NVIDIA-AI-Image-Classification

TensorFlow->TensorRT Image Classification

This contains examples, scripts and code related to image classification using TensorFlow models (from here) converted to TensorRT. Converting TensorFlow models to TensorRT offers significant performance gains on the Jetson TX2 as seen below.

• Models
• Setup
• Download models and create frozen graphs
• Convert frozen graph to TensorRT engine
• Execute TensorRT engine
• Benchmark all models

Models

The table below shows various details related to pretrained models ported from the TensorFlow slim model zoo.

Model	Input Size	TensorRT (TX2 / Half)	TensorRT (TX2 / Float)	TensorFlow (TX2 / Float)	Input Name	Output Name	Preprocessing Fn.
inception_v1	224x224	7.98ms	12.8ms	27.6ms	input	InceptionV1/Logits/SpatialSqueeze	inception
inception_v3	299x299	26.3ms	46.1ms	98.4ms	input	InceptionV3/Logits/SpatialSqueeze	inception
inception_v4	299x299	52.1ms	88.2ms	176ms	input	InceptionV4/Logits/Logits/BiasAdd	inception
inception_resnet_v2	299x299	53.0ms	98.7ms	168ms	input	InceptionResnetV2/Logits/Logits/BiasAdd	inception
resnet_v1_50	224x224	15.7ms	27.1ms	63.9ms	input	resnet_v1_50/SpatialSqueeze	vgg
resnet_v1_101	224x224	29.9ms	51.8ms	107ms	input	resnet_v1_101/SpatialSqueeze	vgg
resnet_v1_152	224x224	42.6ms	78.2ms	157ms	input	resnet_v1_152/SpatialSqueeze	vgg
resnet_v2_50	299x299	27.5ms	44.4ms	92.2ms	input	resnet_v2_50/SpatialSqueeze	inception
resnet_v2_101	299x299	49.2ms	83.1ms	160ms	input	resnet_v2_101/SpatialSqueeze	inception
resnet_v2_152	299x299	74.6ms	124ms	230ms	input	resnet_v2_152/SpatialSqueeze	inception
mobilenet_v1_0p25_128	128x128	2.67ms	2.65ms	15.7ms	input	MobilenetV1/Logits/SpatialSqueeze	inception
mobilenet_v1_0p5_160	160x160	3.95ms	4.00ms	16.9ms	input	MobilenetV1/Logits/SpatialSqueeze	inception
mobilenet_v1_1p0_224	224x224	12.9ms	12.9ms	24.4ms	input	MobilenetV1/Logits/SpatialSqueeze	inception
vgg_16	224x224	38.2ms	79.2ms	171ms	input	vgg_16/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.

Setup

1. Flash the Jetson TX2 using JetPack 3.2. Be sure to install

   • CUDA 9.0
   • OpenCV4Tegra
   • cuDNN
   • TensorRT 3.0

2. Install pip on Jetson TX2.

   sudo apt-get install python-pip
   

3. Install TensorFlow on Jetson TX2.

   1. Download the TensorFlow 1.5.0 pip wheel from here. This build of TensorFlow is provided as a convenience for the purposes of this project.

   2. Install TensorFlow using pip

      sudo pip install tensorflow-1.5.0rc0-cp27-cp27mu-linux_aarch64.whl
      

4. Install uff exporter 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

      tar -xzf TensorRT-3.0.4.Ubuntu-16.04.3.x86_64.cuda-9.0.cudnn7.0.tar.gz
      

   3. Install uff python package using pip

      sudo pip install TensorRT-3.0.4/uff/uff-0.2.0-py2.py3-none-any.whl
      

5. Clone and build this project

   git clone --recursive https://github.com/NVIDIA-Jetson/tf_to_trt_image_classification.git
   cd tf_to_trt_image_classification
   mkdir build
   cd build
   cmake ..
   make 
   cd ..
   

Download models and create frozen graphs

Run the following bash script to download all of the pretrained models.

source scripts/download_models.sh

If there are any models you don't want to use, simply remove the URL from the model list in scripts/download_models.sh.
Next, because the TensorFlow models are provided in checkpoint format, we must convert them to frozen graphs for optimization with TensorRT. Run the scripts/models_to_frozen_graphs.py script.

python scripts/models_to_frozen_graphs.py

If you removed any models in the previous step, you must add 'exclude': true to the corresponding item in the NETS dictionary located in scripts/model_meta.py. If you are following the instructions for executing engines below, you may also need some sample images. Run the following script to download a few images from ImageNet.

source scripts/download_images.sh

Convert frozen graph to TensorRT engine

Run the scripts/convert_plan.py script from the root directory of the project, referencing the models table for relevant parameters. For example, to convert the Inception V1 model run the following

python scripts/convert_plan.py data/frozen_graphs/inception_v1.pb data/plans/inception_v1.plan input 224 224 InceptionV1/Logits/SpatialSqueeze 1 0 float

The inputs to the convert_plan.py script are

1. frozen graph path
2. output plan path
3. input node name
4. input height
5. input width
6. output node name
7. max batch size
8. max workspace size
9. data type (float or half)

This script assumes single output single input image models, and may not work out of the box for models other than those in the table above.

Execute TensorRT engine

Call the examples/classify_image program from the root directory of the project, referencing the models table for relevant parameters. For example, to run the Inception V1 model converted as above

./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 reference, the inputs to the example program are

1. input image path
2. plan file path
3. labels file (one label per line, line number corresponds to index in output)
4. input node name
5. output node name
6. preprocessing function (either vgg or inception)

We provide two image label files in the data folder. Some of the TensorFlow models were trained with an additional "background" class, causing the model to have 1001 outputs instead of 1000. To determine the number of outputs for each model, reference the NETS variable in scripts/model_meta.py.

Benchmark all models

To benchmark all of the models, first convert all of the models that you downloaded above into TensorRT engines. Run the following script to convert all models

python scripts/frozen_graphs_to_plans.py

If you want to change parameters related to TensorRT optimization, just edit the scripts/frozen_graphs_to_plans.py file. Next, to benchmark all of the models run the scripts/test_trt.py script

python scripts/test_trt.py

Once finished, the timing results will be stored at data/test_output_trt.txt. If you want to also benchmark the TensorFlow models, simply run.

python scripts/test_tf.py

The results will be stored at data/test_output_tf.txt. This benchmarking script loads an example image as input, make sure you have downloaded the sample images as above.