################################################################################ # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, publish, distribute, sublicense, # and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL # THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. ################################################################################ [property] gpu-id=0 net-scale-factor=0.007843 model-color-format=1 offsets=127.5;127.5;127.5 labelfile-path=/workspace/deepstream-huhf-unet/model_files/huhf_labels.txt ##Replace following path to your model file model-engine-file=/workspace/deepstream-huhf-unet/model_files/model_huhf_v0_600_cal_int8.etlt_b1_gpu0_int8.engine #current DS cannot parse onnx etlt model, so you need to #convert the etlt model to TensoRT engine first use tao-convert tlt-encoded-model=/workspace/deepstream-huhf-unet/model_files/model_huhf_v0_600_cal_int8.etlt tlt-model-key=nvidia_tlt int8-calib-file=/workspace/deepstream-huhf-unet/model_files/huhf_v0_cal.bin infer-dims=3;512;512 batch-size=1 ## 0=FP32, 1=INT8, 2=FP16 mode network-mode=1 num-detected-classes=16 interval=0 gie-unique-id=1 network-type=100 output-tensor-meta=1 output-blob-names=argmax_1 segmentation-threshold=0.0 ##specify the output tensor order, 0(default value) for CHW and 1 for HWC segmentation-output-order=1 [class-attrs-all] roi-top-offset=0 roi-bottom-offset=0 detected-min-w=0 detected-min-h=0 detected-max-w=0 detected-max-h=0