mindspore/model_zoo/official/cv/inceptionv3

• Contents
• InceptionV3 Description
• Model architecture
• Dataset
• Features
• • Mixed Precision(Ascend)
• Environment Requirements
• Script description
• • Script and sample code
  • Script Parameters
  • Training process
  • • Usage
    • Launch
    • Result
  • Eval process
  • • Usage
    • Launch
    • Result
• Model description
• • Performance
  • • Training Performance
    • • Inference Performance
• Description of Random Situation
• ModelZoo Homepage

Contents

• InceptionV3 Description
• Model Architecture
• Dataset
• Features
  • Mixed Precision
• Environment Requirements
• Script Description
  • Script and Sample Code
  • Training Process
  • Evaluation Process
    • Evaluation
• Model Description
  • Performance
    • Training Performance
    • Inference Performance
• Description of Random Situation
• ModelZoo Homepage

InceptionV3 Description

InceptionV3 by Google is the 3rd version in a series of Deep Learning Convolutional Architectures.

Paper Min Sun, Ali Farhadi, Steve Seitz. Ranking Domain-Specific Highlights by Analyzing Edited Videos[J]. 2014.

Model architecture

The overall network architecture of InceptionV3 is show below:

Link

Dataset

Dataset used can refer to paper.

• 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(Ascend)

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. If you want to try Ascend , please send the application form to ascend@huawei.com. Once approved, you can get the resources.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore tutorials
  • MindSpore API

Script description

Script and sample code

.
└─Inception-v3      
  ├─README.md
  ├─scripts      
	├─run_standalone_train.sh         		  # launch standalone training with ascend platform(1p)
    ├─run_standalone_train_for_gpu.sh         # launch standalone training with gpu platform(1p)
	├─run_distribute_train.sh         		  # launch distributed training with ascend platform(8p)
    ├─run_distribute_train_for_gpu.sh         # launch distributed training with gpu platform(8p)
	├─run_eval.sh                     		  # launch evaluating with ascend platform
    └─run_eval_for_gpu.sh                     # launch evaluating with gpu platform
  ├─src
    ├─config.py                       # parameter configuration
    ├─dataset.py                      # data preprocessing
    ├─inception_v3.py                 # network definition
    ├─loss.py                         # Customized CrossEntropy loss function
    ├─lr_generator.py                 # learning rate generator
  ├─eval.py                           # eval net
  ├─export.py                         # convert checkpoint
  └─train.py                          # train net
  

Script Parameters

Major parameters in train.py and config.py are:   
'random_seed': 1,                # fix random seed
'rank': 0,                       # local rank of distributed
'group_size': 1,                 # world size of distributed
'work_nums': 8,                  # number of workers to read the data
'decay_method': 'cosine',        # learning rate scheduler mode
"loss_scale": 1,                 # loss scale
'batch_size': 128,               # input batchsize
'epoch_size': 250,               # total epoch numbers
'num_classes': 1000,             # dataset class numbers
'smooth_factor': 0.1,            # label smoothing factor
'aux_factor': 0.2,               # loss factor of aux logit
'lr_init': 0.00004,              # initiate learning rate
'lr_max': 0.4,                   # max bound of learning rate
'lr_end': 0.000004,               # min bound of learning rate
'warmup_epochs': 1,              # warmup epoch numbers
'weight_decay': 0.00004,         # weight decay
'momentum': 0.9,                 # momentum
'opt_eps': 1.0,                  # epsilon
'keep_checkpoint_max': 100,      # max numbers to keep checkpoints
'ckpt_path': './checkpoint/',    # save checkpoint path
'is_save_on_master': 1           # save checkpoint on rank0, distributed parameters

Training process

Usage

You can start training using python or shell scripts. The usage of shell scripts as follows:

• 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_DIR 
# standalone training
sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR

Launch

# training example
  python:
      Ascend: python train.py --dataset_path /dataset/train --platform Ascend
      GPU: python train.py --dataset_path /dataset/train --platform GPU

  shell:
      # 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

Training result will be stored in the example path. Checkpoints will be stored at . /checkpoint by default, and training log will be redirected to ./log.txt like followings.

epoch: 0 step: 1251, loss is 5.7787247
Epoch time: 360760.985, per step time: 288.378
epoch: 1 step: 1251, loss is 4.392868
Epoch time: 160917.911, per step time: 128.631

Eval process

Usage

You can start training using python or shell scripts. The usage of shell scripts as follows:

• Ascend: sh run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
• GPU: sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT

Launch

# eval example
  python:
      Ascend: python eval.py --dataset_path DATA_DIR --checkpoint PATH_CHECKPOINT --platform Ascend
      GPU: python eval.py --dataset_path DATA_DIR --checkpoint PATH_CHECKPOINT --platform GPU

  shell:
      Ascend: sh run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
      GPU: sh run_eval_for_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT

checkpoint can be produced in training process.

Result

Evaluation result will be stored in the example path, you can find result like the followings in log.txt.

metric: {'Loss': 1.778, 'Top1-Acc':0.788, 'Top5-Acc':0.942}

Model description

Performance

Training Performance

Parameters	InceptionV3
Model Version
Resource	Ascend 910, cpu:2.60GHz 56cores, memory:314G	NV SMX2 V100-32G
uploaded Date	08/21/2020	08/21/2020
MindSpore Version	0.6.0-beta	0.6.0-beta
Training Parameters	src/config.py	src/config.py
Optimizer	RMSProp	RMSProp
Loss Function	SoftmaxCrossEntropy	SoftmaxCrossEntropy
outputs	probability	probability
Loss	1.98	1.98
Accuracy	ACC1[78.8%] ACC5[94.2%]	ACC1[78.7%] ACC5[94.1%]
Total time	11h	72h
Params (M)	103M	103M
Checkpoint for Fine tuning	313M	312.41
Model for inference

Inference Performance

To be added.

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.