• Contents
• SK-Net Description
• • Description
  • Paper
• Model Architecture
• Dataset
• Features
• • Mixed Precision
• Environment Requirements
• Quick Start
• Script Description
• • Script and Sample Code
  • Script Parameters
  • Training Process
  • • Usage
    • • Running on Ascend
    • Result
  • Evaluation Process
  • • Usage
    • • Running on Ascend
    • Result
• Model Description
• • Performance
  • • Evaluation Performance
    • • SKNet50 on CIFRA10
    • Inference Performance
    • • SKNet50 on CIFAR10
• Description of Random Situation
• ModelZoo Homepage

Contents

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

SK-Net Description

Description

Selective Kernel Networks is inspired by cortical neurons that can dynamically adjust their own receptive field according to different stimuli. It is a product of combining SE operator, Merge-and-Run Mappings, and attention on inception block ideas. Carry out Selective Kernel transformation for all convolution kernels> 1 to make full use of the smaller theory brought by group/depthwise convolution, so that the design of adding multiple channels and dynamic selection will not bring a big overhead

this is example of training SKNET50 with CIFAR-10 dataset in MindSpore. Training SKNet50 for just 90 epochs using 8 Ascend 910, we can reach top-1 accuracy of 94.49% on CIFAR10.

Paper

paper: Xiang Li, Wenhai Wang, Xiaolin Hu, Jian Yang. "Selective Kernel Networks"

Model Architecture

The overall network architecture of Net is show below:
Link

Dataset

Dataset used: CIFAR10

• Dataset size 32*32 colorful images in 10 classes
  • Train：50000 images
  • Test： 10000 images
• Data format：binary files
  • Note：Data will be processed in dataset.py
• Download the dataset, the directory structure is as follows:

├─cifar-10-batches-bin
│
└─cifar-10-verify-bin

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

Quick Start

After installing MindSpore via the official website, you can start training and evaluation as follows:

• Running on Ascend

# 1.standalone training
export DEVICE_ID=0
python train.py --dataset_path=/data/cifar10

#2.run evaluation
export DEVICE_ID=0
python eval.py --checkpoint_path=/resnet/sknet_90.ckpt --dataset_path=/data/cifar10

Script Description

Script and Sample Code

└──SK-Net
  ├── README.md
  ├── scripts
    ├── run_distribute_train.sh            # launch ascend distributed training(8 pcs)
    ├── run_eval.sh                        # launch ascend evaluation
    ├── run_standalone_train.sh            # launch ascend standalone training(1 pcs)
  ├── src
    ├── config.py                          # parameter configuration
    ├── CrossEntropySmooth.py              # loss definition
    ├── dataset.py                         # data preprocessing
    ├── lr_generator.py                    # generate learning rate for each step
    ├── sknet50.py                         # sket50 backbone
    ├── var_init.py                        # convlution init function
    └── util.py                            # group convlution
  ├── export.py                            # export model for inference
  ├── eval.py                              # eval net
  └── train.py                             # train net

Script Parameters

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

• Config for SKNET50, CIFAR10 dataset

"class_num": 10,                # 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
"pretrain_epoch_size": 0,         # epoch size that model has been trained before loading pretrained checkpoint, actual training epoch size is equal to epoch_size minus pretrain_epoch_size
"save_checkpoint": True,          # whether save checkpoint or not
"save_checkpoint_epochs": 5,      # 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": "./ckpt",     # path to save checkpoint relative to the executed path
"warmup_epochs": 5,               # number of warmup epoch
"lr_decay_mode": "ploy",        # decay mode for generating learning rate
"lr_init": 0.01,                     # initial learning rate
"lr_max": 0.00001,                    # maximum learning rate
"lr_end": 0.1,                    # minimum learning rate

Training Process

Usage

Running on Ascend

# distributed training
Usage:
bash run_distribute_train.sh [DATASET_PATH] [RANK_TABLE_FILE] [DEVICE_NUM]

# standalone training
Usage:
export DEVICE_ID=0
bash run_standalone_train.sh [DATASET_PATH]

For distributed training, a hccl configuration file with JSON format needs to be created in advance.

Please follow the instructions in the link hccn_tools.

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 following in log.

Result

• Training SKNET50 with CIFAR10 dataset

# distribute training result(8 pcs)
epoch: 90 step: 195, loss is 1.1160697e-05
epoch time: 35059.094 ms, per step time: 179.790 ms

Evaluation Process

Usage

Running on Ascend

export DEVICE_ID=0
bash run_eval.sh [DATASET_PATH]  [CHECKPOINT_PATH]

Result

• Evaluating SKNet50 with CIFAR10 dataset

result: {'top_5_accuracy': 0.9982972756410257, 'top_1_accuracy': 0.9449118589743589}

Model Description

Performance

Evaluation Performance

SKNet50 on CIFRA10

Parameters	Ascend 910
Model Version	SKNet50
Resource	CentOs 8.2, Ascend 910，CPU 2.60GHz 192cores，Memory 755G
uploaded Date	06/28/2021 (month/day/year)
MindSpore Version	1.2.0
Dataset	CIFAR10
Training Parameters	epoch=90, steps per epoch=195, batch_size = 32
Optimizer	Momentum
Loss Function	Softmax Cross Entropy
outputs	probability
Loss	0.000011160697
Speed	181.3 ms/step（8pcs）
Total time	179 mins
Parameters (M)	13.2M
Checkpoint for Fine tuning	224M (.ckpt file)
Scripts	Link

Inference Performance

SKNet50 on CIFAR10

Parameters	Ascend
Model Version	SKNet50
Resource	Ascend 910
Uploaded Date	06/27/2021 (month/day/year)
MindSpore Version	1.2.0
Dataset	CIFAR10
batch_size	32
Accuracy	94.49%

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.