mindspore/example/resnet50_quant

• ResNet-50_quant Example
• • Description
  • Requirements
  • Example structure
  • Parameter configuration
  • Running the example
  • • Train
    • Usage
    • Launch
    • Result
  • Eval process
  • • Usage
    • Launch
    • • Result

ResNet-50_quant Example

Description

This is an example of training ResNet-50_quant with ImageNet2012 dataset in MindSpore.

Requirements

• Install MindSpore.

• Download the dataset ImageNet2012

Unzip the ImageNet2012 dataset to any path you want and the folder structure should include train and eval dataset as follows:

.  
├── ilsvrc                  # train dataset
└── ilsvrc_eval             # infer dataset: images should be classified into 1000 directories firstly, just like train images

Example structure

.
├── Resnet50_quant        
  ├── Readme.md                      
  ├── scripts 
  │   ├──run_train.sh                  
  │   ├──run_eval.sh                    
  ├── src                              
  │   ├──config.py                     
  │   ├──crossentropy.py                                 
  │   ├──dataset.py
  │   ├──luanch.py       
  │   ├──lr_generator.py                                 
  │   ├──utils.py       
  ├── models                              
  │   ├──resnet_quant.py
  ├── train.py
  ├── eval.py

Parameter configuration

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

"class_num": 1001,                # dataset class number
"batch_size": 32,                 # batch size of input tensor
"loss_scale": 1024,               # loss scale
"momentum": 0.9,                  # momentum optimizer
"weight_decay": 1e-4,             # weight decay 
"epoch_size": 110,                 # only valid for taining, which is always 1 for inference 
"pretrained_epoch_size": 90,       # epoch size that model has been trained before load pretrained checkpoint
"buffer_size": 1000,              # number of queue size in data preprocessing
"image_height": 224,              # image height
"image_width": 224,               # image width
"save_checkpoint": True,          # whether save checkpoint or not
"save_checkpoint_epochs": 1,      # the epoch interval between two checkpoints. By default, the last checkpoint will be saved after the last epoch
"keep_checkpoint_max": 50,        # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./",     # path to save checkpoint relative to the executed path
"warmup_epochs": 0,               # number of warmup epoch
"lr_decay_mode": "cosine",        # decay mode for generating learning rate
"label_smooth": True,             # label smooth
"label_smooth_factor": 0.1,       # label smooth factor
"lr_init": 0,                     # initial learning rate
"lr_max": 0.1,                    # maximum learning rate

Running the example

Train

Usage

• Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]

Launch

# training example
  Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/

Result

Training result will be stored in the example path. Checkpoints will be stored at . /checkpoint by default, and training log will be redirected to ./train/train.log like followings.

epoch: 1 step: 5004, loss is 4.8995576
epoch: 2 step: 5004, loss is 3.9235563
epoch: 3 step: 5004, loss is 3.833077
epoch: 4 step: 5004, loss is 3.2795618
epoch: 5 step: 5004, loss is 3.1978393

Eval process

Usage

• Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]

Launch

# infer example
    Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/checkpoint/resnet50-110_5004.ckpt

checkpoint can be produced in training process.

Result

Inference result will be stored in the example path, whose folder name is "infer". Under this, you can find result like the followings in log.

result: {'acc': 0.75.252054737516005} ckpt=train_parallel0/resnet-110_5004.ckpt