Huawei_Technology
/
mindspore
Not watched
1
0
Fork 0
Code
Releases 13 Wiki evaluate Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain HPC
You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.
17142 Commits
11 Branches
1.3 GB
177 Download
 
 
 
 
 
 
Tree: 5afa307d7e
Branches Tags
${ item.name }
Create branch ${ searchTerm }
from '5afa307d7e'
${ noResults }
mindspore/model_zoo/official/cv/dpn
Yanjun Peng a152c510c5 fix textcnn export on master branch 5 years ago
..
scripts add DPN implementation 5 years ago
src add DPN implementation 5 years ago
README.md add DPN implementation 5 years ago
eval.py add DPN implementation 5 years ago
train.py fix textcnn export on master branch 5 years ago

README.md

Contents

Description

Dual Path Network (DPN) is a convolution-based neural network for the task of image classification. It combines the advantage of both ResNeXt and DenseNet to get higher accuracy. More detail about this model can be found in:

Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, Jiashi Feng. "Dual Path Networks" (NIPS17).

This repository contains a Mindspore implementation of DPNs based upon cypw's original MXNet implementation (https://github.com/cypw/DPNs). The training and validating scripts are also included, and the validation results with cypw’s pretrained weights are shown in the Results section.

Model Architecture

The overall network architecture of DPN is show below:

Link

Dataset

All the models in this repository are trained and validated on ImageNet-1K. The models can achieve the results with the configurations of the dataset preprocessing as follow:

  • For the training dataset:

    • Range (min, max) of the respective size of the original size to be cropped is (0.08, 1.0)

    • Range (min, max) of aspect ratio to be cropped is (0.75, 1.333)

    • The size of input images is reshaped to (width = 224, height = 224)

    • Probability of random horizontal flip is 50%

    • In normalization, the mean is (255*0.485, 255*0.456, 255*0.406) and the standard deviation is (255*0.229, 255*0.224, 255*0.225)

  • For the evaluation dataset:

    • Input size of images is 224*224 (Resize to 256*256 then crops images at the center)
    • In normalization, the mean is (255*0.485, 255*0.456, 255*0.406) and the standard deviation is (255*0.229, 255*0.224, 255*0.225)

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

To run the python scripts in the repository, you need to prepare the environment as follow:

  • Hardware
    • 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.
  • Python and dependencies
    • Python3.7
    • Mindspore 1.0.0
    • Easydict
    • MXNet 1.6.0 if running the script param_convert.py
  • For more information, please check the resources below：

Quick Start

Dataset Preparation

The DPN models use ImageNet-1K dataset to train and validate in this repository. Download the dataset from ImageNet.org. You can place them anywhere and tell the scripts where they are when running.

Running

To train the DPNs, run the shell script scripts/train_standalone.sh with the format below:

sh scripts/train_standalone.sh [device_id] [dataset_dir] [ckpt_path_to_save] [eval_each_epoch] [pretrained_ckpt(optional)]

To validate the DPNs, run the shell script scripts/eval.sh with the format below:

sh scripts/eval.sh [device_id] [dataset_dir] [pretrained_ckpt]

Script Description

Script and Sample Code

The structure of the files in this repository is shown below.

└─ mindspore-dpns
    ├─ scripts
    │   ├─ eval.sh                // launch ascend standalone evaluation
    │   ├─ train_distributed.sh   // launch ascend distributed training
    │   └─ train_standalone.sh    // launch ascend standalone training
    ├─ src
    │   ├─ config.py              // network and running config
    │   ├─ crossentropy.py        // loss function
    │   ├─ dpn.py                 // dpns implementation
    │   ├─ imagenet_dataset.py    // dataset processor and provider
    │   └─ lr_scheduler.py        // dpn learning rate scheduler
    ├─ eval.py                    // evaluation script
    ├─ train.py                   // training script
    └─ README.md                  // descriptions about this repository

Script Parameters

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

  • Configurations for DPN92 with ImageNet-1K dataset
# model config
config.image_size = (224,224)               # inpute image size
config.num_classes = 1000                   # dataset class number
config.backbone =  'dpn92'                  # backbone network
config.is_save_on_master = True

# parallel config
config.num_parallel_workers = 4             # number of workers to read the data
config.rank = 0                             # local rank of distributed
config.group_size =  1                      # group size of distributed

# training config
config.batch_size =  32                     # batch_size
config.global_step =  0                     # start step of learning rate
config.epoch_size = 180                     # epoch_size
config.loss_scale_num =  1024               # loss scale
# optimizer config
config.momentum =  0.9                      # momentum (SGD)
config.weight_decay =  1e-4                 # weight_decay (SGD)
# learning rate config
config.lr_schedule = 'warmup'               # learning rate schedule
config.lr_init = 0.01                       # init learning rate
config.lr_max = 0.1                         # max learning rate
config.factor =  0.1                        # factor of lr to drop
config.epoch_number_to_drop = [5,15]        # learing rate will drop after these epochs
config.warmup_epochs = 5                    # warmup epochs in learning rate schedule

# dataset config
config.dataset = "imagenet-1K"              # dataset
config.label_smooth = False                 # label_smooth
config.label_smooth_factor = 0.0            # label_smooth_factor

# parameter save config
config.keep_checkpoint_max = 3              # only keep the last keep_checkpoint_max checkpoint

Training Process

Training

Running on Ascend

Run scripts/train_standalone.sh to train the model standalone. The usage of the script is:

sh scripts/train_standalone.sh [device_id] [dataset_dir] [ckpt_path_to_save] [eval_each_epoch] [pretrained_ckpt(optional)]

For example, you can run the shell command below to launch the training procedure.

sh scripts/train_standalone.sh 0 /data/dataset/imagenet/ scripts/pretrian/ 0

If eval_each_epoch is 1, it will evaluate after each epoch and save the parameters with the max accurracy. But in this case, the time of one epoch will be longer.

If eval_each_epoch is 0, it will save parameters every some epochs instead of evaluating in the training process.

The script will run training in the background, you can view the results through the file train_log.txt as follows (eval_each_epoch = 0):

epoch: 1 step: 40036, loss is 3.6232593
Epoch time: 10048893.336, per step time: 250.996
...

or as follows (eval_each_epoch = 1):

epoch: 1 step: 40036, loss is 3.6232593
Epoch time: 10048893.336, per step time: 250.996
Save the maximum accuracy checkpoint,the accuracy is 0.2629158669225848
...

The model checkpoint will be saved into [ckpt_path_to_save].

Distributed Training

Running on Ascend

Run scripts/train_distributed.sh to train the model distributed. The usage of the script is:

sh scripts/train_distributed.sh [rank_table] [dataset_dir] [ckpt_path_to_save]  [rank_size] [eval_each_epoch] [pretrained_ckpt(optional)]

For example, you can run the shell command below to launch the training procedure.

sh scripts/train_distributed.sh /home/rank_table.json /data/dataset/imagenet/ ../scripts 8 0 ../pretrain/dpn92.ckpt

The above shell script will run distribute training in the background. You can view the results through the file train_parallel[X]/log.txt as follows:

train_parallel0/log:
epoch: 1 step 20018, loss is 5.74429988861084
Epoch time: 7908183.789, per step time: 395.054, avg loss: 5.744
train_parallel0/log:
epoch: 2 step 20018, loss is 4.53381872177124
Epoch time: 5036189.547, per step time: 251.583, avg loss: 4.534
...
train_parallel1/log:
poch: 1 step 20018, loss is 5.751555442810059
Epoch time: 7895946.079, per step time: 394.442, avg loss: 5.752
train_parallel1/log:
epoch: 2 step 20018, loss is 4.875896453857422
Epoch time: 5036190.008, per step time: 251.583, avg loss: 4.876
...
...

The model checkpoint will be saved into [ckpt_path_to_save].

Evaluation Process

Running on Ascend

Run scripts/eval.sh to evaluate the model with one Ascend processor. The usage of the script is:

sh scripts/eval.sh [device_id] [dataset_dir] [pretrained_ckpt]

For example, you can run the shell command below to launch the validation procedure.

sh scripts/eval.sh 0 /data/dataset/imageNet/ pretrain/dpn92.ckpt

The above shell script will run evaluation in the background. You can view the results through the file eval_log.txt. The result will be achieved as follows:

Evaluation result: {'top_5_accuracy': 0.9449223751600512, 'top_1_accuracy': 0.7911731754161332}.
DPN evaluate success!

Model Description

Performance

The evaluation of model performance is divided into two parts: accuracy and efficiency. The part of accuracy shows the accuracy of the model in classifying images on ImageNet-1K dataset, and it can be evaluated by top-k measure. The part of efficiency reveals the time cost by model training on ImageNet-1K.

All results are validated at image size of 224x224. The dataset preprocessing and training configurations are shown in Dataset section.

Accuracy

The Pretrain tag in the table above means that the model's weights are converted from MXNet directly without further training. Relatively, the Fine tune tag means that the model is fine tuned after converted from MXNet.

DPN92 (Pretrain)

Parameters Ascend
Model Version DPN92 (Pretrain)
Resource Ascend 910
Uploaded Date 09/19/2020 (month/day/year)
MindSpore Version 0.5.0
Dataset ImageNet-1K
outputs probability
train performance Top1:79.12%; Top5:94.49%

DPN98 (Pretrain)

Parameters Ascend
Model Version DPN98 (Pretrain)
Resource Ascend 910
Uploaded Date 09/19/2020 (month/day/year)
MindSpore Version 0.5.0
Dataset ImageNet-1K
outputs probability
train performance Top1:79.90%; Top5:94.81%

DPN131 (Pretrain)

Parameters Ascend
Model Version DPN131 (Pretrain)
Resource Ascend 910
Uploaded Date 09/19/2020 (month/day/year)
MindSpore Version 0.5.0
Dataset ImageNet-1K
outputs probability
train performance Top1:79.96%; Top5:94.81%

DPN92 (Fine tune)

Parameters Ascend
Model Version DPN92 (Pretrain)
Resource Ascend 910
Uploaded Date 09/19/2020 (month/day/year)
MindSpore Version 0.5.0
Dataset ImageNet-1K
epochs 30
outputs probability
train performance Top1:79.30%; Top5:94.58%

DPN92 (Training)

Parameters Ascend
Model Version DPN92 (Train)
Resource Ascend 910
Uploaded Date 11/13/2020 (month/day/year)
MindSpore Version 1.0.0
Dataset ImageNet-1K
epochs 180
outputs probability
train performance Top1:78.91%; Top5:94.53%

Efficiency

DPN92

Parameters Ascend
Model Version DPN92
Resource Ascend 910
Uploaded Date 09/19/2020 (month/day/year)
MindSpore Version 0.5.0
Dataset ImageNet-1K
batch_size 32
outputs probability
speed 1pc:127.90 img/s;8pc:1023.2 img/s

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.

Delete task

Are you sure you want to delete this task? Once this task is deleted, it cannot be recovered.

cancel
confirm

MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.

C++ Python Text Unity3D Asset C other