Low latency than expected #27
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…about camera_tf_trt.py
Finished the first working implementation of the real-time object detection demo script: 'camera_tf_trt.py'
… drawing function
…onvActLayer.cpp (61) - Cuda Error in createFilterTextur'
…xes; fix duplicated logging for tensorflow
Refactor visualization code to the utils/ directory; fix duplicated logging for tensorflow
…th/height setting in command line arguments)
…rflow-1.8 (as specified in the original NVIDIA tf_trt_models/README.md)
…l ones from tensorflow model repository), in which the 'score_threshold' has been modified from 1e-8 to 0.3
Update a working version, tested with JetPack-3.2 and tensorflow 1.8.0
Add download link to tensorflow 1.8.0 wheel for JetPack-3.3
Add support for ssd_mobilenet_v1_egohands
Add data/egohands_label_map.pbtxt
…ohands', 'ssd_inception_v2_egohands', 'faster_rcnn_resnet50_egohands', 'faster_rcnn_resnet101_egohands' and 'faster_rcnn_inception_v2_egohands' models. However, the faster rcnn models are hacky and do not perform well on TX2 yet.
Add support for 'ssd_mobilenet_v2_egohands', 'ssdlite_mobilenet_v2_egohands', 'ssd_inception_v2_egohands', 'faster_rcnn_resnet50_egohands', 'faster_rcnn_resnet101_egohands' and 'faster_rcnn_inception_v2_egohands' models
…TRT) onto GPU, so well as revert number of RPN proposals back to 300; add code to measure tf_sess.run() time
…ection API library: 'detection_boxes', 'detection_scores', 'detection_classes', and 'num_detections'
Add support for 'rfcn_resnet101_egohands', plus some other re-factoring
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I put my latest code in my own GitHub repository: https://github.com/jkjung-avt/tf_trt_models. Feel free to check it out. Meanwhile, I'm not completely sure what your question is. Are you trying to say that TF-TRT fails to optimize 'faster_rcnn_resnet101' at all? |
…Flow Detection Model Zoo instead)
… snapshot tensorflow 'models'
Update my fork to match the latest code in NVIDIA's original repository
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Hello Mr.Jung,
Tanks to you, I was able to test faster rcnn with tensorrt.
But, the latency is lower than I expected on my machine.
The response time is as follows,
In addition, the memory usage is not much different.
My code is as follows,
trt_graph = trt.create_inference_graph(
input_graph_def=frozen_graph,
outputs=output_names,
max_batch_size=1,
max_workspace_size_bytes=1 << 25,
precision_mode='INT8' #'FP32' / 'FP16',
minimum_segment_size=50
)
Did I do something wrong?
I would really appreciate it if you answer for me.