Adding ShuffleNetV2 to ModelZoo

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Update eval.py

Updated ShuffleNetV2 model

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model_zoo/official/cv/shufflenetv2/Readme.md

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+# Contents
+
+- [ShuffleNetV2 Description](#shufflenetv2-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)
+
+# [ShuffleNetV2 Description](#contents)
+
+ShuffleNetV2 is a much faster and more accurate netowrk than the previous networks on different platforms such as Ascend or GPU.
+[Paper](https://arxiv.org/pdf/1807.11164.pdf) Ma, N., Zhang, X., Zheng, H. T., & Sun, J. (2018). Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European conference on computer vision (ECCV) (pp. 116-131).
+
+# [Model architecture](#contents)
+
+The overall network architecture of ShuffleNetV2 is show below:
+
+[Link](https://arxiv.org/pdf/1807.11164.pdf)
+
+# [Dataset](#contents)
+
+Dataset used: [imagenet](http://www.image-net.org/)
+
+- Dataset size: ~125G, 1.2W colorful images in 1000 classes
+	- Train: 120G, 1.2W images
+	- Test: 5G, 50000 images
+- Data format: RGB images.
+	- Note: Data will be processed in src/dataset.py
+
+# [Environment Requirements](#contents)
+
+- Hardware(GPU)
+  - Prepare hardware environment with GPU processor.
+- Framework
+  - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/)
+- For more information, please check the resources below:
+  - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
+  - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
+
+
+# [Script description](#contents)
+
+## [Script and sample code](#contents)
+
+```python
++-- ShuffleNetV2      
+  +-- Readme.md     # descriptions about ShuffleNetV2
+  +-- scripts
+  ¦   +--run_distribute_train_for_gpu.sh   # shell script for distributed training
+  ¦   +--run_eval_for_multi_gpu.sh         # shell script for evaluation
+  ¦   +--run_standalone_train_for_gpu.sh   # shell script for standalone training
+  +-- src
+  ¦   +--config.py      # parameter configuration
+  ¦   +--dataset.py     # creating dataset
+  ¦   +--loss.py        # loss function for network
+  ¦   +--lr_generator.py     # learning rate config
+  +-- train.py      # training script
+  +-- eval.py       # evaluation script
+  +-- blocks.py     # ShuffleNetV2 blocks
+  +-- network.py    # ShuffleNetV2 model network
+```
+
+## [Training process](#contents)
+
+### Usage
+
+
+You can start training using python or shell scripts. The usage of shell scripts as follows:
+
+- Ditributed training on GPU: sh run_distribute_train_for_gpu.sh [DATA_DIR]
+- Standalone training on GPU: sh run_standalone_train_for_gpu.sh [DEVICE_ID] [DATA_DIR]
+
+### Launch
+
+```
+# training example
+  python:
+      GPU: mpirun --allow-run-as-root -n 8 python train.py --is_distributed --platform 'GPU' --dataset_path '~/imagenet/train/' > train.log 2>&1 &
+
+  shell:
+      GPU: sh run_distribute_train_for_gpu.sh ~/imagenet/train/
+```
+
+### Result
+
+Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log  will be redirected to `./train/train.log`.
+
+## [Eval process](#contents)
+
+### Usage
+
+You can start evaluation using python or shell scripts. The usage of shell scripts as follows:
+
+- GPU: sh run_eval_for_multi_gpu.sh [DEVICE_ID] [EPOCH]
+
+### Launch
+
+``` 
+# infer example
+  python:
+      GPU: CUDA_VISIBLE_DEVICES=0 python eval.py --platform 'GPU' --dataset_path '~/imagenet/val/' --epoch 250 > eval.log 2>&1 &
+
+  shell:
+      GPU: sh run_eval_for_multi_gpu.sh 0 250
+```
+
+> checkpoint can be produced in training process.
+
+### Result
+
+Inference result will be stored in the example path, you can find result in `val.log`.

model_zoo/official/cv/shufflenetv2/blocks.py

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+# Copyright 2020 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.
+# ============================================================================
+import mindspore.nn as nn
+import mindspore.ops.operations as P
+
+
+class ShuffleV2Block(nn.Cell):
+    def __init__(self, inp, oup, mid_channels, *, ksize, stride):
+        super(ShuffleV2Block, self).__init__()
+        self.stride = stride
+        ##assert stride in [1, 2]
+
+        self.mid_channels = mid_channels
+        self.ksize = ksize
+        pad = ksize // 2
+        self.pad = pad
+        self.inp = inp
+
+        outputs = oup - inp
+
+        branch_main = [
+            # pw
+            nn.Conv2d(in_channels=inp, out_channels=mid_channels, kernel_size=1, stride=1,
+                      pad_mode='pad', padding=0, has_bias=False),
+            nn.BatchNorm2d(num_features=mid_channels, momentum=0.9),
+            nn.ReLU(),
+            # dw
+            nn.Conv2d(in_channels=mid_channels, out_channels=mid_channels, kernel_size=ksize, stride=stride,
+                      pad_mode='pad', padding=pad, group=mid_channels, has_bias=False),
+            nn.BatchNorm2d(num_features=mid_channels, momentum=0.9),
+            # pw-linear
+            nn.Conv2d(in_channels=mid_channels, out_channels=outputs, kernel_size=1, stride=1,
+                      pad_mode='pad', padding=0, has_bias=False),
+            nn.BatchNorm2d(num_features=outputs, momentum=0.9),
+            nn.ReLU(),
+        ]
+        self.branch_main = nn.SequentialCell(branch_main)
+
+        if stride == 2:
+            branch_proj = [
+                # dw
+                nn.Conv2d(in_channels=inp, out_channels=inp, kernel_size=ksize, stride=stride,
+                          pad_mode='pad', padding=pad, group=inp, has_bias=False),
+                nn.BatchNorm2d(num_features=inp, momentum=0.9),
+                # pw-linear
+                nn.Conv2d(in_channels=inp, out_channels=inp, kernel_size=1, stride=1,
+                          pad_mode='pad', padding=0, has_bias=False),
+                nn.BatchNorm2d(num_features=inp, momentum=0.9),
+                nn.ReLU(),
+            ]
+            self.branch_proj = nn.SequentialCell(branch_proj)
+        else:
+            self.branch_proj = None
+
+    def construct(self, old_x):
+        if self.stride == 1:
+            x_proj, x = self.channel_shuffle(old_x)
+            return P.Concat(1)((x_proj, self.branch_main(x)))
+        if self.stride == 2:
+            x_proj = old_x
+            x = old_x
+            return P.Concat(1)((self.branch_proj(x_proj), self.branch_main(x)))
+        return None
+
+    def channel_shuffle(self, x):
+        batchsize, num_channels, height, width = P.Shape()(x)
+        ##assert (num_channels % 4 == 0)
+        x = P.Reshape()(x, (batchsize * num_channels // 2, 2, height * width,))
+        x = P.Transpose()(x, (1, 0, 2,))
+        x = P.Reshape()(x, (2, -1, num_channels // 2, height, width,))
+        return x[0], x[1]

model_zoo/official/cv/shufflenetv2/eval.py

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+# Copyright 2020 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.
+# ============================================================================
+"""evaluate_imagenet"""
+import argparse
+import os
+
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.train.model import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+from src.config import config_gpu as cfg
+from src.dataset import create_dataset
+from network import ShuffleNetV2
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='image classification evaluation')
+    parser.add_argument('--checkpoint', type=str, default='', help='checkpoint of ShuffleNetV2 (Default: None)')
+    parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
+    parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform')
+    parser.add_argument('--epoch', type=str, default='')
+    args_opt = parser.parse_args()
+
+    if args_opt.platform == 'Ascend':
+        device_id = int(os.getenv('DEVICE_ID'))
+        context.set_context(device_id=device_id)
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, device_id=0)
+    net = ShuffleNetV2(n_class=cfg.num_classes)
+    ckpt = load_checkpoint(args_opt.checkpoint)
+    load_param_into_net(net, ckpt)
+    net.set_train(False)
+    dataset = create_dataset(args_opt.dataset_path, cfg, False)
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False,
+                                            smooth_factor=0.1, num_classes=cfg.num_classes)
+    eval_metrics = {'Loss': nn.Loss(),
+                    'Top1-Acc': nn.Top1CategoricalAccuracy(),
+                    'Top5-Acc': nn.Top5CategoricalAccuracy()}
+    model = Model(net, loss, optimizer=None, metrics=eval_metrics)
+    metrics = model.eval(dataset)
+    print("metric: ", metrics)

model_zoo/official/cv/shufflenetv2/network.py

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+# Copyright 2020 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.
+# ============================================================================
+import numpy as np
+
+from blocks import ShuffleV2Block
+
+from mindspore import Tensor
+import mindspore.nn as nn
+import mindspore.ops.operations as P
+
+
+class ShuffleNetV2(nn.Cell):
+    def __init__(self, input_size=224, n_class=1000, model_size='1.0x'):
+        super(ShuffleNetV2, self).__init__()
+        print('model size is ', model_size)
+
+        self.stage_repeats = [4, 8, 4]
+        self.model_size = model_size
+        if model_size == '0.5x':
+            self.stage_out_channels = [-1, 24, 48, 96, 192, 1024]
+        elif model_size == '1.0x':
+            self.stage_out_channels = [-1, 24, 116, 232, 464, 1024]
+        elif model_size == '1.5x':
+            self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]
+        elif model_size == '2.0x':
+            self.stage_out_channels = [-1, 24, 244, 488, 976, 2048]
+        else:
+            raise NotImplementedError
+
+        # building first layer
+        input_channel = self.stage_out_channels[1]
+        self.first_conv = nn.SequentialCell([
+            nn.Conv2d(in_channels=3, out_channels=input_channel, kernel_size=3, stride=2,
+                      pad_mode='pad', padding=1, has_bias=False),
+            nn.BatchNorm2d(num_features=input_channel, momentum=0.9),
+            nn.ReLU(),
+        ])
+
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
+
+        self.features = []
+        for idxstage in range(len(self.stage_repeats)):
+            numrepeat = self.stage_repeats[idxstage]
+            output_channel = self.stage_out_channels[idxstage+2]
+
+            for i in range(numrepeat):
+                if i == 0:
+                    self.features.append(ShuffleV2Block(input_channel, output_channel,
+                                                        mid_channels=output_channel // 2, ksize=3, stride=2))
+                else:
+                    self.features.append(ShuffleV2Block(input_channel // 2, output_channel,
+                                                        mid_channels=output_channel // 2, ksize=3, stride=1))
+
+                input_channel = output_channel
+
+        self.features = nn.SequentialCell([*self.features])
+
+        self.conv_last = nn.SequentialCell([
+            nn.Conv2d(in_channels=input_channel, out_channels=self.stage_out_channels[-1], kernel_size=1, stride=1,
+                      pad_mode='pad', padding=0, has_bias=False),
+            nn.BatchNorm2d(num_features=self.stage_out_channels[-1], momentum=0.9),
+            nn.ReLU()
+        ])
+        self.globalpool = nn.AvgPool2d(kernel_size=7, stride=7, pad_mode='valid')
+        if self.model_size == '2.0x':
+            self.dropout = nn.Dropout(keep_prob=0.8)
+        self.classifier = nn.SequentialCell([nn.Dense(in_channels=self.stage_out_channels[-1],
+                                                      out_channels=n_class, has_bias=False)])
+        ##TODO init weights
+        self._initialize_weights()
+
+    def construct(self, x):
+        x = self.first_conv(x)
+        x = self.maxpool(x)
+        x = self.features(x)
+        x = self.conv_last(x)
+
+        x = self.globalpool(x)
+        if self.model_size == '2.0x':
+            x = self.dropout(x)
+        x = P.Reshape()(x, (-1, self.stage_out_channels[-1],))
+        x = self.classifier(x)
+        return x
+
+    def _initialize_weights(self):
+        for name, m in self.cells_and_names():
+            if isinstance(m, nn.Conv2d):
+                if 'first' in name:
+                    m.weight.set_parameter_data(Tensor(np.random.normal(0, 0.01,
+                                                                        m.weight.data.shape).astype("float32")))
+                else:
+                    m.weight.set_parameter_data(Tensor(np.random.normal(0, 1.0/m.weight.data.shape[1],
+                                                                        m.weight.data.shape).astype("float32")))
+
+            if isinstance(m, nn.Dense):
+                m.weight.set_parameter_data(Tensor(np.random.normal(0, 0.01, m.weight.data.shape).astype("float32")))

model_zoo/official/cv/shufflenetv2/scripts/run_distribute_train_for_gpu.sh

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+#!/bin/bash
+# Copyright 2020 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.
+# ============================================================================
+DATA_DIR=$1
+mpirun --allow-run-as-root -n 8 python ./train.py --is_distributed --platform 'GPU' --dataset_path $DATA_DIR > train.log 2>&1 &

model_zoo/official/cv/shufflenetv2/scripts/run_eval_for_multi_gpu.sh

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+#!/bin/bash
+# Copyright 2020 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.
+# ============================================================================
+DEVICE_ID=$1
+EPOCH=$2
+CUDA_VISIBLE_DEVICES=$DEVICE_ID python ./eval.py --platform 'GPU' --dataset_path '/home/data/ImageNet_Original/val/' --epoch $EPOCH > eval.log 2>&1 &

model_zoo/official/cv/shufflenetv2/scripts/run_standalone_train_for_gpu.sh

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+#!/bin/bash
+# Copyright 2020 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.
+# ============================================================================
+DEVICE_ID=$1
+DATA_DIR=$2
+CUDA_VISIBLE_DEVICES=$DEVICE_ID python ./train.py --platform 'GPU' --dataset_path $DATA_DIR > train.log 2>&1 &

model_zoo/official/cv/shufflenetv2/src/config.py

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+# Copyright 2020 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.
+# ============================================================================
+"""
+network config setting, will be used in main.py
+"""
+from easydict import EasyDict as edict
+
+
+config_gpu = edict({
+    'random_seed': 1,
+    'rank': 0,
+    'group_size': 1,
+    'work_nums': 8,
+    'epoch_size': 250,
+    'keep_checkpoint_max': 100,
+    'ckpt_path': './checkpoint/',
+    'is_save_on_master': 0,
+
+    ### Dataset Config
+    'batch_size': 128,
+    'num_classes': 1000,
+
+    ### Loss Config
+    'label_smooth_factor': 0.1,
+    'aux_factor': 0.4,
+
+    ### Learning Rate Config
+    'lr_init': 0.5,
+
+    ### Optimization Config
+    'weight_decay': 0.00004,
+    'momentum': 0.9,
+    'opt_eps': 1.0,
+    'rmsprop_decay': 0.9,
+    "loss_scale": 1,
+
+})

model_zoo/official/cv/shufflenetv2/src/dataset.py

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+# Copyright 2020 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.
+# ============================================================================
+"""
+Data operations, will be used in train.py and eval.py
+"""
+import numpy as np
+from src.config import config_gpu as cfg
+
+import mindspore.common.dtype as mstype
+import mindspore.dataset.engine as de
+import mindspore.dataset.transforms.c_transforms as C2
+import mindspore.dataset.transforms.vision.c_transforms as C
+
+
+class toBGR():
+    def __call__(self, img):
+        img = img[:, :, ::-1]
+        img = np.ascontiguousarray(img)
+        return img
+
+def create_dataset(dataset_path, do_train, rank, group_size, repeat_num=1):
+    """
+    create a train or eval dataset
+
+    Args:
+        dataset_path(string): the path of dataset.
+        do_train(bool): whether dataset is used for train or eval.
+        rank (int): The shard ID within num_shards (default=None).
+        group_size (int): Number of shards that the dataset should be divided into (default=None).
+        repeat_num(int): the repeat times of dataset. Default: 1.
+
+    Returns:
+        dataset
+    """
+    if group_size == 1:
+        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=cfg.work_nums, shuffle=True)
+    else:
+        ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=cfg.work_nums, shuffle=True,
+                                     num_shards=group_size, shard_id=rank)
+    # define map operations
+    if do_train:
+        trans = [
+            C.RandomCropDecodeResize(224),
+            C.RandomHorizontalFlip(prob=0.5),
+            C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
+            ]
+    else:
+        trans = [
+            C.Decode(),
+            C.Resize(256),
+            C.CenterCrop(224)
+            ]
+    trans += [
+        toBGR(),
+        C.Rescale(1.0 / 255.0, 0.0),
+        # C.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        C.HWC2CHW(),
+        C2.TypeCast(mstype.float32)
+    ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+    ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=cfg.work_nums)
+    ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=cfg.work_nums)
+    # apply batch operations
+    ds = ds.batch(cfg.batch_size, drop_remainder=True)
+    # apply dataset repeat operation
+    ds = ds.repeat(repeat_num)
+
+    return ds

model_zoo/official/cv/shufflenetv2/src/loss.py

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+# Copyright 2020 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.
+# ============================================================================
+"""define loss function for network."""
+from mindspore.common import dtype as mstype
+from mindspore.nn.loss.loss import _Loss
+from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore import Tensor
+import mindspore.nn as nn
+
+
+class CrossEntropy(_Loss):
+    """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
+    def __init__(self, smooth_factor=0, num_classes=1000, factor=0.4):
+        super(CrossEntropy, self).__init__()
+        self.factor = factor
+        self.onehot = P.OneHot()
+        self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
+        self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
+        self.ce = nn.SoftmaxCrossEntropyWithLogits()
+        self.mean = P.ReduceMean(False)
+
+    def construct(self, logits, label):
+        logit, aux = logits
+        one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
+        loss_logit = self.ce(logit, one_hot_label)
+        loss_logit = self.mean(loss_logit, 0)
+        one_hot_label_aux = self.onehot(label, F.shape(aux)[1], self.on_value, self.off_value)
+        loss_aux = self.ce(aux, one_hot_label_aux)
+        loss_aux = self.mean(loss_aux, 0)
+        return loss_logit + self.factor*loss_aux
+
+
+class CrossEntropy_Val(_Loss):
+    """the redefined loss function with SoftmaxCrossEntropyWithLogits, will be used in inference process"""
+    def __init__(self, smooth_factor=0, num_classes=1000):
+        super(CrossEntropy_Val, self).__init__()
+        self.onehot = P.OneHot()
+        self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
+        self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
+        self.ce = nn.SoftmaxCrossEntropyWithLogits()
+        self.mean = P.ReduceMean(False)
+
+    def construct(self, logits, label):
+        one_hot_label = self.onehot(label, F.shape(logits)[1], self.on_value, self.off_value)
+        loss_logit = self.ce(logits, one_hot_label)
+        loss_logit = self.mean(loss_logit, 0)
+        return loss_logit

model_zoo/official/cv/shufflenetv2/src/lr_generator.py

@@ -0,0 +1,64 @@
+# Copyright 2020 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.
+# ============================================================================
+"""learning rate exponential decay generator"""
+import math
+import numpy as np
+
+
+def get_lr(lr_init, lr_decay_rate, num_epoch_per_decay, total_epochs, steps_per_epoch, is_stair=False):
+    """
+    generate learning rate array
+
+    Args:
+       lr_init(float): init learning rate
+       lr_decay_rate (float):
+       total_epochs(int): total epoch of training
+       steps_per_epoch(int): steps of one epoch
+       is_stair(bool): If `True` decay the learning rate at discrete intervals (default=False)
+
+    Returns:
+       learning_rate, learning rate numpy array
+    """
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    decay_steps = steps_per_epoch * num_epoch_per_decay
+    for i in range(total_steps):
+        p = i/decay_steps
+        if is_stair:
+            p = math.floor(p)
+        lr_each_step.append(lr_init * math.pow(lr_decay_rate, p))
+    learning_rate = np.array(lr_each_step).astype(np.float32)
+    return learning_rate
+
+def get_lr_basic(lr_init, total_epochs, steps_per_epoch, is_stair=False):
+    """
+    generate basic learning rate array
+
+    Args:
+       lr_init(float): init learning rate
+       total_epochs(int): total epochs of training
+       steps_per_epoch(int): steps of one epoch
+       is_stair(bool): If `True` decay the learning rate at discrete intervals (default=False)
+
+    Returns:
+       learning_rate, learning rate numpy array
+    """
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    for i in range(total_steps):
+        lr = lr_init - lr_init * (i) / (total_steps)
+        lr_each_step.append(lr)
+    learning_rate = np.array(lr_each_step).astype(np.float32)
+    return learning_rate

model_zoo/official/cv/shufflenetv2/train.py

@@ -0,0 +1,124 @@
+# Copyright 2020 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_imagenet."""
+import argparse
+import os
+import random
+import numpy as np
+
+from network import ShuffleNetV2
+
+import mindspore.nn as nn
+from mindspore import context
+from mindspore import dataset as de
+from mindspore import ParallelMode
+from mindspore import Tensor
+from mindspore.communication.management import init, get_rank, get_group_size
+from mindspore.nn.optim.momentum import Momentum
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train.model import Model
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+
+from src.config import config_gpu as cfg
+from src.dataset import create_dataset
+from src.lr_generator import get_lr_basic
+
+random.seed(cfg.random_seed)
+np.random.seed(cfg.random_seed)
+de.config.set_seed(cfg.random_seed)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='image classification training')
+    parser.add_argument('--dataset_path', type=str, default='/home/data/imagenet_jpeg/train/', help='Dataset path')
+    parser.add_argument('--resume', type=str, default='', help='resume training with existed checkpoint')
+    parser.add_argument('--is_distributed', action='store_true', default=False,
+                        help='distributed training')
+    parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform')
+    parser.add_argument('--model_size', type=str, default='1.0x', help='ShuffleNetV2 model size parameter')
+    args_opt = parser.parse_args()
+
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False)
+    if os.getenv('DEVICE_ID', "not_set").isdigit():
+        context.set_context(device_id=int(os.getenv('DEVICE_ID')))
+
+    # init distributed
+    if args_opt.is_distributed:
+        if args_opt.platform == "Ascend":
+            init()
+        else:
+            init("nccl")
+        cfg.rank = get_rank()
+        cfg.group_size = get_group_size()
+        parallel_mode = ParallelMode.DATA_PARALLEL
+        context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=cfg.group_size,
+                                          parameter_broadcast=True, mirror_mean=True)
+    else:
+        cfg.rank = 0
+        cfg.group_size = 1
+
+    # dataloader
+    dataset = create_dataset(args_opt.dataset_path, True, cfg.rank, cfg.group_size)
+    batches_per_epoch = dataset.get_dataset_size()
+    print("Batches Per Epoch: ", batches_per_epoch)
+    # network
+    net = ShuffleNetV2(n_class=cfg.num_classes, model_size=args_opt.model_size)
+
+    # loss
+    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean", is_grad=False,
+                                            smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
+
+    # learning rate schedule
+    lr = get_lr_basic(lr_init=cfg.lr_init, total_epochs=cfg.epoch_size,
+                      steps_per_epoch=batches_per_epoch, is_stair=True)
+    lr = Tensor(lr)
+
+    # optimizer
+    decayed_params = []
+    no_decayed_params = []
+    for param in net.trainable_params():
+        if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
+            decayed_params.append(param)
+        else:
+            no_decayed_params.append(param)
+
+    group_params = [{'params': decayed_params, 'weight_decay': cfg.weight_decay},
+                    {'params': no_decayed_params},
+                    {'order_params': net.trainable_params()}]
+    optimizer = Momentum(params=net.trainable_params(), learning_rate=Tensor(lr), momentum=cfg.momentum,
+                         weight_decay=cfg.weight_decay)
+    eval_metrics = {'Loss': nn.Loss(),
+                    'Top1-Acc': nn.Top1CategoricalAccuracy(),
+                    'Top5-Acc': nn.Top5CategoricalAccuracy()}
+
+    if args_opt.resume:
+        ckpt = load_checkpoint(args_opt.resume)
+        load_param_into_net(net, ckpt)
+    model = Model(net, loss_fn=loss, optimizer=optimizer, metrics={'acc'})
+
+    print("============== Starting Training ==============")
+    loss_cb = LossMonitor(per_print_times=batches_per_epoch)
+    time_cb = TimeMonitor(data_size=batches_per_epoch)
+    callbacks = [loss_cb, time_cb]
+    config_ck = CheckpointConfig(save_checkpoint_steps=batches_per_epoch, keep_checkpoint_max=cfg.keep_checkpoint_max)
+    ckpoint_cb = ModelCheckpoint(prefix=f"shufflenet-rank{cfg.rank}", directory=cfg.ckpt_path, config=config_ck)
+    if args_opt.is_distributed & cfg.is_save_on_master:
+        if cfg.rank == 0:
+            callbacks.append(ckpoint_cb)
+        model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
+    else:
+        callbacks.append(ckpoint_cb)
+        model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
+    print("train success")