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research/cv/dnet_nas/ README.md → research/cv/dnet_nas/README.md
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# Contents
- [Contents](#contents)
- [Algorithm Introduction](#algorithm-introduction)
- [Algorithm Description](#algorithm-description)
- [Configuring](#configuring)
- [Dataset](#dataset)
- [Requirements](#requirements)
- [Hardware (Ascend)](#hardware-ascend)
- [Framework](#framework)
- [For more information, please check the resources below](#for-more-information-please-check-the-resources-below)
- [Script Description](#script-description)
- [Scripts and Sample Code](#scripts-and-sample-code)
- [Script Parameter](#script-parameter)
- [Training Process](#training-process)
- [For training](#for-training)
- [Evaluation](#evaluation)
- [Evaluation Process](#evaluation-process)
- [Evaluation Result](#evaluation-result)
- [ModelZoo Homepage](#modelzoo-homepage)
## Algorithm Introduction
The Davincit chips are used in many service scenarios for image and video classification, object detection, and segmentation. In these scenarios, there are various requirements for real-time performance or precision. The current model is optimized based on a backbone network. Therefore, it is important to find a backbone model that runs efficiently and with high accuracy on a D-chip.
## Algorithm Description
The current NAS method has two approaches:
a) Search for the Cell Internal Structure(Micro-level Structure). However, the stacking mode of cells is fixed. Such work has DARTS, PNAS, AmoebaNet.
b) Search for how to stack these Cell's Macro-level structure of channel. The structure of the cell is fixed such cell has Residual Bottleneck, ResNext Block, MobileNet Block. Such work has EffecientNet, RegNet, SM-NAS.
It can be seen that a neural network Micro-level Structure and Macro-level structure are very important for their performance. We expect to use the NAS method to obtain efficient cell designs on D chips and to use these efficient cells to construct an efficient entire network. Therefore, we adopt a two-stage search strategy:
Micro-level Search: Search for efficient cell designs on a D-chip.
Macro-Level Search: Use the efficient cell design obtained in the previous phase to construct an efficient entire network.
### Configuring
For details, see the configuration file src/dnet_nas.yml in the sample code.
```yaml
pipeline: [block_nas, net_nas]
block_nas:
pipe_step:
type: SearchPipeStep
search_algorithm:
type: DblockNas
codec: DblockNasCodec
range:
max_sample: 100
min_sample: 10
search_space:
hyperparameters:
- key: network.backbone.op_num
type: CATEGORY
range: [2, 3]
- key: network.backbone.skip_num
type: CATEGORY
range: [0, 1,]
- key: network.backbone.base_channel
type: CATEGORY
range: [16, 32, 64]
#range: [16, 32, 48, 56, 64]
- key: network.backbone.doublechannel
type: CATEGORY
range: [3, 4]
- key: network.backbone.downsample
type: CATEGORY
range: [3, 4]
model:
model_desc:
modules: ['backbone']
backbone:
type: DNet
n_class: 1000
net_nas:
search_algorithm:
type: DnetNas
codec: DnetNasCodec
policy:
num_mutate: 10
random_ratio: 0.5
range:
max_sample: 100
min_sample: 10
search_space:
hyperparameters:
- key: network.backbone.code_length
type: INT
range: [12,50]
- key: network.backbone.base_channel
type: CATEGORY
range: [32, 64, 128]
- key: network.backbone.final_channel
type: CATEGORY
range: [512, 1024, 2048]
- key: network.backbone.downsample
type: CATEGORY
range: [2, 3]
model:
models_folder: "{local_base_path}/output/block_nas/"
model_desc:
modules: ['backbone']
backbone:
type: DNet
n_class: 1000
```
## Dataset
The benchmark datasets can be downloaded as follows:
[ImageNet2012](https://image-net.org/challenges/LSVRC/2012/).
After downloaded the correspond dataset to the target place, You can configure and use the dataset separately for train and test.
Dataset configuration parameters in src/dnet_nas.yml:
```yaml
type: Imagenet
common:
data_path: "/cache/datasets/ILSVRC/Data/CLS-LOC"
batch_size: 64
n_class: 1000
```
## Requirements
### Hardware (Ascend)
> Prepare hardware environment with Ascend.
### Framework
> [MindSpore](https://www.mindspore.cn/install/en)
### For more information, please check the resources below
[MindSpore Tutorials](https://www.mindspore.cn/tutorials/en/r1.3/index.html)
[MindSpore Python API](https://www.mindspore.cn/docs/api/en/r1.3/index.html)
## Script Description
### Scripts and Sample Code
```bash
dnet_nas
├── eval.py # inference entry
├── train.py # pre-training entry
├── README.md # Readme
├── scripts
│ ├── run_standalone.sh # shell script for standalone train on ascend
│ ├── run_distributed.sh # shell script for distributed train on ascend
└── src
└── dnet_nas.yml # options/hyper-parameters of dnet_nas
```
### Script Parameter
> For details about hyperparameters, see src/dnet_nas.yml.
## Training Process
### For training
- Standalone Ascend Training:
```bash
sh scripts/run_standalone.sh
```
- Distributed Ascend Training:
```bash
sh scripts/run_distributed.sh [RANK_TABLE_FILE]
```
For distributed training, a hccl configuration file with JSON format needs to be created in advance.
Please follow the instructions in the link below:
<https://gitee.com/mindspore/models/tree/master/utils/hccl_tools>.
`$RANK_TABLE_FILE` is needed when you are running a distribute task on ascend.
> Or one can run following script for all tasks.
```bash
python3 train.py
```
## Evaluation
### Evaluation Process
> Inference example:
Modify src/eval.yml:
```bash
models_folder: [CHECKPOINT_PATH]
```
```bash
python3 eval.py
```
### Evaluation Result
The result are evaluated by the value of accuracy, flops, params and inference time.
For details about inference performance, see noah-vega [modelzoo](https://github.com/huawei-noah/vega/blob/master/docs/model_zoo.md#dnet).
## ModelZoo Homepage
Please check the official [homepage](https://gitee.com/mindspore/models).
\ No newline at end of file
# Contents
- [Contents](#contents)
- [Algorithm Introduction](#algorithm-introduction)
- [Algorithm Description](#algorithm-description)
- [Configuring](#configuring)
- [Dataset](#dataset)
- [Requirements](#requirements)
- [Hardware (Ascend)](#hardware-ascend)
- [Framework](#framework)
- [For more information, please check the resources below](#for-more-information-please-check-the-resources-below)
- [Script Description](#script-description)
- [Scripts and Sample Code](#scripts-and-sample-code)
- [Script Parameter](#script-parameter)
- [Training Process](#training-process)
- [For training](#for-training)
- [Evaluation](#evaluation)
- [Evaluation Process](#evaluation-process)
- [Evaluation Result](#evaluation-result)
- [ModelZoo Homepage](#modelzoo-homepage)
## Algorithm Introduction
The Davincit chips are used in many service scenarios for image and video classification, object detection, and segmentation. In these scenarios, there are various requirements for real-time performance or precision. The current model is optimized based on a backbone network. Therefore, it is important to find a backbone model that runs efficiently and with high accuracy on a D-chip.
## Algorithm Description
The current NAS method has two approaches:
a) Search for the Cell Internal Structure(Micro-level Structure). However, the stacking mode of cells is fixed. Such work has DARTS, PNAS, AmoebaNet.
b) Search for how to stack these Cell's Macro-level structure of channel. The structure of the cell is fixed such cell has Residual Bottleneck, ResNext Block, MobileNet Block. Such work has EffecientNet, RegNet, SM-NAS.
It can be seen that a neural network Micro-level Structure and Macro-level structure are very important for their performance. We expect to use the NAS method to obtain efficient cell designs on D chips and to use these efficient cells to construct an efficient entire network. Therefore, we adopt a two-stage search strategy:
Micro-level Search: Search for efficient cell designs on a D-chip.
Macro-Level Search: Use the efficient cell design obtained in the previous phase to construct an efficient entire network.
### Configuring
For details, see the configuration file src/dnet_nas.yml in the sample code.
```yaml
pipeline: [block_nas, net_nas]
block_nas:
pipe_step:
type: SearchPipeStep
search_algorithm:
type: DblockNas
codec: DblockNasCodec
range:
max_sample: 100
min_sample: 10
search_space:
hyperparameters:
- key: network.backbone.op_num
type: CATEGORY
range: [2, 3]
- key: network.backbone.skip_num
type: CATEGORY
range: [0, 1,]
- key: network.backbone.base_channel
type: CATEGORY
range: [16, 32, 64]
#range: [16, 32, 48, 56, 64]
- key: network.backbone.doublechannel
type: CATEGORY
range: [3, 4]
- key: network.backbone.downsample
type: CATEGORY
range: [3, 4]
model:
model_desc:
modules: ['backbone']
backbone:
type: DNet
n_class: 1000
net_nas:
search_algorithm:
type: DnetNas
codec: DnetNasCodec
policy:
num_mutate: 10
random_ratio: 0.5
range:
max_sample: 100
min_sample: 10
search_space:
hyperparameters:
- key: network.backbone.code_length
type: INT
range: [12,50]
- key: network.backbone.base_channel
type: CATEGORY
range: [32, 64, 128]
- key: network.backbone.final_channel
type: CATEGORY
range: [512, 1024, 2048]
- key: network.backbone.downsample
type: CATEGORY
range: [2, 3]
model:
models_folder: "{local_base_path}/output/block_nas/"
model_desc:
modules: ['backbone']
backbone:
type: DNet
n_class: 1000
```
## Dataset
The benchmark datasets can be downloaded as follows:
[ImageNet2012](https://image-net.org/challenges/LSVRC/2012/).
After downloaded the correspond dataset to the target place, You can configure and use the dataset separately for train and test.
Dataset configuration parameters in src/dnet_nas.yml:
```yaml
type: Imagenet
common:
data_path: "/cache/datasets/ILSVRC/Data/CLS-LOC"
batch_size: 64
n_class: 1000
```
## Requirements
### Hardware (Ascend)
> Prepare hardware environment with Ascend.
### Framework
> [MindSpore](https://www.mindspore.cn/install/en)
### For more information, please check the resources below
[MindSpore Tutorials](https://www.mindspore.cn/tutorials/en/r1.3/index.html)
[MindSpore Python API](https://www.mindspore.cn/docs/api/en/r1.3/index.html)
## Script Description
### Scripts and Sample Code
```bash
dnet_nas
├── eval.py # inference entry
├── train.py # pre-training entry
├── README.md # Readme
├── scripts
│ ├── run_standalone.sh # shell script for standalone train on ascend
│ ├── run_distributed.sh # shell script for distributed train on ascend
└── src
└── dnet_nas.yml # options/hyper-parameters of dnet_nas
```
### Script Parameter
> For details about hyperparameters, see src/dnet_nas.yml.
## Training Process
### For training
- Standalone Ascend Training:
```bash
sh scripts/run_standalone.sh
```
- Distributed Ascend Training:
```bash
sh scripts/run_distributed.sh [RANK_TABLE_FILE]
```
For distributed training, a hccl configuration file with JSON format needs to be created in advance.
Please follow the instructions in the link below:
<https://gitee.com/mindspore/models/tree/master/utils/hccl_tools>.
`$RANK_TABLE_FILE` is needed when you are running a distribute task on ascend.
> Or one can run following script for all tasks.
```bash
python3 train.py
```
## Evaluation
### Evaluation Process
> Inference example:
Modify src/eval.yml:
```bash
models_folder: [CHECKPOINT_PATH]
```
```bash
python3 eval.py
```
### Evaluation Result
The result are evaluated by the value of accuracy, flops, params and inference time.
For details about inference performance, see noah-vega [modelzoo](https://github.com/huawei-noah/vega/blob/master/docs/model_zoo.md#dnet).
## ModelZoo Homepage
Please check the official [homepage](https://gitee.com/mindspore/models).
research/cv/dnet_nas/ eval.py → research/cv/dnet_nas/eval.py
+ 20
− 20
View file @ cc6dd3ec
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""eval script"""
import vega
if __name__ == '__main__':
vega.run('./src/eval.yml')
vega.set_backend('mindspore', 'NPU')
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""eval script"""
import vega
if __name__ == '__main__':
vega.run('./src/eval.yml')
vega.set_backend('mindspore', 'NPU')
research/cv/dnet_nas/ requirements.txt → research/cv/dnet_nas/requirements.txt
+ 1
− 1
View file @ cc6dd3ec
noah-vega==1.6.1
noah-vega==1.6.1
research/cv/dnet_nas/ scripts/ run_distributed.sh → research/cv/dnet_nas/scripts/run_distributed.sh
+ 37
− 37
View file @ cc6dd3ec
#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
ulimit -u unlimited
export DEVICE_NUM=8
export RANK_SIZE=8
if [ $# != 1 ]
then
echo "Usage: sh run_distribution.sh [RANK_TABLE_FILE]"
exit 1
fi
if [ ! -f $1 ]
then
echo "error: RANK_TABLE_FILE=$1 is not a file"
exit 1
fi
RANK_TABLE_FILE=$(realpath $1)
export RANK_TABLE_FILE
python3 -m vega.tools.run_pipeline ../src/dnet_nas.yml -b m -d NPU \
> train.log 2>&1 &
#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
ulimit -u unlimited
export DEVICE_NUM=8
export RANK_SIZE=8
if [ $# != 1 ]
then
echo "Usage: sh run_distribution.sh [RANK_TABLE_FILE]"
exit 1
fi
if [ ! -f $1 ]
then
echo "error: RANK_TABLE_FILE=$1 is not a file"
exit 1
fi
RANK_TABLE_FILE=$(realpath $1)
export RANK_TABLE_FILE
python3 -m vega.tools.run_pipeline ../src/dnet_nas.yml -b m -d NPU \
> train.log 2>&1 &
research/cv/dnet_nas/ scripts/ run_standalone.sh → research/cv/dnet_nas/scripts/run_standalone.sh
+ 28
− 28
View file @ cc6dd3ec
#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
ulimit -u unlimited
export DEVICE_NUM=1
export RANK_SIZE=1
if [ $# != 0 ]
then
echo "Usage: sh run_standalone.sh"
exit 1
fi
python3 -m vega.tools.run_pipeline ../src/dnet_nas.yml -b m -d NPU \
> train.log 2>&1 &
#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
ulimit -u unlimited
export DEVICE_NUM=1
export RANK_SIZE=1
if [ $# != 0 ]
then
echo "Usage: sh run_standalone.sh"
exit 1
fi
python3 -m vega.tools.run_pipeline ../src/dnet_nas.yml -b m -d NPU \
> train.log 2>&1 &
research/cv/dnet_nas/ src/ eval.yml → research/cv/dnet_nas/src/eval.yml
+ 0
− 3
View file @ cc6dd3ec
......@@ -16,8 +16,6 @@ eval:
trainer:
with_train: False
type: Trainer
epochs: 160
optimizer:
type: SGD
......@@ -32,4 +30,3 @@ eval:
gamma: 0.5
loss:
type: CrossEntropyLoss
research/cv/dnet_nas/ train.py → research/cv/dnet_nas/train.py
+ 21
− 21
View file @ cc6dd3ec
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""train"""
import vega
if __name__ == '__main__':
vega.run('./src/dnet_nas.yml')
vega.set_backend('mindspore', 'NPU')
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""train"""
import vega
if __name__ == '__main__':
vega.run('./src/dnet_nas.yml')
vega.set_backend('mindspore', 'NPU')
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