mindspore/example/resnet50_imagenet2012

• ResNet-50 Example
• • Description
  • Requirements
  • Example structure
  • Parameter configuration
  • Running the example
  • • Train
    • • Usage
      • Launch
      • Result
    • Infer
    • • Usage
      • Launch
      • Result
    • Running on GPU

ResNet-50 Example

Description

This is an example of training ResNet-50 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

.
├── crossentropy.py                 # CrossEntropy loss function
├── config.py                       # parameter configuration
├── dataset.py                      # data preprocessing
├── eval.py                         # infer script
├── lr_generator.py                 # generate learning rate for each step
├── run_distribute_train.sh         # launch distributed training(8 pcs)
├── run_infer.sh                    # launch infering
├── run_standalone_train.sh         # launch standalone training(1 pcs)
└── train.py                        # train script

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": 90,                 # only valid for taining, which is always 1 for inference 
"pretrained_epoch_size": 1,       # 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": 10,        # 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

# distributed training
Usage: sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)

# standalone training
Usage: sh run_standalone_train.sh [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)

Launch

# distributed training example(8 pcs)
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc

# If you want to load pretrained ckpt file
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc ./pretrained.ckpt

# standalone training example(1 pcs)
sh run_standalone_train.sh dataset/ilsvrc

# If you want to load pretrained ckpt file
sh run_standalone_train.sh dataset/ilsvrc ./pretrained.ckpt

About rank_table.json, you can refer to the distributed training tutorial.

Result

Training result will be stored in the example path, whose folder name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.

# distribute training result(8 pcs)
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

Infer

Usage

# infer
Usage: sh run_infer.sh [DATASET_PATH] [CHECKPOINT_PATH]

Launch

# infer with checkpoint
sh run_infer.sh dataset/ilsvrc_eval train_parallel0/resnet-90_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': 0.7671054737516005} ckpt=train_parallel0/resnet-90_5004.ckpt

Running on GPU

# distributed training example
mpirun -n 8 python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU" --run_distribute=True

# standalone training example
python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU"

# standalone training example with pretrained checkpoint
python train.py --dataset_path=dataset/ilsvrc/train --device_target="GPU" --pre_trained=pretrained.ckpt

# infer example
python eval.py --dataset_path=dataset/ilsvrc/val --device_target="GPU" --checkpoint_path=resnet-90_5004ss.ckpt