mindspore/model_zoo/resnet

• ResNet Example
• • Description
  • Requirements
  • Structure
  • Parameter configuration
  • Running the example
  • • Train
    • • Usage
      • Launch
      • Result
    • Evaluation
    • • Usage
      • Launch
      • Result
    • Running on GPU

ResNet Example

Description

These are examples of training ResNet-50/ResNet-101 with CIFAR-10/ImageNet2012 dataset in MindSpore.
(Training ResNet-101 with dataset CIFAR-10 is unsupported now.)

Requirements

• Install MindSpore.

• Download the dataset CIFAR-10 or ImageNet2012

CIFAR-10

Unzip the CIFAR-10 dataset to any path you want and the folder structure should include train and eval dataset as follows:

.  
└─dataset
  ├─ cifar-10-batches-bin  # train dataset
  └─ cifar-10-verify-bin   # evaluate dataset

ImageNet2012

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

.
└─dataset                 
  ├─ilsvrc                # train dataset 
  └─validation_preprocess # evaluate dataset

Structure

.
└──resnet
  ├── README.md
  ├── script
    ├── run_distribute_train.sh         # launch distributed training(8 pcs)
    ├── run_eval.sh                     # launch evaluation
    └── run_standalone_train.sh         # launch standalone training(1 pcs)
  ├── src
    ├── config.py                       # parameter configuration
    ├── dataset.py                      # data preprocessing
    ├── crossentropy.py                 # loss definition for ImageNet2012 dataset
    ├── lr_generator.py                 # generate learning rate for each step
    └── resnet.py                       # resnet backbone, including resnet50 and resnet101
  ├── eval.py                           # eval net
  └── train.py                          # train net

Parameter configuration

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

• config for ResNet-50, CIFAR-10 dataset

"class_num": 10,                  # dataset class num
"batch_size": 32,                 # batch size of input tensor
"loss_scale": 1024,               # loss scale
"momentum": 0.9,                  # momentum
"weight_decay": 1e-4,             # weight decay 
"epoch_size": 90,                 # only valid for taining, which is always 1 for inference 
"save_checkpoint": True,          # whether save checkpoint or not
"save_checkpoint_steps": 195,     # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
"keep_checkpoint_max": 10,        # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./",     # path to save checkpoint
"warmup_epochs": 5,               # number of warmup epoch
"lr_decay_mode": "poly"           # decay mode can be selected in steps, ploy and default
"lr_init": 0.01,                  # initial learning rate
"lr_end": 0.00001,                # final learning rate
"lr_max": 0.1,                    # maximum learning rate

• config for ResNet-50, ImageNet2012 dataset

"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
"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

• config for ResNet-101, ImageNet2012 dataset

"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
"pretrain_epoch_size": 0,         # epoch size of pretrain checkpoint
"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
Usage: sh run_distribute_train.sh [resnet50|resnet101] [cifar10|imagenet2012] [MINDSPORE_HCCL_CONFIG_PATH] [DATASET_PATH]
       [PRETRAINED_CKPT_PATH](optional)

# standalone training
Usage: sh run_standalone_train.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH]  
       [PRETRAINED_CKPT_PATH](optional)

Launch

# distribute training example
sh run_distribute_train.sh resnet50 cifar10 rank_table.json ~/cifar-10-batches-bin

# standalone training example
sh run_standalone_train.sh resnet50 cifar10 ~/cifar-10-batches-bin

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.

• training ResNet-50 with CIFAR-10 dataset

# distribute training result(8 pcs)
epoch: 1 step: 195, loss is 1.9601055
epoch: 2 step: 195, loss is 1.8555021
epoch: 3 step: 195, loss is 1.6707983
epoch: 4 step: 195, loss is 1.8162166
epoch: 5 step: 195, loss is 1.393667
...

• training ResNet-50 with ImageNet2012 dataset

# 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
...

• training ResNet-101 with ImageNet2012 dataset

# 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
...

Evaluation

Usage

# evaluation
Usage: sh run_eval.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]

Launch

# evaluation example
sh run_eval.sh resnet50 cifar10 ~/cifar10-10-verify-bin ~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt

checkpoint can be produced in training process.

Result

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

• evaluating ResNet-50 with CIFAR-10 dataset

result: {'acc': 0.91446314102564111} ckpt=~/resnet50_cifar10/train_parallel0/resnet-90_195.ckpt

• evaluating ResNet-50 with ImageNet2012 dataset

result: {'acc': 0.7671054737516005} ckpt=train_parallel0/resnet-90_5004.ckpt

• evaluating ResNet-101 with ImageNet2012 dataset

result: {'top_5_accuracy': 0.9429417413572343, 'top_1_accuracy': 0.7853513124199744} ckpt=train_parallel0/resnet-120_5004.ckpt

Running on GPU

# distributed training example
mpirun -n 8 python train.py ---net=resnet50 --dataset=cifar10 -dataset_path=~/cifar-10-batches-bin --device_target="GPU" --run_distribute=True

# standalone training example
python train.py --net=resnet50 --dataset=cifar10 --dataset_path=~/cifar-10-batches-bin --device_target="GPU"

# infer example
python eval.py --net=resnet50 --dataset=cifar10 --dataset_path=~/cifar10-10-verify-bin --device_target="GPU" --checkpoint_path=resnet-90_195.ckpt