mindspore/example/resnet101_imagenet2012

ResNet101 Example

Description

This is an example of training ResNet101 with ImageNet dataset in MindSpore.

Requirements

• Install MindSpore.

• Download the dataset ImageNet.

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

.
└─dataset
    ├─ilsvrc
    │
    └─validation_preprocess

Example structure

.
├── crossentropy.py                 # CrossEntropy loss function
├── config.py                       # parameter configuration
├── dataset.py                      # data preprocessing
├── eval.py                         # eval net
├── lr_generator.py                 # generate learning rate
├── run_distribute_train.sh         # launch distributed training(8p)
├── run_infer.sh                    # launch evaluating
├── run_standalone_train.sh         # launch standalone training(1p)
└── train.py                        # train net

Parameter configuration

Parameters for both training and evaluating 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": 120,                # epoch sizes for training
"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": 1,                # label_smooth
"label_smooth_factor": 0.1,       # label_smooth_factor
"lr": 0.1                         # base learning rate

Running the example

Train

Usage

# distributed training
sh run_distribute_train.sh [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
 
# standalone training
sh run_standalone_train.sh [DATASET_PATH]

Launch

# distributed training example(8p)
sh run_distribute_train.sh rank_table_8p.json dataset/ilsvrc
 
# standalone training example（1p）
sh run_standalone_train.sh dataset/ilsvrc

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". You can find checkpoint file together with result like the followings in log.

# distribute training result(8p)
epoch: 1 step: 5004, loss is 4.805483
epoch: 2 step: 5004, loss is 3.2121816
epoch: 3 step: 5004, loss is 3.429647
epoch: 4 step: 5004, loss is 3.3667371
epoch: 5 step: 5004, loss is 3.1718972
...
epoch: 67 step: 5004, loss is 2.2768745
epoch: 68 step: 5004, loss is 1.7223864
epoch: 69 step: 5004, loss is 2.0665488
epoch: 70 step: 5004, loss is 1.8717369
...

Infer

Usage

# infer
sh run_infer.sh [VALIDATION_DATASET_PATH] [CHECKPOINT_PATH]

Launch

# infer with checkpoint
sh run_infer.sh dataset/validation_preprocess/ train_parallel0/resnet-120_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: {'top_5_accuracy': 0.9429417413572343, 'top_1_accuracy': 0.7853513124199744} ckpt=train_parallel0/resnet-120_5004.ckpt