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mobilenetv2 mobilenetv3 readme normalize, delete mobilenetv3 ascend

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zhaoting 2020-08-19 17:30:20 +08:00
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99
model_zoo/official/cv/mobilenetv2/Readme.md
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@ -1,17 +1,37 @@
# MobileNetV2 Description
# Contents
- [MobileNetV2 Description](#mobilenetv2-description)
- [Model Architecture](#model-architecture)
- [Dataset](#dataset)
- [Features](#features)
- [Mixed Precision](#mixed-precision)
- [Environment Requirements](#environment-requirements)
- [Script Description](#script-description)
- [Script and Sample Code](#script-and-sample-code)
- [Training Process](#training-process)
- [Evaluation Process](#evaluation-process)
- [Evaluation](#evaluation)
- [Model Description](#model-description)
- [Performance](#performance)
- [Training Performance](#evaluation-performance)
- [Inference Performance](#evaluation-performance)
- [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)
# [MobileNetV2 Description](#contents)
MobileNetV2 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
[Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
# Model architecture
# [Model architecture](#contents)
The overall network architecture of MobileNetV2 is show below:
[Link](https://arxiv.org/pdf/1905.02244)
# Dataset
# [Dataset](#contents)
Dataset used: [imagenet](http://www.image-net.org/)
@ -22,10 +42,14 @@ Dataset used: [imagenet](http://www.image-net.org/)
- Note: Data will be processed in src/dataset.py
# Features
# [Features](#contents)
## [Mixed Precision(Ascend)](#contents)
# Environment Requirements
The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) 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](#contents)
- Hardware（Ascend/GPU）
- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources.
@ -36,30 +60,33 @@ Dataset used: [imagenet](http://www.image-net.org/)
- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
# Script description
# [Script description](#contents)
## Script and sample code
## [Script and sample code](#contents)
```python
├── MobileNetV2
├── Readme.md
├── Readme.md # descriptions about MobileNetV2
├── scripts
│ ├──run_train.sh
│ ├──run_eval.sh
│ ├──run_train.sh # shell script for train
│ ├──run_eval.sh # shell script for evaluation
├── src
│ ├──config.py
│ ├──dataset.py
│ ├──luanch.py
│ ├──lr_generator.py
│ ├──mobilenetV2.py
├── train.py
├── eval.py
│ ├──config.py # parameter configuration
│ ├──dataset.py # creating dataset
│ ├──launch.py # start python script
│ ├──lr_generator.py # learning rate config
│ ├──mobilenetV2.py # MobileNetV2 architecture
├── train.py # training script
├── eval.py # evaluation script
```
## Training process
## [Training process](#contents)
### Usage
You can start training using python or shell scripts. The usage of shell scripts as follows:
- Ascend: sh run_train.sh Ascend [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [RANK_TABLE_FILE] [DATASET_PATH] [CKPT_PATH]
- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
@ -67,8 +94,13 @@ Dataset used: [imagenet](http://www.image-net.org/)
```
# training example
Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json ~/imagenet/train/ mobilenet_199.ckpt
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
python:
Ascend: python train.py --dataset_path ~/imagenet/train/ --device_targe Ascend
GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
shell:
Ascend: sh run_train.sh Ascend 8 0,1,2,3,4,5,6,7 hccl_config.json ~/imagenet/train/ mobilenet_199.ckpt
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
```
### Result
@ -82,10 +114,12 @@ epoch: [ 1/200], step:[ 624/ 625], loss:[3.917/3.917], time:[138221.250], lr:
epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
```
## Eval process
## [Eval process](#contents)
### Usage
You can start training using python or shell scripts. The usage of shell scripts as follows:
- Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH]
- GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
@ -93,8 +127,13 @@ epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
```
# infer example
Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
python:
Ascend: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe Ascend
GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU
shell:
Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
```
> checkpoint can be produced in training process.
@ -107,9 +146,9 @@ Inference result will be stored in the example path, you can find result like th
result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
```
# Model description
# [Model description](#contents)
## Performance
## [Performance](#contents)
### Training Performance
@ -147,5 +186,11 @@ result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.
| Total time | | | |
| Model for inference | | | |
# ModelZoo Homepage
[Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
# [Description of Random Situation](#contents)
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
# [ModelZoo Homepage](#contents)
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

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model_zoo/official/cv/mobilenetv2/eval.py
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@ -39,7 +39,7 @@ if __name__ == '__main__':
net = None
if args_opt.device_target == "Ascend":
config = config_ascend
device_id = int(os.getenv('DEVICE_ID'))
device_id = int(os.getenv('DEVICE_ID', '0'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
device_id=device_id, save_graphs=False)
net = mobilenet_v2(num_classes=config.num_classes, device_target="Ascend")

2
model_zoo/official/cv/mobilenetv2/scripts/run_infer.sh
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@ -47,7 +47,7 @@ fi
mkdir ../eval
cd ../eval || exit
# luanch
# launch
python ${BASEPATH}/../eval.py \
--device_target=$1 \
--dataset_path=$2 \

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model_zoo/official/cv/mobilenetv2/src/dataset.py
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@ -35,8 +35,8 @@ def create_dataset(dataset_path, do_train, config, device_target, repeat_num=1,
dataset
"""
if device_target == "Ascend":
rank_size = int(os.getenv("RANK_SIZE"))
rank_id = int(os.getenv("RANK_ID"))
rank_size = int(os.getenv("RANK_SIZE", '1'))
rank_id = int(os.getenv("RANK_ID", '0'))
if rank_size == 1:
ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
else:

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model_zoo/official/cv/mobilenetv2/train.py
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@ -53,11 +53,10 @@ parser.add_argument('--device_target', type=str, default=None, help='run device_
args_opt = parser.parse_args()
if args_opt.device_target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
rank_id = int(os.getenv('RANK_ID'))
rank_size = int(os.getenv('RANK_SIZE'))
device_id = int(os.getenv('DEVICE_ID', '0'))
rank_id = int(os.getenv('RANK_ID', '0'))
rank_size = int(os.getenv('RANK_SIZE', '1'))
run_distribute = rank_size > 1
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
device_id=device_id, save_graphs=False)

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model_zoo/official/cv/mobilenetv3/Readme.md
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@ -1,17 +1,35 @@
# MobileNetV3 Description
# Contents
- [MobileNetV3 Description](#mobilenetv3-description)
- [Model Architecture](#model-architecture)
- [Dataset](#dataset)
- [Environment Requirements](#environment-requirements)
- [Script Description](#script-description)
- [Script and Sample Code](#script-and-sample-code)
- [Training Process](#training-process)
- [Evaluation Process](#evaluation-process)
- [Evaluation](#evaluation)
- [Model Description](#model-description)
- [Performance](#performance)
- [Training Performance](#evaluation-performance)
- [Inference Performance](#evaluation-performance)
- [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)
# [MobileNetV3 Description](#contents)
MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
[Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for mobilenetv3." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
# Model architecture
# [Model architecture](#contents)
The overall network architecture of MobileNetV3 is show below:
[Link](https://arxiv.org/pdf/1905.02244)
# Dataset
# [Dataset](#contents)
Dataset used: [imagenet](http://www.image-net.org/)
@ -22,10 +40,7 @@ Dataset used: [imagenet](http://www.image-net.org/)
- Note: Data will be processed in src/dataset.py
# Features
# Environment Requirements
# [Environment Requirements](#contents)
- Hardware（GPU）
- Prepare hardware environment with GPU processor.
@ -36,37 +51,42 @@ Dataset used: [imagenet](http://www.image-net.org/)
- [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
# Script description
# [Script description](#contents)
## Script and sample code
## [Script and sample code](#contents)
```python
├── MobilenetV3
├── Readme.md
├── MobileNetV3
├── Readme.md # descriptions about MobileNetV3
├── scripts
│ ├──run_train.sh
│ ├──run_eval.sh
│ ├──run_train.sh # shell script for train
│ ├──run_eval.sh # shell script for evaluation
├── src
│ ├──config.py
│ ├──dataset.py
│ ├──luanch.py
│ ├──lr_generator.py
│ ├──mobilenetV2.py
├── train.py
├── eval.py
│ ├──config.py # parameter configuration
│ ├──dataset.py # creating dataset
│ ├──launch.py # start python script
│ ├──lr_generator.py # learning rate config
│ ├──mobilenetV3.py # MobileNetV3 architecture
├── train.py # training script
├── eval.py # evaluation script
```
## Training process
## [Training process](#contents)
### Usage
You can start training using python or shell scripts. The usage of shell scripts as follows:
- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
### Launch
```
# training example
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
python:
GPU: python train.py --dataset_path ~/imagenet/train/ --device_targe GPU
shell:
GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
```
### Result
@ -80,16 +100,22 @@ epoch: [ 1/200], step:[ 624/ 625], loss:[3.917/3.917], time:[138221.250], lr:
epoch time: 138331.250, per step time: 221.330, avg loss: 3.917
```
## Eval process
## [Eval process](#contents)
### Usage
You can start training using python or shell scripts. The usage of shell scripts as follows:
- GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH]
### Launch
```
# infer example
python:
GPU: python eval.py --dataset_path ~/imagenet/val/ --checkpoint_path mobilenet_199.ckpt --device_targe GPU
shell:
GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt
```
@ -103,46 +129,50 @@ Inference result will be stored in the example path, you can find result like th
result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt
```
# Model description
# [Model description](#contents)
## Performance
## [Performance](#contents)
### Training Performance
| Parameters | MobilenetV3 | |
| -------------------------- | ---------------------------------------------------------- | ------------------------- |
| Model Version | | large |
| Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMX2 V100-32G |
| uploaded Date | 05/06/2020 | 05/06/2020 |
| MindSpore Version | 0.3.0 | 0.3.0 |
| Dataset | ImageNet | ImageNet |
| Training Parameters | src/config.py | src/config.py |
| Optimizer | Momentum | Momentum |
| Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy |
| outputs | | |
| Loss | | 1.913 |
| Accuracy | | ACC1[77.57%] ACC5[92.51%] |
| Total time | | |
| Params (M) | | |
| Checkpoint for Fine tuning | | |
| Model for inference | | |
| Parameters | MobilenetV3 |
| -------------------------- | ------------------------- |
| Model Version | large |
| Resource | NV SMX2 V100-32G |
| uploaded Date | 05/06/2020 |
| MindSpore Version | 0.3.0 |
| Dataset | ImageNet |
| Training Parameters | src/config.py |
| Optimizer | Momentum |
| Loss Function | SoftmaxCrossEntropy |
| outputs | |
| Loss | 1.913 |
| Accuracy | ACC1[77.57%] ACC5[92.51%] |
| Total time | |
| Params (M) | |
| Checkpoint for Fine tuning | |
| Model for inference | |
#### Inference Performance
| Parameters | | | |
| -------------------------- | ----------------------------- | ------------------------- | -------------------- |
| Model Version | V1 | | |
| Resource | Huawei 910 | NV SMX2 V100-32G | Huawei 310 |
| uploaded Date | 05/06/2020 | 05/22/2020 | |
| MindSpore Version | 0.2.0 | 0.2.0 | 0.2.0 |
| Dataset | ImageNet, 1.2W | ImageNet, 1.2W | ImageNet, 1.2W |
| batch_size | | 130(8P) | |
| outputs | | | |
| Accuracy | | ACC1[75.43%] ACC5[92.51%] | |
| Speed | | | |
| Total time | | | |
| Model for inference | | | |
| Parameters | |
| -------------------------- | -------------------- |
| Model Version | |
| Resource | NV SMX2 V100-32G |
| uploaded Date | 05/22/2020 |
| MindSpore Version | 0.2.0 |
| Dataset | ImageNet, 1.2W |
| batch_size | 130(8P) |
| outputs | |
| Accuracy | ACC1[75.43%] ACC5[92.51%] |
| Speed | |
| Total time | |
| Model for inference | |
# [Description of Random Situation](#contents)
# ModelZoo Homepage
[Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
# [ModelZoo Homepage](#contents)
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

19
model_zoo/official/cv/mobilenetv3/eval.py
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@ -15,33 +15,26 @@
"""
eval.
"""
import os
import argparse
from mindspore import context
from mindspore import nn
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.common import dtype as mstype
from src.dataset import create_dataset
from src.config import config_ascend, config_gpu
from src.config import config_gpu
from src.mobilenetV3 import mobilenet_v3_large
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
parser.add_argument('--device_target', type=str, default=None, help='run device_target')
parser.add_argument('--device_target', type=str, default="GPU", help='run device_target')
args_opt = parser.parse_args()
if __name__ == '__main__':
config = None
if args_opt.device_target == "Ascend":
config = config_ascend
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
device_id=device_id, save_graphs=False)
elif args_opt.device_target == "GPU":
if args_opt.device_target == "GPU":
config = config_gpu
context.set_context(mode=context.GRAPH_MODE,
device_target="GPU", save_graphs=False)
@ -52,12 +45,6 @@ if __name__ == '__main__':
is_grad=False, sparse=True, reduction='mean')
net = mobilenet_v3_large(num_classes=config.num_classes)
if args_opt.device_target == "Ascend":
net.to_float(mstype.float16)
for _, cell in net.cells_and_names():
if isinstance(cell, nn.Dense):
cell.to_float(mstype.float32)
dataset = create_dataset(dataset_path=args_opt.dataset_path,
do_train=False,
config=config,

16
model_zoo/official/cv/mobilenetv3/train.py
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@ -13,7 +13,7 @@
# limitations under the License.
# ============================================================================
"""train_imagenet."""
import os
import time
import argparse
import random
@ -47,20 +47,10 @@ de.config.set_seed(1)
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
parser.add_argument('--device_target', type=str, default=None, help='run device_target')
parser.add_argument('--device_target', type=str, default="GPU", help='run device_target')
args_opt = parser.parse_args()
if args_opt.device_target == "Ascend":
device_id = int(os.getenv('DEVICE_ID'))
rank_id = int(os.getenv('RANK_ID'))
rank_size = int(os.getenv('RANK_SIZE'))
run_distribute = rank_size > 1
device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
device_id=device_id,
save_graphs=False)
elif args_opt.device_target == "GPU":
if args_opt.device_target == "GPU":
context.set_context(mode=context.GRAPH_MODE,
device_target="GPU",
save_graphs=False)