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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def create_conv_block(in_planes, out_planes, kernel_size=1, stride=1, padding=0,
dilation=1, groups=1, bias=False, bn=True, activation=True):
layers = []
layers.append(nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, groups=groups, bias=bias))
if bn:
layers.append(nn.InstanceNorm2d(out_planes))
if activation:
layers.append(nn.LeakyReLU(inplace=True))
return nn.Sequential(*layers)
def create_conv_block_k1(in_planes, out_planes, kernel_size=1, stride=1, padding=0,
dilation=1, groups=1, bias=False, bn=True, activation=True):
return create_conv_block(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, groups=groups, bias=bias, bn=bn, activation=activation)
def create_conv_block_k2(in_planes, out_planes, kernel_size=2, stride=1, padding=0,
dilation=1, groups=1, bias=False, bn=True, activation=True):
return create_conv_block(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, groups=groups, bias=bias, bn=bn, activation=activation)
def create_conv_block_k3(in_planes, out_planes, kernel_size=3, stride=1, padding=1,
dilation=1, groups=1, bias=False, bn=True, activation=True):
return create_conv_block(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, groups=groups, bias=bias, bn=bn, activation=activation)
class RfbfBlock2d(nn.Module):
def __init__(self, in_planes, out_planes, stride=1, groups=1, droprate=0):
super(RfbfBlock2d, self).__init__()
inter_planes = max(int(np.ceil(out_planes / 8)), 1)
self.groups = groups
self.group_num = inter_planes // groups
self.droprate = droprate
self.branch1 = nn.Sequential(
create_conv_block(in_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
stride=stride, groups=groups),
create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
dilation=(1, 1), groups=groups)
)
self.branch2 = nn.Sequential(
create_conv_block(in_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
stride=stride, groups=groups),
create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
dilation=(1, 1), groups=groups)
)
self.branch3 = nn.Sequential(
create_conv_block(in_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
stride=stride, groups=groups),
create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
dilation=(1, 1), groups=groups)
)
self.branch4 = nn.Sequential(
create_conv_block(in_planes, inter_planes, kernel_size=(7, 7), padding=(3, 3),
stride=stride, groups=groups),
create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
dilation=(1, 1), groups=groups)
)
self.branch5 = nn.Sequential(
create_conv_block(in_planes, inter_planes, kernel_size=(7, 7), padding=(3, 3),
stride=stride, groups=groups),
create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
dilation=(1, 1), groups=groups)
)
self.branch6 = nn.Sequential(
create_conv_block(in_planes, inter_planes, kernel_size=(7, 7), padding=(3, 3),
stride=stride, groups=groups),
create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
dilation=(1, 1), groups=groups)
)
self.branch7 = nn.Sequential(
create_conv_block(in_planes, inter_planes, kernel_size=(9, 9), padding=(4, 4),
stride=stride, groups=groups),
create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
dilation=(1, 1), groups=groups)
)
self.branch8 = nn.Sequential(
create_conv_block(in_planes, inter_planes, kernel_size=(9, 9), padding=(4, 4),
stride=stride, groups=groups),
create_conv_block(inter_planes, inter_planes, kernel_size=(5, 5), padding=(2, 2),
dilation=(1, 1), groups=groups)
)
def forward(self, x):
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
x4 = self.branch4(x)
x5 = self.branch5(x)
x6 = self.branch6(x)
x7 = self.branch7(x)
x8 = self.branch8(x)
if self.groups == 1:
out = torch.cat((x1, x2, x3, x4, x5, x6, x7, x8), 1)
else:
for group in range(self.groups):
group_out = torch.cat((x1[:, group * self.group_num:(group + 1) * self.group_num],
x2[:, group * self.group_num:(group + 1) * self.group_num],
x3[:, group * self.group_num:(group + 1) * self.group_num],
x4[:, group * self.group_num:(group + 1) * self.group_num],
x5[:, group * self.group_num:(group + 1) * self.group_num],
x6[:, group * self.group_num:(group + 1) * self.group_num],
x7[:, group * self.group_num:(group + 1) * self.group_num],
x8[:, group * self.group_num:(group + 1) * self.group_num]), 1)
if group == 0:
out = group_out
else:
out = torch.cat((out, group_out), 1)
if self.droprate > 0:
out = F.dropout2d(out, p=self.droprate, training=self.training)
return out
class ResBasicBlock2d(nn.Module):
def __init__(self, in_planes, out_planes, stride=1, groups=1, droprate=0, se=False):
super(ResBasicBlock2d, self).__init__()
self.conv1 = create_conv_block_k3(in_planes, out_planes, stride=stride, groups=groups)
self.conv2 = create_conv_block_k3(out_planes, out_planes, groups=groups, activation=False)
self.shortcut = None
if stride != 1 or in_planes != out_planes:
self.shortcut = create_conv_block_k1(in_planes, out_planes, stride=stride, groups=groups, activation=False)
self.droprate = droprate
self.se = se
if se:
self.fc1 = nn.Linear(in_features=out_planes, out_features=out_planes // 4)
self.fc2 = nn.Linear(in_features=out_planes // 4, out_features=out_planes)
def forward(self, x):
identity = self.shortcut(x) if self.shortcut is not None else x
out = self.conv1(x)
if self.droprate > 0:
out = F.dropout2d(out, p=self.droprate, training=self.training)
out = self.conv2(out)
if self.se:
original_out = out
out = F.adaptive_avg_pool2d(out, (1, 1))
out = torch.flatten(out, 1)
out = self.fc1(out)
out = F.leaky_relu(out, inplace=True)
out = self.fc2(out)
out = out.sigmoid()
#这里需要测试一下
out = out.view(out.size(0), out.size(1), 1, 1)
out = out * original_out
out = out + identity
out = F.leaky_relu(out, inplace=True)
return out