147 lines
5.1 KiB
Python
147 lines
5.1 KiB
Python
# 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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"""
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This code is refer from:
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https://github.com/LBH1024/CAN/models/densenet.py
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"""
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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 math
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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class Bottleneck(nn.Layer):
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def __init__(self, nChannels, growthRate, use_dropout):
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super(Bottleneck, self).__init__()
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interChannels = 4 * growthRate
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self.bn1 = nn.BatchNorm2D(interChannels)
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self.conv1 = nn.Conv2D(
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nChannels, interChannels, kernel_size=1,
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bias_attr=None) # Xavier initialization
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self.bn2 = nn.BatchNorm2D(growthRate)
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self.conv2 = nn.Conv2D(
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interChannels, growthRate, kernel_size=3, padding=1,
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bias_attr=None) # Xavier initialization
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self.use_dropout = use_dropout
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self.dropout = nn.Dropout(p=0.2)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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if self.use_dropout:
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out = self.dropout(out)
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out = F.relu(self.bn2(self.conv2(out)))
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if self.use_dropout:
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out = self.dropout(out)
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out = paddle.concat([x, out], 1)
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return out
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class SingleLayer(nn.Layer):
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def __init__(self, nChannels, growthRate, use_dropout):
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super(SingleLayer, self).__init__()
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self.bn1 = nn.BatchNorm2D(nChannels)
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self.conv1 = nn.Conv2D(
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nChannels, growthRate, kernel_size=3, padding=1, bias_attr=False)
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self.use_dropout = use_dropout
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self.dropout = nn.Dropout(p=0.2)
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def forward(self, x):
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out = self.conv1(F.relu(x))
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if self.use_dropout:
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out = self.dropout(out)
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out = paddle.concat([x, out], 1)
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return out
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class Transition(nn.Layer):
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def __init__(self, nChannels, out_channels, use_dropout):
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super(Transition, self).__init__()
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self.bn1 = nn.BatchNorm2D(out_channels)
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self.conv1 = nn.Conv2D(
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nChannels, out_channels, kernel_size=1, bias_attr=False)
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self.use_dropout = use_dropout
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self.dropout = nn.Dropout(p=0.2)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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if self.use_dropout:
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out = self.dropout(out)
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out = F.avg_pool2d(out, 2, ceil_mode=True, exclusive=False)
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return out
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class DenseNet(nn.Layer):
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def __init__(self, growthRate, reduction, bottleneck, use_dropout,
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input_channel, **kwargs):
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super(DenseNet, self).__init__()
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nDenseBlocks = 16
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nChannels = 2 * growthRate
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self.conv1 = nn.Conv2D(
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input_channel,
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nChannels,
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kernel_size=7,
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padding=3,
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stride=2,
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bias_attr=False)
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self.dense1 = self._make_dense(nChannels, growthRate, nDenseBlocks,
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bottleneck, use_dropout)
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nChannels += nDenseBlocks * growthRate
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out_channels = int(math.floor(nChannels * reduction))
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self.trans1 = Transition(nChannels, out_channels, use_dropout)
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nChannels = out_channels
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self.dense2 = self._make_dense(nChannels, growthRate, nDenseBlocks,
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bottleneck, use_dropout)
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nChannels += nDenseBlocks * growthRate
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out_channels = int(math.floor(nChannels * reduction))
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self.trans2 = Transition(nChannels, out_channels, use_dropout)
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nChannels = out_channels
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self.dense3 = self._make_dense(nChannels, growthRate, nDenseBlocks,
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bottleneck, use_dropout)
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self.out_channels = out_channels
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def _make_dense(self, nChannels, growthRate, nDenseBlocks, bottleneck,
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use_dropout):
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layers = []
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for i in range(int(nDenseBlocks)):
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if bottleneck:
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layers.append(Bottleneck(nChannels, growthRate, use_dropout))
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else:
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layers.append(SingleLayer(nChannels, growthRate, use_dropout))
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nChannels += growthRate
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return nn.Sequential(*layers)
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def forward(self, inputs):
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x, x_m, y = inputs
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out = self.conv1(x)
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out = F.relu(out)
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out = F.max_pool2d(out, 2, ceil_mode=True)
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out = self.dense1(out)
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out = self.trans1(out)
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out = self.dense2(out)
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out = self.trans2(out)
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out = self.dense3(out)
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return out, x_m, y
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