mindspore2022/model_zoo/official/cv/mobilenetv1

Mobilenet_V1

• Mobilenet_V1
  • MobileNetV1 Description
  • Model architecture
  • Dataset
  • Features
    • Mixed Precision(Ascend)
  • Environment Requirements
  • Script description
    • Script and sample code
  • Training process
    • Usage
    • Launch
    • Result
  • Evaluation process
    • Usage
    • Launch
    • Result
  • Model description
    • Performance
      • Training Performance
  • Description of Random Situation
  • ModelZoo Homepage

MobileNetV1 Description

MobileNetV1 is a efficient network for mobile and embedded vision applications. MobileNetV1 is based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep n.eural networks

Paper Howard A G , Zhu M , Chen B , et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications[J]. 2017.

Model architecture

The overall network architecture of MobileNetV1 is show below:

Link

Dataset

Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

Dataset used: ImageNet2012

• Dataset size 224*224 colorful images in 1000 classes
  • Train：1,281,167 images
  • Test： 50,000 images
• Data format：jpeg
  • Note：Data will be processed in 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 dataset.py

• Download the dataset, the directory structure is as follows:

└─ImageNet_Original
    ├─train                # train dataset
    └─validation_preprocess # evaluate dataset

└─cifar10
    ├─cifar-10-batches-bin  # train dataset
    └─cifar-10-verify-bin  # evaluate dataset

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.

• Framework

  • MindSpore

• For more information, please check the resources below：

  • MindSpore Tutorials
  • MindSpore Python API

• 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 "dataset_path='/cache/data'" on default_config.yaml file.
  #          Set "epoch_size=90" 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 "dataset_path=/cache/data" on the website UI interface.
  #          Add "epoch_size=90" 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/mobilenetv1" 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=90" 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=90" 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/mobilenetv1" 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='./mobilenetv1/mobilenetv1_trained.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='./mobilenetv1/mobilenetv1_trained.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/mobilenetv1" 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='mobilenetv1'" on base_config.yaml file.
   #          Set "file_format='AIR'" 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='mobilenetv1'" on the website UI interface.
   #          Add "file_format='AIR'" 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/mobilenetv1" 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

├── MobileNetV1
  ├── README.md              # descriptions about MobileNetV1
  ├── scripts
  │   ├──run_distribute_train.sh        # shell script for distribute train
  │   ├──run_distribute_train_gpu.sh    # shell script for gpu distribute train
  │   ├──run_standalone_train.sh        # shell script for standalone train
  │   ├──run_standalone_train_gpu.sh    # shell script for gpu standalone train
  │   ├──run_eval.sh                # shell script for evaluation
  ├── src
  │   ├──dataset.py                 # creating dataset
  │   ├──lr_generator.py            # learning rate config
  │   ├──mobilenet_v1_fpn.py        # MobileNetV1 architecture
  │   ├──CrossEntropySmooth.py      # loss function
  │   └──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(cifar10)
  ├── default_config_imagenet.yaml      # Training parameter profile(imagenet)
  ├── default_config_gpu_imagenet.yaml  # Training parameter profile of GPU(imagenet)
  ├── train.py                      # training script
  ├── eval.py                       # evaluation script

Training process

Usage

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

• Ascend: bash run_distribute_train.sh [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH] (optional)

  example: bash run_distribute_train.sh cifar10 /root/hccl_8p_01234567_10.155.170.71.json /home/DataSet/cifar10/cifar-10-batches-bin/

  example: bash run_distribute_train.sh imagenet2012 /root/hccl_8p_01234567_10.155.170.71.json /home/DataSet/ImageNet_Original/

• CPU: bash run_train_CPU.sh [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH] (optional)

• GPU(single device)：bash run_standalone_train_gpu.sh [cifar10|imagenet2012] [DATASET_PATH] PRETRAINED_CKPT_PATH

• GPU(distribute training): bash run_distribute_train_gpu.sh [cifar10|imagenet2012] [CONFIG_PATH] [DATASET_PATH] PRETRAINED_CKPT_PATH

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

Please follow the instructions in the link hccn_tools.

Launch

# training example
  python:
      Ascend: python train.py --device_target Ascend --dataset_path [TRAIN_DATASET_PATH]
      CPU: python train.py --device_target CPU --dataset_path [TRAIN_DATASET_PATH]
      GPU(single device): python train.py --device_target GPU --dateset [DATASET] --dataset_path [TRAIN_DATASET_PATH] --config_path [CONFIG_PATH]
      GPU(distribute training):
      mpirun --allow-run-as-root -n $RANK_SIZE --output-filename log_output --merge-stderr-to-stdout \
        python train.py --config_path=$2 --dataset=$1 --run_distribute=True \
        --device_num=$DEVICE_NUM --dataset_path=$PATH1 &> log.txt &

  shell:
     Ascend: bash run_distribute_train.sh [cifar10|imagenet2012] [RANK_TABLE_FILE] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
     # example: bash run_distribute_train.sh cifar10 /root/hccl_8p_01234567_10.155.170.71.json /home/DataSet/cifar10/cifar-10-batches-bin/
     # example: bash run_distribute_train.sh imagenet2012 /root/hccl_8p_01234567_10.155.170.71.json /home/DataSet/ImageNet_Original/

     CPU: bash run_train_CPU.sh [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
     GPU(single device): bash run_standalone_train_gpu.sh [cifar10|imagenet2012] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
     GPU(distribute training): bash run_distribute_train_gpu.sh [cifar10|imagenet2012] [CONFIG_PATH] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)

Result

Training result will be stored in the example path. Checkpoints will be stored at ckpt_* by default, and training log will be wrote to ./train_parallel*/log with the platform Ascend .

epoch: 89 step: 1251, loss is 2.1829057
Epoch time: 146826.802, per step time: 117.368
epoch: 90 step: 1251, loss is 2.3499017
Epoch time: 150950.623, per step time: 120.664

Training result will be stored in the example path. Checkpoints will be stored at ckpt_* by default, and training log will be wrote to ./train_parallel/log.txt with the platform GPU when distribute training .

epoch: 89 step: 1251, loss is 2.44095
Epoch time: 322114.519, per step time: 257.486
epoch: 90 step: 1251, loss is 2.2521682
Epoch time: 320744.265, per step time: 256.390

Evaluation process

Usage

You can start training using python or shell scripts.If the train method is train or fine tune, should not input the [CHECKPOINT_PATH] The usage of shell scripts as follows:

• Ascend: bash run_eval.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]

  example: bash run_eval.sh cifar10 /home/DataSet/cifar10/cifar-10-verify-bin/ /home/model/mobilenetv1/ckpt/cifar10/mobilenetv1-90_1562.ckpt

  example: bash run_eval.sh imagenet2012 /home/DataSet/ImageNet_Original/ /home/model/mobilenetv1/ckpt/imagenet2012/mobilenetv1-90_625.ckpt

• CPU: bash run_eval_CPU.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]

Launch

# eval example
  python:
      Ascend: python eval.py --dataset [cifar10|imagenet2012] --dataset_path [VAL_DATASET_PATH] --checkpoint_path [CHECKPOINT_PATH]
      CPU: python eval.py --dataset [cifar10|imagenet2012] --dataset_path [VAL_DATASET_PATH] --checkpoint_path [CHECKPOINT_PATH] --device_target CPU
      GPU: python eval.py --dataset [cifar10|imagenet2012] --dataset_path [VAL_DATASET_PATH] --checkpoint_path [CHECKPOINT_PATH] --config_path [CONFIG_PATH] --device_target GPU

  shell:
      Ascend: bash run_eval.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
      # example: bash run_eval.sh cifar10 /home/DataSet/cifar10/cifar-10-verify-bin/ /home/model/mobilenetv1/ckpt/cifar10/mobilenetv1-90_1562.ckpt
      # example: bash run_eval.sh imagenet2012 /home/DataSet/ImageNet_Original/ /home/model/mobilenetv1/ckpt/imagenet2012/mobilenetv1-90_625.ckpt

      CPU: bash run_eval_CPU.sh [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]

checkpoint can be produced in training process.

Result

Inference result will be stored in the example path, you can find result like the followings in eval/log.

Ascend
result: {'top_5_accuracy': 0.9010016025641026, 'top_1_accuracy': 0.7128004807692307} ckpt=./train_parallel0/ckpt_0/mobilenetv1-90_1251.ckpt

GPU
result: {'top_5_accuracy': 0.9011217948717949, 'top_1_accuracy': 0.7129206730769231} ckpt=./ckpt_1/mobilenetv1-90_1251.ckpt

Inference Process

Export MindIR

python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]

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

Infer on Ascend310

Before performing inference, the mindir file must be exported by export.py script. We only provide an example of inference using MINDIR model. Current batch_Size for imagenet2012 dataset can only be set to 1.

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

• MINDIR_PATH specifies path of used "MINDIR" OR "AIR" model.
• DATASET_PATH specifies path of cifar10 datasets
• DEVICE_ID is optional, default value is 0.

Result

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

'top1 acc': 0.71966
'top5 acc': 0.90424

Model description

Performance

Training Performance

Parameters	MobilenetV1	MobilenetV1
Model Version	V1	V1
Resource	Ascend 910 * 4; cpu 2.60GHz, 192cores; memory 755G; OS Euler2.8	GPU NV SMX2 V100-32G
uploaded Date	11/28/2020	06/26/2021
MindSpore Version	1.0.0	1.2.0
Dataset	ImageNet2012	ImageNet2012
Training Parameters	src/config.py	default_config_gpu_imagenet.yaml
Optimizer	Momentum	Momentum
Loss Function	SoftmaxCrossEntropy	SoftmaxCrossEntropy
outputs	probability	probability
Loss	2.3499017	2.2521682
Accuracy	ACC1[71.28%]	ACC1[71.29%]
Total time	225 min	--
Params (M)	3.3 M	--
Checkpoint for Fine tuning	27.3 M	--
Scripts	Link

Description of Random Situation

In train.py, we set the seed which is used by numpy.random, mindspore.common.Initializer, mindspore.ops.composite.random_ops and mindspore.nn.probability.distribution.

ModelZoo Homepage

Please check the official homepage.