DS ver : 5.1
cuda: 11.2
ONNX IR version: 0.0.6
Opset version: 9
Producer name: pytorch
Producer version: 1.8
Domain:
Model version: 0
Doc string:
----------------------------------------------------------------
WARNING: ../nvdsinfer/nvdsinfer_func_utils.cpp:36 [TRT]: TensorRT was linked against cuBLAS/cuBLAS LT 11.3.0 but loaded cuBLAS/cuBLAS LT 11.2.1
INFO: ../nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: Some tactics do not have sufficient workspace memory to run. Increasing workspace size may increase performance, please check verbose output.
INFO: ../nvdsinfer/nvdsinfer_func_utils.cpp:39 [TRT]: Detected 1 inputs and 1 output network tensors.
WARNING: ../nvdsinfer/nvdsinfer_func_utils.cpp:36 [TRT]: TensorRT was linked against cuBLAS/cuBLAS LT 11.3.0 but loaded cuBLAS/cuBLAS LT 11.2.1
0:00:31.737852157 329671 0x55ea2891af30 INFO nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<secondary_gie_0> NvDsInferContext[UID 4]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1749> [UID = 4]: serialize cuda engine to file: /home/ubuntu/models/Onnx/resnet18_224x224.onnx_b64_gpu0_fp16.engine successfully
WARNING: ../nvdsinfer/nvdsinfer_func_utils.cpp:36 [TRT]: TensorRT was linked against cuBLAS/cuBLAS LT 11.3.0 but loaded cuBLAS/cuBLAS LT 11.2.1
ERROR: ../nvdsinfer/nvdsinfer_func_utils.cpp:33 [TRT]: Flatten_47: reshaping failed for tensor: 189
ERROR: ../nvdsinfer/nvdsinfer_func_utils.cpp:33 [TRT]: shapeMachine.cpp (160) - Shape Error in executeReshape: reshape would change volume
ERROR: ../nvdsinfer/nvdsinfer_func_utils.cpp:33 [TRT]: Instruction: RESHAPE{64 512 1 1} {1 512}
INFO: ../nvdsinfer/nvdsinfer_model_builder.cpp:685 [FullDims Engine Info]: layers num: 2
0 INPUT kFLOAT input.1 3x224x224 min: 1x3x224x224 opt: 64x3x224x224 Max: 64x3x224x224
1 OUTPUT kFLOAT output 4 min: 0 opt: 0 Max: 0
iris-app: nvdsinfer_context_impl.cpp:1302: NvDsInferStatus nvdsinfer::NvDsInferContextImpl::allocateBuffers(): Assertion `bindingDims.numElements > 0' failed.
Aborted (core dumped)
Could not understand what the error is precisely.
The engine seems to be generating from the model (can be seen from the logs and the file is present locally) but immediately crashes out. Regarding the original onnx model it’s a plain old resnet18 model trained on torch with the modification in final fc layer to be of 4.
Can you try with trtexec to see if the issue exist?
4x224.onnx ~/Dataset/PoseClassifier/CombinedDataset/TwoClass/train/sleeping/Coffee_room_01_video_1_0157.png
&&&& RUNNING TensorRT.trtexec # /usr/src/tensorrt/bin/trtexec --onnx=resnet18_224x224.onnx /home/ubuntu/Dataset/PoseClassifier/CombinedDataset/TwoClass/train/sleeping/Coffee_room_01_video_1_0157.png
[06/01/2021-15:14:50] [E] Unknown option: /home/ajay/Dataset/PoseClassifier/CombinedDataset/TwoClass/train/sleeping/Coffee_room_01_video_1_0157.png
=== Model Options ===
--uff=<file> UFF model
--onnx=<file> ONNX model
--model=<file> Caffe model (default = no model, random weights used)
--deploy=<file> Caffe prototxt file
--output=<name>[,<name>]* Output names (it can be specified multiple times); at least one output is required for UFF and Caffe
--uffInput=<name>,X,Y,Z Input blob name and its dimensions (X,Y,Z=C,H,W), it can be specified multiple times; at least one is required for UFF models
--uffNHWC Set if inputs are in the NHWC layout instead of NCHW (use X,Y,Z=H,W,C order in --uffInput)
=== Build Options ===
--maxBatch Set max batch size and build an implicit batch engine (default = 1)
--explicitBatch Use explicit batch sizes when building the engine (default = implicit)
--minShapes=spec Build with dynamic shapes using a profile with the min shapes provided
--optShapes=spec Build with dynamic shapes using a profile with the opt shapes provided
--maxShapes=spec Build with dynamic shapes using a profile with the max shapes provided
--minShapesCalib=spec Calibrate with dynamic shapes using a profile with the min shapes provided
--optShapesCalib=spec Calibrate with dynamic shapes using a profile with the opt shapes provided
--maxShapesCalib=spec Calibrate with dynamic shapes using a profile with the max shapes provided
Note: All three of min, opt and max shapes must be supplied.
However, if only opt shapes is supplied then it will be expanded so
that min shapes and max shapes are set to the same values as opt shapes.
In addition, use of dynamic shapes implies explicit batch.
Input names can be wrapped with escaped single quotes (ex: \'Input:0\').
Example input shapes spec: input0:1x3x256x256,input1:1x3x128x128
Each input shape is supplied as a key-value pair where key is the input name and
value is the dimensions (including the batch dimension) to be used for that input.
Each key-value pair has the key and value separated using a colon (:).
Multiple input shapes can be provided via comma-separated key-value pairs.
--inputIOFormats=spec Type and format of each of the input tensors (default = all inputs in fp32:chw)
See --outputIOFormats help for the grammar of type and format list.
Note: If this option is specified, please set comma-separated types and formats for all
inputs following the same order as network inputs ID (even if only one input
needs specifying IO format) or set the type and format once for broadcasting.
--outputIOFormats=spec Type and format of each of the output tensors (default = all outputs in fp32:chw)
Note: If this option is specified, please set comma-separated types and formats for all
outputs following the same order as network outputs ID (even if only one output
needs specifying IO format) or set the type and format once for broadcasting.
IO Formats: spec ::= IOfmt[","spec]
IOfmt ::= type:fmt
type ::= "fp32"|"fp16"|"int32"|"int8"
fmt ::= ("chw"|"chw2"|"chw4"|"hwc8"|"chw16"|"chw32"|"dhwc8")["+"fmt]
--workspace=N Set workspace size in megabytes (default = 16)
--noBuilderCache Disable timing cache in builder (default is to enable timing cache)
--nvtxMode=mode Specify NVTX annotation verbosity. mode ::= default|verbose|none
--minTiming=M Set the minimum number of iterations used in kernel selection (default = 1)
--avgTiming=M Set the number of times averaged in each iteration for kernel selection (default = 8)
--noTF32 Disable tf32 precision (default is to enable tf32, in addition to fp32)
--refit Mark the engine as refittable. This will allow the inspection of refittable layers
and weights within the engine.
--fp16 Enable fp16 precision, in addition to fp32 (default = disabled)
--int8 Enable int8 precision, in addition to fp32 (default = disabled)
--best Enable all precisions to achieve the best performance (default = disabled)
--calib=<file> Read INT8 calibration cache file
--safe Only test the functionality available in safety restricted flows
--saveEngine=<file> Save the serialized engine
--loadEngine=<file> Load a serialized engine
--tacticSources=tactics Specify the tactics to be used by adding (+) or removing (-) tactics from the default
tactic sources (default = all available tactics).
Note: Currently only cuBLAS and cuBLAS LT are listed as optional tactics.
Tactic Sources: tactics ::= [","tactic]
tactic ::= (+|-)lib
lib ::= "cublas"|"cublasLt"
=== Inference Options ===
--batch=N Set batch size for implicit batch engines (default = 1)
--shapes=spec Set input shapes for dynamic shapes inference inputs.
Note: Use of dynamic shapes implies explicit batch.
Input names can be wrapped with escaped single quotes (ex: \'Input:0\').
Example input shapes spec: input0:1x3x256x256, input1:1x3x128x128
Each input shape is supplied as a key-value pair where key is the input name and
value is the dimensions (including the batch dimension) to be used for that input.
Each key-value pair has the key and value separated using a colon (:).
Multiple input shapes can be provided via comma-separated key-value pairs.
--loadInputs=spec Load input values from files (default = generate random inputs). Input names can be wrapped with single quotes (ex: 'Input:0')
Input values spec ::= Ival[","spec]
Ival ::= name":"file
--iterations=N Run at least N inference iterations (default = 10)
--warmUp=N Run for N milliseconds to warmup before measuring performance (default = 200)
--duration=N Run performance measurements for at least N seconds wallclock time (default = 3)
--sleepTime=N Delay inference start with a gap of N milliseconds between launch and compute (default = 0)
--streams=N Instantiate N engines to use concurrently (default = 1)
--exposeDMA Serialize DMA transfers to and from device. (default = disabled)
--noDataTransfers Do not transfer data to and from the device during inference. (default = disabled)
--useSpinWait Actively synchronize on GPU events. This option may decrease synchronization time but increase CPU usage and power (default = disabled)
--threads Enable multithreading to drive engines with independent threads (default = disabled)
--useCudaGraph Use cuda graph to capture engine execution and then launch inference (default = disabled)
--separateProfileRun Do not attach the profiler in the benchmark run; if profiling is enabled, a second profile run will be executed (default = disabled)
--buildOnly Skip inference perf measurement (default = disabled)
=== Build and Inference Batch Options ===
When using implicit batch, the max batch size of the engine, if not given,
is set to the inference batch size;
when using explicit batch, if shapes are specified only for inference, they
will be used also as min/opt/max in the build profile; if shapes are
specified only for the build, the opt shapes will be used also for inference;
if both are specified, they must be compatible; and if explicit batch is
enabled but neither is specified, the model must provide complete static
dimensions, including batch size, for all inputs
=== Reporting Options ===
--verbose Use verbose logging (default = false)
--avgRuns=N Report performance measurements averaged over N consecutive iterations (default = 10)
--percentile=P Report performance for the P percentage (0<=P<=100, 0 representing max perf, and 100 representing min perf; (default = 99%)
--dumpRefit Print the refittable layers and weights from a refittable engine
--dumpOutput Print the output tensor(s) of the last inference iteration (default = disabled)
--dumpProfile Print profile information per layer (default = disabled)
--exportTimes=<file> Write the timing results in a json file (default = disabled)
--exportOutput=<file> Write the output tensors to a json file (default = disabled)
--exportProfile=<file> Write the profile information per layer in a json file (default = disabled)
=== System Options ===
--device=N Select cuda device N (default = 0)
--useDLACore=N Select DLA core N for layers that support DLA (default = none)
--allowGPUFallback When DLA is enabled, allow GPU fallback for unsupported layers (default = disabled)
--plugins Plugin library (.so) to load (can be specified multiple times)
=== Help ===
--help, -h Print this message
&&&& FAILED TensorRT.trtexec # /usr/src/tensorrt/bin/trtexec --onnx=resnet18_224x224.onnx /home/ajay/Dataset/PoseClassifier/CombinedDataset/TwoClass/train/sleeping/Coffee_room_01_video_1_0157.png
It seems to fail
Sorry for the late.
./trtexec --onnx=path --explicitBatch