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Step-by-Step

This document is used to list steps of reproducing Intel Optimized TensorFlow image recognition models tuning zoo result.

Note: Most of those models are both supported in Intel optimized TF 1.15.x and Intel optimized TF 2.x. We use 1.15.2 as an example.

Prerequisite

1. Installation

Recommend python 3.6 or higher version.

pip install -r requirements.txt

2. Prepare Dataset

TensorFlow models repo provides scripts and instructions to download, process and convert the ImageNet dataset to the TF records format. We also prepared related scripts in imagenet_prepare directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.

cd examples/tensorflow/image_recognition
# convert validation subset
bash prepare_dataset.sh --output_dir=./data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
# convert train subset
bash prepare_dataset.sh --output_dir=./data --raw_dir=/PATH/TO/img_raw/train/ --subset=train

3. Prepare pre-trained model

In this version, Intel® Low Precision Optimization Tool just support PB file as input for TensorFlow backend, so we need prepared model pre-trained pb files. For some models pre-trained pb can be found in IntelAI Models, we can found the download link in README file of each model. And for others models in Google models, we can get the pb files by convert the checkpoint files. We will give a example with Inception_v1 to show how to get the pb file by a checkpoint file.

  1. Download the checkpoint file from here
wget http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz
tar -xvf inception_v1_2016_08_28.tar.gz
  1. Exporting the Inference Graph
git clone https://github.com/tensorflow/models
cd models/research/slim
python export_inference_graph.py \
        --alsologtostderr \
        --model_name=inception_v1 \
        --output_file=/tmp/inception_v1_inf_graph.pb

Please note: The ImageNet dataset has 1001, the VGG and ResNet V1 final layers have only 1000 outputs rather than 1001. So we need add the --labels_offset=1 flag in the inference graph exporting command.

  1. Use Netron to get the input/output layer name of inference graph pb, for Inception_v1 the output layer name is InceptionV1/Logits/Predictions/Reshape_1

  2. Freezing the exported Graph, please use the tool freeze_graph.py in tensorflow v1.15.2 repo

python freeze_graph.py \
        --input_graph=/tmp/inception_v1_inf_graph.pb \
        --input_checkpoint=./inception_v1.ckpt \
        --input_binary=true \
        --output_graph=./frozen_inception_v1.pb \
        --output_node_names=InceptionV1/Logits/Predictions/Reshape_1

Run

Note: The model name with * means it comes from models, please follow the step Prepare pre-trained model to get the pb files. The densenet-series comes from tensorflow-densenet, please also follow the step Prepare pre-trained model to get the pb files or use openvino download tools.

git clone https://github.com/openvinotoolkit/open_model_zoo.git
cd open_model_zoo/tools/downloader
pip install -r requirements.in
python downloader.py --name densenet-{121|161|169}-tf -o /PATH/TO/MODEL

1. ResNet50 V1.0

Download pre-trained PB

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/resnet50_fp32_pretrained_model.pb
cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=resnet50v1.0 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/resnet50_fp32_pretrained_model.pb --output_model=./ilit_resnet50_v1.pb

2. ResNet50 V1.5

Download pre-trained PB

wget https://zenodo.org/record/2535873/files/resnet50_v1.pb
cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=resnet50v1.5 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/resnet50_v1.pb --output_model=./ilit_resnet50_v15.pb

3. ResNet101

Download pre-trained PB

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/resnet101_fp32_pretrained_model.pb
cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=resnet101 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/resnet101_fp32_pretrained_model.pb --output_model=./ilit_resnet101.pb

4. MobileNet V1

Download pre-trained PB

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/mobilenet_v1_1.0_224_frozen.pb
cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=mobilenetv1 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/mobilenet_v1_1.0_224_frozen.pb --output_model=./ilit_mobilenetv1.pb

5. MobileNet V2*

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=mobilenetv2 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/frozen_mobilenet_v2.pb --output_model=./ilit_mobilenetv2.pb

6. Inception V1*

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=inception_v1 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/frozen_inception_v1.pb --output_model=./ilit_inceptionv1.pb

7. Inception V2*

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=inception_v2 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/frozen_inception_v2.pb --output_model=./ilit_inceptionv2.pb

8. Inception V3

Download pre-trained PB

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/inceptionv3_fp32_pretrained_model.pb
cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=inception_v3 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/inceptionv3_fp32_pretrained_model.pb --output_model=./ilit_inceptionv3.pb

9. Inception V4

Download pre-trained PB

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/inceptionv4_fp32_pretrained_model.pb
cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=inception_v4 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/inceptionv4_fp32_pretrained_model.pb --output_model=./ilit_inceptionv4.pb

10. Inception ResNet V2*

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=inception_resnet_v2 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/frozen_inception_resnet_v2.pb --output_model=./ilit_irv2.pb

11. VGG 16*

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=vgg16 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/frozen_vgg16.pb --output_model=./ilit_vgg16.pb

12. VGG 19*

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=vgg19 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/frozen_vgg19.pb --output_model=./ilit_vgg19.pb

13. ResNet v2 50

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=resnetv2_50 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/frozen_resnet50v2_50.pb --output_model=./ilit_resnetv2_50.pb

14. ResNet v2 101

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=resnetv2_101 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/frozen_resnetv2_101.pb --output_model=./ilit_resnetv2_101.pb

15. ResNet v2 152

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=resnetv2_152 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/frozen_resnetv2_152.pb --output_model=./ilit_resnetv2_152.pb

16. Densenet-121

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=densenet121 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/densenet121.pb --output_model=./ilit_densenet121

17. Densenet-161

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=densenet161 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/densenet161.pb --output_model=./ilit_densenet161

18. Densenet-169

cd examples/tensorflow/image_recognition
bash run_tuning.sh --topology=densenet169 --dataset_location=/PATH/TO/imagenet/ \
        --input_model=/PATH/TO/densenet169.pb --output_model=./ilit_densenet169

Examples of enabling Intel® Low Precision Optimization Tool auto tuning on TensorFlow ResNet50 V1.5

This is a tutorial of how to enable a TensorFlow image recognition model with Intel® Low Precision Optimization Tool.

User Code Analysis

Intel® Low Precision Optimization Tool supports two usages:

  1. User specifies fp32 "model", calibration dataset "q_dataloader", evaluation dataset "eval_dataloader" and metric in tuning.metric field of model-specific yaml config file.

  2. User specifies fp32 "model", calibration dataset "q_dataloader" and a custom "eval_func" which encapsulates the evaluation dataset and metric by itself.

As ResNet50 V1.5 is a typical image recognition model, use Top-K as metric which is built-in supported by Intel® Low Precision Optimization Tool. So here we integrate Tensorflow ResNet50 V1.5 in IntelAI Models with Intel® Low Precision Optimization Tool by the first use case for simplicity.

Write Yaml config file

In examples directory, there is a template.yaml. We could remove most of items and only keep mandotory item for tuning.

# resnet50_v1_5.yaml

framework:
  - name: tensorflow
    inputs: input_tensor
    outputs: softmax_tensor

calibration:                                         
  - iterations: 5, 10
    algorithm:
        activation: minmax

tuning:
    metric:  
      - topk: 1
    accuracy_criterion:
      - relative: 0.01  
    timeout: 0
    random_seed: 9527

Here we choose topk built-in metric and set accuracy target as tolerating 0.01 relative accuracy loss of baseline. The default tuning strategy is basic strategy. The timeout 0 means early stop as well as a tuning config meet accuracy target.

prepare

There are three preparation steps in here:

  1. Prepare environment
pip install intel-tensorflow==1.15.2 ilit
  1. Get the model source code
git clone -b v1.6.0 https://github.com/IntelAI/models intelai_models
cd intelai_models/models/image_recognition/tensorflow/resnet50v1_5/inference
  1. Prepare the ImageNet dataset and pretrainined PB file
wget https://zenodo.org/record/2535873/files/resnet50_v1.pb
  1. Add load graph and dataloader part in eval_image_classifier_inference.py, which needed in Intel® Low Precision Optimization Tool
def load_graph(model_file):
  """This is a function to load TF graph from pb file

  Args:
      model_file (string): TF pb file local path

  Returns:
      graph: TF graph object
  """
  graph = tf.Graph()
  graph_def = tf.compat.v1.GraphDef()

  import os
  file_ext = os.path.splitext(model_file)[1]

  with open(model_file, "rb") as f:
    if file_ext == '.pbtxt':
      text_format.Merge(f.read(), graph_def)
    else:
      graph_def.ParseFromString(f.read())
  with graph.as_default():
    tf.import_graph_def(graph_def, name='')

  return graph

class Dataloader(object):
  """This is a example class that wrapped the model specified parameters,
    such as dataset path, batch size.
    And more importantly, it provides the ability to iterate the dataset.
  Yields:
      tuple: yield data and label 2 numpy array
  """
  def __init__(self, data_location, subset, input_height, input_width,
                batch_size, num_cores, resize_method='crop'):
    self.batch_size = batch_size
    self.subset = subset
    self.dataset = datasets.ImagenetData(data_location)
    self.total_image = self.dataset.num_examples_per_epoch(self.subset)
    self.preprocessor = self.dataset.get_image_preprocessor()(
        input_height,
        input_width,
        batch_size,
        num_cores,
        resize_method)
    self.n = int(self.total_image / self.batch_size)

  def __iter__(self):
    images, labels, filenames = self.preprocessor.minibatch(self.dataset, subset=self.subset, cache_data=False)
    with tf.compat.v1.Session() as sess:
        for i in range(self.n):
            yield sess.run([images, labels])

code update

After completed preparation steps, we just need add a tuning part in eval_classifier_optimized_graph class.

  def auto_tune(self):
    """This is Intel® Low Precision Optimization Tool tuning part to generate a quantized pb

    Returns:
        graph: it will return a quantized pb
    """
    import ilit
    fp32_graph = load_graph(self.args.input_graph)
    tuner = ilit.Tuner(self.args.config)
    dataloader = Dataloader(self.args.data_location, 'validation',
                            RESNET_IMAGE_SIZE, RESNET_IMAGE_SIZE, self.args.batch_size,
                            num_cores=self.args.num_cores,
                            resize_method='crop')
    q_model = tuner.tune(
                        fp32_graph,
                        q_dataloader=dataloader,
                        eval_func=None,
                        eval_dataloader=dataloader)
    return q_model

Finally, add one line in __main__ function of eval_image_-classifier_inference.py to use Intel® Low Precision Optimization Tool by yourself as below.

q_graph = evaluate_opt_graph.auto_tune()

We can use below cmd to test it.

python eval_image_classifier_inference.py -b 10 -a 28 -e 1 -m resnet50_v1_5 \
        -g /PATH/TO/resnet50_v1.pb -d /PATH/TO/imagenet/

The tune() function will return a best quantized model during timeout constrain.