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| cmake_minimum_required(VERSION 3.1) | ||
| project(trt_image_classification) | ||
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| find_package(OpenCV REQUIRED) | ||
| find_package(CUDA REQUIRED) | ||
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| include_directories(${CMAKE_SOURCE_DIR}) | ||
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| set(CUDA_NVCC_FLAGS --std=c++11) | ||
| set(CMAKE_CXX_STANDARD 11) | ||
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| add_subdirectory(examples) | ||
| add_subdirectory(src) |
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| Installation | ||
| === | ||
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| 1. Flash the Jetson TX2 using JetPack 3.2. Be sure to install | ||
| * CUDA 9.0 | ||
| * OpenCV4Tegra | ||
| * cuDNN | ||
| * TensorRT 3.0 | ||
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| 2. Install TensorFlow on Jetson TX2. | ||
| 1. ... | ||
| 2. ... | ||
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| 3. Install uff converter on Jetson TX2. | ||
| 1. Download TensorRT 3.0.4 for Ubuntu 16.04 and CUDA 9.0 tar package from https://developer.nvidia.com/nvidia-tensorrt-download. | ||
| 2. Extract archive | ||
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| tar -xzf TensorRT-3.0.4.Ubuntu-16.04.3.x86_64.cuda-9.0.cudnn7.0.tar.gz | ||
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| 3. Install uff python package using pip | ||
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| sudo pip install TensorRT-3.0.4/uff/uff-0.2.0-py2.py3-none-any.whl | ||
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| 4. Clone and build this project | ||
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| ``` | ||
| git clone --recursive https://gitlab-master.nvidia.com/jwelsh/trt_image_classification.git | ||
| cd trt_image_classification | ||
| mkdir build | ||
| cd build | ||
| cmake .. | ||
| make | ||
| cd .. | ||
| ``` |
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| Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. | ||
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| Redistribution and use in source and binary forms, with or without | ||
| modification, are permitted provided that the following conditions | ||
| are met: | ||
| * Redistributions of source code must retain the above copyright | ||
| notice, this list of conditions and the following disclaimer. | ||
| * Redistributions in binary form must reproduce the above copyright | ||
| notice, this list of conditions and the following disclaimer in the | ||
| documentation and/or other materials provided with the distribution. | ||
| * Neither the name of NVIDIA CORPORATION nor the names of its | ||
| contributors may be used to endorse or promote products derived | ||
| from this software without specific prior written permission. | ||
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| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY | ||
| EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE | ||
| IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR | ||
| PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR | ||
| CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, | ||
| EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, | ||
| PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR | ||
| PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY | ||
| OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT | ||
| (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
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| TensorFlow->TensorRT Image Classification | ||
| === | ||
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| This contains examples, scripts and code related to image classification using TensorFlow models | ||
| (from [here](https://github.com/tensorflow/models/tree/master/research/slim#Pretrained)) | ||
| converted to TensorRT. Converting TensorFlow models to TensorRT offers significant performance | ||
| gains on the Jetson TX2 as seen [below](#default_models). | ||
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| <a name="quick_start"></a> | ||
| ## Quick Start | ||
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| 1. Follow the [installation guide](INSTALL.md). | ||
| 2. Download the pretrained TensorFlow models and example images. | ||
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| ``` | ||
| source scripts/download_models.sh | ||
| source scripts/download_images.sh | ||
| ``` | ||
| 3. Convert the pretrained models to frozen graphs. | ||
| ``` | ||
| python scripts/models_to_frozen_graphs.py | ||
| ``` | ||
| 4. Convert the frozen graphs to optimized TensorRT engines. | ||
| ``` | ||
| python scripts/frozen_graphs_to_plans.py | ||
| ``` | ||
| 5. Execute the Inception V1 model on a single image. | ||
| ``` | ||
| ./build/examples/classify_image/classify_image data/images/gordon_setter.jpg data/plans/inception_v1.plan data/imagenet_labels_1001.txt input InceptionV1/Logits/SpatialSqueeze inception | ||
| ``` | ||
| For more details, read through the examples [link](examples/README.md). | ||
| <a name="default_models"></a> | ||
| ## Default Models | ||
| The table below shows various details related to the default models ported from the TensorFlow | ||
| slim model zoo. | ||
| | <sub>Model</sub> | <sub>Input Size</sub> | <sub>TensorRT (TX2 / Half)</sub> | <sub>TensorRT (TX2 / Float)</sub> | <sub>TensorFlow (TX2 / Float)</sub> | <sub>Input Name</sub> | <sub>Output Name</sub> | <sub>Preprocessing Fn.</sub> | | ||
| |--- |:---:|:---:|:---:|:---:|---|---|---| | ||
| | <sub>inception_v1</sub> | <sub>224x224</sub> | <sub>7.98ms</sub> | <sub>12.8ms</sub> | <sub>27.6ms</sub> | <sub>input</sub> | <sub>InceptionV1/Logits/SpatialSqueeze</sub> | <sub>inception</sub> | | ||
| | <sub>inception_v3</sub> | <sub>299x299</sub> | <sub>26.3ms</sub> | <sub>46.1ms</sub> | <sub>98.4ms</sub> | <sub>input</sub> | <sub>InceptionV3/Logits/SpatialSqueeze</sub> | <sub>inception</sub> | | ||
| | <sub>inception_v4</sub> | <sub>299x299</sub> | <sub>52.1ms</sub> | <sub>88.2ms</sub> | <sub>176ms</sub> | <sub>input</sub> | <sub>InceptionV4/Logits/Logits/BiasAdd</sub> | <sub>inception</sub> | | ||
| | <sub>inception_resnet_v2</sub> | <sub>299x299</sub> | <sub>53.0ms</sub> | <sub>98.7ms</sub> | <sub>168ms</sub> | <sub>input</sub> | <sub>InceptionResnetV2/Logits/Logits/BiasAdd</sub> | <sub>inception</sub> | | ||
| | <sub>resnet_v1_50</sub> | <sub>224x224</sub> | <sub>15.7ms</sub> | <sub>27.1ms</sub> | <sub>63.9ms</sub> | <sub>input</sub> | <sub>resnet_v1_50/SpatialSqueeze</sub> | <sub>vgg</sub> | | ||
| | <sub>resnet_v1_101</sub> | <sub>224x224</sub> | <sub>29.9ms</sub> | <sub>51.8ms</sub> | <sub>107ms</sub> | <sub>input</sub> | <sub>resnet_v1_101/SpatialSqueeze</sub> | <sub>vgg</sub> | | ||
| | <sub>resnet_v1_152</sub> | <sub>224x224</sub> | <sub>42.6ms</sub> | <sub>78.2ms</sub> | <sub>157ms</sub> | <sub>input</sub> | <sub>resnet_v1_152/SpatialSqueeze</sub> | <sub>vgg</sub> | | ||
| | <sub>resnet_v2_50</sub> | <sub>299x299</sub> | <sub>27.5ms</sub> | <sub>44.4ms</sub> | <sub>92.2ms</sub> | <sub>input</sub> | <sub>resnet_v2_50/SpatialSqueeze</sub> | <sub>inception</sub> | | ||
| | <sub>resnet_v2_101</sub> | <sub>299x299</sub> | <sub>49.2ms</sub> | <sub>83.1ms</sub> | <sub>160ms</sub> | <sub>input</sub> | <sub>resnet_v2_101/SpatialSqueeze</sub> | <sub>inception</sub> | | ||
| | <sub>resnet_v2_152</sub> | <sub>299x299</sub> | <sub>74.6ms</sub> | <sub>124ms</sub> | <sub>230ms</sub> | <sub>input</sub> | <sub>resnet_v2_152/SpatialSqueeze</sub> | <sub>inception</sub> | | ||
| | <sub>mobilenet_v1_0p25_128</sub> | <sub>128x128</sub> | <sub>2.67ms</sub> | <sub>2.65ms</sub> | <sub>15.7ms</sub> | <sub>input</sub> | <sub>MobilenetV1/Logits/SpatialSqueeze</sub> | <sub>inception</sub> | | ||
| | <sub>mobilenet_v1_0p5_160</sub> | <sub>160x160</sub> | <sub>3.95ms</sub> | <sub>4.00ms</sub> | <sub>16.9ms</sub> | <sub>input</sub> | <sub>MobilenetV1/Logits/SpatialSqueeze</sub> | <sub>inception</sub> | | ||
| | <sub>mobilenet_v1_1p0_224</sub> | <sub>224x224</sub> | <sub>12.9ms</sub> | <sub>12.9ms</sub> | <sub>24.4ms</sub> | <sub>input</sub> | <sub>MobilenetV1/Logits/SpatialSqueeze</sub> | <sub>inception</sub> | | ||
| | <sub>vgg_16</sub> | <sub>224x224</sub> | <sub>38.2ms</sub> | <sub>79.2ms</sub> | <sub>171ms</sub> | <sub>input</sub> | <sub>vgg_16/fc8/BiasAdd</sub> | <sub>vgg</sub> | | ||
| <!--| inception_v2 | 224x224 | 10.3ms | 16.9ms | 38.3ms | input | InceptionV2/Logits/SpatialSqueeze | inception |--> | ||
| <!--| vgg_19 | 224x224 | 97.3ms | OOM | input | vgg_19/fc8/BiasAdd | vgg |--> | ||
| The times recorded include data transfer to GPU, network execution, and | ||
| data transfer back from GPU. Time does not include preprocessing. | ||
| See **scripts/test_tf.py**, **scripts/test_trt.py**, and **src/test/test_trt.cu** | ||
| for implementation details. To reproduce the timings run | ||
| ``` | ||
| python scripts/test_tf.py | ||
| python scripts/test_trt.py | ||
| ``` | ||
| The timing results will be located in **data/test_output_tf.txt** and **data/test_output_trt.txt**. Note | ||
| that you must download and convert the models (as in the quick start) prior to running the benchmark scripts. |
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