257 lines
7.6 KiB
Python
257 lines
7.6 KiB
Python
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# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
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from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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from paddle.nn.initializer import KaimingNormal
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import math
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import numpy as np
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import paddle
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from paddle import ParamAttr, reshape, transpose, concat, split
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
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from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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from paddle.nn.initializer import KaimingNormal
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import math
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from paddle.nn.functional import hardswish, hardsigmoid
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from paddle.regularizer import L2Decay
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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num_channels,
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filter_size,
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num_filters,
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stride,
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padding,
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channels=None,
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num_groups=1,
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act='hard_swish'):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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groups=num_groups,
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weight_attr=ParamAttr(initializer=KaimingNormal()),
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bias_attr=False)
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self._batch_norm = BatchNorm(
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num_filters,
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act=act,
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param_attr=ParamAttr(regularizer=L2Decay(0.0)),
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bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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class DepthwiseSeparable(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters1,
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num_filters2,
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num_groups,
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stride,
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scale,
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dw_size=3,
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padding=1,
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use_se=False):
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super(DepthwiseSeparable, self).__init__()
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self.use_se = use_se
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self._depthwise_conv = ConvBNLayer(
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num_channels=num_channels,
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num_filters=int(num_filters1 * scale),
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filter_size=dw_size,
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stride=stride,
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padding=padding,
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num_groups=int(num_groups * scale))
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if use_se:
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self._se = SEModule(int(num_filters1 * scale))
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self._pointwise_conv = ConvBNLayer(
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num_channels=int(num_filters1 * scale),
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filter_size=1,
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num_filters=int(num_filters2 * scale),
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stride=1,
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padding=0)
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def forward(self, inputs):
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y = self._depthwise_conv(inputs)
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if self.use_se:
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y = self._se(y)
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y = self._pointwise_conv(y)
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return y
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class MobileNetV1Enhance(nn.Layer):
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def __init__(self, in_channels=3, scale=0.5, **kwargs):
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super().__init__()
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self.scale = scale
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self.block_list = []
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self.conv1 = ConvBNLayer(
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num_channels=3,
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filter_size=3,
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channels=3,
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num_filters=int(32 * scale),
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stride=2,
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padding=1)
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conv2_1 = DepthwiseSeparable(
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num_channels=int(32 * scale),
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num_filters1=32,
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num_filters2=64,
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num_groups=32,
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stride=1,
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scale=scale)
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self.block_list.append(conv2_1)
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conv2_2 = DepthwiseSeparable(
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num_channels=int(64 * scale),
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num_filters1=64,
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num_filters2=128,
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num_groups=64,
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stride=1,
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scale=scale)
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self.block_list.append(conv2_2)
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conv3_1 = DepthwiseSeparable(
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num_channels=int(128 * scale),
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num_filters1=128,
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num_filters2=128,
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num_groups=128,
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stride=1,
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scale=scale)
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self.block_list.append(conv3_1)
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conv3_2 = DepthwiseSeparable(
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num_channels=int(128 * scale),
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num_filters1=128,
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num_filters2=256,
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num_groups=128,
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stride=(2, 1),
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scale=scale)
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self.block_list.append(conv3_2)
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conv4_1 = DepthwiseSeparable(
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num_channels=int(256 * scale),
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num_filters1=256,
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num_filters2=256,
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num_groups=256,
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stride=1,
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scale=scale)
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self.block_list.append(conv4_1)
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conv4_2 = DepthwiseSeparable(
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num_channels=int(256 * scale),
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num_filters1=256,
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num_filters2=512,
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num_groups=256,
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stride=(2, 1),
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scale=scale)
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self.block_list.append(conv4_2)
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for _ in range(5):
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conv5 = DepthwiseSeparable(
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num_channels=int(512 * scale),
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num_filters1=512,
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num_filters2=512,
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num_groups=512,
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stride=1,
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dw_size=5,
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padding=2,
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scale=scale,
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use_se=False)
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self.block_list.append(conv5)
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conv5_6 = DepthwiseSeparable(
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num_channels=int(512 * scale),
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num_filters1=512,
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num_filters2=1024,
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num_groups=512,
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stride=(2, 1),
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dw_size=5,
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padding=2,
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scale=scale,
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use_se=True)
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self.block_list.append(conv5_6)
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conv6 = DepthwiseSeparable(
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num_channels=int(1024 * scale),
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num_filters1=1024,
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num_filters2=1024,
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num_groups=1024,
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stride=1,
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dw_size=5,
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padding=2,
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use_se=True,
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scale=scale)
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self.block_list.append(conv6)
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self.block_list = nn.Sequential(*self.block_list)
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self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
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self.out_channels = int(1024 * scale)
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def forward(self, inputs):
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y = self.conv1(inputs)
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y = self.block_list(y)
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y = self.pool(y)
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return y
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class SEModule(nn.Layer):
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def __init__(self, channel, reduction=4):
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super(SEModule, self).__init__()
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self.avg_pool = AdaptiveAvgPool2D(1)
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self.conv1 = Conv2D(
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in_channels=channel,
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out_channels=channel // reduction,
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kernel_size=1,
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stride=1,
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padding=0,
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weight_attr=ParamAttr(),
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bias_attr=ParamAttr())
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self.conv2 = Conv2D(
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in_channels=channel // reduction,
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out_channels=channel,
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kernel_size=1,
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stride=1,
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padding=0,
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weight_attr=ParamAttr(),
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bias_attr=ParamAttr())
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def forward(self, inputs):
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outputs = self.avg_pool(inputs)
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outputs = self.conv1(outputs)
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outputs = F.relu(outputs)
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outputs = self.conv2(outputs)
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outputs = hardsigmoid(outputs)
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return paddle.multiply(x=inputs, y=outputs)
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