&&&& RUNNING TensorRT.trtexec [TensorRT v8001] # /usr/src/tensorrt/bin/trtexec --onnx=./yolov3-512.onnx --batch=1 --saveEngine=test.engine --fp16 --verbose [12/16/2021-09:58:03] [I] === Model Options === [12/16/2021-09:58:03] [I] Format: ONNX [12/16/2021-09:58:03] [I] Model: ./yolov3-512.onnx [12/16/2021-09:58:03] [I] Output: [12/16/2021-09:58:03] [I] === Build Options === [12/16/2021-09:58:03] [I] Max batch: explicit [12/16/2021-09:58:03] [I] Workspace: 16 MiB [12/16/2021-09:58:03] [I] minTiming: 1 [12/16/2021-09:58:03] [I] avgTiming: 8 [12/16/2021-09:58:03] [I] Precision: FP32+FP16 [12/16/2021-09:58:03] [I] Calibration: [12/16/2021-09:58:03] [I] Refit: Disabled [12/16/2021-09:58:03] [I] Sparsity: Disabled [12/16/2021-09:58:03] [I] Safe mode: Disabled [12/16/2021-09:58:03] [I] Restricted mode: Disabled [12/16/2021-09:58:03] [I] Save engine: test.engine [12/16/2021-09:58:03] [I] Load engine: [12/16/2021-09:58:03] [I] NVTX verbosity: 0 [12/16/2021-09:58:03] [I] Tactic sources: Using default tactic sources [12/16/2021-09:58:03] [I] timingCacheMode: local [12/16/2021-09:58:03] [I] timingCacheFile: [12/16/2021-09:58:03] [I] Input(s)s format: fp32:CHW [12/16/2021-09:58:03] [I] Output(s)s format: fp32:CHW [12/16/2021-09:58:03] [I] Input build shapes: model [12/16/2021-09:58:03] [I] Input calibration shapes: model [12/16/2021-09:58:03] [I] === System Options === [12/16/2021-09:58:03] [I] Device: 0 [12/16/2021-09:58:03] [I] DLACore: [12/16/2021-09:58:03] [I] Plugins: [12/16/2021-09:58:03] [I] === Inference Options === [12/16/2021-09:58:03] [I] Batch: Explicit [12/16/2021-09:58:03] [I] Input inference shapes: model [12/16/2021-09:58:03] [I] Iterations: 10 [12/16/2021-09:58:03] [I] Duration: 3s (+ 200ms warm up) [12/16/2021-09:58:03] [I] Sleep time: 0ms [12/16/2021-09:58:03] [I] Streams: 1 [12/16/2021-09:58:03] [I] ExposeDMA: Disabled [12/16/2021-09:58:03] [I] Data transfers: Enabled [12/16/2021-09:58:03] [I] Spin-wait: Disabled [12/16/2021-09:58:03] [I] Multithreading: Disabled [12/16/2021-09:58:03] [I] CUDA Graph: Disabled [12/16/2021-09:58:03] [I] Separate profiling: Disabled [12/16/2021-09:58:03] [I] Time Deserialize: Disabled [12/16/2021-09:58:03] [I] Time Refit: Disabled [12/16/2021-09:58:03] [I] Skip inference: Disabled [12/16/2021-09:58:03] [I] Inputs: [12/16/2021-09:58:03] [I] === Reporting Options === [12/16/2021-09:58:03] [I] Verbose: Enabled [12/16/2021-09:58:03] [I] Averages: 10 inferences [12/16/2021-09:58:03] [I] Percentile: 99 [12/16/2021-09:58:03] [I] Dump refittable layers:Disabled [12/16/2021-09:58:03] [I] Dump output: Disabled [12/16/2021-09:58:03] [I] Profile: Disabled [12/16/2021-09:58:03] [I] Export timing to JSON file: [12/16/2021-09:58:03] [I] Export output to JSON file: [12/16/2021-09:58:03] [I] Export profile to JSON file: [12/16/2021-09:58:03] [I] [12/16/2021-09:58:03] [I] === Device Information === [12/16/2021-09:58:03] [I] Selected Device: NVIDIA Tegra X1 [12/16/2021-09:58:03] [I] Compute Capability: 5.3 [12/16/2021-09:58:03] [I] SMs: 1 [12/16/2021-09:58:03] [I] Compute Clock Rate: 0.9216 GHz [12/16/2021-09:58:03] [I] Device Global Memory: 3964 MiB [12/16/2021-09:58:03] [I] Shared Memory per SM: 64 KiB [12/16/2021-09:58:03] [I] Memory Bus Width: 64 bits (ECC disabled) [12/16/2021-09:58:03] [I] Memory Clock Rate: 0.01275 GHz [12/16/2021-09:58:03] [I] [12/16/2021-09:58:03] [I] TensorRT version: 8001 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::GridAnchor_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::GridAnchorRect_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::NMS_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::Reorg_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::Region_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::Clip_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::LReLU_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::PriorBox_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::Normalize_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::ScatterND version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::RPROI_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::BatchedNMS_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::BatchedNMSDynamic_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::FlattenConcat_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::CropAndResize version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::DetectionLayer_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::EfficientNMS_ONNX_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::EfficientNMS_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::Proposal version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::ProposalLayer_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::PyramidROIAlign_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::ResizeNearest_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::Split version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::SpecialSlice_TRT version 1 [12/16/2021-09:58:03] [V] [TRT] Registered plugin creator - ::InstanceNormalization_TRT version 1 [12/16/2021-09:58:06] [I] [TRT] [MemUsageChange] Init CUDA: CPU +203, GPU +0, now: CPU 221, GPU 2563 (MiB) [12/16/2021-09:58:06] [I] Start parsing network model [12/16/2021-09:58:08] [I] [TRT] ---------------------------------------------------------------- [12/16/2021-09:58:08] [I] [TRT] Input filename: ./yolov3-512.onnx [12/16/2021-09:58:08] [I] [TRT] ONNX IR version: 0.0.4 [12/16/2021-09:58:08] [I] [TRT] Opset version: 9 [12/16/2021-09:58:08] [I] [TRT] Producer name: NVIDIA TensorRT sample [12/16/2021-09:58:08] [I] [TRT] Producer version: [12/16/2021-09:58:08] [I] [TRT] Domain: [12/16/2021-09:58:08] [I] [TRT] Model version: 0 [12/16/2021-09:58:08] [I] [TRT] Doc string: [12/16/2021-09:58:08] [I] [TRT] ---------------------------------------------------------------- [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::GridAnchor_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::GridAnchorRect_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::NMS_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::Reorg_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::Region_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::Clip_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::LReLU_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::PriorBox_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::Normalize_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::ScatterND version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::RPROI_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::BatchedNMS_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::BatchedNMSDynamic_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::FlattenConcat_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::CropAndResize version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::DetectionLayer_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::EfficientNMS_ONNX_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::EfficientNMS_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::Proposal version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::ProposalLayer_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::PyramidROIAlign_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::ResizeNearest_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::Split version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::SpecialSlice_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Plugin creator already registered - ::InstanceNormalization_TRT version 1 [12/16/2021-09:58:09] [V] [TRT] Adding network input: 000_net with dtype: float32, dimensions: (5, 3, 512, 512) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 000_net for ONNX tensor: 000_net [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 001_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 001_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 001_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 001_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 001_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 002_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 002_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 002_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 002_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 002_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 003_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 003_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 003_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 003_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 003_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 004_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 004_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 004_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 004_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 004_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 006_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 006_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 006_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 006_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 006_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 007_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 007_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 007_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 007_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 007_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 008_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 008_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 008_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 008_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 008_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 010_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 010_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 010_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 010_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 010_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 011_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 011_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 011_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 011_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 011_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 013_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 013_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 013_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 013_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 013_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 014_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 014_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 014_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 014_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 014_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 015_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 015_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 015_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 015_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 015_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 017_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 017_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 017_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 017_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 017_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 018_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 018_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 018_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 018_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 018_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 020_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 020_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 020_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 020_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 020_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 021_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 021_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 021_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 021_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 021_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 023_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 023_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 023_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 023_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 023_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 024_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 024_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 024_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 024_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 024_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 026_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 026_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 026_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 026_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 026_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 027_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 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090_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 091_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 091_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 091_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 091_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 091_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 092_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 092_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 092_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 092_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 092_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 093_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 093_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 093_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 093_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 093_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 094_convolutional_conv_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 094_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 097_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 097_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 097_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 097_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 097_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 098_upsample_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 100_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 100_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 100_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 100_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 100_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 101_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 101_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 101_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 101_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 101_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 102_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 102_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 102_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 102_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 102_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 103_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 103_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 103_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 103_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 103_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 104_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 104_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 104_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 104_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 104_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 105_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 105_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 105_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 105_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 105_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 106_convolutional_conv_bias [12/16/2021-09:58:09] [V] [TRT] Importing initializer: 106_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Parsing node: 001_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 000_net [12/16/2021-09:58:09] [V] [TRT] Searching for input: 001_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 001_convolutional [Conv] inputs: [000_net -> (5, 3, 512, 512)[FLOAT]], [001_convolutional_conv_weights -> (32, 3, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 3, 512, 512) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 001_convolutional for ONNX node: 001_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 32 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 32, 512, 512) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 001_convolutional for ONNX tensor: 001_convolutional [12/16/2021-09:58:09] [V] [TRT] 001_convolutional [Conv] outputs: [001_convolutional -> (5, 32, 512, 512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 001_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 001_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 001_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 001_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 001_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 001_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 001_convolutional_bn [BatchNormalization] inputs: [001_convolutional -> (5, 32, 512, 512)[FLOAT]], [001_convolutional_bn_scale -> (32)[FLOAT]], [001_convolutional_bn_bias -> (32)[FLOAT]], [001_convolutional_bn_mean -> (32)[FLOAT]], [001_convolutional_bn_var -> (32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 001_convolutional_bn for ONNX node: 001_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 001_convolutional_bn for ONNX tensor: 001_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 001_convolutional_bn [BatchNormalization] outputs: [001_convolutional_bn -> (5, 32, 512, 512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 001_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 001_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 001_convolutional_lrelu [LeakyRelu] inputs: [001_convolutional_bn -> (5, 32, 512, 512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 001_convolutional_lrelu for ONNX node: 001_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 001_convolutional_lrelu for ONNX tensor: 001_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 001_convolutional_lrelu [LeakyRelu] outputs: [001_convolutional_lrelu -> (5, 32, 512, 512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 002_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 001_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 002_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 002_convolutional [Conv] inputs: [001_convolutional_lrelu -> (5, 32, 512, 512)[FLOAT]], [002_convolutional_conv_weights -> (64, 32, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 32, 512, 512) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 002_convolutional for ONNX node: 002_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 64, 256, 256) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 002_convolutional for ONNX tensor: 002_convolutional [12/16/2021-09:58:09] [V] [TRT] 002_convolutional [Conv] outputs: [002_convolutional -> (5, 64, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 002_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 002_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 002_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 002_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 002_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 002_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 002_convolutional_bn [BatchNormalization] inputs: [002_convolutional -> (5, 64, 256, 256)[FLOAT]], [002_convolutional_bn_scale -> (64)[FLOAT]], [002_convolutional_bn_bias -> (64)[FLOAT]], [002_convolutional_bn_mean -> (64)[FLOAT]], [002_convolutional_bn_var -> (64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 002_convolutional_bn for ONNX node: 002_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 002_convolutional_bn for ONNX tensor: 002_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 002_convolutional_bn [BatchNormalization] outputs: [002_convolutional_bn -> (5, 64, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 002_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 002_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 002_convolutional_lrelu [LeakyRelu] inputs: [002_convolutional_bn -> (5, 64, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 002_convolutional_lrelu for ONNX node: 002_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 002_convolutional_lrelu for ONNX tensor: 002_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 002_convolutional_lrelu [LeakyRelu] outputs: [002_convolutional_lrelu -> (5, 64, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 003_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 002_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 003_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 003_convolutional [Conv] inputs: [002_convolutional_lrelu -> (5, 64, 256, 256)[FLOAT]], [003_convolutional_conv_weights -> (32, 64, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 64, 256, 256) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 003_convolutional for ONNX node: 003_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 32 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 32, 256, 256) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 003_convolutional for ONNX tensor: 003_convolutional [12/16/2021-09:58:09] [V] [TRT] 003_convolutional [Conv] outputs: [003_convolutional -> (5, 32, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 003_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 003_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 003_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 003_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 003_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 003_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 003_convolutional_bn [BatchNormalization] inputs: [003_convolutional -> (5, 32, 256, 256)[FLOAT]], [003_convolutional_bn_scale -> (32)[FLOAT]], [003_convolutional_bn_bias -> (32)[FLOAT]], [003_convolutional_bn_mean -> (32)[FLOAT]], [003_convolutional_bn_var -> (32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 003_convolutional_bn for ONNX node: 003_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 003_convolutional_bn for ONNX tensor: 003_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 003_convolutional_bn [BatchNormalization] outputs: [003_convolutional_bn -> (5, 32, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 003_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 003_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 003_convolutional_lrelu [LeakyRelu] inputs: [003_convolutional_bn -> (5, 32, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 003_convolutional_lrelu for ONNX node: 003_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 003_convolutional_lrelu for ONNX tensor: 003_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 003_convolutional_lrelu [LeakyRelu] outputs: [003_convolutional_lrelu -> (5, 32, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 004_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 003_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 004_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 004_convolutional [Conv] inputs: [003_convolutional_lrelu -> (5, 32, 256, 256)[FLOAT]], [004_convolutional_conv_weights -> (64, 32, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 32, 256, 256) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 004_convolutional for ONNX node: 004_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 64, 256, 256) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 004_convolutional for ONNX tensor: 004_convolutional [12/16/2021-09:58:09] [V] [TRT] 004_convolutional [Conv] outputs: [004_convolutional -> (5, 64, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 004_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 004_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 004_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 004_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 004_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 004_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 004_convolutional_bn [BatchNormalization] inputs: [004_convolutional -> (5, 64, 256, 256)[FLOAT]], [004_convolutional_bn_scale -> (64)[FLOAT]], [004_convolutional_bn_bias -> (64)[FLOAT]], [004_convolutional_bn_mean -> (64)[FLOAT]], [004_convolutional_bn_var -> (64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 004_convolutional_bn for ONNX node: 004_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 004_convolutional_bn for ONNX tensor: 004_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 004_convolutional_bn [BatchNormalization] outputs: [004_convolutional_bn -> (5, 64, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 004_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 004_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 004_convolutional_lrelu [LeakyRelu] inputs: [004_convolutional_bn -> (5, 64, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 004_convolutional_lrelu for ONNX node: 004_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 004_convolutional_lrelu for ONNX tensor: 004_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 004_convolutional_lrelu [LeakyRelu] outputs: [004_convolutional_lrelu -> (5, 64, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 005_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 004_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 002_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 005_shortcut [Add] inputs: [004_convolutional_lrelu -> (5, 64, 256, 256)[FLOAT]], [002_convolutional_lrelu -> (5, 64, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 005_shortcut for ONNX node: 005_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 005_shortcut for ONNX tensor: 005_shortcut [12/16/2021-09:58:09] [V] [TRT] 005_shortcut [Add] outputs: [005_shortcut -> (5, 64, 256, 256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 006_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 005_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 006_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 006_convolutional [Conv] inputs: [005_shortcut -> (5, 64, 256, 256)[FLOAT]], [006_convolutional_conv_weights -> (128, 64, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 64, 256, 256) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 006_convolutional for ONNX node: 006_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 128, 128) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 006_convolutional for ONNX tensor: 006_convolutional [12/16/2021-09:58:09] [V] [TRT] 006_convolutional [Conv] outputs: [006_convolutional -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 006_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 006_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 006_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 006_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 006_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 006_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 006_convolutional_bn [BatchNormalization] inputs: [006_convolutional -> (5, 128, 128, 128)[FLOAT]], [006_convolutional_bn_scale -> (128)[FLOAT]], [006_convolutional_bn_bias -> (128)[FLOAT]], [006_convolutional_bn_mean -> (128)[FLOAT]], [006_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 006_convolutional_bn for ONNX node: 006_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 006_convolutional_bn for ONNX tensor: 006_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 006_convolutional_bn [BatchNormalization] outputs: [006_convolutional_bn -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 006_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 006_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 006_convolutional_lrelu [LeakyRelu] inputs: [006_convolutional_bn -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 006_convolutional_lrelu for ONNX node: 006_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 006_convolutional_lrelu for ONNX tensor: 006_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 006_convolutional_lrelu [LeakyRelu] outputs: [006_convolutional_lrelu -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 007_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 006_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 007_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 007_convolutional [Conv] inputs: [006_convolutional_lrelu -> (5, 128, 128, 128)[FLOAT]], [007_convolutional_conv_weights -> (64, 128, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 128, 128) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 007_convolutional for ONNX node: 007_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 64, 128, 128) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 007_convolutional for ONNX tensor: 007_convolutional [12/16/2021-09:58:09] [V] [TRT] 007_convolutional [Conv] outputs: [007_convolutional -> (5, 64, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 007_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 007_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 007_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 007_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 007_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 007_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 007_convolutional_bn [BatchNormalization] inputs: [007_convolutional -> (5, 64, 128, 128)[FLOAT]], [007_convolutional_bn_scale -> (64)[FLOAT]], [007_convolutional_bn_bias -> (64)[FLOAT]], [007_convolutional_bn_mean -> (64)[FLOAT]], [007_convolutional_bn_var -> (64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 007_convolutional_bn for ONNX node: 007_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 007_convolutional_bn for ONNX tensor: 007_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 007_convolutional_bn [BatchNormalization] outputs: [007_convolutional_bn -> (5, 64, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 007_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 007_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 007_convolutional_lrelu [LeakyRelu] inputs: [007_convolutional_bn -> (5, 64, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 007_convolutional_lrelu for ONNX node: 007_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 007_convolutional_lrelu for ONNX tensor: 007_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 007_convolutional_lrelu [LeakyRelu] outputs: [007_convolutional_lrelu -> (5, 64, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 008_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 007_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 008_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 008_convolutional [Conv] inputs: [007_convolutional_lrelu -> (5, 64, 128, 128)[FLOAT]], [008_convolutional_conv_weights -> (128, 64, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 64, 128, 128) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 008_convolutional for ONNX node: 008_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 128, 128) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 008_convolutional for ONNX tensor: 008_convolutional [12/16/2021-09:58:09] [V] [TRT] 008_convolutional [Conv] outputs: [008_convolutional -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 008_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 008_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 008_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 008_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 008_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 008_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 008_convolutional_bn [BatchNormalization] inputs: [008_convolutional -> (5, 128, 128, 128)[FLOAT]], [008_convolutional_bn_scale -> (128)[FLOAT]], [008_convolutional_bn_bias -> (128)[FLOAT]], [008_convolutional_bn_mean -> (128)[FLOAT]], [008_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 008_convolutional_bn for ONNX node: 008_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 008_convolutional_bn for ONNX tensor: 008_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 008_convolutional_bn [BatchNormalization] outputs: [008_convolutional_bn -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 008_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 008_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 008_convolutional_lrelu [LeakyRelu] inputs: [008_convolutional_bn -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 008_convolutional_lrelu for ONNX node: 008_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 008_convolutional_lrelu for ONNX tensor: 008_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 008_convolutional_lrelu [LeakyRelu] outputs: [008_convolutional_lrelu -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 009_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 008_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 006_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 009_shortcut [Add] inputs: [008_convolutional_lrelu -> (5, 128, 128, 128)[FLOAT]], [006_convolutional_lrelu -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 009_shortcut for ONNX node: 009_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 009_shortcut for ONNX tensor: 009_shortcut [12/16/2021-09:58:09] [V] [TRT] 009_shortcut [Add] outputs: [009_shortcut -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 010_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 009_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 010_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 010_convolutional [Conv] inputs: [009_shortcut -> (5, 128, 128, 128)[FLOAT]], [010_convolutional_conv_weights -> (64, 128, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 128, 128) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 010_convolutional for ONNX node: 010_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 64 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 64, 128, 128) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 010_convolutional for ONNX tensor: 010_convolutional [12/16/2021-09:58:09] [V] [TRT] 010_convolutional [Conv] outputs: [010_convolutional -> (5, 64, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 010_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 010_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 010_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 010_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 010_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 010_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 010_convolutional_bn [BatchNormalization] inputs: [010_convolutional -> (5, 64, 128, 128)[FLOAT]], [010_convolutional_bn_scale -> (64)[FLOAT]], [010_convolutional_bn_bias -> (64)[FLOAT]], [010_convolutional_bn_mean -> (64)[FLOAT]], [010_convolutional_bn_var -> (64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 010_convolutional_bn for ONNX node: 010_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 010_convolutional_bn for ONNX tensor: 010_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 010_convolutional_bn [BatchNormalization] outputs: [010_convolutional_bn -> (5, 64, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 010_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 010_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 010_convolutional_lrelu [LeakyRelu] inputs: [010_convolutional_bn -> (5, 64, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 010_convolutional_lrelu for ONNX node: 010_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 010_convolutional_lrelu for ONNX tensor: 010_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 010_convolutional_lrelu [LeakyRelu] outputs: [010_convolutional_lrelu -> (5, 64, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 011_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 010_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 011_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 011_convolutional [Conv] inputs: [010_convolutional_lrelu -> (5, 64, 128, 128)[FLOAT]], [011_convolutional_conv_weights -> (128, 64, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 64, 128, 128) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 011_convolutional for ONNX node: 011_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 128, 128) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 011_convolutional for ONNX tensor: 011_convolutional [12/16/2021-09:58:09] [V] [TRT] 011_convolutional [Conv] outputs: [011_convolutional -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 011_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 011_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 011_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 011_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 011_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 011_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 011_convolutional_bn [BatchNormalization] inputs: [011_convolutional -> (5, 128, 128, 128)[FLOAT]], [011_convolutional_bn_scale -> (128)[FLOAT]], [011_convolutional_bn_bias -> (128)[FLOAT]], [011_convolutional_bn_mean -> (128)[FLOAT]], [011_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 011_convolutional_bn for ONNX node: 011_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 011_convolutional_bn for ONNX tensor: 011_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 011_convolutional_bn [BatchNormalization] outputs: [011_convolutional_bn -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 011_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 011_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 011_convolutional_lrelu [LeakyRelu] inputs: [011_convolutional_bn -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 011_convolutional_lrelu for ONNX node: 011_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 011_convolutional_lrelu for ONNX tensor: 011_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 011_convolutional_lrelu [LeakyRelu] outputs: [011_convolutional_lrelu -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 012_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 011_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 009_shortcut [12/16/2021-09:58:09] [V] [TRT] 012_shortcut [Add] inputs: [011_convolutional_lrelu -> (5, 128, 128, 128)[FLOAT]], [009_shortcut -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 012_shortcut for ONNX node: 012_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 012_shortcut for ONNX tensor: 012_shortcut [12/16/2021-09:58:09] [V] [TRT] 012_shortcut [Add] outputs: [012_shortcut -> (5, 128, 128, 128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 013_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 012_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 013_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 013_convolutional [Conv] inputs: [012_shortcut -> (5, 128, 128, 128)[FLOAT]], [013_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 128, 128) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 013_convolutional for ONNX node: 013_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 013_convolutional for ONNX tensor: 013_convolutional [12/16/2021-09:58:09] [V] [TRT] 013_convolutional [Conv] outputs: [013_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 013_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 013_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 013_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 013_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 013_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 013_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 013_convolutional_bn [BatchNormalization] inputs: [013_convolutional -> (5, 256, 64, 64)[FLOAT]], [013_convolutional_bn_scale -> (256)[FLOAT]], [013_convolutional_bn_bias -> (256)[FLOAT]], [013_convolutional_bn_mean -> (256)[FLOAT]], [013_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 013_convolutional_bn for ONNX node: 013_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 013_convolutional_bn for ONNX tensor: 013_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 013_convolutional_bn [BatchNormalization] outputs: [013_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 013_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 013_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 013_convolutional_lrelu [LeakyRelu] inputs: [013_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 013_convolutional_lrelu for ONNX node: 013_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 013_convolutional_lrelu for ONNX tensor: 013_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 013_convolutional_lrelu [LeakyRelu] outputs: [013_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 014_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 013_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 014_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 014_convolutional [Conv] inputs: [013_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [014_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 014_convolutional for ONNX node: 014_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 014_convolutional for ONNX tensor: 014_convolutional [12/16/2021-09:58:09] [V] [TRT] 014_convolutional [Conv] outputs: [014_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 014_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 014_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 014_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 014_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 014_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 014_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 014_convolutional_bn [BatchNormalization] inputs: [014_convolutional -> (5, 128, 64, 64)[FLOAT]], [014_convolutional_bn_scale -> (128)[FLOAT]], [014_convolutional_bn_bias -> (128)[FLOAT]], [014_convolutional_bn_mean -> (128)[FLOAT]], [014_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 014_convolutional_bn for ONNX node: 014_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 014_convolutional_bn for ONNX tensor: 014_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 014_convolutional_bn [BatchNormalization] outputs: [014_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 014_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 014_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 014_convolutional_lrelu [LeakyRelu] inputs: [014_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 014_convolutional_lrelu for ONNX node: 014_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 014_convolutional_lrelu for ONNX tensor: 014_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 014_convolutional_lrelu [LeakyRelu] outputs: [014_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 015_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 014_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 015_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 015_convolutional [Conv] inputs: [014_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [015_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 015_convolutional for ONNX node: 015_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 015_convolutional for ONNX tensor: 015_convolutional [12/16/2021-09:58:09] [V] [TRT] 015_convolutional [Conv] outputs: [015_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 015_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 015_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 015_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 015_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 015_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 015_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 015_convolutional_bn [BatchNormalization] inputs: [015_convolutional -> (5, 256, 64, 64)[FLOAT]], [015_convolutional_bn_scale -> (256)[FLOAT]], [015_convolutional_bn_bias -> (256)[FLOAT]], [015_convolutional_bn_mean -> (256)[FLOAT]], [015_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 015_convolutional_bn for ONNX node: 015_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 015_convolutional_bn for ONNX tensor: 015_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 015_convolutional_bn [BatchNormalization] outputs: [015_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 015_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 015_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 015_convolutional_lrelu [LeakyRelu] inputs: [015_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 015_convolutional_lrelu for ONNX node: 015_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 015_convolutional_lrelu for ONNX tensor: 015_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 015_convolutional_lrelu [LeakyRelu] outputs: [015_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 016_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 015_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 013_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 016_shortcut [Add] inputs: [015_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [013_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 016_shortcut for ONNX node: 016_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 016_shortcut for ONNX tensor: 016_shortcut [12/16/2021-09:58:09] [V] [TRT] 016_shortcut [Add] outputs: [016_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 017_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 016_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 017_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 017_convolutional [Conv] inputs: [016_shortcut -> (5, 256, 64, 64)[FLOAT]], [017_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 017_convolutional for ONNX node: 017_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 017_convolutional for ONNX tensor: 017_convolutional [12/16/2021-09:58:09] [V] [TRT] 017_convolutional [Conv] outputs: [017_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 017_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 017_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 017_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 017_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 017_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 017_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 017_convolutional_bn [BatchNormalization] inputs: [017_convolutional -> (5, 128, 64, 64)[FLOAT]], [017_convolutional_bn_scale -> (128)[FLOAT]], [017_convolutional_bn_bias -> (128)[FLOAT]], [017_convolutional_bn_mean -> (128)[FLOAT]], [017_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 017_convolutional_bn for ONNX node: 017_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 017_convolutional_bn for ONNX tensor: 017_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 017_convolutional_bn [BatchNormalization] outputs: [017_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 017_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 017_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 017_convolutional_lrelu [LeakyRelu] inputs: [017_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 017_convolutional_lrelu for ONNX node: 017_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 017_convolutional_lrelu for ONNX tensor: 017_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 017_convolutional_lrelu [LeakyRelu] outputs: [017_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 018_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 017_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 018_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 018_convolutional [Conv] inputs: [017_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [018_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 018_convolutional for ONNX node: 018_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 018_convolutional for ONNX tensor: 018_convolutional [12/16/2021-09:58:09] [V] [TRT] 018_convolutional [Conv] outputs: [018_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 018_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 018_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 018_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 018_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 018_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 018_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 018_convolutional_bn [BatchNormalization] inputs: [018_convolutional -> (5, 256, 64, 64)[FLOAT]], [018_convolutional_bn_scale -> (256)[FLOAT]], [018_convolutional_bn_bias -> (256)[FLOAT]], [018_convolutional_bn_mean -> (256)[FLOAT]], [018_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 018_convolutional_bn for ONNX node: 018_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 018_convolutional_bn for ONNX tensor: 018_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 018_convolutional_bn [BatchNormalization] outputs: [018_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 018_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 018_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 018_convolutional_lrelu [LeakyRelu] inputs: [018_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 018_convolutional_lrelu for ONNX node: 018_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 018_convolutional_lrelu for ONNX tensor: 018_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 018_convolutional_lrelu [LeakyRelu] outputs: [018_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 019_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 018_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 016_shortcut [12/16/2021-09:58:09] [V] [TRT] 019_shortcut [Add] inputs: [018_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [016_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 019_shortcut for ONNX node: 019_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 019_shortcut for ONNX tensor: 019_shortcut [12/16/2021-09:58:09] [V] [TRT] 019_shortcut [Add] outputs: [019_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 020_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 019_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 020_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 020_convolutional [Conv] inputs: [019_shortcut -> (5, 256, 64, 64)[FLOAT]], [020_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 020_convolutional for ONNX node: 020_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 020_convolutional for ONNX tensor: 020_convolutional [12/16/2021-09:58:09] [V] [TRT] 020_convolutional [Conv] outputs: [020_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 020_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 020_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 020_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 020_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 020_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 020_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 020_convolutional_bn [BatchNormalization] inputs: [020_convolutional -> (5, 128, 64, 64)[FLOAT]], [020_convolutional_bn_scale -> (128)[FLOAT]], [020_convolutional_bn_bias -> (128)[FLOAT]], [020_convolutional_bn_mean -> (128)[FLOAT]], [020_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 020_convolutional_bn for ONNX node: 020_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 020_convolutional_bn for ONNX tensor: 020_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 020_convolutional_bn [BatchNormalization] outputs: [020_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 020_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 020_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 020_convolutional_lrelu [LeakyRelu] inputs: [020_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 020_convolutional_lrelu for ONNX node: 020_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 020_convolutional_lrelu for ONNX tensor: 020_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 020_convolutional_lrelu [LeakyRelu] outputs: [020_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 021_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 020_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 021_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 021_convolutional [Conv] inputs: [020_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [021_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 021_convolutional for ONNX node: 021_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 021_convolutional for ONNX tensor: 021_convolutional [12/16/2021-09:58:09] [V] [TRT] 021_convolutional [Conv] outputs: [021_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 021_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 021_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 021_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 021_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 021_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 021_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 021_convolutional_bn [BatchNormalization] inputs: [021_convolutional -> (5, 256, 64, 64)[FLOAT]], [021_convolutional_bn_scale -> (256)[FLOAT]], [021_convolutional_bn_bias -> (256)[FLOAT]], [021_convolutional_bn_mean -> (256)[FLOAT]], [021_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 021_convolutional_bn for ONNX node: 021_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 021_convolutional_bn for ONNX tensor: 021_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 021_convolutional_bn [BatchNormalization] outputs: [021_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 021_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 021_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 021_convolutional_lrelu [LeakyRelu] inputs: [021_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 021_convolutional_lrelu for ONNX node: 021_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 021_convolutional_lrelu for ONNX tensor: 021_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 021_convolutional_lrelu [LeakyRelu] outputs: [021_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 022_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 021_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 019_shortcut [12/16/2021-09:58:09] [V] [TRT] 022_shortcut [Add] inputs: [021_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [019_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 022_shortcut for ONNX node: 022_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 022_shortcut for ONNX tensor: 022_shortcut [12/16/2021-09:58:09] [V] [TRT] 022_shortcut [Add] outputs: [022_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 023_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 022_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 023_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 023_convolutional [Conv] inputs: [022_shortcut -> (5, 256, 64, 64)[FLOAT]], [023_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 023_convolutional for ONNX node: 023_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 023_convolutional for ONNX tensor: 023_convolutional [12/16/2021-09:58:09] [V] [TRT] 023_convolutional [Conv] outputs: [023_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 023_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 023_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 023_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 023_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 023_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 023_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 023_convolutional_bn [BatchNormalization] inputs: [023_convolutional -> (5, 128, 64, 64)[FLOAT]], [023_convolutional_bn_scale -> (128)[FLOAT]], [023_convolutional_bn_bias -> (128)[FLOAT]], [023_convolutional_bn_mean -> (128)[FLOAT]], [023_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 023_convolutional_bn for ONNX node: 023_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 023_convolutional_bn for ONNX tensor: 023_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 023_convolutional_bn [BatchNormalization] outputs: [023_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 023_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 023_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 023_convolutional_lrelu [LeakyRelu] inputs: [023_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 023_convolutional_lrelu for ONNX node: 023_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 023_convolutional_lrelu for ONNX tensor: 023_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 023_convolutional_lrelu [LeakyRelu] outputs: [023_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 024_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 023_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 024_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 024_convolutional [Conv] inputs: [023_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [024_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 024_convolutional for ONNX node: 024_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 024_convolutional for ONNX tensor: 024_convolutional [12/16/2021-09:58:09] [V] [TRT] 024_convolutional [Conv] outputs: [024_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 024_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 024_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 024_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 024_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 024_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 024_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 024_convolutional_bn [BatchNormalization] inputs: [024_convolutional -> (5, 256, 64, 64)[FLOAT]], [024_convolutional_bn_scale -> (256)[FLOAT]], [024_convolutional_bn_bias -> (256)[FLOAT]], [024_convolutional_bn_mean -> (256)[FLOAT]], [024_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 024_convolutional_bn for ONNX node: 024_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 024_convolutional_bn for ONNX tensor: 024_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 024_convolutional_bn [BatchNormalization] outputs: [024_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 024_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 024_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 024_convolutional_lrelu [LeakyRelu] inputs: [024_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 024_convolutional_lrelu for ONNX node: 024_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 024_convolutional_lrelu for ONNX tensor: 024_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 024_convolutional_lrelu [LeakyRelu] outputs: [024_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 025_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 024_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 022_shortcut [12/16/2021-09:58:09] [V] [TRT] 025_shortcut [Add] inputs: [024_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [022_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 025_shortcut for ONNX node: 025_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 025_shortcut for ONNX tensor: 025_shortcut [12/16/2021-09:58:09] [V] [TRT] 025_shortcut [Add] outputs: [025_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 026_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 025_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 026_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 026_convolutional [Conv] inputs: [025_shortcut -> (5, 256, 64, 64)[FLOAT]], [026_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 026_convolutional for ONNX node: 026_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 026_convolutional for ONNX tensor: 026_convolutional [12/16/2021-09:58:09] [V] [TRT] 026_convolutional [Conv] outputs: [026_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 026_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 026_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 026_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 026_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 026_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 026_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 026_convolutional_bn [BatchNormalization] inputs: [026_convolutional -> (5, 128, 64, 64)[FLOAT]], [026_convolutional_bn_scale -> (128)[FLOAT]], [026_convolutional_bn_bias -> (128)[FLOAT]], [026_convolutional_bn_mean -> (128)[FLOAT]], [026_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 026_convolutional_bn for ONNX node: 026_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 026_convolutional_bn for ONNX tensor: 026_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 026_convolutional_bn [BatchNormalization] outputs: [026_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 026_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 026_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 026_convolutional_lrelu [LeakyRelu] inputs: [026_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 026_convolutional_lrelu for ONNX node: 026_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 026_convolutional_lrelu for ONNX tensor: 026_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 026_convolutional_lrelu [LeakyRelu] outputs: [026_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 027_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 026_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 027_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 027_convolutional [Conv] inputs: [026_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [027_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 027_convolutional for ONNX node: 027_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 027_convolutional for ONNX tensor: 027_convolutional [12/16/2021-09:58:09] [V] [TRT] 027_convolutional [Conv] outputs: [027_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 027_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 027_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 027_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 027_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 027_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 027_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 027_convolutional_bn [BatchNormalization] inputs: [027_convolutional -> (5, 256, 64, 64)[FLOAT]], [027_convolutional_bn_scale -> (256)[FLOAT]], [027_convolutional_bn_bias -> (256)[FLOAT]], [027_convolutional_bn_mean -> (256)[FLOAT]], [027_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 027_convolutional_bn for ONNX node: 027_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 027_convolutional_bn for ONNX tensor: 027_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 027_convolutional_bn [BatchNormalization] outputs: [027_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 027_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 027_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 027_convolutional_lrelu [LeakyRelu] inputs: [027_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 027_convolutional_lrelu for ONNX node: 027_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 027_convolutional_lrelu for ONNX tensor: 027_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 027_convolutional_lrelu [LeakyRelu] outputs: [027_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 028_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 027_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 025_shortcut [12/16/2021-09:58:09] [V] [TRT] 028_shortcut [Add] inputs: [027_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [025_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 028_shortcut for ONNX node: 028_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 028_shortcut for ONNX tensor: 028_shortcut [12/16/2021-09:58:09] [V] [TRT] 028_shortcut [Add] outputs: [028_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 029_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 028_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 029_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 029_convolutional [Conv] inputs: [028_shortcut -> (5, 256, 64, 64)[FLOAT]], [029_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 029_convolutional for ONNX node: 029_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 029_convolutional for ONNX tensor: 029_convolutional [12/16/2021-09:58:09] [V] [TRT] 029_convolutional [Conv] outputs: [029_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 029_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 029_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 029_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 029_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 029_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 029_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 029_convolutional_bn [BatchNormalization] inputs: [029_convolutional -> (5, 128, 64, 64)[FLOAT]], [029_convolutional_bn_scale -> (128)[FLOAT]], [029_convolutional_bn_bias -> (128)[FLOAT]], [029_convolutional_bn_mean -> (128)[FLOAT]], [029_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 029_convolutional_bn for ONNX node: 029_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 029_convolutional_bn for ONNX tensor: 029_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 029_convolutional_bn [BatchNormalization] outputs: [029_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 029_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 029_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 029_convolutional_lrelu [LeakyRelu] inputs: [029_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 029_convolutional_lrelu for ONNX node: 029_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 029_convolutional_lrelu for ONNX tensor: 029_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 029_convolutional_lrelu [LeakyRelu] outputs: [029_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 030_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 029_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 030_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 030_convolutional [Conv] inputs: [029_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [030_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 030_convolutional for ONNX node: 030_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 030_convolutional for ONNX tensor: 030_convolutional [12/16/2021-09:58:09] [V] [TRT] 030_convolutional [Conv] outputs: [030_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 030_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 030_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 030_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 030_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 030_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 030_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 030_convolutional_bn [BatchNormalization] inputs: [030_convolutional -> (5, 256, 64, 64)[FLOAT]], [030_convolutional_bn_scale -> (256)[FLOAT]], [030_convolutional_bn_bias -> (256)[FLOAT]], [030_convolutional_bn_mean -> (256)[FLOAT]], [030_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 030_convolutional_bn for ONNX node: 030_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 030_convolutional_bn for ONNX tensor: 030_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 030_convolutional_bn [BatchNormalization] outputs: [030_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 030_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 030_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 030_convolutional_lrelu [LeakyRelu] inputs: [030_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 030_convolutional_lrelu for ONNX node: 030_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 030_convolutional_lrelu for ONNX tensor: 030_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 030_convolutional_lrelu [LeakyRelu] outputs: [030_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 031_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 030_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 028_shortcut [12/16/2021-09:58:09] [V] [TRT] 031_shortcut [Add] inputs: [030_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [028_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 031_shortcut for ONNX node: 031_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 031_shortcut for ONNX tensor: 031_shortcut [12/16/2021-09:58:09] [V] [TRT] 031_shortcut [Add] outputs: [031_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 032_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 031_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 032_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 032_convolutional [Conv] inputs: [031_shortcut -> (5, 256, 64, 64)[FLOAT]], [032_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 032_convolutional for ONNX node: 032_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 032_convolutional for ONNX tensor: 032_convolutional [12/16/2021-09:58:09] [V] [TRT] 032_convolutional [Conv] outputs: [032_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 032_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 032_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 032_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 032_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 032_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 032_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 032_convolutional_bn [BatchNormalization] inputs: [032_convolutional -> (5, 128, 64, 64)[FLOAT]], [032_convolutional_bn_scale -> (128)[FLOAT]], [032_convolutional_bn_bias -> (128)[FLOAT]], [032_convolutional_bn_mean -> (128)[FLOAT]], [032_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 032_convolutional_bn for ONNX node: 032_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 032_convolutional_bn for ONNX tensor: 032_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 032_convolutional_bn [BatchNormalization] outputs: [032_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 032_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 032_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 032_convolutional_lrelu [LeakyRelu] inputs: [032_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 032_convolutional_lrelu for ONNX node: 032_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 032_convolutional_lrelu for ONNX tensor: 032_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 032_convolutional_lrelu [LeakyRelu] outputs: [032_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 033_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 032_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 033_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 033_convolutional [Conv] inputs: [032_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [033_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 033_convolutional for ONNX node: 033_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 033_convolutional for ONNX tensor: 033_convolutional [12/16/2021-09:58:09] [V] [TRT] 033_convolutional [Conv] outputs: [033_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 033_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 033_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 033_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 033_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 033_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 033_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 033_convolutional_bn [BatchNormalization] inputs: [033_convolutional -> (5, 256, 64, 64)[FLOAT]], [033_convolutional_bn_scale -> (256)[FLOAT]], [033_convolutional_bn_bias -> (256)[FLOAT]], [033_convolutional_bn_mean -> (256)[FLOAT]], [033_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 033_convolutional_bn for ONNX node: 033_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 033_convolutional_bn for ONNX tensor: 033_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 033_convolutional_bn [BatchNormalization] outputs: [033_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 033_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 033_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 033_convolutional_lrelu [LeakyRelu] inputs: [033_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 033_convolutional_lrelu for ONNX node: 033_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 033_convolutional_lrelu for ONNX tensor: 033_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 033_convolutional_lrelu [LeakyRelu] outputs: [033_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 034_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 033_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 031_shortcut [12/16/2021-09:58:09] [V] [TRT] 034_shortcut [Add] inputs: [033_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [031_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 034_shortcut for ONNX node: 034_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 034_shortcut for ONNX tensor: 034_shortcut [12/16/2021-09:58:09] [V] [TRT] 034_shortcut [Add] outputs: [034_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 035_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 034_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 035_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 035_convolutional [Conv] inputs: [034_shortcut -> (5, 256, 64, 64)[FLOAT]], [035_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 035_convolutional for ONNX node: 035_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 035_convolutional for ONNX tensor: 035_convolutional [12/16/2021-09:58:09] [V] [TRT] 035_convolutional [Conv] outputs: [035_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 035_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 035_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 035_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 035_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 035_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 035_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 035_convolutional_bn [BatchNormalization] inputs: [035_convolutional -> (5, 128, 64, 64)[FLOAT]], [035_convolutional_bn_scale -> (128)[FLOAT]], [035_convolutional_bn_bias -> (128)[FLOAT]], [035_convolutional_bn_mean -> (128)[FLOAT]], [035_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 035_convolutional_bn for ONNX node: 035_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 035_convolutional_bn for ONNX tensor: 035_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 035_convolutional_bn [BatchNormalization] outputs: [035_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 035_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 035_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 035_convolutional_lrelu [LeakyRelu] inputs: [035_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 035_convolutional_lrelu for ONNX node: 035_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 035_convolutional_lrelu for ONNX tensor: 035_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 035_convolutional_lrelu [LeakyRelu] outputs: [035_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 036_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 035_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 036_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 036_convolutional [Conv] inputs: [035_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [036_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 036_convolutional for ONNX node: 036_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 036_convolutional for ONNX tensor: 036_convolutional [12/16/2021-09:58:09] [V] [TRT] 036_convolutional [Conv] outputs: [036_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 036_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 036_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 036_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 036_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 036_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 036_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 036_convolutional_bn [BatchNormalization] inputs: [036_convolutional -> (5, 256, 64, 64)[FLOAT]], [036_convolutional_bn_scale -> (256)[FLOAT]], [036_convolutional_bn_bias -> (256)[FLOAT]], [036_convolutional_bn_mean -> (256)[FLOAT]], [036_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 036_convolutional_bn for ONNX node: 036_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 036_convolutional_bn for ONNX tensor: 036_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 036_convolutional_bn [BatchNormalization] outputs: [036_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 036_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 036_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 036_convolutional_lrelu [LeakyRelu] inputs: [036_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 036_convolutional_lrelu for ONNX node: 036_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 036_convolutional_lrelu for ONNX tensor: 036_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 036_convolutional_lrelu [LeakyRelu] outputs: [036_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 037_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 036_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 034_shortcut [12/16/2021-09:58:09] [V] [TRT] 037_shortcut [Add] inputs: [036_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [034_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 037_shortcut for ONNX node: 037_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 037_shortcut for ONNX tensor: 037_shortcut [12/16/2021-09:58:09] [V] [TRT] 037_shortcut [Add] outputs: [037_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 038_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 037_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 038_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 038_convolutional [Conv] inputs: [037_shortcut -> (5, 256, 64, 64)[FLOAT]], [038_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 038_convolutional for ONNX node: 038_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 038_convolutional for ONNX tensor: 038_convolutional [12/16/2021-09:58:09] [V] [TRT] 038_convolutional [Conv] outputs: [038_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 038_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 038_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 038_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 038_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 038_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 038_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 038_convolutional_bn [BatchNormalization] inputs: [038_convolutional -> (5, 512, 32, 32)[FLOAT]], [038_convolutional_bn_scale -> (512)[FLOAT]], [038_convolutional_bn_bias -> (512)[FLOAT]], [038_convolutional_bn_mean -> (512)[FLOAT]], [038_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 038_convolutional_bn for ONNX node: 038_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 038_convolutional_bn for ONNX tensor: 038_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 038_convolutional_bn [BatchNormalization] outputs: [038_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 038_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 038_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 038_convolutional_lrelu [LeakyRelu] inputs: [038_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 038_convolutional_lrelu for ONNX node: 038_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 038_convolutional_lrelu for ONNX tensor: 038_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 038_convolutional_lrelu [LeakyRelu] outputs: [038_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 039_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 038_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 039_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 039_convolutional [Conv] inputs: [038_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [039_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 039_convolutional for ONNX node: 039_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 039_convolutional for ONNX tensor: 039_convolutional [12/16/2021-09:58:09] [V] [TRT] 039_convolutional [Conv] outputs: [039_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 039_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 039_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 039_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 039_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 039_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 039_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 039_convolutional_bn [BatchNormalization] inputs: [039_convolutional -> (5, 256, 32, 32)[FLOAT]], [039_convolutional_bn_scale -> (256)[FLOAT]], [039_convolutional_bn_bias -> (256)[FLOAT]], [039_convolutional_bn_mean -> (256)[FLOAT]], [039_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 039_convolutional_bn for ONNX node: 039_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 039_convolutional_bn for ONNX tensor: 039_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 039_convolutional_bn [BatchNormalization] outputs: [039_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 039_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 039_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 039_convolutional_lrelu [LeakyRelu] inputs: [039_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 039_convolutional_lrelu for ONNX node: 039_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 039_convolutional_lrelu for ONNX tensor: 039_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 039_convolutional_lrelu [LeakyRelu] outputs: [039_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 040_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 039_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 040_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 040_convolutional [Conv] inputs: [039_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [040_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 040_convolutional for ONNX node: 040_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 040_convolutional for ONNX tensor: 040_convolutional [12/16/2021-09:58:09] [V] [TRT] 040_convolutional [Conv] outputs: [040_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 040_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 040_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 040_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 040_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 040_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 040_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 040_convolutional_bn [BatchNormalization] inputs: [040_convolutional -> (5, 512, 32, 32)[FLOAT]], [040_convolutional_bn_scale -> (512)[FLOAT]], [040_convolutional_bn_bias -> (512)[FLOAT]], [040_convolutional_bn_mean -> (512)[FLOAT]], [040_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 040_convolutional_bn for ONNX node: 040_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 040_convolutional_bn for ONNX tensor: 040_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 040_convolutional_bn [BatchNormalization] outputs: [040_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 040_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 040_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 040_convolutional_lrelu [LeakyRelu] inputs: [040_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 040_convolutional_lrelu for ONNX node: 040_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 040_convolutional_lrelu for ONNX tensor: 040_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 040_convolutional_lrelu [LeakyRelu] outputs: [040_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 041_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 040_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 038_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 041_shortcut [Add] inputs: [040_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [038_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 041_shortcut for ONNX node: 041_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 041_shortcut for ONNX tensor: 041_shortcut [12/16/2021-09:58:09] [V] [TRT] 041_shortcut [Add] outputs: [041_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 042_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 041_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 042_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 042_convolutional [Conv] inputs: [041_shortcut -> (5, 512, 32, 32)[FLOAT]], [042_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 042_convolutional for ONNX node: 042_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 042_convolutional for ONNX tensor: 042_convolutional [12/16/2021-09:58:09] [V] [TRT] 042_convolutional [Conv] outputs: [042_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 042_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 042_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 042_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 042_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 042_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 042_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 042_convolutional_bn [BatchNormalization] inputs: [042_convolutional -> (5, 256, 32, 32)[FLOAT]], [042_convolutional_bn_scale -> (256)[FLOAT]], [042_convolutional_bn_bias -> (256)[FLOAT]], [042_convolutional_bn_mean -> (256)[FLOAT]], [042_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 042_convolutional_bn for ONNX node: 042_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 042_convolutional_bn for ONNX tensor: 042_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 042_convolutional_bn [BatchNormalization] outputs: [042_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 042_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 042_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 042_convolutional_lrelu [LeakyRelu] inputs: [042_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 042_convolutional_lrelu for ONNX node: 042_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 042_convolutional_lrelu for ONNX tensor: 042_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 042_convolutional_lrelu [LeakyRelu] outputs: [042_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 043_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 042_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 043_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 043_convolutional [Conv] inputs: [042_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [043_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 043_convolutional for ONNX node: 043_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 043_convolutional for ONNX tensor: 043_convolutional [12/16/2021-09:58:09] [V] [TRT] 043_convolutional [Conv] outputs: [043_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 043_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 043_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 043_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 043_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 043_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 043_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 043_convolutional_bn [BatchNormalization] inputs: [043_convolutional -> (5, 512, 32, 32)[FLOAT]], [043_convolutional_bn_scale -> (512)[FLOAT]], [043_convolutional_bn_bias -> (512)[FLOAT]], [043_convolutional_bn_mean -> (512)[FLOAT]], [043_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 043_convolutional_bn for ONNX node: 043_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 043_convolutional_bn for ONNX tensor: 043_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 043_convolutional_bn [BatchNormalization] outputs: [043_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 043_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 043_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 043_convolutional_lrelu [LeakyRelu] inputs: [043_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 043_convolutional_lrelu for ONNX node: 043_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 043_convolutional_lrelu for ONNX tensor: 043_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 043_convolutional_lrelu [LeakyRelu] outputs: [043_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 044_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 043_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 041_shortcut [12/16/2021-09:58:09] [V] [TRT] 044_shortcut [Add] inputs: [043_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [041_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 044_shortcut for ONNX node: 044_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 044_shortcut for ONNX tensor: 044_shortcut [12/16/2021-09:58:09] [V] [TRT] 044_shortcut [Add] outputs: [044_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 045_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 044_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 045_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 045_convolutional [Conv] inputs: [044_shortcut -> (5, 512, 32, 32)[FLOAT]], [045_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 045_convolutional for ONNX node: 045_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 045_convolutional for ONNX tensor: 045_convolutional [12/16/2021-09:58:09] [V] [TRT] 045_convolutional [Conv] outputs: [045_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 045_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 045_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 045_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 045_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 045_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 045_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 045_convolutional_bn [BatchNormalization] inputs: [045_convolutional -> (5, 256, 32, 32)[FLOAT]], [045_convolutional_bn_scale -> (256)[FLOAT]], [045_convolutional_bn_bias -> (256)[FLOAT]], [045_convolutional_bn_mean -> (256)[FLOAT]], [045_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 045_convolutional_bn for ONNX node: 045_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 045_convolutional_bn for ONNX tensor: 045_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 045_convolutional_bn [BatchNormalization] outputs: [045_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 045_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 045_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 045_convolutional_lrelu [LeakyRelu] inputs: [045_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 045_convolutional_lrelu for ONNX node: 045_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 045_convolutional_lrelu for ONNX tensor: 045_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 045_convolutional_lrelu [LeakyRelu] outputs: [045_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 046_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 045_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 046_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 046_convolutional [Conv] inputs: [045_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [046_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 046_convolutional for ONNX node: 046_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 046_convolutional for ONNX tensor: 046_convolutional [12/16/2021-09:58:09] [V] [TRT] 046_convolutional [Conv] outputs: [046_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 046_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 046_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 046_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 046_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 046_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 046_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 046_convolutional_bn [BatchNormalization] inputs: [046_convolutional -> (5, 512, 32, 32)[FLOAT]], [046_convolutional_bn_scale -> (512)[FLOAT]], [046_convolutional_bn_bias -> (512)[FLOAT]], [046_convolutional_bn_mean -> (512)[FLOAT]], [046_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 046_convolutional_bn for ONNX node: 046_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 046_convolutional_bn for ONNX tensor: 046_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 046_convolutional_bn [BatchNormalization] outputs: [046_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 046_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 046_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 046_convolutional_lrelu [LeakyRelu] inputs: [046_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 046_convolutional_lrelu for ONNX node: 046_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 046_convolutional_lrelu for ONNX tensor: 046_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 046_convolutional_lrelu [LeakyRelu] outputs: [046_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 047_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 046_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 044_shortcut [12/16/2021-09:58:09] [V] [TRT] 047_shortcut [Add] inputs: [046_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [044_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 047_shortcut for ONNX node: 047_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 047_shortcut for ONNX tensor: 047_shortcut [12/16/2021-09:58:09] [V] [TRT] 047_shortcut [Add] outputs: [047_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 048_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 047_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 048_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 048_convolutional [Conv] inputs: [047_shortcut -> (5, 512, 32, 32)[FLOAT]], [048_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 048_convolutional for ONNX node: 048_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 048_convolutional for ONNX tensor: 048_convolutional [12/16/2021-09:58:09] [V] [TRT] 048_convolutional [Conv] outputs: [048_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 048_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 048_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 048_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 048_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 048_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 048_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 048_convolutional_bn [BatchNormalization] inputs: [048_convolutional -> (5, 256, 32, 32)[FLOAT]], [048_convolutional_bn_scale -> (256)[FLOAT]], [048_convolutional_bn_bias -> (256)[FLOAT]], [048_convolutional_bn_mean -> (256)[FLOAT]], [048_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 048_convolutional_bn for ONNX node: 048_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 048_convolutional_bn for ONNX tensor: 048_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 048_convolutional_bn [BatchNormalization] outputs: [048_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 048_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 048_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 048_convolutional_lrelu [LeakyRelu] inputs: [048_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 048_convolutional_lrelu for ONNX node: 048_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 048_convolutional_lrelu for ONNX tensor: 048_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 048_convolutional_lrelu [LeakyRelu] outputs: [048_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 049_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 048_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 049_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 049_convolutional [Conv] inputs: [048_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [049_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 049_convolutional for ONNX node: 049_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 049_convolutional for ONNX tensor: 049_convolutional [12/16/2021-09:58:09] [V] [TRT] 049_convolutional [Conv] outputs: [049_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 049_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 049_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 049_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 049_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 049_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 049_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 049_convolutional_bn [BatchNormalization] inputs: [049_convolutional -> (5, 512, 32, 32)[FLOAT]], [049_convolutional_bn_scale -> (512)[FLOAT]], [049_convolutional_bn_bias -> (512)[FLOAT]], [049_convolutional_bn_mean -> (512)[FLOAT]], [049_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 049_convolutional_bn for ONNX node: 049_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 049_convolutional_bn for ONNX tensor: 049_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 049_convolutional_bn [BatchNormalization] outputs: [049_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 049_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 049_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 049_convolutional_lrelu [LeakyRelu] inputs: [049_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 049_convolutional_lrelu for ONNX node: 049_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 049_convolutional_lrelu for ONNX tensor: 049_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 049_convolutional_lrelu [LeakyRelu] outputs: [049_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 050_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 049_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 047_shortcut [12/16/2021-09:58:09] [V] [TRT] 050_shortcut [Add] inputs: [049_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [047_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 050_shortcut for ONNX node: 050_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 050_shortcut for ONNX tensor: 050_shortcut [12/16/2021-09:58:09] [V] [TRT] 050_shortcut [Add] outputs: [050_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 051_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 050_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 051_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 051_convolutional [Conv] inputs: [050_shortcut -> (5, 512, 32, 32)[FLOAT]], [051_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 051_convolutional for ONNX node: 051_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 051_convolutional for ONNX tensor: 051_convolutional [12/16/2021-09:58:09] [V] [TRT] 051_convolutional [Conv] outputs: [051_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 051_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 051_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 051_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 051_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 051_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 051_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 051_convolutional_bn [BatchNormalization] inputs: [051_convolutional -> (5, 256, 32, 32)[FLOAT]], [051_convolutional_bn_scale -> (256)[FLOAT]], [051_convolutional_bn_bias -> (256)[FLOAT]], [051_convolutional_bn_mean -> (256)[FLOAT]], [051_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 051_convolutional_bn for ONNX node: 051_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 051_convolutional_bn for ONNX tensor: 051_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 051_convolutional_bn [BatchNormalization] outputs: [051_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 051_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 051_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 051_convolutional_lrelu [LeakyRelu] inputs: [051_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 051_convolutional_lrelu for ONNX node: 051_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 051_convolutional_lrelu for ONNX tensor: 051_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 051_convolutional_lrelu [LeakyRelu] outputs: [051_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 052_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 051_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 052_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 052_convolutional [Conv] inputs: [051_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [052_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 052_convolutional for ONNX node: 052_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 052_convolutional for ONNX tensor: 052_convolutional [12/16/2021-09:58:09] [V] [TRT] 052_convolutional [Conv] outputs: [052_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 052_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 052_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 052_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 052_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 052_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 052_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 052_convolutional_bn [BatchNormalization] inputs: [052_convolutional -> (5, 512, 32, 32)[FLOAT]], [052_convolutional_bn_scale -> (512)[FLOAT]], [052_convolutional_bn_bias -> (512)[FLOAT]], [052_convolutional_bn_mean -> (512)[FLOAT]], [052_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 052_convolutional_bn for ONNX node: 052_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 052_convolutional_bn for ONNX tensor: 052_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 052_convolutional_bn [BatchNormalization] outputs: [052_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 052_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 052_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 052_convolutional_lrelu [LeakyRelu] inputs: [052_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 052_convolutional_lrelu for ONNX node: 052_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 052_convolutional_lrelu for ONNX tensor: 052_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 052_convolutional_lrelu [LeakyRelu] outputs: [052_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 053_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 052_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 050_shortcut [12/16/2021-09:58:09] [V] [TRT] 053_shortcut [Add] inputs: [052_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [050_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 053_shortcut for ONNX node: 053_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 053_shortcut for ONNX tensor: 053_shortcut [12/16/2021-09:58:09] [V] [TRT] 053_shortcut [Add] outputs: [053_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 054_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 053_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 054_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 054_convolutional [Conv] inputs: [053_shortcut -> (5, 512, 32, 32)[FLOAT]], [054_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 054_convolutional for ONNX node: 054_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 054_convolutional for ONNX tensor: 054_convolutional [12/16/2021-09:58:09] [V] [TRT] 054_convolutional [Conv] outputs: [054_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 054_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 054_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 054_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 054_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 054_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 054_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 054_convolutional_bn [BatchNormalization] inputs: [054_convolutional -> (5, 256, 32, 32)[FLOAT]], [054_convolutional_bn_scale -> (256)[FLOAT]], [054_convolutional_bn_bias -> (256)[FLOAT]], [054_convolutional_bn_mean -> (256)[FLOAT]], [054_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 054_convolutional_bn for ONNX node: 054_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 054_convolutional_bn for ONNX tensor: 054_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 054_convolutional_bn [BatchNormalization] outputs: [054_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 054_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 054_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 054_convolutional_lrelu [LeakyRelu] inputs: [054_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 054_convolutional_lrelu for ONNX node: 054_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 054_convolutional_lrelu for ONNX tensor: 054_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 054_convolutional_lrelu [LeakyRelu] outputs: [054_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 055_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 054_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 055_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 055_convolutional [Conv] inputs: [054_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [055_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 055_convolutional for ONNX node: 055_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 055_convolutional for ONNX tensor: 055_convolutional [12/16/2021-09:58:09] [V] [TRT] 055_convolutional [Conv] outputs: [055_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 055_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 055_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 055_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 055_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 055_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 055_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 055_convolutional_bn [BatchNormalization] inputs: [055_convolutional -> (5, 512, 32, 32)[FLOAT]], [055_convolutional_bn_scale -> (512)[FLOAT]], [055_convolutional_bn_bias -> (512)[FLOAT]], [055_convolutional_bn_mean -> (512)[FLOAT]], [055_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 055_convolutional_bn for ONNX node: 055_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 055_convolutional_bn for ONNX tensor: 055_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 055_convolutional_bn [BatchNormalization] outputs: [055_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 055_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 055_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 055_convolutional_lrelu [LeakyRelu] inputs: [055_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 055_convolutional_lrelu for ONNX node: 055_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 055_convolutional_lrelu for ONNX tensor: 055_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 055_convolutional_lrelu [LeakyRelu] outputs: [055_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 056_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 055_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 053_shortcut [12/16/2021-09:58:09] [V] [TRT] 056_shortcut [Add] inputs: [055_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [053_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 056_shortcut for ONNX node: 056_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 056_shortcut for ONNX tensor: 056_shortcut [12/16/2021-09:58:09] [V] [TRT] 056_shortcut [Add] outputs: [056_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 057_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 056_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 057_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 057_convolutional [Conv] inputs: [056_shortcut -> (5, 512, 32, 32)[FLOAT]], [057_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 057_convolutional for ONNX node: 057_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 057_convolutional for ONNX tensor: 057_convolutional [12/16/2021-09:58:09] [V] [TRT] 057_convolutional [Conv] outputs: [057_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 057_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 057_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 057_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 057_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 057_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 057_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 057_convolutional_bn [BatchNormalization] inputs: [057_convolutional -> (5, 256, 32, 32)[FLOAT]], [057_convolutional_bn_scale -> (256)[FLOAT]], [057_convolutional_bn_bias -> (256)[FLOAT]], [057_convolutional_bn_mean -> (256)[FLOAT]], [057_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 057_convolutional_bn for ONNX node: 057_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 057_convolutional_bn for ONNX tensor: 057_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 057_convolutional_bn [BatchNormalization] outputs: [057_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 057_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 057_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 057_convolutional_lrelu [LeakyRelu] inputs: [057_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 057_convolutional_lrelu for ONNX node: 057_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 057_convolutional_lrelu for ONNX tensor: 057_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 057_convolutional_lrelu [LeakyRelu] outputs: [057_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 058_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 057_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 058_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 058_convolutional [Conv] inputs: [057_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [058_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 058_convolutional for ONNX node: 058_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 058_convolutional for ONNX tensor: 058_convolutional [12/16/2021-09:58:09] [V] [TRT] 058_convolutional [Conv] outputs: [058_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 058_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 058_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 058_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 058_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 058_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 058_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 058_convolutional_bn [BatchNormalization] inputs: [058_convolutional -> (5, 512, 32, 32)[FLOAT]], [058_convolutional_bn_scale -> (512)[FLOAT]], [058_convolutional_bn_bias -> (512)[FLOAT]], [058_convolutional_bn_mean -> (512)[FLOAT]], [058_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 058_convolutional_bn for ONNX node: 058_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 058_convolutional_bn for ONNX tensor: 058_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 058_convolutional_bn [BatchNormalization] outputs: [058_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 058_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 058_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 058_convolutional_lrelu [LeakyRelu] inputs: [058_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 058_convolutional_lrelu for ONNX node: 058_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 058_convolutional_lrelu for ONNX tensor: 058_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 058_convolutional_lrelu [LeakyRelu] outputs: [058_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 059_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 058_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 056_shortcut [12/16/2021-09:58:09] [V] [TRT] 059_shortcut [Add] inputs: [058_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [056_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 059_shortcut for ONNX node: 059_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 059_shortcut for ONNX tensor: 059_shortcut [12/16/2021-09:58:09] [V] [TRT] 059_shortcut [Add] outputs: [059_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 060_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 059_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 060_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 060_convolutional [Conv] inputs: [059_shortcut -> (5, 512, 32, 32)[FLOAT]], [060_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 060_convolutional for ONNX node: 060_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 060_convolutional for ONNX tensor: 060_convolutional [12/16/2021-09:58:09] [V] [TRT] 060_convolutional [Conv] outputs: [060_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 060_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 060_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 060_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 060_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 060_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 060_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 060_convolutional_bn [BatchNormalization] inputs: [060_convolutional -> (5, 256, 32, 32)[FLOAT]], [060_convolutional_bn_scale -> (256)[FLOAT]], [060_convolutional_bn_bias -> (256)[FLOAT]], [060_convolutional_bn_mean -> (256)[FLOAT]], [060_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 060_convolutional_bn for ONNX node: 060_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 060_convolutional_bn for ONNX tensor: 060_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 060_convolutional_bn [BatchNormalization] outputs: [060_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 060_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 060_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 060_convolutional_lrelu [LeakyRelu] inputs: [060_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 060_convolutional_lrelu for ONNX node: 060_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 060_convolutional_lrelu for ONNX tensor: 060_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 060_convolutional_lrelu [LeakyRelu] outputs: [060_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 061_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 060_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 061_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 061_convolutional [Conv] inputs: [060_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [061_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 061_convolutional for ONNX node: 061_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 061_convolutional for ONNX tensor: 061_convolutional [12/16/2021-09:58:09] [V] [TRT] 061_convolutional [Conv] outputs: [061_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 061_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 061_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 061_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 061_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 061_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 061_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 061_convolutional_bn [BatchNormalization] inputs: [061_convolutional -> (5, 512, 32, 32)[FLOAT]], [061_convolutional_bn_scale -> (512)[FLOAT]], [061_convolutional_bn_bias -> (512)[FLOAT]], [061_convolutional_bn_mean -> (512)[FLOAT]], [061_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 061_convolutional_bn for ONNX node: 061_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 061_convolutional_bn for ONNX tensor: 061_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 061_convolutional_bn [BatchNormalization] outputs: [061_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 061_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 061_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 061_convolutional_lrelu [LeakyRelu] inputs: [061_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 061_convolutional_lrelu for ONNX node: 061_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 061_convolutional_lrelu for ONNX tensor: 061_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 061_convolutional_lrelu [LeakyRelu] outputs: [061_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 062_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 061_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 059_shortcut [12/16/2021-09:58:09] [V] [TRT] 062_shortcut [Add] inputs: [061_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [059_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 062_shortcut for ONNX node: 062_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 062_shortcut for ONNX tensor: 062_shortcut [12/16/2021-09:58:09] [V] [TRT] 062_shortcut [Add] outputs: [062_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 063_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 062_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 063_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 063_convolutional [Conv] inputs: [062_shortcut -> (5, 512, 32, 32)[FLOAT]], [063_convolutional_conv_weights -> (1024, 512, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 063_convolutional for ONNX node: 063_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (2, 2), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 1024 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 063_convolutional for ONNX tensor: 063_convolutional [12/16/2021-09:58:09] [V] [TRT] 063_convolutional [Conv] outputs: [063_convolutional -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 063_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 063_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 063_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 063_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 063_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 063_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 063_convolutional_bn [BatchNormalization] inputs: [063_convolutional -> (5, 1024, 16, 16)[FLOAT]], [063_convolutional_bn_scale -> (1024)[FLOAT]], [063_convolutional_bn_bias -> (1024)[FLOAT]], [063_convolutional_bn_mean -> (1024)[FLOAT]], [063_convolutional_bn_var -> (1024)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 063_convolutional_bn for ONNX node: 063_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 063_convolutional_bn for ONNX tensor: 063_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 063_convolutional_bn [BatchNormalization] outputs: [063_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 063_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 063_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 063_convolutional_lrelu [LeakyRelu] inputs: [063_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 063_convolutional_lrelu for ONNX node: 063_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 063_convolutional_lrelu for ONNX tensor: 063_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 063_convolutional_lrelu [LeakyRelu] outputs: [063_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 064_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 063_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 064_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 064_convolutional [Conv] inputs: [063_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [064_convolutional_conv_weights -> (512, 1024, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 064_convolutional for ONNX node: 064_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 064_convolutional for ONNX tensor: 064_convolutional [12/16/2021-09:58:09] [V] [TRT] 064_convolutional [Conv] outputs: [064_convolutional -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 064_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 064_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 064_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 064_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 064_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 064_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 064_convolutional_bn [BatchNormalization] inputs: [064_convolutional -> (5, 512, 16, 16)[FLOAT]], [064_convolutional_bn_scale -> (512)[FLOAT]], [064_convolutional_bn_bias -> (512)[FLOAT]], [064_convolutional_bn_mean -> (512)[FLOAT]], [064_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 064_convolutional_bn for ONNX node: 064_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 064_convolutional_bn for ONNX tensor: 064_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 064_convolutional_bn [BatchNormalization] outputs: [064_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 064_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 064_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 064_convolutional_lrelu [LeakyRelu] inputs: [064_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 064_convolutional_lrelu for ONNX node: 064_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 064_convolutional_lrelu for ONNX tensor: 064_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 064_convolutional_lrelu [LeakyRelu] outputs: [064_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 065_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 064_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 065_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 065_convolutional [Conv] inputs: [064_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [065_convolutional_conv_weights -> (1024, 512, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 065_convolutional for ONNX node: 065_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 1024 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 065_convolutional for ONNX tensor: 065_convolutional [12/16/2021-09:58:09] [V] [TRT] 065_convolutional [Conv] outputs: [065_convolutional -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 065_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 065_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 065_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 065_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 065_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 065_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 065_convolutional_bn [BatchNormalization] inputs: [065_convolutional -> (5, 1024, 16, 16)[FLOAT]], [065_convolutional_bn_scale -> (1024)[FLOAT]], [065_convolutional_bn_bias -> (1024)[FLOAT]], [065_convolutional_bn_mean -> (1024)[FLOAT]], [065_convolutional_bn_var -> (1024)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 065_convolutional_bn for ONNX node: 065_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 065_convolutional_bn for ONNX tensor: 065_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 065_convolutional_bn [BatchNormalization] outputs: [065_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 065_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 065_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 065_convolutional_lrelu [LeakyRelu] inputs: [065_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 065_convolutional_lrelu for ONNX node: 065_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 065_convolutional_lrelu for ONNX tensor: 065_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 065_convolutional_lrelu [LeakyRelu] outputs: [065_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 066_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 065_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 063_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 066_shortcut [Add] inputs: [065_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [063_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 066_shortcut for ONNX node: 066_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 066_shortcut for ONNX tensor: 066_shortcut [12/16/2021-09:58:09] [V] [TRT] 066_shortcut [Add] outputs: [066_shortcut -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 067_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 066_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 067_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 067_convolutional [Conv] inputs: [066_shortcut -> (5, 1024, 16, 16)[FLOAT]], [067_convolutional_conv_weights -> (512, 1024, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 067_convolutional for ONNX node: 067_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 067_convolutional for ONNX tensor: 067_convolutional [12/16/2021-09:58:09] [V] [TRT] 067_convolutional [Conv] outputs: [067_convolutional -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 067_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 067_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 067_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 067_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 067_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 067_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 067_convolutional_bn [BatchNormalization] inputs: [067_convolutional -> (5, 512, 16, 16)[FLOAT]], [067_convolutional_bn_scale -> (512)[FLOAT]], [067_convolutional_bn_bias -> (512)[FLOAT]], [067_convolutional_bn_mean -> (512)[FLOAT]], [067_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 067_convolutional_bn for ONNX node: 067_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 067_convolutional_bn for ONNX tensor: 067_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 067_convolutional_bn [BatchNormalization] outputs: [067_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 067_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 067_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 067_convolutional_lrelu [LeakyRelu] inputs: [067_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 067_convolutional_lrelu for ONNX node: 067_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 067_convolutional_lrelu for ONNX tensor: 067_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 067_convolutional_lrelu [LeakyRelu] outputs: [067_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 068_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 067_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 068_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 068_convolutional [Conv] inputs: [067_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [068_convolutional_conv_weights -> (1024, 512, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 068_convolutional for ONNX node: 068_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 1024 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 068_convolutional for ONNX tensor: 068_convolutional [12/16/2021-09:58:09] [V] [TRT] 068_convolutional [Conv] outputs: [068_convolutional -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 068_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 068_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 068_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 068_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 068_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 068_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 068_convolutional_bn [BatchNormalization] inputs: [068_convolutional -> (5, 1024, 16, 16)[FLOAT]], [068_convolutional_bn_scale -> (1024)[FLOAT]], [068_convolutional_bn_bias -> (1024)[FLOAT]], [068_convolutional_bn_mean -> (1024)[FLOAT]], [068_convolutional_bn_var -> (1024)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 068_convolutional_bn for ONNX node: 068_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 068_convolutional_bn for ONNX tensor: 068_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 068_convolutional_bn [BatchNormalization] outputs: [068_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 068_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 068_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 068_convolutional_lrelu [LeakyRelu] inputs: [068_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 068_convolutional_lrelu for ONNX node: 068_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 068_convolutional_lrelu for ONNX tensor: 068_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 068_convolutional_lrelu [LeakyRelu] outputs: [068_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 069_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 068_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 066_shortcut [12/16/2021-09:58:09] [V] [TRT] 069_shortcut [Add] inputs: [068_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [066_shortcut -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 069_shortcut for ONNX node: 069_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 069_shortcut for ONNX tensor: 069_shortcut [12/16/2021-09:58:09] [V] [TRT] 069_shortcut [Add] outputs: [069_shortcut -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 070_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 069_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 070_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 070_convolutional [Conv] inputs: [069_shortcut -> (5, 1024, 16, 16)[FLOAT]], [070_convolutional_conv_weights -> (512, 1024, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 070_convolutional for ONNX node: 070_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 070_convolutional for ONNX tensor: 070_convolutional [12/16/2021-09:58:09] [V] [TRT] 070_convolutional [Conv] outputs: [070_convolutional -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 070_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 070_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 070_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 070_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 070_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 070_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 070_convolutional_bn [BatchNormalization] inputs: [070_convolutional -> (5, 512, 16, 16)[FLOAT]], [070_convolutional_bn_scale -> (512)[FLOAT]], [070_convolutional_bn_bias -> (512)[FLOAT]], [070_convolutional_bn_mean -> (512)[FLOAT]], [070_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 070_convolutional_bn for ONNX node: 070_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 070_convolutional_bn for ONNX tensor: 070_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 070_convolutional_bn [BatchNormalization] outputs: [070_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 070_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 070_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 070_convolutional_lrelu [LeakyRelu] inputs: [070_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 070_convolutional_lrelu for ONNX node: 070_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 070_convolutional_lrelu for ONNX tensor: 070_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 070_convolutional_lrelu [LeakyRelu] outputs: [070_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 071_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 070_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 071_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 071_convolutional [Conv] inputs: [070_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [071_convolutional_conv_weights -> (1024, 512, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 071_convolutional for ONNX node: 071_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 1024 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 071_convolutional for ONNX tensor: 071_convolutional [12/16/2021-09:58:09] [V] [TRT] 071_convolutional [Conv] outputs: [071_convolutional -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 071_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 071_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 071_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 071_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 071_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 071_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 071_convolutional_bn [BatchNormalization] inputs: [071_convolutional -> (5, 1024, 16, 16)[FLOAT]], [071_convolutional_bn_scale -> (1024)[FLOAT]], [071_convolutional_bn_bias -> (1024)[FLOAT]], [071_convolutional_bn_mean -> (1024)[FLOAT]], [071_convolutional_bn_var -> (1024)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 071_convolutional_bn for ONNX node: 071_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 071_convolutional_bn for ONNX tensor: 071_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 071_convolutional_bn [BatchNormalization] outputs: [071_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 071_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 071_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 071_convolutional_lrelu [LeakyRelu] inputs: [071_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 071_convolutional_lrelu for ONNX node: 071_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 071_convolutional_lrelu for ONNX tensor: 071_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 071_convolutional_lrelu [LeakyRelu] outputs: [071_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 072_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 071_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 069_shortcut [12/16/2021-09:58:09] [V] [TRT] 072_shortcut [Add] inputs: [071_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [069_shortcut -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 072_shortcut for ONNX node: 072_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 072_shortcut for ONNX tensor: 072_shortcut [12/16/2021-09:58:09] [V] [TRT] 072_shortcut [Add] outputs: [072_shortcut -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 073_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 072_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 073_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 073_convolutional [Conv] inputs: [072_shortcut -> (5, 1024, 16, 16)[FLOAT]], [073_convolutional_conv_weights -> (512, 1024, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 073_convolutional for ONNX node: 073_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 073_convolutional for ONNX tensor: 073_convolutional [12/16/2021-09:58:09] [V] [TRT] 073_convolutional [Conv] outputs: [073_convolutional -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 073_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 073_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 073_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 073_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 073_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 073_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 073_convolutional_bn [BatchNormalization] inputs: [073_convolutional -> (5, 512, 16, 16)[FLOAT]], [073_convolutional_bn_scale -> (512)[FLOAT]], [073_convolutional_bn_bias -> (512)[FLOAT]], [073_convolutional_bn_mean -> (512)[FLOAT]], [073_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 073_convolutional_bn for ONNX node: 073_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 073_convolutional_bn for ONNX tensor: 073_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 073_convolutional_bn [BatchNormalization] outputs: [073_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 073_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 073_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 073_convolutional_lrelu [LeakyRelu] inputs: [073_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 073_convolutional_lrelu for ONNX node: 073_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 073_convolutional_lrelu for ONNX tensor: 073_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 073_convolutional_lrelu [LeakyRelu] outputs: [073_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 074_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 073_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 074_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 074_convolutional [Conv] inputs: [073_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [074_convolutional_conv_weights -> (1024, 512, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 074_convolutional for ONNX node: 074_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 1024 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 074_convolutional for ONNX tensor: 074_convolutional [12/16/2021-09:58:09] [V] [TRT] 074_convolutional [Conv] outputs: [074_convolutional -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 074_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 074_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 074_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 074_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 074_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 074_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 074_convolutional_bn [BatchNormalization] inputs: [074_convolutional -> (5, 1024, 16, 16)[FLOAT]], [074_convolutional_bn_scale -> (1024)[FLOAT]], [074_convolutional_bn_bias -> (1024)[FLOAT]], [074_convolutional_bn_mean -> (1024)[FLOAT]], [074_convolutional_bn_var -> (1024)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 074_convolutional_bn for ONNX node: 074_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 074_convolutional_bn for ONNX tensor: 074_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 074_convolutional_bn [BatchNormalization] outputs: [074_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 074_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 074_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 074_convolutional_lrelu [LeakyRelu] inputs: [074_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 074_convolutional_lrelu for ONNX node: 074_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 074_convolutional_lrelu for ONNX tensor: 074_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 074_convolutional_lrelu [LeakyRelu] outputs: [074_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 075_shortcut [Add] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 074_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 072_shortcut [12/16/2021-09:58:09] [V] [TRT] 075_shortcut [Add] inputs: [074_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [072_shortcut -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 075_shortcut for ONNX node: 075_shortcut [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 075_shortcut for ONNX tensor: 075_shortcut [12/16/2021-09:58:09] [V] [TRT] 075_shortcut [Add] outputs: [075_shortcut -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 076_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 075_shortcut [12/16/2021-09:58:09] [V] [TRT] Searching for input: 076_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 076_convolutional [Conv] inputs: [075_shortcut -> (5, 1024, 16, 16)[FLOAT]], [076_convolutional_conv_weights -> (512, 1024, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 076_convolutional for ONNX node: 076_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 076_convolutional for ONNX tensor: 076_convolutional [12/16/2021-09:58:09] [V] [TRT] 076_convolutional [Conv] outputs: [076_convolutional -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 076_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 076_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 076_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 076_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 076_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 076_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 076_convolutional_bn [BatchNormalization] inputs: [076_convolutional -> (5, 512, 16, 16)[FLOAT]], [076_convolutional_bn_scale -> (512)[FLOAT]], [076_convolutional_bn_bias -> (512)[FLOAT]], [076_convolutional_bn_mean -> (512)[FLOAT]], [076_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 076_convolutional_bn for ONNX node: 076_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 076_convolutional_bn for ONNX tensor: 076_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 076_convolutional_bn [BatchNormalization] outputs: [076_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 076_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 076_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 076_convolutional_lrelu [LeakyRelu] inputs: [076_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 076_convolutional_lrelu for ONNX node: 076_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 076_convolutional_lrelu for ONNX tensor: 076_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 076_convolutional_lrelu [LeakyRelu] outputs: [076_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 077_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 076_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 077_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 077_convolutional [Conv] inputs: [076_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [077_convolutional_conv_weights -> (1024, 512, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 077_convolutional for ONNX node: 077_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 1024 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 077_convolutional for ONNX tensor: 077_convolutional [12/16/2021-09:58:09] [V] [TRT] 077_convolutional [Conv] outputs: [077_convolutional -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 077_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 077_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 077_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 077_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 077_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 077_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 077_convolutional_bn [BatchNormalization] inputs: [077_convolutional -> (5, 1024, 16, 16)[FLOAT]], [077_convolutional_bn_scale -> (1024)[FLOAT]], [077_convolutional_bn_bias -> (1024)[FLOAT]], [077_convolutional_bn_mean -> (1024)[FLOAT]], [077_convolutional_bn_var -> (1024)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 077_convolutional_bn for ONNX node: 077_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 077_convolutional_bn for ONNX tensor: 077_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 077_convolutional_bn [BatchNormalization] outputs: [077_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 077_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 077_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 077_convolutional_lrelu [LeakyRelu] inputs: [077_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 077_convolutional_lrelu for ONNX node: 077_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 077_convolutional_lrelu for ONNX tensor: 077_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 077_convolutional_lrelu [LeakyRelu] outputs: [077_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 078_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 077_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 078_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 078_convolutional [Conv] inputs: [077_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [078_convolutional_conv_weights -> (512, 1024, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 078_convolutional for ONNX node: 078_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 078_convolutional for ONNX tensor: 078_convolutional [12/16/2021-09:58:09] [V] [TRT] 078_convolutional [Conv] outputs: [078_convolutional -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 078_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 078_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 078_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 078_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 078_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 078_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 078_convolutional_bn [BatchNormalization] inputs: [078_convolutional -> (5, 512, 16, 16)[FLOAT]], [078_convolutional_bn_scale -> (512)[FLOAT]], [078_convolutional_bn_bias -> (512)[FLOAT]], [078_convolutional_bn_mean -> (512)[FLOAT]], [078_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 078_convolutional_bn for ONNX node: 078_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 078_convolutional_bn for ONNX tensor: 078_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 078_convolutional_bn [BatchNormalization] outputs: [078_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 078_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 078_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 078_convolutional_lrelu [LeakyRelu] inputs: [078_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 078_convolutional_lrelu for ONNX node: 078_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 078_convolutional_lrelu for ONNX tensor: 078_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 078_convolutional_lrelu [LeakyRelu] outputs: [078_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 079_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 078_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 079_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 079_convolutional [Conv] inputs: [078_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [079_convolutional_conv_weights -> (1024, 512, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 079_convolutional for ONNX node: 079_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 1024 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 079_convolutional for ONNX tensor: 079_convolutional [12/16/2021-09:58:09] [V] [TRT] 079_convolutional [Conv] outputs: [079_convolutional -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 079_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 079_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 079_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 079_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 079_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 079_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 079_convolutional_bn [BatchNormalization] inputs: [079_convolutional -> (5, 1024, 16, 16)[FLOAT]], [079_convolutional_bn_scale -> (1024)[FLOAT]], [079_convolutional_bn_bias -> (1024)[FLOAT]], [079_convolutional_bn_mean -> (1024)[FLOAT]], [079_convolutional_bn_var -> (1024)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 079_convolutional_bn for ONNX node: 079_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 079_convolutional_bn for ONNX tensor: 079_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 079_convolutional_bn [BatchNormalization] outputs: [079_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 079_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 079_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 079_convolutional_lrelu [LeakyRelu] inputs: [079_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 079_convolutional_lrelu for ONNX node: 079_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 079_convolutional_lrelu for ONNX tensor: 079_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 079_convolutional_lrelu [LeakyRelu] outputs: [079_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 080_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 079_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 080_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 080_convolutional [Conv] inputs: [079_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [080_convolutional_conv_weights -> (512, 1024, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 080_convolutional for ONNX node: 080_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 080_convolutional for ONNX tensor: 080_convolutional [12/16/2021-09:58:09] [V] [TRT] 080_convolutional [Conv] outputs: [080_convolutional -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 080_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 080_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 080_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 080_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 080_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 080_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 080_convolutional_bn [BatchNormalization] inputs: [080_convolutional -> (5, 512, 16, 16)[FLOAT]], [080_convolutional_bn_scale -> (512)[FLOAT]], [080_convolutional_bn_bias -> (512)[FLOAT]], [080_convolutional_bn_mean -> (512)[FLOAT]], [080_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 080_convolutional_bn for ONNX node: 080_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 080_convolutional_bn for ONNX tensor: 080_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 080_convolutional_bn [BatchNormalization] outputs: [080_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 080_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 080_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 080_convolutional_lrelu [LeakyRelu] inputs: [080_convolutional_bn -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 080_convolutional_lrelu for ONNX node: 080_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 080_convolutional_lrelu for ONNX tensor: 080_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 080_convolutional_lrelu [LeakyRelu] outputs: [080_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 081_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 080_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 081_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 081_convolutional [Conv] inputs: [080_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [081_convolutional_conv_weights -> (1024, 512, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 081_convolutional for ONNX node: 081_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 1024 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 081_convolutional for ONNX tensor: 081_convolutional [12/16/2021-09:58:09] [V] [TRT] 081_convolutional [Conv] outputs: [081_convolutional -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 081_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 081_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 081_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 081_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 081_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 081_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 081_convolutional_bn [BatchNormalization] inputs: [081_convolutional -> (5, 1024, 16, 16)[FLOAT]], [081_convolutional_bn_scale -> (1024)[FLOAT]], [081_convolutional_bn_bias -> (1024)[FLOAT]], [081_convolutional_bn_mean -> (1024)[FLOAT]], [081_convolutional_bn_var -> (1024)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 081_convolutional_bn for ONNX node: 081_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 081_convolutional_bn for ONNX tensor: 081_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 081_convolutional_bn [BatchNormalization] outputs: [081_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 081_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 081_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 081_convolutional_lrelu [LeakyRelu] inputs: [081_convolutional_bn -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 081_convolutional_lrelu for ONNX node: 081_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 081_convolutional_lrelu for ONNX tensor: 081_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 081_convolutional_lrelu [LeakyRelu] outputs: [081_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 082_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 081_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 082_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Searching for input: 082_convolutional_conv_bias [12/16/2021-09:58:09] [V] [TRT] 082_convolutional [Conv] inputs: [081_convolutional_lrelu -> (5, 1024, 16, 16)[FLOAT]], [082_convolutional_conv_weights -> (33, 1024, 1, 1)[FLOAT]], [082_convolutional_conv_bias -> (33)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 1024, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 082_convolutional for ONNX node: 082_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 33 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 33, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 082_convolutional_368 for ONNX tensor: 082_convolutional [12/16/2021-09:58:09] [V] [TRT] 082_convolutional [Conv] outputs: [082_convolutional -> (5, 33, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 085_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 080_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 085_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 085_convolutional [Conv] inputs: [080_convolutional_lrelu -> (5, 512, 16, 16)[FLOAT]], [085_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 085_convolutional for ONNX node: 085_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 16, 16) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 085_convolutional for ONNX tensor: 085_convolutional [12/16/2021-09:58:09] [V] [TRT] 085_convolutional [Conv] outputs: [085_convolutional -> (5, 256, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 085_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 085_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 085_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 085_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 085_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 085_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 085_convolutional_bn [BatchNormalization] inputs: [085_convolutional -> (5, 256, 16, 16)[FLOAT]], [085_convolutional_bn_scale -> (256)[FLOAT]], [085_convolutional_bn_bias -> (256)[FLOAT]], [085_convolutional_bn_mean -> (256)[FLOAT]], [085_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 085_convolutional_bn for ONNX node: 085_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 085_convolutional_bn for ONNX tensor: 085_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 085_convolutional_bn [BatchNormalization] outputs: [085_convolutional_bn -> (5, 256, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 085_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 085_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 085_convolutional_lrelu [LeakyRelu] inputs: [085_convolutional_bn -> (5, 256, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 085_convolutional_lrelu for ONNX node: 085_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 085_convolutional_lrelu for ONNX tensor: 085_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 085_convolutional_lrelu [LeakyRelu] outputs: [085_convolutional_lrelu -> (5, 256, 16, 16)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 086_upsample [Upsample] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 085_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 086_upsample_scale [12/16/2021-09:58:09] [V] [TRT] 086_upsample [Upsample] inputs: [085_convolutional_lrelu -> (5, 256, 16, 16)[FLOAT]], [086_upsample_scale -> (4)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 086_upsample for ONNX node: 086_upsample [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 086_upsample for ONNX tensor: 086_upsample [12/16/2021-09:58:09] [V] [TRT] 086_upsample [Upsample] outputs: [086_upsample -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 087_route [Concat] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 086_upsample [12/16/2021-09:58:09] [V] [TRT] Searching for input: 062_shortcut [12/16/2021-09:58:09] [V] [TRT] 087_route [Concat] inputs: [086_upsample -> (5, 256, 32, 32)[FLOAT]], [062_shortcut -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 087_route for ONNX node: 087_route [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 087_route for ONNX tensor: 087_route [12/16/2021-09:58:09] [V] [TRT] 087_route [Concat] outputs: [087_route -> (5, 768, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 088_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 087_route [12/16/2021-09:58:09] [V] [TRT] Searching for input: 088_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 088_convolutional [Conv] inputs: [087_route -> (5, 768, 32, 32)[FLOAT]], [088_convolutional_conv_weights -> (256, 768, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 768, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 088_convolutional for ONNX node: 088_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 088_convolutional for ONNX tensor: 088_convolutional [12/16/2021-09:58:09] [V] [TRT] 088_convolutional [Conv] outputs: [088_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 088_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 088_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 088_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 088_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 088_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 088_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 088_convolutional_bn [BatchNormalization] inputs: [088_convolutional -> (5, 256, 32, 32)[FLOAT]], [088_convolutional_bn_scale -> (256)[FLOAT]], [088_convolutional_bn_bias -> (256)[FLOAT]], [088_convolutional_bn_mean -> (256)[FLOAT]], [088_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 088_convolutional_bn for ONNX node: 088_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 088_convolutional_bn for ONNX tensor: 088_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 088_convolutional_bn [BatchNormalization] outputs: [088_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 088_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 088_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 088_convolutional_lrelu [LeakyRelu] inputs: [088_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 088_convolutional_lrelu for ONNX node: 088_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 088_convolutional_lrelu for ONNX tensor: 088_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 088_convolutional_lrelu [LeakyRelu] outputs: [088_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 089_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 088_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 089_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 089_convolutional [Conv] inputs: [088_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [089_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 089_convolutional for ONNX node: 089_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 089_convolutional for ONNX tensor: 089_convolutional [12/16/2021-09:58:09] [V] [TRT] 089_convolutional [Conv] outputs: [089_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 089_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 089_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 089_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 089_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 089_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 089_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 089_convolutional_bn [BatchNormalization] inputs: [089_convolutional -> (5, 512, 32, 32)[FLOAT]], [089_convolutional_bn_scale -> (512)[FLOAT]], [089_convolutional_bn_bias -> (512)[FLOAT]], [089_convolutional_bn_mean -> (512)[FLOAT]], [089_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 089_convolutional_bn for ONNX node: 089_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 089_convolutional_bn for ONNX tensor: 089_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 089_convolutional_bn [BatchNormalization] outputs: [089_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 089_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 089_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 089_convolutional_lrelu [LeakyRelu] inputs: [089_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 089_convolutional_lrelu for ONNX node: 089_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 089_convolutional_lrelu for ONNX tensor: 089_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 089_convolutional_lrelu [LeakyRelu] outputs: [089_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 090_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 089_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 090_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 090_convolutional [Conv] inputs: [089_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [090_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 090_convolutional for ONNX node: 090_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 090_convolutional for ONNX tensor: 090_convolutional [12/16/2021-09:58:09] [V] [TRT] 090_convolutional [Conv] outputs: [090_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 090_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 090_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 090_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 090_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 090_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 090_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 090_convolutional_bn [BatchNormalization] inputs: [090_convolutional -> (5, 256, 32, 32)[FLOAT]], [090_convolutional_bn_scale -> (256)[FLOAT]], [090_convolutional_bn_bias -> (256)[FLOAT]], [090_convolutional_bn_mean -> (256)[FLOAT]], [090_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 090_convolutional_bn for ONNX node: 090_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 090_convolutional_bn for ONNX tensor: 090_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 090_convolutional_bn [BatchNormalization] outputs: [090_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 090_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 090_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 090_convolutional_lrelu [LeakyRelu] inputs: [090_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 090_convolutional_lrelu for ONNX node: 090_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 090_convolutional_lrelu for ONNX tensor: 090_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 090_convolutional_lrelu [LeakyRelu] outputs: [090_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 091_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 090_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 091_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 091_convolutional [Conv] inputs: [090_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [091_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 091_convolutional for ONNX node: 091_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 091_convolutional for ONNX tensor: 091_convolutional [12/16/2021-09:58:09] [V] [TRT] 091_convolutional [Conv] outputs: [091_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 091_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 091_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 091_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 091_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 091_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 091_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 091_convolutional_bn [BatchNormalization] inputs: [091_convolutional -> (5, 512, 32, 32)[FLOAT]], [091_convolutional_bn_scale -> (512)[FLOAT]], [091_convolutional_bn_bias -> (512)[FLOAT]], [091_convolutional_bn_mean -> (512)[FLOAT]], [091_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 091_convolutional_bn for ONNX node: 091_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 091_convolutional_bn for ONNX tensor: 091_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 091_convolutional_bn [BatchNormalization] outputs: [091_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 091_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 091_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 091_convolutional_lrelu [LeakyRelu] inputs: [091_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 091_convolutional_lrelu for ONNX node: 091_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 091_convolutional_lrelu for ONNX tensor: 091_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 091_convolutional_lrelu [LeakyRelu] outputs: [091_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 092_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 091_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 092_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 092_convolutional [Conv] inputs: [091_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [092_convolutional_conv_weights -> (256, 512, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 092_convolutional for ONNX node: 092_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 092_convolutional for ONNX tensor: 092_convolutional [12/16/2021-09:58:09] [V] [TRT] 092_convolutional [Conv] outputs: [092_convolutional -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 092_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 092_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 092_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 092_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 092_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 092_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 092_convolutional_bn [BatchNormalization] inputs: [092_convolutional -> (5, 256, 32, 32)[FLOAT]], [092_convolutional_bn_scale -> (256)[FLOAT]], [092_convolutional_bn_bias -> (256)[FLOAT]], [092_convolutional_bn_mean -> (256)[FLOAT]], [092_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 092_convolutional_bn for ONNX node: 092_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 092_convolutional_bn for ONNX tensor: 092_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 092_convolutional_bn [BatchNormalization] outputs: [092_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 092_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 092_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 092_convolutional_lrelu [LeakyRelu] inputs: [092_convolutional_bn -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 092_convolutional_lrelu for ONNX node: 092_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 092_convolutional_lrelu for ONNX tensor: 092_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 092_convolutional_lrelu [LeakyRelu] outputs: [092_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 093_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 092_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 093_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 093_convolutional [Conv] inputs: [092_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [093_convolutional_conv_weights -> (512, 256, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 093_convolutional for ONNX node: 093_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 512 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 093_convolutional for ONNX tensor: 093_convolutional [12/16/2021-09:58:09] [V] [TRT] 093_convolutional [Conv] outputs: [093_convolutional -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 093_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 093_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 093_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 093_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 093_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 093_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 093_convolutional_bn [BatchNormalization] inputs: [093_convolutional -> (5, 512, 32, 32)[FLOAT]], [093_convolutional_bn_scale -> (512)[FLOAT]], [093_convolutional_bn_bias -> (512)[FLOAT]], [093_convolutional_bn_mean -> (512)[FLOAT]], [093_convolutional_bn_var -> (512)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 093_convolutional_bn for ONNX node: 093_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 093_convolutional_bn for ONNX tensor: 093_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 093_convolutional_bn [BatchNormalization] outputs: [093_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 093_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 093_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 093_convolutional_lrelu [LeakyRelu] inputs: [093_convolutional_bn -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 093_convolutional_lrelu for ONNX node: 093_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 093_convolutional_lrelu for ONNX tensor: 093_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 093_convolutional_lrelu [LeakyRelu] outputs: [093_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 094_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 093_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 094_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Searching for input: 094_convolutional_conv_bias [12/16/2021-09:58:09] [V] [TRT] 094_convolutional [Conv] inputs: [093_convolutional_lrelu -> (5, 512, 32, 32)[FLOAT]], [094_convolutional_conv_weights -> (33, 512, 1, 1)[FLOAT]], [094_convolutional_conv_bias -> (33)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 512, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 094_convolutional for ONNX node: 094_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 33 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 33, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 094_convolutional_369 for ONNX tensor: 094_convolutional [12/16/2021-09:58:09] [V] [TRT] 094_convolutional [Conv] outputs: [094_convolutional -> (5, 33, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 097_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 092_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 097_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 097_convolutional [Conv] inputs: [092_convolutional_lrelu -> (5, 256, 32, 32)[FLOAT]], [097_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 097_convolutional for ONNX node: 097_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 32, 32) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 097_convolutional for ONNX tensor: 097_convolutional [12/16/2021-09:58:09] [V] [TRT] 097_convolutional [Conv] outputs: [097_convolutional -> (5, 128, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 097_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 097_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 097_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 097_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 097_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 097_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 097_convolutional_bn [BatchNormalization] inputs: [097_convolutional -> (5, 128, 32, 32)[FLOAT]], [097_convolutional_bn_scale -> (128)[FLOAT]], [097_convolutional_bn_bias -> (128)[FLOAT]], [097_convolutional_bn_mean -> (128)[FLOAT]], [097_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 097_convolutional_bn for ONNX node: 097_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 097_convolutional_bn for ONNX tensor: 097_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 097_convolutional_bn [BatchNormalization] outputs: [097_convolutional_bn -> (5, 128, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 097_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 097_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 097_convolutional_lrelu [LeakyRelu] inputs: [097_convolutional_bn -> (5, 128, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 097_convolutional_lrelu for ONNX node: 097_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 097_convolutional_lrelu for ONNX tensor: 097_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 097_convolutional_lrelu [LeakyRelu] outputs: [097_convolutional_lrelu -> (5, 128, 32, 32)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 098_upsample [Upsample] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 097_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 098_upsample_scale [12/16/2021-09:58:09] [V] [TRT] 098_upsample [Upsample] inputs: [097_convolutional_lrelu -> (5, 128, 32, 32)[FLOAT]], [098_upsample_scale -> (4)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 098_upsample for ONNX node: 098_upsample [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 098_upsample for ONNX tensor: 098_upsample [12/16/2021-09:58:09] [V] [TRT] 098_upsample [Upsample] outputs: [098_upsample -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 099_route [Concat] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 098_upsample [12/16/2021-09:58:09] [V] [TRT] Searching for input: 037_shortcut [12/16/2021-09:58:09] [V] [TRT] 099_route [Concat] inputs: [098_upsample -> (5, 128, 64, 64)[FLOAT]], [037_shortcut -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 099_route for ONNX node: 099_route [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 099_route for ONNX tensor: 099_route [12/16/2021-09:58:09] [V] [TRT] 099_route [Concat] outputs: [099_route -> (5, 384, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 100_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 099_route [12/16/2021-09:58:09] [V] [TRT] Searching for input: 100_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 100_convolutional [Conv] inputs: [099_route -> (5, 384, 64, 64)[FLOAT]], [100_convolutional_conv_weights -> (128, 384, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 384, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 100_convolutional for ONNX node: 100_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 100_convolutional for ONNX tensor: 100_convolutional [12/16/2021-09:58:09] [V] [TRT] 100_convolutional [Conv] outputs: [100_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 100_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 100_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 100_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 100_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 100_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 100_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 100_convolutional_bn [BatchNormalization] inputs: [100_convolutional -> (5, 128, 64, 64)[FLOAT]], [100_convolutional_bn_scale -> (128)[FLOAT]], [100_convolutional_bn_bias -> (128)[FLOAT]], [100_convolutional_bn_mean -> (128)[FLOAT]], [100_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 100_convolutional_bn for ONNX node: 100_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 100_convolutional_bn for ONNX tensor: 100_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 100_convolutional_bn [BatchNormalization] outputs: [100_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 100_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 100_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 100_convolutional_lrelu [LeakyRelu] inputs: [100_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 100_convolutional_lrelu for ONNX node: 100_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 100_convolutional_lrelu for ONNX tensor: 100_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 100_convolutional_lrelu [LeakyRelu] outputs: [100_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 101_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 100_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 101_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 101_convolutional [Conv] inputs: [100_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [101_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 101_convolutional for ONNX node: 101_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 101_convolutional for ONNX tensor: 101_convolutional [12/16/2021-09:58:09] [V] [TRT] 101_convolutional [Conv] outputs: [101_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 101_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 101_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 101_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 101_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 101_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 101_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 101_convolutional_bn [BatchNormalization] inputs: [101_convolutional -> (5, 256, 64, 64)[FLOAT]], [101_convolutional_bn_scale -> (256)[FLOAT]], [101_convolutional_bn_bias -> (256)[FLOAT]], [101_convolutional_bn_mean -> (256)[FLOAT]], [101_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 101_convolutional_bn for ONNX node: 101_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 101_convolutional_bn for ONNX tensor: 101_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 101_convolutional_bn [BatchNormalization] outputs: [101_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 101_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 101_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 101_convolutional_lrelu [LeakyRelu] inputs: [101_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 101_convolutional_lrelu for ONNX node: 101_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 101_convolutional_lrelu for ONNX tensor: 101_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 101_convolutional_lrelu [LeakyRelu] outputs: [101_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 102_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 101_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 102_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 102_convolutional [Conv] inputs: [101_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [102_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 102_convolutional for ONNX node: 102_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 102_convolutional for ONNX tensor: 102_convolutional [12/16/2021-09:58:09] [V] [TRT] 102_convolutional [Conv] outputs: [102_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 102_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 102_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 102_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 102_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 102_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 102_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 102_convolutional_bn [BatchNormalization] inputs: [102_convolutional -> (5, 128, 64, 64)[FLOAT]], [102_convolutional_bn_scale -> (128)[FLOAT]], [102_convolutional_bn_bias -> (128)[FLOAT]], [102_convolutional_bn_mean -> (128)[FLOAT]], [102_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 102_convolutional_bn for ONNX node: 102_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 102_convolutional_bn for ONNX tensor: 102_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 102_convolutional_bn [BatchNormalization] outputs: [102_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 102_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 102_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 102_convolutional_lrelu [LeakyRelu] inputs: [102_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 102_convolutional_lrelu for ONNX node: 102_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 102_convolutional_lrelu for ONNX tensor: 102_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 102_convolutional_lrelu [LeakyRelu] outputs: [102_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 103_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 102_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 103_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 103_convolutional [Conv] inputs: [102_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [103_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 103_convolutional for ONNX node: 103_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 103_convolutional for ONNX tensor: 103_convolutional [12/16/2021-09:58:09] [V] [TRT] 103_convolutional [Conv] outputs: [103_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 103_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 103_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 103_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 103_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 103_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 103_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 103_convolutional_bn [BatchNormalization] inputs: [103_convolutional -> (5, 256, 64, 64)[FLOAT]], [103_convolutional_bn_scale -> (256)[FLOAT]], [103_convolutional_bn_bias -> (256)[FLOAT]], [103_convolutional_bn_mean -> (256)[FLOAT]], [103_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 103_convolutional_bn for ONNX node: 103_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 103_convolutional_bn for ONNX tensor: 103_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 103_convolutional_bn [BatchNormalization] outputs: [103_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 103_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 103_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 103_convolutional_lrelu [LeakyRelu] inputs: [103_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 103_convolutional_lrelu for ONNX node: 103_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 103_convolutional_lrelu for ONNX tensor: 103_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 103_convolutional_lrelu [LeakyRelu] outputs: [103_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 104_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 103_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 104_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 104_convolutional [Conv] inputs: [103_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [104_convolutional_conv_weights -> (128, 256, 1, 1)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 104_convolutional for ONNX node: 104_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 128 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 104_convolutional for ONNX tensor: 104_convolutional [12/16/2021-09:58:09] [V] [TRT] 104_convolutional [Conv] outputs: [104_convolutional -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 104_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 104_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 104_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 104_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 104_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 104_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 104_convolutional_bn [BatchNormalization] inputs: [104_convolutional -> (5, 128, 64, 64)[FLOAT]], [104_convolutional_bn_scale -> (128)[FLOAT]], [104_convolutional_bn_bias -> (128)[FLOAT]], [104_convolutional_bn_mean -> (128)[FLOAT]], [104_convolutional_bn_var -> (128)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 104_convolutional_bn for ONNX node: 104_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 104_convolutional_bn for ONNX tensor: 104_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 104_convolutional_bn [BatchNormalization] outputs: [104_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 104_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 104_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 104_convolutional_lrelu [LeakyRelu] inputs: [104_convolutional_bn -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 104_convolutional_lrelu for ONNX node: 104_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 104_convolutional_lrelu for ONNX tensor: 104_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 104_convolutional_lrelu [LeakyRelu] outputs: [104_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 105_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 104_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 105_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] 105_convolutional [Conv] inputs: [104_convolutional_lrelu -> (5, 128, 64, 64)[FLOAT]], [105_convolutional_conv_weights -> (256, 128, 3, 3)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 128, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 105_convolutional for ONNX node: 105_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (3, 3), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 256 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 105_convolutional for ONNX tensor: 105_convolutional [12/16/2021-09:58:09] [V] [TRT] 105_convolutional [Conv] outputs: [105_convolutional -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 105_convolutional_bn [BatchNormalization] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 105_convolutional [12/16/2021-09:58:09] [V] [TRT] Searching for input: 105_convolutional_bn_scale [12/16/2021-09:58:09] [V] [TRT] Searching for input: 105_convolutional_bn_bias [12/16/2021-09:58:09] [V] [TRT] Searching for input: 105_convolutional_bn_mean [12/16/2021-09:58:09] [V] [TRT] Searching for input: 105_convolutional_bn_var [12/16/2021-09:58:09] [V] [TRT] 105_convolutional_bn [BatchNormalization] inputs: [105_convolutional -> (5, 256, 64, 64)[FLOAT]], [105_convolutional_bn_scale -> (256)[FLOAT]], [105_convolutional_bn_bias -> (256)[FLOAT]], [105_convolutional_bn_mean -> (256)[FLOAT]], [105_convolutional_bn_var -> (256)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 105_convolutional_bn for ONNX node: 105_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 105_convolutional_bn for ONNX tensor: 105_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 105_convolutional_bn [BatchNormalization] outputs: [105_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 105_convolutional_lrelu [LeakyRelu] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 105_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] 105_convolutional_lrelu [LeakyRelu] inputs: [105_convolutional_bn -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Registering layer: 105_convolutional_lrelu for ONNX node: 105_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 105_convolutional_lrelu for ONNX tensor: 105_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] 105_convolutional_lrelu [LeakyRelu] outputs: [105_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Parsing node: 106_convolutional [Conv] [12/16/2021-09:58:09] [V] [TRT] Searching for input: 105_convolutional_lrelu [12/16/2021-09:58:09] [V] [TRT] Searching for input: 106_convolutional_conv_weights [12/16/2021-09:58:09] [V] [TRT] Searching for input: 106_convolutional_conv_bias [12/16/2021-09:58:09] [V] [TRT] 106_convolutional [Conv] inputs: [105_convolutional_lrelu -> (5, 256, 64, 64)[FLOAT]], [106_convolutional_conv_weights -> (33, 256, 1, 1)[FLOAT]], [106_convolutional_conv_bias -> (33)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Convolution input dimensions: (5, 256, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering layer: 106_convolutional for ONNX node: 106_convolutional [12/16/2021-09:58:09] [V] [TRT] Using kernel: (1, 1), strides: (1, 1), prepadding: (0, 0), postpadding: (0, 0), dilations: (1, 1), numOutputs: 33 [12/16/2021-09:58:09] [V] [TRT] Convolution output dimensions: (5, 33, 64, 64) [12/16/2021-09:58:09] [V] [TRT] Registering tensor: 106_convolutional_370 for ONNX tensor: 106_convolutional [12/16/2021-09:58:09] [V] [TRT] 106_convolutional [Conv] outputs: [106_convolutional -> (5, 33, 64, 64)[FLOAT]], [12/16/2021-09:58:09] [V] [TRT] Marking 082_convolutional_368 as output: 082_convolutional [12/16/2021-09:58:09] [V] [TRT] Marking 094_convolutional_369 as output: 094_convolutional [12/16/2021-09:58:09] [V] [TRT] Marking 106_convolutional_370 as output: 106_convolutional [12/16/2021-09:58:09] [I] Finish parsing network model [12/16/2021-09:58:09] [I] [TRT] [MemUsageChange] Init CUDA: CPU +0, GPU +0, now: CPU 457, GPU 3128 (MiB) [12/16/2021-09:58:09] [I] [TRT] [MemUsageSnapshot] Builder begin: CPU 457 MiB, GPU 3128 MiB [12/16/2021-09:58:09] [V] [TRT] Applying generic optimizations to the graph for inference. [12/16/2021-09:58:09] [V] [TRT] Original: 246 layers [12/16/2021-09:58:09] [V] [TRT] After dead-layer removal: 246 layers [12/16/2021-09:58:09] [V] [TRT] After Myelin optimization: 246 layers [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 001_convolutional with scale 001_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 002_convolutional with scale 002_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 003_convolutional with scale 003_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 004_convolutional with scale 004_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 006_convolutional with scale 006_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 007_convolutional with scale 007_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 008_convolutional with scale 008_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 010_convolutional with scale 010_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 011_convolutional with scale 011_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 013_convolutional with scale 013_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 014_convolutional with scale 014_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 015_convolutional with scale 015_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 017_convolutional with scale 017_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 018_convolutional with scale 018_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 020_convolutional with scale 020_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 021_convolutional with scale 021_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 023_convolutional with scale 023_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 024_convolutional with scale 024_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 026_convolutional with scale 026_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 027_convolutional with scale 027_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 029_convolutional with scale 029_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 030_convolutional with scale 030_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 032_convolutional with scale 032_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 033_convolutional with scale 033_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 035_convolutional with scale 035_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 036_convolutional with scale 036_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 038_convolutional with scale 038_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 039_convolutional with scale 039_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 040_convolutional with scale 040_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 042_convolutional with scale 042_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 043_convolutional with scale 043_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 045_convolutional with scale 045_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 046_convolutional with scale 046_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 048_convolutional with scale 048_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 049_convolutional with scale 049_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 051_convolutional with scale 051_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 052_convolutional with scale 052_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 054_convolutional with scale 054_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 055_convolutional with scale 055_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 057_convolutional with scale 057_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 058_convolutional with scale 058_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 060_convolutional with scale 060_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 061_convolutional with scale 061_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 063_convolutional with scale 063_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 064_convolutional with scale 064_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 065_convolutional with scale 065_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 067_convolutional with scale 067_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 068_convolutional with scale 068_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 070_convolutional with scale 070_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 071_convolutional with scale 071_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 073_convolutional with scale 073_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 074_convolutional with scale 074_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 076_convolutional with scale 076_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 077_convolutional with scale 077_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 078_convolutional with scale 078_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 079_convolutional with scale 079_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 080_convolutional with scale 080_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 081_convolutional with scale 081_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 085_convolutional with scale 085_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 088_convolutional with scale 088_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 089_convolutional with scale 089_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 090_convolutional with scale 090_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 091_convolutional with scale 091_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 092_convolutional with scale 092_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 093_convolutional with scale 093_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 097_convolutional with scale 097_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 100_convolutional with scale 100_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 101_convolutional with scale 101_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 102_convolutional with scale 102_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 103_convolutional with scale 103_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 104_convolutional with scale 104_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] Fusing convolution weights from 105_convolutional with scale 105_convolutional_bn [12/16/2021-09:58:09] [V] [TRT] After scale fusion: 174 layers [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 004_convolutional_lrelu with 005_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 008_convolutional_lrelu with 009_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 011_convolutional_lrelu with 012_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 015_convolutional_lrelu with 016_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 018_convolutional_lrelu with 019_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 021_convolutional_lrelu with 022_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 024_convolutional_lrelu with 025_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 027_convolutional_lrelu with 028_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 030_convolutional_lrelu with 031_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 033_convolutional_lrelu with 034_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 036_convolutional_lrelu with 037_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 040_convolutional_lrelu with 041_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 043_convolutional_lrelu with 044_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 046_convolutional_lrelu with 047_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 049_convolutional_lrelu with 050_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 052_convolutional_lrelu with 053_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 055_convolutional_lrelu with 056_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 058_convolutional_lrelu with 059_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 061_convolutional_lrelu with 062_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 065_convolutional_lrelu with 066_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 068_convolutional_lrelu with 069_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 071_convolutional_lrelu with 072_shortcut [12/16/2021-09:58:09] [V] [TRT] PointWiseFusion: Fusing 074_convolutional_lrelu with 075_shortcut [12/16/2021-09:58:09] [V] [TRT] After vertical fusions: 151 layers [12/16/2021-09:58:09] [V] [TRT] After dupe layer removal: 151 layers [12/16/2021-09:58:09] [V] [TRT] After final dead-layer removal: 151 layers [12/16/2021-09:58:09] [V] [TRT] After tensor merging: 151 layers [12/16/2021-09:58:09] [V] [TRT] Eliminating concatenation 087_route [12/16/2021-09:58:09] [V] [TRT] Generating copy for 086_upsample to 087_route because input does not support striding. [12/16/2021-09:58:09] [V] [TRT] Retargeting 062_shortcut to 087_route [12/16/2021-09:58:09] [V] [TRT] Eliminating concatenation 099_route [12/16/2021-09:58:09] [V] [TRT] Generating copy for 098_upsample to 099_route because input does not support striding. [12/16/2021-09:58:09] [V] [TRT] Retargeting 037_shortcut to 099_route [12/16/2021-09:58:09] [V] [TRT] After concat removal: 151 layers [12/16/2021-09:58:10] [V] [TRT] Graph construction and optimization completed in 0.566444 seconds. [12/16/2021-09:58:10] [I] [TRT] ---------- Layers Running on DLA ---------- [12/16/2021-09:58:10] [I] [TRT] ---------- Layers Running on GPU ---------- [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 001_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 001_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 002_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 002_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 003_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 003_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 004_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(004_convolutional_lrelu, 005_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 006_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 006_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 007_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 007_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 008_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(008_convolutional_lrelu, 009_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 010_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 010_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 011_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(011_convolutional_lrelu, 012_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 013_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 013_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 014_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 014_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 015_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(015_convolutional_lrelu, 016_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 017_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 017_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 018_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(018_convolutional_lrelu, 019_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 020_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 020_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 021_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(021_convolutional_lrelu, 022_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 023_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 023_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 024_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(024_convolutional_lrelu, 025_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 026_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 026_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 027_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(027_convolutional_lrelu, 028_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 029_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 029_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 030_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(030_convolutional_lrelu, 031_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 032_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 032_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 033_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(033_convolutional_lrelu, 034_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 035_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 035_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 036_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(036_convolutional_lrelu, 037_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 038_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 038_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 039_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 039_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 040_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(040_convolutional_lrelu, 041_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 042_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 042_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 043_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(043_convolutional_lrelu, 044_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 045_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 045_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 046_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(046_convolutional_lrelu, 047_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 048_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 048_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 049_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(049_convolutional_lrelu, 050_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 051_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 051_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 052_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(052_convolutional_lrelu, 053_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 054_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 054_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 055_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(055_convolutional_lrelu, 056_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 057_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 057_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 058_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(058_convolutional_lrelu, 059_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 060_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 060_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 061_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(061_convolutional_lrelu, 062_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 063_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 063_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 064_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 064_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 065_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(065_convolutional_lrelu, 066_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 067_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 067_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 068_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(068_convolutional_lrelu, 069_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 070_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 070_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 071_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(071_convolutional_lrelu, 072_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 073_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 073_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 074_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] PWN(074_convolutional_lrelu, 075_shortcut) [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 076_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 076_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 077_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 077_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 078_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 078_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 079_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 079_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 080_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 080_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 081_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 081_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 082_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 085_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 085_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 086_upsample [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 086_upsample copy [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 088_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 088_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 089_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 089_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 090_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 090_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 091_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 091_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 092_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 092_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 093_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 093_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 094_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 097_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 097_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 098_upsample [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 098_upsample copy [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 100_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 100_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 101_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 101_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 102_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 102_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 103_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 103_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 104_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 104_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 105_convolutional [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 105_convolutional_lrelu [12/16/2021-09:58:10] [I] [TRT] [GpuLayer] 106_convolutional [12/16/2021-09:58:12] [V] [TRT] Using cublas a tactic source [12/16/2021-09:58:12] [I] [TRT] [MemUsageChange] Init cuBLAS/cuBLASLt: CPU +158, GPU +240, now: CPU 850, GPU 3510 (MiB) [12/16/2021-09:58:12] [V] [TRT] Using cuDNN as a tactic source [12/16/2021-09:58:15] [I] [TRT] [MemUsageChange] Init cuDNN: CPU +241, GPU +349, now: CPU 1091, GPU 3859 (MiB) [12/16/2021-09:58:15] [12/16/2021-09:58:15] [V] [TRT] Constructing optimization profile number 0 [1/1]. [12/16/2021-09:58:15] [V] [TRT] *************** Autotuning Reformat:Float(786432,262144,512,1) -> Float(786432,1,1536,3) *************** [12/16/2021-09:58:15] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:58:16] [V] [TRT] Tactic: 1002 Time: 86.5496 [12/16/2021-09:58:16] [V] [TRT] Tactic: 0 Time: 5.72908 [12/16/2021-09:58:16] [V] [TRT] Fastest Tactic: 0 Time: 5.72908 [12/16/2021-09:58:16] [V] [TRT] *************** Autotuning Reformat:Float(786432,262144,512,1) -> Half(786432,262144,512,1) *************** [12/16/2021-09:58:16] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:58:16] [V] [TRT] Tactic: 1002 Time: 4.80268 [12/16/2021-09:58:16] [V] [TRT] Tactic: 0 Time: 3.97187 [12/16/2021-09:58:16] [V] [TRT] Fastest Tactic: 0 Time: 3.97187 [12/16/2021-09:58:16] [V] [TRT] *************** Autotuning Reformat:Float(786432,262144,512,1) -> Half(524288,262144:2,512,1) *************** [12/16/2021-09:58:16] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:58:17] [V] [TRT] Tactic: 1002 Time: 10.7994 [12/16/2021-09:58:17] [V] [TRT] Tactic: 0 Time: 4.22917 [12/16/2021-09:58:17] [V] [TRT] Fastest Tactic: 0 Time: 4.22917 [12/16/2021-09:58:17] [V] [TRT] *************** Autotuning format combination: Float(786432,262144,512,1) -> Float(8388608,262144,512,1) *************** [12/16/2021-09:58:17] [V] [TRT] --------------- Timing Runner: 001_convolutional (CudaDepthwiseConvolution) [12/16/2021-09:58:17] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-09:58:17] [V] [TRT] --------------- Timing Runner: 001_convolutional (FusedConvActConvolution) [12/16/2021-09:58:17] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-09:58:17] [V] [TRT] --------------- Timing Runner: 001_convolutional (CudnnConvolution) [12/16/2021-09:58:22] [V] [TRT] Tactic: 0 Time: 68.2027 [12/16/2021-09:58:23] [V] [TRT] Tactic: 1 Time: 52.3286 [12/16/2021-09:58:23] [V] [TRT] Tactic: 2 skipped. Scratch requested: 141557760, available: 16777216 [12/16/2021-09:58:28] [V] [TRT] Tactic: 5 Time: 327.034 [12/16/2021-09:58:29] [V] [TRT] Tactic: 6 Time: 74.654 [12/16/2021-09:58:29] [I] [TRT] Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output. [12/16/2021-09:58:29] [V] [TRT] Fastest Tactic: 1 Time: 52.3286 [12/16/2021-09:58:29] [V] [TRT] --------------- Timing Runner: 001_convolutional (CaskConvolution) [12/16/2021-09:58:29] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-09:58:30] [V] [TRT] Tactic: 1062367460111450758 Time: 35.2758 [12/16/2021-09:58:30] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v0 Tactic: 1754984623894446479 [12/16/2021-09:58:31] [V] [TRT] Tactic: 1754984623894446479 Time: 33.4662 [12/16/2021-09:58:31] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v0 Tactic: 3611739942397549984 [12/16/2021-09:58:33] [V] [TRT] Tactic: 3611739942397549984 Time: 113.746 [12/16/2021-09:58:33] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148n_nt_v1 Tactic: 3827454225649558724 [12/16/2021-09:58:34] [V] [TRT] Tactic: 3827454225649558724 Time: 88.8604 [12/16/2021-09:58:34] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v1 Tactic: 4337000649858996379 [12/16/2021-09:58:35] [V] [TRT] Tactic: 4337000649858996379 Time: 53.6191 [12/16/2021-09:58:35] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-09:58:37] [V] [TRT] Tactic: 4501471010995462441 Time: 108.844 [12/16/2021-09:58:37] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-09:58:38] [V] [TRT] Tactic: 5137655947464784826 Time: 53.5864 [12/16/2021-09:58:38] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-09:58:40] [V] [TRT] Tactic: 5288347012147084929 Time: 111.729 [12/16/2021-09:58:40] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148m_nt_v1 Tactic: 5921334924264294896 [12/16/2021-09:58:41] [V] [TRT] Tactic: 5921334924264294896 Time: 56.5367 [12/16/2021-09:58:41] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-09:58:42] [V] [TRT] Tactic: 6645123197870846056 Time: 52.7498 [12/16/2021-09:58:42] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-09:58:43] [V] [TRT] Tactic: 7144526460361122478 Time: 32.3176 [12/16/2021-09:58:43] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v1 Tactic: 7852627285308570038 [12/16/2021-09:58:45] [V] [TRT] Tactic: 7852627285308570038 Time: 92.2291 [12/16/2021-09:58:45] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v1 Tactic: -9137461792520977713 [12/16/2021-09:58:47] [V] [TRT] Tactic: -9137461792520977713 Time: 107.086 [12/16/2021-09:58:47] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148n_nt_v0 Tactic: -8776506421218919509 [12/16/2021-09:58:48] [V] [TRT] Tactic: -8776506421218919509 Time: 86.3774 [12/16/2021-09:58:48] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-09:58:50] [V] [TRT] Tactic: -8262349710178828730 Time: 113.238 [12/16/2021-09:58:50] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v0 Tactic: -8133971918129952780 [12/16/2021-09:58:51] [V] [TRT] Tactic: -8133971918129952780 Time: 53.4889 [12/16/2021-09:58:51] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v1 Tactic: -6092040395344634144 [12/16/2021-09:58:52] [V] [TRT] Tactic: -6092040395344634144 Time: 36.3405 [12/16/2021-09:58:52] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-09:58:53] [V] [TRT] Tactic: -4787320710726427159 Time: 33.8524 [12/16/2021-09:58:53] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-09:58:53] [V] [TRT] Tactic: -3456450830548107839 Time: 33.5038 [12/16/2021-09:58:53] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v0 Tactic: -2318106587342035239 [12/16/2021-09:58:55] [V] [TRT] Tactic: -2318106587342035239 Time: 88.1268 [12/16/2021-09:58:55] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_mobile_relu_tile148t_nt_v0 Tactic: -1343271414618805657 [12/16/2021-09:58:56] [V] [TRT] Tactic: -1343271414618805657 Time: 54.9146 [12/16/2021-09:58:56] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-09:58:57] [V] [TRT] Tactic: -1218658103698133241 Time: 53.1656 [12/16/2021-09:58:57] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-09:58:58] [V] [TRT] Tactic: -836875257600482091 Time: 52.1384 [12/16/2021-09:58:58] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-09:59:00] [V] [TRT] Tactic: -410470605513481746 Time: 104.968 [12/16/2021-09:59:00] [V] [TRT] Fastest Tactic: 7144526460361122478 Time: 32.3176 [12/16/2021-09:59:00] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 7144526460361122478 [12/16/2021-09:59:00] [V] [TRT] *************** Autotuning format combination: Float(786432,1,1536,3) -> Float(8388608,1,16384,32) *************** [12/16/2021-09:59:00] [V] [TRT] --------------- Timing Runner: 001_convolutional (CudnnConvolution) [12/16/2021-09:59:00] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-09:59:00] [V] [TRT] --------------- Timing Runner: 001_convolutional (CaskConvolution) [12/16/2021-09:59:00] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-09:59:00] [V] [TRT] *************** Autotuning format combination: Half(786432,262144,512,1) -> Half(8388608,262144,512,1) *************** [12/16/2021-09:59:00] [V] [TRT] --------------- Timing Runner: 001_convolutional (CudnnConvolution) [12/16/2021-09:59:01] [V] [TRT] Tactic: 0 Time: 62.1499 [12/16/2021-09:59:01] [V] [TRT] Tactic: 1 skipped. Scratch requested: 85504512, available: 16777216 [12/16/2021-09:59:01] [V] [TRT] Tactic: 2 skipped. Scratch requested: 70778880, available: 16777216 [12/16/2021-09:59:06] [V] [TRT] Tactic: 5 Time: 318.768 [12/16/2021-09:59:06] [V] [TRT] Tactic: 6 skipped. Scratch requested: 183525376, available: 16777216 [12/16/2021-09:59:06] [V] [TRT] Fastest Tactic: 0 Time: 62.1499 [12/16/2021-09:59:06] [V] [TRT] --------------- Timing Runner: 001_convolutional (CaskConvolution) [12/16/2021-09:59:06] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-09:59:06] [V] [TRT] Setting workspace to 183525376enables more tactics for profiling [12/16/2021-09:59:06] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CudnnConvolution Tactic: 0 [12/16/2021-09:59:06] [V] [TRT] *************** Autotuning format combination: Half(524288,262144:2,512,1) -> Half(4194304,262144:2,512,1) *************** [12/16/2021-09:59:06] [V] [TRT] --------------- Timing Runner: 001_convolutional (FusedConvActConvolution) [12/16/2021-09:59:06] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-09:59:06] [V] [TRT] --------------- Timing Runner: 001_convolutional (CudnnConvolution) [12/16/2021-09:59:06] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-09:59:06] [V] [TRT] --------------- Timing Runner: 001_convolutional (CaskConvolution) [12/16/2021-09:59:06] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-09:59:07] [V] [TRT] Tactic: 3564772625446233998 Time: 26.3252 [12/16/2021-09:59:07] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_large_nn_v1 Tactic: 3650389455493082349 [12/16/2021-09:59:07] [V] [TRT] Tactic: 3650389455493082349 Time: 27.3011 [12/16/2021-09:59:07] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_winograd_fp16x2_128x128_ldg1_ldg4_relu_tile148m_nt_v1 Tactic: 4772821744921268633 [12/16/2021-09:59:08] [V] [TRT] Tactic: 4772821744921268633 Time: 45.0824 [12/16/2021-09:59:08] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-09:59:08] [V] [TRT] Tactic: 5319956359050645452 Time: 24.9437 [12/16/2021-09:59:08] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-09:59:09] [V] [TRT] Tactic: 7205456024582378848 Time: 38.8069 [12/16/2021-09:59:09] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_large_nn_v1 Tactic: -6490690591794140522 [12/16/2021-09:59:10] [V] [TRT] Tactic: -6490690591794140522 Time: 39.6261 [12/16/2021-09:59:10] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_large_nn_v1 Tactic: -4686027666808657977 [12/16/2021-09:59:11] [V] [TRT] Tactic: -4686027666808657977 Time: 78.4753 [12/16/2021-09:59:11] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-09:59:12] [V] [TRT] Tactic: -4212163711445252890 Time: 75.6086 [12/16/2021-09:59:12] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-09:59:14] [V] [TRT] Tactic: -3898373634979201110 Time: 78.8694 [12/16/2021-09:59:14] [V] [TRT] 001_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-09:59:14] [V] [TRT] Tactic: -2409163523992614473 Time: 37.8829 [12/16/2021-09:59:14] [V] [TRT] Fastest Tactic: 5319956359050645452 Time: 24.9437 [12/16/2021-09:59:14] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 5319956359050645452 [12/16/2021-09:59:14] [V] [TRT] *************** Autotuning Reformat:Float(8388608,262144,512,1) -> Float(8388608,1,16384,32) *************** [12/16/2021-09:59:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:16] [V] [TRT] Tactic: 1002 Time: 88.9534 [12/16/2021-09:59:18] [V] [TRT] Tactic: 0 Time: 88.2592 [12/16/2021-09:59:18] [V] [TRT] Fastest Tactic: 0 Time: 88.2592 [12/16/2021-09:59:18] [V] [TRT] *************** Autotuning Reformat:Float(8388608,262144,512,1) -> Float(262144,262144:32,512,1) *************** [12/16/2021-09:59:18] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:20] [V] [TRT] Tactic: 1002 Time: 91.0818 [12/16/2021-09:59:22] [V] [TRT] Tactic: 0 Time: 148.402 [12/16/2021-09:59:22] [V] [TRT] Fastest Tactic: 1002 Time: 91.0818 [12/16/2021-09:59:22] [V] [TRT] *************** Autotuning Reformat:Float(8388608,262144,512,1) -> Half(8388608,262144,512,1) *************** [12/16/2021-09:59:22] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:23] [V] [TRT] Tactic: 1002 Time: 51.1206 [12/16/2021-09:59:24] [V] [TRT] Tactic: 0 Time: 42.2437 [12/16/2021-09:59:24] [V] [TRT] Fastest Tactic: 0 Time: 42.2437 [12/16/2021-09:59:24] [V] [TRT] *************** Autotuning Reformat:Float(8388608,262144,512,1) -> Half(4194304,262144:2,512,1) *************** [12/16/2021-09:59:24] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:26] [V] [TRT] Tactic: 1002 Time: 62.3631 [12/16/2021-09:59:26] [V] [TRT] Tactic: 0 Time: 33.7854 [12/16/2021-09:59:26] [V] [TRT] Fastest Tactic: 0 Time: 33.7854 [12/16/2021-09:59:26] [V] [TRT] *************** Autotuning Reformat:Float(8388608,1,16384,32) -> Float(8388608,262144,512,1) *************** [12/16/2021-09:59:26] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:28] [V] [TRT] Tactic: 1002 Time: 89.9118 [12/16/2021-09:59:31] [V] [TRT] Tactic: 0 Time: 142.486 [12/16/2021-09:59:31] [V] [TRT] Fastest Tactic: 1002 Time: 89.9118 [12/16/2021-09:59:31] [V] [TRT] *************** Autotuning Reformat:Float(8388608,1,16384,32) -> Float(262144,262144:32,512,1) *************** [12/16/2021-09:59:31] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:32] [V] [TRT] Tactic: 1002 Time: 39.1397 [12/16/2021-09:59:37] [V] [TRT] Tactic: 0 Time: 289.02 [12/16/2021-09:59:37] [V] [TRT] Fastest Tactic: 1002 Time: 39.1397 [12/16/2021-09:59:37] [V] [TRT] *************** Autotuning Reformat:Float(8388608,1,16384,32) -> Half(8388608,262144,512,1) *************** [12/16/2021-09:59:37] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:37] [V] [TRT] Tactic: 1002 Time: 38.6906 [12/16/2021-09:59:40] [V] [TRT] Tactic: 0 Time: 139.191 [12/16/2021-09:59:40] [V] [TRT] Fastest Tactic: 1002 Time: 38.6906 [12/16/2021-09:59:40] [V] [TRT] *************** Autotuning Reformat:Float(8388608,1,16384,32) -> Half(4194304,262144:2,512,1) *************** [12/16/2021-09:59:40] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:41] [V] [TRT] Tactic: 1002 Time: 52.8497 [12/16/2021-09:59:44] [V] [TRT] Tactic: 0 Time: 150.16 [12/16/2021-09:59:44] [V] [TRT] Fastest Tactic: 1002 Time: 52.8497 [12/16/2021-09:59:44] [V] [TRT] *************** Autotuning Reformat:Float(262144,262144:32,512,1) -> Float(8388608,262144,512,1) *************** [12/16/2021-09:59:44] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:45] [V] [TRT] Tactic: 1002 Time: 89.849 [12/16/2021-09:59:48] [V] [TRT] Tactic: 0 Time: 142.243 [12/16/2021-09:59:48] [V] [TRT] Fastest Tactic: 1002 Time: 89.849 [12/16/2021-09:59:48] [V] [TRT] *************** Autotuning Reformat:Float(262144,262144:32,512,1) -> Float(8388608,1,16384,32) *************** [12/16/2021-09:59:48] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:49] [V] [TRT] Tactic: 1002 Time: 39.0617 [12/16/2021-09:59:50] [V] [TRT] Tactic: 0 Time: 71.5079 [12/16/2021-09:59:50] [V] [TRT] Fastest Tactic: 1002 Time: 39.0617 [12/16/2021-09:59:50] [V] [TRT] *************** Autotuning Reformat:Float(262144,262144:32,512,1) -> Half(8388608,262144,512,1) *************** [12/16/2021-09:59:50] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:51] [V] [TRT] Tactic: 1002 Time: 41.8144 [12/16/2021-09:59:54] [V] [TRT] Tactic: 0 Time: 139.151 [12/16/2021-09:59:54] [V] [TRT] Fastest Tactic: 1002 Time: 41.8144 [12/16/2021-09:59:54] [V] [TRT] *************** Autotuning Reformat:Float(262144,262144:32,512,1) -> Half(4194304,262144:2,512,1) *************** [12/16/2021-09:59:54] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:55] [V] [TRT] Tactic: 1002 Time: 52.8509 [12/16/2021-09:59:57] [V] [TRT] Tactic: 0 Time: 150.169 [12/16/2021-09:59:57] [V] [TRT] Fastest Tactic: 1002 Time: 52.8509 [12/16/2021-09:59:57] [V] [TRT] *************** Autotuning Reformat:Half(8388608,262144,512,1) -> Float(8388608,262144,512,1) *************** [12/16/2021-09:59:57] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-09:59:58] [V] [TRT] Tactic: 1002 Time: 51.5802 [12/16/2021-09:59:59] [V] [TRT] Tactic: 0 Time: 36.0709 [12/16/2021-09:59:59] [V] [TRT] Fastest Tactic: 0 Time: 36.0709 [12/16/2021-09:59:59] [V] [TRT] *************** Autotuning Reformat:Half(8388608,262144,512,1) -> Float(8388608,1,16384,32) *************** [12/16/2021-09:59:59] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:00:00] [V] [TRT] Tactic: 1002 Time: 36.1903 [12/16/2021-10:00:01] [V] [TRT] Tactic: 0 Time: 68.4451 [12/16/2021-10:00:01] [V] [TRT] Fastest Tactic: 1002 Time: 36.1903 [12/16/2021-10:00:01] [V] [TRT] *************** Autotuning Reformat:Half(8388608,262144,512,1) -> Float(262144,262144:32,512,1) *************** [12/16/2021-10:00:01] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:00:02] [V] [TRT] Tactic: 1002 Time: 36.1796 [12/16/2021-10:00:04] [V] [TRT] Tactic: 0 Time: 146.335 [12/16/2021-10:00:04] [V] [TRT] Fastest Tactic: 1002 Time: 36.1796 [12/16/2021-10:00:04] [V] [TRT] *************** Autotuning Reformat:Half(8388608,262144,512,1) -> Half(4194304,262144:2,512,1) *************** [12/16/2021-10:00:04] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:00:05] [V] [TRT] Tactic: 1002 Time: 35.269 [12/16/2021-10:00:06] [V] [TRT] Tactic: 0 Time: 33.4427 [12/16/2021-10:00:06] [V] [TRT] Fastest Tactic: 0 Time: 33.4427 [12/16/2021-10:00:06] [V] [TRT] *************** Autotuning Reformat:Half(4194304,262144:2,512,1) -> Float(8388608,262144,512,1) *************** [12/16/2021-10:00:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:00:07] [V] [TRT] Tactic: 1002 Time: 50.4133 [12/16/2021-10:00:07] [V] [TRT] Tactic: 0 Time: 29.44 [12/16/2021-10:00:07] [V] [TRT] Fastest Tactic: 0 Time: 29.44 [12/16/2021-10:00:07] [V] [TRT] *************** Autotuning Reformat:Half(4194304,262144:2,512,1) -> Float(8388608,1,16384,32) *************** [12/16/2021-10:00:07] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:00:08] [V] [TRT] Tactic: 1002 Time: 36.8266 [12/16/2021-10:00:10] [V] [TRT] Tactic: 0 Time: 72.6817 [12/16/2021-10:00:10] [V] [TRT] Fastest Tactic: 1002 Time: 36.8266 [12/16/2021-10:00:10] [V] [TRT] *************** Autotuning Reformat:Half(4194304,262144:2,512,1) -> Float(262144,262144:32,512,1) *************** [12/16/2021-10:00:10] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:00:10] [V] [TRT] Tactic: 1002 Time: 36.8368 [12/16/2021-10:00:13] [V] [TRT] Tactic: 0 Time: 150.012 [12/16/2021-10:00:13] [V] [TRT] Fastest Tactic: 1002 Time: 36.8368 [12/16/2021-10:00:13] [V] [TRT] *************** Autotuning Reformat:Half(4194304,262144:2,512,1) -> Half(8388608,262144,512,1) *************** [12/16/2021-10:00:13] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:00:15] [V] [TRT] Tactic: 1002 Time: 118.985 [12/16/2021-10:00:16] [V] [TRT] Tactic: 0 Time: 28.5972 [12/16/2021-10:00:16] [V] [TRT] Fastest Tactic: 0 Time: 28.5972 [12/16/2021-10:00:16] [V] [TRT] *************** Autotuning format combination: Float(8388608,262144,512,1) -> Float(8388608,262144,512,1) *************** [12/16/2021-10:00:16] [V] [TRT] --------------- Timing Runner: 001_convolutional_lrelu (PointWiseV2) [12/16/2021-10:00:17] [V] [TRT] Tactic: 0 Time: 34.9032 [12/16/2021-10:00:19] [V] [TRT] Tactic: 1 Time: 24.9872 [12/16/2021-10:00:20] [V] [TRT] Tactic: 2 Time: 21.7143 [12/16/2021-10:00:22] [V] [TRT] Tactic: 3 Time: 21.3628 [12/16/2021-10:00:23] [V] [TRT] Tactic: 4 Time: 17.2596 [12/16/2021-10:00:24] [V] [TRT] Tactic: 5 Time: 16.7289 [12/16/2021-10:00:26] [V] [TRT] Tactic: 6 Time: 21.1842 [12/16/2021-10:00:27] [V] [TRT] Tactic: 7 Time: 16.9843 [12/16/2021-10:00:28] [V] [TRT] Tactic: 8 Time: 17.0079 [12/16/2021-10:00:30] [V] [TRT] Tactic: 9 Time: 16.887 [12/16/2021-10:00:31] [V] [TRT] Tactic: 28 Time: 34.1582 [12/16/2021-10:00:31] [V] [TRT] Fastest Tactic: 5 Time: 16.7289 [12/16/2021-10:00:31] [V] [TRT] --------------- Timing Runner: 001_convolutional_lrelu (PointWise) [12/16/2021-10:00:33] [V] [TRT] Tactic: 128 Time: 81.6603 [12/16/2021-10:00:34] [V] [TRT] Tactic: 256 Time: 81.948 [12/16/2021-10:00:36] [V] [TRT] Tactic: 512 Time: 82.5139 [12/16/2021-10:00:36] [V] [TRT] Fastest Tactic: 128 Time: 81.6603 [12/16/2021-10:00:36] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:00:36] [V] [TRT] *************** Autotuning format combination: Float(8388608,1,16384,32) -> Float(8388608,1,16384,32) *************** [12/16/2021-10:00:36] [V] [TRT] --------------- Timing Runner: 001_convolutional_lrelu (PointWiseV2) [12/16/2021-10:00:37] [V] [TRT] Tactic: 0 Time: 34.9308 [12/16/2021-10:00:37] [V] [TRT] Tactic: 1 Time: 25.0613 [12/16/2021-10:00:38] [V] [TRT] Tactic: 2 Time: 21.6984 [12/16/2021-10:00:39] [V] [TRT] Tactic: 3 Time: 21.5745 [12/16/2021-10:00:39] [V] [TRT] Tactic: 4 Time: 17.3514 [12/16/2021-10:00:40] [V] [TRT] Tactic: 5 Time: 16.771 [12/16/2021-10:00:41] [V] [TRT] Tactic: 6 Time: 21.2392 [12/16/2021-10:00:41] [V] [TRT] Tactic: 7 Time: 16.9439 [12/16/2021-10:00:42] [V] [TRT] Tactic: 8 Time: 17.0191 [12/16/2021-10:00:42] [V] [TRT] Tactic: 9 Time: 17.0371 [12/16/2021-10:00:43] [V] [TRT] Tactic: 28 Time: 34.1667 [12/16/2021-10:00:43] [V] [TRT] Fastest Tactic: 5 Time: 16.771 [12/16/2021-10:00:43] [V] [TRT] --------------- Timing Runner: 001_convolutional_lrelu (PointWise) [12/16/2021-10:00:45] [V] [TRT] Tactic: 128 Time: 81.6508 [12/16/2021-10:00:46] [V] [TRT] Tactic: 256 Time: 81.9339 [12/16/2021-10:00:48] [V] [TRT] Tactic: 512 Time: 82.4939 [12/16/2021-10:00:48] [V] [TRT] Fastest Tactic: 128 Time: 81.6508 [12/16/2021-10:00:48] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:00:48] [V] [TRT] *************** Autotuning format combination: Float(262144,262144:32,512,1) -> Float(262144,262144:32,512,1) *************** [12/16/2021-10:00:48] [V] [TRT] --------------- Timing Runner: 001_convolutional_lrelu (PointWiseV2) [12/16/2021-10:00:49] [V] [TRT] Tactic: 24 Time: 22.1457 [12/16/2021-10:00:51] [V] [TRT] Tactic: 25 Time: 20.834 [12/16/2021-10:00:52] [V] [TRT] Tactic: 26 Time: 20.9753 [12/16/2021-10:00:54] [V] [TRT] Tactic: 27 Time: 20.5152 [12/16/2021-10:00:55] [V] [TRT] Tactic: 31 Time: 22.2166 [12/16/2021-10:00:55] [V] [TRT] Fastest Tactic: 27 Time: 20.5152 [12/16/2021-10:00:55] [V] [TRT] --------------- Timing Runner: 001_convolutional_lrelu (PointWise) [12/16/2021-10:00:57] [V] [TRT] Tactic: 128 Time: 81.636 [12/16/2021-10:00:58] [V] [TRT] Tactic: 256 Time: 81.9394 [12/16/2021-10:01:00] [V] [TRT] Tactic: 512 Time: 82.4898 [12/16/2021-10:01:00] [V] [TRT] Fastest Tactic: 128 Time: 81.636 [12/16/2021-10:01:00] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 27 [12/16/2021-10:01:00] [V] [TRT] *************** Autotuning format combination: Half(8388608,262144,512,1) -> Half(8388608,262144,512,1) *************** [12/16/2021-10:01:00] [V] [TRT] --------------- Timing Runner: 001_convolutional_lrelu (PointWiseV2) [12/16/2021-10:01:01] [V] [TRT] Tactic: 0 Time: 34.7279 [12/16/2021-10:01:03] [V] [TRT] Tactic: 1 Time: 24.2946 [12/16/2021-10:01:04] [V] [TRT] Tactic: 2 Time: 23.4099 [12/16/2021-10:01:05] [V] [TRT] Tactic: 3 Time: 19.1056 [12/16/2021-10:01:06] [V] [TRT] Tactic: 4 Time: 14.8717 [12/16/2021-10:01:07] [V] [TRT] Tactic: 5 Time: 15.6075 [12/16/2021-10:01:09] [V] [TRT] Tactic: 6 Time: 17.4027 [12/16/2021-10:01:10] [V] [TRT] Tactic: 7 Time: 12.2784 [12/16/2021-10:01:11] [V] [TRT] Tactic: 8 Time: 10.8865 [12/16/2021-10:01:12] [V] [TRT] Tactic: 9 Time: 12.3393 [12/16/2021-10:01:13] [V] [TRT] Tactic: 28 Time: 33.8111 [12/16/2021-10:01:13] [V] [TRT] Fastest Tactic: 8 Time: 10.8865 [12/16/2021-10:01:13] [V] [TRT] --------------- Timing Runner: 001_convolutional_lrelu (PointWise) [12/16/2021-10:01:15] [V] [TRT] Tactic: 128 Time: 73.4703 [12/16/2021-10:01:16] [V] [TRT] Tactic: 256 Time: 73.0237 [12/16/2021-10:01:17] [V] [TRT] Tactic: 512 Time: 71.264 [12/16/2021-10:01:17] [V] [TRT] Fastest Tactic: 512 Time: 71.264 [12/16/2021-10:01:17] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:01:17] [V] [TRT] *************** Autotuning format combination: Half(4194304,262144:2,512,1) -> Half(4194304,262144:2,512,1) *************** [12/16/2021-10:01:17] [V] [TRT] --------------- Timing Runner: 001_convolutional_lrelu (PointWiseV2) [12/16/2021-10:01:19] [V] [TRT] Tactic: 0 Time: 22.3105 [12/16/2021-10:01:20] [V] [TRT] Tactic: 1 Time: 17.9637 [12/16/2021-10:01:21] [V] [TRT] Tactic: 2 Time: 19.7702 [12/16/2021-10:01:22] [V] [TRT] Tactic: 3 Time: 15.8943 [12/16/2021-10:01:23] [V] [TRT] Tactic: 4 Time: 16.4805 [12/16/2021-10:01:25] [V] [TRT] Tactic: 5 Time: 17.7311 [12/16/2021-10:01:26] [V] [TRT] Tactic: 6 Time: 15.3856 [12/16/2021-10:01:27] [V] [TRT] Tactic: 7 Time: 15.8775 [12/16/2021-10:01:28] [V] [TRT] Tactic: 8 Time: 16.6743 [12/16/2021-10:01:30] [V] [TRT] Tactic: 9 Time: 18.6006 [12/16/2021-10:01:31] [V] [TRT] Tactic: 10 Time: 37.8212 [12/16/2021-10:01:32] [V] [TRT] Tactic: 11 Time: 25.9988 [12/16/2021-10:01:34] [V] [TRT] Tactic: 12 Time: 25.0845 [12/16/2021-10:01:35] [V] [TRT] Tactic: 13 Time: 19.4921 [12/16/2021-10:01:36] [V] [TRT] Tactic: 14 Time: 15.8483 [12/16/2021-10:01:37] [V] [TRT] Tactic: 15 Time: 16.9281 [12/16/2021-10:01:39] [V] [TRT] Tactic: 16 Time: 17.6624 [12/16/2021-10:01:40] [V] [TRT] Tactic: 17 Time: 12.4469 [12/16/2021-10:01:41] [V] [TRT] Tactic: 18 Time: 11.6717 [12/16/2021-10:01:42] [V] [TRT] Tactic: 19 Time: 13.874 [12/16/2021-10:01:43] [V] [TRT] Tactic: 28 Time: 21.8468 [12/16/2021-10:01:45] [V] [TRT] Tactic: 29 Time: 36.6327 [12/16/2021-10:01:45] [V] [TRT] Fastest Tactic: 18 Time: 11.6717 [12/16/2021-10:01:45] [V] [TRT] --------------- Timing Runner: 001_convolutional_lrelu (PointWise) [12/16/2021-10:01:46] [V] [TRT] Tactic: 128 Time: 73.4102 [12/16/2021-10:01:47] [V] [TRT] Tactic: 256 Time: 73.0453 [12/16/2021-10:01:49] [V] [TRT] Tactic: 512 Time: 71.2834 [12/16/2021-10:01:49] [V] [TRT] Fastest Tactic: 512 Time: 71.2834 [12/16/2021-10:01:49] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Float(8388608,262144,512,1) -> Float(8388608,1,16384,32) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Float(8388608,262144,512,1) -> Half(8388608,262144,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Float(8388608,262144,512,1) -> Half(4194304,262144:2,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Float(8388608,1,16384,32) -> Float(8388608,262144,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Float(8388608,1,16384,32) -> Half(8388608,262144,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Float(8388608,1,16384,32) -> Half(4194304,262144:2,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Float(262144,262144:32,512,1) -> Float(8388608,262144,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Float(262144,262144:32,512,1) -> Float(8388608,1,16384,32) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Float(262144,262144:32,512,1) -> Half(8388608,262144,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Float(262144,262144:32,512,1) -> Half(4194304,262144:2,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Half(8388608,262144,512,1) -> Float(8388608,262144,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Half(8388608,262144,512,1) -> Float(8388608,1,16384,32) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Half(8388608,262144,512,1) -> Half(4194304,262144:2,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Half(4194304,262144:2,512,1) -> Float(8388608,262144,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Half(4194304,262144:2,512,1) -> Float(8388608,1,16384,32) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning Reformat:Half(4194304,262144:2,512,1) -> Half(8388608,262144,512,1) *************** [12/16/2021-10:01:49] [V] [TRT] *************** Autotuning format combination: Float(8388608,262144,512,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:01:49] [V] [TRT] --------------- Timing Runner: 002_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:01:49] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:01:49] [V] [TRT] --------------- Timing Runner: 002_convolutional (FusedConvActConvolution) [12/16/2021-10:01:49] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:01:49] [V] [TRT] --------------- Timing Runner: 002_convolutional (CudnnConvolution) [12/16/2021-10:01:49] [V] [TRT] Tactic: 0 skipped. Scratch requested: 168428544, available: 16777216 [12/16/2021-10:01:49] [V] [TRT] Tactic: 1 skipped. Scratch requested: 168897024, available: 16777216 [12/16/2021-10:01:49] [V] [TRT] Tactic: 2 skipped. Scratch requested: 545915904, available: 16777216 [12/16/2021-10:01:49] [V] [TRT] Tactic: 5 skipped. Scratch requested: 181519360, available: 16777216 [12/16/2021-10:01:49] [V] [TRT] Fastest Tactic: -3360065831133338131 Time: 3.40282e+38 [12/16/2021-10:01:49] [V] [TRT] --------------- Timing Runner: 002_convolutional (CaskConvolution) [12/16/2021-10:01:49] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:01:50] [V] [TRT] Tactic: 1062367460111450758 Time: 85.2428 [12/16/2021-10:01:50] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v0 Tactic: 1754984623894446479 [12/16/2021-10:01:52] [V] [TRT] Tactic: 1754984623894446479 Time: 95.2463 [12/16/2021-10:01:52] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v0 Tactic: 3611739942397549984 [12/16/2021-10:01:54] [V] [TRT] Tactic: 3611739942397549984 Time: 134.588 [12/16/2021-10:01:54] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v1 Tactic: 4337000649858996379 [12/16/2021-10:01:55] [V] [TRT] Tactic: 4337000649858996379 Time: 67.3922 [12/16/2021-10:01:55] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:01:58] [V] [TRT] Tactic: 4501471010995462441 Time: 132.721 [12/16/2021-10:01:58] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:01:59] [V] [TRT] Tactic: 5137655947464784826 Time: 65.2109 [12/16/2021-10:01:59] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:02:01] [V] [TRT] Tactic: 5288347012147084929 Time: 132.4 [12/16/2021-10:02:01] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-10:02:03] [V] [TRT] Tactic: 6645123197870846056 Time: 66.6945 [12/16/2021-10:02:03] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-10:02:04] [V] [TRT] Tactic: 7144526460361122478 Time: 89.2257 [12/16/2021-10:02:04] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v1 Tactic: -9137461792520977713 [12/16/2021-10:02:06] [V] [TRT] Tactic: -9137461792520977713 Time: 133.968 [12/16/2021-10:02:06] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-10:02:09] [V] [TRT] Tactic: -8262349710178828730 Time: 134.707 [12/16/2021-10:02:09] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v0 Tactic: -8133971918129952780 [12/16/2021-10:02:10] [V] [TRT] Tactic: -8133971918129952780 Time: 75.0023 [12/16/2021-10:02:10] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v1 Tactic: -6092040395344634144 [12/16/2021-10:02:12] [V] [TRT] Tactic: -6092040395344634144 Time: 88.0527 [12/16/2021-10:02:12] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-10:02:13] [V] [TRT] Tactic: -4787320710726427159 Time: 94.0751 [12/16/2021-10:02:14] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-10:02:15] [V] [TRT] Tactic: -3456450830548107839 Time: 79.2019 [12/16/2021-10:02:15] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-10:02:16] [V] [TRT] Tactic: -1218658103698133241 Time: 74.4558 [12/16/2021-10:02:16] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-10:02:18] [V] [TRT] Tactic: -836875257600482091 Time: 72.8436 [12/16/2021-10:02:18] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-10:02:20] [V] [TRT] Tactic: -410470605513481746 Time: 130.035 [12/16/2021-10:02:20] [V] [TRT] Fastest Tactic: 5137655947464784826 Time: 65.2109 [12/16/2021-10:02:20] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 5137655947464784826 [12/16/2021-10:02:20] [V] [TRT] *************** Autotuning format combination: Float(8388608,1,16384,32) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:02:20] [V] [TRT] --------------- Timing Runner: 002_convolutional (CudnnConvolution) [12/16/2021-10:02:20] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:02:20] [V] [TRT] --------------- Timing Runner: 002_convolutional (CaskConvolution) [12/16/2021-10:02:20] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -9153228964338181824 [12/16/2021-10:02:22] [V] [TRT] Tactic: -9153228964338181824 Time: 122.755 [12/16/2021-10:02:22] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -7394439838318485025 [12/16/2021-10:02:23] [V] [TRT] Tactic: -7394439838318485025 Time: 68.2104 [12/16/2021-10:02:23] [V] [TRT] Fastest Tactic: -7394439838318485025 Time: 68.2104 [12/16/2021-10:02:23] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -7394439838318485025 [12/16/2021-10:02:23] [V] [TRT] *************** Autotuning format combination: Half(8388608,262144,512,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:02:23] [V] [TRT] --------------- Timing Runner: 002_convolutional (CudnnConvolution) [12/16/2021-10:02:23] [V] [TRT] Tactic: 0 skipped. Scratch requested: 84214272, available: 16777216 [12/16/2021-10:02:23] [V] [TRT] Tactic: 1 skipped. Scratch requested: 84645888, available: 16777216 [12/16/2021-10:02:23] [V] [TRT] Tactic: 2 skipped. Scratch requested: 272957952, available: 16777216 [12/16/2021-10:02:23] [V] [TRT] Tactic: 5 skipped. Scratch requested: 97305088, available: 16777216 [12/16/2021-10:02:23] [V] [TRT] Fastest Tactic: -3360065831133338131 Time: 3.40282e+38 [12/16/2021-10:02:23] [V] [TRT] --------------- Timing Runner: 002_convolutional (CaskConvolution) [12/16/2021-10:02:23] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:02:23] [V] [TRT] *************** Autotuning format combination: Half(4194304,262144:2,512,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:02:23] [V] [TRT] --------------- Timing Runner: 002_convolutional (FusedConvActConvolution) [12/16/2021-10:02:23] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:02:23] [V] [TRT] --------------- Timing Runner: 002_convolutional (CudnnConvolution) [12/16/2021-10:02:23] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:02:23] [V] [TRT] --------------- Timing Runner: 002_convolutional (CaskConvolution) [12/16/2021-10:02:23] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-10:02:24] [V] [TRT] Tactic: 3564772625446233998 Time: 44.6331 [12/16/2021-10:02:24] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_large_nn_v1 Tactic: 3650389455493082349 [12/16/2021-10:02:25] [V] [TRT] Tactic: 3650389455493082349 Time: 46.4473 [12/16/2021-10:02:25] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-10:02:26] [V] [TRT] Tactic: 5319956359050645452 Time: 41.4126 [12/16/2021-10:02:26] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-10:02:26] [V] [TRT] Tactic: 7205456024582378848 Time: 34.5737 [12/16/2021-10:02:26] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_large_nn_v1 Tactic: -6490690591794140522 [12/16/2021-10:02:27] [V] [TRT] Tactic: -6490690591794140522 Time: 34.9496 [12/16/2021-10:02:27] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_large_nn_v1 Tactic: -4686027666808657977 [12/16/2021-10:02:28] [V] [TRT] Tactic: -4686027666808657977 Time: 70.2938 [12/16/2021-10:02:28] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-10:02:29] [V] [TRT] Tactic: -4212163711445252890 Time: 67.9345 [12/16/2021-10:02:29] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-10:02:31] [V] [TRT] Tactic: -3898373634979201110 Time: 69.3451 [12/16/2021-10:02:31] [V] [TRT] 002_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-10:02:31] [V] [TRT] Tactic: -2409163523992614473 Time: 33.808 [12/16/2021-10:02:31] [V] [TRT] Fastest Tactic: -2409163523992614473 Time: 33.808 [12/16/2021-10:02:31] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -2409163523992614473 [12/16/2021-10:02:31] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:02:31] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:32] [V] [TRT] Tactic: 1002 Time: 21.4468 [12/16/2021-10:02:33] [V] [TRT] Tactic: 0 Time: 39.6483 [12/16/2021-10:02:33] [V] [TRT] Fastest Tactic: 1002 Time: 21.4468 [12/16/2021-10:02:33] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:02:33] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:33] [V] [TRT] Tactic: 1002 Time: 21.4636 [12/16/2021-10:02:34] [V] [TRT] Tactic: 0 Time: 67.2514 [12/16/2021-10:02:34] [V] [TRT] Fastest Tactic: 1002 Time: 21.4636 [12/16/2021-10:02:34] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:02:34] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:35] [V] [TRT] Tactic: 1002 Time: 25.5897 [12/16/2021-10:02:35] [V] [TRT] Tactic: 0 Time: 21.1467 [12/16/2021-10:02:35] [V] [TRT] Fastest Tactic: 0 Time: 21.1467 [12/16/2021-10:02:35] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:02:35] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:36] [V] [TRT] Tactic: 1002 Time: 31.3286 [12/16/2021-10:02:36] [V] [TRT] Tactic: 0 Time: 16.9155 [12/16/2021-10:02:36] [V] [TRT] Fastest Tactic: 0 Time: 16.9155 [12/16/2021-10:02:36] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:02:36] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:37] [V] [TRT] Tactic: 1002 Time: 25.6787 [12/16/2021-10:02:38] [V] [TRT] Tactic: 0 Time: 71.4924 [12/16/2021-10:02:38] [V] [TRT] Fastest Tactic: 1002 Time: 25.6787 [12/16/2021-10:02:38] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:02:38] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:38] [V] [TRT] Tactic: 1002 Time: 19.602 [12/16/2021-10:02:41] [V] [TRT] Tactic: 0 Time: 142.733 [12/16/2021-10:02:41] [V] [TRT] Fastest Tactic: 1002 Time: 19.602 [12/16/2021-10:02:41] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:02:41] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:41] [V] [TRT] Tactic: 1002 Time: 19.365 [12/16/2021-10:02:42] [V] [TRT] Tactic: 0 Time: 70.1517 [12/16/2021-10:02:42] [V] [TRT] Fastest Tactic: 1002 Time: 19.365 [12/16/2021-10:02:42] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:02:42] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:43] [V] [TRT] Tactic: 1002 Time: 26.8665 [12/16/2021-10:02:44] [V] [TRT] Tactic: 0 Time: 75.0297 [12/16/2021-10:02:44] [V] [TRT] Fastest Tactic: 1002 Time: 26.8665 [12/16/2021-10:02:44] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:02:44] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:45] [V] [TRT] Tactic: 1002 Time: 25.5425 [12/16/2021-10:02:46] [V] [TRT] Tactic: 0 Time: 70.4166 [12/16/2021-10:02:46] [V] [TRT] Fastest Tactic: 1002 Time: 25.5425 [12/16/2021-10:02:46] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:02:46] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:46] [V] [TRT] Tactic: 1002 Time: 19.5576 [12/16/2021-10:02:47] [V] [TRT] Tactic: 0 Time: 35.7941 [12/16/2021-10:02:47] [V] [TRT] Fastest Tactic: 1002 Time: 19.5576 [12/16/2021-10:02:47] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:02:47] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:47] [V] [TRT] Tactic: 1002 Time: 20.933 [12/16/2021-10:02:49] [V] [TRT] Tactic: 0 Time: 68.9541 [12/16/2021-10:02:49] [V] [TRT] Fastest Tactic: 1002 Time: 20.933 [12/16/2021-10:02:49] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:02:49] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:49] [V] [TRT] Tactic: 1002 Time: 26.8693 [12/16/2021-10:02:50] [V] [TRT] Tactic: 0 Time: 73.9953 [12/16/2021-10:02:50] [V] [TRT] Fastest Tactic: 1002 Time: 26.8693 [12/16/2021-10:02:50] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:02:50] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:51] [V] [TRT] Tactic: 1002 Time: 25.8186 [12/16/2021-10:02:51] [V] [TRT] Tactic: 0 Time: 18.0564 [12/16/2021-10:02:51] [V] [TRT] Fastest Tactic: 0 Time: 18.0564 [12/16/2021-10:02:51] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:02:51] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:52] [V] [TRT] Tactic: 1002 Time: 18.1231 [12/16/2021-10:02:52] [V] [TRT] Tactic: 0 Time: 33.1691 [12/16/2021-10:02:52] [V] [TRT] Fastest Tactic: 1002 Time: 18.1231 [12/16/2021-10:02:52] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:02:52] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:53] [V] [TRT] Tactic: 1002 Time: 18.125 [12/16/2021-10:02:54] [V] [TRT] Tactic: 0 Time: 66.5798 [12/16/2021-10:02:54] [V] [TRT] Fastest Tactic: 1002 Time: 18.125 [12/16/2021-10:02:54] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:02:54] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:54] [V] [TRT] Tactic: 1002 Time: 17.496 [12/16/2021-10:02:54] [V] [TRT] Tactic: 0 Time: 16.7469 [12/16/2021-10:02:54] [V] [TRT] Fastest Tactic: 0 Time: 16.7469 [12/16/2021-10:02:54] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:02:54] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:55] [V] [TRT] Tactic: 1002 Time: 25.2017 [12/16/2021-10:02:55] [V] [TRT] Tactic: 0 Time: 14.7285 [12/16/2021-10:02:55] [V] [TRT] Fastest Tactic: 0 Time: 14.7285 [12/16/2021-10:02:55] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:02:55] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:56] [V] [TRT] Tactic: 1002 Time: 18.4205 [12/16/2021-10:02:56] [V] [TRT] Tactic: 0 Time: 36.3638 [12/16/2021-10:02:56] [V] [TRT] Fastest Tactic: 1002 Time: 18.4205 [12/16/2021-10:02:56] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:02:56] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:57] [V] [TRT] Tactic: 1002 Time: 18.4203 [12/16/2021-10:02:58] [V] [TRT] Tactic: 0 Time: 68.3647 [12/16/2021-10:02:58] [V] [TRT] Fastest Tactic: 1002 Time: 18.4203 [12/16/2021-10:02:58] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:02:58] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:02:59] [V] [TRT] Tactic: 1002 Time: 40.1101 [12/16/2021-10:02:59] [V] [TRT] Tactic: 0 Time: 14.3085 [12/16/2021-10:02:59] [V] [TRT] Fastest Tactic: 0 Time: 14.3085 [12/16/2021-10:02:59] [V] [TRT] *************** Autotuning format combination: Float(4194304,65536,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:02:59] [V] [TRT] --------------- Timing Runner: 002_convolutional_lrelu (PointWiseV2) [12/16/2021-10:02:59] [V] [TRT] Tactic: 0 Time: 17.4752 [12/16/2021-10:03:00] [V] [TRT] Tactic: 1 Time: 12.5179 [12/16/2021-10:03:00] [V] [TRT] Tactic: 2 Time: 10.858 [12/16/2021-10:03:00] [V] [TRT] Tactic: 3 Time: 10.6508 [12/16/2021-10:03:00] [V] [TRT] Tactic: 4 Time: 8.60814 [12/16/2021-10:03:01] [V] [TRT] Tactic: 5 Time: 8.31279 [12/16/2021-10:03:01] [V] [TRT] Tactic: 6 Time: 10.5705 [12/16/2021-10:03:01] [V] [TRT] Tactic: 7 Time: 8.46378 [12/16/2021-10:03:01] [V] [TRT] Tactic: 8 Time: 8.45501 [12/16/2021-10:03:02] [V] [TRT] Tactic: 9 Time: 8.40705 [12/16/2021-10:03:02] [V] [TRT] Tactic: 28 Time: 17.0967 [12/16/2021-10:03:02] [V] [TRT] Fastest Tactic: 5 Time: 8.31279 [12/16/2021-10:03:02] [V] [TRT] --------------- Timing Runner: 002_convolutional_lrelu (PointWise) [12/16/2021-10:03:03] [V] [TRT] Tactic: 128 Time: 40.8458 [12/16/2021-10:03:04] [V] [TRT] Tactic: 256 Time: 40.9844 [12/16/2021-10:03:04] [V] [TRT] Tactic: 512 Time: 41.2696 [12/16/2021-10:03:04] [V] [TRT] Fastest Tactic: 128 Time: 40.8458 [12/16/2021-10:03:04] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:03:04] [V] [TRT] *************** Autotuning format combination: Float(4194304,1,16384,64) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:03:04] [V] [TRT] --------------- Timing Runner: 002_convolutional_lrelu (PointWiseV2) [12/16/2021-10:03:05] [V] [TRT] Tactic: 0 Time: 17.4632 [12/16/2021-10:03:05] [V] [TRT] Tactic: 1 Time: 12.4905 [12/16/2021-10:03:05] [V] [TRT] Tactic: 2 Time: 10.8491 [12/16/2021-10:03:06] [V] [TRT] Tactic: 3 Time: 10.6682 [12/16/2021-10:03:06] [V] [TRT] Tactic: 4 Time: 8.62031 [12/16/2021-10:03:06] [V] [TRT] Tactic: 5 Time: 8.30835 [12/16/2021-10:03:06] [V] [TRT] Tactic: 6 Time: 10.5696 [12/16/2021-10:03:07] [V] [TRT] Tactic: 7 Time: 8.46403 [12/16/2021-10:03:07] [V] [TRT] Tactic: 8 Time: 8.45275 [12/16/2021-10:03:07] [V] [TRT] Tactic: 9 Time: 8.41506 [12/16/2021-10:03:08] [V] [TRT] Tactic: 28 Time: 17.0954 [12/16/2021-10:03:08] [V] [TRT] Fastest Tactic: 5 Time: 8.30835 [12/16/2021-10:03:08] [V] [TRT] --------------- Timing Runner: 002_convolutional_lrelu (PointWise) [12/16/2021-10:03:08] [V] [TRT] Tactic: 128 Time: 40.8572 [12/16/2021-10:03:09] [V] [TRT] Tactic: 256 Time: 40.987 [12/16/2021-10:03:10] [V] [TRT] Tactic: 512 Time: 41.2645 [12/16/2021-10:03:10] [V] [TRT] Fastest Tactic: 128 Time: 40.8572 [12/16/2021-10:03:10] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:03:10] [V] [TRT] *************** Autotuning format combination: Float(131072,65536:32,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:03:10] [V] [TRT] --------------- Timing Runner: 002_convolutional_lrelu (PointWiseV2) [12/16/2021-10:03:10] [V] [TRT] Tactic: 24 Time: 11.0227 [12/16/2021-10:03:11] [V] [TRT] Tactic: 25 Time: 10.3142 [12/16/2021-10:03:11] [V] [TRT] Tactic: 26 Time: 10.4803 [12/16/2021-10:03:11] [V] [TRT] Tactic: 27 Time: 10.256 [12/16/2021-10:03:11] [V] [TRT] Tactic: 31 Time: 11.124 [12/16/2021-10:03:11] [V] [TRT] Fastest Tactic: 27 Time: 10.256 [12/16/2021-10:03:11] [V] [TRT] --------------- Timing Runner: 002_convolutional_lrelu (PointWise) [12/16/2021-10:03:12] [V] [TRT] Tactic: 128 Time: 40.8451 [12/16/2021-10:03:13] [V] [TRT] Tactic: 256 Time: 40.9857 [12/16/2021-10:03:14] [V] [TRT] Tactic: 512 Time: 41.2621 [12/16/2021-10:03:14] [V] [TRT] Fastest Tactic: 128 Time: 40.8451 [12/16/2021-10:03:14] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 27 [12/16/2021-10:03:14] [V] [TRT] *************** Autotuning format combination: Half(4194304,65536,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:03:14] [V] [TRT] --------------- Timing Runner: 002_convolutional_lrelu (PointWiseV2) [12/16/2021-10:03:14] [V] [TRT] Tactic: 0 Time: 17.3795 [12/16/2021-10:03:14] [V] [TRT] Tactic: 1 Time: 12.169 [12/16/2021-10:03:15] [V] [TRT] Tactic: 2 Time: 11.7199 [12/16/2021-10:03:15] [V] [TRT] Tactic: 3 Time: 9.54436 [12/16/2021-10:03:15] [V] [TRT] Tactic: 4 Time: 7.45197 [12/16/2021-10:03:15] [V] [TRT] Tactic: 5 Time: 7.81399 [12/16/2021-10:03:15] [V] [TRT] Tactic: 6 Time: 8.69369 [12/16/2021-10:03:16] [V] [TRT] Tactic: 7 Time: 6.14382 [12/16/2021-10:03:16] [V] [TRT] Tactic: 8 Time: 5.4474 [12/16/2021-10:03:16] [V] [TRT] Tactic: 9 Time: 6.17566 [12/16/2021-10:03:16] [V] [TRT] Tactic: 28 Time: 16.9171 [12/16/2021-10:03:16] [V] [TRT] Fastest Tactic: 8 Time: 5.4474 [12/16/2021-10:03:16] [V] [TRT] --------------- Timing Runner: 002_convolutional_lrelu (PointWise) [12/16/2021-10:03:17] [V] [TRT] Tactic: 128 Time: 36.7503 [12/16/2021-10:03:17] [V] [TRT] Tactic: 256 Time: 36.5264 [12/16/2021-10:03:18] [V] [TRT] Tactic: 512 Time: 35.6403 [12/16/2021-10:03:18] [V] [TRT] Fastest Tactic: 512 Time: 35.6403 [12/16/2021-10:03:18] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:03:18] [V] [TRT] *************** Autotuning format combination: Half(2097152,65536:2,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:03:18] [V] [TRT] --------------- Timing Runner: 002_convolutional_lrelu (PointWiseV2) [12/16/2021-10:03:18] [V] [TRT] Tactic: 0 Time: 11.173 [12/16/2021-10:03:19] [V] [TRT] Tactic: 1 Time: 8.97423 [12/16/2021-10:03:19] [V] [TRT] Tactic: 2 Time: 9.88663 [12/16/2021-10:03:19] [V] [TRT] Tactic: 3 Time: 7.93325 [12/16/2021-10:03:19] [V] [TRT] Tactic: 4 Time: 8.23648 [12/16/2021-10:03:19] [V] [TRT] Tactic: 5 Time: 8.89583 [12/16/2021-10:03:20] [V] [TRT] Tactic: 6 Time: 7.70445 [12/16/2021-10:03:20] [V] [TRT] Tactic: 7 Time: 7.91535 [12/16/2021-10:03:20] [V] [TRT] Tactic: 8 Time: 8.34378 [12/16/2021-10:03:20] [V] [TRT] Tactic: 9 Time: 9.30561 [12/16/2021-10:03:21] [V] [TRT] Tactic: 10 Time: 18.9292 [12/16/2021-10:03:21] [V] [TRT] Tactic: 11 Time: 13.0175 [12/16/2021-10:03:21] [V] [TRT] Tactic: 12 Time: 12.5607 [12/16/2021-10:03:21] [V] [TRT] Tactic: 13 Time: 9.7492 [12/16/2021-10:03:21] [V] [TRT] Tactic: 14 Time: 7.93302 [12/16/2021-10:03:22] [V] [TRT] Tactic: 15 Time: 8.47521 [12/16/2021-10:03:22] [V] [TRT] Tactic: 16 Time: 8.81428 [12/16/2021-10:03:22] [V] [TRT] Tactic: 17 Time: 6.2048 [12/16/2021-10:03:22] [V] [TRT] Tactic: 18 Time: 5.84208 [12/16/2021-10:03:22] [V] [TRT] Tactic: 19 Time: 6.94406 [12/16/2021-10:03:23] [V] [TRT] Tactic: 28 Time: 10.9269 [12/16/2021-10:03:23] [V] [TRT] Tactic: 29 Time: 18.3316 [12/16/2021-10:03:23] [V] [TRT] Fastest Tactic: 18 Time: 5.84208 [12/16/2021-10:03:23] [V] [TRT] --------------- Timing Runner: 002_convolutional_lrelu (PointWise) [12/16/2021-10:03:24] [V] [TRT] Tactic: 128 Time: 36.766 [12/16/2021-10:03:24] [V] [TRT] Tactic: 256 Time: 36.5313 [12/16/2021-10:03:25] [V] [TRT] Tactic: 512 Time: 35.6417 [12/16/2021-10:03:25] [V] [TRT] Fastest Tactic: 512 Time: 35.6417 [12/16/2021-10:03:25] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:03:25] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:03:25] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:25] [V] [TRT] Tactic: 1002 Time: 21.4859 [12/16/2021-10:03:26] [V] [TRT] Tactic: 0 Time: 39.577 [12/16/2021-10:03:26] [V] [TRT] Fastest Tactic: 1002 Time: 21.4859 [12/16/2021-10:03:26] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:03:26] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:27] [V] [TRT] Tactic: 1002 Time: 21.5733 [12/16/2021-10:03:28] [V] [TRT] Tactic: 0 Time: 67.2035 [12/16/2021-10:03:28] [V] [TRT] Fastest Tactic: 1002 Time: 21.5733 [12/16/2021-10:03:28] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:03:28] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:28] [V] [TRT] Tactic: 1002 Time: 25.5977 [12/16/2021-10:03:29] [V] [TRT] Tactic: 0 Time: 21.3885 [12/16/2021-10:03:29] [V] [TRT] Fastest Tactic: 0 Time: 21.3885 [12/16/2021-10:03:29] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:03:29] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:29] [V] [TRT] Tactic: 1002 Time: 31.678 [12/16/2021-10:03:30] [V] [TRT] Tactic: 0 Time: 17.101 [12/16/2021-10:03:30] [V] [TRT] Fastest Tactic: 0 Time: 17.101 [12/16/2021-10:03:30] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:03:30] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:30] [V] [TRT] Tactic: 1002 Time: 25.8838 [12/16/2021-10:03:31] [V] [TRT] Tactic: 0 Time: 71.473 [12/16/2021-10:03:31] [V] [TRT] Fastest Tactic: 1002 Time: 25.8838 [12/16/2021-10:03:31] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:03:31] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:32] [V] [TRT] Tactic: 1002 Time: 19.6003 [12/16/2021-10:03:34] [V] [TRT] Tactic: 0 Time: 142.744 [12/16/2021-10:03:34] [V] [TRT] Fastest Tactic: 1002 Time: 19.6003 [12/16/2021-10:03:34] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:03:34] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:35] [V] [TRT] Tactic: 1002 Time: 19.3572 [12/16/2021-10:03:36] [V] [TRT] Tactic: 0 Time: 70.1358 [12/16/2021-10:03:36] [V] [TRT] Fastest Tactic: 1002 Time: 19.3572 [12/16/2021-10:03:36] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:03:36] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:36] [V] [TRT] Tactic: 1002 Time: 26.8624 [12/16/2021-10:03:38] [V] [TRT] Tactic: 0 Time: 75.2041 [12/16/2021-10:03:38] [V] [TRT] Fastest Tactic: 1002 Time: 26.8624 [12/16/2021-10:03:38] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:03:38] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:38] [V] [TRT] Tactic: 1002 Time: 25.4887 [12/16/2021-10:03:39] [V] [TRT] Tactic: 0 Time: 70.4925 [12/16/2021-10:03:39] [V] [TRT] Fastest Tactic: 1002 Time: 25.4887 [12/16/2021-10:03:39] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:03:39] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:40] [V] [TRT] Tactic: 1002 Time: 19.5598 [12/16/2021-10:03:41] [V] [TRT] Tactic: 0 Time: 35.7991 [12/16/2021-10:03:41] [V] [TRT] Fastest Tactic: 1002 Time: 19.5598 [12/16/2021-10:03:41] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:03:41] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:41] [V] [TRT] Tactic: 1002 Time: 21.1722 [12/16/2021-10:03:42] [V] [TRT] Tactic: 0 Time: 69.0071 [12/16/2021-10:03:42] [V] [TRT] Fastest Tactic: 1002 Time: 21.1722 [12/16/2021-10:03:42] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:03:42] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:43] [V] [TRT] Tactic: 1002 Time: 26.8692 [12/16/2021-10:03:44] [V] [TRT] Tactic: 0 Time: 74.0541 [12/16/2021-10:03:44] [V] [TRT] Fastest Tactic: 1002 Time: 26.8692 [12/16/2021-10:03:44] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:03:44] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:44] [V] [TRT] Tactic: 1002 Time: 25.8221 [12/16/2021-10:03:45] [V] [TRT] Tactic: 0 Time: 18.2592 [12/16/2021-10:03:45] [V] [TRT] Fastest Tactic: 0 Time: 18.2592 [12/16/2021-10:03:45] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:03:45] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:45] [V] [TRT] Tactic: 1002 Time: 18.3333 [12/16/2021-10:03:46] [V] [TRT] Tactic: 0 Time: 33.5064 [12/16/2021-10:03:46] [V] [TRT] Fastest Tactic: 1002 Time: 18.3333 [12/16/2021-10:03:46] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:03:46] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:46] [V] [TRT] Tactic: 1002 Time: 18.328 [12/16/2021-10:03:47] [V] [TRT] Tactic: 0 Time: 66.7302 [12/16/2021-10:03:47] [V] [TRT] Fastest Tactic: 1002 Time: 18.328 [12/16/2021-10:03:47] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:03:47] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:48] [V] [TRT] Tactic: 1002 Time: 17.5641 [12/16/2021-10:03:48] [V] [TRT] Tactic: 0 Time: 16.9279 [12/16/2021-10:03:48] [V] [TRT] Fastest Tactic: 0 Time: 16.9279 [12/16/2021-10:03:48] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:03:48] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:49] [V] [TRT] Tactic: 1002 Time: 25.4723 [12/16/2021-10:03:49] [V] [TRT] Tactic: 0 Time: 14.8626 [12/16/2021-10:03:49] [V] [TRT] Fastest Tactic: 0 Time: 14.8626 [12/16/2021-10:03:49] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:03:49] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:49] [V] [TRT] Tactic: 1002 Time: 18.6315 [12/16/2021-10:03:50] [V] [TRT] Tactic: 0 Time: 36.7693 [12/16/2021-10:03:50] [V] [TRT] Fastest Tactic: 1002 Time: 18.6315 [12/16/2021-10:03:50] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:03:50] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:50] [V] [TRT] Tactic: 1002 Time: 18.6292 [12/16/2021-10:03:52] [V] [TRT] Tactic: 0 Time: 68.5591 [12/16/2021-10:03:52] [V] [TRT] Fastest Tactic: 1002 Time: 18.6292 [12/16/2021-10:03:52] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:03:52] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:03:52] [V] [TRT] Tactic: 1002 Time: 40.2648 [12/16/2021-10:03:53] [V] [TRT] Tactic: 0 Time: 14.4588 [12/16/2021-10:03:53] [V] [TRT] Fastest Tactic: 0 Time: 14.4588 [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] *************** Autotuning format combination: Float(4194304,65536,256,1) -> Float(2097152,65536,256,1) *************** [12/16/2021-10:03:53] [V] [TRT] --------------- Timing Runner: 003_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:03:53] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:03:53] [V] [TRT] --------------- Timing Runner: 003_convolutional (FusedConvActConvolution) [12/16/2021-10:03:53] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:03:53] [V] [TRT] --------------- Timing Runner: 003_convolutional (CudnnConvolution) [12/16/2021-10:03:53] [V] [TRT] Tactic: 0 Time: 23.8282 [12/16/2021-10:03:53] [V] [TRT] Tactic: 1 Time: 18.1857 [12/16/2021-10:03:53] [V] [TRT] Tactic: 2 skipped. Scratch requested: 83886080, available: 16777216 [12/16/2021-10:03:53] [V] [TRT] Tactic: 4 skipped. Scratch requested: 1251221504, available: 16777216 [12/16/2021-10:03:53] [V] [TRT] Tactic: 5 skipped. Scratch requested: 128925696, available: 16777216 [12/16/2021-10:03:53] [V] [TRT] Fastest Tactic: 1 Time: 18.1857 [12/16/2021-10:03:53] [V] [TRT] --------------- Timing Runner: 003_convolutional (CublasConvolution) [12/16/2021-10:03:53] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:03:53] [V] [TRT] --------------- Timing Runner: 003_convolutional (CaskConvolution) [12/16/2021-10:03:53] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:03:54] [V] [TRT] Tactic: 1062367460111450758 Time: 14.1427 [12/16/2021-10:03:54] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_interior_nn_v0 Tactic: 1698681053543049347 [12/16/2021-10:03:54] [V] [TRT] Tactic: 1698681053543049347 Time: 12.5427 [12/16/2021-10:03:54] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:03:55] [V] [TRT] Tactic: 4501471010995462441 Time: 40.4064 [12/16/2021-10:03:55] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:03:55] [V] [TRT] Tactic: 5137655947464784826 Time: 19.911 [12/16/2021-10:03:55] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:03:56] [V] [TRT] Tactic: 5288347012147084929 Time: 41.3532 [12/16/2021-10:03:56] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_interior_nn_v1 Tactic: 5326823351883942011 [12/16/2021-10:03:57] [V] [TRT] Tactic: 5326823351883942011 Time: 38.8406 [12/16/2021-10:03:57] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_interior_nn_v0 Tactic: 5500448035057547314 [12/16/2021-10:03:57] [V] [TRT] Tactic: 5500448035057547314 Time: 21.6505 [12/16/2021-10:03:57] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-10:03:57] [V] [TRT] Tactic: 6645123197870846056 Time: 20.2966 [12/16/2021-10:03:57] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-10:03:58] [V] [TRT] Tactic: 7144526460361122478 Time: 14.1819 [12/16/2021-10:03:58] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-10:03:59] [V] [TRT] Tactic: -8262349710178828730 Time: 42.1251 [12/16/2021-10:03:59] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_interior_nn_v1 Tactic: -6576203419454146580 [12/16/2021-10:03:59] [V] [TRT] Tactic: -6576203419454146580 Time: 13.3415 [12/16/2021-10:03:59] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-10:03:59] [V] [TRT] Tactic: -4787320710726427159 Time: 14.8507 [12/16/2021-10:03:59] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-10:03:59] [V] [TRT] Tactic: -3456450830548107839 Time: 13.606 [12/16/2021-10:04:00] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-10:04:00] [V] [TRT] Tactic: -1218658103698133241 Time: 22.7516 [12/16/2021-10:04:00] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-10:04:00] [V] [TRT] Tactic: -836875257600482091 Time: 22.3174 [12/16/2021-10:04:00] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-10:04:01] [V] [TRT] Tactic: -410470605513481746 Time: 39.6703 [12/16/2021-10:04:01] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_interior_nn_v0 Tactic: -377491875521947884 [12/16/2021-10:04:02] [V] [TRT] Tactic: -377491875521947884 Time: 40.7379 [12/16/2021-10:04:02] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_interior_nn_v1 Tactic: -37215280111360163 [12/16/2021-10:04:02] [V] [TRT] Tactic: -37215280111360163 Time: 19.9349 [12/16/2021-10:04:02] [V] [TRT] Fastest Tactic: 1698681053543049347 Time: 12.5427 [12/16/2021-10:04:02] [V] [TRT] Setting workspace to 128925696enables more tactics for profiling [12/16/2021-10:04:02] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 1698681053543049347 [12/16/2021-10:04:02] [V] [TRT] *************** Autotuning format combination: Float(4194304,1,16384,64) -> Float(2097152,1,8192,32) *************** [12/16/2021-10:04:02] [V] [TRT] --------------- Timing Runner: 003_convolutional (CudnnConvolution) [12/16/2021-10:04:02] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:04:02] [V] [TRT] --------------- Timing Runner: 003_convolutional (CublasConvolution) [12/16/2021-10:04:02] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:04:02] [V] [TRT] --------------- Timing Runner: 003_convolutional (CaskConvolution) [12/16/2021-10:04:02] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_interior_nhwc_tn_v1 Tactic: 3886731678879822788 [12/16/2021-10:04:03] [V] [TRT] Tactic: 3886731678879822788 Time: 23.925 [12/16/2021-10:04:03] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_interior_nhwc_tn_v1 Tactic: 6629944304117643200 [12/16/2021-10:04:03] [V] [TRT] Tactic: 6629944304117643200 Time: 31.8187 [12/16/2021-10:04:03] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -9153228964338181824 [12/16/2021-10:04:04] [V] [TRT] Tactic: -9153228964338181824 Time: 32.0938 [12/16/2021-10:04:04] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -7394439838318485025 [12/16/2021-10:04:04] [V] [TRT] Tactic: -7394439838318485025 Time: 23.6167 [12/16/2021-10:04:04] [V] [TRT] Fastest Tactic: -7394439838318485025 Time: 23.6167 [12/16/2021-10:04:04] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -7394439838318485025 [12/16/2021-10:04:04] [V] [TRT] *************** Autotuning format combination: Half(4194304,65536,256,1) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:04] [V] [TRT] --------------- Timing Runner: 003_convolutional (CudnnConvolution) [12/16/2021-10:04:05] [V] [TRT] Tactic: 0 Time: 23.2334 [12/16/2021-10:04:05] [V] [TRT] Tactic: 1 Time: 20.7014 [12/16/2021-10:04:05] [V] [TRT] Tactic: 2 skipped. Scratch requested: 41943040, available: 16777216 [12/16/2021-10:04:05] [V] [TRT] Tactic: 4 skipped. Scratch requested: 1251221504, available: 16777216 [12/16/2021-10:04:05] [V] [TRT] Tactic: 5 skipped. Scratch requested: 128925696, available: 16777216 [12/16/2021-10:04:05] [V] [TRT] Fastest Tactic: 1 Time: 20.7014 [12/16/2021-10:04:05] [V] [TRT] --------------- Timing Runner: 003_convolutional (CublasConvolution) [12/16/2021-10:04:05] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:04:05] [V] [TRT] --------------- Timing Runner: 003_convolutional (CaskConvolution) [12/16/2021-10:04:05] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:04:05] [V] [TRT] Setting workspace to 128925696enables more tactics for profiling [12/16/2021-10:04:05] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CudnnConvolution Tactic: 1 [12/16/2021-10:04:05] [V] [TRT] *************** Autotuning format combination: Half(2097152,65536:2,256,1) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:05] [V] [TRT] --------------- Timing Runner: 003_convolutional (CaskConvolution) [12/16/2021-10:04:05] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:04:05] [V] [TRT] *************** Autotuning format combination: Half(2097152,65536:2,256,1) -> Half(1048576,65536:2,256,1) *************** [12/16/2021-10:04:05] [V] [TRT] --------------- Timing Runner: 003_convolutional (FusedConvActConvolution) [12/16/2021-10:04:05] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:04:05] [V] [TRT] --------------- Timing Runner: 003_convolutional (CudnnConvolution) [12/16/2021-10:04:05] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:04:05] [V] [TRT] --------------- Timing Runner: 003_convolutional (CublasConvolution) [12/16/2021-10:04:05] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:04:05] [V] [TRT] --------------- Timing Runner: 003_convolutional (CaskConvolution) [12/16/2021-10:04:05] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_interior_nn_v1 Tactic: 3066127711859985668 [12/16/2021-10:04:05] [V] [TRT] Tactic: 3066127711859985668 Time: 7.52738 [12/16/2021-10:04:05] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-10:04:06] [V] [TRT] Tactic: 3564772625446233998 Time: 8.0579 [12/16/2021-10:04:06] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-10:04:06] [V] [TRT] Tactic: 5319956359050645452 Time: 7.67066 [12/16/2021-10:04:06] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-10:04:06] [V] [TRT] Tactic: 7205456024582378848 Time: 11.5928 [12/16/2021-10:04:06] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_interior_nn_v1 Tactic: 8163473458334948789 [12/16/2021-10:04:06] [V] [TRT] Tactic: 8163473458334948789 Time: 11.4552 [12/16/2021-10:04:06] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-10:04:07] [V] [TRT] Tactic: -4212163711445252890 Time: 23.0607 [12/16/2021-10:04:07] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-10:04:07] [V] [TRT] Tactic: -3898373634979201110 Time: 23.2276 [12/16/2021-10:04:07] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-10:04:07] [V] [TRT] Tactic: -2409163523992614473 Time: 11.4623 [12/16/2021-10:04:07] [V] [TRT] 003_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_interior_nn_v1 Tactic: -1716393687483585322 [12/16/2021-10:04:08] [V] [TRT] Tactic: -1716393687483585322 Time: 22.7281 [12/16/2021-10:04:08] [V] [TRT] Fastest Tactic: 3066127711859985668 Time: 7.52738 [12/16/2021-10:04:08] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 3066127711859985668 [12/16/2021-10:04:08] [V] [TRT] *************** Autotuning Reformat:Float(2097152,65536,256,1) -> Float(2097152,1,8192,32) *************** [12/16/2021-10:04:08] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:08] [V] [TRT] Tactic: 1002 Time: 22.4909 [12/16/2021-10:04:09] [V] [TRT] Tactic: 0 Time: 20.5568 [12/16/2021-10:04:09] [V] [TRT] Fastest Tactic: 0 Time: 20.5568 [12/16/2021-10:04:09] [V] [TRT] *************** Autotuning Reformat:Float(2097152,65536,256,1) -> Float(65536,65536:32,256,1) *************** [12/16/2021-10:04:09] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:09] [V] [TRT] Tactic: 1002 Time: 22.4626 [12/16/2021-10:04:10] [V] [TRT] Tactic: 0 Time: 33.6024 [12/16/2021-10:04:10] [V] [TRT] Fastest Tactic: 1002 Time: 22.4626 [12/16/2021-10:04:10] [V] [TRT] *************** Autotuning Reformat:Float(2097152,65536,256,1) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:10] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:10] [V] [TRT] Tactic: 1002 Time: 12.8021 [12/16/2021-10:04:10] [V] [TRT] Tactic: 0 Time: 10.5796 [12/16/2021-10:04:10] [V] [TRT] Fastest Tactic: 0 Time: 10.5796 [12/16/2021-10:04:10] [V] [TRT] *************** Autotuning Reformat:Float(2097152,65536,256,1) -> Half(1048576,65536:2,256,1) *************** [12/16/2021-10:04:10] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:10] [V] [TRT] Tactic: 1002 Time: 15.6133 [12/16/2021-10:04:11] [V] [TRT] Tactic: 0 Time: 8.46025 [12/16/2021-10:04:11] [V] [TRT] Fastest Tactic: 0 Time: 8.46025 [12/16/2021-10:04:11] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,8192,32) -> Float(2097152,65536,256,1) *************** [12/16/2021-10:04:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:11] [V] [TRT] Tactic: 1002 Time: 22.4653 [12/16/2021-10:04:12] [V] [TRT] Tactic: 0 Time: 35.2339 [12/16/2021-10:04:12] [V] [TRT] Fastest Tactic: 1002 Time: 22.4653 [12/16/2021-10:04:12] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,8192,32) -> Float(65536,65536:32,256,1) *************** [12/16/2021-10:04:12] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:12] [V] [TRT] Tactic: 1002 Time: 9.80046 [12/16/2021-10:04:13] [V] [TRT] Tactic: 0 Time: 69.9793 [12/16/2021-10:04:13] [V] [TRT] Fastest Tactic: 1002 Time: 9.80046 [12/16/2021-10:04:13] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,8192,32) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:13] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:13] [V] [TRT] Tactic: 1002 Time: 9.67977 [12/16/2021-10:04:14] [V] [TRT] Tactic: 0 Time: 34.4955 [12/16/2021-10:04:14] [V] [TRT] Fastest Tactic: 1002 Time: 9.67977 [12/16/2021-10:04:14] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,8192,32) -> Half(1048576,65536:2,256,1) *************** [12/16/2021-10:04:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:14] [V] [TRT] Tactic: 1002 Time: 13.3842 [12/16/2021-10:04:15] [V] [TRT] Tactic: 0 Time: 37.0754 [12/16/2021-10:04:15] [V] [TRT] Fastest Tactic: 1002 Time: 13.3842 [12/16/2021-10:04:15] [V] [TRT] *************** Autotuning Reformat:Float(65536,65536:32,256,1) -> Float(2097152,65536,256,1) *************** [12/16/2021-10:04:15] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:15] [V] [TRT] Tactic: 1002 Time: 22.7296 [12/16/2021-10:04:16] [V] [TRT] Tactic: 0 Time: 35.285 [12/16/2021-10:04:16] [V] [TRT] Fastest Tactic: 1002 Time: 22.7296 [12/16/2021-10:04:16] [V] [TRT] *************** Autotuning Reformat:Float(65536,65536:32,256,1) -> Float(2097152,1,8192,32) *************** [12/16/2021-10:04:16] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:16] [V] [TRT] Tactic: 1002 Time: 9.88925 [12/16/2021-10:04:16] [V] [TRT] Tactic: 0 Time: 18.101 [12/16/2021-10:04:16] [V] [TRT] Fastest Tactic: 1002 Time: 9.88925 [12/16/2021-10:04:16] [V] [TRT] *************** Autotuning Reformat:Float(65536,65536:32,256,1) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:16] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:16] [V] [TRT] Tactic: 1002 Time: 10.5891 [12/16/2021-10:04:17] [V] [TRT] Tactic: 0 Time: 34.51 [12/16/2021-10:04:17] [V] [TRT] Fastest Tactic: 1002 Time: 10.5891 [12/16/2021-10:04:17] [V] [TRT] *************** Autotuning Reformat:Float(65536,65536:32,256,1) -> Half(1048576,65536:2,256,1) *************** [12/16/2021-10:04:17] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:17] [V] [TRT] Tactic: 1002 Time: 13.3859 [12/16/2021-10:04:18] [V] [TRT] Tactic: 0 Time: 37.094 [12/16/2021-10:04:18] [V] [TRT] Fastest Tactic: 1002 Time: 13.3859 [12/16/2021-10:04:18] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536,256,1) -> Float(2097152,65536,256,1) *************** [12/16/2021-10:04:18] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:18] [V] [TRT] Tactic: 1002 Time: 13.0609 [12/16/2021-10:04:18] [V] [TRT] Tactic: 0 Time: 9.13442 [12/16/2021-10:04:18] [V] [TRT] Fastest Tactic: 0 Time: 9.13442 [12/16/2021-10:04:18] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536,256,1) -> Float(2097152,1,8192,32) *************** [12/16/2021-10:04:18] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:19] [V] [TRT] Tactic: 1002 Time: 9.17628 [12/16/2021-10:04:19] [V] [TRT] Tactic: 0 Time: 17.1142 [12/16/2021-10:04:19] [V] [TRT] Fastest Tactic: 1002 Time: 9.17628 [12/16/2021-10:04:19] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536,256,1) -> Float(65536,65536:32,256,1) *************** [12/16/2021-10:04:19] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:19] [V] [TRT] Tactic: 1002 Time: 9.17671 [12/16/2021-10:04:20] [V] [TRT] Tactic: 0 Time: 33.3867 [12/16/2021-10:04:20] [V] [TRT] Fastest Tactic: 1002 Time: 9.17671 [12/16/2021-10:04:20] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536,256,1) -> Half(1048576,65536:2,256,1) *************** [12/16/2021-10:04:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:20] [V] [TRT] Tactic: 1002 Time: 8.8201 [12/16/2021-10:04:20] [V] [TRT] Tactic: 0 Time: 8.4724 [12/16/2021-10:04:20] [V] [TRT] Fastest Tactic: 0 Time: 8.4724 [12/16/2021-10:04:20] [V] [TRT] *************** Autotuning Reformat:Half(1048576,65536:2,256,1) -> Float(2097152,65536,256,1) *************** [12/16/2021-10:04:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:20] [V] [TRT] Tactic: 1002 Time: 12.7466 [12/16/2021-10:04:20] [V] [TRT] Tactic: 0 Time: 7.43481 [12/16/2021-10:04:20] [V] [TRT] Fastest Tactic: 0 Time: 7.43481 [12/16/2021-10:04:20] [V] [TRT] *************** Autotuning Reformat:Half(1048576,65536:2,256,1) -> Float(2097152,1,8192,32) *************** [12/16/2021-10:04:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:21] [V] [TRT] Tactic: 1002 Time: 9.33406 [12/16/2021-10:04:21] [V] [TRT] Tactic: 0 Time: 18.3968 [12/16/2021-10:04:21] [V] [TRT] Fastest Tactic: 1002 Time: 9.33406 [12/16/2021-10:04:21] [V] [TRT] *************** Autotuning Reformat:Half(1048576,65536:2,256,1) -> Float(65536,65536:32,256,1) *************** [12/16/2021-10:04:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:21] [V] [TRT] Tactic: 1002 Time: 9.32878 [12/16/2021-10:04:22] [V] [TRT] Tactic: 0 Time: 34.4178 [12/16/2021-10:04:22] [V] [TRT] Fastest Tactic: 1002 Time: 9.32878 [12/16/2021-10:04:22] [V] [TRT] *************** Autotuning Reformat:Half(1048576,65536:2,256,1) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:22] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:04:22] [V] [TRT] Tactic: 1002 Time: 30.0225 [12/16/2021-10:04:22] [V] [TRT] Tactic: 0 Time: 7.2313 [12/16/2021-10:04:22] [V] [TRT] Fastest Tactic: 0 Time: 7.2313 [12/16/2021-10:04:22] [V] [TRT] *************** Autotuning format combination: Float(2097152,65536,256,1) -> Float(2097152,65536,256,1) *************** [12/16/2021-10:04:22] [V] [TRT] --------------- Timing Runner: 003_convolutional_lrelu (PointWiseV2) [12/16/2021-10:04:23] [V] [TRT] Tactic: 0 Time: 8.83038 [12/16/2021-10:04:23] [V] [TRT] Tactic: 1 Time: 6.2991 [12/16/2021-10:04:23] [V] [TRT] Tactic: 2 Time: 5.48606 [12/16/2021-10:04:23] [V] [TRT] Tactic: 3 Time: 5.35822 [12/16/2021-10:04:23] [V] [TRT] Tactic: 4 Time: 4.32391 [12/16/2021-10:04:23] [V] [TRT] Tactic: 5 Time: 4.14702 [12/16/2021-10:04:24] [V] [TRT] Tactic: 6 Time: 5.2969 [12/16/2021-10:04:24] [V] [TRT] Tactic: 7 Time: 4.23574 [12/16/2021-10:04:24] [V] [TRT] Tactic: 8 Time: 4.22939 [12/16/2021-10:04:24] [V] [TRT] Tactic: 9 Time: 4.20716 [12/16/2021-10:04:24] [V] [TRT] Tactic: 28 Time: 8.64146 [12/16/2021-10:04:24] [V] [TRT] Fastest Tactic: 5 Time: 4.14702 [12/16/2021-10:04:24] [V] [TRT] --------------- Timing Runner: 003_convolutional_lrelu (PointWise) [12/16/2021-10:04:25] [V] [TRT] Tactic: 128 Time: 20.6494 [12/16/2021-10:04:25] [V] [TRT] Tactic: 256 Time: 20.7223 [12/16/2021-10:04:25] [V] [TRT] Tactic: 512 Time: 20.8627 [12/16/2021-10:04:25] [V] [TRT] Fastest Tactic: 128 Time: 20.6494 [12/16/2021-10:04:25] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:04:25] [V] [TRT] *************** Autotuning format combination: Float(2097152,1,8192,32) -> Float(2097152,1,8192,32) *************** [12/16/2021-10:04:25] [V] [TRT] --------------- Timing Runner: 003_convolutional_lrelu (PointWiseV2) [12/16/2021-10:04:26] [V] [TRT] Tactic: 0 Time: 8.82969 [12/16/2021-10:04:26] [V] [TRT] Tactic: 1 Time: 6.29695 [12/16/2021-10:04:26] [V] [TRT] Tactic: 2 Time: 5.48396 [12/16/2021-10:04:26] [V] [TRT] Tactic: 3 Time: 5.35368 [12/16/2021-10:04:26] [V] [TRT] Tactic: 4 Time: 4.34071 [12/16/2021-10:04:26] [V] [TRT] Tactic: 5 Time: 4.14698 [12/16/2021-10:04:26] [V] [TRT] Tactic: 6 Time: 5.29813 [12/16/2021-10:04:27] [V] [TRT] Tactic: 7 Time: 4.22804 [12/16/2021-10:04:27] [V] [TRT] Tactic: 8 Time: 4.23215 [12/16/2021-10:04:27] [V] [TRT] Tactic: 9 Time: 4.20615 [12/16/2021-10:04:27] [V] [TRT] Tactic: 28 Time: 8.55259 [12/16/2021-10:04:27] [V] [TRT] Fastest Tactic: 5 Time: 4.14698 [12/16/2021-10:04:27] [V] [TRT] --------------- Timing Runner: 003_convolutional_lrelu (PointWise) [12/16/2021-10:04:27] [V] [TRT] Tactic: 128 Time: 20.4315 [12/16/2021-10:04:28] [V] [TRT] Tactic: 256 Time: 20.4991 [12/16/2021-10:04:28] [V] [TRT] Tactic: 512 Time: 20.6377 [12/16/2021-10:04:28] [V] [TRT] Fastest Tactic: 128 Time: 20.4315 [12/16/2021-10:04:28] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:04:28] [V] [TRT] *************** Autotuning format combination: Float(65536,65536:32,256,1) -> Float(65536,65536:32,256,1) *************** [12/16/2021-10:04:28] [V] [TRT] --------------- Timing Runner: 003_convolutional_lrelu (PointWiseV2) [12/16/2021-10:04:28] [V] [TRT] Tactic: 24 Time: 5.50643 [12/16/2021-10:04:28] [V] [TRT] Tactic: 25 Time: 5.18755 [12/16/2021-10:04:29] [V] [TRT] Tactic: 26 Time: 5.20928 [12/16/2021-10:04:29] [V] [TRT] Tactic: 27 Time: 5.13322 [12/16/2021-10:04:29] [V] [TRT] Tactic: 31 Time: 5.53549 [12/16/2021-10:04:29] [V] [TRT] Fastest Tactic: 27 Time: 5.13322 [12/16/2021-10:04:29] [V] [TRT] --------------- Timing Runner: 003_convolutional_lrelu (PointWise) [12/16/2021-10:04:29] [V] [TRT] Tactic: 128 Time: 20.4305 [12/16/2021-10:04:30] [V] [TRT] Tactic: 256 Time: 20.5021 [12/16/2021-10:04:30] [V] [TRT] Tactic: 512 Time: 20.8713 [12/16/2021-10:04:30] [V] [TRT] Fastest Tactic: 128 Time: 20.4305 [12/16/2021-10:04:30] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 27 [12/16/2021-10:04:30] [V] [TRT] *************** Autotuning format combination: Half(2097152,65536,256,1) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:30] [V] [TRT] --------------- Timing Runner: 003_convolutional_lrelu (PointWiseV2) [12/16/2021-10:04:30] [V] [TRT] Tactic: 0 Time: 8.79197 [12/16/2021-10:04:30] [V] [TRT] Tactic: 1 Time: 6.15304 [12/16/2021-10:04:31] [V] [TRT] Tactic: 2 Time: 5.92313 [12/16/2021-10:04:31] [V] [TRT] Tactic: 3 Time: 4.81311 [12/16/2021-10:04:31] [V] [TRT] Tactic: 4 Time: 3.77526 [12/16/2021-10:04:31] [V] [TRT] Tactic: 5 Time: 3.95387 [12/16/2021-10:04:31] [V] [TRT] Tactic: 6 Time: 4.36917 [12/16/2021-10:04:31] [V] [TRT] Tactic: 7 Time: 3.10676 [12/16/2021-10:04:31] [V] [TRT] Tactic: 8 Time: 2.76139 [12/16/2021-10:04:31] [V] [TRT] Tactic: 9 Time: 3.12852 [12/16/2021-10:04:31] [V] [TRT] Tactic: 28 Time: 8.55356 [12/16/2021-10:04:31] [V] [TRT] Fastest Tactic: 8 Time: 2.76139 [12/16/2021-10:04:31] [V] [TRT] --------------- Timing Runner: 003_convolutional_lrelu (PointWise) [12/16/2021-10:04:32] [V] [TRT] Tactic: 128 Time: 18.6063 [12/16/2021-10:04:32] [V] [TRT] Tactic: 256 Time: 18.4711 [12/16/2021-10:04:32] [V] [TRT] Tactic: 512 Time: 18.0252 [12/16/2021-10:04:32] [V] [TRT] Fastest Tactic: 512 Time: 18.0252 [12/16/2021-10:04:32] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:04:32] [V] [TRT] *************** Autotuning format combination: Half(1048576,65536:2,256,1) -> Half(1048576,65536:2,256,1) *************** [12/16/2021-10:04:32] [V] [TRT] --------------- Timing Runner: 003_convolutional_lrelu (PointWiseV2) [12/16/2021-10:04:32] [V] [TRT] Tactic: 0 Time: 5.6475 [12/16/2021-10:04:33] [V] [TRT] Tactic: 1 Time: 4.52792 [12/16/2021-10:04:33] [V] [TRT] Tactic: 2 Time: 4.94583 [12/16/2021-10:04:33] [V] [TRT] Tactic: 3 Time: 3.99168 [12/16/2021-10:04:33] [V] [TRT] Tactic: 4 Time: 4.15477 [12/16/2021-10:04:33] [V] [TRT] Tactic: 5 Time: 4.47466 [12/16/2021-10:04:33] [V] [TRT] Tactic: 6 Time: 3.86513 [12/16/2021-10:04:33] [V] [TRT] Tactic: 7 Time: 3.9777 [12/16/2021-10:04:33] [V] [TRT] Tactic: 8 Time: 4.19322 [12/16/2021-10:04:33] [V] [TRT] Tactic: 9 Time: 4.68191 [12/16/2021-10:04:34] [V] [TRT] Tactic: 10 Time: 9.57507 [12/16/2021-10:04:34] [V] [TRT] Tactic: 11 Time: 6.57749 [12/16/2021-10:04:34] [V] [TRT] Tactic: 12 Time: 6.34779 [12/16/2021-10:04:34] [V] [TRT] Tactic: 13 Time: 4.90062 [12/16/2021-10:04:34] [V] [TRT] Tactic: 14 Time: 4.0102 [12/16/2021-10:04:34] [V] [TRT] Tactic: 15 Time: 4.28745 [12/16/2021-10:04:34] [V] [TRT] Tactic: 16 Time: 4.44816 [12/16/2021-10:04:34] [V] [TRT] Tactic: 17 Time: 3.15267 [12/16/2021-10:04:34] [V] [TRT] Tactic: 18 Time: 2.96161 [12/16/2021-10:04:35] [V] [TRT] Tactic: 19 Time: 3.51573 [12/16/2021-10:04:35] [V] [TRT] Tactic: 28 Time: 5.52442 [12/16/2021-10:04:35] [V] [TRT] Tactic: 29 Time: 9.27466 [12/16/2021-10:04:35] [V] [TRT] Fastest Tactic: 18 Time: 2.96161 [12/16/2021-10:04:35] [V] [TRT] --------------- Timing Runner: 003_convolutional_lrelu (PointWise) [12/16/2021-10:04:35] [V] [TRT] Tactic: 128 Time: 18.6099 [12/16/2021-10:04:36] [V] [TRT] Tactic: 256 Time: 18.4833 [12/16/2021-10:04:36] [V] [TRT] Tactic: 512 Time: 18.0241 [12/16/2021-10:04:36] [V] [TRT] Fastest Tactic: 512 Time: 18.0241 [12/16/2021-10:04:36] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,65536,256,1) -> Float(2097152,1,8192,32) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,65536,256,1) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,65536,256,1) -> Half(1048576,65536:2,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,8192,32) -> Float(2097152,65536,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,8192,32) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,8192,32) -> Half(1048576,65536:2,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,65536:32,256,1) -> Float(2097152,65536,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,65536:32,256,1) -> Float(2097152,1,8192,32) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,65536:32,256,1) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,65536:32,256,1) -> Half(1048576,65536:2,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536,256,1) -> Float(2097152,65536,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536,256,1) -> Float(2097152,1,8192,32) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536,256,1) -> Half(1048576,65536:2,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,65536:2,256,1) -> Float(2097152,65536,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,65536:2,256,1) -> Float(2097152,1,8192,32) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,65536:2,256,1) -> Half(2097152,65536,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] *************** Autotuning format combination: Float(2097152,65536,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:04:36] [V] [TRT] --------------- Timing Runner: 004_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:04:36] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:04:36] [V] [TRT] --------------- Timing Runner: 004_convolutional (FusedConvActConvolution) [12/16/2021-10:04:36] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:04:36] [V] [TRT] --------------- Timing Runner: 004_convolutional (CudnnConvolution) [12/16/2021-10:04:40] [V] [TRT] Tactic: 0 Time: 223.086 [12/16/2021-10:04:41] [V] [TRT] Tactic: 1 Time: 75.4157 [12/16/2021-10:04:41] [V] [TRT] Tactic: 2 skipped. Scratch requested: 377487360, available: 16777216 [12/16/2021-10:04:45] [V] [TRT] Tactic: 5 Time: 250.759 [12/16/2021-10:04:46] [V] [TRT] Tactic: 6 Time: 63.6619 [12/16/2021-10:04:46] [V] [TRT] Fastest Tactic: 6 Time: 63.6619 [12/16/2021-10:04:46] [V] [TRT] --------------- Timing Runner: 004_convolutional (CaskConvolution) [12/16/2021-10:04:46] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:04:48] [V] [TRT] Tactic: 1062367460111450758 Time: 84.8111 [12/16/2021-10:04:48] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v0 Tactic: 1754984623894446479 [12/16/2021-10:04:49] [V] [TRT] Tactic: 1754984623894446479 Time: 90.0611 [12/16/2021-10:04:49] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v0 Tactic: 3611739942397549984 [12/16/2021-10:04:51] [V] [TRT] Tactic: 3611739942397549984 Time: 135.264 [12/16/2021-10:04:51] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148n_nt_v1 Tactic: 3827454225649558724 [12/16/2021-10:04:53] [V] [TRT] Tactic: 3827454225649558724 Time: 87.4116 [12/16/2021-10:04:53] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v1 Tactic: 4337000649858996379 [12/16/2021-10:04:54] [V] [TRT] Tactic: 4337000649858996379 Time: 68.5178 [12/16/2021-10:04:54] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:04:56] [V] [TRT] Tactic: 4501471010995462441 Time: 133.925 [12/16/2021-10:04:56] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:04:57] [V] [TRT] Tactic: 5137655947464784826 Time: 65.4931 [12/16/2021-10:04:57] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:05:00] [V] [TRT] Tactic: 5288347012147084929 Time: 134.466 [12/16/2021-10:05:00] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148m_nt_v1 Tactic: 5921334924264294896 [12/16/2021-10:05:01] [V] [TRT] Tactic: 5921334924264294896 Time: 60.4915 [12/16/2021-10:05:01] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-10:05:02] [V] [TRT] Tactic: 6645123197870846056 Time: 67.6479 [12/16/2021-10:05:02] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-10:05:03] [V] [TRT] Tactic: 7144526460361122478 Time: 84.4715 [12/16/2021-10:05:03] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v1 Tactic: 7852627285308570038 [12/16/2021-10:05:05] [V] [TRT] Tactic: 7852627285308570038 Time: 88.1057 [12/16/2021-10:05:05] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v1 Tactic: -9137461792520977713 [12/16/2021-10:05:07] [V] [TRT] Tactic: -9137461792520977713 Time: 135.274 [12/16/2021-10:05:07] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148n_nt_v0 Tactic: -8776506421218919509 [12/16/2021-10:05:09] [V] [TRT] Tactic: -8776506421218919509 Time: 86.8984 [12/16/2021-10:05:09] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-10:05:11] [V] [TRT] Tactic: -8262349710178828730 Time: 135.417 [12/16/2021-10:05:11] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v0 Tactic: -8133971918129952780 [12/16/2021-10:05:12] [V] [TRT] Tactic: -8133971918129952780 Time: 73.1053 [12/16/2021-10:05:12] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v1 Tactic: -6092040395344634144 [12/16/2021-10:05:14] [V] [TRT] Tactic: -6092040395344634144 Time: 87.4176 [12/16/2021-10:05:14] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-10:05:15] [V] [TRT] Tactic: -4787320710726427159 Time: 90.0237 [12/16/2021-10:05:15] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-10:05:16] [V] [TRT] Tactic: -3456450830548107839 Time: 78.2359 [12/16/2021-10:05:16] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v0 Tactic: -2318106587342035239 [12/16/2021-10:05:18] [V] [TRT] Tactic: -2318106587342035239 Time: 86.2423 [12/16/2021-10:05:18] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_mobile_relu_tile148t_nt_v0 Tactic: -1343271414618805657 [12/16/2021-10:05:19] [V] [TRT] Tactic: -1343271414618805657 Time: 54.4876 [12/16/2021-10:05:19] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-10:05:20] [V] [TRT] Tactic: -1218658103698133241 Time: 74.2936 [12/16/2021-10:05:20] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-10:05:21] [V] [TRT] Tactic: -836875257600482091 Time: 72.1201 [12/16/2021-10:05:21] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-10:05:24] [V] [TRT] Tactic: -410470605513481746 Time: 133.091 [12/16/2021-10:05:24] [V] [TRT] Fastest Tactic: -1343271414618805657 Time: 54.4876 [12/16/2021-10:05:24] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -1343271414618805657 [12/16/2021-10:05:24] [V] [TRT] *************** Autotuning format combination: Float(2097152,1,8192,32) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:05:24] [V] [TRT] --------------- Timing Runner: 004_convolutional (CudnnConvolution) [12/16/2021-10:05:24] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:05:24] [V] [TRT] --------------- Timing Runner: 004_convolutional (CaskConvolution) [12/16/2021-10:05:24] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -9153228964338181824 [12/16/2021-10:05:25] [V] [TRT] Tactic: -9153228964338181824 Time: 112.463 [12/16/2021-10:05:26] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -7394439838318485025 [12/16/2021-10:05:27] [V] [TRT] Tactic: -7394439838318485025 Time: 67.8228 [12/16/2021-10:05:27] [V] [TRT] Fastest Tactic: -7394439838318485025 Time: 67.8228 [12/16/2021-10:05:27] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -7394439838318485025 [12/16/2021-10:05:27] [V] [TRT] *************** Autotuning format combination: Half(2097152,65536,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:05:27] [V] [TRT] --------------- Timing Runner: 004_convolutional (CudnnConvolution) [12/16/2021-10:05:30] [V] [TRT] Tactic: 0 Time: 232.442 [12/16/2021-10:05:30] [V] [TRT] Tactic: 1 skipped. Scratch requested: 42385920, available: 16777216 [12/16/2021-10:05:30] [V] [TRT] Tactic: 2 skipped. Scratch requested: 188743680, available: 16777216 [12/16/2021-10:05:34] [V] [TRT] Tactic: 5 Time: 244.28 [12/16/2021-10:05:34] [V] [TRT] Tactic: 6 skipped. Scratch requested: 126109184, available: 16777216 [12/16/2021-10:05:34] [V] [TRT] Fastest Tactic: 0 Time: 232.442 [12/16/2021-10:05:34] [V] [TRT] --------------- Timing Runner: 004_convolutional (CaskConvolution) [12/16/2021-10:05:34] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:05:34] [V] [TRT] Setting workspace to 126109184enables more tactics for profiling [12/16/2021-10:05:34] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CudnnConvolution Tactic: 0 [12/16/2021-10:05:34] [V] [TRT] *************** Autotuning format combination: Half(1048576,65536:2,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:05:34] [V] [TRT] --------------- Timing Runner: 004_convolutional (FusedConvActConvolution) [12/16/2021-10:05:34] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:05:34] [V] [TRT] --------------- Timing Runner: 004_convolutional (CudnnConvolution) [12/16/2021-10:05:34] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:05:34] [V] [TRT] --------------- Timing Runner: 004_convolutional (CaskConvolution) [12/16/2021-10:05:34] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-10:05:35] [V] [TRT] Tactic: 3564772625446233998 Time: 44.4983 [12/16/2021-10:05:35] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_large_nn_v1 Tactic: 3650389455493082349 [12/16/2021-10:05:36] [V] [TRT] Tactic: 3650389455493082349 Time: 46.0233 [12/16/2021-10:05:36] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_winograd_fp16x2_128x128_ldg1_ldg4_relu_tile148m_nt_v1 Tactic: 4772821744921268633 [12/16/2021-10:05:37] [V] [TRT] Tactic: 4772821744921268633 Time: 31.0322 [12/16/2021-10:05:37] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-10:05:37] [V] [TRT] Tactic: 5319956359050645452 Time: 40.8872 [12/16/2021-10:05:37] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-10:05:38] [V] [TRT] Tactic: 7205456024582378848 Time: 34.9304 [12/16/2021-10:05:38] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_large_nn_v1 Tactic: -6490690591794140522 [12/16/2021-10:05:38] [V] [TRT] Tactic: -6490690591794140522 Time: 35.7568 [12/16/2021-10:05:38] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_large_nn_v1 Tactic: -4686027666808657977 [12/16/2021-10:05:40] [V] [TRT] Tactic: -4686027666808657977 Time: 70.3759 [12/16/2021-10:05:40] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-10:05:41] [V] [TRT] Tactic: -4212163711445252890 Time: 68.6613 [12/16/2021-10:05:41] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-10:05:42] [V] [TRT] Tactic: -3898373634979201110 Time: 69.7716 [12/16/2021-10:05:42] [V] [TRT] 004_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-10:05:43] [V] [TRT] Tactic: -2409163523992614473 Time: 34.132 [12/16/2021-10:05:43] [V] [TRT] Fastest Tactic: 4772821744921268633 Time: 31.0322 [12/16/2021-10:05:43] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 4772821744921268633 [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] *************** Autotuning format combination: Float(4194304,65536,256,1), Float(4194304,65536,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:05:43] [V] [TRT] --------------- Timing Runner: PWN(004_convolutional_lrelu, 005_shortcut) (PointWiseV2) [12/16/2021-10:05:44] [V] [TRT] Tactic: 0 Time: 19.0212 [12/16/2021-10:05:45] [V] [TRT] Tactic: 1 Time: 14.9666 [12/16/2021-10:05:46] [V] [TRT] Tactic: 2 Time: 12.8908 [12/16/2021-10:05:47] [V] [TRT] Tactic: 3 Time: 13.9301 [12/16/2021-10:05:49] [V] [TRT] Tactic: 4 Time: 12.3915 [12/16/2021-10:05:50] [V] [TRT] Tactic: 5 Time: 12.5702 [12/16/2021-10:05:51] [V] [TRT] Tactic: 6 Time: 13.9226 [12/16/2021-10:05:52] [V] [TRT] Tactic: 7 Time: 12.6533 [12/16/2021-10:05:53] [V] [TRT] Tactic: 8 Time: 12.4774 [12/16/2021-10:05:55] [V] [TRT] Tactic: 9 Time: 12.5791 [12/16/2021-10:05:56] [V] [TRT] Tactic: 28 Time: 18.7309 [12/16/2021-10:05:56] [V] [TRT] Fastest Tactic: 4 Time: 12.3915 [12/16/2021-10:05:56] [V] [TRT] --------------- Timing Runner: PWN(004_convolutional_lrelu, 005_shortcut) (PointWise) [12/16/2021-10:05:57] [V] [TRT] Tactic: 128 Time: 54.5792 [12/16/2021-10:05:58] [V] [TRT] Tactic: 256 Time: 54.8299 [12/16/2021-10:05:59] [V] [TRT] Tactic: 512 Time: 55.0973 [12/16/2021-10:05:59] [V] [TRT] Fastest Tactic: 128 Time: 54.5792 [12/16/2021-10:05:59] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 4 [12/16/2021-10:05:59] [V] [TRT] *************** Autotuning format combination: Float(4194304,1,16384,64), Float(4194304,1,16384,64) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:05:59] [V] [TRT] --------------- Timing Runner: PWN(004_convolutional_lrelu, 005_shortcut) (PointWiseV2) [12/16/2021-10:06:00] [V] [TRT] Tactic: 0 Time: 19.0138 [12/16/2021-10:06:00] [V] [TRT] Tactic: 1 Time: 15.0005 [12/16/2021-10:06:00] [V] [TRT] Tactic: 2 Time: 12.8966 [12/16/2021-10:06:01] [V] [TRT] Tactic: 3 Time: 13.9551 [12/16/2021-10:06:01] [V] [TRT] Tactic: 4 Time: 12.3962 [12/16/2021-10:06:02] [V] [TRT] Tactic: 5 Time: 12.5824 [12/16/2021-10:06:02] [V] [TRT] Tactic: 6 Time: 13.9146 [12/16/2021-10:06:02] [V] [TRT] Tactic: 7 Time: 12.7812 [12/16/2021-10:06:03] [V] [TRT] Tactic: 8 Time: 12.4751 [12/16/2021-10:06:03] [V] [TRT] Tactic: 9 Time: 12.7749 [12/16/2021-10:06:04] [V] [TRT] Tactic: 28 Time: 18.7033 [12/16/2021-10:06:04] [V] [TRT] Fastest Tactic: 4 Time: 12.3962 [12/16/2021-10:06:04] [V] [TRT] --------------- Timing Runner: PWN(004_convolutional_lrelu, 005_shortcut) (PointWise) [12/16/2021-10:06:05] [V] [TRT] Tactic: 128 Time: 54.5708 [12/16/2021-10:06:06] [V] [TRT] Tactic: 256 Time: 54.845 [12/16/2021-10:06:07] [V] [TRT] Tactic: 512 Time: 55.0803 [12/16/2021-10:06:07] [V] [TRT] Fastest Tactic: 128 Time: 54.5708 [12/16/2021-10:06:07] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 4 [12/16/2021-10:06:07] [V] [TRT] *************** Autotuning format combination: Float(131072,65536:32,256,1), Float(131072,65536:32,256,1) -> Float(131072,65536:32,256,1) *************** [12/16/2021-10:06:07] [V] [TRT] --------------- Timing Runner: PWN(004_convolutional_lrelu, 005_shortcut) (PointWiseV2) [12/16/2021-10:06:08] [V] [TRT] Tactic: 24 Time: 19.1119 [12/16/2021-10:06:09] [V] [TRT] Tactic: 25 Time: 16.1757 [12/16/2021-10:06:11] [V] [TRT] Tactic: 26 Time: 16.5131 [12/16/2021-10:06:12] [V] [TRT] Tactic: 27 Time: 16.1504 [12/16/2021-10:06:13] [V] [TRT] Tactic: 31 Time: 19.092 [12/16/2021-10:06:13] [V] [TRT] Fastest Tactic: 27 Time: 16.1504 [12/16/2021-10:06:13] [V] [TRT] --------------- Timing Runner: PWN(004_convolutional_lrelu, 005_shortcut) (PointWise) [12/16/2021-10:06:14] [V] [TRT] Tactic: 128 Time: 54.5599 [12/16/2021-10:06:15] [V] [TRT] Tactic: 256 Time: 54.8269 [12/16/2021-10:06:16] [V] [TRT] Tactic: 512 Time: 55.0749 [12/16/2021-10:06:16] [V] [TRT] Fastest Tactic: 128 Time: 54.5599 [12/16/2021-10:06:16] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 27 [12/16/2021-10:06:16] [V] [TRT] *************** Autotuning format combination: Half(4194304,65536,256,1), Half(4194304,65536,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:06:16] [V] [TRT] --------------- Timing Runner: PWN(004_convolutional_lrelu, 005_shortcut) (PointWiseV2) [12/16/2021-10:06:18] [V] [TRT] Tactic: 0 Time: 19.0217 [12/16/2021-10:06:19] [V] [TRT] Tactic: 1 Time: 14.5612 [12/16/2021-10:06:20] [V] [TRT] Tactic: 2 Time: 13.1417 [12/16/2021-10:06:21] [V] [TRT] Tactic: 3 Time: 11.1875 [12/16/2021-10:06:22] [V] [TRT] Tactic: 4 Time: 9.91026 [12/16/2021-10:06:23] [V] [TRT] Tactic: 5 Time: 9.31202 [12/16/2021-10:06:24] [V] [TRT] Tactic: 6 Time: 10.797 [12/16/2021-10:06:25] [V] [TRT] Tactic: 7 Time: 8.49529 [12/16/2021-10:06:26] [V] [TRT] Tactic: 8 Time: 7.88752 [12/16/2021-10:06:27] [V] [TRT] Tactic: 9 Time: 8.52303 [12/16/2021-10:06:29] [V] [TRT] Tactic: 28 Time: 18.6208 [12/16/2021-10:06:29] [V] [TRT] Fastest Tactic: 8 Time: 7.88752 [12/16/2021-10:06:29] [V] [TRT] --------------- Timing Runner: PWN(004_convolutional_lrelu, 005_shortcut) (PointWise) [12/16/2021-10:06:30] [V] [TRT] Tactic: 128 Time: 51.4431 [12/16/2021-10:06:30] [V] [TRT] Tactic: 256 Time: 51.3735 [12/16/2021-10:06:31] [V] [TRT] Tactic: 512 Time: 50.2195 [12/16/2021-10:06:31] [V] [TRT] Fastest Tactic: 512 Time: 50.2195 [12/16/2021-10:06:31] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:06:31] [V] [TRT] *************** Autotuning format combination: Half(2097152,65536:2,256,1), Half(2097152,65536:2,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:06:31] [V] [TRT] --------------- Timing Runner: PWN(004_convolutional_lrelu, 005_shortcut) (PointWiseV2) [12/16/2021-10:06:32] [V] [TRT] Tactic: 0 Time: 14.6432 [12/16/2021-10:06:34] [V] [TRT] Tactic: 1 Time: 12.7341 [12/16/2021-10:06:35] [V] [TRT] Tactic: 2 Time: 13.7855 [12/16/2021-10:06:36] [V] [TRT] Tactic: 3 Time: 12.226 [12/16/2021-10:06:37] [V] [TRT] Tactic: 4 Time: 12.6762 [12/16/2021-10:06:38] [V] [TRT] Tactic: 5 Time: 13.6889 [12/16/2021-10:06:39] [V] [TRT] Tactic: 6 Time: 12.0227 [12/16/2021-10:06:40] [V] [TRT] Tactic: 7 Time: 12.4284 [12/16/2021-10:06:42] [V] [TRT] Tactic: 8 Time: 13.533 [12/16/2021-10:06:43] [V] [TRT] Tactic: 9 Time: 16.6885 [12/16/2021-10:06:44] [V] [TRT] Tactic: 10 Time: 20.8236 [12/16/2021-10:06:45] [V] [TRT] Tactic: 11 Time: 16.0617 [12/16/2021-10:06:47] [V] [TRT] Tactic: 12 Time: 14.1807 [12/16/2021-10:06:48] [V] [TRT] Tactic: 13 Time: 11.9011 [12/16/2021-10:06:49] [V] [TRT] Tactic: 14 Time: 11.8421 [12/16/2021-10:06:50] [V] [TRT] Tactic: 15 Time: 11.5374 [12/16/2021-10:06:51] [V] [TRT] Tactic: 16 Time: 11.0689 [12/16/2021-10:06:52] [V] [TRT] Tactic: 17 Time: 9.91396 [12/16/2021-10:06:53] [V] [TRT] Tactic: 18 Time: 9.81911 [12/16/2021-10:06:54] [V] [TRT] Tactic: 19 Time: 10.1698 [12/16/2021-10:06:55] [V] [TRT] Tactic: 28 Time: 14.6239 [12/16/2021-10:06:57] [V] [TRT] Tactic: 29 Time: 20.3366 [12/16/2021-10:06:57] [V] [TRT] Fastest Tactic: 18 Time: 9.81911 [12/16/2021-10:06:57] [V] [TRT] --------------- Timing Runner: PWN(004_convolutional_lrelu, 005_shortcut) (PointWise) [12/16/2021-10:06:58] [V] [TRT] Tactic: 128 Time: 51.1331 [12/16/2021-10:06:58] [V] [TRT] Tactic: 256 Time: 51.4092 [12/16/2021-10:06:59] [V] [TRT] Tactic: 512 Time: 50.2359 [12/16/2021-10:06:59] [V] [TRT] Fastest Tactic: 512 Time: 50.2359 [12/16/2021-10:06:59] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Float(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Float(4194304,1,16384,64) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Float(131072,65536:32,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Half(4194304,65536,256,1) -> Half(2097152,65536:2,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,65536,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Float(4194304,1,16384,64) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning Reformat:Half(2097152,65536:2,256,1) -> Half(4194304,65536,256,1) *************** [12/16/2021-10:06:59] [V] [TRT] *************** Autotuning format combination: Float(4194304,65536,256,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:06:59] [V] [TRT] --------------- Timing Runner: 006_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:06:59] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:06:59] [V] [TRT] --------------- Timing Runner: 006_convolutional (FusedConvActConvolution) [12/16/2021-10:06:59] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:06:59] [V] [TRT] --------------- Timing Runner: 006_convolutional (CudnnConvolution) [12/16/2021-10:06:59] [V] [TRT] Tactic: 0 skipped. Scratch requested: 84542976, available: 16777216 [12/16/2021-10:06:59] [V] [TRT] Tactic: 1 skipped. Scratch requested: 84937728, available: 16777216 [12/16/2021-10:06:59] [V] [TRT] Tactic: 2 skipped. Scratch requested: 273286656, available: 16777216 [12/16/2021-10:06:59] [V] [TRT] Tactic: 5 skipped. Scratch requested: 128550400, available: 16777216 [12/16/2021-10:06:59] [V] [TRT] Fastest Tactic: -3360065831133338131 Time: 3.40282e+38 [12/16/2021-10:06:59] [V] [TRT] --------------- Timing Runner: 006_convolutional (CaskConvolution) [12/16/2021-10:06:59] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:07:01] [V] [TRT] Tactic: 1062367460111450758 Time: 81.2248 [12/16/2021-10:07:01] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v0 Tactic: 1754984623894446479 [12/16/2021-10:07:02] [V] [TRT] Tactic: 1754984623894446479 Time: 92.1621 [12/16/2021-10:07:02] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v0 Tactic: 3611739942397549984 [12/16/2021-10:07:03] [V] [TRT] Tactic: 3611739942397549984 Time: 65.5276 [12/16/2021-10:07:03] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v1 Tactic: 4337000649858996379 [12/16/2021-10:07:05] [V] [TRT] Tactic: 4337000649858996379 Time: 65.1787 [12/16/2021-10:07:05] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:07:06] [V] [TRT] Tactic: 4501471010995462441 Time: 64.5858 [12/16/2021-10:07:06] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:07:07] [V] [TRT] Tactic: 5137655947464784826 Time: 63.431 [12/16/2021-10:07:07] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:07:08] [V] [TRT] Tactic: 5288347012147084929 Time: 64.3582 [12/16/2021-10:07:08] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-10:07:09] [V] [TRT] Tactic: 6645123197870846056 Time: 64.2889 [12/16/2021-10:07:09] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-10:07:11] [V] [TRT] Tactic: 7144526460361122478 Time: 86.1319 [12/16/2021-10:07:11] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v1 Tactic: -9137461792520977713 [12/16/2021-10:07:12] [V] [TRT] Tactic: -9137461792520977713 Time: 65.6572 [12/16/2021-10:07:12] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-10:07:13] [V] [TRT] Tactic: -8262349710178828730 Time: 65.661 [12/16/2021-10:07:13] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v0 Tactic: -8133971918129952780 [12/16/2021-10:07:14] [V] [TRT] Tactic: -8133971918129952780 Time: 71.8364 [12/16/2021-10:07:14] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v1 Tactic: -6092040395344634144 [12/16/2021-10:07:16] [V] [TRT] Tactic: -6092040395344634144 Time: 84.4782 [12/16/2021-10:07:16] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-10:07:17] [V] [TRT] Tactic: -4787320710726427159 Time: 91.6033 [12/16/2021-10:07:17] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-10:07:18] [V] [TRT] Tactic: -3456450830548107839 Time: 75.2576 [12/16/2021-10:07:18] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-10:07:20] [V] [TRT] Tactic: -1218658103698133241 Time: 71.3571 [12/16/2021-10:07:20] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-10:07:21] [V] [TRT] Tactic: -836875257600482091 Time: 69.5643 [12/16/2021-10:07:21] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-10:07:22] [V] [TRT] Tactic: -410470605513481746 Time: 63.9483 [12/16/2021-10:07:22] [V] [TRT] Fastest Tactic: 5137655947464784826 Time: 63.431 [12/16/2021-10:07:22] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 5137655947464784826 [12/16/2021-10:07:22] [V] [TRT] *************** Autotuning format combination: Float(4194304,1,16384,64) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:07:22] [V] [TRT] --------------- Timing Runner: 006_convolutional (CudnnConvolution) [12/16/2021-10:07:22] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:07:22] [V] [TRT] --------------- Timing Runner: 006_convolutional (CaskConvolution) [12/16/2021-10:07:22] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -9153228964338181824 [12/16/2021-10:07:24] [V] [TRT] Tactic: -9153228964338181824 Time: 106.421 [12/16/2021-10:07:24] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -7394439838318485025 [12/16/2021-10:07:25] [V] [TRT] Tactic: -7394439838318485025 Time: 64.3749 [12/16/2021-10:07:25] [V] [TRT] Fastest Tactic: -7394439838318485025 Time: 64.3749 [12/16/2021-10:07:25] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -7394439838318485025 [12/16/2021-10:07:25] [V] [TRT] *************** Autotuning format combination: Half(4194304,65536,256,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:07:25] [V] [TRT] --------------- Timing Runner: 006_convolutional (CudnnConvolution) [12/16/2021-10:07:25] [V] [TRT] Tactic: 0 skipped. Scratch requested: 42271744, available: 16777216 [12/16/2021-10:07:25] [V] [TRT] Tactic: 1 skipped. Scratch requested: 42519040, available: 16777216 [12/16/2021-10:07:25] [V] [TRT] Tactic: 2 skipped. Scratch requested: 136643584, available: 16777216 [12/16/2021-10:07:25] [V] [TRT] Tactic: 5 skipped. Scratch requested: 86279168, available: 16777216 [12/16/2021-10:07:25] [V] [TRT] Fastest Tactic: -3360065831133338131 Time: 3.40282e+38 [12/16/2021-10:07:25] [V] [TRT] --------------- Timing Runner: 006_convolutional (CaskConvolution) [12/16/2021-10:07:25] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:07:25] [V] [TRT] *************** Autotuning format combination: Half(2097152,65536:2,256,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:07:25] [V] [TRT] --------------- Timing Runner: 006_convolutional (FusedConvActConvolution) [12/16/2021-10:07:25] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:07:25] [V] [TRT] --------------- Timing Runner: 006_convolutional (CudnnConvolution) [12/16/2021-10:07:25] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:07:25] [V] [TRT] --------------- Timing Runner: 006_convolutional (CaskConvolution) [12/16/2021-10:07:25] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-10:07:26] [V] [TRT] Tactic: 3564772625446233998 Time: 41.0936 [12/16/2021-10:07:26] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_large_nn_v1 Tactic: 3650389455493082349 [12/16/2021-10:07:26] [V] [TRT] Tactic: 3650389455493082349 Time: 42.872 [12/16/2021-10:07:26] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-10:07:27] [V] [TRT] Tactic: 5319956359050645452 Time: 38.0341 [12/16/2021-10:07:27] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-10:07:28] [V] [TRT] Tactic: 7205456024582378848 Time: 32.6516 [12/16/2021-10:07:28] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_large_nn_v1 Tactic: -6490690591794140522 [12/16/2021-10:07:28] [V] [TRT] Tactic: -6490690591794140522 Time: 32.9748 [12/16/2021-10:07:28] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_large_nn_v1 Tactic: -4686027666808657977 [12/16/2021-10:07:29] [V] [TRT] Tactic: -4686027666808657977 Time: 33.1957 [12/16/2021-10:07:29] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-10:07:29] [V] [TRT] Tactic: -4212163711445252890 Time: 32.0484 [12/16/2021-10:07:29] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-10:07:30] [V] [TRT] Tactic: -3898373634979201110 Time: 32.966 [12/16/2021-10:07:30] [V] [TRT] 006_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-10:07:30] [V] [TRT] Tactic: -2409163523992614473 Time: 32.3634 [12/16/2021-10:07:30] [V] [TRT] Fastest Tactic: -4212163711445252890 Time: 32.0484 [12/16/2021-10:07:30] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -4212163711445252890 [12/16/2021-10:07:30] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:07:30] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:31] [V] [TRT] Tactic: 1002 Time: 10.859 [12/16/2021-10:07:31] [V] [TRT] Tactic: 0 Time: 19.7778 [12/16/2021-10:07:31] [V] [TRT] Fastest Tactic: 1002 Time: 10.859 [12/16/2021-10:07:31] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:07:31] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:31] [V] [TRT] Tactic: 1002 Time: 10.8061 [12/16/2021-10:07:32] [V] [TRT] Tactic: 0 Time: 33.7159 [12/16/2021-10:07:32] [V] [TRT] Fastest Tactic: 1002 Time: 10.8061 [12/16/2021-10:07:32] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:07:32] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:32] [V] [TRT] Tactic: 1002 Time: 12.9613 [12/16/2021-10:07:32] [V] [TRT] Tactic: 0 Time: 10.7107 [12/16/2021-10:07:32] [V] [TRT] Fastest Tactic: 0 Time: 10.7107 [12/16/2021-10:07:32] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:07:32] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:33] [V] [TRT] Tactic: 1002 Time: 15.8873 [12/16/2021-10:07:33] [V] [TRT] Tactic: 0 Time: 8.5664 [12/16/2021-10:07:33] [V] [TRT] Fastest Tactic: 0 Time: 8.5664 [12/16/2021-10:07:33] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:07:33] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:33] [V] [TRT] Tactic: 1002 Time: 12.9721 [12/16/2021-10:07:34] [V] [TRT] Tactic: 0 Time: 37.9677 [12/16/2021-10:07:34] [V] [TRT] Fastest Tactic: 1002 Time: 12.9721 [12/16/2021-10:07:34] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:07:34] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:34] [V] [TRT] Tactic: 1002 Time: 9.92275 [12/16/2021-10:07:35] [V] [TRT] Tactic: 0 Time: 68.0538 [12/16/2021-10:07:35] [V] [TRT] Fastest Tactic: 1002 Time: 9.92275 [12/16/2021-10:07:35] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:07:35] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:35] [V] [TRT] Tactic: 1002 Time: 9.8018 [12/16/2021-10:07:36] [V] [TRT] Tactic: 0 Time: 37.9485 [12/16/2021-10:07:36] [V] [TRT] Fastest Tactic: 1002 Time: 9.8018 [12/16/2021-10:07:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:07:36] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:36] [V] [TRT] Tactic: 1002 Time: 13.6565 [12/16/2021-10:07:37] [V] [TRT] Tactic: 0 Time: 39.6969 [12/16/2021-10:07:37] [V] [TRT] Fastest Tactic: 1002 Time: 13.6565 [12/16/2021-10:07:37] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:07:37] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:37] [V] [TRT] Tactic: 1002 Time: 12.7546 [12/16/2021-10:07:38] [V] [TRT] Tactic: 0 Time: 35.2506 [12/16/2021-10:07:38] [V] [TRT] Fastest Tactic: 1002 Time: 12.7546 [12/16/2021-10:07:38] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:07:38] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:38] [V] [TRT] Tactic: 1002 Time: 9.89624 [12/16/2021-10:07:38] [V] [TRT] Tactic: 0 Time: 18.1129 [12/16/2021-10:07:38] [V] [TRT] Fastest Tactic: 1002 Time: 9.89624 [12/16/2021-10:07:38] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:07:38] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:39] [V] [TRT] Tactic: 1002 Time: 10.5982 [12/16/2021-10:07:39] [V] [TRT] Tactic: 0 Time: 34.5318 [12/16/2021-10:07:39] [V] [TRT] Fastest Tactic: 1002 Time: 10.5982 [12/16/2021-10:07:39] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:07:39] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:39] [V] [TRT] Tactic: 1002 Time: 13.6517 [12/16/2021-10:07:40] [V] [TRT] Tactic: 0 Time: 37.0806 [12/16/2021-10:07:40] [V] [TRT] Fastest Tactic: 1002 Time: 13.6517 [12/16/2021-10:07:40] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:07:40] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:40] [V] [TRT] Tactic: 1002 Time: 13.0731 [12/16/2021-10:07:41] [V] [TRT] Tactic: 0 Time: 9.14138 [12/16/2021-10:07:41] [V] [TRT] Fastest Tactic: 0 Time: 9.14138 [12/16/2021-10:07:41] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:07:41] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:41] [V] [TRT] Tactic: 1002 Time: 9.18333 [12/16/2021-10:07:41] [V] [TRT] Tactic: 0 Time: 17.5545 [12/16/2021-10:07:41] [V] [TRT] Fastest Tactic: 1002 Time: 9.18333 [12/16/2021-10:07:41] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:07:41] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:41] [V] [TRT] Tactic: 1002 Time: 9.18462 [12/16/2021-10:07:42] [V] [TRT] Tactic: 0 Time: 33.4354 [12/16/2021-10:07:42] [V] [TRT] Fastest Tactic: 1002 Time: 9.18462 [12/16/2021-10:07:42] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:07:42] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:42] [V] [TRT] Tactic: 1002 Time: 8.86282 [12/16/2021-10:07:42] [V] [TRT] Tactic: 0 Time: 8.47451 [12/16/2021-10:07:42] [V] [TRT] Fastest Tactic: 0 Time: 8.47451 [12/16/2021-10:07:42] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:07:42] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:42] [V] [TRT] Tactic: 1002 Time: 12.7676 [12/16/2021-10:07:43] [V] [TRT] Tactic: 0 Time: 7.38702 [12/16/2021-10:07:43] [V] [TRT] Fastest Tactic: 0 Time: 7.38702 [12/16/2021-10:07:43] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:07:43] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:43] [V] [TRT] Tactic: 1002 Time: 9.32775 [12/16/2021-10:07:43] [V] [TRT] Tactic: 0 Time: 18.3969 [12/16/2021-10:07:43] [V] [TRT] Fastest Tactic: 1002 Time: 9.32775 [12/16/2021-10:07:43] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:07:43] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:43] [V] [TRT] Tactic: 1002 Time: 9.33063 [12/16/2021-10:07:44] [V] [TRT] Tactic: 0 Time: 34.0277 [12/16/2021-10:07:44] [V] [TRT] Fastest Tactic: 1002 Time: 9.33063 [12/16/2021-10:07:44] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:07:44] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:44] [V] [TRT] Tactic: 1002 Time: 19.1051 [12/16/2021-10:07:44] [V] [TRT] Tactic: 0 Time: 7.21949 [12/16/2021-10:07:44] [V] [TRT] Fastest Tactic: 0 Time: 7.21949 [12/16/2021-10:07:44] [V] [TRT] *************** Autotuning format combination: Float(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:07:44] [V] [TRT] --------------- Timing Runner: 006_convolutional_lrelu (PointWiseV2) [12/16/2021-10:07:45] [V] [TRT] Tactic: 0 Time: 8.83868 [12/16/2021-10:07:45] [V] [TRT] Tactic: 1 Time: 6.30077 [12/16/2021-10:07:45] [V] [TRT] Tactic: 2 Time: 5.48978 [12/16/2021-10:07:45] [V] [TRT] Tactic: 3 Time: 5.35999 [12/16/2021-10:07:45] [V] [TRT] Tactic: 4 Time: 4.32456 [12/16/2021-10:07:45] [V] [TRT] Tactic: 5 Time: 4.14643 [12/16/2021-10:07:46] [V] [TRT] Tactic: 6 Time: 5.30391 [12/16/2021-10:07:46] [V] [TRT] Tactic: 7 Time: 4.23202 [12/16/2021-10:07:46] [V] [TRT] Tactic: 8 Time: 4.22686 [12/16/2021-10:07:46] [V] [TRT] Tactic: 9 Time: 4.20762 [12/16/2021-10:07:46] [V] [TRT] Tactic: 28 Time: 8.64076 [12/16/2021-10:07:46] [V] [TRT] Fastest Tactic: 5 Time: 4.14643 [12/16/2021-10:07:46] [V] [TRT] --------------- Timing Runner: 006_convolutional_lrelu (PointWise) [12/16/2021-10:07:46] [V] [TRT] Tactic: 128 Time: 20.6543 [12/16/2021-10:07:47] [V] [TRT] Tactic: 256 Time: 20.7316 [12/16/2021-10:07:47] [V] [TRT] Tactic: 512 Time: 20.8725 [12/16/2021-10:07:47] [V] [TRT] Fastest Tactic: 128 Time: 20.6543 [12/16/2021-10:07:47] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:07:47] [V] [TRT] *************** Autotuning format combination: Float(2097152,1,16384,128) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:07:47] [V] [TRT] --------------- Timing Runner: 006_convolutional_lrelu (PointWiseV2) [12/16/2021-10:07:47] [V] [TRT] Tactic: 0 Time: 8.8337 [12/16/2021-10:07:48] [V] [TRT] Tactic: 1 Time: 6.30564 [12/16/2021-10:07:48] [V] [TRT] Tactic: 2 Time: 5.48603 [12/16/2021-10:07:48] [V] [TRT] Tactic: 3 Time: 5.35201 [12/16/2021-10:07:48] [V] [TRT] Tactic: 4 Time: 4.32486 [12/16/2021-10:07:48] [V] [TRT] Tactic: 5 Time: 4.1471 [12/16/2021-10:07:48] [V] [TRT] Tactic: 6 Time: 5.3019 [12/16/2021-10:07:48] [V] [TRT] Tactic: 7 Time: 4.23247 [12/16/2021-10:07:49] [V] [TRT] Tactic: 8 Time: 4.23864 [12/16/2021-10:07:49] [V] [TRT] Tactic: 9 Time: 4.20447 [12/16/2021-10:07:49] [V] [TRT] Tactic: 28 Time: 8.63887 [12/16/2021-10:07:49] [V] [TRT] Fastest Tactic: 5 Time: 4.1471 [12/16/2021-10:07:49] [V] [TRT] --------------- Timing Runner: 006_convolutional_lrelu (PointWise) [12/16/2021-10:07:49] [V] [TRT] Tactic: 128 Time: 20.6531 [12/16/2021-10:07:50] [V] [TRT] Tactic: 256 Time: 20.7299 [12/16/2021-10:07:50] [V] [TRT] Tactic: 512 Time: 20.8743 [12/16/2021-10:07:50] [V] [TRT] Fastest Tactic: 128 Time: 20.6531 [12/16/2021-10:07:50] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:07:50] [V] [TRT] *************** Autotuning format combination: Float(65536,16384:32,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:07:50] [V] [TRT] --------------- Timing Runner: 006_convolutional_lrelu (PointWiseV2) [12/16/2021-10:07:50] [V] [TRT] Tactic: 24 Time: 5.50434 [12/16/2021-10:07:50] [V] [TRT] Tactic: 25 Time: 5.2059 [12/16/2021-10:07:51] [V] [TRT] Tactic: 26 Time: 5.23969 [12/16/2021-10:07:51] [V] [TRT] Tactic: 27 Time: 5.14958 [12/16/2021-10:07:51] [V] [TRT] Tactic: 31 Time: 5.52409 [12/16/2021-10:07:51] [V] [TRT] Fastest Tactic: 27 Time: 5.14958 [12/16/2021-10:07:51] [V] [TRT] --------------- Timing Runner: 006_convolutional_lrelu (PointWise) [12/16/2021-10:07:51] [V] [TRT] Tactic: 128 Time: 20.6567 [12/16/2021-10:07:52] [V] [TRT] Tactic: 256 Time: 20.7356 [12/16/2021-10:07:52] [V] [TRT] Tactic: 512 Time: 20.872 [12/16/2021-10:07:52] [V] [TRT] Fastest Tactic: 128 Time: 20.6567 [12/16/2021-10:07:52] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 27 [12/16/2021-10:07:52] [V] [TRT] *************** Autotuning format combination: Half(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:07:52] [V] [TRT] --------------- Timing Runner: 006_convolutional_lrelu (PointWiseV2) [12/16/2021-10:07:52] [V] [TRT] Tactic: 0 Time: 8.79363 [12/16/2021-10:07:52] [V] [TRT] Tactic: 1 Time: 6.14643 [12/16/2021-10:07:52] [V] [TRT] Tactic: 2 Time: 5.92275 [12/16/2021-10:07:53] [V] [TRT] Tactic: 3 Time: 4.8097 [12/16/2021-10:07:53] [V] [TRT] Tactic: 4 Time: 3.77105 [12/16/2021-10:07:53] [V] [TRT] Tactic: 5 Time: 3.95658 [12/16/2021-10:07:53] [V] [TRT] Tactic: 6 Time: 4.37748 [12/16/2021-10:07:53] [V] [TRT] Tactic: 7 Time: 3.10832 [12/16/2021-10:07:53] [V] [TRT] Tactic: 8 Time: 2.76226 [12/16/2021-10:07:53] [V] [TRT] Tactic: 9 Time: 3.12983 [12/16/2021-10:07:53] [V] [TRT] Tactic: 28 Time: 8.55665 [12/16/2021-10:07:53] [V] [TRT] Fastest Tactic: 8 Time: 2.76226 [12/16/2021-10:07:53] [V] [TRT] --------------- Timing Runner: 006_convolutional_lrelu (PointWise) [12/16/2021-10:07:54] [V] [TRT] Tactic: 128 Time: 18.6015 [12/16/2021-10:07:54] [V] [TRT] Tactic: 256 Time: 18.4737 [12/16/2021-10:07:54] [V] [TRT] Tactic: 512 Time: 18.0284 [12/16/2021-10:07:54] [V] [TRT] Fastest Tactic: 512 Time: 18.0284 [12/16/2021-10:07:54] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:07:54] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384:2,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:07:54] [V] [TRT] --------------- Timing Runner: 006_convolutional_lrelu (PointWiseV2) [12/16/2021-10:07:54] [V] [TRT] Tactic: 0 Time: 5.64473 [12/16/2021-10:07:55] [V] [TRT] Tactic: 1 Time: 4.53391 [12/16/2021-10:07:55] [V] [TRT] Tactic: 2 Time: 4.94587 [12/16/2021-10:07:55] [V] [TRT] Tactic: 3 Time: 3.9913 [12/16/2021-10:07:55] [V] [TRT] Tactic: 4 Time: 4.14374 [12/16/2021-10:07:55] [V] [TRT] Tactic: 5 Time: 4.4727 [12/16/2021-10:07:55] [V] [TRT] Tactic: 6 Time: 3.87121 [12/16/2021-10:07:55] [V] [TRT] Tactic: 7 Time: 3.97912 [12/16/2021-10:07:55] [V] [TRT] Tactic: 8 Time: 4.19883 [12/16/2021-10:07:55] [V] [TRT] Tactic: 9 Time: 4.67775 [12/16/2021-10:07:56] [V] [TRT] Tactic: 10 Time: 9.57714 [12/16/2021-10:07:56] [V] [TRT] Tactic: 11 Time: 6.57916 [12/16/2021-10:07:56] [V] [TRT] Tactic: 12 Time: 6.34937 [12/16/2021-10:07:56] [V] [TRT] Tactic: 13 Time: 4.90678 [12/16/2021-10:07:56] [V] [TRT] Tactic: 14 Time: 4.01636 [12/16/2021-10:07:56] [V] [TRT] Tactic: 15 Time: 4.29025 [12/16/2021-10:07:56] [V] [TRT] Tactic: 16 Time: 4.44352 [12/16/2021-10:07:56] [V] [TRT] Tactic: 17 Time: 3.143 [12/16/2021-10:07:56] [V] [TRT] Tactic: 18 Time: 2.96126 [12/16/2021-10:07:57] [V] [TRT] Tactic: 19 Time: 3.51654 [12/16/2021-10:07:57] [V] [TRT] Tactic: 28 Time: 5.51865 [12/16/2021-10:07:57] [V] [TRT] Tactic: 29 Time: 9.27498 [12/16/2021-10:07:57] [V] [TRT] Fastest Tactic: 18 Time: 2.96126 [12/16/2021-10:07:57] [V] [TRT] --------------- Timing Runner: 006_convolutional_lrelu (PointWise) [12/16/2021-10:07:57] [V] [TRT] Tactic: 128 Time: 18.6197 [12/16/2021-10:07:58] [V] [TRT] Tactic: 256 Time: 18.4727 [12/16/2021-10:07:58] [V] [TRT] Tactic: 512 Time: 18.0293 [12/16/2021-10:07:58] [V] [TRT] Fastest Tactic: 512 Time: 18.0293 [12/16/2021-10:07:58] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:07:58] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:07:58] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:58] [V] [TRT] Tactic: 1002 Time: 10.8823 [12/16/2021-10:07:58] [V] [TRT] Tactic: 0 Time: 19.7849 [12/16/2021-10:07:58] [V] [TRT] Fastest Tactic: 1002 Time: 10.8823 [12/16/2021-10:07:58] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:07:58] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:07:59] [V] [TRT] Tactic: 1002 Time: 10.821 [12/16/2021-10:07:59] [V] [TRT] Tactic: 0 Time: 33.704 [12/16/2021-10:07:59] [V] [TRT] Fastest Tactic: 1002 Time: 10.821 [12/16/2021-10:07:59] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:07:59] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:00] [V] [TRT] Tactic: 1002 Time: 12.9501 [12/16/2021-10:08:00] [V] [TRT] Tactic: 0 Time: 10.6981 [12/16/2021-10:08:00] [V] [TRT] Fastest Tactic: 0 Time: 10.6981 [12/16/2021-10:08:00] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:08:00] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:00] [V] [TRT] Tactic: 1002 Time: 15.8773 [12/16/2021-10:08:00] [V] [TRT] Tactic: 0 Time: 8.56159 [12/16/2021-10:08:00] [V] [TRT] Fastest Tactic: 0 Time: 8.56159 [12/16/2021-10:08:00] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:08:00] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:01] [V] [TRT] Tactic: 1002 Time: 12.9533 [12/16/2021-10:08:01] [V] [TRT] Tactic: 0 Time: 37.9577 [12/16/2021-10:08:01] [V] [TRT] Fastest Tactic: 1002 Time: 12.9533 [12/16/2021-10:08:01] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:08:01] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:01] [V] [TRT] Tactic: 1002 Time: 9.92099 [12/16/2021-10:08:03] [V] [TRT] Tactic: 0 Time: 68.0811 [12/16/2021-10:08:03] [V] [TRT] Fastest Tactic: 1002 Time: 9.92099 [12/16/2021-10:08:03] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:08:03] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:03] [V] [TRT] Tactic: 1002 Time: 9.79479 [12/16/2021-10:08:03] [V] [TRT] Tactic: 0 Time: 37.9409 [12/16/2021-10:08:03] [V] [TRT] Fastest Tactic: 1002 Time: 9.79479 [12/16/2021-10:08:03] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:08:03] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:04] [V] [TRT] Tactic: 1002 Time: 13.6407 [12/16/2021-10:08:04] [V] [TRT] Tactic: 0 Time: 39.663 [12/16/2021-10:08:04] [V] [TRT] Fastest Tactic: 1002 Time: 13.6407 [12/16/2021-10:08:04] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:08:04] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:05] [V] [TRT] Tactic: 1002 Time: 12.7796 [12/16/2021-10:08:05] [V] [TRT] Tactic: 0 Time: 35.2576 [12/16/2021-10:08:05] [V] [TRT] Fastest Tactic: 1002 Time: 12.7796 [12/16/2021-10:08:05] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:08:05] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:05] [V] [TRT] Tactic: 1002 Time: 9.89305 [12/16/2021-10:08:06] [V] [TRT] Tactic: 0 Time: 18.1023 [12/16/2021-10:08:06] [V] [TRT] Fastest Tactic: 1002 Time: 9.89305 [12/16/2021-10:08:06] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:08:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:06] [V] [TRT] Tactic: 1002 Time: 10.5943 [12/16/2021-10:08:07] [V] [TRT] Tactic: 0 Time: 34.5139 [12/16/2021-10:08:07] [V] [TRT] Fastest Tactic: 1002 Time: 10.5943 [12/16/2021-10:08:07] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:08:07] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:07] [V] [TRT] Tactic: 1002 Time: 13.6467 [12/16/2021-10:08:08] [V] [TRT] Tactic: 0 Time: 37.0771 [12/16/2021-10:08:08] [V] [TRT] Fastest Tactic: 1002 Time: 13.6467 [12/16/2021-10:08:08] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:08:08] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:08] [V] [TRT] Tactic: 1002 Time: 13.0758 [12/16/2021-10:08:08] [V] [TRT] Tactic: 0 Time: 9.13719 [12/16/2021-10:08:08] [V] [TRT] Fastest Tactic: 0 Time: 9.13719 [12/16/2021-10:08:08] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:08:08] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:08] [V] [TRT] Tactic: 1002 Time: 9.18221 [12/16/2021-10:08:08] [V] [TRT] Tactic: 0 Time: 17.7873 [12/16/2021-10:08:08] [V] [TRT] Fastest Tactic: 1002 Time: 9.18221 [12/16/2021-10:08:08] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:08:08] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:09] [V] [TRT] Tactic: 1002 Time: 9.18171 [12/16/2021-10:08:09] [V] [TRT] Tactic: 0 Time: 33.424 [12/16/2021-10:08:09] [V] [TRT] Fastest Tactic: 1002 Time: 9.18171 [12/16/2021-10:08:09] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:08:09] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:09] [V] [TRT] Tactic: 1002 Time: 8.88665 [12/16/2021-10:08:10] [V] [TRT] Tactic: 0 Time: 8.47704 [12/16/2021-10:08:10] [V] [TRT] Fastest Tactic: 0 Time: 8.47704 [12/16/2021-10:08:10] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:08:10] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:10] [V] [TRT] Tactic: 1002 Time: 12.7669 [12/16/2021-10:08:10] [V] [TRT] Tactic: 0 Time: 7.38626 [12/16/2021-10:08:10] [V] [TRT] Fastest Tactic: 0 Time: 7.38626 [12/16/2021-10:08:10] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:08:10] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:10] [V] [TRT] Tactic: 1002 Time: 9.32547 [12/16/2021-10:08:11] [V] [TRT] Tactic: 0 Time: 18.3908 [12/16/2021-10:08:11] [V] [TRT] Fastest Tactic: 1002 Time: 9.32547 [12/16/2021-10:08:11] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:08:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:11] [V] [TRT] Tactic: 1002 Time: 9.32725 [12/16/2021-10:08:11] [V] [TRT] Tactic: 0 Time: 34.0466 [12/16/2021-10:08:11] [V] [TRT] Fastest Tactic: 1002 Time: 9.32725 [12/16/2021-10:08:11] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:08:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:12] [V] [TRT] Tactic: 1002 Time: 19.0966 [12/16/2021-10:08:12] [V] [TRT] Tactic: 0 Time: 7.21872 [12/16/2021-10:08:12] [V] [TRT] Fastest Tactic: 0 Time: 7.21872 [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] *************** Autotuning format combination: Float(2097152,16384,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:08:12] [V] [TRT] --------------- Timing Runner: 007_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:08:12] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:12] [V] [TRT] --------------- Timing Runner: 007_convolutional (FusedConvActConvolution) [12/16/2021-10:08:12] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:12] [V] [TRT] --------------- Timing Runner: 007_convolutional (CudnnConvolution) [12/16/2021-10:08:12] [V] [TRT] Tactic: 0 Time: 28.0692 [12/16/2021-10:08:13] [V] [TRT] Tactic: 1 Time: 10.9741 [12/16/2021-10:08:13] [V] [TRT] Tactic: 2 skipped. Scratch requested: 41943040, available: 16777216 [12/16/2021-10:08:13] [V] [TRT] Tactic: 4 skipped. Scratch requested: 1175748608, available: 16777216 [12/16/2021-10:08:13] [V] [TRT] Tactic: 5 skipped. Scratch requested: 34537472, available: 16777216 [12/16/2021-10:08:13] [V] [TRT] Fastest Tactic: 1 Time: 10.9741 [12/16/2021-10:08:13] [V] [TRT] --------------- Timing Runner: 007_convolutional (CublasConvolution) [12/16/2021-10:08:13] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:13] [V] [TRT] --------------- Timing Runner: 007_convolutional (CaskConvolution) [12/16/2021-10:08:13] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:08:13] [V] [TRT] Tactic: 1062367460111450758 Time: 11.5875 [12/16/2021-10:08:13] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_interior_nn_v0 Tactic: 1698681053543049347 [12/16/2021-10:08:13] [V] [TRT] Tactic: 1698681053543049347 Time: 10.418 [12/16/2021-10:08:13] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:08:13] [V] [TRT] Tactic: 4501471010995462441 Time: 16.9806 [12/16/2021-10:08:13] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:08:14] [V] [TRT] Tactic: 5137655947464784826 Time: 8.41928 [12/16/2021-10:08:14] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:08:14] [V] [TRT] Tactic: 5288347012147084929 Time: 17.229 [12/16/2021-10:08:14] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_interior_nn_v1 Tactic: 5326823351883942011 [12/16/2021-10:08:14] [V] [TRT] Tactic: 5326823351883942011 Time: 16.3943 [12/16/2021-10:08:14] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_interior_nn_v0 Tactic: 5500448035057547314 [12/16/2021-10:08:14] [V] [TRT] Tactic: 5500448035057547314 Time: 9.4719 [12/16/2021-10:08:14] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-10:08:15] [V] [TRT] Tactic: 6645123197870846056 Time: 8.49736 [12/16/2021-10:08:15] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-10:08:15] [V] [TRT] Tactic: 7144526460361122478 Time: 11.7649 [12/16/2021-10:08:15] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-10:08:15] [V] [TRT] Tactic: -8262349710178828730 Time: 17.4276 [12/16/2021-10:08:15] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_interior_nn_v1 Tactic: -6576203419454146580 [12/16/2021-10:08:15] [V] [TRT] Tactic: -6576203419454146580 Time: 10.6624 [12/16/2021-10:08:15] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-10:08:16] [V] [TRT] Tactic: -4787320710726427159 Time: 12.2919 [12/16/2021-10:08:16] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-10:08:16] [V] [TRT] Tactic: -3456450830548107839 Time: 11.013 [12/16/2021-10:08:16] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-10:08:16] [V] [TRT] Tactic: -1218658103698133241 Time: 9.52567 [12/16/2021-10:08:16] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-10:08:16] [V] [TRT] Tactic: -836875257600482091 Time: 9.34501 [12/16/2021-10:08:16] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-10:08:17] [V] [TRT] Tactic: -410470605513481746 Time: 16.6642 [12/16/2021-10:08:17] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_interior_nn_v0 Tactic: -377491875521947884 [12/16/2021-10:08:17] [V] [TRT] Tactic: -377491875521947884 Time: 16.9081 [12/16/2021-10:08:17] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_interior_nn_v1 Tactic: -37215280111360163 [12/16/2021-10:08:17] [V] [TRT] Tactic: -37215280111360163 Time: 8.35911 [12/16/2021-10:08:17] [V] [TRT] Fastest Tactic: -37215280111360163 Time: 8.35911 [12/16/2021-10:08:17] [V] [TRT] Setting workspace to 34537472enables more tactics for profiling [12/16/2021-10:08:17] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -37215280111360163 [12/16/2021-10:08:17] [V] [TRT] *************** Autotuning format combination: Float(2097152,1,16384,128) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:08:17] [V] [TRT] --------------- Timing Runner: 007_convolutional (CudnnConvolution) [12/16/2021-10:08:17] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:17] [V] [TRT] --------------- Timing Runner: 007_convolutional (CublasConvolution) [12/16/2021-10:08:17] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:17] [V] [TRT] --------------- Timing Runner: 007_convolutional (CaskConvolution) [12/16/2021-10:08:17] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_interior_nhwc_tn_v1 Tactic: 3886731678879822788 [12/16/2021-10:08:17] [V] [TRT] Tactic: 3886731678879822788 Time: 9.29419 [12/16/2021-10:08:17] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_interior_nhwc_tn_v1 Tactic: 6629944304117643200 [12/16/2021-10:08:18] [V] [TRT] Tactic: 6629944304117643200 Time: 20.9652 [12/16/2021-10:08:18] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -9153228964338181824 [12/16/2021-10:08:18] [V] [TRT] Tactic: -9153228964338181824 Time: 21.3054 [12/16/2021-10:08:18] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -7394439838318485025 [12/16/2021-10:08:18] [V] [TRT] Tactic: -7394439838318485025 Time: 9.31215 [12/16/2021-10:08:18] [V] [TRT] Fastest Tactic: 3886731678879822788 Time: 9.29419 [12/16/2021-10:08:18] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 3886731678879822788 [12/16/2021-10:08:18] [V] [TRT] *************** Autotuning format combination: Half(2097152,16384,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:18] [V] [TRT] --------------- Timing Runner: 007_convolutional (CudnnConvolution) [12/16/2021-10:08:19] [V] [TRT] Tactic: 0 Time: 29.3696 [12/16/2021-10:08:19] [V] [TRT] Tactic: 1 Time: 11.4474 [12/16/2021-10:08:19] [V] [TRT] Tactic: 2 skipped. Scratch requested: 20971520, available: 16777216 [12/16/2021-10:08:19] [V] [TRT] Tactic: 4 skipped. Scratch requested: 1175748608, available: 16777216 [12/16/2021-10:08:19] [V] [TRT] Tactic: 5 skipped. Scratch requested: 34537472, available: 16777216 [12/16/2021-10:08:19] [V] [TRT] Fastest Tactic: 1 Time: 11.4474 [12/16/2021-10:08:19] [V] [TRT] --------------- Timing Runner: 007_convolutional (CublasConvolution) [12/16/2021-10:08:19] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:19] [V] [TRT] --------------- Timing Runner: 007_convolutional (CaskConvolution) [12/16/2021-10:08:19] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:19] [V] [TRT] Setting workspace to 34537472enables more tactics for profiling [12/16/2021-10:08:19] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CudnnConvolution Tactic: 1 [12/16/2021-10:08:19] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384:2,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:19] [V] [TRT] --------------- Timing Runner: 007_convolutional (CaskConvolution) [12/16/2021-10:08:19] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:19] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384:2,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:08:19] [V] [TRT] --------------- Timing Runner: 007_convolutional (FusedConvActConvolution) [12/16/2021-10:08:19] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:19] [V] [TRT] --------------- Timing Runner: 007_convolutional (CudnnConvolution) [12/16/2021-10:08:19] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:19] [V] [TRT] --------------- Timing Runner: 007_convolutional (CublasConvolution) [12/16/2021-10:08:19] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:19] [V] [TRT] --------------- Timing Runner: 007_convolutional (CaskConvolution) [12/16/2021-10:08:19] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_interior_nn_v1 Tactic: 3066127711859985668 [12/16/2021-10:08:19] [V] [TRT] Tactic: 3066127711859985668 Time: 5.85897 [12/16/2021-10:08:19] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-10:08:19] [V] [TRT] Tactic: 3564772625446233998 Time: 6.35609 [12/16/2021-10:08:19] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-10:08:19] [V] [TRT] Tactic: 5319956359050645452 Time: 6.04152 [12/16/2021-10:08:19] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-10:08:19] [V] [TRT] Tactic: 7205456024582378848 Time: 4.62456 [12/16/2021-10:08:19] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_interior_nn_v1 Tactic: 8163473458334948789 [12/16/2021-10:08:20] [V] [TRT] Tactic: 8163473458334948789 Time: 4.5097 [12/16/2021-10:08:20] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-10:08:20] [V] [TRT] Tactic: -4212163711445252890 Time: 9.07693 [12/16/2021-10:08:20] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-10:08:20] [V] [TRT] Tactic: -3898373634979201110 Time: 9.21912 [12/16/2021-10:08:20] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-10:08:20] [V] [TRT] Tactic: -2409163523992614473 Time: 4.5545 [12/16/2021-10:08:20] [V] [TRT] 007_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_interior_nn_v1 Tactic: -1716393687483585322 [12/16/2021-10:08:20] [V] [TRT] Tactic: -1716393687483585322 Time: 8.94641 [12/16/2021-10:08:20] [V] [TRT] Fastest Tactic: 8163473458334948789 Time: 4.5097 [12/16/2021-10:08:20] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 8163473458334948789 [12/16/2021-10:08:20] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:08:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:20] [V] [TRT] Tactic: 1002 Time: 5.40658 [12/16/2021-10:08:20] [V] [TRT] Tactic: 0 Time: 9.88824 [12/16/2021-10:08:20] [V] [TRT] Fastest Tactic: 1002 Time: 5.40658 [12/16/2021-10:08:20] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Float(32768,16384:32,128,1) *************** [12/16/2021-10:08:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:21] [V] [TRT] Tactic: 1002 Time: 5.42272 [12/16/2021-10:08:21] [V] [TRT] Tactic: 0 Time: 16.8487 [12/16/2021-10:08:21] [V] [TRT] Fastest Tactic: 1002 Time: 5.42272 [12/16/2021-10:08:21] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:21] [V] [TRT] Tactic: 1002 Time: 6.48535 [12/16/2021-10:08:21] [V] [TRT] Tactic: 0 Time: 5.35965 [12/16/2021-10:08:21] [V] [TRT] Fastest Tactic: 0 Time: 5.35965 [12/16/2021-10:08:21] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:08:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:21] [V] [TRT] Tactic: 1002 Time: 7.93242 [12/16/2021-10:08:21] [V] [TRT] Tactic: 0 Time: 4.289 [12/16/2021-10:08:21] [V] [TRT] Fastest Tactic: 0 Time: 4.289 [12/16/2021-10:08:21] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:08:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:22] [V] [TRT] Tactic: 1002 Time: 6.40322 [12/16/2021-10:08:22] [V] [TRT] Tactic: 0 Time: 17.8552 [12/16/2021-10:08:22] [V] [TRT] Fastest Tactic: 1002 Time: 6.40322 [12/16/2021-10:08:22] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Float(32768,16384:32,128,1) *************** [12/16/2021-10:08:22] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:22] [V] [TRT] Tactic: 1002 Time: 4.96527 [12/16/2021-10:08:23] [V] [TRT] Tactic: 0 Time: 35.2851 [12/16/2021-10:08:23] [V] [TRT] Fastest Tactic: 1002 Time: 4.96527 [12/16/2021-10:08:23] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:23] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:23] [V] [TRT] Tactic: 1002 Time: 4.89917 [12/16/2021-10:08:23] [V] [TRT] Tactic: 0 Time: 17.5196 [12/16/2021-10:08:23] [V] [TRT] Fastest Tactic: 1002 Time: 4.89917 [12/16/2021-10:08:23] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:08:23] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:23] [V] [TRT] Tactic: 1002 Time: 6.80495 [12/16/2021-10:08:23] [V] [TRT] Tactic: 0 Time: 18.7991 [12/16/2021-10:08:23] [V] [TRT] Fastest Tactic: 1002 Time: 6.80495 [12/16/2021-10:08:23] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:08:23] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:24] [V] [TRT] Tactic: 1002 Time: 6.39723 [12/16/2021-10:08:24] [V] [TRT] Tactic: 0 Time: 17.6166 [12/16/2021-10:08:24] [V] [TRT] Fastest Tactic: 1002 Time: 6.39723 [12/16/2021-10:08:24] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:08:24] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:24] [V] [TRT] Tactic: 1002 Time: 4.95426 [12/16/2021-10:08:24] [V] [TRT] Tactic: 0 Time: 9.06023 [12/16/2021-10:08:24] [V] [TRT] Fastest Tactic: 1002 Time: 4.95426 [12/16/2021-10:08:24] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:24] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:24] [V] [TRT] Tactic: 1002 Time: 5.30513 [12/16/2021-10:08:25] [V] [TRT] Tactic: 0 Time: 17.2556 [12/16/2021-10:08:25] [V] [TRT] Fastest Tactic: 1002 Time: 5.30513 [12/16/2021-10:08:25] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:08:25] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:25] [V] [TRT] Tactic: 1002 Time: 6.80615 [12/16/2021-10:08:25] [V] [TRT] Tactic: 0 Time: 18.5103 [12/16/2021-10:08:25] [V] [TRT] Fastest Tactic: 1002 Time: 6.80615 [12/16/2021-10:08:25] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:08:25] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:25] [V] [TRT] Tactic: 1002 Time: 6.54156 [12/16/2021-10:08:25] [V] [TRT] Tactic: 0 Time: 4.5723 [12/16/2021-10:08:25] [V] [TRT] Fastest Tactic: 0 Time: 4.5723 [12/16/2021-10:08:25] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:08:25] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:25] [V] [TRT] Tactic: 1002 Time: 4.6008 [12/16/2021-10:08:26] [V] [TRT] Tactic: 0 Time: 8.36506 [12/16/2021-10:08:26] [V] [TRT] Fastest Tactic: 1002 Time: 4.6008 [12/16/2021-10:08:26] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Float(32768,16384:32,128,1) *************** [12/16/2021-10:08:26] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:26] [V] [TRT] Tactic: 1002 Time: 4.60201 [12/16/2021-10:08:26] [V] [TRT] Tactic: 0 Time: 16.728 [12/16/2021-10:08:26] [V] [TRT] Fastest Tactic: 1002 Time: 4.60201 [12/16/2021-10:08:26] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:08:26] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:26] [V] [TRT] Tactic: 1002 Time: 4.4145 [12/16/2021-10:08:26] [V] [TRT] Tactic: 0 Time: 4.24363 [12/16/2021-10:08:26] [V] [TRT] Fastest Tactic: 0 Time: 4.24363 [12/16/2021-10:08:26] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:08:26] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:26] [V] [TRT] Tactic: 1002 Time: 6.39465 [12/16/2021-10:08:26] [V] [TRT] Tactic: 0 Time: 3.70008 [12/16/2021-10:08:26] [V] [TRT] Fastest Tactic: 0 Time: 3.70008 [12/16/2021-10:08:26] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:08:26] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:26] [V] [TRT] Tactic: 1002 Time: 4.67579 [12/16/2021-10:08:27] [V] [TRT] Tactic: 0 Time: 9.20842 [12/16/2021-10:08:27] [V] [TRT] Fastest Tactic: 1002 Time: 4.67579 [12/16/2021-10:08:27] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Float(32768,16384:32,128,1) *************** [12/16/2021-10:08:27] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:27] [V] [TRT] Tactic: 1002 Time: 4.67244 [12/16/2021-10:08:27] [V] [TRT] Tactic: 0 Time: 17.0261 [12/16/2021-10:08:27] [V] [TRT] Fastest Tactic: 1002 Time: 4.67244 [12/16/2021-10:08:27] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:27] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:08:27] [V] [TRT] Tactic: 1002 Time: 10.0609 [12/16/2021-10:08:27] [V] [TRT] Tactic: 0 Time: 3.61244 [12/16/2021-10:08:27] [V] [TRT] Fastest Tactic: 0 Time: 3.61244 [12/16/2021-10:08:27] [V] [TRT] *************** Autotuning format combination: Float(1048576,16384,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:08:27] [V] [TRT] --------------- Timing Runner: 007_convolutional_lrelu (PointWiseV2) [12/16/2021-10:08:27] [V] [TRT] Tactic: 0 Time: 4.42367 [12/16/2021-10:08:27] [V] [TRT] Tactic: 1 Time: 3.15701 [12/16/2021-10:08:27] [V] [TRT] Tactic: 2 Time: 2.74881 [12/16/2021-10:08:28] [V] [TRT] Tactic: 3 Time: 2.67569 [12/16/2021-10:08:28] [V] [TRT] Tactic: 4 Time: 2.16477 [12/16/2021-10:08:28] [V] [TRT] Tactic: 5 Time: 2.08109 [12/16/2021-10:08:28] [V] [TRT] Tactic: 6 Time: 2.65165 [12/16/2021-10:08:28] [V] [TRT] Tactic: 7 Time: 2.12258 [12/16/2021-10:08:28] [V] [TRT] Tactic: 8 Time: 2.11499 [12/16/2021-10:08:28] [V] [TRT] Tactic: 9 Time: 2.10562 [12/16/2021-10:08:28] [V] [TRT] Tactic: 28 Time: 4.33055 [12/16/2021-10:08:28] [V] [TRT] Fastest Tactic: 5 Time: 2.08109 [12/16/2021-10:08:28] [V] [TRT] --------------- Timing Runner: 007_convolutional_lrelu (PointWise) [12/16/2021-10:08:28] [V] [TRT] Tactic: 128 Time: 10.3394 [12/16/2021-10:08:29] [V] [TRT] Tactic: 256 Time: 10.3733 [12/16/2021-10:08:29] [V] [TRT] Tactic: 512 Time: 10.441 [12/16/2021-10:08:29] [V] [TRT] Fastest Tactic: 128 Time: 10.3394 [12/16/2021-10:08:29] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:08:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,8192,64) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:08:29] [V] [TRT] --------------- Timing Runner: 007_convolutional_lrelu (PointWiseV2) [12/16/2021-10:08:29] [V] [TRT] Tactic: 0 Time: 4.42497 [12/16/2021-10:08:29] [V] [TRT] Tactic: 1 Time: 3.1543 [12/16/2021-10:08:29] [V] [TRT] Tactic: 2 Time: 2.74893 [12/16/2021-10:08:29] [V] [TRT] Tactic: 3 Time: 2.67393 [12/16/2021-10:08:29] [V] [TRT] Tactic: 4 Time: 2.16754 [12/16/2021-10:08:29] [V] [TRT] Tactic: 5 Time: 2.0778 [12/16/2021-10:08:29] [V] [TRT] Tactic: 6 Time: 2.64859 [12/16/2021-10:08:29] [V] [TRT] Tactic: 7 Time: 2.11861 [12/16/2021-10:08:29] [V] [TRT] Tactic: 8 Time: 2.11616 [12/16/2021-10:08:29] [V] [TRT] Tactic: 9 Time: 2.10648 [12/16/2021-10:08:30] [V] [TRT] Tactic: 28 Time: 4.32925 [12/16/2021-10:08:30] [V] [TRT] Fastest Tactic: 5 Time: 2.0778 [12/16/2021-10:08:30] [V] [TRT] --------------- Timing Runner: 007_convolutional_lrelu (PointWise) [12/16/2021-10:08:30] [V] [TRT] Tactic: 128 Time: 10.3309 [12/16/2021-10:08:30] [V] [TRT] Tactic: 256 Time: 10.3676 [12/16/2021-10:08:30] [V] [TRT] Tactic: 512 Time: 10.4372 [12/16/2021-10:08:30] [V] [TRT] Fastest Tactic: 128 Time: 10.3309 [12/16/2021-10:08:30] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:08:30] [V] [TRT] *************** Autotuning format combination: Float(32768,16384:32,128,1) -> Float(32768,16384:32,128,1) *************** [12/16/2021-10:08:30] [V] [TRT] --------------- Timing Runner: 007_convolutional_lrelu (PointWiseV2) [12/16/2021-10:08:30] [V] [TRT] Tactic: 24 Time: 2.77931 [12/16/2021-10:08:30] [V] [TRT] Tactic: 25 Time: 2.59159 [12/16/2021-10:08:30] [V] [TRT] Tactic: 26 Time: 2.61352 [12/16/2021-10:08:31] [V] [TRT] Tactic: 27 Time: 2.57792 [12/16/2021-10:08:31] [V] [TRT] Tactic: 31 Time: 2.78324 [12/16/2021-10:08:31] [V] [TRT] Fastest Tactic: 27 Time: 2.57792 [12/16/2021-10:08:31] [V] [TRT] --------------- Timing Runner: 007_convolutional_lrelu (PointWise) [12/16/2021-10:08:31] [V] [TRT] Tactic: 128 Time: 10.3333 [12/16/2021-10:08:31] [V] [TRT] Tactic: 256 Time: 10.3685 [12/16/2021-10:08:31] [V] [TRT] Tactic: 512 Time: 10.4409 [12/16/2021-10:08:31] [V] [TRT] Fastest Tactic: 128 Time: 10.3333 [12/16/2021-10:08:31] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 27 [12/16/2021-10:08:31] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:31] [V] [TRT] --------------- Timing Runner: 007_convolutional_lrelu (PointWiseV2) [12/16/2021-10:08:31] [V] [TRT] Tactic: 0 Time: 4.40163 [12/16/2021-10:08:31] [V] [TRT] Tactic: 1 Time: 3.08109 [12/16/2021-10:08:31] [V] [TRT] Tactic: 2 Time: 2.96688 [12/16/2021-10:08:31] [V] [TRT] Tactic: 3 Time: 2.40754 [12/16/2021-10:08:32] [V] [TRT] Tactic: 4 Time: 1.88979 [12/16/2021-10:08:32] [V] [TRT] Tactic: 5 Time: 1.98089 [12/16/2021-10:08:32] [V] [TRT] Tactic: 6 Time: 2.18777 [12/16/2021-10:08:32] [V] [TRT] Tactic: 7 Time: 1.55523 [12/16/2021-10:08:32] [V] [TRT] Tactic: 8 Time: 1.38678 [12/16/2021-10:08:32] [V] [TRT] Tactic: 9 Time: 1.56867 [12/16/2021-10:08:32] [V] [TRT] Tactic: 28 Time: 4.28304 [12/16/2021-10:08:32] [V] [TRT] Fastest Tactic: 8 Time: 1.38678 [12/16/2021-10:08:32] [V] [TRT] --------------- Timing Runner: 007_convolutional_lrelu (PointWise) [12/16/2021-10:08:32] [V] [TRT] Tactic: 128 Time: 9.31581 [12/16/2021-10:08:32] [V] [TRT] Tactic: 256 Time: 9.24582 [12/16/2021-10:08:32] [V] [TRT] Tactic: 512 Time: 9.01511 [12/16/2021-10:08:32] [V] [TRT] Fastest Tactic: 512 Time: 9.01511 [12/16/2021-10:08:32] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:08:32] [V] [TRT] *************** Autotuning format combination: Half(524288,16384:2,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:08:32] [V] [TRT] --------------- Timing Runner: 007_convolutional_lrelu (PointWiseV2) [12/16/2021-10:08:32] [V] [TRT] Tactic: 0 Time: 2.82667 [12/16/2021-10:08:33] [V] [TRT] Tactic: 1 Time: 2.26422 [12/16/2021-10:08:33] [V] [TRT] Tactic: 2 Time: 2.47436 [12/16/2021-10:08:33] [V] [TRT] Tactic: 3 Time: 1.99979 [12/16/2021-10:08:33] [V] [TRT] Tactic: 4 Time: 2.08379 [12/16/2021-10:08:33] [V] [TRT] Tactic: 5 Time: 2.24003 [12/16/2021-10:08:33] [V] [TRT] Tactic: 6 Time: 1.9309 [12/16/2021-10:08:33] [V] [TRT] Tactic: 7 Time: 1.98746 [12/16/2021-10:08:33] [V] [TRT] Tactic: 8 Time: 2.09536 [12/16/2021-10:08:33] [V] [TRT] Tactic: 9 Time: 2.33769 [12/16/2021-10:08:33] [V] [TRT] Tactic: 10 Time: 4.79254 [12/16/2021-10:08:33] [V] [TRT] Tactic: 11 Time: 3.29163 [12/16/2021-10:08:33] [V] [TRT] Tactic: 12 Time: 3.17659 [12/16/2021-10:08:33] [V] [TRT] Tactic: 13 Time: 2.45169 [12/16/2021-10:08:33] [V] [TRT] Tactic: 14 Time: 2.01008 [12/16/2021-10:08:33] [V] [TRT] Tactic: 15 Time: 2.14816 [12/16/2021-10:08:33] [V] [TRT] Tactic: 16 Time: 2.22953 [12/16/2021-10:08:34] [V] [TRT] Tactic: 17 Time: 1.57083 [12/16/2021-10:08:34] [V] [TRT] Tactic: 18 Time: 1.48582 [12/16/2021-10:08:34] [V] [TRT] Tactic: 19 Time: 1.76325 [12/16/2021-10:08:34] [V] [TRT] Tactic: 28 Time: 2.76048 [12/16/2021-10:08:34] [V] [TRT] Tactic: 29 Time: 4.64103 [12/16/2021-10:08:34] [V] [TRT] Fastest Tactic: 18 Time: 1.48582 [12/16/2021-10:08:34] [V] [TRT] --------------- Timing Runner: 007_convolutional_lrelu (PointWise) [12/16/2021-10:08:34] [V] [TRT] Tactic: 128 Time: 9.30755 [12/16/2021-10:08:34] [V] [TRT] Tactic: 256 Time: 9.2443 [12/16/2021-10:08:34] [V] [TRT] Tactic: 512 Time: 9.01546 [12/16/2021-10:08:34] [V] [TRT] Fastest Tactic: 512 Time: 9.01546 [12/16/2021-10:08:34] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] *************** Autotuning format combination: Float(1048576,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:08:34] [V] [TRT] --------------- Timing Runner: 008_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:08:34] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:34] [V] [TRT] --------------- Timing Runner: 008_convolutional (FusedConvActConvolution) [12/16/2021-10:08:34] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:08:34] [V] [TRT] --------------- Timing Runner: 008_convolutional (CudnnConvolution) [12/16/2021-10:08:36] [V] [TRT] Tactic: 0 Time: 117.941 [12/16/2021-10:08:37] [V] [TRT] Tactic: 1 Time: 67.8904 [12/16/2021-10:08:37] [V] [TRT] Tactic: 2 skipped. Scratch requested: 188743680, available: 16777216 [12/16/2021-10:08:37] [V] [TRT] Tactic: 5 skipped. Scratch requested: 44007424, available: 16777216 [12/16/2021-10:08:38] [V] [TRT] Tactic: 6 Time: 50.4485 [12/16/2021-10:08:38] [V] [TRT] Fastest Tactic: 6 Time: 50.4485 [12/16/2021-10:08:38] [V] [TRT] --------------- Timing Runner: 008_convolutional (CaskConvolution) [12/16/2021-10:08:38] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:08:40] [V] [TRT] Tactic: 1062367460111450758 Time: 78.5155 [12/16/2021-10:08:40] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v0 Tactic: 1754984623894446479 [12/16/2021-10:08:41] [V] [TRT] Tactic: 1754984623894446479 Time: 86.3336 [12/16/2021-10:08:41] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v0 Tactic: 3611739942397549984 [12/16/2021-10:08:42] [V] [TRT] Tactic: 3611739942397549984 Time: 65.2821 [12/16/2021-10:08:42] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148n_nt_v1 Tactic: 3827454225649558724 [12/16/2021-10:08:43] [V] [TRT] Tactic: 3827454225649558724 Time: 72.1747 [12/16/2021-10:08:43] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v1 Tactic: 4337000649858996379 [12/16/2021-10:08:44] [V] [TRT] Tactic: 4337000649858996379 Time: 65.6261 [12/16/2021-10:08:44] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:08:45] [V] [TRT] Tactic: 4501471010995462441 Time: 64.4612 [12/16/2021-10:08:45] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:08:47] [V] [TRT] Tactic: 5137655947464784826 Time: 62.59 [12/16/2021-10:08:47] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:08:48] [V] [TRT] Tactic: 5288347012147084929 Time: 64.2268 [12/16/2021-10:08:48] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148m_nt_v1 Tactic: 5921334924264294896 [12/16/2021-10:08:48] [V] [TRT] Tactic: 5921334924264294896 Time: 48.2545 [12/16/2021-10:08:48] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-10:08:49] [V] [TRT] Tactic: 6645123197870846056 Time: 64.0219 [12/16/2021-10:08:49] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-10:08:51] [V] [TRT] Tactic: 7144526460361122478 Time: 80.5372 [12/16/2021-10:08:51] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v1 Tactic: 7852627285308570038 [12/16/2021-10:08:52] [V] [TRT] Tactic: 7852627285308570038 Time: 70.4007 [12/16/2021-10:08:52] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v1 Tactic: -9137461792520977713 [12/16/2021-10:08:53] [V] [TRT] Tactic: -9137461792520977713 Time: 65.6172 [12/16/2021-10:08:53] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148n_nt_v0 Tactic: -8776506421218919509 [12/16/2021-10:08:54] [V] [TRT] Tactic: -8776506421218919509 Time: 72.5672 [12/16/2021-10:08:54] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-10:08:55] [V] [TRT] Tactic: -8262349710178828730 Time: 65.2689 [12/16/2021-10:08:55] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v0 Tactic: -8133971918129952780 [12/16/2021-10:08:57] [V] [TRT] Tactic: -8133971918129952780 Time: 70.0824 [12/16/2021-10:08:57] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v1 Tactic: -6092040395344634144 [12/16/2021-10:08:58] [V] [TRT] Tactic: -6092040395344634144 Time: 81.629 [12/16/2021-10:08:58] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-10:08:59] [V] [TRT] Tactic: -4787320710726427159 Time: 85.9118 [12/16/2021-10:08:59] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-10:09:01] [V] [TRT] Tactic: -3456450830548107839 Time: 72.9564 [12/16/2021-10:09:01] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v0 Tactic: -2318106587342035239 [12/16/2021-10:09:02] [V] [TRT] Tactic: -2318106587342035239 Time: 71.2928 [12/16/2021-10:09:02] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_mobile_relu_tile148t_nt_v0 Tactic: -1343271414618805657 [12/16/2021-10:09:03] [V] [TRT] Tactic: -1343271414618805657 Time: 45.5767 [12/16/2021-10:09:03] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-10:09:04] [V] [TRT] Tactic: -1218658103698133241 Time: 69.6539 [12/16/2021-10:09:04] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-10:09:05] [V] [TRT] Tactic: -836875257600482091 Time: 67.8961 [12/16/2021-10:09:05] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-10:09:06] [V] [TRT] Tactic: -410470605513481746 Time: 62.9822 [12/16/2021-10:09:06] [V] [TRT] Fastest Tactic: -1343271414618805657 Time: 45.5767 [12/16/2021-10:09:06] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -1343271414618805657 [12/16/2021-10:09:06] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,8192,64) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:06] [V] [TRT] --------------- Timing Runner: 008_convolutional (CudnnConvolution) [12/16/2021-10:09:06] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:06] [V] [TRT] --------------- Timing Runner: 008_convolutional (CaskConvolution) [12/16/2021-10:09:06] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -9153228964338181824 [12/16/2021-10:09:07] [V] [TRT] Tactic: -9153228964338181824 Time: 90.9141 [12/16/2021-10:09:07] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -7394439838318485025 [12/16/2021-10:09:08] [V] [TRT] Tactic: -7394439838318485025 Time: 63.3179 [12/16/2021-10:09:08] [V] [TRT] Fastest Tactic: -7394439838318485025 Time: 63.3179 [12/16/2021-10:09:08] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -7394439838318485025 [12/16/2021-10:09:08] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:08] [V] [TRT] --------------- Timing Runner: 008_convolutional (CudnnConvolution) [12/16/2021-10:09:10] [V] [TRT] Tactic: 0 Time: 115.328 [12/16/2021-10:09:12] [V] [TRT] Tactic: 1 Time: 73.0502 [12/16/2021-10:09:12] [V] [TRT] Tactic: 2 skipped. Scratch requested: 94371840, available: 16777216 [12/16/2021-10:09:12] [V] [TRT] Tactic: 5 skipped. Scratch requested: 44007424, available: 16777216 [12/16/2021-10:09:12] [V] [TRT] Tactic: 6 skipped. Scratch requested: 64030720, available: 16777216 [12/16/2021-10:09:12] [V] [TRT] Fastest Tactic: 1 Time: 73.0502 [12/16/2021-10:09:12] [V] [TRT] --------------- Timing Runner: 008_convolutional (CaskConvolution) [12/16/2021-10:09:12] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:12] [V] [TRT] Setting workspace to 64030720enables more tactics for profiling [12/16/2021-10:09:12] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CudnnConvolution Tactic: 1 [12/16/2021-10:09:12] [V] [TRT] *************** Autotuning format combination: Half(524288,16384:2,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:12] [V] [TRT] --------------- Timing Runner: 008_convolutional (FusedConvActConvolution) [12/16/2021-10:09:12] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:12] [V] [TRT] --------------- Timing Runner: 008_convolutional (CudnnConvolution) [12/16/2021-10:09:12] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:12] [V] [TRT] --------------- Timing Runner: 008_convolutional (CaskConvolution) [12/16/2021-10:09:12] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-10:09:12] [V] [TRT] Tactic: 3564772625446233998 Time: 40.44 [12/16/2021-10:09:12] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_large_nn_v1 Tactic: 3650389455493082349 [12/16/2021-10:09:13] [V] [TRT] Tactic: 3650389455493082349 Time: 42.2844 [12/16/2021-10:09:13] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_winograd_fp16x2_128x128_ldg1_ldg4_relu_tile148m_nt_v1 Tactic: 4772821744921268633 [12/16/2021-10:09:13] [V] [TRT] Tactic: 4772821744921268633 Time: 26.0404 [12/16/2021-10:09:13] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-10:09:14] [V] [TRT] Tactic: 5319956359050645452 Time: 37.3809 [12/16/2021-10:09:14] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-10:09:15] [V] [TRT] Tactic: 7205456024582378848 Time: 32.6183 [12/16/2021-10:09:15] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_large_nn_v1 Tactic: -6490690591794140522 [12/16/2021-10:09:15] [V] [TRT] Tactic: -6490690591794140522 Time: 32.9812 [12/16/2021-10:09:15] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_large_nn_v1 Tactic: -4686027666808657977 [12/16/2021-10:09:16] [V] [TRT] Tactic: -4686027666808657977 Time: 33.0965 [12/16/2021-10:09:16] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-10:09:16] [V] [TRT] Tactic: -4212163711445252890 Time: 31.7376 [12/16/2021-10:09:16] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-10:09:17] [V] [TRT] Tactic: -3898373634979201110 Time: 32.7326 [12/16/2021-10:09:17] [V] [TRT] 008_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-10:09:17] [V] [TRT] Tactic: -2409163523992614473 Time: 31.8818 [12/16/2021-10:09:17] [V] [TRT] Fastest Tactic: 4772821744921268633 Time: 26.0404 [12/16/2021-10:09:17] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 4772821744921268633 [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] *************** Autotuning format combination: Float(2097152,16384,128,1), Float(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:17] [V] [TRT] --------------- Timing Runner: PWN(008_convolutional_lrelu, 009_shortcut) (PointWiseV2) [12/16/2021-10:09:18] [V] [TRT] Tactic: 0 Time: 9.51567 [12/16/2021-10:09:18] [V] [TRT] Tactic: 1 Time: 7.49716 [12/16/2021-10:09:18] [V] [TRT] Tactic: 2 Time: 6.45671 [12/16/2021-10:09:18] [V] [TRT] Tactic: 3 Time: 6.96618 [12/16/2021-10:09:18] [V] [TRT] Tactic: 4 Time: 6.19376 [12/16/2021-10:09:19] [V] [TRT] Tactic: 5 Time: 6.25573 [12/16/2021-10:09:19] [V] [TRT] Tactic: 6 Time: 6.95393 [12/16/2021-10:09:19] [V] [TRT] Tactic: 7 Time: 6.31311 [12/16/2021-10:09:19] [V] [TRT] Tactic: 8 Time: 6.23521 [12/16/2021-10:09:19] [V] [TRT] Tactic: 9 Time: 6.29441 [12/16/2021-10:09:20] [V] [TRT] Tactic: 28 Time: 9.34363 [12/16/2021-10:09:20] [V] [TRT] Fastest Tactic: 4 Time: 6.19376 [12/16/2021-10:09:20] [V] [TRT] --------------- Timing Runner: PWN(008_convolutional_lrelu, 009_shortcut) (PointWise) [12/16/2021-10:09:20] [V] [TRT] Tactic: 128 Time: 27.2739 [12/16/2021-10:09:21] [V] [TRT] Tactic: 256 Time: 27.4085 [12/16/2021-10:09:21] [V] [TRT] Tactic: 512 Time: 27.5219 [12/16/2021-10:09:21] [V] [TRT] Fastest Tactic: 128 Time: 27.2739 [12/16/2021-10:09:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 4 [12/16/2021-10:09:21] [V] [TRT] *************** Autotuning format combination: Float(2097152,1,16384,128), Float(2097152,1,16384,128) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:21] [V] [TRT] --------------- Timing Runner: PWN(008_convolutional_lrelu, 009_shortcut) (PointWiseV2) [12/16/2021-10:09:21] [V] [TRT] Tactic: 0 Time: 9.50611 [12/16/2021-10:09:22] [V] [TRT] Tactic: 1 Time: 7.50027 [12/16/2021-10:09:22] [V] [TRT] Tactic: 2 Time: 6.46266 [12/16/2021-10:09:22] [V] [TRT] Tactic: 3 Time: 6.9612 [12/16/2021-10:09:22] [V] [TRT] Tactic: 4 Time: 6.18827 [12/16/2021-10:09:22] [V] [TRT] Tactic: 5 Time: 6.25706 [12/16/2021-10:09:23] [V] [TRT] Tactic: 6 Time: 6.95528 [12/16/2021-10:09:23] [V] [TRT] Tactic: 7 Time: 6.31294 [12/16/2021-10:09:23] [V] [TRT] Tactic: 8 Time: 6.23316 [12/16/2021-10:09:23] [V] [TRT] Tactic: 9 Time: 6.2927 [12/16/2021-10:09:23] [V] [TRT] Tactic: 28 Time: 9.35607 [12/16/2021-10:09:23] [V] [TRT] Fastest Tactic: 4 Time: 6.18827 [12/16/2021-10:09:23] [V] [TRT] --------------- Timing Runner: PWN(008_convolutional_lrelu, 009_shortcut) (PointWise) [12/16/2021-10:09:24] [V] [TRT] Tactic: 128 Time: 27.2797 [12/16/2021-10:09:24] [V] [TRT] Tactic: 256 Time: 27.3991 [12/16/2021-10:09:25] [V] [TRT] Tactic: 512 Time: 27.53 [12/16/2021-10:09:25] [V] [TRT] Fastest Tactic: 128 Time: 27.2797 [12/16/2021-10:09:25] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 4 [12/16/2021-10:09:25] [V] [TRT] *************** Autotuning format combination: Float(65536,16384:32,128,1), Float(65536,16384:32,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:25] [V] [TRT] --------------- Timing Runner: PWN(008_convolutional_lrelu, 009_shortcut) (PointWiseV2) [12/16/2021-10:09:25] [V] [TRT] Tactic: 24 Time: 9.5309 [12/16/2021-10:09:25] [V] [TRT] Tactic: 25 Time: 8.04881 [12/16/2021-10:09:26] [V] [TRT] Tactic: 26 Time: 8.28371 [12/16/2021-10:09:26] [V] [TRT] Tactic: 27 Time: 8.11611 [12/16/2021-10:09:26] [V] [TRT] Tactic: 31 Time: 9.53751 [12/16/2021-10:09:26] [V] [TRT] Fastest Tactic: 25 Time: 8.04881 [12/16/2021-10:09:26] [V] [TRT] --------------- Timing Runner: PWN(008_convolutional_lrelu, 009_shortcut) (PointWise) [12/16/2021-10:09:27] [V] [TRT] Tactic: 128 Time: 27.271 [12/16/2021-10:09:27] [V] [TRT] Tactic: 256 Time: 27.3988 [12/16/2021-10:09:28] [V] [TRT] Tactic: 512 Time: 27.5263 [12/16/2021-10:09:28] [V] [TRT] Fastest Tactic: 128 Time: 27.271 [12/16/2021-10:09:28] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 25 [12/16/2021-10:09:28] [V] [TRT] *************** Autotuning format combination: Half(2097152,16384,128,1), Half(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:28] [V] [TRT] --------------- Timing Runner: PWN(008_convolutional_lrelu, 009_shortcut) (PointWiseV2) [12/16/2021-10:09:28] [V] [TRT] Tactic: 0 Time: 9.50408 [12/16/2021-10:09:28] [V] [TRT] Tactic: 1 Time: 7.28434 [12/16/2021-10:09:28] [V] [TRT] Tactic: 2 Time: 6.56895 [12/16/2021-10:09:28] [V] [TRT] Tactic: 3 Time: 5.59785 [12/16/2021-10:09:29] [V] [TRT] Tactic: 4 Time: 4.95607 [12/16/2021-10:09:29] [V] [TRT] Tactic: 5 Time: 4.64701 [12/16/2021-10:09:29] [V] [TRT] Tactic: 6 Time: 5.40111 [12/16/2021-10:09:29] [V] [TRT] Tactic: 7 Time: 4.25792 [12/16/2021-10:09:29] [V] [TRT] Tactic: 8 Time: 3.93702 [12/16/2021-10:09:29] [V] [TRT] Tactic: 9 Time: 4.2206 [12/16/2021-10:09:29] [V] [TRT] Tactic: 28 Time: 9.31234 [12/16/2021-10:09:29] [V] [TRT] Fastest Tactic: 8 Time: 3.93702 [12/16/2021-10:09:29] [V] [TRT] --------------- Timing Runner: PWN(008_convolutional_lrelu, 009_shortcut) (PointWise) [12/16/2021-10:09:30] [V] [TRT] Tactic: 128 Time: 25.7432 [12/16/2021-10:09:30] [V] [TRT] Tactic: 256 Time: 25.6845 [12/16/2021-10:09:31] [V] [TRT] Tactic: 512 Time: 25.0946 [12/16/2021-10:09:31] [V] [TRT] Fastest Tactic: 512 Time: 25.0946 [12/16/2021-10:09:31] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:09:31] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384:2,128,1), Half(1048576,16384:2,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:31] [V] [TRT] --------------- Timing Runner: PWN(008_convolutional_lrelu, 009_shortcut) (PointWiseV2) [12/16/2021-10:09:31] [V] [TRT] Tactic: 0 Time: 7.32312 [12/16/2021-10:09:31] [V] [TRT] Tactic: 1 Time: 6.37729 [12/16/2021-10:09:31] [V] [TRT] Tactic: 2 Time: 6.85961 [12/16/2021-10:09:31] [V] [TRT] Tactic: 3 Time: 6.10494 [12/16/2021-10:09:31] [V] [TRT] Tactic: 4 Time: 6.3399 [12/16/2021-10:09:32] [V] [TRT] Tactic: 5 Time: 6.84488 [12/16/2021-10:09:32] [V] [TRT] Tactic: 6 Time: 6.00944 [12/16/2021-10:09:32] [V] [TRT] Tactic: 7 Time: 6.21717 [12/16/2021-10:09:32] [V] [TRT] Tactic: 8 Time: 6.76672 [12/16/2021-10:09:32] [V] [TRT] Tactic: 9 Time: 8.37408 [12/16/2021-10:09:32] [V] [TRT] Tactic: 10 Time: 10.5274 [12/16/2021-10:09:33] [V] [TRT] Tactic: 11 Time: 8.11904 [12/16/2021-10:09:33] [V] [TRT] Tactic: 12 Time: 7.1723 [12/16/2021-10:09:33] [V] [TRT] Tactic: 13 Time: 6.01126 [12/16/2021-10:09:33] [V] [TRT] Tactic: 14 Time: 5.99171 [12/16/2021-10:09:33] [V] [TRT] Tactic: 15 Time: 5.77012 [12/16/2021-10:09:33] [V] [TRT] Tactic: 16 Time: 5.53716 [12/16/2021-10:09:34] [V] [TRT] Tactic: 17 Time: 5.01257 [12/16/2021-10:09:34] [V] [TRT] Tactic: 18 Time: 4.9656 [12/16/2021-10:09:34] [V] [TRT] Tactic: 19 Time: 5.1437 [12/16/2021-10:09:34] [V] [TRT] Tactic: 28 Time: 7.33939 [12/16/2021-10:09:34] [V] [TRT] Tactic: 29 Time: 10.2766 [12/16/2021-10:09:34] [V] [TRT] Fastest Tactic: 18 Time: 4.9656 [12/16/2021-10:09:34] [V] [TRT] --------------- Timing Runner: PWN(008_convolutional_lrelu, 009_shortcut) (PointWise) [12/16/2021-10:09:35] [V] [TRT] Tactic: 128 Time: 25.7242 [12/16/2021-10:09:35] [V] [TRT] Tactic: 256 Time: 25.6841 [12/16/2021-10:09:36] [V] [TRT] Tactic: 512 Time: 25.0997 [12/16/2021-10:09:36] [V] [TRT] Fastest Tactic: 512 Time: 25.0997 [12/16/2021-10:09:36] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(2097152,16384,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(2097152,1,16384,128) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Half(2097152,16384,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384:2,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] --------------- Timing Runner: 010_convolutional (CaskConvolution) [12/16/2021-10:09:36] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384:2,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Float(32768,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Float(32768,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Float(32768,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Float(32768,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(1048576,16384,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,8192,64) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(32768,16384:32,128,1) -> Float(32768,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Half(524288,16384:2,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,16384,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,8192,64) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(32768,16384:32,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384,128,1) -> Half(524288,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Float(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Float(1048576,1,8192,64) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(524288,16384:2,128,1) -> Half(1048576,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(1048576,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,8192,64) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Half(524288,16384:2,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(2097152,16384,128,1), Float(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(2097152,1,16384,128), Float(2097152,1,16384,128) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(65536,16384:32,128,1), Float(65536,16384:32,128,1) -> Float(65536,16384:32,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Half(2097152,16384,128,1), Half(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384:2,128,1), Half(1048576,16384:2,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(2097152,1,16384,128) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Float(65536,16384:32,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(2097152,16384,128,1) -> Half(1048576,16384:2,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Float(2097152,1,16384,128) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning Reformat:Half(1048576,16384:2,128,1) -> Half(2097152,16384,128,1) *************** [12/16/2021-10:09:36] [V] [TRT] *************** Autotuning format combination: Float(2097152,16384,128,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:09:36] [V] [TRT] --------------- Timing Runner: 013_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:09:36] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:36] [V] [TRT] --------------- Timing Runner: 013_convolutional (FusedConvActConvolution) [12/16/2021-10:09:36] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:36] [V] [TRT] --------------- Timing Runner: 013_convolutional (CudnnConvolution) [12/16/2021-10:09:36] [V] [TRT] Tactic: 0 skipped. Scratch requested: 42600960, available: 16777216 [12/16/2021-10:09:36] [V] [TRT] Tactic: 1 skipped. Scratch requested: 43806720, available: 16777216 [12/16/2021-10:09:36] [V] [TRT] Tactic: 2 skipped. Scratch requested: 136972800, available: 16777216 [12/16/2021-10:09:36] [V] [TRT] Tactic: 5 skipped. Scratch requested: 201918976, available: 16777216 [12/16/2021-10:09:36] [V] [TRT] Fastest Tactic: -3360065831133338131 Time: 3.40282e+38 [12/16/2021-10:09:36] [V] [TRT] --------------- Timing Runner: 013_convolutional (CaskConvolution) [12/16/2021-10:09:36] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:09:37] [V] [TRT] Tactic: 1062367460111450758 Time: 77.5469 [12/16/2021-10:09:37] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v0 Tactic: 1754984623894446479 [12/16/2021-10:09:38] [V] [TRT] Tactic: 1754984623894446479 Time: 87.3071 [12/16/2021-10:09:38] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v0 Tactic: 3611739942397549984 [12/16/2021-10:09:39] [V] [TRT] Tactic: 3611739942397549984 Time: 63.1741 [12/16/2021-10:09:39] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v1 Tactic: 4337000649858996379 [12/16/2021-10:09:40] [V] [TRT] Tactic: 4337000649858996379 Time: 63.3362 [12/16/2021-10:09:40] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:09:42] [V] [TRT] Tactic: 4501471010995462441 Time: 62.9798 [12/16/2021-10:09:42] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:09:43] [V] [TRT] Tactic: 5137655947464784826 Time: 61.2827 [12/16/2021-10:09:43] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:09:44] [V] [TRT] Tactic: 5288347012147084929 Time: 62.3804 [12/16/2021-10:09:44] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-10:09:45] [V] [TRT] Tactic: 6645123197870846056 Time: 62.6 [12/16/2021-10:09:45] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-10:09:46] [V] [TRT] Tactic: 7144526460361122478 Time: 81.8537 [12/16/2021-10:09:46] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v1 Tactic: -9137461792520977713 [12/16/2021-10:09:47] [V] [TRT] Tactic: -9137461792520977713 Time: 63.3273 [12/16/2021-10:09:47] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-10:09:48] [V] [TRT] Tactic: -8262349710178828730 Time: 63.5355 [12/16/2021-10:09:48] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v0 Tactic: -8133971918129952780 [12/16/2021-10:09:49] [V] [TRT] Tactic: -8133971918129952780 Time: 70.3588 [12/16/2021-10:09:49] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v1 Tactic: -6092040395344634144 [12/16/2021-10:09:51] [V] [TRT] Tactic: -6092040395344634144 Time: 80.1792 [12/16/2021-10:09:51] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-10:09:52] [V] [TRT] Tactic: -4787320710726427159 Time: 87.1849 [12/16/2021-10:09:52] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-10:09:53] [V] [TRT] Tactic: -3456450830548107839 Time: 71.4078 [12/16/2021-10:09:53] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-10:09:55] [V] [TRT] Tactic: -1218658103698133241 Time: 70.0621 [12/16/2021-10:09:55] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-10:09:56] [V] [TRT] Tactic: -836875257600482091 Time: 69.5764 [12/16/2021-10:09:56] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-10:09:57] [V] [TRT] Tactic: -410470605513481746 Time: 61.8442 [12/16/2021-10:09:57] [V] [TRT] Fastest Tactic: 5137655947464784826 Time: 61.2827 [12/16/2021-10:09:57] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 5137655947464784826 [12/16/2021-10:09:57] [V] [TRT] *************** Autotuning format combination: Float(2097152,1,16384,128) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:09:57] [V] [TRT] --------------- Timing Runner: 013_convolutional (CudnnConvolution) [12/16/2021-10:09:57] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:57] [V] [TRT] --------------- Timing Runner: 013_convolutional (CaskConvolution) [12/16/2021-10:09:57] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -9153228964338181824 [12/16/2021-10:09:58] [V] [TRT] Tactic: -9153228964338181824 Time: 95.7341 [12/16/2021-10:09:58] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -7394439838318485025 [12/16/2021-10:09:59] [V] [TRT] Tactic: -7394439838318485025 Time: 61.2954 [12/16/2021-10:09:59] [V] [TRT] Fastest Tactic: -7394439838318485025 Time: 61.2954 [12/16/2021-10:09:59] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -7394439838318485025 [12/16/2021-10:09:59] [V] [TRT] *************** Autotuning format combination: Half(2097152,16384,128,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:09:59] [V] [TRT] --------------- Timing Runner: 013_convolutional (CudnnConvolution) [12/16/2021-10:09:59] [V] [TRT] Tactic: 0 skipped. Scratch requested: 21300736, available: 16777216 [12/16/2021-10:09:59] [V] [TRT] Tactic: 1 skipped. Scratch requested: 32404992, available: 16777216 [12/16/2021-10:09:59] [V] [TRT] Tactic: 2 skipped. Scratch requested: 68486656, available: 16777216 [12/16/2021-10:09:59] [V] [TRT] Tactic: 5 skipped. Scratch requested: 180618752, available: 16777216 [12/16/2021-10:09:59] [V] [TRT] Fastest Tactic: -3360065831133338131 Time: 3.40282e+38 [12/16/2021-10:09:59] [V] [TRT] --------------- Timing Runner: 013_convolutional (CaskConvolution) [12/16/2021-10:09:59] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:59] [V] [TRT] *************** Autotuning format combination: Half(1048576,16384:2,128,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:09:59] [V] [TRT] --------------- Timing Runner: 013_convolutional (FusedConvActConvolution) [12/16/2021-10:09:59] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:59] [V] [TRT] --------------- Timing Runner: 013_convolutional (CudnnConvolution) [12/16/2021-10:09:59] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:09:59] [V] [TRT] --------------- Timing Runner: 013_convolutional (CaskConvolution) [12/16/2021-10:09:59] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-10:10:00] [V] [TRT] Tactic: 3564772625446233998 Time: 38.8179 [12/16/2021-10:10:00] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_large_nn_v1 Tactic: 3650389455493082349 [12/16/2021-10:10:01] [V] [TRT] Tactic: 3650389455493082349 Time: 40.9721 [12/16/2021-10:10:01] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-10:10:01] [V] [TRT] Tactic: 5319956359050645452 Time: 36.0206 [12/16/2021-10:10:01] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-10:10:02] [V] [TRT] Tactic: 7205456024582378848 Time: 31.6475 [12/16/2021-10:10:02] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_large_nn_v1 Tactic: -6490690591794140522 [12/16/2021-10:10:02] [V] [TRT] Tactic: -6490690591794140522 Time: 31.9694 [12/16/2021-10:10:02] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_large_nn_v1 Tactic: -4686027666808657977 [12/16/2021-10:10:03] [V] [TRT] Tactic: -4686027666808657977 Time: 32.0206 [12/16/2021-10:10:03] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-10:10:03] [V] [TRT] Tactic: -4212163711445252890 Time: 30.8763 [12/16/2021-10:10:03] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-10:10:04] [V] [TRT] Tactic: -3898373634979201110 Time: 31.706 [12/16/2021-10:10:04] [V] [TRT] 013_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-10:10:05] [V] [TRT] Tactic: -2409163523992614473 Time: 31.0084 [12/16/2021-10:10:05] [V] [TRT] Fastest Tactic: -4212163711445252890 Time: 30.8763 [12/16/2021-10:10:05] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -4212163711445252890 [12/16/2021-10:10:05] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:05] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:05] [V] [TRT] Tactic: 1002 Time: 5.46124 [12/16/2021-10:10:05] [V] [TRT] Tactic: 0 Time: 9.73343 [12/16/2021-10:10:05] [V] [TRT] Fastest Tactic: 1002 Time: 5.46124 [12/16/2021-10:10:05] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:10:05] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:05] [V] [TRT] Tactic: 1002 Time: 5.47089 [12/16/2021-10:10:05] [V] [TRT] Tactic: 0 Time: 16.8606 [12/16/2021-10:10:05] [V] [TRT] Fastest Tactic: 1002 Time: 5.47089 [12/16/2021-10:10:05] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:05] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:05] [V] [TRT] Tactic: 1002 Time: 6.48479 [12/16/2021-10:10:06] [V] [TRT] Tactic: 0 Time: 5.36007 [12/16/2021-10:10:06] [V] [TRT] Fastest Tactic: 0 Time: 5.36007 [12/16/2021-10:10:06] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:06] [V] [TRT] Tactic: 1002 Time: 7.92389 [12/16/2021-10:10:06] [V] [TRT] Tactic: 0 Time: 4.28452 [12/16/2021-10:10:06] [V] [TRT] Fastest Tactic: 0 Time: 4.28452 [12/16/2021-10:10:06] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:06] [V] [TRT] Tactic: 1002 Time: 6.51159 [12/16/2021-10:10:06] [V] [TRT] Tactic: 0 Time: 19.3333 [12/16/2021-10:10:06] [V] [TRT] Fastest Tactic: 1002 Time: 6.51159 [12/16/2021-10:10:06] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:10:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:06] [V] [TRT] Tactic: 1002 Time: 4.97695 [12/16/2021-10:10:07] [V] [TRT] Tactic: 0 Time: 33.9401 [12/16/2021-10:10:07] [V] [TRT] Fastest Tactic: 1002 Time: 4.97695 [12/16/2021-10:10:07] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:07] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:07] [V] [TRT] Tactic: 1002 Time: 4.89751 [12/16/2021-10:10:07] [V] [TRT] Tactic: 0 Time: 18.9454 [12/16/2021-10:10:07] [V] [TRT] Fastest Tactic: 1002 Time: 4.89751 [12/16/2021-10:10:07] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:07] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:08] [V] [TRT] Tactic: 1002 Time: 6.80469 [12/16/2021-10:10:08] [V] [TRT] Tactic: 0 Time: 20.0901 [12/16/2021-10:10:08] [V] [TRT] Fastest Tactic: 1002 Time: 6.80469 [12/16/2021-10:10:08] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:08] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:08] [V] [TRT] Tactic: 1002 Time: 6.29704 [12/16/2021-10:10:08] [V] [TRT] Tactic: 0 Time: 17.6193 [12/16/2021-10:10:08] [V] [TRT] Fastest Tactic: 1002 Time: 6.29704 [12/16/2021-10:10:08] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:08] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:08] [V] [TRT] Tactic: 1002 Time: 4.9543 [12/16/2021-10:10:09] [V] [TRT] Tactic: 0 Time: 9.05863 [12/16/2021-10:10:09] [V] [TRT] Fastest Tactic: 1002 Time: 4.9543 [12/16/2021-10:10:09] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:09] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:09] [V] [TRT] Tactic: 1002 Time: 5.30389 [12/16/2021-10:10:09] [V] [TRT] Tactic: 0 Time: 17.2514 [12/16/2021-10:10:09] [V] [TRT] Fastest Tactic: 1002 Time: 5.30389 [12/16/2021-10:10:09] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:09] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:09] [V] [TRT] Tactic: 1002 Time: 6.80673 [12/16/2021-10:10:09] [V] [TRT] Tactic: 0 Time: 18.4977 [12/16/2021-10:10:09] [V] [TRT] Fastest Tactic: 1002 Time: 6.80673 [12/16/2021-10:10:09] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:09] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:10] [V] [TRT] Tactic: 1002 Time: 6.54345 [12/16/2021-10:10:10] [V] [TRT] Tactic: 0 Time: 4.57216 [12/16/2021-10:10:10] [V] [TRT] Fastest Tactic: 0 Time: 4.57216 [12/16/2021-10:10:10] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:10] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:10] [V] [TRT] Tactic: 1002 Time: 4.59977 [12/16/2021-10:10:10] [V] [TRT] Tactic: 0 Time: 8.70266 [12/16/2021-10:10:10] [V] [TRT] Fastest Tactic: 1002 Time: 4.59977 [12/16/2021-10:10:10] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:10:10] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:10] [V] [TRT] Tactic: 1002 Time: 4.59691 [12/16/2021-10:10:10] [V] [TRT] Tactic: 0 Time: 16.6804 [12/16/2021-10:10:10] [V] [TRT] Fastest Tactic: 1002 Time: 4.59691 [12/16/2021-10:10:10] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:10] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:10] [V] [TRT] Tactic: 1002 Time: 4.3885 [12/16/2021-10:10:11] [V] [TRT] Tactic: 0 Time: 4.23994 [12/16/2021-10:10:11] [V] [TRT] Fastest Tactic: 0 Time: 4.23994 [12/16/2021-10:10:11] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:11] [V] [TRT] Tactic: 1002 Time: 6.18146 [12/16/2021-10:10:11] [V] [TRT] Tactic: 0 Time: 3.69021 [12/16/2021-10:10:11] [V] [TRT] Fastest Tactic: 0 Time: 3.69021 [12/16/2021-10:10:11] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:11] [V] [TRT] Tactic: 1002 Time: 4.67019 [12/16/2021-10:10:11] [V] [TRT] Tactic: 0 Time: 9.20471 [12/16/2021-10:10:11] [V] [TRT] Fastest Tactic: 1002 Time: 4.67019 [12/16/2021-10:10:11] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:10:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:11] [V] [TRT] Tactic: 1002 Time: 4.66852 [12/16/2021-10:10:11] [V] [TRT] Tactic: 0 Time: 16.4937 [12/16/2021-10:10:11] [V] [TRT] Fastest Tactic: 1002 Time: 4.66852 [12/16/2021-10:10:11] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:12] [V] [TRT] Tactic: 1002 Time: 9.41081 [12/16/2021-10:10:12] [V] [TRT] Tactic: 0 Time: 3.60365 [12/16/2021-10:10:12] [V] [TRT] Fastest Tactic: 0 Time: 3.60365 [12/16/2021-10:10:12] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:12] [V] [TRT] --------------- Timing Runner: 013_convolutional_lrelu (PointWiseV2) [12/16/2021-10:10:12] [V] [TRT] Tactic: 0 Time: 4.42174 [12/16/2021-10:10:12] [V] [TRT] Tactic: 1 Time: 3.15281 [12/16/2021-10:10:12] [V] [TRT] Tactic: 2 Time: 2.74913 [12/16/2021-10:10:12] [V] [TRT] Tactic: 3 Time: 2.69004 [12/16/2021-10:10:12] [V] [TRT] Tactic: 4 Time: 2.17335 [12/16/2021-10:10:12] [V] [TRT] Tactic: 5 Time: 2.0812 [12/16/2021-10:10:12] [V] [TRT] Tactic: 6 Time: 2.6637 [12/16/2021-10:10:12] [V] [TRT] Tactic: 7 Time: 2.12032 [12/16/2021-10:10:12] [V] [TRT] Tactic: 8 Time: 2.11341 [12/16/2021-10:10:12] [V] [TRT] Tactic: 9 Time: 2.10663 [12/16/2021-10:10:13] [V] [TRT] Tactic: 28 Time: 4.32861 [12/16/2021-10:10:13] [V] [TRT] Fastest Tactic: 5 Time: 2.0812 [12/16/2021-10:10:13] [V] [TRT] --------------- Timing Runner: 013_convolutional_lrelu (PointWise) [12/16/2021-10:10:13] [V] [TRT] Tactic: 128 Time: 10.3303 [12/16/2021-10:10:13] [V] [TRT] Tactic: 256 Time: 10.3688 [12/16/2021-10:10:13] [V] [TRT] Tactic: 512 Time: 10.4402 [12/16/2021-10:10:13] [V] [TRT] Fastest Tactic: 128 Time: 10.3303 [12/16/2021-10:10:13] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:10:13] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:13] [V] [TRT] --------------- Timing Runner: 013_convolutional_lrelu (PointWiseV2) [12/16/2021-10:10:13] [V] [TRT] Tactic: 0 Time: 4.42641 [12/16/2021-10:10:13] [V] [TRT] Tactic: 1 Time: 3.15673 [12/16/2021-10:10:13] [V] [TRT] Tactic: 2 Time: 2.75088 [12/16/2021-10:10:13] [V] [TRT] Tactic: 3 Time: 2.67958 [12/16/2021-10:10:14] [V] [TRT] Tactic: 4 Time: 2.16481 [12/16/2021-10:10:14] [V] [TRT] Tactic: 5 Time: 2.07855 [12/16/2021-10:10:14] [V] [TRT] Tactic: 6 Time: 2.6527 [12/16/2021-10:10:14] [V] [TRT] Tactic: 7 Time: 2.12167 [12/16/2021-10:10:14] [V] [TRT] Tactic: 8 Time: 2.11514 [12/16/2021-10:10:14] [V] [TRT] Tactic: 9 Time: 2.10184 [12/16/2021-10:10:14] [V] [TRT] Tactic: 28 Time: 4.32734 [12/16/2021-10:10:14] [V] [TRT] Fastest Tactic: 5 Time: 2.07855 [12/16/2021-10:10:14] [V] [TRT] --------------- Timing Runner: 013_convolutional_lrelu (PointWise) [12/16/2021-10:10:14] [V] [TRT] Tactic: 128 Time: 10.3277 [12/16/2021-10:10:14] [V] [TRT] Tactic: 256 Time: 10.3659 [12/16/2021-10:10:15] [V] [TRT] Tactic: 512 Time: 10.4401 [12/16/2021-10:10:15] [V] [TRT] Fastest Tactic: 128 Time: 10.3277 [12/16/2021-10:10:15] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:10:15] [V] [TRT] *************** Autotuning format combination: Float(32768,4096:32,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:10:15] [V] [TRT] --------------- Timing Runner: 013_convolutional_lrelu (PointWiseV2) [12/16/2021-10:10:15] [V] [TRT] Tactic: 24 Time: 2.78377 [12/16/2021-10:10:15] [V] [TRT] Tactic: 25 Time: 2.59087 [12/16/2021-10:10:15] [V] [TRT] Tactic: 26 Time: 2.63395 [12/16/2021-10:10:15] [V] [TRT] Tactic: 27 Time: 2.58252 [12/16/2021-10:10:15] [V] [TRT] Tactic: 31 Time: 2.7798 [12/16/2021-10:10:15] [V] [TRT] Fastest Tactic: 27 Time: 2.58252 [12/16/2021-10:10:15] [V] [TRT] --------------- Timing Runner: 013_convolutional_lrelu (PointWise) [12/16/2021-10:10:15] [V] [TRT] Tactic: 128 Time: 10.3295 [12/16/2021-10:10:15] [V] [TRT] Tactic: 256 Time: 10.3662 [12/16/2021-10:10:16] [V] [TRT] Tactic: 512 Time: 10.4366 [12/16/2021-10:10:16] [V] [TRT] Fastest Tactic: 128 Time: 10.3295 [12/16/2021-10:10:16] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 27 [12/16/2021-10:10:16] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:16] [V] [TRT] --------------- Timing Runner: 013_convolutional_lrelu (PointWiseV2) [12/16/2021-10:10:16] [V] [TRT] Tactic: 0 Time: 4.40206 [12/16/2021-10:10:16] [V] [TRT] Tactic: 1 Time: 3.08438 [12/16/2021-10:10:16] [V] [TRT] Tactic: 2 Time: 2.96516 [12/16/2021-10:10:16] [V] [TRT] Tactic: 3 Time: 2.40921 [12/16/2021-10:10:16] [V] [TRT] Tactic: 4 Time: 1.89084 [12/16/2021-10:10:16] [V] [TRT] Tactic: 5 Time: 1.98152 [12/16/2021-10:10:16] [V] [TRT] Tactic: 6 Time: 2.19277 [12/16/2021-10:10:16] [V] [TRT] Tactic: 7 Time: 1.55284 [12/16/2021-10:10:16] [V] [TRT] Tactic: 8 Time: 1.38603 [12/16/2021-10:10:16] [V] [TRT] Tactic: 9 Time: 1.5694 [12/16/2021-10:10:16] [V] [TRT] Tactic: 28 Time: 4.28314 [12/16/2021-10:10:16] [V] [TRT] Fastest Tactic: 8 Time: 1.38603 [12/16/2021-10:10:16] [V] [TRT] --------------- Timing Runner: 013_convolutional_lrelu (PointWise) [12/16/2021-10:10:17] [V] [TRT] Tactic: 128 Time: 9.30021 [12/16/2021-10:10:17] [V] [TRT] Tactic: 256 Time: 9.24031 [12/16/2021-10:10:17] [V] [TRT] Tactic: 512 Time: 9.01894 [12/16/2021-10:10:17] [V] [TRT] Fastest Tactic: 512 Time: 9.01894 [12/16/2021-10:10:17] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:10:17] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:17] [V] [TRT] --------------- Timing Runner: 013_convolutional_lrelu (PointWiseV2) [12/16/2021-10:10:17] [V] [TRT] Tactic: 0 Time: 2.82351 [12/16/2021-10:10:17] [V] [TRT] Tactic: 1 Time: 2.26729 [12/16/2021-10:10:17] [V] [TRT] Tactic: 2 Time: 2.47495 [12/16/2021-10:10:17] [V] [TRT] Tactic: 3 Time: 1.99302 [12/16/2021-10:10:17] [V] [TRT] Tactic: 4 Time: 2.08341 [12/16/2021-10:10:17] [V] [TRT] Tactic: 5 Time: 2.23453 [12/16/2021-10:10:17] [V] [TRT] Tactic: 6 Time: 1.92904 [12/16/2021-10:10:17] [V] [TRT] Tactic: 7 Time: 1.99 [12/16/2021-10:10:17] [V] [TRT] Tactic: 8 Time: 2.09534 [12/16/2021-10:10:17] [V] [TRT] Tactic: 9 Time: 2.34789 [12/16/2021-10:10:18] [V] [TRT] Tactic: 10 Time: 4.79266 [12/16/2021-10:10:18] [V] [TRT] Tactic: 11 Time: 3.29564 [12/16/2021-10:10:18] [V] [TRT] Tactic: 12 Time: 3.17852 [12/16/2021-10:10:18] [V] [TRT] Tactic: 13 Time: 2.45084 [12/16/2021-10:10:18] [V] [TRT] Tactic: 14 Time: 2.01081 [12/16/2021-10:10:18] [V] [TRT] Tactic: 15 Time: 2.151 [12/16/2021-10:10:18] [V] [TRT] Tactic: 16 Time: 2.22194 [12/16/2021-10:10:18] [V] [TRT] Tactic: 17 Time: 1.57315 [12/16/2021-10:10:18] [V] [TRT] Tactic: 18 Time: 1.48628 [12/16/2021-10:10:18] [V] [TRT] Tactic: 19 Time: 1.7646 [12/16/2021-10:10:18] [V] [TRT] Tactic: 28 Time: 2.76456 [12/16/2021-10:10:18] [V] [TRT] Tactic: 29 Time: 4.64456 [12/16/2021-10:10:18] [V] [TRT] Fastest Tactic: 18 Time: 1.48628 [12/16/2021-10:10:18] [V] [TRT] --------------- Timing Runner: 013_convolutional_lrelu (PointWise) [12/16/2021-10:10:18] [V] [TRT] Tactic: 128 Time: 9.31512 [12/16/2021-10:10:19] [V] [TRT] Tactic: 256 Time: 9.24344 [12/16/2021-10:10:19] [V] [TRT] Tactic: 512 Time: 9.01801 [12/16/2021-10:10:19] [V] [TRT] Fastest Tactic: 512 Time: 9.01801 [12/16/2021-10:10:19] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:10:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:19] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:19] [V] [TRT] Tactic: 1002 Time: 5.48446 [12/16/2021-10:10:19] [V] [TRT] Tactic: 0 Time: 9.73587 [12/16/2021-10:10:19] [V] [TRT] Fastest Tactic: 1002 Time: 5.48446 [12/16/2021-10:10:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:10:19] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:19] [V] [TRT] Tactic: 1002 Time: 5.4753 [12/16/2021-10:10:19] [V] [TRT] Tactic: 0 Time: 16.8745 [12/16/2021-10:10:19] [V] [TRT] Fastest Tactic: 1002 Time: 5.4753 [12/16/2021-10:10:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:19] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:20] [V] [TRT] Tactic: 1002 Time: 6.48273 [12/16/2021-10:10:20] [V] [TRT] Tactic: 0 Time: 5.35832 [12/16/2021-10:10:20] [V] [TRT] Fastest Tactic: 0 Time: 5.35832 [12/16/2021-10:10:20] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:20] [V] [TRT] Tactic: 1002 Time: 7.91973 [12/16/2021-10:10:20] [V] [TRT] Tactic: 0 Time: 4.28534 [12/16/2021-10:10:20] [V] [TRT] Fastest Tactic: 0 Time: 4.28534 [12/16/2021-10:10:20] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:20] [V] [TRT] Tactic: 1002 Time: 6.52488 [12/16/2021-10:10:20] [V] [TRT] Tactic: 0 Time: 19.3391 [12/16/2021-10:10:20] [V] [TRT] Fastest Tactic: 1002 Time: 6.52488 [12/16/2021-10:10:20] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:10:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:21] [V] [TRT] Tactic: 1002 Time: 4.97841 [12/16/2021-10:10:21] [V] [TRT] Tactic: 0 Time: 33.9887 [12/16/2021-10:10:21] [V] [TRT] Fastest Tactic: 1002 Time: 4.97841 [12/16/2021-10:10:21] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:21] [V] [TRT] Tactic: 1002 Time: 4.89839 [12/16/2021-10:10:22] [V] [TRT] Tactic: 0 Time: 18.9494 [12/16/2021-10:10:22] [V] [TRT] Fastest Tactic: 1002 Time: 4.89839 [12/16/2021-10:10:22] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:22] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:22] [V] [TRT] Tactic: 1002 Time: 6.80552 [12/16/2021-10:10:22] [V] [TRT] Tactic: 0 Time: 20.1212 [12/16/2021-10:10:22] [V] [TRT] Fastest Tactic: 1002 Time: 6.80552 [12/16/2021-10:10:22] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:22] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:22] [V] [TRT] Tactic: 1002 Time: 6.30937 [12/16/2021-10:10:22] [V] [TRT] Tactic: 0 Time: 17.6135 [12/16/2021-10:10:22] [V] [TRT] Fastest Tactic: 1002 Time: 6.30937 [12/16/2021-10:10:22] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:22] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:23] [V] [TRT] Tactic: 1002 Time: 4.95783 [12/16/2021-10:10:23] [V] [TRT] Tactic: 0 Time: 9.05928 [12/16/2021-10:10:23] [V] [TRT] Fastest Tactic: 1002 Time: 4.95783 [12/16/2021-10:10:23] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:23] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:23] [V] [TRT] Tactic: 1002 Time: 5.3072 [12/16/2021-10:10:23] [V] [TRT] Tactic: 0 Time: 17.2413 [12/16/2021-10:10:23] [V] [TRT] Fastest Tactic: 1002 Time: 5.3072 [12/16/2021-10:10:23] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:23] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:23] [V] [TRT] Tactic: 1002 Time: 6.81101 [12/16/2021-10:10:24] [V] [TRT] Tactic: 0 Time: 18.5019 [12/16/2021-10:10:24] [V] [TRT] Fastest Tactic: 1002 Time: 6.81101 [12/16/2021-10:10:24] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:24] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:24] [V] [TRT] Tactic: 1002 Time: 6.54272 [12/16/2021-10:10:24] [V] [TRT] Tactic: 0 Time: 4.57537 [12/16/2021-10:10:24] [V] [TRT] Fastest Tactic: 0 Time: 4.57537 [12/16/2021-10:10:24] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:24] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:24] [V] [TRT] Tactic: 1002 Time: 4.59997 [12/16/2021-10:10:24] [V] [TRT] Tactic: 0 Time: 8.68887 [12/16/2021-10:10:24] [V] [TRT] Fastest Tactic: 1002 Time: 4.59997 [12/16/2021-10:10:24] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:10:24] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:24] [V] [TRT] Tactic: 1002 Time: 4.5978 [12/16/2021-10:10:25] [V] [TRT] Tactic: 0 Time: 16.6972 [12/16/2021-10:10:25] [V] [TRT] Fastest Tactic: 1002 Time: 4.5978 [12/16/2021-10:10:25] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:25] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:25] [V] [TRT] Tactic: 1002 Time: 4.38538 [12/16/2021-10:10:25] [V] [TRT] Tactic: 0 Time: 4.24193 [12/16/2021-10:10:25] [V] [TRT] Fastest Tactic: 0 Time: 4.24193 [12/16/2021-10:10:25] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:25] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:25] [V] [TRT] Tactic: 1002 Time: 6.18373 [12/16/2021-10:10:25] [V] [TRT] Tactic: 0 Time: 3.69154 [12/16/2021-10:10:25] [V] [TRT] Fastest Tactic: 0 Time: 3.69154 [12/16/2021-10:10:25] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:25] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:25] [V] [TRT] Tactic: 1002 Time: 4.67015 [12/16/2021-10:10:25] [V] [TRT] Tactic: 0 Time: 9.20878 [12/16/2021-10:10:25] [V] [TRT] Fastest Tactic: 1002 Time: 4.67015 [12/16/2021-10:10:25] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:10:25] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:25] [V] [TRT] Tactic: 1002 Time: 4.67015 [12/16/2021-10:10:26] [V] [TRT] Tactic: 0 Time: 16.4943 [12/16/2021-10:10:26] [V] [TRT] Fastest Tactic: 1002 Time: 4.67015 [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:26] [V] [TRT] Tactic: 1002 Time: 9.32915 [12/16/2021-10:10:26] [V] [TRT] Tactic: 0 Time: 3.60483 [12/16/2021-10:10:26] [V] [TRT] Fastest Tactic: 0 Time: 3.60483 [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:10:26] [V] [TRT] --------------- Timing Runner: 014_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:10:26] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:26] [V] [TRT] --------------- Timing Runner: 014_convolutional (FusedConvActConvolution) [12/16/2021-10:10:26] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:26] [V] [TRT] --------------- Timing Runner: 014_convolutional (CudnnConvolution) [12/16/2021-10:10:26] [V] [TRT] Tactic: 0 Time: 14.1156 [12/16/2021-10:10:26] [V] [TRT] Tactic: 1 Time: 8.82291 [12/16/2021-10:10:26] [V] [TRT] Tactic: 2 skipped. Scratch requested: 20971520, available: 16777216 [12/16/2021-10:10:26] [V] [TRT] Tactic: 4 skipped. Scratch requested: 1150681088, available: 16777216 [12/16/2021-10:10:26] [V] [TRT] Tactic: 5 skipped. Scratch requested: 37879808, available: 16777216 [12/16/2021-10:10:26] [V] [TRT] Fastest Tactic: 1 Time: 8.82291 [12/16/2021-10:10:26] [V] [TRT] --------------- Timing Runner: 014_convolutional (CublasConvolution) [12/16/2021-10:10:26] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:26] [V] [TRT] --------------- Timing Runner: 014_convolutional (CaskConvolution) [12/16/2021-10:10:26] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:10:26] [V] [TRT] Tactic: 1062367460111450758 Time: 10.325 [12/16/2021-10:10:26] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_interior_nn_v0 Tactic: 1698681053543049347 [12/16/2021-10:10:27] [V] [TRT] Tactic: 1698681053543049347 Time: 9.62283 [12/16/2021-10:10:27] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:10:27] [V] [TRT] Tactic: 4501471010995462441 Time: 7.76724 [12/16/2021-10:10:27] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:10:27] [V] [TRT] Tactic: 5137655947464784826 Time: 7.65716 [12/16/2021-10:10:27] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:10:27] [V] [TRT] Tactic: 5288347012147084929 Time: 7.78754 [12/16/2021-10:10:27] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_interior_nn_v1 Tactic: 5326823351883942011 [12/16/2021-10:10:27] [V] [TRT] Tactic: 5326823351883942011 Time: 7.5079 [12/16/2021-10:10:27] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_interior_nn_v0 Tactic: 5500448035057547314 [12/16/2021-10:10:27] [V] [TRT] Tactic: 5500448035057547314 Time: 8.47887 [12/16/2021-10:10:27] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-10:10:28] [V] [TRT] Tactic: 6645123197870846056 Time: 7.81587 [12/16/2021-10:10:28] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-10:10:28] [V] [TRT] Tactic: 7144526460361122478 Time: 11.0046 [12/16/2021-10:10:28] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-10:10:28] [V] [TRT] Tactic: -8262349710178828730 Time: 7.92956 [12/16/2021-10:10:28] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_interior_nn_v1 Tactic: -6576203419454146580 [12/16/2021-10:10:28] [V] [TRT] Tactic: -6576203419454146580 Time: 9.28605 [12/16/2021-10:10:28] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-10:10:28] [V] [TRT] Tactic: -4787320710726427159 Time: 11.4293 [12/16/2021-10:10:28] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-10:10:29] [V] [TRT] Tactic: -3456450830548107839 Time: 9.72636 [12/16/2021-10:10:29] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-10:10:29] [V] [TRT] Tactic: -1218658103698133241 Time: 8.83986 [12/16/2021-10:10:29] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-10:10:29] [V] [TRT] Tactic: -836875257600482091 Time: 8.66376 [12/16/2021-10:10:29] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-10:10:29] [V] [TRT] Tactic: -410470605513481746 Time: 7.72702 [12/16/2021-10:10:29] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_interior_nn_v0 Tactic: -377491875521947884 [12/16/2021-10:10:29] [V] [TRT] Tactic: -377491875521947884 Time: 7.76257 [12/16/2021-10:10:29] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_interior_nn_v1 Tactic: -37215280111360163 [12/16/2021-10:10:29] [V] [TRT] Tactic: -37215280111360163 Time: 7.43305 [12/16/2021-10:10:29] [V] [TRT] Fastest Tactic: -37215280111360163 Time: 7.43305 [12/16/2021-10:10:29] [V] [TRT] Setting workspace to 37879808enables more tactics for profiling [12/16/2021-10:10:29] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -37215280111360163 [12/16/2021-10:10:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256) -> Float(524288,1,8192,128) *************** [12/16/2021-10:10:29] [V] [TRT] --------------- Timing Runner: 014_convolutional (CudnnConvolution) [12/16/2021-10:10:29] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:29] [V] [TRT] --------------- Timing Runner: 014_convolutional (CublasConvolution) [12/16/2021-10:10:29] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:29] [V] [TRT] --------------- Timing Runner: 014_convolutional (CaskConvolution) [12/16/2021-10:10:29] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_interior_nhwc_tn_v1 Tactic: 3886731678879822788 [12/16/2021-10:10:29] [V] [TRT] Tactic: 3886731678879822788 Time: 7.95225 [12/16/2021-10:10:29] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_interior_nhwc_tn_v1 Tactic: 6629944304117643200 [12/16/2021-10:10:30] [V] [TRT] Tactic: 6629944304117643200 Time: 15.6826 [12/16/2021-10:10:30] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -9153228964338181824 [12/16/2021-10:10:30] [V] [TRT] Tactic: -9153228964338181824 Time: 15.8749 [12/16/2021-10:10:30] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -7394439838318485025 [12/16/2021-10:10:30] [V] [TRT] Tactic: -7394439838318485025 Time: 7.94941 [12/16/2021-10:10:30] [V] [TRT] Fastest Tactic: -7394439838318485025 Time: 7.94941 [12/16/2021-10:10:30] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -7394439838318485025 [12/16/2021-10:10:30] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:30] [V] [TRT] --------------- Timing Runner: 014_convolutional (CudnnConvolution) [12/16/2021-10:10:30] [V] [TRT] Tactic: 0 Time: 13.9006 [12/16/2021-10:10:31] [V] [TRT] Tactic: 1 Time: 8.37367 [12/16/2021-10:10:31] [V] [TRT] Tactic: 2 Time: 14.5582 [12/16/2021-10:10:31] [V] [TRT] Tactic: 4 skipped. Scratch requested: 1150681088, available: 16777216 [12/16/2021-10:10:31] [V] [TRT] Tactic: 5 skipped. Scratch requested: 37879808, available: 16777216 [12/16/2021-10:10:31] [V] [TRT] Fastest Tactic: 1 Time: 8.37367 [12/16/2021-10:10:31] [V] [TRT] --------------- Timing Runner: 014_convolutional (CublasConvolution) [12/16/2021-10:10:31] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:31] [V] [TRT] --------------- Timing Runner: 014_convolutional (CaskConvolution) [12/16/2021-10:10:31] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:31] [V] [TRT] Setting workspace to 37879808enables more tactics for profiling [12/16/2021-10:10:31] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CudnnConvolution Tactic: 1 [12/16/2021-10:10:31] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:31] [V] [TRT] --------------- Timing Runner: 014_convolutional (CaskConvolution) [12/16/2021-10:10:31] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:31] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:10:31] [V] [TRT] --------------- Timing Runner: 014_convolutional (FusedConvActConvolution) [12/16/2021-10:10:31] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:31] [V] [TRT] --------------- Timing Runner: 014_convolutional (CudnnConvolution) [12/16/2021-10:10:31] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:31] [V] [TRT] --------------- Timing Runner: 014_convolutional (CublasConvolution) [12/16/2021-10:10:31] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:31] [V] [TRT] --------------- Timing Runner: 014_convolutional (CaskConvolution) [12/16/2021-10:10:31] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_interior_nn_v1 Tactic: 3066127711859985668 [12/16/2021-10:10:31] [V] [TRT] Tactic: 3066127711859985668 Time: 5.04307 [12/16/2021-10:10:31] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-10:10:31] [V] [TRT] Tactic: 3564772625446233998 Time: 5.47275 [12/16/2021-10:10:31] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-10:10:31] [V] [TRT] Tactic: 5319956359050645452 Time: 5.19839 [12/16/2021-10:10:31] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-10:10:31] [V] [TRT] Tactic: 7205456024582378848 Time: 4.03853 [12/16/2021-10:10:31] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_interior_nn_v1 Tactic: 8163473458334948789 [12/16/2021-10:10:31] [V] [TRT] Tactic: 8163473458334948789 Time: 3.89629 [12/16/2021-10:10:31] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-10:10:31] [V] [TRT] Tactic: -4212163711445252890 Time: 3.94631 [12/16/2021-10:10:31] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-10:10:32] [V] [TRT] Tactic: -3898373634979201110 Time: 3.99479 [12/16/2021-10:10:32] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-10:10:32] [V] [TRT] Tactic: -2409163523992614473 Time: 3.95679 [12/16/2021-10:10:32] [V] [TRT] 014_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_interior_nn_v1 Tactic: -1716393687483585322 [12/16/2021-10:10:32] [V] [TRT] Tactic: -1716393687483585322 Time: 3.88372 [12/16/2021-10:10:32] [V] [TRT] Fastest Tactic: -1716393687483585322 Time: 3.88372 [12/16/2021-10:10:32] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -1716393687483585322 [12/16/2021-10:10:32] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:10:32] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:32] [V] [TRT] Tactic: 1002 Time: 2.74246 [12/16/2021-10:10:32] [V] [TRT] Tactic: 0 Time: 4.75015 [12/16/2021-10:10:32] [V] [TRT] Fastest Tactic: 1002 Time: 2.74246 [12/16/2021-10:10:32] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:10:32] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:32] [V] [TRT] Tactic: 1002 Time: 2.7499 [12/16/2021-10:10:32] [V] [TRT] Tactic: 0 Time: 8.42105 [12/16/2021-10:10:32] [V] [TRT] Fastest Tactic: 1002 Time: 2.7499 [12/16/2021-10:10:32] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:32] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:32] [V] [TRT] Tactic: 1002 Time: 3.24634 [12/16/2021-10:10:32] [V] [TRT] Tactic: 0 Time: 2.68458 [12/16/2021-10:10:32] [V] [TRT] Fastest Tactic: 0 Time: 2.68458 [12/16/2021-10:10:32] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:10:32] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:32] [V] [TRT] Tactic: 1002 Time: 3.96018 [12/16/2021-10:10:32] [V] [TRT] Tactic: 0 Time: 2.14658 [12/16/2021-10:10:32] [V] [TRT] Fastest Tactic: 0 Time: 2.14658 [12/16/2021-10:10:32] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:10:32] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:32] [V] [TRT] Tactic: 1002 Time: 3.21772 [12/16/2021-10:10:33] [V] [TRT] Tactic: 0 Time: 9.48982 [12/16/2021-10:10:33] [V] [TRT] Fastest Tactic: 1002 Time: 3.21772 [12/16/2021-10:10:33] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:10:33] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:33] [V] [TRT] Tactic: 1002 Time: 2.48895 [12/16/2021-10:10:33] [V] [TRT] Tactic: 0 Time: 16.978 [12/16/2021-10:10:33] [V] [TRT] Fastest Tactic: 1002 Time: 2.48895 [12/16/2021-10:10:33] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:33] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:33] [V] [TRT] Tactic: 1002 Time: 2.45358 [12/16/2021-10:10:33] [V] [TRT] Tactic: 0 Time: 9.49008 [12/16/2021-10:10:33] [V] [TRT] Fastest Tactic: 1002 Time: 2.45358 [12/16/2021-10:10:33] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:10:33] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:33] [V] [TRT] Tactic: 1002 Time: 3.37539 [12/16/2021-10:10:33] [V] [TRT] Tactic: 0 Time: 9.89398 [12/16/2021-10:10:33] [V] [TRT] Fastest Tactic: 1002 Time: 3.37539 [12/16/2021-10:10:33] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:10:33] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:33] [V] [TRT] Tactic: 1002 Time: 3.1609 [12/16/2021-10:10:34] [V] [TRT] Tactic: 0 Time: 8.80734 [12/16/2021-10:10:34] [V] [TRT] Fastest Tactic: 1002 Time: 3.1609 [12/16/2021-10:10:34] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:10:34] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:34] [V] [TRT] Tactic: 1002 Time: 2.48352 [12/16/2021-10:10:34] [V] [TRT] Tactic: 0 Time: 4.53611 [12/16/2021-10:10:34] [V] [TRT] Fastest Tactic: 1002 Time: 2.48352 [12/16/2021-10:10:34] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:34] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:34] [V] [TRT] Tactic: 1002 Time: 2.6593 [12/16/2021-10:10:34] [V] [TRT] Tactic: 0 Time: 8.629 [12/16/2021-10:10:34] [V] [TRT] Fastest Tactic: 1002 Time: 2.6593 [12/16/2021-10:10:34] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:10:34] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:34] [V] [TRT] Tactic: 1002 Time: 3.38281 [12/16/2021-10:10:34] [V] [TRT] Tactic: 0 Time: 9.25891 [12/16/2021-10:10:34] [V] [TRT] Fastest Tactic: 1002 Time: 3.38281 [12/16/2021-10:10:34] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:10:34] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:34] [V] [TRT] Tactic: 1002 Time: 3.27821 [12/16/2021-10:10:34] [V] [TRT] Tactic: 0 Time: 2.29183 [12/16/2021-10:10:34] [V] [TRT] Fastest Tactic: 0 Time: 2.29183 [12/16/2021-10:10:34] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:10:34] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:34] [V] [TRT] Tactic: 1002 Time: 2.3093 [12/16/2021-10:10:34] [V] [TRT] Tactic: 0 Time: 4.22126 [12/16/2021-10:10:34] [V] [TRT] Fastest Tactic: 1002 Time: 2.3093 [12/16/2021-10:10:34] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:10:34] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:35] [V] [TRT] Tactic: 1002 Time: 2.30706 [12/16/2021-10:10:35] [V] [TRT] Tactic: 0 Time: 8.35311 [12/16/2021-10:10:35] [V] [TRT] Fastest Tactic: 1002 Time: 2.30706 [12/16/2021-10:10:35] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:10:35] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:35] [V] [TRT] Tactic: 1002 Time: 2.21415 [12/16/2021-10:10:35] [V] [TRT] Tactic: 0 Time: 2.12452 [12/16/2021-10:10:35] [V] [TRT] Fastest Tactic: 0 Time: 2.12452 [12/16/2021-10:10:35] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:10:35] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:35] [V] [TRT] Tactic: 1002 Time: 3.09845 [12/16/2021-10:10:35] [V] [TRT] Tactic: 0 Time: 1.85018 [12/16/2021-10:10:35] [V] [TRT] Fastest Tactic: 0 Time: 1.85018 [12/16/2021-10:10:35] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:10:35] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:35] [V] [TRT] Tactic: 1002 Time: 2.34328 [12/16/2021-10:10:35] [V] [TRT] Tactic: 0 Time: 4.60374 [12/16/2021-10:10:35] [V] [TRT] Fastest Tactic: 1002 Time: 2.34328 [12/16/2021-10:10:35] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:10:35] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:35] [V] [TRT] Tactic: 1002 Time: 2.34264 [12/16/2021-10:10:35] [V] [TRT] Tactic: 0 Time: 8.23089 [12/16/2021-10:10:35] [V] [TRT] Fastest Tactic: 1002 Time: 2.34264 [12/16/2021-10:10:35] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:35] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:10:35] [V] [TRT] Tactic: 1002 Time: 4.68979 [12/16/2021-10:10:35] [V] [TRT] Tactic: 0 Time: 1.8082 [12/16/2021-10:10:35] [V] [TRT] Fastest Tactic: 0 Time: 1.8082 [12/16/2021-10:10:35] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:10:35] [V] [TRT] --------------- Timing Runner: 014_convolutional_lrelu (PointWiseV2) [12/16/2021-10:10:35] [V] [TRT] Tactic: 0 Time: 2.2149 [12/16/2021-10:10:35] [V] [TRT] Tactic: 1 Time: 1.58045 [12/16/2021-10:10:35] [V] [TRT] Tactic: 2 Time: 1.3788 [12/16/2021-10:10:36] [V] [TRT] Tactic: 3 Time: 1.34361 [12/16/2021-10:10:36] [V] [TRT] Tactic: 4 Time: 1.08496 [12/16/2021-10:10:36] [V] [TRT] Tactic: 5 Time: 1.04061 [12/16/2021-10:10:36] [V] [TRT] Tactic: 6 Time: 1.32531 [12/16/2021-10:10:36] [V] [TRT] Tactic: 7 Time: 1.06034 [12/16/2021-10:10:36] [V] [TRT] Tactic: 8 Time: 1.06258 [12/16/2021-10:10:36] [V] [TRT] Tactic: 9 Time: 1.05488 [12/16/2021-10:10:36] [V] [TRT] Tactic: 28 Time: 2.16667 [12/16/2021-10:10:36] [V] [TRT] Fastest Tactic: 5 Time: 1.04061 [12/16/2021-10:10:36] [V] [TRT] --------------- Timing Runner: 014_convolutional_lrelu (PointWise) [12/16/2021-10:10:36] [V] [TRT] Tactic: 128 Time: 5.1726 [12/16/2021-10:10:36] [V] [TRT] Tactic: 256 Time: 5.18951 [12/16/2021-10:10:36] [V] [TRT] Tactic: 512 Time: 5.22588 [12/16/2021-10:10:36] [V] [TRT] Fastest Tactic: 128 Time: 5.1726 [12/16/2021-10:10:36] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:10:36] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(524288,1,8192,128) *************** [12/16/2021-10:10:36] [V] [TRT] --------------- Timing Runner: 014_convolutional_lrelu (PointWiseV2) [12/16/2021-10:10:36] [V] [TRT] Tactic: 0 Time: 2.21626 [12/16/2021-10:10:36] [V] [TRT] Tactic: 1 Time: 1.57932 [12/16/2021-10:10:36] [V] [TRT] Tactic: 2 Time: 1.37859 [12/16/2021-10:10:36] [V] [TRT] Tactic: 3 Time: 1.33859 [12/16/2021-10:10:36] [V] [TRT] Tactic: 4 Time: 1.08449 [12/16/2021-10:10:36] [V] [TRT] Tactic: 5 Time: 1.03781 [12/16/2021-10:10:36] [V] [TRT] Tactic: 6 Time: 1.32887 [12/16/2021-10:10:36] [V] [TRT] Tactic: 7 Time: 1.06057 [12/16/2021-10:10:37] [V] [TRT] Tactic: 8 Time: 1.05949 [12/16/2021-10:10:37] [V] [TRT] Tactic: 9 Time: 1.05277 [12/16/2021-10:10:37] [V] [TRT] Tactic: 28 Time: 2.16673 [12/16/2021-10:10:37] [V] [TRT] Fastest Tactic: 5 Time: 1.03781 [12/16/2021-10:10:37] [V] [TRT] --------------- Timing Runner: 014_convolutional_lrelu (PointWise) [12/16/2021-10:10:37] [V] [TRT] Tactic: 128 Time: 5.1738 [12/16/2021-10:10:37] [V] [TRT] Tactic: 256 Time: 5.18979 [12/16/2021-10:10:37] [V] [TRT] Tactic: 512 Time: 5.22322 [12/16/2021-10:10:37] [V] [TRT] Fastest Tactic: 128 Time: 5.1738 [12/16/2021-10:10:37] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:10:37] [V] [TRT] *************** Autotuning format combination: Float(16384,4096:32,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:10:37] [V] [TRT] --------------- Timing Runner: 014_convolutional_lrelu (PointWiseV2) [12/16/2021-10:10:37] [V] [TRT] Tactic: 24 Time: 1.37543 [12/16/2021-10:10:37] [V] [TRT] Tactic: 25 Time: 1.30512 [12/16/2021-10:10:37] [V] [TRT] Tactic: 26 Time: 1.31428 [12/16/2021-10:10:37] [V] [TRT] Tactic: 27 Time: 1.28945 [12/16/2021-10:10:37] [V] [TRT] Tactic: 31 Time: 1.38796 [12/16/2021-10:10:37] [V] [TRT] Fastest Tactic: 27 Time: 1.28945 [12/16/2021-10:10:37] [V] [TRT] --------------- Timing Runner: 014_convolutional_lrelu (PointWise) [12/16/2021-10:10:37] [V] [TRT] Tactic: 128 Time: 5.17172 [12/16/2021-10:10:37] [V] [TRT] Tactic: 256 Time: 5.18767 [12/16/2021-10:10:37] [V] [TRT] Tactic: 512 Time: 5.22414 [12/16/2021-10:10:37] [V] [TRT] Fastest Tactic: 128 Time: 5.17172 [12/16/2021-10:10:37] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 27 [12/16/2021-10:10:37] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:37] [V] [TRT] --------------- Timing Runner: 014_convolutional_lrelu (PointWiseV2) [12/16/2021-10:10:38] [V] [TRT] Tactic: 0 Time: 2.20554 [12/16/2021-10:10:38] [V] [TRT] Tactic: 1 Time: 1.5442 [12/16/2021-10:10:38] [V] [TRT] Tactic: 2 Time: 1.48714 [12/16/2021-10:10:38] [V] [TRT] Tactic: 3 Time: 1.20904 [12/16/2021-10:10:38] [V] [TRT] Tactic: 4 Time: 0.95112 [12/16/2021-10:10:38] [V] [TRT] Tactic: 5 Time: 0.996517 [12/16/2021-10:10:38] [V] [TRT] Tactic: 6 Time: 1.10016 [12/16/2021-10:10:38] [V] [TRT] Tactic: 7 Time: 0.785163 [12/16/2021-10:10:38] [V] [TRT] Tactic: 8 Time: 0.698594 [12/16/2021-10:10:38] [V] [TRT] Tactic: 9 Time: 0.790612 [12/16/2021-10:10:38] [V] [TRT] Tactic: 28 Time: 2.14637 [12/16/2021-10:10:38] [V] [TRT] Fastest Tactic: 8 Time: 0.698594 [12/16/2021-10:10:38] [V] [TRT] --------------- Timing Runner: 014_convolutional_lrelu (PointWise) [12/16/2021-10:10:38] [V] [TRT] Tactic: 128 Time: 4.66352 [12/16/2021-10:10:38] [V] [TRT] Tactic: 256 Time: 4.63177 [12/16/2021-10:10:38] [V] [TRT] Tactic: 512 Time: 4.51197 [12/16/2021-10:10:38] [V] [TRT] Fastest Tactic: 512 Time: 4.51197 [12/16/2021-10:10:38] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:10:38] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:10:38] [V] [TRT] --------------- Timing Runner: 014_convolutional_lrelu (PointWiseV2) [12/16/2021-10:10:38] [V] [TRT] Tactic: 0 Time: 1.41744 [12/16/2021-10:10:38] [V] [TRT] Tactic: 1 Time: 1.13835 [12/16/2021-10:10:38] [V] [TRT] Tactic: 2 Time: 1.23947 [12/16/2021-10:10:38] [V] [TRT] Tactic: 3 Time: 1.00251 [12/16/2021-10:10:38] [V] [TRT] Tactic: 4 Time: 1.04768 [12/16/2021-10:10:38] [V] [TRT] Tactic: 5 Time: 1.1218 [12/16/2021-10:10:38] [V] [TRT] Tactic: 6 Time: 0.96957 [12/16/2021-10:10:38] [V] [TRT] Tactic: 7 Time: 0.998444 [12/16/2021-10:10:38] [V] [TRT] Tactic: 8 Time: 1.05287 [12/16/2021-10:10:38] [V] [TRT] Tactic: 9 Time: 1.17224 [12/16/2021-10:10:39] [V] [TRT] Tactic: 10 Time: 2.40181 [12/16/2021-10:10:39] [V] [TRT] Tactic: 11 Time: 1.65099 [12/16/2021-10:10:39] [V] [TRT] Tactic: 12 Time: 1.59415 [12/16/2021-10:10:39] [V] [TRT] Tactic: 13 Time: 1.22986 [12/16/2021-10:10:39] [V] [TRT] Tactic: 14 Time: 1.01115 [12/16/2021-10:10:39] [V] [TRT] Tactic: 15 Time: 1.08027 [12/16/2021-10:10:39] [V] [TRT] Tactic: 16 Time: 1.12356 [12/16/2021-10:10:39] [V] [TRT] Tactic: 17 Time: 0.791549 [12/16/2021-10:10:39] [V] [TRT] Tactic: 18 Time: 0.748138 [12/16/2021-10:10:39] [V] [TRT] Tactic: 19 Time: 0.887162 [12/16/2021-10:10:39] [V] [TRT] Tactic: 28 Time: 1.38486 [12/16/2021-10:10:39] [V] [TRT] Tactic: 29 Time: 2.32678 [12/16/2021-10:10:39] [V] [TRT] Fastest Tactic: 18 Time: 0.748138 [12/16/2021-10:10:39] [V] [TRT] --------------- Timing Runner: 014_convolutional_lrelu (PointWise) [12/16/2021-10:10:39] [V] [TRT] Tactic: 128 Time: 4.66251 [12/16/2021-10:10:39] [V] [TRT] Tactic: 256 Time: 4.62908 [12/16/2021-10:10:39] [V] [TRT] Tactic: 512 Time: 4.5127 [12/16/2021-10:10:39] [V] [TRT] Fastest Tactic: 512 Time: 4.5127 [12/16/2021-10:10:39] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:10:39] [V] [TRT] --------------- Timing Runner: 015_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:10:39] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:39] [V] [TRT] --------------- Timing Runner: 015_convolutional (FusedConvActConvolution) [12/16/2021-10:10:39] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:10:39] [V] [TRT] --------------- Timing Runner: 015_convolutional (CudnnConvolution) [12/16/2021-10:10:41] [V] [TRT] Tactic: 0 Time: 111.448 [12/16/2021-10:10:42] [V] [TRT] Tactic: 1 Time: 63.8341 [12/16/2021-10:10:42] [V] [TRT] Tactic: 2 skipped. Scratch requested: 94371840, available: 16777216 [12/16/2021-10:10:42] [V] [TRT] Tactic: 5 skipped. Scratch requested: 159318016, available: 16777216 [12/16/2021-10:10:43] [V] [TRT] Tactic: 6 Time: 40.3661 [12/16/2021-10:10:43] [V] [TRT] Fastest Tactic: 6 Time: 40.3661 [12/16/2021-10:10:43] [V] [TRT] --------------- Timing Runner: 015_convolutional (CaskConvolution) [12/16/2021-10:10:43] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:10:44] [V] [TRT] Tactic: 1062367460111450758 Time: 76.7793 [12/16/2021-10:10:44] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v0 Tactic: 1754984623894446479 [12/16/2021-10:10:45] [V] [TRT] Tactic: 1754984623894446479 Time: 85.3029 [12/16/2021-10:10:45] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v0 Tactic: 3611739942397549984 [12/16/2021-10:10:46] [V] [TRT] Tactic: 3611739942397549984 Time: 63.1209 [12/16/2021-10:10:46] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148n_nt_v1 Tactic: 3827454225649558724 [12/16/2021-10:10:47] [V] [TRT] Tactic: 3827454225649558724 Time: 58.6668 [12/16/2021-10:10:47] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v1 Tactic: 4337000649858996379 [12/16/2021-10:10:48] [V] [TRT] Tactic: 4337000649858996379 Time: 63.3565 [12/16/2021-10:10:48] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:10:50] [V] [TRT] Tactic: 4501471010995462441 Time: 62.8905 [12/16/2021-10:10:50] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:10:51] [V] [TRT] Tactic: 5137655947464784826 Time: 61.0763 [12/16/2021-10:10:51] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:10:52] [V] [TRT] Tactic: 5288347012147084929 Time: 62.1987 [12/16/2021-10:10:52] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148m_nt_v1 Tactic: 5921334924264294896 [12/16/2021-10:10:52] [V] [TRT] Tactic: 5921334924264294896 Time: 41.3227 [12/16/2021-10:10:52] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-10:10:53] [V] [TRT] Tactic: 6645123197870846056 Time: 62.5026 [12/16/2021-10:10:53] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-10:10:55] [V] [TRT] Tactic: 7144526460361122478 Time: 79.3492 [12/16/2021-10:10:55] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v1 Tactic: 7852627285308570038 [12/16/2021-10:10:56] [V] [TRT] Tactic: 7852627285308570038 Time: 59.6014 [12/16/2021-10:10:56] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v1 Tactic: -9137461792520977713 [12/16/2021-10:10:57] [V] [TRT] Tactic: -9137461792520977713 Time: 63.4447 [12/16/2021-10:10:57] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148n_nt_v0 Tactic: -8776506421218919509 [12/16/2021-10:10:58] [V] [TRT] Tactic: -8776506421218919509 Time: 58.6119 [12/16/2021-10:10:58] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-10:10:59] [V] [TRT] Tactic: -8262349710178828730 Time: 63.4903 [12/16/2021-10:10:59] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v0 Tactic: -8133971918129952780 [12/16/2021-10:11:00] [V] [TRT] Tactic: -8133971918129952780 Time: 69.3815 [12/16/2021-10:11:00] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v1 Tactic: -6092040395344634144 [12/16/2021-10:11:01] [V] [TRT] Tactic: -6092040395344634144 Time: 79.6598 [12/16/2021-10:11:01] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-10:11:03] [V] [TRT] Tactic: -4787320710726427159 Time: 85.6013 [12/16/2021-10:11:03] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-10:11:04] [V] [TRT] Tactic: -3456450830548107839 Time: 70.9811 [12/16/2021-10:11:04] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_relu_tile148t_nt_v0 Tactic: -2318106587342035239 [12/16/2021-10:11:05] [V] [TRT] Tactic: -2318106587342035239 Time: 59.4293 [12/16/2021-10:11:05] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_winograd_128x128_ldg1_ldg4_mobile_relu_tile148t_nt_v0 Tactic: -1343271414618805657 [12/16/2021-10:11:05] [V] [TRT] Tactic: -1343271414618805657 Time: 37.7586 [12/16/2021-10:11:05] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-10:11:06] [V] [TRT] Tactic: -1218658103698133241 Time: 68.8535 [12/16/2021-10:11:06] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-10:11:08] [V] [TRT] Tactic: -836875257600482091 Time: 66.1008 [12/16/2021-10:11:08] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-10:11:09] [V] [TRT] Tactic: -410470605513481746 Time: 61.3991 [12/16/2021-10:11:09] [V] [TRT] Fastest Tactic: -1343271414618805657 Time: 37.7586 [12/16/2021-10:11:09] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -1343271414618805657 [12/16/2021-10:11:09] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:09] [V] [TRT] --------------- Timing Runner: 015_convolutional (CudnnConvolution) [12/16/2021-10:11:09] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:09] [V] [TRT] --------------- Timing Runner: 015_convolutional (CaskConvolution) [12/16/2021-10:11:09] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -9153228964338181824 [12/16/2021-10:11:10] [V] [TRT] Tactic: -9153228964338181824 Time: 82.1119 [12/16/2021-10:11:10] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -7394439838318485025 [12/16/2021-10:11:11] [V] [TRT] Tactic: -7394439838318485025 Time: 60.7183 [12/16/2021-10:11:11] [V] [TRT] Fastest Tactic: -7394439838318485025 Time: 60.7183 [12/16/2021-10:11:11] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -7394439838318485025 [12/16/2021-10:11:11] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:11] [V] [TRT] --------------- Timing Runner: 015_convolutional (CudnnConvolution) [12/16/2021-10:11:13] [V] [TRT] Tactic: 0 Time: 111.455 [12/16/2021-10:11:14] [V] [TRT] Tactic: 1 Time: 62.7431 [12/16/2021-10:11:14] [V] [TRT] Tactic: 2 skipped. Scratch requested: 47185920, available: 16777216 [12/16/2021-10:11:14] [V] [TRT] Tactic: 5 skipped. Scratch requested: 159318016, available: 16777216 [12/16/2021-10:11:14] [V] [TRT] Tactic: 6 skipped. Scratch requested: 35916288, available: 16777216 [12/16/2021-10:11:14] [V] [TRT] Fastest Tactic: 1 Time: 62.7431 [12/16/2021-10:11:14] [V] [TRT] --------------- Timing Runner: 015_convolutional (CaskConvolution) [12/16/2021-10:11:14] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:14] [V] [TRT] Setting workspace to 35916288enables more tactics for profiling [12/16/2021-10:11:14] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CudnnConvolution Tactic: 1 [12/16/2021-10:11:14] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:14] [V] [TRT] --------------- Timing Runner: 015_convolutional (FusedConvActConvolution) [12/16/2021-10:11:14] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:14] [V] [TRT] --------------- Timing Runner: 015_convolutional (CudnnConvolution) [12/16/2021-10:11:14] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:14] [V] [TRT] --------------- Timing Runner: 015_convolutional (CaskConvolution) [12/16/2021-10:11:14] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-10:11:14] [V] [TRT] Tactic: 3564772625446233998 Time: 38.6624 [12/16/2021-10:11:14] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_large_nn_v1 Tactic: 3650389455493082349 [12/16/2021-10:11:15] [V] [TRT] Tactic: 3650389455493082349 Time: 40.3887 [12/16/2021-10:11:15] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_winograd_fp16x2_128x128_ldg1_ldg4_relu_tile148m_nt_v1 Tactic: 4772821744921268633 [12/16/2021-10:11:15] [V] [TRT] Tactic: 4772821744921268633 Time: 21.6105 [12/16/2021-10:11:15] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-10:11:16] [V] [TRT] Tactic: 5319956359050645452 Time: 35.5623 [12/16/2021-10:11:16] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-10:11:17] [V] [TRT] Tactic: 7205456024582378848 Time: 31.5688 [12/16/2021-10:11:17] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_large_nn_v1 Tactic: -6490690591794140522 [12/16/2021-10:11:17] [V] [TRT] Tactic: -6490690591794140522 Time: 31.9013 [12/16/2021-10:11:17] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_large_nn_v1 Tactic: -4686027666808657977 [12/16/2021-10:11:18] [V] [TRT] Tactic: -4686027666808657977 Time: 32.0024 [12/16/2021-10:11:18] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-10:11:18] [V] [TRT] Tactic: -4212163711445252890 Time: 30.7508 [12/16/2021-10:11:18] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-10:11:19] [V] [TRT] Tactic: -3898373634979201110 Time: 31.756 [12/16/2021-10:11:19] [V] [TRT] 015_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-10:11:19] [V] [TRT] Tactic: -2409163523992614473 Time: 30.861 [12/16/2021-10:11:19] [V] [TRT] Fastest Tactic: 4772821744921268633 Time: 21.6105 [12/16/2021-10:11:19] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 4772821744921268633 [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1), Float(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:19] [V] [TRT] --------------- Timing Runner: PWN(015_convolutional_lrelu, 016_shortcut) (PointWiseV2) [12/16/2021-10:11:19] [V] [TRT] Tactic: 0 Time: 4.76204 [12/16/2021-10:11:19] [V] [TRT] Tactic: 1 Time: 3.74575 [12/16/2021-10:11:20] [V] [TRT] Tactic: 2 Time: 3.23078 [12/16/2021-10:11:20] [V] [TRT] Tactic: 3 Time: 3.48913 [12/16/2021-10:11:20] [V] [TRT] Tactic: 4 Time: 3.10499 [12/16/2021-10:11:20] [V] [TRT] Tactic: 5 Time: 3.13143 [12/16/2021-10:11:20] [V] [TRT] Tactic: 6 Time: 3.48568 [12/16/2021-10:11:20] [V] [TRT] Tactic: 7 Time: 3.16363 [12/16/2021-10:11:20] [V] [TRT] Tactic: 8 Time: 3.1213 [12/16/2021-10:11:20] [V] [TRT] Tactic: 9 Time: 3.14932 [12/16/2021-10:11:20] [V] [TRT] Tactic: 28 Time: 4.68551 [12/16/2021-10:11:20] [V] [TRT] Fastest Tactic: 4 Time: 3.10499 [12/16/2021-10:11:20] [V] [TRT] --------------- Timing Runner: PWN(015_convolutional_lrelu, 016_shortcut) (PointWise) [12/16/2021-10:11:21] [V] [TRT] Tactic: 128 Time: 13.6542 [12/16/2021-10:11:21] [V] [TRT] Tactic: 256 Time: 13.7146 [12/16/2021-10:11:21] [V] [TRT] Tactic: 512 Time: 13.7772 [12/16/2021-10:11:21] [V] [TRT] Fastest Tactic: 128 Time: 13.6542 [12/16/2021-10:11:21] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 4 [12/16/2021-10:11:21] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256), Float(1048576,1,16384,256) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:21] [V] [TRT] --------------- Timing Runner: PWN(015_convolutional_lrelu, 016_shortcut) (PointWiseV2) [12/16/2021-10:11:21] [V] [TRT] Tactic: 0 Time: 4.76461 [12/16/2021-10:11:21] [V] [TRT] Tactic: 1 Time: 3.75115 [12/16/2021-10:11:22] [V] [TRT] Tactic: 2 Time: 3.23575 [12/16/2021-10:11:22] [V] [TRT] Tactic: 3 Time: 3.48691 [12/16/2021-10:11:22] [V] [TRT] Tactic: 4 Time: 3.1025 [12/16/2021-10:11:22] [V] [TRT] Tactic: 5 Time: 3.13437 [12/16/2021-10:11:22] [V] [TRT] Tactic: 6 Time: 3.48426 [12/16/2021-10:11:22] [V] [TRT] Tactic: 7 Time: 3.16022 [12/16/2021-10:11:22] [V] [TRT] Tactic: 8 Time: 3.12314 [12/16/2021-10:11:22] [V] [TRT] Tactic: 9 Time: 3.14742 [12/16/2021-10:11:22] [V] [TRT] Tactic: 28 Time: 4.68628 [12/16/2021-10:11:22] [V] [TRT] Fastest Tactic: 4 Time: 3.1025 [12/16/2021-10:11:22] [V] [TRT] --------------- Timing Runner: PWN(015_convolutional_lrelu, 016_shortcut) (PointWise) [12/16/2021-10:11:23] [V] [TRT] Tactic: 128 Time: 13.6532 [12/16/2021-10:11:23] [V] [TRT] Tactic: 256 Time: 13.7166 [12/16/2021-10:11:23] [V] [TRT] Tactic: 512 Time: 13.7781 [12/16/2021-10:11:23] [V] [TRT] Fastest Tactic: 128 Time: 13.6532 [12/16/2021-10:11:23] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 4 [12/16/2021-10:11:23] [V] [TRT] *************** Autotuning format combination: Float(32768,4096:32,64,1), Float(32768,4096:32,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:23] [V] [TRT] --------------- Timing Runner: PWN(015_convolutional_lrelu, 016_shortcut) (PointWiseV2) [12/16/2021-10:11:23] [V] [TRT] Tactic: 24 Time: 4.76847 [12/16/2021-10:11:23] [V] [TRT] Tactic: 25 Time: 4.06668 [12/16/2021-10:11:24] [V] [TRT] Tactic: 26 Time: 4.15804 [12/16/2021-10:11:24] [V] [TRT] Tactic: 27 Time: 4.07115 [12/16/2021-10:11:24] [V] [TRT] Tactic: 31 Time: 4.7731 [12/16/2021-10:11:24] [V] [TRT] Fastest Tactic: 25 Time: 4.06668 [12/16/2021-10:11:24] [V] [TRT] --------------- Timing Runner: PWN(015_convolutional_lrelu, 016_shortcut) (PointWise) [12/16/2021-10:11:24] [V] [TRT] Tactic: 128 Time: 13.6481 [12/16/2021-10:11:24] [V] [TRT] Tactic: 256 Time: 13.7112 [12/16/2021-10:11:25] [V] [TRT] Tactic: 512 Time: 13.7661 [12/16/2021-10:11:25] [V] [TRT] Fastest Tactic: 128 Time: 13.6481 [12/16/2021-10:11:25] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 25 [12/16/2021-10:11:25] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1), Half(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:25] [V] [TRT] --------------- Timing Runner: PWN(015_convolutional_lrelu, 016_shortcut) (PointWiseV2) [12/16/2021-10:11:25] [V] [TRT] Tactic: 0 Time: 4.75949 [12/16/2021-10:11:25] [V] [TRT] Tactic: 1 Time: 3.65249 [12/16/2021-10:11:25] [V] [TRT] Tactic: 2 Time: 3.30121 [12/16/2021-10:11:25] [V] [TRT] Tactic: 3 Time: 2.80826 [12/16/2021-10:11:25] [V] [TRT] Tactic: 4 Time: 2.48775 [12/16/2021-10:11:25] [V] [TRT] Tactic: 5 Time: 2.33091 [12/16/2021-10:11:25] [V] [TRT] Tactic: 6 Time: 2.70819 [12/16/2021-10:11:25] [V] [TRT] Tactic: 7 Time: 2.13747 [12/16/2021-10:11:25] [V] [TRT] Tactic: 8 Time: 1.97566 [12/16/2021-10:11:25] [V] [TRT] Tactic: 9 Time: 2.12234 [12/16/2021-10:11:25] [V] [TRT] Tactic: 28 Time: 4.66568 [12/16/2021-10:11:25] [V] [TRT] Fastest Tactic: 8 Time: 1.97566 [12/16/2021-10:11:25] [V] [TRT] --------------- Timing Runner: PWN(015_convolutional_lrelu, 016_shortcut) (PointWise) [12/16/2021-10:11:26] [V] [TRT] Tactic: 128 Time: 12.8908 [12/16/2021-10:11:26] [V] [TRT] Tactic: 256 Time: 12.8578 [12/16/2021-10:11:26] [V] [TRT] Tactic: 512 Time: 12.5616 [12/16/2021-10:11:26] [V] [TRT] Fastest Tactic: 512 Time: 12.5616 [12/16/2021-10:11:26] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:11:26] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1), Half(524288,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:26] [V] [TRT] --------------- Timing Runner: PWN(015_convolutional_lrelu, 016_shortcut) (PointWiseV2) [12/16/2021-10:11:26] [V] [TRT] Tactic: 0 Time: 3.65667 [12/16/2021-10:11:26] [V] [TRT] Tactic: 1 Time: 3.18814 [12/16/2021-10:11:26] [V] [TRT] Tactic: 2 Time: 3.44621 [12/16/2021-10:11:26] [V] [TRT] Tactic: 3 Time: 3.05505 [12/16/2021-10:11:27] [V] [TRT] Tactic: 4 Time: 3.17913 [12/16/2021-10:11:27] [V] [TRT] Tactic: 5 Time: 3.42837 [12/16/2021-10:11:27] [V] [TRT] Tactic: 6 Time: 3.01116 [12/16/2021-10:11:27] [V] [TRT] Tactic: 7 Time: 3.10282 [12/16/2021-10:11:27] [V] [TRT] Tactic: 8 Time: 3.38172 [12/16/2021-10:11:27] [V] [TRT] Tactic: 9 Time: 4.18005 [12/16/2021-10:11:27] [V] [TRT] Tactic: 10 Time: 5.27504 [12/16/2021-10:11:27] [V] [TRT] Tactic: 11 Time: 4.07066 [12/16/2021-10:11:27] [V] [TRT] Tactic: 12 Time: 3.59462 [12/16/2021-10:11:27] [V] [TRT] Tactic: 13 Time: 3.01361 [12/16/2021-10:11:27] [V] [TRT] Tactic: 14 Time: 3.00477 [12/16/2021-10:11:27] [V] [TRT] Tactic: 15 Time: 2.89376 [12/16/2021-10:11:28] [V] [TRT] Tactic: 16 Time: 2.77702 [12/16/2021-10:11:28] [V] [TRT] Tactic: 17 Time: 2.51281 [12/16/2021-10:11:28] [V] [TRT] Tactic: 18 Time: 2.4919 [12/16/2021-10:11:28] [V] [TRT] Tactic: 19 Time: 2.58076 [12/16/2021-10:11:28] [V] [TRT] Tactic: 28 Time: 3.69173 [12/16/2021-10:11:28] [V] [TRT] Tactic: 29 Time: 5.14895 [12/16/2021-10:11:28] [V] [TRT] Fastest Tactic: 18 Time: 2.4919 [12/16/2021-10:11:28] [V] [TRT] --------------- Timing Runner: PWN(015_convolutional_lrelu, 016_shortcut) (PointWise) [12/16/2021-10:11:28] [V] [TRT] Tactic: 128 Time: 12.8909 [12/16/2021-10:11:28] [V] [TRT] Tactic: 256 Time: 12.8503 [12/16/2021-10:11:29] [V] [TRT] Tactic: 512 Time: 12.561 [12/16/2021-10:11:29] [V] [TRT] Fastest Tactic: 512 Time: 12.561 [12/16/2021-10:11:29] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] --------------- Timing Runner: 017_convolutional (CaskConvolution) [12/16/2021-10:11:29] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(16384,4096:32,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1), Float(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256), Float(1048576,1,16384,256) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(32768,4096:32,64,1), Float(32768,4096:32,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1), Half(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1), Half(524288,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] --------------- Timing Runner: 020_convolutional (CaskConvolution) [12/16/2021-10:11:29] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(16384,4096:32,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1), Float(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256), Float(1048576,1,16384,256) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(32768,4096:32,64,1), Float(32768,4096:32,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1), Half(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1), Half(524288,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] --------------- Timing Runner: 023_convolutional (CaskConvolution) [12/16/2021-10:11:29] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(16384,4096:32,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1), Float(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256), Float(1048576,1,16384,256) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(32768,4096:32,64,1), Float(32768,4096:32,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1), Half(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1), Half(524288,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] --------------- Timing Runner: 026_convolutional (CaskConvolution) [12/16/2021-10:11:29] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(16384,4096:32,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1), Float(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256), Float(1048576,1,16384,256) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(32768,4096:32,64,1), Float(32768,4096:32,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1), Half(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1), Half(524288,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] --------------- Timing Runner: 029_convolutional (CaskConvolution) [12/16/2021-10:11:29] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(16384,4096:32,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1), Float(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256), Float(1048576,1,16384,256) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(32768,4096:32,64,1), Float(32768,4096:32,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1), Half(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1), Half(524288,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] --------------- Timing Runner: 032_convolutional (CaskConvolution) [12/16/2021-10:11:29] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(16384,4096:32,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1), Float(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256), Float(1048576,1,16384,256) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(32768,4096:32,64,1), Float(32768,4096:32,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1), Half(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1), Half(524288,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] --------------- Timing Runner: 035_convolutional (CaskConvolution) [12/16/2021-10:11:29] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(16384,4096:32,64,1) -> Float(16384,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,8192,128) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(16384,4096:32,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096,64,1) -> Half(262144,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Float(524288,1,8192,128) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(262144,4096:2,64,1) -> Half(524288,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(524288,1,8192,128) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(524288,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Half(262144,4096:2,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(1048576,1,16384,256) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Float(32768,4096:32,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(1048576,4096,64,1) -> Half(524288,4096:2,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(1048576,1,16384,256) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Float(32768,4096:32,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning Reformat:Half(524288,4096:2,64,1) -> Half(1048576,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] *************** Autotuning format combination: Float(1048576,4096,64,1), Float(1048576,4096,64,1) -> Float(1572864,4096,64,1) *************** [12/16/2021-10:11:29] [V] [TRT] --------------- Timing Runner: PWN(036_convolutional_lrelu, 037_shortcut) (PointWiseV2) [12/16/2021-10:11:30] [V] [TRT] Tactic: 0 Time: 6.43953 [12/16/2021-10:11:31] [V] [TRT] Tactic: 1 Time: 4.488 [12/16/2021-10:11:32] [V] [TRT] Tactic: 2 Time: 3.98521 [12/16/2021-10:11:33] [V] [TRT] Tactic: 3 Time: 3.57003 [12/16/2021-10:11:34] [V] [TRT] Tactic: 4 Time: 3.09838 [12/16/2021-10:11:34] [V] [TRT] Tactic: 5 Time: 3.11579 [12/16/2021-10:11:35] [V] [TRT] Tactic: 6 Time: 3.50724 [12/16/2021-10:11:36] [V] [TRT] Tactic: 7 Time: 3.15702 [12/16/2021-10:11:37] [V] [TRT] Tactic: 8 Time: 3.12158 [12/16/2021-10:11:38] [V] [TRT] Tactic: 9 Time: 3.14598 [12/16/2021-10:11:39] [V] [TRT] Tactic: 28 Time: 6.25855 [12/16/2021-10:11:39] [V] [TRT] Fastest Tactic: 4 Time: 3.09838 [12/16/2021-10:11:39] [V] [TRT] --------------- Timing Runner: PWN(036_convolutional_lrelu, 037_shortcut) (PointWise) [12/16/2021-10:11:40] [V] [TRT] Tactic: 128 Time: 16.2513 [12/16/2021-10:11:40] [V] [TRT] Tactic: 256 Time: 16.3047 [12/16/2021-10:11:40] [V] [TRT] Tactic: 512 Time: 16.3598 [12/16/2021-10:11:40] [V] [TRT] Fastest Tactic: 128 Time: 16.2513 [12/16/2021-10:11:40] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 4 [12/16/2021-10:11:40] [V] [TRT] *************** Autotuning format combination: Float(1048576,1,16384,256), Float(1048576,1,16384,256) -> Float(1572864,1,24576,384) *************** [12/16/2021-10:11:40] [V] [TRT] --------------- Timing Runner: PWN(036_convolutional_lrelu, 037_shortcut) (PointWiseV2) [12/16/2021-10:11:40] [V] [TRT] Tactic: 0 Time: 6.4398 [12/16/2021-10:11:40] [V] [TRT] Tactic: 1 Time: 4.49435 [12/16/2021-10:11:41] [V] [TRT] Tactic: 2 Time: 3.98595 [12/16/2021-10:11:41] [V] [TRT] Tactic: 3 Time: 3.59716 [12/16/2021-10:11:41] [V] [TRT] Tactic: 4 Time: 3.12144 [12/16/2021-10:11:41] [V] [TRT] Tactic: 5 Time: 3.16065 [12/16/2021-10:11:41] [V] [TRT] Tactic: 6 Time: 3.52952 [12/16/2021-10:11:41] [V] [TRT] Tactic: 7 Time: 3.20914 [12/16/2021-10:11:41] [V] [TRT] Tactic: 8 Time: 3.15916 [12/16/2021-10:11:41] [V] [TRT] Tactic: 9 Time: 3.1841 [12/16/2021-10:11:41] [V] [TRT] Tactic: 28 Time: 6.25661 [12/16/2021-10:11:41] [V] [TRT] Fastest Tactic: 4 Time: 3.12144 [12/16/2021-10:11:41] [V] [TRT] --------------- Timing Runner: PWN(036_convolutional_lrelu, 037_shortcut) (PointWise) [12/16/2021-10:11:42] [V] [TRT] Tactic: 128 Time: 16.2496 [12/16/2021-10:11:42] [V] [TRT] Tactic: 256 Time: 16.3034 [12/16/2021-10:11:42] [V] [TRT] Tactic: 512 Time: 16.3574 [12/16/2021-10:11:42] [V] [TRT] Fastest Tactic: 128 Time: 16.2496 [12/16/2021-10:11:42] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 4 [12/16/2021-10:11:42] [V] [TRT] *************** Autotuning format combination: Float(32768,4096:32,64,1), Float(32768,4096:32,64,1) -> Float(49152,4096:32,64,1) *************** [12/16/2021-10:11:42] [V] [TRT] --------------- Timing Runner: PWN(036_convolutional_lrelu, 037_shortcut) (PointWiseV2) [12/16/2021-10:11:43] [V] [TRT] Tactic: 24 Time: 4.77539 [12/16/2021-10:11:44] [V] [TRT] Tactic: 25 Time: 4.07449 [12/16/2021-10:11:45] [V] [TRT] Tactic: 26 Time: 4.14786 [12/16/2021-10:11:46] [V] [TRT] Tactic: 27 Time: 4.06232 [12/16/2021-10:11:47] [V] [TRT] Tactic: 31 Time: 4.77818 [12/16/2021-10:11:47] [V] [TRT] Fastest Tactic: 27 Time: 4.06232 [12/16/2021-10:11:47] [V] [TRT] --------------- Timing Runner: PWN(036_convolutional_lrelu, 037_shortcut) (PointWise) [12/16/2021-10:11:48] [V] [TRT] Tactic: 128 Time: 16.2562 [12/16/2021-10:11:48] [V] [TRT] Tactic: 256 Time: 16.3138 [12/16/2021-10:11:48] [V] [TRT] Tactic: 512 Time: 16.3651 [12/16/2021-10:11:48] [V] [TRT] Fastest Tactic: 128 Time: 16.2562 [12/16/2021-10:11:48] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 27 [12/16/2021-10:11:48] [V] [TRT] *************** Autotuning format combination: Half(1048576,4096,64,1), Half(1048576,4096,64,1) -> Half(1572864,4096,64,1) *************** [12/16/2021-10:11:48] [V] [TRT] --------------- Timing Runner: PWN(036_convolutional_lrelu, 037_shortcut) (PointWiseV2) [12/16/2021-10:11:49] [V] [TRT] Tactic: 0 Time: 6.45806 [12/16/2021-10:11:50] [V] [TRT] Tactic: 1 Time: 4.64625 [12/16/2021-10:11:51] [V] [TRT] Tactic: 2 Time: 4.09283 [12/16/2021-10:11:52] [V] [TRT] Tactic: 3 Time: 3.13051 [12/16/2021-10:11:53] [V] [TRT] Tactic: 4 Time: 2.93725 [12/16/2021-10:11:54] [V] [TRT] Tactic: 5 Time: 2.72187 [12/16/2021-10:11:55] [V] [TRT] Tactic: 6 Time: 2.79324 [12/16/2021-10:11:55] [V] [TRT] Tactic: 7 Time: 2.21502 [12/16/2021-10:11:56] [V] [TRT] Tactic: 8 Time: 2.0837 [12/16/2021-10:11:57] [V] [TRT] Tactic: 9 Time: 2.1341 [12/16/2021-10:11:58] [V] [TRT] Tactic: 28 Time: 6.31194 [12/16/2021-10:11:58] [V] [TRT] Fastest Tactic: 8 Time: 2.0837 [12/16/2021-10:11:58] [V] [TRT] --------------- Timing Runner: PWN(036_convolutional_lrelu, 037_shortcut) (PointWise) [12/16/2021-10:11:59] [V] [TRT] Tactic: 128 Time: 13.8534 [12/16/2021-10:11:59] [V] [TRT] Tactic: 256 Time: 13.6994 [12/16/2021-10:11:59] [V] [TRT] Tactic: 512 Time: 13.2909 [12/16/2021-10:11:59] [V] [TRT] Fastest Tactic: 512 Time: 13.2909 [12/16/2021-10:11:59] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:11:59] [V] [TRT] *************** Autotuning format combination: Half(524288,4096:2,64,1), Half(524288,4096:2,64,1) -> Half(786432,4096:2,64,1) *************** [12/16/2021-10:11:59] [V] [TRT] --------------- Timing Runner: PWN(036_convolutional_lrelu, 037_shortcut) (PointWiseV2) [12/16/2021-10:12:00] [V] [TRT] Tactic: 0 Time: 4.17229 [12/16/2021-10:12:01] [V] [TRT] Tactic: 1 Time: 3.31188 [12/16/2021-10:12:02] [V] [TRT] Tactic: 2 Time: 3.35487 [12/16/2021-10:12:03] [V] [TRT] Tactic: 3 Time: 3.07775 [12/16/2021-10:12:04] [V] [TRT] Tactic: 4 Time: 3.12708 [12/16/2021-10:12:05] [V] [TRT] Tactic: 5 Time: 3.45949 [12/16/2021-10:12:06] [V] [TRT] Tactic: 6 Time: 3.03331 [12/16/2021-10:12:06] [V] [TRT] Tactic: 7 Time: 3.11196 [12/16/2021-10:12:07] [V] [TRT] Tactic: 8 Time: 3.42833 [12/16/2021-10:12:08] [V] [TRT] Tactic: 9 Time: 4.18137 [12/16/2021-10:12:09] [V] [TRT] Tactic: 10 Time: 6.98901 [12/16/2021-10:12:10] [V] [TRT] Tactic: 11 Time: 5.04469 [12/16/2021-10:12:11] [V] [TRT] Tactic: 12 Time: 4.49605 [12/16/2021-10:12:12] [V] [TRT] Tactic: 13 Time: 3.42025 [12/16/2021-10:12:13] [V] [TRT] Tactic: 14 Time: 3.53488 [12/16/2021-10:12:14] [V] [TRT] Tactic: 15 Time: 3.33247 [12/16/2021-10:12:15] [V] [TRT] Tactic: 16 Time: 2.86189 [12/16/2021-10:12:16] [V] [TRT] Tactic: 17 Time: 2.71351 [12/16/2021-10:12:17] [V] [TRT] Tactic: 18 Time: 2.76926 [12/16/2021-10:12:18] [V] [TRT] Tactic: 19 Time: 2.78237 [12/16/2021-10:12:19] [V] [TRT] Tactic: 28 Time: 4.08366 [12/16/2021-10:12:20] [V] [TRT] Tactic: 29 Time: 6.76736 [12/16/2021-10:12:20] [V] [TRT] Fastest Tactic: 17 Time: 2.71351 [12/16/2021-10:12:20] [V] [TRT] --------------- Timing Runner: PWN(036_convolutional_lrelu, 037_shortcut) (PointWise) [12/16/2021-10:12:20] [V] [TRT] Tactic: 128 Time: 13.8439 [12/16/2021-10:12:20] [V] [TRT] Tactic: 256 Time: 13.6954 [12/16/2021-10:12:20] [V] [TRT] Tactic: 512 Time: 13.2815 [12/16/2021-10:12:20] [V] [TRT] Fastest Tactic: 512 Time: 13.2815 [12/16/2021-10:12:20] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 17 [12/16/2021-10:12:20] [V] [TRT] *************** Autotuning Reformat:Float(1572864,4096,64,1) -> Float(1572864,1,24576,384) *************** [12/16/2021-10:12:20] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:21] [V] [TRT] Tactic: 1002 Time: 5.46804 [12/16/2021-10:12:21] [V] [TRT] Tactic: 0 Time: 9.88234 [12/16/2021-10:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 5.46804 [12/16/2021-10:12:21] [V] [TRT] *************** Autotuning Reformat:Float(1572864,4096,64,1) -> Float(49152,4096:32,64,1) *************** [12/16/2021-10:12:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:21] [V] [TRT] Tactic: 1002 Time: 5.47693 [12/16/2021-10:12:21] [V] [TRT] Tactic: 0 Time: 16.8584 [12/16/2021-10:12:21] [V] [TRT] Fastest Tactic: 1002 Time: 5.47693 [12/16/2021-10:12:21] [V] [TRT] *************** Autotuning Reformat:Float(1572864,4096,64,1) -> Half(1572864,4096,64,1) *************** [12/16/2021-10:12:21] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:21] [V] [TRT] Tactic: 1002 Time: 7.68454 [12/16/2021-10:12:22] [V] [TRT] Tactic: 0 Time: 7.22605 [12/16/2021-10:12:22] [V] [TRT] Fastest Tactic: 0 Time: 7.22605 [12/16/2021-10:12:22] [V] [TRT] *************** Autotuning Reformat:Float(1572864,4096,64,1) -> Half(786432,4096:2,64,1) *************** [12/16/2021-10:12:22] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:22] [V] [TRT] Tactic: 1002 Time: 7.92047 [12/16/2021-10:12:22] [V] [TRT] Tactic: 0 Time: 4.28318 [12/16/2021-10:12:22] [V] [TRT] Fastest Tactic: 0 Time: 4.28318 [12/16/2021-10:12:22] [V] [TRT] *************** Autotuning Reformat:Float(1572864,1,24576,384) -> Float(1572864,4096,64,1) *************** [12/16/2021-10:12:22] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:22] [V] [TRT] Tactic: 1002 Time: 6.50416 [12/16/2021-10:12:22] [V] [TRT] Tactic: 0 Time: 19.3591 [12/16/2021-10:12:22] [V] [TRT] Fastest Tactic: 1002 Time: 6.50416 [12/16/2021-10:12:22] [V] [TRT] *************** Autotuning Reformat:Float(1572864,1,24576,384) -> Float(49152,4096:32,64,1) *************** [12/16/2021-10:12:22] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:22] [V] [TRT] Tactic: 1002 Time: 4.98044 [12/16/2021-10:12:23] [V] [TRT] Tactic: 0 Time: 34.0627 [12/16/2021-10:12:23] [V] [TRT] Fastest Tactic: 1002 Time: 4.98044 [12/16/2021-10:12:23] [V] [TRT] *************** Autotuning Reformat:Float(1572864,1,24576,384) -> Half(1572864,4096,64,1) *************** [12/16/2021-10:12:23] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:23] [V] [TRT] Tactic: 1002 Time: 4.89657 [12/16/2021-10:12:23] [V] [TRT] Tactic: 0 Time: 18.9228 [12/16/2021-10:12:23] [V] [TRT] Fastest Tactic: 1002 Time: 4.89657 [12/16/2021-10:12:23] [V] [TRT] *************** Autotuning Reformat:Float(1572864,1,24576,384) -> Half(786432,4096:2,64,1) *************** [12/16/2021-10:12:23] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:24] [V] [TRT] Tactic: 1002 Time: 6.79943 [12/16/2021-10:12:24] [V] [TRT] Tactic: 0 Time: 20.0729 [12/16/2021-10:12:24] [V] [TRT] Fastest Tactic: 1002 Time: 6.79943 [12/16/2021-10:12:24] [V] [TRT] *************** Autotuning Reformat:Float(49152,4096:32,64,1) -> Float(1572864,4096,64,1) *************** [12/16/2021-10:12:24] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:24] [V] [TRT] Tactic: 1002 Time: 6.29587 [12/16/2021-10:12:24] [V] [TRT] Tactic: 0 Time: 17.5981 [12/16/2021-10:12:24] [V] [TRT] Fastest Tactic: 1002 Time: 6.29587 [12/16/2021-10:12:24] [V] [TRT] *************** Autotuning Reformat:Float(49152,4096:32,64,1) -> Float(1572864,1,24576,384) *************** [12/16/2021-10:12:24] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:24] [V] [TRT] Tactic: 1002 Time: 4.95665 [12/16/2021-10:12:25] [V] [TRT] Tactic: 0 Time: 9.05758 [12/16/2021-10:12:25] [V] [TRT] Fastest Tactic: 1002 Time: 4.95665 [12/16/2021-10:12:25] [V] [TRT] *************** Autotuning Reformat:Float(49152,4096:32,64,1) -> Half(1572864,4096,64,1) *************** [12/16/2021-10:12:25] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:25] [V] [TRT] Tactic: 1002 Time: 5.30366 [12/16/2021-10:12:25] [V] [TRT] Tactic: 0 Time: 17.2515 [12/16/2021-10:12:25] [V] [TRT] Fastest Tactic: 1002 Time: 5.30366 [12/16/2021-10:12:25] [V] [TRT] *************** Autotuning Reformat:Float(49152,4096:32,64,1) -> Half(786432,4096:2,64,1) *************** [12/16/2021-10:12:25] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:25] [V] [TRT] Tactic: 1002 Time: 6.80546 [12/16/2021-10:12:26] [V] [TRT] Tactic: 0 Time: 18.513 [12/16/2021-10:12:26] [V] [TRT] Fastest Tactic: 1002 Time: 6.80546 [12/16/2021-10:12:26] [V] [TRT] *************** Autotuning Reformat:Half(1572864,4096,64,1) -> Float(1572864,4096,64,1) *************** [12/16/2021-10:12:26] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:26] [V] [TRT] Tactic: 1002 Time: 7.75575 [12/16/2021-10:12:26] [V] [TRT] Tactic: 0 Time: 7.27256 [12/16/2021-10:12:26] [V] [TRT] Fastest Tactic: 0 Time: 7.27256 [12/16/2021-10:12:26] [V] [TRT] *************** Autotuning Reformat:Half(1572864,4096,64,1) -> Float(1572864,1,24576,384) *************** [12/16/2021-10:12:26] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:26] [V] [TRT] Tactic: 1002 Time: 4.59903 [12/16/2021-10:12:26] [V] [TRT] Tactic: 0 Time: 8.7935 [12/16/2021-10:12:26] [V] [TRT] Fastest Tactic: 1002 Time: 4.59903 [12/16/2021-10:12:26] [V] [TRT] *************** Autotuning Reformat:Half(1572864,4096,64,1) -> Float(49152,4096:32,64,1) *************** [12/16/2021-10:12:26] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:26] [V] [TRT] Tactic: 1002 Time: 4.59773 [12/16/2021-10:12:27] [V] [TRT] Tactic: 0 Time: 16.676 [12/16/2021-10:12:27] [V] [TRT] Fastest Tactic: 1002 Time: 4.59773 [12/16/2021-10:12:27] [V] [TRT] *************** Autotuning Reformat:Half(1572864,4096,64,1) -> Half(786432,4096:2,64,1) *************** [12/16/2021-10:12:27] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:27] [V] [TRT] Tactic: 1002 Time: 4.38124 [12/16/2021-10:12:27] [V] [TRT] Tactic: 0 Time: 4.23859 [12/16/2021-10:12:27] [V] [TRT] Fastest Tactic: 0 Time: 4.23859 [12/16/2021-10:12:27] [V] [TRT] *************** Autotuning Reformat:Half(786432,4096:2,64,1) -> Float(1572864,4096,64,1) *************** [12/16/2021-10:12:27] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:27] [V] [TRT] Tactic: 1002 Time: 6.17869 [12/16/2021-10:12:27] [V] [TRT] Tactic: 0 Time: 8.53158 [12/16/2021-10:12:27] [V] [TRT] Fastest Tactic: 1002 Time: 6.17869 [12/16/2021-10:12:27] [V] [TRT] *************** Autotuning Reformat:Half(786432,4096:2,64,1) -> Float(1572864,1,24576,384) *************** [12/16/2021-10:12:27] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:27] [V] [TRT] Tactic: 1002 Time: 4.66576 [12/16/2021-10:12:27] [V] [TRT] Tactic: 0 Time: 9.19667 [12/16/2021-10:12:27] [V] [TRT] Fastest Tactic: 1002 Time: 4.66576 [12/16/2021-10:12:27] [V] [TRT] *************** Autotuning Reformat:Half(786432,4096:2,64,1) -> Float(49152,4096:32,64,1) *************** [12/16/2021-10:12:27] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:27] [V] [TRT] Tactic: 1002 Time: 4.66821 [12/16/2021-10:12:28] [V] [TRT] Tactic: 0 Time: 16.4856 [12/16/2021-10:12:28] [V] [TRT] Fastest Tactic: 1002 Time: 4.66821 [12/16/2021-10:12:28] [V] [TRT] *************** Autotuning Reformat:Half(786432,4096:2,64,1) -> Half(1572864,4096,64,1) *************** [12/16/2021-10:12:28] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:28] [V] [TRT] Tactic: 1002 Time: 9.41436 [12/16/2021-10:12:28] [V] [TRT] Tactic: 0 Time: 7.70452 [12/16/2021-10:12:28] [V] [TRT] Fastest Tactic: 0 Time: 7.70452 [12/16/2021-10:12:28] [V] [TRT] *************** Autotuning Reformat:Float(1572864,4096,64,1) -> Float(1572864,1,24576,384) *************** [12/16/2021-10:12:28] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:28] [V] [TRT] Tactic: 1002 Time: 5.45398 [12/16/2021-10:12:28] [V] [TRT] Tactic: 0 Time: 9.79688 [12/16/2021-10:12:28] [V] [TRT] Fastest Tactic: 1002 Time: 5.45398 [12/16/2021-10:12:28] [V] [TRT] *************** Autotuning Reformat:Float(1572864,4096,64,1) -> Half(1572864,4096,64,1) *************** [12/16/2021-10:12:28] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:29] [V] [TRT] Tactic: 1002 Time: 7.68199 [12/16/2021-10:12:29] [V] [TRT] Tactic: 0 Time: 7.22436 [12/16/2021-10:12:29] [V] [TRT] Fastest Tactic: 0 Time: 7.22436 [12/16/2021-10:12:29] [V] [TRT] *************** Autotuning Reformat:Float(1572864,4096,64,1) -> Half(786432,4096:2,64,1) *************** [12/16/2021-10:12:29] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:29] [V] [TRT] Tactic: 1002 Time: 7.91671 [12/16/2021-10:12:29] [V] [TRT] Tactic: 0 Time: 8.6419 [12/16/2021-10:12:29] [V] [TRT] Fastest Tactic: 1002 Time: 7.91671 [12/16/2021-10:12:29] [V] [TRT] *************** Autotuning Reformat:Float(1572864,1,24576,384) -> Float(1572864,4096,64,1) *************** [12/16/2021-10:12:29] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:29] [V] [TRT] Tactic: 1002 Time: 6.49334 [12/16/2021-10:12:30] [V] [TRT] Tactic: 0 Time: 20.435 [12/16/2021-10:12:30] [V] [TRT] Fastest Tactic: 1002 Time: 6.49334 [12/16/2021-10:12:30] [V] [TRT] *************** Autotuning Reformat:Float(1572864,1,24576,384) -> Half(1572864,4096,64,1) *************** [12/16/2021-10:12:30] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:30] [V] [TRT] Tactic: 1002 Time: 4.89679 [12/16/2021-10:12:30] [V] [TRT] Tactic: 0 Time: 20.0039 [12/16/2021-10:12:30] [V] [TRT] Fastest Tactic: 1002 Time: 4.89679 [12/16/2021-10:12:30] [V] [TRT] *************** Autotuning Reformat:Float(1572864,1,24576,384) -> Half(786432,4096:2,64,1) *************** [12/16/2021-10:12:30] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:30] [V] [TRT] Tactic: 1002 Time: 6.80882 [12/16/2021-10:12:30] [V] [TRT] Tactic: 0 Time: 21.1523 [12/16/2021-10:12:30] [V] [TRT] Fastest Tactic: 1002 Time: 6.80882 [12/16/2021-10:12:30] [V] [TRT] *************** Autotuning Reformat:Float(49152,4096:32,64,1) -> Float(1572864,4096,64,1) *************** [12/16/2021-10:12:30] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:31] [V] [TRT] Tactic: 1002 Time: 6.32853 [12/16/2021-10:12:31] [V] [TRT] Tactic: 0 Time: 17.5986 [12/16/2021-10:12:31] [V] [TRT] Fastest Tactic: 1002 Time: 6.32853 [12/16/2021-10:12:31] [V] [TRT] *************** Autotuning Reformat:Float(49152,4096:32,64,1) -> Float(1572864,1,24576,384) *************** [12/16/2021-10:12:31] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:31] [V] [TRT] Tactic: 1002 Time: 4.9599 [12/16/2021-10:12:31] [V] [TRT] Tactic: 0 Time: 9.05762 [12/16/2021-10:12:31] [V] [TRT] Fastest Tactic: 1002 Time: 4.9599 [12/16/2021-10:12:31] [V] [TRT] *************** Autotuning Reformat:Float(49152,4096:32,64,1) -> Half(1572864,4096,64,1) *************** [12/16/2021-10:12:31] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:31] [V] [TRT] Tactic: 1002 Time: 5.30439 [12/16/2021-10:12:32] [V] [TRT] Tactic: 0 Time: 17.2544 [12/16/2021-10:12:32] [V] [TRT] Fastest Tactic: 1002 Time: 5.30439 [12/16/2021-10:12:32] [V] [TRT] *************** Autotuning Reformat:Float(49152,4096:32,64,1) -> Half(786432,4096:2,64,1) *************** [12/16/2021-10:12:32] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:32] [V] [TRT] Tactic: 1002 Time: 6.80388 [12/16/2021-10:12:32] [V] [TRT] Tactic: 0 Time: 18.5006 [12/16/2021-10:12:32] [V] [TRT] Fastest Tactic: 1002 Time: 6.80388 [12/16/2021-10:12:32] [V] [TRT] *************** Autotuning Reformat:Half(1572864,4096,64,1) -> Float(1572864,4096,64,1) *************** [12/16/2021-10:12:32] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:32] [V] [TRT] Tactic: 1002 Time: 7.75965 [12/16/2021-10:12:32] [V] [TRT] Tactic: 0 Time: 4.57331 [12/16/2021-10:12:32] [V] [TRT] Fastest Tactic: 0 Time: 4.57331 [12/16/2021-10:12:32] [V] [TRT] *************** Autotuning Reformat:Half(1572864,4096,64,1) -> Float(1572864,1,24576,384) *************** [12/16/2021-10:12:32] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:33] [V] [TRT] Tactic: 1002 Time: 4.60046 [12/16/2021-10:12:33] [V] [TRT] Tactic: 0 Time: 8.69804 [12/16/2021-10:12:33] [V] [TRT] Fastest Tactic: 1002 Time: 4.60046 [12/16/2021-10:12:33] [V] [TRT] *************** Autotuning Reformat:Half(1572864,4096,64,1) -> Half(786432,4096:2,64,1) *************** [12/16/2021-10:12:33] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:33] [V] [TRT] Tactic: 1002 Time: 4.39268 [12/16/2021-10:12:33] [V] [TRT] Tactic: 0 Time: 4.24057 [12/16/2021-10:12:33] [V] [TRT] Fastest Tactic: 0 Time: 4.24057 [12/16/2021-10:12:33] [V] [TRT] *************** Autotuning Reformat:Half(786432,4096:2,64,1) -> Float(1572864,4096,64,1) *************** [12/16/2021-10:12:33] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:33] [V] [TRT] Tactic: 1002 Time: 6.18322 [12/16/2021-10:12:33] [V] [TRT] Tactic: 0 Time: 3.69069 [12/16/2021-10:12:33] [V] [TRT] Fastest Tactic: 0 Time: 3.69069 [12/16/2021-10:12:33] [V] [TRT] *************** Autotuning Reformat:Half(786432,4096:2,64,1) -> Float(1572864,1,24576,384) *************** [12/16/2021-10:12:33] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:33] [V] [TRT] Tactic: 1002 Time: 4.6702 [12/16/2021-10:12:33] [V] [TRT] Tactic: 0 Time: 9.2093 [12/16/2021-10:12:33] [V] [TRT] Fastest Tactic: 1002 Time: 4.6702 [12/16/2021-10:12:33] [V] [TRT] *************** Autotuning Reformat:Half(786432,4096:2,64,1) -> Half(1572864,4096,64,1) *************** [12/16/2021-10:12:33] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:12:34] [V] [TRT] Tactic: 1002 Time: 9.42054 [12/16/2021-10:12:34] [V] [TRT] Tactic: 0 Time: 7.70758 [12/16/2021-10:12:34] [V] [TRT] Fastest Tactic: 0 Time: 7.70758 [12/16/2021-10:12:34] [V] [TRT] *************** Autotuning format combination: Float(1572864,4096,64,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:12:34] [V] [TRT] --------------- Timing Runner: 038_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:12:34] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:12:34] [V] [TRT] --------------- Timing Runner: 038_convolutional (FusedConvActConvolution) [12/16/2021-10:12:34] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:12:34] [V] [TRT] --------------- Timing Runner: 038_convolutional (CudnnConvolution) [12/16/2021-10:12:34] [V] [TRT] Tactic: 0 skipped. Scratch requested: 32448000, available: 16777216 [12/16/2021-10:12:34] [V] [TRT] Tactic: 1 skipped. Scratch requested: 69295616, available: 16777216 [12/16/2021-10:12:34] [V] [TRT] Tactic: 2 skipped. Scratch requested: 79633920, available: 16777216 [12/16/2021-10:12:34] [V] [TRT] Tactic: 5 skipped. Scratch requested: 657929216, available: 16777216 [12/16/2021-10:12:34] [V] [TRT] Fastest Tactic: -3360065831133338131 Time: 3.40282e+38 [12/16/2021-10:12:34] [V] [TRT] --------------- Timing Runner: 038_convolutional (CaskConvolution) [12/16/2021-10:12:34] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:12:35] [V] [TRT] Tactic: 1062367460111450758 Time: 77.5851 [12/16/2021-10:12:35] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v0 Tactic: 1754984623894446479 [12/16/2021-10:12:36] [V] [TRT] Tactic: 1754984623894446479 Time: 86.9394 [12/16/2021-10:12:36] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v0 Tactic: 3611739942397549984 [12/16/2021-10:12:37] [V] [TRT] Tactic: 3611739942397549984 Time: 62.6665 [12/16/2021-10:12:37] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v1 Tactic: 4337000649858996379 [12/16/2021-10:12:39] [V] [TRT] Tactic: 4337000649858996379 Time: 63.065 [12/16/2021-10:12:39] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:12:40] [V] [TRT] Tactic: 4501471010995462441 Time: 62.0499 [12/16/2021-10:12:40] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:12:41] [V] [TRT] Tactic: 5137655947464784826 Time: 60.5769 [12/16/2021-10:12:41] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:12:42] [V] [TRT] Tactic: 5288347012147084929 Time: 61.4278 [12/16/2021-10:12:42] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v1 Tactic: 6645123197870846056 [12/16/2021-10:12:43] [V] [TRT] Tactic: 6645123197870846056 Time: 62.1446 [12/16/2021-10:12:43] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v0 Tactic: 7144526460361122478 [12/16/2021-10:12:44] [V] [TRT] Tactic: 7144526460361122478 Time: 79.8421 [12/16/2021-10:12:44] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_large_nn_v1 Tactic: -9137461792520977713 [12/16/2021-10:12:45] [V] [TRT] Tactic: -9137461792520977713 Time: 62.5642 [12/16/2021-10:12:45] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v0 Tactic: -8262349710178828730 [12/16/2021-10:12:46] [V] [TRT] Tactic: -8262349710178828730 Time: 62.5871 [12/16/2021-10:12:46] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_large_nn_v0 Tactic: -8133971918129952780 [12/16/2021-10:12:47] [V] [TRT] Tactic: -8133971918129952780 Time: 68.3546 [12/16/2021-10:12:47] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_large_nn_v1 Tactic: -6092040395344634144 [12/16/2021-10:12:49] [V] [TRT] Tactic: -6092040395344634144 Time: 80.5613 [12/16/2021-10:12:49] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v0 Tactic: -4787320710726427159 [12/16/2021-10:12:50] [V] [TRT] Tactic: -4787320710726427159 Time: 87.0866 [12/16/2021-10:12:50] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_small_nn_v1 Tactic: -3456450830548107839 [12/16/2021-10:12:51] [V] [TRT] Tactic: -3456450830548107839 Time: 70.4029 [12/16/2021-10:12:51] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_medium_nn_v0 Tactic: -1218658103698133241 [12/16/2021-10:12:52] [V] [TRT] Tactic: -1218658103698133241 Time: 68.7966 [12/16/2021-10:12:52] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v0 Tactic: -836875257600482091 [12/16/2021-10:12:53] [V] [TRT] Tactic: -836875257600482091 Time: 65.3905 [12/16/2021-10:12:53] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v1 Tactic: -410470605513481746 [12/16/2021-10:12:54] [V] [TRT] Tactic: -410470605513481746 Time: 60.4505 [12/16/2021-10:12:54] [V] [TRT] Fastest Tactic: -410470605513481746 Time: 60.4505 [12/16/2021-10:12:54] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -410470605513481746 [12/16/2021-10:12:54] [V] [TRT] *************** Autotuning format combination: Float(1572864,1,24576,384) -> Float(524288,1,16384,512) *************** [12/16/2021-10:12:54] [V] [TRT] --------------- Timing Runner: 038_convolutional (CudnnConvolution) [12/16/2021-10:12:54] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:12:54] [V] [TRT] --------------- Timing Runner: 038_convolutional (CaskConvolution) [12/16/2021-10:12:55] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x32_sliced1x4_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -9153228964338181824 [12/16/2021-10:12:56] [V] [TRT] Tactic: -9153228964338181824 Time: 90.0264 [12/16/2021-10:12:56] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_scudnn_128x64_sliced1x2_ldg4_relu_exp_small_nhwc_tn_v1 Tactic: -7394439838318485025 [12/16/2021-10:12:57] [V] [TRT] Tactic: -7394439838318485025 Time: 61.3674 [12/16/2021-10:12:57] [V] [TRT] Fastest Tactic: -7394439838318485025 Time: 61.3674 [12/16/2021-10:12:57] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -7394439838318485025 [12/16/2021-10:12:57] [V] [TRT] *************** Autotuning format combination: Half(1572864,4096,64,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:12:57] [V] [TRT] --------------- Timing Runner: 038_convolutional (CudnnConvolution) [12/16/2021-10:12:59] [V] [TRT] Tactic: 0 Time: 116.48 [12/16/2021-10:12:59] [V] [TRT] Tactic: 1 skipped. Scratch requested: 34651648, available: 16777216 [12/16/2021-10:12:59] [V] [TRT] Tactic: 2 skipped. Scratch requested: 39817216, available: 16777216 [12/16/2021-10:12:59] [V] [TRT] Tactic: 5 skipped. Scratch requested: 630889472, available: 16777216 [12/16/2021-10:12:59] [V] [TRT] Fastest Tactic: 0 Time: 116.48 [12/16/2021-10:12:59] [V] [TRT] --------------- Timing Runner: 038_convolutional (CaskConvolution) [12/16/2021-10:12:59] [V] [TRT] CaskConvolution has no valid tactics for this config, skipping [12/16/2021-10:12:59] [V] [TRT] Setting workspace to 630889472enables more tactics for profiling [12/16/2021-10:12:59] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CudnnConvolution Tactic: 0 [12/16/2021-10:12:59] [V] [TRT] *************** Autotuning format combination: Half(786432,4096:2,64,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:12:59] [V] [TRT] --------------- Timing Runner: 038_convolutional (FusedConvActConvolution) [12/16/2021-10:12:59] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:12:59] [V] [TRT] --------------- Timing Runner: 038_convolutional (CudnnConvolution) [12/16/2021-10:12:59] [V] [TRT] CudnnConvolution has no valid tactics for this config, skipping [12/16/2021-10:12:59] [V] [TRT] --------------- Timing Runner: 038_convolutional (CaskConvolution) [12/16/2021-10:12:59] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_medium_nn_v1 Tactic: 3564772625446233998 [12/16/2021-10:13:00] [V] [TRT] Tactic: 3564772625446233998 Time: 38.7997 [12/16/2021-10:13:00] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_large_nn_v1 Tactic: 3650389455493082349 [12/16/2021-10:13:00] [V] [TRT] Tactic: 3650389455493082349 Time: 39.994 [12/16/2021-10:13:00] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x32_relu_small_nn_v1 Tactic: 5319956359050645452 [12/16/2021-10:13:01] [V] [TRT] Tactic: 5319956359050645452 Time: 35.6341 [12/16/2021-10:13:01] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_medium_nn_v1 Tactic: 7205456024582378848 [12/16/2021-10:13:01] [V] [TRT] Tactic: 7205456024582378848 Time: 31.128 [12/16/2021-10:13:01] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_large_nn_v1 Tactic: -6490690591794140522 [12/16/2021-10:13:02] [V] [TRT] Tactic: -6490690591794140522 Time: 31.4668 [12/16/2021-10:13:02] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_large_nn_v1 Tactic: -4686027666808657977 [12/16/2021-10:13:03] [V] [TRT] Tactic: -4686027666808657977 Time: 31.4604 [12/16/2021-10:13:03] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_small_nn_v1 Tactic: -4212163711445252890 [12/16/2021-10:13:03] [V] [TRT] Tactic: -4212163711445252890 Time: 30.4095 [12/16/2021-10:13:03] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x128_relu_medium_nn_v1 Tactic: -3898373634979201110 [12/16/2021-10:13:04] [V] [TRT] Tactic: -3898373634979201110 Time: 31.3485 [12/16/2021-10:13:04] [V] [TRT] 038_convolutional Set Tactic Name: maxwell_fp16x2_hcudnn_fp16x2_128x64_relu_small_nn_v1 Tactic: -2409163523992614473 [12/16/2021-10:13:04] [V] [TRT] Tactic: -2409163523992614473 Time: 30.4341 [12/16/2021-10:13:04] [V] [TRT] Fastest Tactic: -4212163711445252890 Time: 30.4095 [12/16/2021-10:13:04] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: -4212163711445252890 [12/16/2021-10:13:04] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:04] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:04] [V] [TRT] Tactic: 1002 Time: 2.65404 [12/16/2021-10:13:04] [V] [TRT] Tactic: 0 Time: 4.45634 [12/16/2021-10:13:04] [V] [TRT] Fastest Tactic: 1002 Time: 2.65404 [12/16/2021-10:13:04] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Float(16384,1024:32,32,1) *************** [12/16/2021-10:13:04] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:04] [V] [TRT] Tactic: 1002 Time: 2.6992 [12/16/2021-10:13:04] [V] [TRT] Tactic: 0 Time: 6.11132 [12/16/2021-10:13:04] [V] [TRT] Fastest Tactic: 1002 Time: 2.6992 [12/16/2021-10:13:04] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:04] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:04] [V] [TRT] Tactic: 1002 Time: 3.24577 [12/16/2021-10:13:05] [V] [TRT] Tactic: 0 Time: 2.68545 [12/16/2021-10:13:05] [V] [TRT] Fastest Tactic: 0 Time: 2.68545 [12/16/2021-10:13:05] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:05] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:05] [V] [TRT] Tactic: 1002 Time: 3.87973 [12/16/2021-10:13:05] [V] [TRT] Tactic: 0 Time: 2.14793 [12/16/2021-10:13:05] [V] [TRT] Fastest Tactic: 0 Time: 2.14793 [12/16/2021-10:13:05] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:05] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:05] [V] [TRT] Tactic: 1002 Time: 3.23738 [12/16/2021-10:13:05] [V] [TRT] Tactic: 0 Time: 4.19728 [12/16/2021-10:13:05] [V] [TRT] Fastest Tactic: 1002 Time: 3.23738 [12/16/2021-10:13:05] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Float(16384,1024:32,32,1) *************** [12/16/2021-10:13:05] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:05] [V] [TRT] Tactic: 1002 Time: 2.50328 [12/16/2021-10:13:05] [V] [TRT] Tactic: 0 Time: 9.23285 [12/16/2021-10:13:05] [V] [TRT] Fastest Tactic: 1002 Time: 2.50328 [12/16/2021-10:13:05] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:05] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:05] [V] [TRT] Tactic: 1002 Time: 2.45424 [12/16/2021-10:13:05] [V] [TRT] Tactic: 0 Time: 4.12638 [12/16/2021-10:13:05] [V] [TRT] Fastest Tactic: 1002 Time: 2.45424 [12/16/2021-10:13:05] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:05] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:05] [V] [TRT] Tactic: 1002 Time: 3.31221 [12/16/2021-10:13:05] [V] [TRT] Tactic: 0 Time: 4.73792 [12/16/2021-10:13:05] [V] [TRT] Fastest Tactic: 1002 Time: 3.31221 [12/16/2021-10:13:05] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:05] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:05] [V] [TRT] Tactic: 1002 Time: 3.16428 [12/16/2021-10:13:06] [V] [TRT] Tactic: 0 Time: 4.6649 [12/16/2021-10:13:06] [V] [TRT] Fastest Tactic: 1002 Time: 3.16428 [12/16/2021-10:13:06] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:06] [V] [TRT] Tactic: 1002 Time: 2.4848 [12/16/2021-10:13:06] [V] [TRT] Tactic: 0 Time: 4.53757 [12/16/2021-10:13:06] [V] [TRT] Fastest Tactic: 1002 Time: 2.4848 [12/16/2021-10:13:06] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:06] [V] [TRT] Tactic: 1002 Time: 2.66086 [12/16/2021-10:13:06] [V] [TRT] Tactic: 0 Time: 4.62305 [12/16/2021-10:13:06] [V] [TRT] Fastest Tactic: 1002 Time: 2.66086 [12/16/2021-10:13:06] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:06] [V] [TRT] Tactic: 1002 Time: 3.31745 [12/16/2021-10:13:06] [V] [TRT] Tactic: 0 Time: 5.24387 [12/16/2021-10:13:06] [V] [TRT] Fastest Tactic: 1002 Time: 3.31745 [12/16/2021-10:13:06] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:06] [V] [TRT] Tactic: 1002 Time: 3.27888 [12/16/2021-10:13:06] [V] [TRT] Tactic: 0 Time: 2.29327 [12/16/2021-10:13:06] [V] [TRT] Fastest Tactic: 0 Time: 2.29327 [12/16/2021-10:13:06] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:06] [V] [TRT] Tactic: 1002 Time: 2.3091 [12/16/2021-10:13:06] [V] [TRT] Tactic: 0 Time: 4.16203 [12/16/2021-10:13:06] [V] [TRT] Fastest Tactic: 1002 Time: 2.3091 [12/16/2021-10:13:06] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Float(16384,1024:32,32,1) *************** [12/16/2021-10:13:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:06] [V] [TRT] Tactic: 1002 Time: 2.30907 [12/16/2021-10:13:06] [V] [TRT] Tactic: 0 Time: 6.14936 [12/16/2021-10:13:06] [V] [TRT] Fastest Tactic: 1002 Time: 2.30907 [12/16/2021-10:13:06] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:06] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:06] [V] [TRT] Tactic: 1002 Time: 2.17678 [12/16/2021-10:13:07] [V] [TRT] Tactic: 0 Time: 2.12659 [12/16/2021-10:13:07] [V] [TRT] Fastest Tactic: 0 Time: 2.12659 [12/16/2021-10:13:07] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:07] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:07] [V] [TRT] Tactic: 1002 Time: 3.09708 [12/16/2021-10:13:07] [V] [TRT] Tactic: 0 Time: 1.84876 [12/16/2021-10:13:07] [V] [TRT] Fastest Tactic: 0 Time: 1.84876 [12/16/2021-10:13:07] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:07] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:07] [V] [TRT] Tactic: 1002 Time: 2.34676 [12/16/2021-10:13:07] [V] [TRT] Tactic: 0 Time: 4.60492 [12/16/2021-10:13:07] [V] [TRT] Fastest Tactic: 1002 Time: 2.34676 [12/16/2021-10:13:07] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Float(16384,1024:32,32,1) *************** [12/16/2021-10:13:07] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:07] [V] [TRT] Tactic: 1002 Time: 2.3463 [12/16/2021-10:13:07] [V] [TRT] Tactic: 0 Time: 6.11674 [12/16/2021-10:13:07] [V] [TRT] Fastest Tactic: 1002 Time: 2.3463 [12/16/2021-10:13:07] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:07] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:07] [V] [TRT] Tactic: 1002 Time: 4.61708 [12/16/2021-10:13:07] [V] [TRT] Tactic: 0 Time: 1.80824 [12/16/2021-10:13:07] [V] [TRT] Fastest Tactic: 0 Time: 1.80824 [12/16/2021-10:13:07] [V] [TRT] *************** Autotuning format combination: Float(524288,1024,32,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:07] [V] [TRT] --------------- Timing Runner: 038_convolutional_lrelu (PointWiseV2) [12/16/2021-10:13:07] [V] [TRT] Tactic: 0 Time: 2.21662 [12/16/2021-10:13:07] [V] [TRT] Tactic: 1 Time: 1.57834 [12/16/2021-10:13:07] [V] [TRT] Tactic: 2 Time: 1.37691 [12/16/2021-10:13:07] [V] [TRT] Tactic: 3 Time: 1.33979 [12/16/2021-10:13:07] [V] [TRT] Tactic: 4 Time: 1.08716 [12/16/2021-10:13:07] [V] [TRT] Tactic: 5 Time: 1.03797 [12/16/2021-10:13:07] [V] [TRT] Tactic: 6 Time: 1.32811 [12/16/2021-10:13:07] [V] [TRT] Tactic: 7 Time: 1.06142 [12/16/2021-10:13:07] [V] [TRT] Tactic: 8 Time: 1.06012 [12/16/2021-10:13:07] [V] [TRT] Tactic: 9 Time: 1.05471 [12/16/2021-10:13:08] [V] [TRT] Tactic: 28 Time: 2.16816 [12/16/2021-10:13:08] [V] [TRT] Fastest Tactic: 5 Time: 1.03797 [12/16/2021-10:13:08] [V] [TRT] --------------- Timing Runner: 038_convolutional_lrelu (PointWise) [12/16/2021-10:13:08] [V] [TRT] Tactic: 128 Time: 5.17212 [12/16/2021-10:13:08] [V] [TRT] Tactic: 256 Time: 5.1883 [12/16/2021-10:13:08] [V] [TRT] Tactic: 512 Time: 5.22528 [12/16/2021-10:13:08] [V] [TRT] Fastest Tactic: 128 Time: 5.17212 [12/16/2021-10:13:08] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:13:08] [V] [TRT] *************** Autotuning format combination: Float(524288,1,16384,512) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:08] [V] [TRT] --------------- Timing Runner: 038_convolutional_lrelu (PointWiseV2) [12/16/2021-10:13:08] [V] [TRT] Tactic: 0 Time: 2.21609 [12/16/2021-10:13:08] [V] [TRT] Tactic: 1 Time: 1.57839 [12/16/2021-10:13:08] [V] [TRT] Tactic: 2 Time: 1.38111 [12/16/2021-10:13:08] [V] [TRT] Tactic: 3 Time: 1.34324 [12/16/2021-10:13:08] [V] [TRT] Tactic: 4 Time: 1.08808 [12/16/2021-10:13:08] [V] [TRT] Tactic: 5 Time: 1.03722 [12/16/2021-10:13:08] [V] [TRT] Tactic: 6 Time: 1.32833 [12/16/2021-10:13:08] [V] [TRT] Tactic: 7 Time: 1.06044 [12/16/2021-10:13:08] [V] [TRT] Tactic: 8 Time: 1.06283 [12/16/2021-10:13:08] [V] [TRT] Tactic: 9 Time: 1.05272 [12/16/2021-10:13:08] [V] [TRT] Tactic: 28 Time: 2.16686 [12/16/2021-10:13:08] [V] [TRT] Fastest Tactic: 5 Time: 1.03722 [12/16/2021-10:13:08] [V] [TRT] --------------- Timing Runner: 038_convolutional_lrelu (PointWise) [12/16/2021-10:13:08] [V] [TRT] Tactic: 128 Time: 5.1732 [12/16/2021-10:13:09] [V] [TRT] Tactic: 256 Time: 5.18908 [12/16/2021-10:13:09] [V] [TRT] Tactic: 512 Time: 5.2262 [12/16/2021-10:13:09] [V] [TRT] Fastest Tactic: 128 Time: 5.1732 [12/16/2021-10:13:09] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 5 [12/16/2021-10:13:09] [V] [TRT] *************** Autotuning format combination: Float(16384,1024:32,32,1) -> Float(16384,1024:32,32,1) *************** [12/16/2021-10:13:09] [V] [TRT] --------------- Timing Runner: 038_convolutional_lrelu (PointWiseV2) [12/16/2021-10:13:09] [V] [TRT] Tactic: 24 Time: 1.38851 [12/16/2021-10:13:09] [V] [TRT] Tactic: 25 Time: 1.3031 [12/16/2021-10:13:09] [V] [TRT] Tactic: 26 Time: 1.32582 [12/16/2021-10:13:09] [V] [TRT] Tactic: 27 Time: 1.29313 [12/16/2021-10:13:09] [V] [TRT] Tactic: 31 Time: 1.3884 [12/16/2021-10:13:09] [V] [TRT] Fastest Tactic: 27 Time: 1.29313 [12/16/2021-10:13:09] [V] [TRT] --------------- Timing Runner: 038_convolutional_lrelu (PointWise) [12/16/2021-10:13:09] [V] [TRT] Tactic: 128 Time: 5.17084 [12/16/2021-10:13:09] [V] [TRT] Tactic: 256 Time: 5.18781 [12/16/2021-10:13:09] [V] [TRT] Tactic: 512 Time: 5.22418 [12/16/2021-10:13:09] [V] [TRT] Fastest Tactic: 128 Time: 5.17084 [12/16/2021-10:13:09] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 27 [12/16/2021-10:13:09] [V] [TRT] *************** Autotuning format combination: Half(524288,1024,32,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:09] [V] [TRT] --------------- Timing Runner: 038_convolutional_lrelu (PointWiseV2) [12/16/2021-10:13:09] [V] [TRT] Tactic: 0 Time: 2.20613 [12/16/2021-10:13:09] [V] [TRT] Tactic: 1 Time: 1.54504 [12/16/2021-10:13:09] [V] [TRT] Tactic: 2 Time: 1.48642 [12/16/2021-10:13:09] [V] [TRT] Tactic: 3 Time: 1.20721 [12/16/2021-10:13:09] [V] [TRT] Tactic: 4 Time: 0.949948 [12/16/2021-10:13:09] [V] [TRT] Tactic: 5 Time: 0.99623 [12/16/2021-10:13:09] [V] [TRT] Tactic: 6 Time: 1.10044 [12/16/2021-10:13:09] [V] [TRT] Tactic: 7 Time: 0.785631 [12/16/2021-10:13:09] [V] [TRT] Tactic: 8 Time: 0.699408 [12/16/2021-10:13:10] [V] [TRT] Tactic: 9 Time: 0.790703 [12/16/2021-10:13:10] [V] [TRT] Tactic: 28 Time: 2.14725 [12/16/2021-10:13:10] [V] [TRT] Fastest Tactic: 8 Time: 0.699408 [12/16/2021-10:13:10] [V] [TRT] --------------- Timing Runner: 038_convolutional_lrelu (PointWise) [12/16/2021-10:13:10] [V] [TRT] Tactic: 128 Time: 4.66377 [12/16/2021-10:13:10] [V] [TRT] Tactic: 256 Time: 4.63289 [12/16/2021-10:13:10] [V] [TRT] Tactic: 512 Time: 4.51381 [12/16/2021-10:13:10] [V] [TRT] Fastest Tactic: 512 Time: 4.51381 [12/16/2021-10:13:10] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 8 [12/16/2021-10:13:10] [V] [TRT] *************** Autotuning format combination: Half(262144,1024:2,32,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:10] [V] [TRT] --------------- Timing Runner: 038_convolutional_lrelu (PointWiseV2) [12/16/2021-10:13:10] [V] [TRT] Tactic: 0 Time: 1.41635 [12/16/2021-10:13:10] [V] [TRT] Tactic: 1 Time: 1.13826 [12/16/2021-10:13:10] [V] [TRT] Tactic: 2 Time: 1.23877 [12/16/2021-10:13:10] [V] [TRT] Tactic: 3 Time: 0.998502 [12/16/2021-10:13:10] [V] [TRT] Tactic: 4 Time: 1.04479 [12/16/2021-10:13:10] [V] [TRT] Tactic: 5 Time: 1.12312 [12/16/2021-10:13:10] [V] [TRT] Tactic: 6 Time: 0.967956 [12/16/2021-10:13:10] [V] [TRT] Tactic: 7 Time: 0.997839 [12/16/2021-10:13:10] [V] [TRT] Tactic: 8 Time: 1.05113 [12/16/2021-10:13:10] [V] [TRT] Tactic: 9 Time: 1.18007 [12/16/2021-10:13:10] [V] [TRT] Tactic: 10 Time: 2.40345 [12/16/2021-10:13:10] [V] [TRT] Tactic: 11 Time: 1.65268 [12/16/2021-10:13:10] [V] [TRT] Tactic: 12 Time: 1.59406 [12/16/2021-10:13:10] [V] [TRT] Tactic: 13 Time: 1.2311 [12/16/2021-10:13:10] [V] [TRT] Tactic: 14 Time: 1.01119 [12/16/2021-10:13:10] [V] [TRT] Tactic: 15 Time: 1.08098 [12/16/2021-10:13:10] [V] [TRT] Tactic: 16 Time: 1.11538 [12/16/2021-10:13:10] [V] [TRT] Tactic: 17 Time: 0.792389 [12/16/2021-10:13:11] [V] [TRT] Tactic: 18 Time: 0.748704 [12/16/2021-10:13:11] [V] [TRT] Tactic: 19 Time: 0.887637 [12/16/2021-10:13:11] [V] [TRT] Tactic: 28 Time: 1.38615 [12/16/2021-10:13:11] [V] [TRT] Tactic: 29 Time: 2.32715 [12/16/2021-10:13:11] [V] [TRT] Fastest Tactic: 18 Time: 0.748704 [12/16/2021-10:13:11] [V] [TRT] --------------- Timing Runner: 038_convolutional_lrelu (PointWise) [12/16/2021-10:13:11] [V] [TRT] Tactic: 128 Time: 4.66632 [12/16/2021-10:13:11] [V] [TRT] Tactic: 256 Time: 4.63579 [12/16/2021-10:13:11] [V] [TRT] Tactic: 512 Time: 4.5137 [12/16/2021-10:13:11] [V] [TRT] Fastest Tactic: 512 Time: 4.5137 [12/16/2021-10:13:11] [V] [TRT] >>>>>>>>>>>>>>> Chose Runner Type: PointWiseV2 Tactic: 18 [12/16/2021-10:13:11] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:11] [V] [TRT] Tactic: 1002 Time: 2.65898 [12/16/2021-10:13:11] [V] [TRT] Tactic: 0 Time: 4.46074 [12/16/2021-10:13:11] [V] [TRT] Fastest Tactic: 1002 Time: 2.65898 [12/16/2021-10:13:11] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Float(16384,1024:32,32,1) *************** [12/16/2021-10:13:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:11] [V] [TRT] Tactic: 1002 Time: 2.7006 [12/16/2021-10:13:11] [V] [TRT] Tactic: 0 Time: 6.11281 [12/16/2021-10:13:11] [V] [TRT] Fastest Tactic: 1002 Time: 2.7006 [12/16/2021-10:13:11] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:11] [V] [TRT] Tactic: 1002 Time: 3.24848 [12/16/2021-10:13:11] [V] [TRT] Tactic: 0 Time: 2.68678 [12/16/2021-10:13:11] [V] [TRT] Fastest Tactic: 0 Time: 2.68678 [12/16/2021-10:13:11] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:11] [V] [TRT] Tactic: 1002 Time: 3.8797 [12/16/2021-10:13:11] [V] [TRT] Tactic: 0 Time: 2.14775 [12/16/2021-10:13:11] [V] [TRT] Fastest Tactic: 0 Time: 2.14775 [12/16/2021-10:13:11] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:11] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:12] [V] [TRT] Tactic: 1002 Time: 3.25902 [12/16/2021-10:13:12] [V] [TRT] Tactic: 0 Time: 4.18981 [12/16/2021-10:13:12] [V] [TRT] Fastest Tactic: 1002 Time: 3.25902 [12/16/2021-10:13:12] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Float(16384,1024:32,32,1) *************** [12/16/2021-10:13:12] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:12] [V] [TRT] Tactic: 1002 Time: 2.503 [12/16/2021-10:13:12] [V] [TRT] Tactic: 0 Time: 9.2385 [12/16/2021-10:13:12] [V] [TRT] Fastest Tactic: 1002 Time: 2.503 [12/16/2021-10:13:12] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:12] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:12] [V] [TRT] Tactic: 1002 Time: 2.45531 [12/16/2021-10:13:12] [V] [TRT] Tactic: 0 Time: 4.1291 [12/16/2021-10:13:12] [V] [TRT] Fastest Tactic: 1002 Time: 2.45531 [12/16/2021-10:13:12] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:12] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:12] [V] [TRT] Tactic: 1002 Time: 3.31252 [12/16/2021-10:13:12] [V] [TRT] Tactic: 0 Time: 4.73325 [12/16/2021-10:13:12] [V] [TRT] Fastest Tactic: 1002 Time: 3.31252 [12/16/2021-10:13:12] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:12] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:12] [V] [TRT] Tactic: 1002 Time: 3.16689 [12/16/2021-10:13:12] [V] [TRT] Tactic: 0 Time: 4.66486 [12/16/2021-10:13:12] [V] [TRT] Fastest Tactic: 1002 Time: 3.16689 [12/16/2021-10:13:12] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:12] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:12] [V] [TRT] Tactic: 1002 Time: 2.4864 [12/16/2021-10:13:12] [V] [TRT] Tactic: 0 Time: 4.53725 [12/16/2021-10:13:12] [V] [TRT] Fastest Tactic: 1002 Time: 2.4864 [12/16/2021-10:13:12] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:12] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:13] [V] [TRT] Tactic: 1002 Time: 2.65953 [12/16/2021-10:13:13] [V] [TRT] Tactic: 0 Time: 4.62493 [12/16/2021-10:13:13] [V] [TRT] Fastest Tactic: 1002 Time: 2.65953 [12/16/2021-10:13:13] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:13] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:13] [V] [TRT] Tactic: 1002 Time: 3.31585 [12/16/2021-10:13:13] [V] [TRT] Tactic: 0 Time: 5.24122 [12/16/2021-10:13:13] [V] [TRT] Fastest Tactic: 1002 Time: 3.31585 [12/16/2021-10:13:13] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:13] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:13] [V] [TRT] Tactic: 1002 Time: 3.27891 [12/16/2021-10:13:13] [V] [TRT] Tactic: 0 Time: 2.29198 [12/16/2021-10:13:13] [V] [TRT] Fastest Tactic: 0 Time: 2.29198 [12/16/2021-10:13:13] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:13] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:13] [V] [TRT] Tactic: 1002 Time: 2.30871 [12/16/2021-10:13:13] [V] [TRT] Tactic: 0 Time: 4.16852 [12/16/2021-10:13:13] [V] [TRT] Fastest Tactic: 1002 Time: 2.30871 [12/16/2021-10:13:13] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Float(16384,1024:32,32,1) *************** [12/16/2021-10:13:13] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:13] [V] [TRT] Tactic: 1002 Time: 2.30998 [12/16/2021-10:13:13] [V] [TRT] Tactic: 0 Time: 6.14973 [12/16/2021-10:13:13] [V] [TRT] Fastest Tactic: 1002 Time: 2.30998 [12/16/2021-10:13:13] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:13] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:13] [V] [TRT] Tactic: 1002 Time: 2.17376 [12/16/2021-10:13:13] [V] [TRT] Tactic: 0 Time: 2.1265 [12/16/2021-10:13:13] [V] [TRT] Fastest Tactic: 0 Time: 2.1265 [12/16/2021-10:13:13] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:13] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:13] [V] [TRT] Tactic: 1002 Time: 3.09644 [12/16/2021-10:13:13] [V] [TRT] Tactic: 0 Time: 1.84828 [12/16/2021-10:13:13] [V] [TRT] Fastest Tactic: 0 Time: 1.84828 [12/16/2021-10:13:13] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:13] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:13] [V] [TRT] Tactic: 1002 Time: 2.3454 [12/16/2021-10:13:14] [V] [TRT] Tactic: 0 Time: 4.60407 [12/16/2021-10:13:14] [V] [TRT] Fastest Tactic: 1002 Time: 2.3454 [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Float(16384,1024:32,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:14] [V] [TRT] Tactic: 1002 Time: 2.34548 [12/16/2021-10:13:14] [V] [TRT] Tactic: 0 Time: 6.11537 [12/16/2021-10:13:14] [V] [TRT] Fastest Tactic: 1002 Time: 2.34548 [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] --------------- Timing Runner: Optimizer Reformat (Reformat) [12/16/2021-10:13:14] [V] [TRT] Tactic: 1002 Time: 4.59743 [12/16/2021-10:13:14] [V] [TRT] Tactic: 0 Time: 1.80774 [12/16/2021-10:13:14] [V] [TRT] Fastest Tactic: 0 Time: 1.80774 [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Float(524288,1024,32,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Float(524288,1,16384,512) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Float(16384,1024:32,32,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Half(524288,1024,32,1) -> Half(262144,1024:2,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Float(524288,1024,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Float(524288,1,16384,512) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning Reformat:Half(262144,1024:2,32,1) -> Half(524288,1024,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] *************** Autotuning format combination: Float(524288,1024,32,1) -> Float(262144,1024,32,1) *************** [12/16/2021-10:13:14] [V] [TRT] --------------- Timing Runner: 039_convolutional (CudaDepthwiseConvolution) [12/16/2021-10:13:14] [V] [TRT] CudaDepthwiseConvolution has no valid tactics for this config, skipping [12/16/2021-10:13:14] [V] [TRT] --------------- Timing Runner: 039_convolutional (FusedConvActConvolution) [12/16/2021-10:13:14] [V] [TRT] FusedConvActConvolution has no valid tactics for this config, skipping [12/16/2021-10:13:14] [V] [TRT] --------------- Timing Runner: 039_convolutional (CudnnConvolution) [12/16/2021-10:13:14] [V] [TRT] Tactic: 0 Time: 12.3355 [12/16/2021-10:13:14] [V] [TRT] Tactic: 1 Time: 7.75753 [12/16/2021-10:13:14] [V] [TRT] Tactic: 2 Time: 12.8664 [12/16/2021-10:13:14] [V] [TRT] Tactic: 4 skipped. Scratch requested: 1163657216, available: 16777216 [12/16/2021-10:13:14] [V] [TRT] Tactic: 5 skipped. Scratch requested: 34537472, available: 16777216 [12/16/2021-10:13:14] [V] [TRT] Fastest Tactic: 1 Time: 7.75753 [12/16/2021-10:13:14] [V] [TRT] --------------- Timing Runner: 039_convolutional (CublasConvolution) [12/16/2021-10:13:14] [V] [TRT] CublasConvolution has no valid tactics for this config, skipping [12/16/2021-10:13:14] [V] [TRT] --------------- Timing Runner: 039_convolutional (CaskConvolution) [12/16/2021-10:13:14] [V] [TRT] 039_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_medium_nn_v1 Tactic: 1062367460111450758 [12/16/2021-10:13:15] [V] [TRT] Tactic: 1062367460111450758 Time: 9.18531 [12/16/2021-10:13:15] [V] [TRT] 039_convolutional Set Tactic Name: maxwell_scudnn_128x32_relu_interior_nn_v0 Tactic: 1698681053543049347 [12/16/2021-10:13:15] [V] [TRT] Tactic: 1698681053543049347 Time: 8.81143 [12/16/2021-10:13:15] [V] [TRT] 039_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_medium_nn_v1 Tactic: 4501471010995462441 [12/16/2021-10:13:15] [V] [TRT] Tactic: 4501471010995462441 Time: 7.20482 [12/16/2021-10:13:15] [V] [TRT] 039_convolutional Set Tactic Name: maxwell_scudnn_128x64_relu_small_nn_v1 Tactic: 5137655947464784826 [12/16/2021-10:13:15] [V] [TRT] Tactic: 5137655947464784826 Time: 7.09706 [12/16/2021-10:13:15] [V] [TRT] 039_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_small_nn_v0 Tactic: 5288347012147084929 [12/16/2021-10:13:15] [V] [TRT] Tactic: 5288347012147084929 Time: 7.18921 [12/16/2021-10:13:15] [V] [TRT] 039_convolutional Set Tactic Name: maxwell_scudnn_128x128_relu_interior_nn_v1 Tactic: 5326823351883942011