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Add keras saved_model examples (#1201)
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...w/image_recognition/keras_models/inception_resnet_v2/quantization/ptq/README.md
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| Step-by-Step | ||
| ============ | ||
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| This document is used to enable Tensorflow Keras models using Intel® Neural Compressor. | ||
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| ## Prerequisite | ||
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| ### 1. Installation | ||
| ```shell | ||
| # Install Intel® Neural Compressor | ||
| pip install neural-compressor | ||
| ``` | ||
| ### 2. Install Intel Tensorflow | ||
| ```shell | ||
| pip install intel-tensorflow | ||
| ``` | ||
| > Note: Supported Tensorflow [Version](../../../../../../../README.md). | ||
| ### 3. Prepare Pretrained model | ||
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| The pretrained model is provided by [Keras Applications](https://keras.io/api/applications/). prepare the model, Run as follow: | ||
| ``` | ||
| python prepare_model.py --output_model=/path/to/model | ||
| ``` | ||
| `--output_model ` the model should be saved as SavedModel format or H5 format. | ||
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| ## Run Command | ||
| ```shell | ||
| bash run_tuning.sh --config=inception_resnet_v2.yaml --input_model=./path/to/model --output_model=./result --eval_data=/path/to/evaluation/dataset --calib_data=/path/to/calibration/dataset | ||
| bash run_benchmark.sh --config=inception_resnet_v2.yaml --input_model=./path/to/model --mode=performance --eval_data=/path/to/evaluation/dataset | ||
| ``` | ||
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...ge_recognition/keras_models/inception_resnet_v2/quantization/ptq/inception_resnet_v2.yaml
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| # | ||
| # Copyright (c) 2021 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| version: 1.0 | ||
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| model: # mandatory. used to specify model specific information. | ||
| name: inception_resnet_v2 | ||
| framework: tensorflow # mandatory. supported values are tensorflow, pytorch, pytorch_ipex, onnxrt_integer, onnxrt_qlinear or mxnet; allow new framework backend extension. | ||
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| quantization: # optional. tuning constraints on model-wise for advance user to reduce tuning space. | ||
| calibration: | ||
| sampling_size: 50, 100 # optional. default value is 100. used to set how many samples should be used in calibration. | ||
| model_wise: # optional. tuning constraints on model-wise for advance user to reduce tuning space. | ||
| activation: | ||
| algorithm: minmax | ||
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| evaluation: # optional. required if user doesn't provide eval_func in neural_compressor.Quantization. | ||
| accuracy: | ||
| performance: # optional. used to benchmark performance of passing model. | ||
| iteration: 100 | ||
| configs: | ||
| cores_per_instance: 4 | ||
| num_of_instance: 7 | ||
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| tuning: | ||
| accuracy_criterion: | ||
| relative: 0.01 # optional. default value is relative, other value is absolute. this example allows relative accuracy loss: 1%. | ||
| exit_policy: | ||
| timeout: 0 # optional. tuning timeout (seconds). default value is 0 which means early stop. combine with max_trials field to decide when to exit. | ||
| random_seed: 9527 # optional. random seed for deterministic tuning. |
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...es/tensorflow/image_recognition/keras_models/inception_resnet_v2/quantization/ptq/main.py
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| # | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # Copyright (c) 2018 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| # | ||
| import time | ||
| import shutil | ||
| import numpy as np | ||
| from argparse import ArgumentParser | ||
| from neural_compressor import data | ||
| import tensorflow as tf | ||
| tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) | ||
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| flags = tf.compat.v1.flags | ||
| FLAGS = flags.FLAGS | ||
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| ## Required parameters | ||
| flags.DEFINE_string( | ||
| 'input_model', None, 'Run inference with specified pb graph.') | ||
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| flags.DEFINE_string( | ||
| 'output_model', None, 'The output model of the quantized model.') | ||
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| flags.DEFINE_string( | ||
| 'mode', 'performance', 'define benchmark mode for accuracy or performance') | ||
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| flags.DEFINE_bool( | ||
| 'tune', False, 'whether to tune the model') | ||
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| flags.DEFINE_bool( | ||
| 'benchmark', False, 'whether to benchmark the model') | ||
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| flags.DEFINE_string( | ||
| 'config', 'bert.yaml', 'yaml configuration of the model') | ||
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| flags.DEFINE_string( | ||
| 'calib_data', None, 'location of calibration dataset') | ||
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| flags.DEFINE_string( | ||
| 'eval_data', None, 'location of evaluate dataset') | ||
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| from neural_compressor.experimental.metric.metric import TensorflowTopK | ||
| from neural_compressor.experimental.data.transforms.transform import ComposeTransform | ||
| from neural_compressor.experimental.data.datasets.dataset import TensorflowImageRecord | ||
| from neural_compressor.experimental.data.transforms.imagenet_transform import LabelShift | ||
| from neural_compressor.experimental.data.dataloaders.default_dataloader import DefaultDataLoader | ||
| from neural_compressor.data.transforms.imagenet_transform import BilinearImagenetTransform | ||
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| eval_dataset = TensorflowImageRecord(root=FLAGS.eval_data, transform=ComposeTransform(transform_list= \ | ||
| [BilinearImagenetTransform(height=299, width=299)])) | ||
| if FLAGS.benchmark and FLAGS.mode == 'performance': | ||
| eval_dataloader = DefaultDataLoader(dataset=eval_dataset, batch_size=1) | ||
| else: | ||
| eval_dataloader = DefaultDataLoader(dataset=eval_dataset, batch_size=32) | ||
| if FLAGS.calib_data: | ||
| calib_dataset = TensorflowImageRecord(root=FLAGS.calib_data, transform=ComposeTransform(transform_list= \ | ||
| [BilinearImagenetTransform(height=299, width=299)])) | ||
| calib_dataloader = DefaultDataLoader(dataset=calib_dataset, batch_size=10) | ||
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| def evaluate(model, measurer=None): | ||
| """ | ||
| Custom Evaluate function to inference the model for specified metric on validation dataset. | ||
| Args: | ||
| model ([tf.saved_model.load]): The model will be the class of tf.saved_model.load(quantized_model_path). | ||
| measurer (object, optional): for precise benchmark measurement. | ||
| Returns: | ||
| [float]: evaluation result, the larger is better. | ||
| """ | ||
| infer = model.signatures["serving_default"] | ||
| output_dict_keys = infer.structured_outputs.keys() | ||
| output_name = list(output_dict_keys )[0] | ||
| postprocess = LabelShift(label_shift=1) | ||
| metric = TensorflowTopK(k=1) | ||
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| def eval_func(dataloader, metric): | ||
| results = [] | ||
| for idx, (inputs, labels) in enumerate(dataloader): | ||
| inputs = np.array(inputs) | ||
| input_tensor = tf.constant(inputs) | ||
| if measurer: | ||
| measurer.start() | ||
| predictions = infer(input_tensor)[output_name] | ||
| if measurer: | ||
| measurer.end() | ||
| predictions = predictions.numpy() | ||
| predictions, labels = postprocess((predictions, labels)) | ||
| metric.update(predictions, labels) | ||
| return results | ||
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| results = eval_func(eval_dataloader, metric) | ||
| acc = metric.result() | ||
| return acc | ||
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| def main(_): | ||
| if FLAGS.tune: | ||
| from neural_compressor.experimental import Quantization, common | ||
| quantizer = Quantization(FLAGS.config) | ||
| quantizer.model = common.Model(FLAGS.input_model) | ||
| quantizer.eval_func = evaluate | ||
| quantizer.calib_dataloader = calib_dataloader | ||
| q_model = quantizer.fit() | ||
| q_model.save(FLAGS.output_model) | ||
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| if FLAGS.benchmark: | ||
| from neural_compressor.experimental import Benchmark, common | ||
| evaluator = Benchmark(FLAGS.config) | ||
| evaluator.model = common.Model(FLAGS.input_model) | ||
| evaluator.b_func = evaluate | ||
| evaluator.b_dataloader = eval_dataloader | ||
| evaluator(FLAGS.mode) | ||
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| if __name__ == "__main__": | ||
| tf.compat.v1.app.run() |
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...flow/image_recognition/keras_models/inception_resnet_v2/quantization/ptq/prepare_model.py
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| import argparse | ||
| import tensorflow as tf | ||
| def get_inception_resnet_v2_model(saved_path): | ||
| model = tf.keras.applications.InceptionResNetV2(weights='imagenet') | ||
| model.save(saved_path) | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser( | ||
| description='Export pretained keras model', | ||
| formatter_class=argparse.ArgumentDefaultsHelpFormatter) | ||
| parser.add_argument( | ||
| '--output_model', | ||
| type=str, | ||
| help='path to exported model file') | ||
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| args = parser.parse_args() | ||
| get_inception_resnet_v2_model(args.output_model) |
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...flow/image_recognition/keras_models/inception_resnet_v2/quantization/ptq/run_benchmark.sh
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| #!/bin/bash | ||
| set -x | ||
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| function main { | ||
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| init_params "$@" | ||
| run_benchmark | ||
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| } | ||
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| # init params | ||
| function init_params { | ||
| for var in "$@" | ||
| do | ||
| case $var in | ||
| --config=*) | ||
| config=$(echo $var |cut -f2 -d=) | ||
| ;; | ||
| --input_model=*) | ||
| input_model=$(echo $var |cut -f2 -d=) | ||
| ;; | ||
| --mode=*) | ||
| mode=$(echo $var |cut -f2 -d=) | ||
| ;; | ||
| --eval_data=*) | ||
| eval_data=$(echo $var |cut -f2 -d=) | ||
| ;; | ||
| esac | ||
| done | ||
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| } | ||
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| # run_tuning | ||
| function run_benchmark { | ||
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| python main.py \ | ||
| --input_model ${input_model} \ | ||
| --config ${config} \ | ||
| --benchmark \ | ||
| --mode ${mode} \ | ||
| --eval_data ${eval_data} | ||
| } | ||
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| main "$@" |
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