mindspore/model_zoo/official/cv/srcnn

• Contents
• NASNet Description
• Model architecture
• Dataset
• Environment Requirements
• Script description
• • Script and sample code
  • Script Parameters
  • Training Process
  • • Dataset
    • Usage
    • Launch
  • Evaluation Process
  • • Usage
    • Launch
    • Result
• Model description
• • Performance
  • • Training Performance
    • Inference Performance
• ModelZoo Homepage

Contents

• SRCNN Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
  • Script and Sample Code
  • Script Parameters
  • Training Process
  • Evaluation Process
• Model Description
  • Performance
    • Training Performance
    • Inference Performance
• ModelZoo Homepage

NASNet Description

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.

Paper: Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. Image Super-Resolution Using Deep Convolutional Networks. 2014.

Model architecture

The overall network architecture of SRCNN is show below:

Link

Dataset

• Training Dataset
  • ILSVRC2013_DET_train: 395918 images, 200 classes
• Evaluation Dataset
  • Set5: 5 images
  • Set14: 14 images
    • Set5 & Set14 download url: http://vllab.ucmerced.edu/wlai24/LapSRN/results/SR_testing_datasets.zip
  • BSDS200: 200 images
    • BSDS200 download url: http://vllab.ucmerced.edu/wlai24/LapSRN/results/SR_training_datasets.zip
• Data format: RGB images.
  • Note: Data will be processed in src/dataset.py

Environment Requirements

• Hardware GPU
  • Prepare hardware environment with GPU processor.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API

Script description

Script and sample code

.
└─srcnn
  ├─README.md
  ├─scripts
    ├─run_distribute_train_gpu.sh     # launch distributed training with gpu platform
    └─run_eval_gpu.sh                 # launch evaluating with gpu platform
  ├─src
    ├─config.py                       # parameter configuration
    ├─dataset.py                      # data preprocessing
    ├─metric.py                       # accuracy metric
    ├─utils.py                        # some functions which is commonly used
    ├─srcnn.py                        # network definition
├─create_dataset.py                   # generating mindrecord training dataset
├─eval.py                             # eval net
└─train.py                            # train net  

Script Parameters

Parameters for both training and evaluating can be set in config.py.

'lr': 1e-4,                             # learning rate
'patch_size': 33,                       # patch_size
'stride': 99,                           # stride
'scale': 2,                             # image scale
'epoch_size': 20,                       # total epoch numbers
'batch_size': 16,                       # input batchsize
'save_checkpoint': True,                # whether saving ckpt file
'keep_checkpoint_max': 10,              # max numbers to keep checkpoints
'save_checkpoint_path': 'outputs/'      # save checkpoint path

Training Process

Dataset

To create dataset, download the training dataset firstly and then convert them to mindrecord files. We can deal with it as follows.

    python create_dataset.py --src_folder=/dataset/ILSVRC2013_DET_train --output_folder=/dataset/mindrecord_dir

Usage

GPU:
    sh run_distribute_train_gpu.sh DEVICE_NUM VISIABLE_DEVICES(0,1,2,3,4,5,6,7) DATASET_PATH

Launch

# distributed training example(8p) for GPU
sh run_distribute_train_gpu.sh 8 0,1,2,3,4,5,6,7 /dataset/train
# standalone training example for GPU
sh run_distribute_train_gpu.sh 1 0 /dataset/train

You can find checkpoint file together with result in log.

Evaluation Process

Usage

# Evaluation
sh run_eval_gpu.sh DEVICE_ID DATASET_PATH CHECKPOINT_PATH

Launch

# Evaluation with checkpoint
sh run_eval_gpu.sh 1 /dataset/val /ckpt_dir/srcnn-20_*.ckpt

Result

Evaluation result will be stored in the scripts path. Under this, you can find result like the followings in log.

result {'PSNR': 36.72421418219669}

Model description

Performance

Training Performance

Parameters	SRCNN
Resource	NV PCIE V100-32G
uploaded Date	03/02/2021
MindSpore Version	master
Dataset	ImageNet2013 scale:2
Training Parameters	src/config.py
Optimizer	Adam
Loss Function	MSELoss
Loss	0.00179
Total time	1 h 8ps
Checkpoint for Fine tuning	671 K(.ckpt file)

Inference Performance

Parameters
Resource	NV PCIE V100-32G
uploaded Date	03/02/2021
MindSpore Version	master
Dataset	Set5/Set14/BSDS200 scale:2
batch_size	1
PSNR	36.72/32.58/33.81

ModelZoo Homepage

Please check the official homepage.