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cpp_infer

cpp_infer

 
 

infer

infer

 
 

modelarts

modelarts

 
 

scripts

scripts

 
 

src

src

 
 

README.md

README.md

 
 

README_CN.md

README_CN.md

 
 

default_config.yaml

default_config.yaml

 
 

default_config_cpu.yaml

default_config_cpu.yaml

 
 

default_config_gpu.yaml

default_config_gpu.yaml

 
 

eval.py

eval.py

 
 

eval_onnx.py

eval_onnx.py

 
 

export.py

export.py

 
 

mindspore_hub_conf.py

mindspore_hub_conf.py

 
 

postprocess.py

postprocess.py

 
 

requirements.txt

requirements.txt

 
 

train.py

train.py

 
 

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Contents

InceptionV3 Description

InceptionV3 by Google is the 3rd version in a series of Deep Learning Convolutional Architectures. Inception v3 mainly focuses on burning less computational power by modifying the previous Inception architectures. This idea was proposed in the paper Rethinking the Inception Architecture for Computer Vision, published in 2015.

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 used: ImageNet2012

  • 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

Dataset used: CIFAR-10

  • Dataset size: 175M, 60,000 32*32 colorful images in 10 classes
    • Train: 146M, 50,000 images
    • Test: 29M, 10,000 images
  • Data format：binary files
    • 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）

  • Prepare hardware environment with Ascend processor.

  • Framework

  • For more information, please check the resources below：

  • Running on ModelArts

    # Train 8p with Ascend
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on default_config.yaml file.
#          Set "distribute=True" on default_config.yaml file.
#          Set "need_modelarts_dataset_unzip=True" on default_config.yaml file.
#          Set "modelarts_dataset_unzip_name='ImageNet_Original'" on default_config.yaml file.
#          Set "lr_init=0.00004" on default_config.yaml file.
#          Set "dataset_path='/cache/data'" on default_config.yaml file.
#          Set "epoch_size=250" on default_config.yaml file.
#          (optional)Set "checkpoint_url='s3://dir_to_your_pretrained/'" 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 "need_modelarts_dataset_unzip=True" on the website UI interface.
#          Add "modelarts_dataset_unzip_name='ImageNet_Original'" on the website UI interface.
#          Add "distribute=True" on the website UI interface.
#          Add "lr_init=0.00004" on the website UI interface.
#          Add "dataset_path=/cache/data" on the website UI interface.
#          Add "epoch_size=250" on the website UI interface.
#          (optional)Add "checkpoint_url='s3://dir_to_your_pretrained/'" on the website UI interface.
#          Add other parameters on the website UI interface.
# (2) Prepare model code
# (3) Upload or copy your pretrained model to S3 bucket if you want to finetune.
# (4) Perform a or b. (suggested option a)
#       a. First, zip MindRecord dataset to one zip file.
#          Second, upload your zip dataset to S3 bucket.(you could also upload the origin mindrecord dataset, but it can be so slow.)
#       b. Upload the original coco dataset to S3 bucket.
#           (Data set conversion occurs during training process and costs a lot of time. it happens every time you train.)
# (5) Set the code directory to "/path/inceptionv3" on the website UI interface.
# (6) Set the startup file to "train.py" on the website UI interface.
# (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (8) Create your job.
#
# Train 1p with Ascend
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on default_config.yaml file.
#          Set "need_modelarts_dataset_unzip=True" on default_config.yaml file.
#          Set "modelarts_dataset_unzip_name='ImageNet_Original'" on default_config.yaml file.
#          Set "dataset_path='/cache/data'" on default_config.yaml file.
#          Set "epoch_size=250" on default_config.yaml file.
#          (optional)Set "checkpoint_url='s3://dir_to_your_pretrained/'" 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 "need_modelarts_dataset_unzip=True" on the website UI interface.
#          Add "modelarts_dataset_unzip_name='ImageNet_Original'" on the website UI interface.
#          Add "dataset_path='/cache/data'" on the website UI interface.
#          Add "epoch_size=250" on the website UI interface.
#          (optional)Add "checkpoint_url='s3://dir_to_your_pretrained/'" on the website UI interface.
#          Add other parameters on the website UI interface.
# (2) Prepare model code
# (3) Upload or copy your pretrained model to S3 bucket if you want to finetune.
# (4) Perform a or b. (suggested option a)
#       a. zip MindRecord dataset to one zip file.
#          Second, upload your zip dataset to S3 bucket.(you could also upload the origin mindrecord dataset, but it can be so slow.)
#       b. Upload the original coco dataset to S3 bucket.
#           (Data set conversion occurs during training process and costs a lot of time. it happens every time you train.)
# (5) Set the code directory to "/path/inceptionv3" on the website UI interface.
# (6) Set the startup file to "train.py" on the website UI interface.
# (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (8) Create your job.
#
# Eval 1p with Ascend
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on default_config.yaml file.
#          Set "need_modelarts_dataset_unzip=True" on default_config.yaml file.
#          Set "modelarts_dataset_unzip_name='ImageNet_Original'" on default_config.yaml file.
#          Set "checkpoint_url='s3://dir_to_your_trained_model/'" on base_config.yaml file.
#          Set "checkpoint='./inceptionv3/inceptionv3-rank3_1-247_1251.ckpt'" on default_config.yaml file.
#          Set "dataset_path='/cache/data'" 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 "need_modelarts_dataset_unzip=True" on the website UI interface.
#          Add "modelarts_dataset_unzip_name='ImageNet_Original'" on the website UI interface.
#          Add "checkpoint_url='s3://dir_to_your_trained_model/'" on the website UI interface.
#          Add "checkpoint='./inceptionv3/inceptionv3-rank3_1-247_1251.ckpt'" on the website UI interface.
#          Add "dataset_path='/cache/data'" on the website UI interface.
#          Add other parameters on the website UI interface.
# (2) Prepare model code
# (3) Upload or copy your trained model to S3 bucket.
# (4) Perform a or b. (suggested option a)
#       a. First, zip MindRecord dataset to one zip file.
#          Second, upload your zip dataset to S3 bucket.(you could also upload the origin mindrecord dataset, but it can be so slow.)
#       b. Upload the original coco dataset to S3 bucket.
#           (Data set conversion occurs during training process and costs a lot of time. it happens every time you train.)
# (5) Set the code directory to "/path/inceptionv3" on the website UI interface.
# (6) Set the startup file to "eval.py" on the website UI interface.
# (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (8) Create your job.

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

  1. Export s8 multiscale and flip with voc val dataset on modelarts, evaluating steps are as follows:

    # (1) Perform a or b.
#       a. Set "enable_modelarts=True" on base_config.yaml file.
#          Set "file_name='inceptionv3'" on base_config.yaml file.
#          Set "file_format='MINDIR'" on base_config.yaml file.
#          Set "checkpoint_url='/The path of checkpoint in S3/'" on beta_config.yaml file.
#          Set "ckpt_file='/cache/checkpoint_path/model.ckpt'" on base_config.yaml file.
#          Set other parameters on base_config.yaml file you need.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add "file_name='inceptionv3'" on the website UI interface.
#          Add "file_format='MINDIR'" on the website UI interface.
#          Add "checkpoint_url='/The path of checkpoint in S3/'" on the website UI interface.
#          Add "ckpt_file='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
#          Add other parameters on the website UI interface.
# (2) Upload or copy your trained model to S3 bucket.
# (3) Set the code directory to "/path/inceptionv3" on the website UI interface.
# (4) Set the startup file to "export.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

.
└─Inception-v3
  ├─README_CN.md
  ├─README.md
  ├─ascend310_infer                           # application for 310 inference
  ├─infer
  ├─modelarts
  ├─scripts
    ├─run_standalone_train_cpu.sh             # launch standalone training with cpu platform
    ├─run_standalone_train_gpu.sh             # launch standalone training with gpu platform(1p)
    ├─run_distribute_train_gpu.sh             # launch distributed training with gpu platform(8p)
    ├─run_standalone_train.sh                 # launch standalone training with ascend platform(1p)
    ├─run_distribute_train.sh                 # launch distributed training with ascend platform(8p)
    ├─run_infer_310.sh                        # shell script for 310 inference
    ├─run_eval_cpu.sh                         # launch evaluation with cpu platform
    ├─run_eval_gpu.sh                         # launch evaluation with gpu platform
    ├─run_eval_onnx_gpu.sh                    # launch ONNX model evaluation with gpu platform
    └─run_eval.sh                             # launch evaluating with ascend platform
  ├─src
    ├─dataset.py                      # data preprocessing
    ├─inception_v3.py                 # network definition
    ├─loss.py                         # Customized CrossEntropy loss function
    ├─lr_generator.py                 # learning rate generator
    └─model_utils
      ├─config.py               # Processing configuration parameters
      ├─device_adapter.py       # Get cloud ID
      ├─local_adapter.py        # Get local ID
      └─moxing_adapter.py       # Parameter processing
  ├─default_config.yaml             # Training parameter profile(ascend)
  ├─default_config_cpu.yaml         # Training parameter profile(cpu)
  ├─default_config_gpu.yaml         # Training parameter profile(gpu)
  ├─eval.py                           # eval net
  ├─eval_onnx.py                      # eval ONNX model
  ├─export.py                         # convert checkpoint
  ├─mindspore_hub_conf.py             # create network model
  ├─postprogress.py                   # post process for 310 inference
  └─train.py                          # train net

Script Parameters

Major parameters in train.py and config.py are:
'random_seed'                # fix random seed
'work_nums'                  # number of workers to read the data
'decay_method'               # learning rate scheduler mode
"loss_scale"                 # loss scale
'batch_size'                 # input batchsize
'epoch_size'                 # total epoch numbers
'num_classes'                # dataset class numbers
'ds_type'                    # dataset type, such as: imagenet, cifar10
'ds_sink_mode'               # whether enable dataset sink mode
'smooth_factor'              # label smoothing factor
'aux_factor'                 # loss factor of aux logit
'lr_init'                    # initiate learning rate
'lr_max'                     # max bound of learning rate
'lr_end'                     # min bound of learning rate
'warmup_epochs'              # warmup epoch numbers
'weight_decay'               # weight decay
'momentum'                   # momentum
'opt_eps'                    # epsilon
'keep_checkpoint_max'        # max numbers to keep checkpoints
'ckpt_path'                  # save checkpoint path
'onnx_file'                  # save exported ONNX model path
'is_save_on_master'          # save checkpoint on rank0, distributed parameters
'dropout_keep_prob'          # the keep rate, between 0 and 1, e.g. keep_prob = 0.9, means dropping out 10% of input units
'has_bias'                   # specifies whether the layer uses a bias vector.
'amp_level'                  # option for argument `level` in `mindspore.amp.build_train_network`, level for mixed
                             # precision training. Supports [O0, O2, O3].

Training process

Usage

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

  • Ascend:

please modify your *.yaml file(default default_config.yaml), ds_type: cifar10 when using cifar10 dataset, ds_type: imagenet when using imagenet dataset.

# distribute training(8p)
bash run_distribute_train.sh [RANK_TABLE_FILE] [DATA_PATH] [CKPT_PATH]
# example: bash run_distribute_train.sh ~/hccl_8p.json /home/DataSet/cifar10/ ./ckpt/

# standalone training
bash scripts/run_standalone_train.sh [DEVICE_ID] [DATA_PATH] [CKPT_PATH]
# example: bash scripts/run_standalone_train.sh 0 /home/DataSet/cifar10/ ./ckpt/
  • CPU:
# standalone training
bash scripts/run_standalone_train_cpu.sh DATA_PATH

  1. RANK_TABLE_FILE can refer to Link. For large models like InceptionV3, it's better to export an external environment variable export HCCL_CONNECT_TIMEOUT=600 to extend hccl connection checking time from the default 120 seconds to 600 seconds. Otherwise, the connection could be timeout since compiling time increases with the growth of model size.

  2. This is processor cores binding operation regarding the device_num and total processor numbers. If you are not expect to do it, remove the operations taskset in scripts/run_distribute_train.sh

Launch

# training example
  python:
      Ascend: python train.py --config_path CONFIG_FILE --dataset_path DATA_PATH --platform Ascend
      CPU: python train.py --config_path CONFIG_FILE --dataset_path DATA_PATH --platform CPU

  shell:
      Ascend:
      bash run_distribute_train.sh [RANK_TABLE_FILE] [DATA_PATH] [CKPT_PATH]
      # example: bash run_distribute_train.sh /root/hccl_8p_012345467_10.155.170.71.json /home/DataSet/cifar10/ ./ckpt/

      # standalone training example
      bash scripts/run_standalone_train.sh [DEVICE_ID] [DATA_PATH] [CKPT_PATH]
      # example: bash scripts/run_standalone_train.sh 0 /home/DataSet/cifar10/ ./ckpt/

      CPU:
      bash script/run_standalone_train_cpu.sh DATA_PATH ./ckpt

Result

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

Ascend

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

CPU

epoch: 1 step: 390, loss is 2.7072601
epoch time: 6334572.124 ms, per step time: 16242.493 ms
epoch: 2 step: 390, loss is 2.5908582
epoch time: 6217897.644 ms, per step time: 15943.327 ms
epoch: 3 step: 390, loss is 2.5612416
epoch time: 6358482.104 ms, per step time: 16303.800 ms
...

Eval process

Usage

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

  • Ascend:
    bash run_eval.sh [DEVICE_ID] [DATA_DIR] [PATH_CHECKPOINT]
    # example: bash run_eval.sh 0 /home/DataSet/cifar10/ /home/model/inceptionv3/ckpt/inception_v3-rank0-2_1251.ckpt
  • CPU:
    bash run_eval_cpu.sh DATA_PATH PATH_CHECKPOINT

Launch

# eval example
  python:
      Ascend: python eval.py --config_path CONFIG_FILE --dataset_path DATA_PATH --checkpoint PATH_CHECKPOINT --platform Ascend
      CPU: python eval.py --config_path CONFIG_FILE --dataset_path DATA_PATH --checkpoint PATH_CHECKPOINT --platform CPU

  shell:
      Ascend: bash scripts/run_eval.sh DEVICE_ID DATA_PATH PATH_CHECKPOINT
      CPU: bash run_eval_cpu.sh DATA_PATH 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 eval.log.

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

Model Export

python export.py --config_path CONFIG_FILE --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format[EXPORT_FORMAT]

EXPORT_FORMAT should be in ["AIR", "MINDIR", "ONNX"]

ONNX Export And Evaluation

ONNX Export

python export.py --ckpt_file [CKPT_PATH] --device_target [DEVICE_TARGET] --file_format "ONNX"
# example:python export.py --ckpt_file /home/models/official/cv/Inception/inceptionv3/inceptionv3_ascend_v160_imagenet2012_official_cv_top1acc78.69_top5acc94.3.ckpt --device_target "GPU" --file_format "ONNX"

ONNX Evaluation

    bash run_eval_onnx_gpu.sh [DEVICE_ID] [DATA_DIR] [PATH_ONNX]
    # example: bash run_eval_onnx_gpu.sh 2 /home/data/ /home/models/official/cv/Inception/inceptionv3/inceptionv3.onnx

Inference Process

Before inference, please refer to MindSpore Inference with C++ Deployment Guide to set environment variables.

Usage

Before performing inference, the model file must be exported by export script on the Ascend910 environment.

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

-NOTE: Ascend310 inference use Imagenet dataset . The label of the image is the number of folder which is started from 0 after sorting. This file can be converted by script from models/utils/cpp_infer/imgid2label.py.

result

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

accuracy:78.742

Model description

Performance

Evaluation Performance

Parameters Ascend
Model Version InceptionV3
Resource Ascend 910; cpu 2.60GHz, 192cores; memory 755G; OS Euler2.8
uploaded Date 07/05/2021
MindSpore Version 1.3.0
Dataset 1200k images
Batch_size 128
Training Parameters src/model_utils/default_config.yaml
Optimizer RMSProp
Loss Function SoftmaxCrossEntropy
Outputs probability
Loss 1.98
Total time (8p) 10h
Params (M) 103M
Checkpoint for Fine tuning 313M
Model for inference 92M (.onnx file)
Speed 1pc:1200 img/s;8pc:9500 img/s
Scripts inceptionv3 script

Inference Performance

Parameters Ascend
Model Version InceptionV3
Resource Ascend 910; cpu 2.60GHz, 192cores; memory 755G; OS Euler2.8
Uploaded Date 07/05/2021
MindSpore Version 1.3.0
Dataset 50k images
Batch_size 128
Outputs probability
Accuracy ACC1[78.8%] ACC5[94.2%]
Total time 2mins

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.

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