irvingzhang0512
GitHub
parent
8d8e2c9b0c
commit
501160d262
25
tsm/CMakeLists.txt
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25
tsm/CMakeLists.txt
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@ -0,0 +1,25 @@
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cmake_minimum_required(VERSION 2.6)
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project(TSM)
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add_definitions(-std=c++11)
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option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
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set(CMAKE_CXX_STANDARD 11)
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set(CMAKE_BUILD_TYPE Debug)
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include_directories(${PROJECT_SOURCE_DIR}/include)
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# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
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# cuda
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include_directories(/usr/local/cuda/include)
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link_directories(/usr/local/cuda/lib64)
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# tensorrt
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include_directories(/home/ubuntu/TensorRT/include/)
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link_directories(/home/ubuntu/TensorRT/lib/)
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add_executable(tsm_r50 ${PROJECT_SOURCE_DIR}/tsm_r50.cpp)
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target_link_libraries(tsm_r50 nvinfer)
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target_link_libraries(tsm_r50 cudart)
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add_definitions(-O2 -pthread)
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@ -23,9 +23,12 @@ More details about the shift module(which is the core of TSM) could to [test_shi
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python gen_wts.py /path/to/pytorch.pth --out-filename /path/to/tensorrt.wts
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```
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+ Step 3: Modify configs in `tsm_r50.py`.
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+ Step 3: Test Python API.
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+ Modify configs in `tsm_r50.py`.
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+ Inference with `tsm_r50.py`.
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```python
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# Supported settings
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BATCH_SIZE = 1
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NUM_SEGMENTS = 8
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INPUT_H = 224
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@ -34,10 +37,8 @@ OUTPUT_SIZE = 400
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SHIFT_DIV = 8
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```
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+ Step 4: Inference with `tsm_r50.py`.
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```shell
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usage: tsm_r50.py [-h] [--tensorrt-weights TENSORRT_WEIGHTS] [--input-video INPUT_VIDEO] [--save-engine-path SAVE_ENGINE_PATH] [--load-engine-path LOAD_ENGINE_PATH] [--test-mmaction2] [--mmaction2-config MMACTION2_CONFIG] [--mmaction2-checkpoint MMACTION2_CHECKPOINT]
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usage: tsm_r50.py [-h] [--tensorrt-weights TENSORRT_WEIGHTS] [--input-video INPUT_VIDEO] [--save-engine-path SAVE_ENGINE_PATH] [--load-engine-path LOAD_ENGINE_PATH] [--test-mmaction2] [--mmaction2-config MMACTION2_CONFIG] [--mmaction2-checkpoint MMACTION2_CHECKPOINT] [--test-cpp] [--cpp-result-path CPP_RESULT_PATH]
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optional arguments:
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-h, --help show this help message and exit
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@ -54,8 +55,18 @@ optional arguments:
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Path to MMAction2 config file
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--mmaction2-checkpoint MMACTION2_CHECKPOINT
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Path to MMAction2 checkpoint url or file path
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--test-cpp Compare Python API results with C++ API results
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--cpp-result-path CPP_RESULT_PATH
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Path to C++ API results
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```
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+ Step 4: Test C++ API.
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+ Mocify Configs in `tsm_r50.cpp`.
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+ Build from source code: `mkdir build && cd build && cmake .. && make`
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+ Generate Engine file: `./tsm_r50 -s`
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+ Inference with genrated engine file and write predictions to local: `./tsm_r50 -d`
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+ Compare results with Python API: `python tsm_r50.py --tensorrt-weights /path/to/tensorrt.weights --test-cpp --cpp-result-file /path/to/cpp-result.txt`
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## TODO
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+ [x] Python Shift module.
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@ -63,4 +74,4 @@ optional arguments:
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+ [x] Python API Definition
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+ [x] Test with mmaction2 demo
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+ [x] Tutorial
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+ [ ] C++ API Definition
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+ [x] C++ API Definition
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28
tsm/demo.sh
28
tsm/demo.sh
@ -5,16 +5,16 @@ wget https://download.openmmlab.com/mmaction/recognition/tsm/tsm_r50_1x1x8_50e_k
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# Step 2: Convert pytorch checkpoints to TensorRT weights
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python gen_wts.py tsm_r50_1x1x8_50e_kinetics400_rgb_20200607-af7fb746.pth --out-filename ./tsm_r50_kinetics400_mmaction2.wts
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# Step 3: Skip this step since we use default settings.
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# Step 4: Inference
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# 1) Save local engine file to `./tsm_r50_kinetics400_mmaction2.trt`.
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# Step 3: Test Python API.
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# 3.1 Skip this step since we use default settings.
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# 3.2 Inference
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# 3.2.1 Save local engine file to `./tsm_r50_kinetics400_mmaction2.trt`.
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python tsm_r50.py \
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--tensorrt-weights ./tsm_r50_kinetics400_mmaction2.wts \
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--save-engine-path ./tsm_r50_kinetics400_mmaction2.trt
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# 2) Predict the recognition result using a single video `demo.mp4`.
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# Should print `Result class id 6`, aka `arm wrestling`
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# 3.2.2 Predict the recognition result using a single video `demo.mp4`.
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# Should print `Result class id 6`, aka `arm wrestling`
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# Download demo video
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wget https://raw.githubusercontent.com/open-mmlab/mmaction2/master/demo/demo.mp4
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# # use *.wts as input
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@ -24,8 +24,8 @@ wget https://raw.githubusercontent.com/open-mmlab/mmaction2/master/demo/demo.mp4
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python tsm_r50.py --load-engine-path ./tsm_r50_kinetics400_mmaction2.trt \
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--input-video ./demo.mp4
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# 3) Optional: Compare inference result with MMAction2 TSM-R50 model
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# Have to install MMAction2 First, please refer to https://github.com/open-mmlab/mmaction2/blob/master/docs/install.md
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# 3.2.3 Optional: Compare inference result with MMAction2 TSM-R50 model
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# Have to install MMAction2 First, please refer to https://github.com/open-mmlab/mmaction2/blob/master/docs/install.md
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# pip3 install pytest-runner
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# pip3 install mmcv
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# pip3 install mmaction2
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@ -41,3 +41,15 @@ python tsm_r50.py --load-engine-path ./tsm_r50_kinetics400_mmaction2.trt \
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# --test-mmaction2 \
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# --mmaction2-config mmaction2_tsm_r50_config.py \
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# --mmaction2-checkpoint tsm_r50_1x1x8_50e_kinetics400_rgb_20200607-af7fb746.pth
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# Step 4: Test Python API.
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# 4.1 Skip this step since we use default settings.
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# 4.2 Build CPP
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mkdir build && cd build && cmake .. && make
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# 4.3 Generate Engine file
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./tsm_r50 -s
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# 4.4 Get Predictions
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./tsm_r50 -d
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# 4.5 Compare C++ Results with Python Results
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cd ..
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python tsm_r50.py --test-cpp --tensorrt-weights ./tsm_r50_kinetics400_mmaction2.wts
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503
tsm/logging.h
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503
tsm/logging.h
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/*
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* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef TENSORRT_LOGGING_H
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#define TENSORRT_LOGGING_H
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#include "NvInferRuntimeCommon.h"
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#include <cassert>
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#include <ctime>
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#include <iomanip>
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#include <iostream>
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#include <ostream>
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#include <sstream>
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#include <string>
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using Severity = nvinfer1::ILogger::Severity;
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class LogStreamConsumerBuffer : public std::stringbuf
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{
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public:
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LogStreamConsumerBuffer(std::ostream& stream, const std::string& prefix, bool shouldLog)
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: mOutput(stream)
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, mPrefix(prefix)
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, mShouldLog(shouldLog)
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{
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}
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LogStreamConsumerBuffer(LogStreamConsumerBuffer&& other)
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: mOutput(other.mOutput)
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{
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}
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~LogStreamConsumerBuffer()
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{
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// std::streambuf::pbase() gives a pointer to the beginning of the buffered part of the output sequence
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// std::streambuf::pptr() gives a pointer to the current position of the output sequence
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// if the pointer to the beginning is not equal to the pointer to the current position,
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// call putOutput() to log the output to the stream
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if (pbase() != pptr())
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{
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putOutput();
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}
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}
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// synchronizes the stream buffer and returns 0 on success
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// synchronizing the stream buffer consists of inserting the buffer contents into the stream,
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// resetting the buffer and flushing the stream
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virtual int sync()
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{
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putOutput();
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return 0;
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}
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void putOutput()
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{
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if (mShouldLog)
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{
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// prepend timestamp
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std::time_t timestamp = std::time(nullptr);
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tm* tm_local = std::localtime(×tamp);
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std::cout << "[";
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std::cout << std::setw(2) << std::setfill('0') << 1 + tm_local->tm_mon << "/";
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std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_mday << "/";
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std::cout << std::setw(4) << std::setfill('0') << 1900 + tm_local->tm_year << "-";
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std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_hour << ":";
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std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_min << ":";
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std::cout << std::setw(2) << std::setfill('0') << tm_local->tm_sec << "] ";
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// std::stringbuf::str() gets the string contents of the buffer
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// insert the buffer contents pre-appended by the appropriate prefix into the stream
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mOutput << mPrefix << str();
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// set the buffer to empty
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str("");
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// flush the stream
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mOutput.flush();
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}
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}
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void setShouldLog(bool shouldLog)
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{
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mShouldLog = shouldLog;
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}
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private:
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std::ostream& mOutput;
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std::string mPrefix;
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bool mShouldLog;
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};
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//!
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//! \class LogStreamConsumerBase
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//! \brief Convenience object used to initialize LogStreamConsumerBuffer before std::ostream in LogStreamConsumer
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//!
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class LogStreamConsumerBase
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{
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public:
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LogStreamConsumerBase(std::ostream& stream, const std::string& prefix, bool shouldLog)
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: mBuffer(stream, prefix, shouldLog)
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{
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}
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protected:
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LogStreamConsumerBuffer mBuffer;
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};
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//!
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//! \class LogStreamConsumer
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//! \brief Convenience object used to facilitate use of C++ stream syntax when logging messages.
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//! Order of base classes is LogStreamConsumerBase and then std::ostream.
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//! This is because the LogStreamConsumerBase class is used to initialize the LogStreamConsumerBuffer member field
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//! in LogStreamConsumer and then the address of the buffer is passed to std::ostream.
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//! This is necessary to prevent the address of an uninitialized buffer from being passed to std::ostream.
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//! Please do not change the order of the parent classes.
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//!
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class LogStreamConsumer : protected LogStreamConsumerBase, public std::ostream
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{
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public:
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//! \brief Creates a LogStreamConsumer which logs messages with level severity.
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//! Reportable severity determines if the messages are severe enough to be logged.
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LogStreamConsumer(Severity reportableSeverity, Severity severity)
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: LogStreamConsumerBase(severityOstream(severity), severityPrefix(severity), severity <= reportableSeverity)
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, std::ostream(&mBuffer) // links the stream buffer with the stream
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, mShouldLog(severity <= reportableSeverity)
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, mSeverity(severity)
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{
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}
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LogStreamConsumer(LogStreamConsumer&& other)
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: LogStreamConsumerBase(severityOstream(other.mSeverity), severityPrefix(other.mSeverity), other.mShouldLog)
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, std::ostream(&mBuffer) // links the stream buffer with the stream
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, mShouldLog(other.mShouldLog)
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, mSeverity(other.mSeverity)
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{
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}
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void setReportableSeverity(Severity reportableSeverity)
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{
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mShouldLog = mSeverity <= reportableSeverity;
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mBuffer.setShouldLog(mShouldLog);
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}
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private:
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static std::ostream& severityOstream(Severity severity)
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{
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return severity >= Severity::kINFO ? std::cout : std::cerr;
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}
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static std::string severityPrefix(Severity severity)
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{
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switch (severity)
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{
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case Severity::kINTERNAL_ERROR: return "[F] ";
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case Severity::kERROR: return "[E] ";
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case Severity::kWARNING: return "[W] ";
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case Severity::kINFO: return "[I] ";
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case Severity::kVERBOSE: return "[V] ";
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default: assert(0); return "";
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}
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}
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bool mShouldLog;
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Severity mSeverity;
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};
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//! \class Logger
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//!
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//! \brief Class which manages logging of TensorRT tools and samples
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//!
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//! \details This class provides a common interface for TensorRT tools and samples to log information to the console,
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//! and supports logging two types of messages:
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//!
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//! - Debugging messages with an associated severity (info, warning, error, or internal error/fatal)
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//! - Test pass/fail messages
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//!
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//! The advantage of having all samples use this class for logging as opposed to emitting directly to stdout/stderr is
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//! that the logic for controlling the verbosity and formatting of sample output is centralized in one location.
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//!
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//! In the future, this class could be extended to support dumping test results to a file in some standard format
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//! (for example, JUnit XML), and providing additional metadata (e.g. timing the duration of a test run).
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//!
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//! TODO: For backwards compatibility with existing samples, this class inherits directly from the nvinfer1::ILogger
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//! interface, which is problematic since there isn't a clean separation between messages coming from the TensorRT
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//! library and messages coming from the sample.
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//!
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//! In the future (once all samples are updated to use Logger::getTRTLogger() to access the ILogger) we can refactor the
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//! class to eliminate the inheritance and instead make the nvinfer1::ILogger implementation a member of the Logger
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//! object.
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class Logger : public nvinfer1::ILogger
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{
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public:
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Logger(Severity severity = Severity::kWARNING)
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: mReportableSeverity(severity)
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{
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}
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//!
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//! \enum TestResult
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//! \brief Represents the state of a given test
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//!
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enum class TestResult
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{
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kRUNNING, //!< The test is running
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kPASSED, //!< The test passed
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kFAILED, //!< The test failed
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kWAIVED //!< The test was waived
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};
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//!
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//! \brief Forward-compatible method for retrieving the nvinfer::ILogger associated with this Logger
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//! \return The nvinfer1::ILogger associated with this Logger
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//!
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//! TODO Once all samples are updated to use this method to register the logger with TensorRT,
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//! we can eliminate the inheritance of Logger from ILogger
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//!
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nvinfer1::ILogger& getTRTLogger()
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{
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return *this;
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}
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//!
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//! \brief Implementation of the nvinfer1::ILogger::log() virtual method
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//!
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//! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
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//! inheritance from nvinfer1::ILogger
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//!
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void log(Severity severity, const char* msg) override
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{
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LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
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}
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//!
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//! \brief Method for controlling the verbosity of logging output
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//!
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//! \param severity The logger will only emit messages that have severity of this level or higher.
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//!
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void setReportableSeverity(Severity severity)
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{
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mReportableSeverity = severity;
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}
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//!
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//! \brief Opaque handle that holds logging information for a particular test
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//!
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//! This object is an opaque handle to information used by the Logger to print test results.
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//! The sample must call Logger::defineTest() in order to obtain a TestAtom that can be used
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//! with Logger::reportTest{Start,End}().
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//!
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class TestAtom
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{
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public:
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TestAtom(TestAtom&&) = default;
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private:
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friend class Logger;
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TestAtom(bool started, const std::string& name, const std::string& cmdline)
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: mStarted(started)
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, mName(name)
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, mCmdline(cmdline)
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{
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}
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bool mStarted;
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std::string mName;
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std::string mCmdline;
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};
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//!
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//! \brief Define a test for logging
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//!
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//! \param[in] name The name of the test. This should be a string starting with
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//! "TensorRT" and containing dot-separated strings containing
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//! the characters [A-Za-z0-9_].
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//! For example, "TensorRT.sample_googlenet"
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//! \param[in] cmdline The command line used to reproduce the test
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//
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//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
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//!
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static TestAtom defineTest(const std::string& name, const std::string& cmdline)
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{
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return TestAtom(false, name, cmdline);
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}
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//!
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//! \brief A convenience overloaded version of defineTest() that accepts an array of command-line arguments
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//! as input
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//!
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//! \param[in] name The name of the test
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//! \param[in] argc The number of command-line arguments
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//! \param[in] argv The array of command-line arguments (given as C strings)
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//!
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//! \return a TestAtom that can be used in Logger::reportTest{Start,End}().
|
||||
static TestAtom defineTest(const std::string& name, int argc, char const* const* argv)
|
||||
{
|
||||
auto cmdline = genCmdlineString(argc, argv);
|
||||
return defineTest(name, cmdline);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has started.
|
||||
//!
|
||||
//! \pre reportTestStart() has not been called yet for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has started
|
||||
//!
|
||||
static void reportTestStart(TestAtom& testAtom)
|
||||
{
|
||||
reportTestResult(testAtom, TestResult::kRUNNING);
|
||||
assert(!testAtom.mStarted);
|
||||
testAtom.mStarted = true;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief Report that a test has ended.
|
||||
//!
|
||||
//! \pre reportTestStart() has been called for the given testAtom
|
||||
//!
|
||||
//! \param[in] testAtom The handle to the test that has ended
|
||||
//! \param[in] result The result of the test. Should be one of TestResult::kPASSED,
|
||||
//! TestResult::kFAILED, TestResult::kWAIVED
|
||||
//!
|
||||
static void reportTestEnd(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
assert(result != TestResult::kRUNNING);
|
||||
assert(testAtom.mStarted);
|
||||
reportTestResult(testAtom, result);
|
||||
}
|
||||
|
||||
static int reportPass(const TestAtom& testAtom)
|
||||
{
|
||||
reportTestEnd(testAtom, TestResult::kPASSED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportFail(const TestAtom& testAtom)
|
||||
{
|
||||
reportTestEnd(testAtom, TestResult::kFAILED);
|
||||
return EXIT_FAILURE;
|
||||
}
|
||||
|
||||
static int reportWaive(const TestAtom& testAtom)
|
||||
{
|
||||
reportTestEnd(testAtom, TestResult::kWAIVED);
|
||||
return EXIT_SUCCESS;
|
||||
}
|
||||
|
||||
static int reportTest(const TestAtom& testAtom, bool pass)
|
||||
{
|
||||
return pass ? reportPass(testAtom) : reportFail(testAtom);
|
||||
}
|
||||
|
||||
Severity getReportableSeverity() const
|
||||
{
|
||||
return mReportableSeverity;
|
||||
}
|
||||
|
||||
private:
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a log message with the given severity
|
||||
//!
|
||||
static const char* severityPrefix(Severity severity)
|
||||
{
|
||||
switch (severity)
|
||||
{
|
||||
case Severity::kINTERNAL_ERROR: return "[F] ";
|
||||
case Severity::kERROR: return "[E] ";
|
||||
case Severity::kWARNING: return "[W] ";
|
||||
case Severity::kINFO: return "[I] ";
|
||||
case Severity::kVERBOSE: return "[V] ";
|
||||
default: assert(0); return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate string for prefixing a test result message with the given result
|
||||
//!
|
||||
static const char* testResultString(TestResult result)
|
||||
{
|
||||
switch (result)
|
||||
{
|
||||
case TestResult::kRUNNING: return "RUNNING";
|
||||
case TestResult::kPASSED: return "PASSED";
|
||||
case TestResult::kFAILED: return "FAILED";
|
||||
case TestResult::kWAIVED: return "WAIVED";
|
||||
default: assert(0); return "";
|
||||
}
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief returns an appropriate output stream (cout or cerr) to use with the given severity
|
||||
//!
|
||||
static std::ostream& severityOstream(Severity severity)
|
||||
{
|
||||
return severity >= Severity::kINFO ? std::cout : std::cerr;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief method that implements logging test results
|
||||
//!
|
||||
static void reportTestResult(const TestAtom& testAtom, TestResult result)
|
||||
{
|
||||
severityOstream(Severity::kINFO) << "&&&& " << testResultString(result) << " " << testAtom.mName << " # "
|
||||
<< testAtom.mCmdline << std::endl;
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief generate a command line string from the given (argc, argv) values
|
||||
//!
|
||||
static std::string genCmdlineString(int argc, char const* const* argv)
|
||||
{
|
||||
std::stringstream ss;
|
||||
for (int i = 0; i < argc; i++)
|
||||
{
|
||||
if (i > 0)
|
||||
ss << " ";
|
||||
ss << argv[i];
|
||||
}
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
Severity mReportableSeverity;
|
||||
};
|
||||
|
||||
namespace
|
||||
{
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kVERBOSE
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_VERBOSE(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_VERBOSE(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kVERBOSE);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINFO
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_INFO(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_INFO(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINFO);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kWARNING
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_WARN(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_WARN(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kWARNING);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kERROR
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_ERROR(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_ERROR(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kERROR);
|
||||
}
|
||||
|
||||
//!
|
||||
//! \brief produces a LogStreamConsumer object that can be used to log messages of severity kINTERNAL_ERROR
|
||||
// ("fatal" severity)
|
||||
//!
|
||||
//! Example usage:
|
||||
//!
|
||||
//! LOG_FATAL(logger) << "hello world" << std::endl;
|
||||
//!
|
||||
inline LogStreamConsumer LOG_FATAL(const Logger& logger)
|
||||
{
|
||||
return LogStreamConsumer(logger.getReportableSeverity(), Severity::kINTERNAL_ERROR);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
#endif // TENSORRT_LOGGING_H
|
||||
422
tsm/tsm_r50.cpp
Normal file
422
tsm/tsm_r50.cpp
Normal file
@ -0,0 +1,422 @@
|
||||
#include "NvInfer.h"
|
||||
#include "cuda_runtime_api.h"
|
||||
#include "logging.h"
|
||||
#include <fstream>
|
||||
#include <iostream>
|
||||
#include <map>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
#include <chrono>
|
||||
#include <cmath>
|
||||
#include <cstring>
|
||||
|
||||
#define CHECK(status) \
|
||||
do\
|
||||
{\
|
||||
auto ret = (status);\
|
||||
if (ret != 0)\
|
||||
{\
|
||||
std::cerr << "Cuda failure: " << ret << std::endl;\
|
||||
abort();\
|
||||
}\
|
||||
} while (0)
|
||||
|
||||
static const int INPUT_H = 224;
|
||||
static const int INPUT_W = 224;
|
||||
static const int OUTPUT_SIZE = 400;
|
||||
static const int NUM_SEGMENTS = 8;
|
||||
static const int SHIFT_DIV = 8;
|
||||
|
||||
const char* INPUT_BLOB_NAME = "data";
|
||||
const char* OUTPUT_BLOB_NAME = "prob";
|
||||
const char* WEIGHTS_PATH = "../tsm_r50_kinetics400_mmaction2.wts";
|
||||
const char* ENGINE_PATH = "./tsm_r50_kinetics400_mmaction2_cpp.trt";
|
||||
const char* RESULT_PATH = "./result.txt";
|
||||
|
||||
using namespace nvinfer1;
|
||||
|
||||
static Logger gLogger;
|
||||
|
||||
// Load weights from files shared with TensorRT samples.
|
||||
// TensorRT weight files have a simple space delimited format:
|
||||
// [type] [size] <data x size in hex>
|
||||
std::map<std::string, Weights> loadWeights(const std::string file)
|
||||
{
|
||||
std::cout << "Loading weights: " << file << std::endl;
|
||||
std::map<std::string, Weights> weightMap;
|
||||
|
||||
// Open weights file
|
||||
std::ifstream input(file);
|
||||
assert(input.is_open() && "Unable to load weight file.");
|
||||
|
||||
// Read number of weight blobs
|
||||
int32_t count;
|
||||
input >> count;
|
||||
assert(count > 0 && "Invalid weight map file.");
|
||||
|
||||
while (count--)
|
||||
{
|
||||
Weights wt{DataType::kFLOAT, nullptr, 0};
|
||||
uint32_t size;
|
||||
|
||||
// Read name and type of blob
|
||||
std::string name;
|
||||
input >> name >> std::dec >> size;
|
||||
wt.type = DataType::kFLOAT;
|
||||
|
||||
// Load blob
|
||||
uint32_t* val = reinterpret_cast<uint32_t*>(malloc(sizeof(val) * size));
|
||||
for (uint32_t x = 0, y = size; x < y; ++x)
|
||||
{
|
||||
input >> std::hex >> val[x];
|
||||
}
|
||||
wt.values = val;
|
||||
|
||||
wt.count = size;
|
||||
weightMap[name] = wt;
|
||||
}
|
||||
|
||||
return weightMap;
|
||||
}
|
||||
|
||||
void print(char* name, ITensor* tensor) {
|
||||
Dims dim = tensor->getDimensions();
|
||||
std::cout << name << " " << dim.d[0] << " " << dim.d[1] << " " << dim.d[2] << " " << dim.d[3] <<std::endl;
|
||||
}
|
||||
|
||||
IConcatenationLayer* addShift(INetworkDefinition *network, ITensor& input, Dims4 inputShape, int numSegments, int shiftDiv) {
|
||||
int fold = int(inputShape.d[1] / shiftDiv);
|
||||
float* zeros = reinterpret_cast<float*>(malloc(sizeof(zeros) * fold*inputShape.d[2]*inputShape.d[3]));
|
||||
memset(zeros, 0, sizeof(zeros) * fold*inputShape.d[2]*inputShape.d[3]);
|
||||
Weights zeros_weights{DataType::kFLOAT, zeros, fold*inputShape.d[2]*inputShape.d[3]};
|
||||
|
||||
// left
|
||||
ISliceLayer* left1 = network->addSlice(input, Dims4{1, 0, 0, 0}, Dims4{numSegments - 1, fold, inputShape.d[2], inputShape.d[3]}, Dims4{1, 1, 1, 1});
|
||||
IConstantLayer* left2 = network->addConstant(Dims4{1, fold, inputShape.d[2], inputShape.d[3]}, zeros_weights);
|
||||
ITensor* tensorsLeft[] = {left1->getOutput(0), left2->getOutput(0)};
|
||||
IConcatenationLayer* left = network->addConcatenation(tensorsLeft, 2);
|
||||
left->setAxis(0);
|
||||
|
||||
// mid
|
||||
IConstantLayer* mid1 = network->addConstant(Dims4{1, fold, inputShape.d[2], inputShape.d[3]}, zeros_weights);
|
||||
ISliceLayer* mid2 = network->addSlice(input, Dims4{0, fold, 0, 0}, Dims4{numSegments - 1, fold, inputShape.d[2], inputShape.d[3]}, Dims4{1, 1, 1, 1});
|
||||
ITensor* tensorsMid[] = {mid1->getOutput(0), mid2->getOutput(0)};
|
||||
IConcatenationLayer* mid = network->addConcatenation(tensorsMid, 2);
|
||||
mid->setAxis(0);
|
||||
|
||||
// right
|
||||
ISliceLayer* right = network->addSlice(input, Dims4{0, 2 * fold, 0, 0}, Dims4{numSegments, inputShape.d[1] - 2 * fold, inputShape.d[2], inputShape.d[3]}, Dims4{1, 1, 1, 1});
|
||||
|
||||
// concatenate left/mid/right
|
||||
ITensor* tensors[] = {left->getOutput(0), mid->getOutput(0), right->getOutput(0)};
|
||||
IConcatenationLayer* concat = network->addConcatenation(tensors, 3);
|
||||
concat->setAxis(1);
|
||||
return concat;
|
||||
}
|
||||
|
||||
IScaleLayer* addBatchNorm2d(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, std::string lname, float eps) {
|
||||
float *gamma = (float*)weightMap[lname + ".weight"].values;
|
||||
float *beta = (float*)weightMap[lname + ".bias"].values;
|
||||
float *mean = (float*)weightMap[lname + ".running_mean"].values;
|
||||
float *var = (float*)weightMap[lname + ".running_var"].values;
|
||||
int len = weightMap[lname + ".running_var"].count;
|
||||
|
||||
float *scval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
scval[i] = gamma[i] / sqrt(var[i] + eps);
|
||||
}
|
||||
Weights scale{DataType::kFLOAT, scval, len};
|
||||
|
||||
float *shval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
shval[i] = beta[i] - mean[i] * gamma[i] / sqrt(var[i] + eps);
|
||||
}
|
||||
Weights shift{DataType::kFLOAT, shval, len};
|
||||
|
||||
float *pval = reinterpret_cast<float*>(malloc(sizeof(float) * len));
|
||||
for (int i = 0; i < len; i++) {
|
||||
pval[i] = 1.0;
|
||||
}
|
||||
Weights power{DataType::kFLOAT, pval, len};
|
||||
|
||||
weightMap[lname + ".scale"] = scale;
|
||||
weightMap[lname + ".shift"] = shift;
|
||||
weightMap[lname + ".power"] = power;
|
||||
IScaleLayer* scale_1 = network->addScale(input, ScaleMode::kCHANNEL, shift, scale, power);
|
||||
assert(scale_1);
|
||||
return scale_1;
|
||||
}
|
||||
|
||||
IActivationLayer* bottleneck(INetworkDefinition *network, std::map<std::string, Weights>& weightMap, ITensor& input, int inch, int outch, int stride, std::string lname, Dims4 inputShape) {
|
||||
IConcatenationLayer* shift = addShift(network, input, inputShape, NUM_SEGMENTS, SHIFT_DIV);
|
||||
assert(shift);
|
||||
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
IConvolutionLayer* conv1 = network->addConvolution(*shift->getOutput(0), outch, DimsHW{1, 1}, weightMap[lname + "conv1.weight"], emptywts);
|
||||
|
||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), lname + "bn1", 1e-5);
|
||||
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu1);
|
||||
|
||||
IConvolutionLayer* conv2 = network->addConvolution(*relu1->getOutput(0), outch, DimsHW{3, 3}, weightMap[lname + "conv2.weight"], emptywts);
|
||||
assert(conv2);
|
||||
conv2->setStride(DimsHW{stride, stride});
|
||||
conv2->setPadding(DimsHW{1, 1});
|
||||
|
||||
IScaleLayer* bn2 = addBatchNorm2d(network, weightMap, *conv2->getOutput(0), lname + "bn2", 1e-5);
|
||||
|
||||
IActivationLayer* relu2 = network->addActivation(*bn2->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu2);
|
||||
|
||||
IConvolutionLayer* conv3 = network->addConvolution(*relu2->getOutput(0), outch * 4, DimsHW{1, 1}, weightMap[lname + "conv3.weight"], emptywts);
|
||||
assert(conv3);
|
||||
|
||||
IScaleLayer* bn3 = addBatchNorm2d(network, weightMap, *conv3->getOutput(0), lname + "bn3", 1e-5);
|
||||
|
||||
IElementWiseLayer* ew1;
|
||||
if (stride != 1 || inch != outch * 4) {
|
||||
IConvolutionLayer* conv4 = network->addConvolution(input, outch * 4, DimsHW{1, 1}, weightMap[lname + "downsample.0.weight"], emptywts);
|
||||
assert(conv4);
|
||||
conv4->setStride(DimsHW{stride, stride});
|
||||
|
||||
IScaleLayer* bn4 = addBatchNorm2d(network, weightMap, *conv4->getOutput(0), lname + "downsample.1", 1e-5);
|
||||
ew1 = network->addElementWise(*bn4->getOutput(0), *bn3->getOutput(0), ElementWiseOperation::kSUM);
|
||||
} else {
|
||||
ew1 = network->addElementWise(input, *bn3->getOutput(0), ElementWiseOperation::kSUM);
|
||||
}
|
||||
IActivationLayer* relu3 = network->addActivation(*ew1->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu3);
|
||||
return relu3;
|
||||
}
|
||||
|
||||
// Creat the engine using only the API and not any parser.
|
||||
ICudaEngine* createEngine(unsigned int maxBatchSize, IBuilder* builder, DataType dt)
|
||||
{
|
||||
INetworkDefinition* network = builder->createNetwork();
|
||||
|
||||
// Create input tensor of shape {NUM_SEGMENTS, 3, INPUT_H, INPUT_W } with name INPUT_BLOB_NAME
|
||||
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims4{NUM_SEGMENTS, 3, INPUT_H, INPUT_W});
|
||||
assert(data);
|
||||
print("input", data);
|
||||
|
||||
std::map<std::string, Weights> weightMap = loadWeights(WEIGHTS_PATH);
|
||||
Weights emptywts{DataType::kFLOAT, nullptr, 0};
|
||||
|
||||
IConvolutionLayer* conv1 = network->addConvolution(*data, 64, DimsHW{7, 7}, weightMap["conv1.weight"], emptywts);
|
||||
assert(conv1);
|
||||
conv1->setStride(DimsHW{2, 2});
|
||||
conv1->setPadding(DimsHW{3, 3});
|
||||
|
||||
IScaleLayer* bn1 = addBatchNorm2d(network, weightMap, *conv1->getOutput(0), "bn1", 1e-5);
|
||||
|
||||
// Add activation layer using the ReLU algorithm.
|
||||
IActivationLayer* relu1 = network->addActivation(*bn1->getOutput(0), ActivationType::kRELU);
|
||||
assert(relu1);
|
||||
|
||||
// Add max pooling layer with stride of 2x2 and kernel size of 2x2.
|
||||
IPoolingLayer* pool1 = network->addPooling(*relu1->getOutput(0), PoolingType::kMAX, DimsHW{3, 3});
|
||||
assert(pool1);
|
||||
pool1->setStride(DimsHW{2, 2});
|
||||
pool1->setPadding(DimsHW{1, 1});
|
||||
|
||||
int curHeight = int(INPUT_H / 4);
|
||||
int curWidth = int(INPUT_W / 4);
|
||||
IActivationLayer* x = bottleneck(network, weightMap, *pool1->getOutput(0), 64, 64, 1, "layer1.0.", Dims4{NUM_SEGMENTS, 64, curHeight, curWidth});
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 256, 64, 1, "layer1.1.", Dims4{NUM_SEGMENTS, 256, curHeight, curWidth});
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 256, 64, 1, "layer1.2.", Dims4{NUM_SEGMENTS, 256, curHeight, curWidth});
|
||||
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 256, 128, 2, "layer2.0.", Dims4{NUM_SEGMENTS, 256, curHeight, curWidth});
|
||||
curHeight = int(INPUT_H / 8);
|
||||
curWidth = int(INPUT_W / 8);
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.1.", Dims4{NUM_SEGMENTS, 512, curHeight, curWidth});
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.2.", Dims4{NUM_SEGMENTS, 512, curHeight, curWidth});
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 128, 1, "layer2.3.", Dims4{NUM_SEGMENTS, 512, curHeight, curWidth});
|
||||
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 512, 256, 2, "layer3.0.", Dims4{NUM_SEGMENTS, 512, curHeight, curWidth});
|
||||
curHeight = int(INPUT_H / 16);
|
||||
curWidth = int(INPUT_W / 16);
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.1.", Dims4{NUM_SEGMENTS, 1024, curHeight, curWidth});
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.2.", Dims4{NUM_SEGMENTS, 1024, curHeight, curWidth});
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.3.", Dims4{NUM_SEGMENTS, 1024, curHeight, curWidth});
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.4.", Dims4{NUM_SEGMENTS, 1024, curHeight, curWidth});
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 256, 1, "layer3.5.", Dims4{NUM_SEGMENTS, 1024, curHeight, curWidth});
|
||||
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 1024, 512, 2, "layer4.0.", Dims4{NUM_SEGMENTS, 1024, curHeight, curWidth});
|
||||
curHeight = int(INPUT_H / 32);
|
||||
curWidth = int(INPUT_W / 32);
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 2048, 512, 1, "layer4.1.", Dims4{NUM_SEGMENTS, 2048, curHeight, curWidth});
|
||||
x = bottleneck(network, weightMap, *x->getOutput(0), 2048, 512, 1, "layer4.2.", Dims4{NUM_SEGMENTS, 2048, curHeight, curWidth});
|
||||
|
||||
IPoolingLayer* pool2 = network->addPooling(*x->getOutput(0), PoolingType::kAVERAGE, DimsHW{curHeight, curWidth});
|
||||
assert(pool2);
|
||||
pool2->setStride(DimsHW{1, 1});
|
||||
|
||||
IFullyConnectedLayer* fc1 = network->addFullyConnected(*pool2->getOutput(0), OUTPUT_SIZE, weightMap["fc.weight"], weightMap["fc.bias"]);
|
||||
assert(fc1);
|
||||
|
||||
IReduceLayer* reduce = network->addReduce(*fc1->getOutput(0), ReduceOperation::kAVG, 1, false);
|
||||
assert(reduce);
|
||||
|
||||
ISoftMaxLayer* softmax = network->addSoftMax(*reduce->getOutput(0));
|
||||
assert(softmax);
|
||||
softmax->setAxes(1);
|
||||
|
||||
softmax->getOutput(0)->setName(OUTPUT_BLOB_NAME);
|
||||
network->markOutput(*softmax->getOutput(0));
|
||||
|
||||
// Build engine
|
||||
builder->setMaxBatchSize(maxBatchSize);
|
||||
ICudaEngine* engine = builder->buildCudaEngine(*network);
|
||||
|
||||
// Don't need the network any more
|
||||
network->destroy();
|
||||
|
||||
// Release host memory
|
||||
for (auto& mem : weightMap)
|
||||
{
|
||||
free((void*) (mem.second.values));
|
||||
}
|
||||
|
||||
return engine;
|
||||
}
|
||||
|
||||
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream)
|
||||
{
|
||||
// Create builder
|
||||
IBuilder* builder = createInferBuilder(gLogger);
|
||||
|
||||
// Create model to populate the network, then set the outputs and create an engine
|
||||
ICudaEngine* engine = createEngine(maxBatchSize, builder, DataType::kFLOAT);
|
||||
assert(engine != nullptr);
|
||||
|
||||
// Serialize the engine
|
||||
(*modelStream) = engine->serialize();
|
||||
|
||||
// Close everything down
|
||||
engine->destroy();
|
||||
builder->destroy();
|
||||
}
|
||||
|
||||
void doInference(IExecutionContext& context, float* input, float* output, int batchSize)
|
||||
{
|
||||
const ICudaEngine& engine = context.getEngine();
|
||||
|
||||
// Pointers to input and output device buffers to pass to engine.
|
||||
// Engine requires exactly IEngine::getNbBindings() number of buffers.
|
||||
assert(engine.getNbBindings() == 2);
|
||||
void* buffers[2];
|
||||
|
||||
// In order to bind the buffers, we need to know the names of the input and output tensors.
|
||||
// Note that indices are guaranteed to be less than IEngine::getNbBindings()
|
||||
const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
|
||||
const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
|
||||
|
||||
// Create GPU buffers on device
|
||||
CHECK(cudaMalloc(&buffers[inputIndex], batchSize * NUM_SEGMENTS * 3 * INPUT_H * INPUT_W * sizeof(float)));
|
||||
CHECK(cudaMalloc(&buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float)));
|
||||
|
||||
// Create stream
|
||||
cudaStream_t stream;
|
||||
CHECK(cudaStreamCreate(&stream));
|
||||
|
||||
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
|
||||
CHECK(cudaMemcpyAsync(buffers[inputIndex], input, batchSize * NUM_SEGMENTS * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
|
||||
context.enqueue(batchSize, buffers, stream, nullptr);
|
||||
CHECK(cudaMemcpyAsync(output, buffers[outputIndex], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
|
||||
cudaStreamSynchronize(stream);
|
||||
|
||||
// Release stream and buffers
|
||||
cudaStreamDestroy(stream);
|
||||
CHECK(cudaFree(buffers[inputIndex]));
|
||||
CHECK(cudaFree(buffers[outputIndex]));
|
||||
}
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
if (argc != 2) {
|
||||
std::cerr << "arguments not right!" << std::endl;
|
||||
std::cerr << "./tsm_r50 -s // serialize model to plan file" << std::endl;
|
||||
std::cerr << "./tsm_r50 -d // deserialize plan file and run inference" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// create a model using the API directly and serialize it to a stream
|
||||
char *trtModelStream{nullptr};
|
||||
size_t size{0};
|
||||
|
||||
if (std::string(argv[1]) == "-s") {
|
||||
IHostMemory* modelStream{nullptr};
|
||||
APIToModel(1, &modelStream);
|
||||
assert(modelStream != nullptr);
|
||||
|
||||
std::ofstream p(ENGINE_PATH, std::ios::binary);
|
||||
if (!p)
|
||||
{
|
||||
std::cerr << "could not open plan output file" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
|
||||
modelStream->destroy();
|
||||
return 1;
|
||||
} else if (std::string(argv[1]) == "-d") {
|
||||
std::ifstream file(ENGINE_PATH, std::ios::binary);
|
||||
if (file.good()) {
|
||||
file.seekg(0, file.end);
|
||||
size = file.tellg();
|
||||
file.seekg(0, file.beg);
|
||||
trtModelStream = new char[size];
|
||||
assert(trtModelStream);
|
||||
file.read(trtModelStream, size);
|
||||
file.close();
|
||||
}
|
||||
} else {
|
||||
return -1;
|
||||
}
|
||||
|
||||
|
||||
// Subtract mean from image
|
||||
static float data[NUM_SEGMENTS * 3 * INPUT_H * INPUT_W];
|
||||
for (int i = 0; i < NUM_SEGMENTS * 3 * INPUT_H * INPUT_W; i++)
|
||||
data[i] = 1.0;
|
||||
|
||||
IRuntime* runtime = createInferRuntime(gLogger);
|
||||
assert(runtime != nullptr);
|
||||
ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size, nullptr);
|
||||
assert(engine != nullptr);
|
||||
IExecutionContext* context = engine->createExecutionContext();
|
||||
assert(context != nullptr);
|
||||
delete[] trtModelStream;
|
||||
|
||||
// Run inference
|
||||
static float prob[OUTPUT_SIZE];
|
||||
doInference(*context, data, prob, 1);
|
||||
|
||||
// Destroy the engine
|
||||
context->destroy();
|
||||
engine->destroy();
|
||||
runtime->destroy();
|
||||
|
||||
// Print histogram of the output distribution
|
||||
std::cout << "\nOutput:\n\n";
|
||||
for (unsigned int i = 0; i < 10; i++)
|
||||
{
|
||||
std::cout << prob[i] << ", ";
|
||||
}
|
||||
std::cout << std::endl;
|
||||
for (unsigned int i = 0; i < 10; i++)
|
||||
{
|
||||
std::cout << prob[OUTPUT_SIZE - 10 + i] << ", ";
|
||||
}
|
||||
std::cout << std::endl;
|
||||
std::fstream writer(RESULT_PATH, std::ios::out);
|
||||
|
||||
writer << prob[0];
|
||||
for(int i = 1; i < OUTPUT_SIZE ; i++) {
|
||||
writer << " " << prob[i];
|
||||
}
|
||||
writer.close();
|
||||
|
||||
return 0;
|
||||
}
|
||||
@ -399,7 +399,33 @@ def main(args):
|
||||
assert_array_almost_equal(host_out.reshape(-1),
|
||||
pytorch_results.reshape(-1),
|
||||
decimal=4)
|
||||
print("TEST PASSED")
|
||||
print("MMAction2 TEST PASSED")
|
||||
|
||||
if args.test_cpp:
|
||||
assert args.cpp_result_path, "Should set --cpp-result-path"
|
||||
assert os.path.exists(args.cpp_result_path),\
|
||||
f"{args.cpp_result} doesn't exist"
|
||||
|
||||
# C++ API fixed inputs
|
||||
inputs = np.ones((BATCH_SIZE, NUM_SEGMENTS, 3, INPUT_H, INPUT_W),
|
||||
dtype=np.float32)
|
||||
|
||||
# TensorRT inference
|
||||
np.copyto(host_in, inputs.ravel())
|
||||
do_inference(context, host_in, host_out, BATCH_SIZE)
|
||||
|
||||
# Read cpp inference results
|
||||
with open(args.cpp_result_path, "r") as f:
|
||||
data = f.read().strip()
|
||||
cpp_results = np.array([float(d)
|
||||
for d in data.split(" ")]).astype(np.float32)
|
||||
|
||||
# test
|
||||
from numpy.testing import assert_array_almost_equal
|
||||
assert_array_almost_equal(host_out.reshape(-1),
|
||||
cpp_results.reshape(-1),
|
||||
decimal=4)
|
||||
print("CPP TEST PASSED")
|
||||
|
||||
if args.input_video:
|
||||
# Get ONE prediction result from ONE video
|
||||
@ -467,5 +493,12 @@ if __name__ == '__main__':
|
||||
type=str,
|
||||
default=None,
|
||||
help="Path to MMAction2 checkpoint url or file path")
|
||||
parser.add_argument("--test-cpp",
|
||||
action='store_true',
|
||||
help="Compare Python API results with C++ API results")
|
||||
parser.add_argument("--cpp-result-path",
|
||||
type=str,
|
||||
default='./build/result.txt',
|
||||
help="Path to C++ API results")
|
||||
|
||||
main(parser.parse_args())
|
||||
|
||||
Loading…
Reference in New Issue
Block a user