forked from OSSInnovation/mindspore
!12807 MindSpore社区网络模型征集活动——SRCNN
From: @fuyu-wang Reviewed-by: Signed-off-by:
This commit is contained in:
Gitee
commit
2a7916b7bc
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# Contents
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- [SRCNN Description](#srcnn-description)
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- [Model Architecture](#model-architecture)
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- [Dataset](#dataset)
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- [Environment Requirements](#environment-requirements)
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- [Quick Start](#quick-start)
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- [Script Description](#script-description)
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- [Script and Sample Code](#script-and-sample-code)
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- [Script Parameters](#script-parameters)
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- [Training Process](#training-process)
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- [Evaluation Process](#evaluation-process)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Training Performance](#evaluation-performance)
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- [Inference Performance](#evaluation-performance)
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- [ModelZoo Homepage](#modelzoo-homepage)
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# [NASNet Description](#contents)
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SRCNN learns an end-to-end mapping between low- and high-resolution images, with little extra pre/post-processing beyond the optimization. With a lightweight structure, the SRCNN has achieved superior performance than the state-of-the-art methods.
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[Paper](https://arxiv.org/pdf/1501.00092.pdf): Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Image Super-Resolution Using Deep Convolutional Networks. 2014.
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# [Model architecture](#contents)
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The overall network architecture of SRCNN is show below:
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[Link](https://arxiv.org/pdf/1501.00092.pdf)
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# [Dataset](#contents)
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- Training Dataset
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- ILSVRC2013_DET_train: 395918 images, 200 classes
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- Evaluation Dataset
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- Set5: 5 images
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- Set14: 14 images
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- Set5 & Set14 download url: http://vllab.ucmerced.edu/wlai24/LapSRN/results/SR_testing_datasets.zip
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- BSDS200: 200 images
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- BSDS200 download url: http://vllab.ucmerced.edu/wlai24/LapSRN/results/SR_training_datasets.zip
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- Data format: RGB images.
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- Note: Data will be processed in src/dataset.py
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# [Environment Requirements](#contents)
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- Hardware GPU
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- Prepare hardware environment with GPU processor.
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- Framework
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- [MindSpore](https://www.mindspore.cn/install/en)
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- For more information, please check the resources below：
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- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
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- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
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# [Script description](#contents)
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## [Script and sample code](#contents)
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```python
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.
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└─srcnn
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├─README.md
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├─scripts
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├─run_distribute_train_gpu.sh # launch distributed training with gpu platform
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└─run_eval_gpu.sh # launch evaluating with gpu platform
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├─src
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├─config.py # parameter configuration
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├─dataset.py # data preprocessing
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├─metric.py # accuracy metric
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├─utils.py # some functions which is commonly used
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├─srcnn.py # network definition
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├─create_dataset.py # generating mindrecord training dataset
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├─eval.py # eval net
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└─train.py # train net
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```
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## [Script Parameters](#contents)
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Parameters for both training and evaluating can be set in config.py.
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```python
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'lr': 1e-4, # learning rate
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'patch_size': 33, # patch_size
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'stride': 99, # stride
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'scale': 2, # image scale
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'epoch_size': 20, # total epoch numbers
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'batch_size': 16, # input batchsize
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'save_checkpoint': True, # whether saving ckpt file
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'keep_checkpoint_max': 10, # max numbers to keep checkpoints
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'save_checkpoint_path': 'outputs/' # save checkpoint path
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```
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## [Training Process](#contents)
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### Dataset
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To create dataset, download the training dataset firstly and then convert them to mindrecord files. We can deal with it as follows.
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```shell
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python create_dataset.py --src_folder=/dataset/ILSVRC2013_DET_train --output_folder=/dataset/mindrecord_dir
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```
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### Usage
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```bash
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GPU:
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sh run_distribute_train_gpu.sh DEVICE_NUM VISIABLE_DEVICES(0,1,2,3,4,5,6,7) DATASET_PATH
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```
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### Launch
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```bash
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# distributed training example(8p) for GPU
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sh run_distribute_train_gpu.sh 8 0,1,2,3,4,5,6,7 /dataset/train
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# standalone training example for GPU
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sh run_distribute_train_gpu.sh 1 0 /dataset/train
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```
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You can find checkpoint file together with result in log.
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## [Evaluation Process](#contents)
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### Usage
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```bash
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# Evaluation
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sh run_eval_gpu.sh DEVICE_ID DATASET_PATH CHECKPOINT_PATH
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```
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### Launch
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```bash
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# Evaluation with checkpoint
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sh run_eval_gpu.sh 1 /dataset/val /ckpt_dir/srcnn-20_*.ckpt
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```
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### Result
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Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log.
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result {'PSNR': 36.72421418219669}
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# [Model description](#contents)
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## [Performance](#contents)
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### Training Performance
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| Parameters | SRCNN |
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| -------------------------- | ------------------------- |
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| Resource | NV PCIE V100-32G |
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| uploaded Date | 03/02/2021 |
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| MindSpore Version | master |
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| Dataset | ImageNet2013 scale:2 |
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| Training Parameters | src/config.py |
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| Optimizer | Adam |
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| Loss Function | MSELoss |
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| Loss | 0.00179 |
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| Total time | 1 h 8ps |
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| Checkpoint for Fine tuning | 671 K(.ckpt file) |
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### Inference Performance
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| Parameters | |
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| -------------------------- | -------------------------- |
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| Resource | NV PCIE V100-32G |
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| uploaded Date | 03/02/2021 |
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| MindSpore Version | master |
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| Dataset | Set5/Set14/BSDS200 scale:2 |
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| batch_size | 1 |
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| PSNR | 36.72/32.58/33.81 |
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# [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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"""Create Dataset."""
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import os
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import argparse
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import glob
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import numpy as np
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import PIL.Image as pil_image
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from PIL import ImageFile
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from mindspore.mindrecord import FileWriter
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from src.config import srcnn_cfg as config
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from src.utils import convert_rgb_to_y
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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parser = argparse.ArgumentParser(description='Generate dataset file.')
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parser.add_argument("--src_folder", type=str, required=True, help="Raw data folder.")
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parser.add_argument("--output_folder", type=str, required=True, help="Dataset output path.")
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if __name__ == '__main__':
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args, _ = parser.parse_known_args()
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if not os.path.exists(args.output_folder):
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os.makedirs(args.output_folder)
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prefix = "srcnn.mindrecord"
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file_num = 32
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patch_size = config.patch_size
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stride = config.stride
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scale = config.scale
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mindrecord_path = os.path.join(args.output_folder, prefix)
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writer = FileWriter(mindrecord_path, file_num)
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srcnn_json = {
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"lr": {"type": "float32", "shape": [1, patch_size, patch_size]},
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"hr": {"type": "float32", "shape": [1, patch_size, patch_size]},
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}
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writer.add_schema(srcnn_json, "srcnn_json")
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image_list = []
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file_list = sorted(os.listdir(args.src_folder))
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for file_name in file_list:
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path = os.path.join(args.src_folder, file_name)
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if os.path.isfile(path):
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image_list.append(path)
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else:
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for image_path in sorted(glob.glob('{}/*'.format(path))):
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image_list.append(image_path)
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print("image_list size ", len(image_list), flush=True)
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for path in image_list:
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hr = pil_image.open(path).convert('RGB')
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hr_width = (hr.width // scale) * scale
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hr_height = (hr.height // scale) * scale
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hr = hr.resize((hr_width, hr_height), resample=pil_image.BICUBIC)
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lr = hr.resize((hr_width // scale, hr_height // scale), resample=pil_image.BICUBIC)
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lr = lr.resize((lr.width * scale, lr.height * scale), resample=pil_image.BICUBIC)
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hr = np.array(hr).astype(np.float32)
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lr = np.array(lr).astype(np.float32)
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hr = convert_rgb_to_y(hr)
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lr = convert_rgb_to_y(lr)
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for i in range(0, lr.shape[0] - patch_size + 1, stride):
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for j in range(0, lr.shape[1] - patch_size + 1, stride):
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lr_res = np.expand_dims(lr[i:i + patch_size, j:j + patch_size] / 255., 0)
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hr_res = np.expand_dims(hr[i:i + patch_size, j:j + patch_size] / 255., 0)
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row = {"lr": lr_res, "hr": hr_res}
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writer.write_raw_data([row])
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writer.commit()
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print("Finish!")
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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"""srcnn evaluation"""
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import argparse
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import context, Tensor
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.config import srcnn_cfg as config
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from src.dataset import create_eval_dataset
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from src.srcnn import SRCNN
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from src.metric import SRCNNpsnr
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description="srcnn eval")
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parser.add_argument('--dataset_path', type=str, required=True, help="Dataset, default is None.")
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parser.add_argument('--checkpoint_path', type=str, required=True, help="checkpoint file path")
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parser.add_argument('--device_target', type=str, default='GPU', choices=("GPU"),
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help="Device target, support GPU.")
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args, _ = parser.parse_known_args()
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if args.device_target == "GPU":
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context.set_context(mode=context.GRAPH_MODE,
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device_target=args.device_target,
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save_graphs=False)
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else:
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raise ValueError("Unsupported device target.")
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eval_ds = create_eval_dataset(args.dataset_path)
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net = SRCNN()
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lr = Tensor(config.lr, ms.float32)
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opt = nn.Adam(params=net.trainable_params(), learning_rate=lr, eps=1e-07)
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loss = nn.MSELoss(reduction='mean')
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param_dict = load_checkpoint(args.checkpoint_path)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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model = Model(net, loss_fn=loss, optimizer=opt, metrics={'PSNR': SRCNNpsnr()})
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res = model.eval(eval_ds, dataset_sink_mode=False)
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print("result ", res)
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Pillow
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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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.
|
||||
# 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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# ============================================================================
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if [ $# -lt 3 ]
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then
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echo "Usage: sh run_distribute_train_gpu.sh [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [PRE_TRAINED](optional)"
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exit 1
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fi
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if [ $1 -lt 1 ] && [ $1 -gt 8 ]
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then
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echo "error: DEVICE_NUM=$1 is not in (1-8)"
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exit 1
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fi
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export DEVICE_NUM=$1
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export RANK_SIZE=$1
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BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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if [ -d "train_parallel" ];
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then
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rm -rf train_parallel
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fi
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mkdir train_parallel
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cd train_parallel || exit
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export CUDA_VISIBLE_DEVICES="$2"
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if [ -f $4 ] # pre_trained ckpt
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then
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if [ $1 -gt 1 ]
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then
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mpirun -n $1 --allow-run-as-root python3 ${BASEPATH}/../train.py \
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--dataset_path=$3 \
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--run_distribute=True \
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--pre_trained=$4 > log.txt 2>&1 &
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else
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python3 ${BASEPATH}/../train.py \
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--dataset_path=$3 \
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--pre_trained=$4 > log.txt 2>&1 &
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fi
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else
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if [ $1 -gt 1 ]
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then
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mpirun -n $1 --allow-run-as-root python3 ${BASEPATH}/../train.py \
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--run_distribute=True \
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--dataset_path=$3 > log.txt 2>&1 &
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else
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python3 ${BASEPATH}/../train.py \
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--dataset_path=$3 > log.txt 2>&1 &
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fi
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fi
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|
|
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
if [ $# -lt 3 ]
|
||||
then
|
||||
echo "Usage: sh run_eval_gpu.sh [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]"
|
||||
exit 1
|
||||
fi
|
||||
|
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# check checkpoint file
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||||
if [ ! -f $3 ]
|
||||
then
|
||||
echo "error: CHECKPOINT_PATH=$3 is not a file"
|
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exit 1
|
||||
fi
|
||||
|
||||
BASEPATH=$(cd "`dirname $0`" || exit; pwd)
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||||
export PYTHONPATH=${BASEPATH}:$PYTHONPATH
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||||
|
||||
if [ -d "./eval" ];
|
||||
then
|
||||
rm -rf ./eval
|
||||
fi
|
||||
mkdir ./eval
|
||||
|
||||
export CUDA_VISIBLE_DEVICES="$1"
|
||||
|
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python3 ${BASEPATH}/../eval.py \
|
||||
--dataset_path=$2 \
|
||||
--checkpoint_path=$3 > eval/eval.log 2>&1 &
|
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|
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|
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# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""Network parameters."""
|
||||
|
||||
from easydict import EasyDict as edict
|
||||
|
||||
srcnn_cfg = edict({
|
||||
'lr': 1e-4,
|
||||
'patch_size': 33,
|
||||
'stride': 99,
|
||||
'scale': 2,
|
||||
'epoch_size': 20,
|
||||
'batch_size': 16,
|
||||
'save_checkpoint': True,
|
||||
'keep_checkpoint_max': 10,
|
||||
'save_checkpoint_path': 'outputs/'
|
||||
})
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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 glob
|
||||
import numpy as np
|
||||
import PIL.Image as pil_image
|
||||
|
||||
import mindspore.dataset as ds
|
||||
|
||||
from src.config import srcnn_cfg as config
|
||||
from src.utils import convert_rgb_to_y
|
||||
|
||||
class EvalDataset:
|
||||
def __init__(self, images_dir):
|
||||
self.images_dir = images_dir
|
||||
scale = config.scale
|
||||
self.lr_group = []
|
||||
self.hr_group = []
|
||||
for image_path in sorted(glob.glob('{}/*'.format(images_dir))):
|
||||
hr = pil_image.open(image_path).convert('RGB')
|
||||
hr_width = (hr.width // scale) * scale
|
||||
hr_height = (hr.height // scale) * scale
|
||||
hr = hr.resize((hr_width, hr_height), resample=pil_image.BICUBIC)
|
||||
lr = hr.resize((hr_width // scale, hr_height // scale), resample=pil_image.BICUBIC)
|
||||
lr = lr.resize((lr.width * scale, lr.height * scale), resample=pil_image.BICUBIC)
|
||||
hr = np.array(hr).astype(np.float32)
|
||||
lr = np.array(lr).astype(np.float32)
|
||||
hr = convert_rgb_to_y(hr)
|
||||
lr = convert_rgb_to_y(lr)
|
||||
|
||||
self.lr_group.append(lr)
|
||||
self.hr_group.append(hr)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.lr_group)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return np.expand_dims(self.lr_group[idx] / 255., 0), np.expand_dims(self.hr_group[idx] / 255., 0)
|
||||
|
||||
def create_train_dataset(mindrecord_file, batch_size=1, shard_id=0, num_shard=1, num_parallel_workers=4):
|
||||
data_set = ds.MindDataset(mindrecord_file, columns_list=["lr", "hr"], num_shards=num_shard,
|
||||
shard_id=shard_id, num_parallel_workers=num_parallel_workers, shuffle=True)
|
||||
data_set = data_set.batch(batch_size, drop_remainder=True)
|
||||
return data_set
|
||||
|
||||
def create_eval_dataset(images_dir, batch_size=1):
|
||||
dataset = EvalDataset(images_dir)
|
||||
data_set = ds.GeneratorDataset(dataset, ["lr", "hr"], shuffle=False)
|
||||
data_set = data_set.batch(batch_size, drop_remainder=True)
|
||||
return data_set
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""Metric for accuracy evaluation."""
|
||||
from mindspore import nn
|
||||
import numpy as np
|
||||
|
||||
class SRCNNpsnr(nn.Metric):
|
||||
def __init__(self):
|
||||
super(SRCNNpsnr).__init__()
|
||||
self.clear()
|
||||
|
||||
def clear(self):
|
||||
self.val = 0
|
||||
self.sum = 0
|
||||
self.count = 0
|
||||
|
||||
def update(self, *inputs):
|
||||
if len(inputs) != 2:
|
||||
raise ValueError('SRCNNpsnr need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
|
||||
|
||||
y_pred = self._convert_data(inputs[0])
|
||||
y = self._convert_data(inputs[1])
|
||||
|
||||
n = len(inputs)
|
||||
val = 10. * np.log10(1. / np.mean((y_pred - y) ** 2))
|
||||
|
||||
self.val = val
|
||||
self.sum += val * n
|
||||
self.count += n
|
||||
|
||||
def eval(self):
|
||||
if self.count == 0:
|
||||
raise RuntimeError('PSNR can not be calculated, because the number of samples is 0.')
|
||||
return self.sum / self.count
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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 mindspore.nn as nn
|
||||
|
||||
class SRCNN(nn.Cell):
|
||||
def __init__(self, num_channels=1):
|
||||
super(SRCNN, self).__init__()
|
||||
self.conv1 = nn.Conv2d(num_channels, 64, kernel_size=9, padding=9 // 2, pad_mode='pad')
|
||||
self.conv2 = nn.Conv2d(64, 32, kernel_size=5, padding=5 // 2, pad_mode='pad')
|
||||
self.conv3 = nn.Conv2d(32, num_channels, kernel_size=5, padding=5 // 2, pad_mode='pad')
|
||||
self.relu = nn.ReLU()
|
||||
|
||||
def construct(self, x):
|
||||
x = self.relu(self.conv1(x))
|
||||
x = self.relu(self.conv2(x))
|
||||
x = self.conv3(x)
|
||||
return x
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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 numpy as np
|
||||
|
||||
def convert_rgb_to_y(img):
|
||||
if isinstance(img, np.ndarray):
|
||||
return 16. + (64.738 * img[:, :, 0] + 129.057 * img[:, :, 1] + 25.064 * img[:, :, 2]) / 256.
|
||||
raise Exception('Unknown Type', type(img))
|
||||
|
||||
def convert_rgb_to_ycbcr(img):
|
||||
if isinstance(img, np.ndarray):
|
||||
y = 16. + (64.738 * img[:, :, 0] + 129.057 * img[:, :, 1] + 25.064 * img[:, :, 2]) / 256.
|
||||
cb = 128. + (-37.945 * img[:, :, 0] - 74.494 * img[:, :, 1] + 112.439 * img[:, :, 2]) / 256.
|
||||
cr = 128. + (112.439 * img[:, :, 0] - 94.154 * img[:, :, 1] - 18.285 * img[:, :, 2]) / 256.
|
||||
return np.array([y, cb, cr]).transpose([1, 2, 0])
|
||||
raise Exception('Unknown Type', type(img))
|
||||
|
||||
def convert_ycbcr_to_rgb(img):
|
||||
if isinstance(img, np.ndarray):
|
||||
r = 298.082 * img[:, :, 0] / 256. + 408.583 * img[:, :, 2] / 256. - 222.921
|
||||
g = 298.082 * img[:, :, 0] / 256. - 100.291 * img[:, :, 1] / 256. - 208.120 * img[:, :, 2] / 256. + 135.576
|
||||
b = 298.082 * img[:, :, 0] / 256. + 516.412 * img[:, :, 1] / 256. - 276.836
|
||||
return np.array([r, g, b]).transpose([1, 2, 0])
|
||||
raise Exception('Unknown Type', type(img))
|
||||
|
|
@ -0,0 +1,105 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""srcnn training"""
|
||||
|
||||
import os
|
||||
import argparse
|
||||
import ast
|
||||
|
||||
import mindspore as ms
|
||||
import mindspore.nn as nn
|
||||
from mindspore import context, Tensor
|
||||
from mindspore.common import set_seed
|
||||
from mindspore.train.model import Model
|
||||
from mindspore.train.callback import TimeMonitor, LossMonitor, CheckpointConfig, ModelCheckpoint
|
||||
from mindspore.communication.management import init, get_rank, get_group_size
|
||||
from mindspore.train.model import ParallelMode
|
||||
|
||||
from src.config import srcnn_cfg as config
|
||||
from src.dataset import create_train_dataset
|
||||
from src.srcnn import SRCNN
|
||||
|
||||
set_seed(1)
|
||||
|
||||
def filter_checkpoint_parameter_by_list(origin_dict, param_filter):
|
||||
"""remove useless parameters according to filter_list"""
|
||||
for key in list(origin_dict.keys()):
|
||||
for name in param_filter:
|
||||
if name in key:
|
||||
print("Delete parameter from checkpoint: ", key)
|
||||
del origin_dict[key]
|
||||
break
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description="srcnn training")
|
||||
parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
|
||||
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
|
||||
parser.add_argument('--device_target', type=str, default='GPU', choices=("GPU"),
|
||||
help="Device target, support GPU.")
|
||||
parser.add_argument('--pre_trained', type=str, default='', help='model_path, local pretrained model to load')
|
||||
parser.add_argument("--run_distribute", type=ast.literal_eval, default=False,
|
||||
help="Run distribute, default: false.")
|
||||
parser.add_argument("--filter_weight", type=ast.literal_eval, default=False,
|
||||
help="Filter head weight parameters, default is False.")
|
||||
args, _ = parser.parse_known_args()
|
||||
|
||||
|
||||
if args.device_target == "GPU":
|
||||
context.set_context(mode=context.GRAPH_MODE,
|
||||
device_target=args.device_target,
|
||||
save_graphs=False)
|
||||
else:
|
||||
raise ValueError("Unsupported device target.")
|
||||
|
||||
rank = 0
|
||||
device_num = 1
|
||||
if args.run_distribute:
|
||||
init()
|
||||
rank = get_rank()
|
||||
device_num = get_group_size()
|
||||
context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL)
|
||||
|
||||
train_dataset = create_train_dataset(args.dataset_path, batch_size=config.batch_size,
|
||||
shard_id=rank, num_shard=device_num)
|
||||
|
||||
step_size = train_dataset.get_dataset_size()
|
||||
|
||||
# define net
|
||||
net = SRCNN()
|
||||
|
||||
# init weight
|
||||
if args.pre_trained:
|
||||
param_dict = load_checkpoint(args.pre_trained)
|
||||
if args.filter_weight:
|
||||
filter_list = [x.name for x in net.end_point.get_parameters()]
|
||||
filter_checkpoint_parameter_by_list(param_dict, filter_list)
|
||||
load_param_into_net(net, param_dict)
|
||||
|
||||
lr = Tensor(config.lr, ms.float32)
|
||||
|
||||
opt = nn.Adam(params=net.trainable_params(), learning_rate=lr, eps=1e-07)
|
||||
loss = nn.MSELoss(reduction='mean')
|
||||
model = Model(net, loss_fn=loss, optimizer=opt)
|
||||
|
||||
# define callbacks
|
||||
callbacks = [LossMonitor(), TimeMonitor(data_size=step_size)]
|
||||
if config.save_checkpoint and rank == 0:
|
||||
config_ck = CheckpointConfig(save_checkpoint_steps=step_size,
|
||||
keep_checkpoint_max=config.keep_checkpoint_max)
|
||||
save_ckpt_path = os.path.join(config.save_checkpoint_path, 'ckpt_' + str(rank) + '/')
|
||||
ckpt_cb = ModelCheckpoint(prefix="srcnn", directory=save_ckpt_path, config=config_ck)
|
||||
callbacks.append(ckpt_cb)
|
||||
|
||||
model.train(config.epoch_size, train_dataset, callbacks=callbacks)
|
||||
Loading…
Reference in New Issue