mindspore/model_zoo/official/cv/resnext50

• ResNext50 Example
• • Description
  • Requirements
  • Structure
  • Parameter Configuration
  • Running the example
  • • Train
    • • Usage
      • Launch
      • Result
    • Evaluation
    • • Usage
      • Launch
      • Result

ResNext50 Example

Description

This is an example of training ResNext50 in MindSpore.

Requirements

• Install Mindspore.
• Downlaod the dataset.

Structure

.
└─resnext50      
  ├─README.md
  ├─scripts      
    ├─run_standalone_train.sh         # launch standalone training(1p)
    ├─run_distribute_train.sh         # launch distributed training(8p)
    └─run_eval.sh                     # launch evaluating
  ├─src
    ├─backbone
      ├─_init_.py                     # initalize
      ├─resnet.py                     # resnext50 backbone
    ├─utils
      ├─_init_.py                     # initalize
      ├─cunstom_op.py                 # network operation
      ├─logging.py                    # print log
      ├─optimizers_init_.py           # get parameters
      ├─sampler.py                    # distributed sampler
      ├─var_init_.py                  # calculate gain value
    ├─_init_.py                       # initalize
    ├─config.py                       # parameter configuration
    ├─crossentropy.py                 # CrossEntropy loss function
    ├─dataset.py                      # data preprocessing
    ├─head.py                         # commom head
    ├─image_classification.py         # get resnet
    ├─linear_warmup.py                # linear warmup learning rate
    ├─warmup_cosine_annealing.py      # learning rate each step
    ├─warmup_step_lr.py               # warmup step learning rate
  ├─eval.py                           # eval net
  └─train.py                          # train net
  

Parameter Configuration

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

"image_height": '224,224'                 # image size
"num_classes": 1000,                      # dataset class number
"per_batch_size": 128,                    # batch size of input tensor
"lr": 0.05,                               # base learning rate
"lr_scheduler": 'cosine_annealing',       # learning rate mode
"lr_epochs": '30,60,90,120',              # epoch of lr changing
"lr_gamma": 0.1,                          # decrease lr by a factor of exponential lr_scheduler
"eta_min": 0,                             # eta_min in cosine_annealing scheduler
"T_max": 150,                             # T-max in cosine_annealing scheduler
"max_epoch": 150,                         # max epoch num to train the model
"backbone": 'resnext50',                  # backbone metwork
"warmup_epochs" : 1,                      # warmup epoch
"weight_decay": 0.0001,                   # weight decay
"momentum": 0.9,                          # momentum
"is_dynamic_loss_scale": 0,               # dynamic loss scale
"loss_scale": 1024,                       # loss scale
"label_smooth": 1,                        # label_smooth
"label_smooth_factor": 0.1,               # label_smooth_factor
"ckpt_interval": 2000,                    # ckpt_interval
"ckpt_path": 'outputs/',                  # checkpoint save location
"is_save_on_master": 1,
"rank": 0,                                # local rank of distributed
"group_size": 1                           # world size of distributed

Running the example

Train

Usage

# distribute training example(8p)
sh run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
# standalone training
sh run_standalone_train.sh DEVICE_ID DATA_PATH

Launch

# distributed training example(8p) for Ascend
sh scripts/run_distribute_train.sh RANK_TABLE_FILE /dataset/train
# standalone training example for Ascend
sh scripts/run_standalone_train.sh 0 /dataset/train

# distributed training example(8p) for GPU
sh scripts/run_distribute_train_for_gpu.sh /dataset/train
# standalone training example for GPU
sh scripts/run_standalone_train_for_gpu.sh 0 /dataset/train

Result

You can find checkpoint file together with result in log.

Evaluation

Usage

# Evaluation
sh run_eval.sh DEVICE_ID DATA_PATH PRETRAINED_CKPT_PATH PLATFORM

PLATFORM is Ascend or GPU, default is Ascend.

Launch

# Evaluation with checkpoint
sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext50_100.ckpt Ascend

checkpoint can be produced in training process.

Result

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

acc=78.16%(TOP1)
acc=93.88%(TOP5)