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import numpy as np
import torch
import torch.nn as nn
class ConvBNLayer(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel,
stride=1,
act='ReLU'):
super(ConvBNLayer, self).__init__()
self.act_flag = act
self.conv = nn.Conv2d(in_channels,
out_channels,
kernel_size=2 if stride == (1, 1) else kernel,
stride=stride,
padding=(kernel - 1) // 2,
dilation=2 if stride == (1, 1) else 1)
self.bn = nn.BatchNorm2d(out_channels)
self.act = nn.ReLU(True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.act_flag != 'None':
x = self.act(x)
return x
class Shortcut(nn.Module):
def __init__(self, in_channels, out_channels, stride, is_first=False):
super(Shortcut, self).__init__()
self.use_conv = True
if in_channels != out_channels or stride != 1 or is_first is True:
if stride == (1, 1):
self.conv = ConvBNLayer(in_channels, out_channels, 1, 1)
else:
self.conv = ConvBNLayer(in_channels, out_channels, 1, stride)
else:
self.use_conv = False
def forward(self, x):
if self.use_conv:
x = self.conv(x)
return x
class BottleneckBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super(BottleneckBlock, self).__init__()
self.conv0 = ConvBNLayer(in_channels, out_channels, kernel=1)
self.conv1 = ConvBNLayer(out_channels,
out_channels,
kernel=3,
stride=stride)
self.conv2 = ConvBNLayer(out_channels,
out_channels * 4,
kernel=1,
act='None')
self.short = Shortcut(in_channels, out_channels * 4, stride=stride)
self.out_channels = out_channels * 4
self.relu = nn.ReLU(True)
def forward(self, x):
y = self.conv0(x)
y = self.conv1(y)
y = self.conv2(y)
y = y + self.short(x)
y = self.relu(y)
return y
class BasicBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, is_first):
super(BasicBlock, self).__init__()
self.conv0 = ConvBNLayer(in_channels,
out_channels,
kernel=3,
stride=stride)
self.conv1 = ConvBNLayer(out_channels,
out_channels,
kernel=3,
act='None')
self.short = Shortcut(in_channels, out_channels, stride, is_first)
self.out_chanels = out_channels
self.relu = nn.ReLU(True)
def forward(self, x):
y = self.conv0(x)
y = self.conv1(y)
y = y + self.short(x)
y = self.relu(y)
return y
class ResNet_FPN(nn.Module):
def __init__(self, in_channels=1, layers=50, **kwargs):
super(ResNet_FPN, self).__init__()
supported_layers = {
18: {
'depth': [2, 2, 2, 2],
'block_class': BasicBlock
},
34: {
'depth': [3, 4, 6, 3],
'block_class': BasicBlock
},
50: {
'depth': [3, 4, 6, 3],
'block_class': BottleneckBlock
},
101: {
'depth': [3, 4, 23, 3],
'block_class': BottleneckBlock
},
152: {
'depth': [3, 8, 36, 3],
'block_class': BottleneckBlock
}
}
stride_list = [(2, 2), (
2,
2,
), (1, 1), (1, 1)]
num_filters = [64, 128, 256, 512]
self.depth = supported_layers[layers]['depth']
self.F = []
# print(f"in_channels:{in_channels}")
self.conv = ConvBNLayer(in_channels=in_channels,
out_channels=64,
kernel=7,
stride=2) #64*256 ->32*128
self.block_list = nn.ModuleList()
in_ch = 64
if layers >= 50:
for block in range(len(self.depth)):
for i in range(self.depth[block]):
self.block_list.append(
BottleneckBlock(
in_channels=in_ch,
out_channels=num_filters[block],
stride=stride_list[block] if i == 0 else 1))
in_ch = num_filters[block] * 4
else:
for block in range(len(self.depth)):
for i in range(self.depth[block]):
if i == 0 and block != 0:
stride = (2, 1)
else:
stride = (1, 1)
basic_block = BasicBlock(
in_channels=in_ch,
out_channels=num_filters[block],
stride=stride_list[block] if i == 0 else 1,
is_first=block == i == 0)
in_ch = basic_block.out_chanels
self.block_list.append(basic_block)
out_ch_list = [in_ch // 4, in_ch // 2, in_ch]
self.base_block = nn.ModuleList()
self.conv_trans = []
self.bn_block = []
for i in [-2, -3]:
in_channels = out_ch_list[i + 1] + out_ch_list[i]
self.base_block.append(
nn.Conv2d(in_channels, out_ch_list[i], kernel_size=1)) #进行升通道
self.base_block.append(
nn.Conv2d(out_ch_list[i],
out_ch_list[i],
kernel_size=3,
padding=1)) #进行合并
self.base_block.append(
nn.Sequential(nn.BatchNorm2d(out_ch_list[i]), nn.ReLU(True)))
self.base_block.append(nn.Conv2d(out_ch_list[i], 512, kernel_size=1))
self.out_channels = 512
def forward(self, x):
# print(f"before resnetfpn x.shape:{x.shape}")
x = self.conv(x)
fpn_list = []
F = []
for i in range(len(self.depth)):
fpn_list.append(np.sum(self.depth[:i + 1]))
for i, block in enumerate(self.block_list):
x = block(x)
for number in fpn_list:
if i + 1 == number:
F.append(x)
base = F[-1]
j = 0
for i, block in enumerate(self.base_block):
if i % 3 == 0 and i < 6:
j = j + 1
b, c, w, h = F[-j - 1].size()
if [w, h] == list(base.size()[2:]):
base = base
else:
base = self.conv_trans[j - 1](base)
base = self.bn_block[j - 1](base)
base = torch.cat([base, F[-j - 1]], dim=1)
base = block(base)
return base
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