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densenet.py
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densenet.py
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.droprate = dropRate
def forward(self, x):
out = self.conv1(self.relu(self.bn1(x)))
if self.droprate > 0:
out = F.dropout(out, p=self.droprate, training=self.training)
return torch.cat([x, out], 1)
class BottleneckBlock(nn.Module):
def __init__(self, in_planes, out_planes, dropRate=0.0):
super(BottleneckBlock, self).__init__()
inter_planes = out_planes * 4
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_planes, inter_planes, kernel_size=1, stride=1,
padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(inter_planes)
self.conv2 = nn.Conv2d(inter_planes, out_planes, kernel_size=3, stride=1,
padding=1, bias=False)
self.droprate = dropRate
def forward(self, x):
out = self.conv1(self.relu(self.bn1(x)))
if self.droprate > 0:
out = F.dropout(out, p=self.droprate, inplace=False, training=self.training)
out = self.conv2(self.relu(self.bn2(out)))
if self.droprate > 0:
out = F.dropout(out, p=self.droprate, inplace=False, training=self.training)
return torch.cat([x, out], 1)
class TransitionBlock(nn.Module):
def __init__(self, in_planes, out_planes, dropRate=0.0):
super(TransitionBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1,
padding=0, bias=False)
self.droprate = dropRate
def forward(self, x):
out = self.conv1(self.relu(self.bn1(x)))
if self.droprate > 0:
out = F.dropout(out, p=self.droprate, inplace=False, training=self.training)
return F.avg_pool2d(out, 2)
class DenseBlock(nn.Module):
def __init__(self, nb_layers, in_planes, growth_rate, block, dropRate=0.0):
super(DenseBlock, self).__init__()
self.layer = self._make_layer(block, in_planes, growth_rate, nb_layers, dropRate)
def _make_layer(self, block, in_planes, growth_rate, nb_layers, dropRate):
layers = []
for i in range(int(nb_layers)):
layers.append(block(in_planes+i*growth_rate, growth_rate, dropRate))
return nn.Sequential(*layers)
def forward(self, x):
return self.layer(x)
class DenseNet3(nn.Module):
def __init__(self, depth, num_classes, growth_rate=12,
reduction=0.5, bottleneck=True, dropRate=0.0):
super(DenseNet3, self).__init__()
in_planes = 2 * growth_rate
n = (depth - 4) / 3
if bottleneck == True:
n = n/2
block = BottleneckBlock
else:
block = BasicBlock
# 1st conv before any dense block
self.conv1 = nn.Conv2d(3, in_planes, kernel_size=3, stride=1,
padding=1, bias=False)
# 1st block
self.block1 = DenseBlock(n, in_planes, growth_rate, block, dropRate)
in_planes = int(in_planes+n*growth_rate)
self.trans1 = TransitionBlock(in_planes, int(math.floor(in_planes*reduction)), dropRate=dropRate)
in_planes = int(math.floor(in_planes*reduction))
# 2nd block
self.block2 = DenseBlock(n, in_planes, growth_rate, block, dropRate)
in_planes = int(in_planes+n*growth_rate)
self.trans2 = TransitionBlock(in_planes, int(math.floor(in_planes*reduction)), dropRate=dropRate)
in_planes = int(math.floor(in_planes*reduction))
# 3rd block
self.block3 = DenseBlock(n, in_planes, growth_rate, block, dropRate)
in_planes = int(in_planes+n*growth_rate)
# global average pooling and classifier
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu = nn.ReLU(inplace=True)
self.fc = nn.Linear(in_planes, num_classes)
self.in_planes = in_planes
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.bias.data.zero_()
def forward(self, x):
out = self.conv1(x)
out = self.trans1(self.block1(out))
out = self.trans2(self.block2(out))
out = self.block3(out)
out = self.relu(self.bn1(out))
out = F.avg_pool2d(out, 8)
out = out.view(-1, self.in_planes)
return self.fc(out)
# function to extact the multiple features
def feature_list(self, x):
out_list = []
out = self.conv1(x)
out_list.append(out)
out = self.trans1(self.block1(out))
out_list.append(out)
out = self.trans2(self.block2(out))
out_list.append(out)
out = self.block3(out)
out = self.relu(self.bn1(out))
out_list.append(out)
out = F.avg_pool2d(out, 8)
out = out.view(-1, self.in_planes)
return self.fc(out), out_list
def intermediate_forward(self, x, layer_index):
out = self.conv1(x)
if layer_index == 1:
out = self.trans1(self.block1(out))
elif layer_index == 2:
out = self.trans1(self.block1(out))
out = self.trans2(self.block2(out))
elif layer_index == 3:
out = self.trans1(self.block1(out))
out = self.trans2(self.block2(out))
out = self.block3(out)
out = self.relu(self.bn1(out))
return out
# function to extact the penultimate features
def penultimate_forward(self, x):
out = self.conv1(x)
out = self.trans1(self.block1(out))
out = self.trans2(self.block2(out))
out = self.block3(out)
penultimate = self.relu(self.bn1(out))
out = F.avg_pool2d(penultimate, 8)
out = out.view(-1, self.in_planes)
return self.fc(out), penultimate