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109 lines
4.6 KiB
109 lines
4.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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from blocks import ShuffleV2Block
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from mindspore import Tensor
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import mindspore.nn as nn
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import mindspore.ops.operations as P
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class ShuffleNetV2(nn.Cell):
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def __init__(self, input_size=224, n_class=1000, model_size='1.0x'):
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super(ShuffleNetV2, self).__init__()
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print('model size is ', model_size)
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self.stage_repeats = [4, 8, 4]
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self.model_size = model_size
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if model_size == '0.5x':
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self.stage_out_channels = [-1, 24, 48, 96, 192, 1024]
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elif model_size == '1.0x':
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self.stage_out_channels = [-1, 24, 116, 232, 464, 1024]
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elif model_size == '1.5x':
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self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]
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elif model_size == '2.0x':
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self.stage_out_channels = [-1, 24, 244, 488, 976, 2048]
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else:
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raise NotImplementedError
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# building first layer
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input_channel = self.stage_out_channels[1]
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self.first_conv = nn.SequentialCell([
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nn.Conv2d(in_channels=3, out_channels=input_channel, kernel_size=3, stride=2,
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pad_mode='pad', padding=1, has_bias=False),
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nn.BatchNorm2d(num_features=input_channel, momentum=0.9),
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nn.ReLU(),
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])
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode='same')
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self.features = []
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for idxstage in range(len(self.stage_repeats)):
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numrepeat = self.stage_repeats[idxstage]
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output_channel = self.stage_out_channels[idxstage+2]
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for i in range(numrepeat):
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if i == 0:
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self.features.append(ShuffleV2Block(input_channel, output_channel,
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mid_channels=output_channel // 2, ksize=3, stride=2))
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else:
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self.features.append(ShuffleV2Block(input_channel // 2, output_channel,
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mid_channels=output_channel // 2, ksize=3, stride=1))
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input_channel = output_channel
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self.features = nn.SequentialCell([*self.features])
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self.conv_last = nn.SequentialCell([
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nn.Conv2d(in_channels=input_channel, out_channels=self.stage_out_channels[-1], kernel_size=1, stride=1,
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pad_mode='pad', padding=0, has_bias=False),
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nn.BatchNorm2d(num_features=self.stage_out_channels[-1], momentum=0.9),
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nn.ReLU()
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])
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self.globalpool = nn.AvgPool2d(kernel_size=7, stride=7, pad_mode='valid')
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if self.model_size == '2.0x':
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self.dropout = nn.Dropout(keep_prob=0.8)
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self.classifier = nn.SequentialCell([nn.Dense(in_channels=self.stage_out_channels[-1],
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out_channels=n_class, has_bias=False)])
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##TODO init weights
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self._initialize_weights()
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def construct(self, x):
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x = self.first_conv(x)
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x = self.maxpool(x)
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x = self.features(x)
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x = self.conv_last(x)
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x = self.globalpool(x)
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if self.model_size == '2.0x':
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x = self.dropout(x)
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x = P.Reshape()(x, (-1, self.stage_out_channels[-1],))
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x = self.classifier(x)
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return x
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def _initialize_weights(self):
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for name, m in self.cells_and_names():
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if isinstance(m, nn.Conv2d):
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if 'first' in name:
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m.weight.set_parameter_data(Tensor(np.random.normal(0, 0.01,
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m.weight.data.shape).astype("float32")))
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else:
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m.weight.set_parameter_data(Tensor(np.random.normal(0, 1.0/m.weight.data.shape[1],
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m.weight.data.shape).astype("float32")))
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if isinstance(m, nn.Dense):
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m.weight.set_parameter_data(Tensor(np.random.normal(0, 0.01, m.weight.data.shape).astype("float32")))
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