add dnet

research/cv/dnet_nas/ README.md

+# 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

research/cv/dnet_nas/ eval.py

+# 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

+noah-vega==1.6.1

research/cv/dnet_nas/ scripts/ run_distributed.sh

+#!/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

+#!/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/ dnet_nas.yml

+general:
+    backend: mindspore
+    quota: "model_valid"
+
+
+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
+
+    trainer:
+        type: Trainer
+        epochs: 1
+        loss:
+            type: CrossEntropyLoss
+
+    dataset:
+        type: Imagenet
+        common:
+            data_path: "/cache/datasets/ILSVRC/Data/CLS-LOC"
+            batch_size: 64
+            n_class: 1000
+
+net_nas:
+    pipe_step:
+        type: SearchPipeStep
+
+    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
+
+    trainer:
+        type: Trainer
+        epochs: 2
+        loss:
+            type: CrossEntropyLoss
+
+    dataset:
+        type: Imagenet
+        common:
+            data_path: "/cache/datasets/ILSVRC/Data/CLS-LOC"
+            batch_size: 64
+            n_class: 1000
+
+fullytrain:
+    pipe_step:
+        type: TrainPipeStep
+        models_folder: "{local_base_path}/output/net_nas/"
+
+    trainer:
+        ref: block_nas.trainer
+        epochs: 160
+        optimizer:
+            type: SGD
+            params:
+                lr: 0.1  # 0.001 for mindspore
+                momentum: 0.9
+                weight_decay: 0.0001  # 0.01 for mindspore
+        lr_scheduler:
+            type: MultiStepLR
+            params:
+                milestones: [60, 120]
+                gamma: 0.5
+    dataset:
+        ref: block_nas.dataset

research/cv/dnet_nas/ src/ eval.yml

+general:
+    backend: mindspore
+
+pipeline: [eval]
+
+eval:
+    pipe_step:
+        type: TrainPipeStep
+        models_folder: ~
+    dataset:
+        type: Imagenet
+        common:
+            data_path: "/cache/datasets/ILSVRC/Data/CLS-LOC"
+            batch_size: 64
+            n_class: 1000
+    trainer:
+        with_train: False
+        type: Trainer
+
+
+        epochs: 160
+        optimizer:
+            type: SGD
+            params:
+                lr: 0.1  # 0.001 for mindspore
+                momentum: 0.9
+                weight_decay: 0.0001  # 0.01 for mindspore
+        lr_scheduler:
+            type: MultiStepLR
+            params:
+                milestones: [60, 120]
+                gamma: 0.5
+        loss:
+            type: CrossEntropyLoss
+

research/cv/dnet_nas/ train.py

+# 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')