mindspore2022/model_zoo/official/cv/resnext

Contents

• ResNeXt Description
• Model Architecture
• Dataset
• Features
• Mixed Precision
• Environment Requirements
• Quick Start
• Script Description
  • Script and Sample Code
  • Script Parameters
  • Training Process
  • Evaluation Process
  • Model Export
  • Inference Process
• Model Description
  • Performance
    • Training Performance
    • Inference Performance
• Description of Random Situation
• ModelZoo Homepage

ResNeXt Description

ResNeXt is a simple, highly modularized network architecture for image classification. It designs results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set in ResNeXt. This strategy exposes a new dimension, which we call “cardinality” (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width.

Paper: Xie S, Girshick R, Dollár, Piotr, et al. Aggregated Residual Transformations for Deep Neural Networks. 2016.

Model architecture

The overall network architecture of ResNeXt is show below:

Link

Dataset

Dataset used: imagenet

• Dataset size: ~125G, 1.2W colorful images in 1000 classes
• Train: 120G, 1.2W images
• Test: 5G, 50000 images
• Data format: RGB images
• Note: Data will be processed in src/dataset.py

Features

Mixed Precision

The mixed precision training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.

For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.

Environment Requirements

• Hardware（Ascend/GPU）
• Prepare hardware environment with Ascend or GPU processor.
• Framework
• MindSpore
• For more information, please check the resources below：
• MindSpore Tutorials
• MindSpore Python API

If you want to run in modelarts, please check the official documentation of modelarts, and you can start training and evaluation as follows:

# run distributed training on modelarts example
# (1) First, Perform a or b.
#       a. Set "enable_modelarts=True" on yaml file.
#          Set other parameters on yaml file you need.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add other parameters on the website UI interface.
# (2) Set the code directory to "/path/resnext" on the website UI interface.
# (3) Set the startup file to "train.py" on the website UI interface.
# (4) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (5) Create your job.

# run evaluation on modelarts example
# (1) Copy or upload your trained model to S3 bucket.
# (2) Perform a or b.
#       a. Set "enable_modelarts=True" on yaml file.
#          Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
#          Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
#          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
# (3) Set the code directory to "/path/resnext" on the website UI interface.
# (4) Set the startup file to "eval.py" on the website UI interface.
# (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (6) Create your job.

Script description

Script and sample code

.
└─resnext
  ├─README.md
  ├─scripts
    ├─run_standalone_train.sh         # launch standalone training for ascend(1p)
    ├─run_distribute_train.sh         # launch distributed training for ascend(8p)
    ├─run_standalone_train_for_gpu.sh # launch standalone training for gpu(1p)
    ├─run_distribute_train_for_gpu.sh # launch distributed training for gpu(8p)
    └─run_eval.sh                     # launch evaluating
  ├─src
    ├─backbone
      ├─_init_.py                     # initialize
      ├─resnet.py                     # resnext backbone
    ├─utils
      ├─_init_.py                     # initialize
      ├─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                       # initialize
    ├─config.py                       # parameter configuration
    ├─crossentropy.py                 # CrossEntropy loss function
    ├─dataset.py                      # data preprocessing
    ├─head.py                         # common 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
  ├── model_utils
    ├──config.py                      # parameter configuration
    ├──device_adapter.py              # device adapter
    ├──local_adapter.py               # local adapter
    ├──moxing_adapter.py              # moxing adapter
  ├── default_config.yaml             # parameter configuration
  ├──eval.py                          # eval net
  ├──train.py                         # train net
  ├──export.py                        # export mindir script
  ├──mindspore_hub_conf.py            # mindspore hub interface

Script Parameters

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
"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

Training Process

Usage

You can start training by python script:

python train.py --data_path ~/imagenet/train/ --device_target Ascend --run_distribute 0

or shell script:

Ascend:
    # 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
GPU:
    # distribute training example(8p)
    sh run_distribute_train_for_gpu.sh DATA_PATH
    # standalone training
    sh run_standalone_train_for_gpu.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

You can find checkpoint file together with result in log.

Evaluation Process

Usage

You can start training by python script:

python eval.py --data_path ~/imagenet/val/ --device_target Ascend --checkpoint_file_path resnext.ckpt

or shell script:

# Evaluation
sh scripts/run_eval.sh DEVICE_ID DATA_PATH CHECKPOINT_FILE_PATH DEVICE_TARGET

PLATFORM is Ascend or GPU, default is Ascend.

Launch

# Evaluation with checkpoint
sh scripts/run_eval.sh 0 /opt/npu/datasets/classification/val /resnext_100.ckpt Ascend

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)

Model Export

Export MindIR on local

python export.py --device_target [PLATFORM] --checkpoint_file_path [CKPT_PATH] --file_format [EXPORT_FORMAT]

The checkpoint_file_path parameter is required. EXPORT_FORMAT should be in ["AIR", "MINDIR"].

Export on ModelArts (If you want to run in modelarts, please check the official documentation of modelarts, and you can start as follows)

# Export on ModelArts
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on default_config.yaml file.
#          Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
#          Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file.
#          Set "file_name='./resnext50'" on default_config.yaml file.
#          Set "file_format='AIR'" on default_config.yaml file.
#          Set other parameters on default_config.yaml file you need.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
#          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
#          Add "file_name='./resnext50'" on the website UI interface.
#          Add "file_format='AIR'" on the website UI interface.
#          Add other parameters on the website UI interface.
# (2) Set the config_path="/path/yaml file" on the website UI interface.
# (3) Set the code directory to "/path/resnext50" on the website UI interface.
# (4) Set the startup file to "export.py" on the website UI interface.
# (5) Set the "Output file path" and "Job log path" to your path on the website UI interface.
# (6) Create your job.

Inference Process

Usage

Before performing inference, the mindir file must be exported by export.py. Currently, only batchsize 1 is supported.

# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]

DEVICE_ID is optional, default value is 0.

result

Inference result is saved in current path, you can find result in acc.log file.

Total data:50000, top1 accuracy:0.78462, top5 accuracy:0.94182

Total data:50000, top1 accuracy:0.79858, top5 accuracy:0.94716

Model description

Performance

Training Performance

Parameters	ResNeXt50
Resource	Ascend 910; cpu 2.60GHz, 192cores; memory 755G; OS Euler2.8	NV SMX2 V100-32G
uploaded Date	06/30/2020	07/23/2020
MindSpore Version	0.5.0	0.6.0
Dataset	ImageNet	ImageNet
Training Parameters	default_config.yaml	default_config.yaml
Optimizer	Momentum	Momentum
Loss Function	SoftmaxCrossEntropy	SoftmaxCrossEntropy
Loss	1.76592	1.8965
Accuracy	78%(TOP1)	77.8%(TOP1)
Total time	7.8 h 8ps	21.5 h 8ps
Checkpoint for Fine tuning	192 M(.ckpt file)	192 M(.ckpt file)

Parameters	ResNeXt101
Resource	Ascend 910; cpu 2.60GHz, 192cores; memory 755G; OS Euler2.8
uploaded Date	22/06/2021 (month/day/year)
MindSpore Version	1.2.0
Dataset	ImageNet
Training Parameters	default_config.yaml
Optimizer	Momentum
Loss Function	SoftmaxCrossEntropy
Accuracy	79.56%%(TOP1)
train performance	196.33image/sec 1ps

Inference Performance

Parameters	ResNeXt50
Resource	Ascend 910; OS Euler2.8	NV SMX2 V100-32G	Ascend 310
uploaded Date	06/30/2020	07/23/2020	07/23/2020
MindSpore Version	0.5.0	0.6.0	0.6.0
Dataset	ImageNet, 1.2W	ImageNet, 1.2W	ImageNet, 1.2W
batch_size	1	1	1
outputs	probability	probability	probability
Accuracy	acc=78.16%(TOP1)	acc=78.05%(TOP1)

Parameters	Ascend
Model Version	ResNeXt101
Resource	Ascend 310; OS Euler2.8
Uploaded Date	22/06/2021 (month/day/year)
MindSpore Version	1.2.0
Dataset	ImageNet
batch_size	1
outputs	Accuracy
Accuracy	TOP1: 79.85%

Description of Random Situation

In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.

ModelZoo Homepage

Please check the official homepage.