mindspore2022/model_zoo/official/cv/xception

Contents

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

Xception Description

Xception by Google is extreme version of Inception. With a modified depthwise separable convolution, it is even better than Inception-v3. This paper was published in 2017.

Paper Franois Chollet. Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE, 2017.

Model architecture

The overall network architecture of Xception is show below:

Link

Dataset

Dataset used can refer to paper.

• Dataset size: 125G, 1250k colorful images in 1000 classes
  • Train: 120G, 1200k images
  • Test: 5G, 50k 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

Script description

Script and sample code

.
└─Xception
  ├─README.md
  ├─ascend310_infer                #application for 310 inference
  ├─scripts
    ├─run_standalone_train.sh      # launch standalone training with ascend platform(1p)
    ├─run_distribute_train.sh      # launch distributed training with ascend platform(8p)
    ├─run_train_gpu_fp32.sh        # launch standalone or distributed fp32 training with gpu platform(1p or 8p)
    ├─run_train_gpu_fp16.sh        # launch standalone or distributed fp16 training with gpu platform(1p or 8p)
    ├─run_eval.sh                  # launch evaluating with ascend platform
    ├─run_infer_310.sh             # shell script for 310 inference
    └─run_eval_gpu.sh              # launch evaluating with gpu platform
  ├─src
    ├─model_utils
        ├─config.py                # parsing parameter configuration file of "*.yaml"
        ├─device_adapter.py        # local or ModelArts training
        ├─local_adapter.py         # get related environment variables on local
        └─moxing_adapter.py        # get related environment variables abd transfer data on ModelArts
    ├─dataset.py                   # data preprocessing
    ├─Xception.py                  # network definition
    ├─loss.py                      # Customized CrossEntropy loss function
    └─lr_generator.py              # learning rate generator
  ├─default_config.yaml            # parameter configuration
  ├─mindspore_hub_conf.py          # mindspore hub interface
  ├─train.py                       # train net
  ├─postprogress.py                # post process for 310 inference
  ├─export.py                      # export net
  └─eval.py                        # eval net

Script Parameters

Parameters for both training and evaluation can be set in default_config.yaml.

• Config on ascend

Major parameters in train.py and config.py are:
'num_classes': 1000                # dataset class numbers
'batch_size': 128                  # input batchsize
'loss_scale': 1024                 # loss scale
'momentum': 0.9                    # momentum
'weight_decay': 1e-4               # weight decay
'epoch_size': 250                  # total epoch numbers
'save_checkpoint': True            # save checkpoint
'save_checkpoint_epochs': 1        # save checkpoint epochs
'keep_checkpoint_max': 5           # max numbers to keep checkpoints
'save_checkpoint_path': "./"       # save checkpoint path
'warmup_epochs': 1                 # warmup epoch numbers
'lr_decay_mode': "liner"           # lr decay mode
'use_label_smooth': True           # use label smooth
'finish_epoch': 0                  # finished epochs numbers
'label_smooth_factor': 0.1         # label smoothing factor
'lr_init': 0.00004                 # initiate learning rate
'lr_max': 0.4                      # max bound of learning rate
'lr_end': 0.00004                  # min bound of learning rate

• Config on gpu

Major parameters in train.py and config.py are:
'num_classes': 1000                # dataset class numbers
'batch_size': 64                  # input batchsize
'loss_scale': 1024                 # loss scale
'momentum': 0.9                    # momentum
'weight_decay': 1e-4               # weight decay
'epoch_size': 250                  # total epoch numbers
'save_checkpoint': True            # save checkpoint
'save_checkpoint_epochs': 1        # save checkpoint epochs
'keep_checkpoint_max': 5           # max numbers to keep checkpoints
'save_checkpoint_path': "./gpu-ckpt"       # save checkpoint path
'warmup_epochs': 1                 # warmup epoch numbers
'lr_decay_mode': "linear"           # lr decay mode
'use_label_smooth': True           # use label smooth
'finish_epoch': 0                  # finished epochs numbers
'label_smooth_factor': 0.1         # label smoothing factor
'lr_init': 0.00004                 # initiate learning rate
'lr_max': 0.4                      # max bound of learning rate
'lr_end': 0.00004                  # min bound of learning rate

Training process

Usage

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

• Ascend:

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

• GPU:

# fp32 distributed training example(8p)
bash scripts/run_train_gpu_fp32.sh DEVICE_NUM DATASET_PATH PRETRAINED_CKPT_PATH(optional)

# fp32 standalone training example
bash scripts/run_train_gpu_fp32.sh 1 DATASET_PATH PRETRAINED_CKPT_PATH(optional)

# fp16 distributed training example(8p)
bash scripts/run_train_gpu_fp16.sh DEVICE_NUM DATASET_PATH PRETRAINED_CKPT_PATH(optional)

# fp16 standalone training example
bash scripts/run_train_gpu_fp16.sh 1 DATASET_PATH PRETRAINED_CKPT_PATH(optional)

# infer example
bash run_eval_gpu.sh DEVICE_ID DATASET_PATH CHECKPOINT_PATH

#ascend310 infer example
bash run_infer_310.sh MINDIR_PATH DATA_PATH LABEL_FILE DEVICE_ID

Notes: RANK_TABLE_FILE can refer to Link, and the device_ip can be got as Link.

Launch

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

  shell:
      Ascend:
      # distribute training example(8p)
      bash scripts/run_distribute_train.sh RANK_TABLE_FILE DATA_PATH
      # standalone training
      bash scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
      GPU:
      # fp16 training example(8p)
      bash scripts/run_train_gpu_fp16.sh DEVICE_NUM DATA_PATH
      # fp32 training example(8p)
      bash scripts/run_train_gpu_fp32.sh DEVICE_NUM DATA_PATH

Result

Training result will be stored in the example path. Checkpoints will be stored at ./ckpt_0 for Ascend and ./gpu_ckpt for GPU by default, and training log will be redirected to log.txt fo Ascend and log_gpu.txt for GPU like following.

• Ascend:

epoch: 1 step: 1251, loss is 4.8427444
epoch time: 701242.350 ms, per step time: 560.545 ms
epoch: 2 step: 1251, loss is 4.0637593
epoch time: 598591.422 ms, per step time: 478.490 ms

• GPU:

epoch: 1 step: 20018, loss is 5.479554
epoch time: 5664051.330 ms, per step time: 282.948 ms
epoch: 2 step: 20018, loss is 5.179064
epoch time: 5628609.779 ms, per step time: 281.177 ms

Training with 8 cards on ModelArts

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

# (1) Upload the code folder to S3 bucket.
# (2) Click to "create training task" on the website UI interface.
# (3) Set the code directory to "/{path}/xception" on the website UI interface.
# (4) Set the startup file to /{path}/xception/train.py" on the website UI interface.
# (5) Perform a or b.
#     a. setting parameters in /{path}/xception/default_config.yaml.
#         1. Set ”enable_modelarts: True“
#         2. Set “is_distributed: True”
#         3. Set “modelarts_dataset_unzip_name: {folder_name}", if the data is uploaded in the form of zip package.
#         4. Set “folder_name_under_zip_file: {path}”, (dateset path under the unzip folder, such as './ImageNet_Original/train')
#     b. adding on the website UI interface.
#         1. Add ”enable_modelarts=True“
#         2. Add “is_distributed: True”
#         3. Add “modelarts_dataset_unzip_name: {folder_name}", if the data is uploaded in the form of zip package.
#         4. Add “folder_name_under_zip_file: {path}”, (dateset path under the unzip folder, such as './ImageNet_Original/train')
# (6) Upload the mindrecdrd dataset to S3 bucket.
# (7) Check the "data storage location" on the website UI interface and set the "Dataset path" path.
# (8) Set the "Output file path" and "Job log path" to your path on the website UI interface.
# (9) Under the item "resource pool selection", select the specification of 8 cards..
# (10) Create your job.

Eval process

Usage

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

• Ascend:

bash scripts/run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT

• GPU:

bash scripts/run_eval_gpu.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT

Launch

# eval example
  python:
      Ascend: python eval.py --device_target Ascend --checkpoint_path PATH_CHECKPOINT --dataset_path DATA_DIR
      GPU: python eval.py --device_target GPU --checkpoint_path PATH_CHECKPOINT --dataset_path DATA_DIR

  shell:
      Ascend: bash scripts/run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
      GPU: bash scripts/run_eval_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 following in eval.log on ascend and eval_gpu.log on gpu.

• Evaluating with ascend

result: {'Loss': 1.7797744848789312, 'Top_1_Acc': 0.7985777243589743, 'Top_5_Acc': 0.9485777243589744}

• Evaluating with gpu

result: {'Loss': 1.7846775874590903, 'Top_1_Acc': 0.798735595390525, 'Top_5_Acc': 0.9498439500640204}

Evaluating with single card on ModelArts

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

# (1) Upload the code folder 'xception' to S3 bucket.
# (2) Click to "create training task" on the website UI interface.
# (3) Set the code directory to "/{path}/xception" on the website UI interface.
# (4) Set the startup file to /{path}/xception/eval.py" on the website UI interface.
# (5) Perform a or b.
#     a. setting parameters in /{path}/xception/default_config.yaml.
#         1. Set ”enable_modelarts: True“
#         2. Set “checkpoint_path: ./{path}/*.ckpt”('load_checkpoint_path' indicates the path of the weight file to be evaluated relative to the file `eval.py`, and the weight file must be included in the code directory.)
#         3. Set “modelarts_dataset_unzip_name: {folder_name}", if the data is uploaded in the form of zip package.
#         4. Set “folder_name_under_zip_file: {path}”, (dateset path under the unzip folder, such as './ImageNet_Original/validation_preprocess')
#     b. adding on the website UI interface.
#         1. Add ”enable_modelarts: True“
#         2. Add “checkpoint_path: ./{path}/*.ckpt”('load_checkpoint_path' indicates the path of the weight file to be evaluated relative to the file `eval.py`, and the weight file must be included in the code directory.)
#         3. Add “modelarts_dataset_unzip_name: {folder_name}", if the data is uploaded in the form of zip package.
#         4. Add “folder_name_under_zip_file: {path}”, (dateset path under the unzip folder, such as './ImageNet_Original/validation_preprocess')
# (6) Upload the dataset(not mindrecord format) to S3 bucket.
# (7) Check the "data storage location" on the website UI interface and set the "Dataset path" path.
# (8) Set the "Output file path" and "Job log path" to your path on the website UI interface.
# (9) Under the item "resource pool selection", select the specification of a single card.
# (10) Create your job.

Export process

• Export on local

  python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT] --batch_size [BATCH_SIZE]
  

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)

  # (1) Upload the code folder to S3 bucket.
  # (2) Click to "create training task" on the website UI interface.
  # (3) Set the code directory to "/{path}/xception" on the website UI interface.
  # (4) Set the startup file to /{path}/xception/export.py" on the website UI interface.
  # (5) Perform a or b.
  #     a. setting parameters in /{path}/xception/default_config.yaml.
  #         1. Set ”enable_modelarts: True“
  #         2. Set “ckpt_file: ./{path}/*.ckpt”('ckpt_file' indicates the path of the weight file to be exported relative to the file `export.py`, and the weight file must be included in the code directory.)
  #         3. Set ”file_name: xception“
  #         4. Set ”file_format：MINDIR“
  #     b. adding on the website UI interface.
  #         1. Add ”enable_modelarts=True“
  #         2. Add “ckpt_file=./{path}/*.ckpt”('ckpt_file' indicates the path of the weight file to be exported relative to the file `export.py`, and the weight file must be included in the code directory.)
  #         3. Add ”file_name=xception“
  #         4. Add ”file_format=MINDIR“
  # (7) Check the "data storage location" on the website UI interface and set the "Dataset path" path (This step is useless, but necessary.).
  # (8) Set the "Output file path" and "Job log path" to your path on the website UI interface.
  # (9) Under the item "resource pool selection", select the specification of a single card.
  # (10) Create your job.
  # You will see xception.mindir under {Output file path}.
  

Inference process

Inference

Before performing inference, we need to export model first. Air model can only be exported in Ascend 910 environment, mindir model can be exported in any environment. Current batch_ size can only be set to 1.

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

-Note: the Imagenet data set is used in densnet121 network. The label of the picture is the number from 0 after sorting the folder.

Inference result will be stored in the script path, you can find result like the followings in acc.log.

Top_1_Acc: 0.79886%, Top_5_Acc: 0.94882%

Model description

Performance

Training Performance

Parameters	Ascend	GPU
Model Version	Xception	Xception
Resource	HUAWEI CLOUD Modelarts	HUAWEI CLOUD Modelarts
uploaded Date	12/10/2020	02/09/2021
MindSpore Version	1.1.0	1.1.0
Dataset	1200k images	1200k images
Batch_size	128	64
Training Parameters	src/config.py	src/config.py
Optimizer	Momentum	Momentum
Loss Function	CrossEntropySmooth	CrossEntropySmooth
Loss	1.78	1.78
Accuracy (8p)	Top1[79.8%] Top5[94.8%]	Top1[79.8%] Top5[94.9%]
Per step time (8p)	479 ms/step	282 ms/step
Total time (8p)	42h	51h
Params (M)	180M	180M
Scripts	Xception script	Xception script

Inference Performance

Parameters	Ascend	GPU
Model Version	Xception	Xception
Resource	HUAWEI CLOUD Modelarts	HUAWEI CLOUD Modelarts
Uploaded Date	12/10/2020	02/09/2021
MindSpore Version	1.1.0	1.1.0
Dataset	50k images	50k images
Batch_size	128	64
Accuracy	Top1[79.8%] Top5[94.8%]	Top1[79.8%] Top5[94.9%]
Total time	3mins	4.7mins

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.