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# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
"""
This code is refer from:
https://github.com/LBH1024/CAN/models/densenet.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
class Bottleneck(nn.Layer):
def __init__(self, nChannels, growthRate, use_dropout):
super(Bottleneck, self).__init__()
interChannels = 4 * growthRate
self.bn1 = nn.BatchNorm2D(interChannels)
self.conv1 = nn.Conv2D(
nChannels, interChannels, kernel_size=1,
bias_attr=None) # Xavier initialization
self.bn2 = nn.BatchNorm2D(growthRate)
self.conv2 = nn.Conv2D(
interChannels, growthRate, kernel_size=3, padding=1,
bias_attr=None) # Xavier initialization
self.use_dropout = use_dropout
self.dropout = nn.Dropout(p=0.2)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
if self.use_dropout:
out = self.dropout(out)
out = F.relu(self.bn2(self.conv2(out)))
if self.use_dropout:
out = self.dropout(out)
out = paddle.concat([x, out], 1)
return out
class SingleLayer(nn.Layer):
def __init__(self, nChannels, growthRate, use_dropout):
super(SingleLayer, self).__init__()
self.bn1 = nn.BatchNorm2D(nChannels)
self.conv1 = nn.Conv2D(
nChannels, growthRate, kernel_size=3, padding=1, bias_attr=False)
self.use_dropout = use_dropout
self.dropout = nn.Dropout(p=0.2)
def forward(self, x):
out = self.conv1(F.relu(x))
if self.use_dropout:
out = self.dropout(out)
out = paddle.concat([x, out], 1)
return out
class Transition(nn.Layer):
def __init__(self, nChannels, out_channels, use_dropout):
super(Transition, self).__init__()
self.bn1 = nn.BatchNorm2D(out_channels)
self.conv1 = nn.Conv2D(
nChannels, out_channels, kernel_size=1, bias_attr=False)
self.use_dropout = use_dropout
self.dropout = nn.Dropout(p=0.2)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
if self.use_dropout:
out = self.dropout(out)
out = F.avg_pool2d(out, 2, ceil_mode=True, exclusive=False)
return out
class DenseNet(nn.Layer):
def __init__(self, growthRate, reduction, bottleneck, use_dropout,
input_channel, **kwargs):
super(DenseNet, self).__init__()
nDenseBlocks = 16
nChannels = 2 * growthRate
self.conv1 = nn.Conv2D(
input_channel,
nChannels,
kernel_size=7,
padding=3,
stride=2,
bias_attr=False)
self.dense1 = self._make_dense(nChannels, growthRate, nDenseBlocks,
bottleneck, use_dropout)
nChannels += nDenseBlocks * growthRate
out_channels = int(math.floor(nChannels * reduction))
self.trans1 = Transition(nChannels, out_channels, use_dropout)
nChannels = out_channels
self.dense2 = self._make_dense(nChannels, growthRate, nDenseBlocks,
bottleneck, use_dropout)
nChannels += nDenseBlocks * growthRate
out_channels = int(math.floor(nChannels * reduction))
self.trans2 = Transition(nChannels, out_channels, use_dropout)
nChannels = out_channels
self.dense3 = self._make_dense(nChannels, growthRate, nDenseBlocks,
bottleneck, use_dropout)
self.out_channels = out_channels
def _make_dense(self, nChannels, growthRate, nDenseBlocks, bottleneck,
use_dropout):
layers = []
for i in range(int(nDenseBlocks)):
if bottleneck:
layers.append(Bottleneck(nChannels, growthRate, use_dropout))
else:
layers.append(SingleLayer(nChannels, growthRate, use_dropout))
nChannels += growthRate
return nn.Sequential(*layers)
def forward(self, inputs):
x, x_m, y = inputs
out = self.conv1(x)
out = F.relu(out)
out = F.max_pool2d(out, 2, ceil_mode=True)
out = self.dense1(out)
out = self.trans1(out)
out = self.dense2(out)
out = self.trans2(out)
out = self.dense3(out)
return out, x_m, y
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