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Running
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A10G
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import torch.nn as nn
import torch
class ResidualConv(nn.Module):
def __init__(self, input_dim, output_dim, stride, padding):
super(ResidualConv, self).__init__()
self.conv_block = nn.Sequential(
nn.BatchNorm2d(input_dim),
nn.ReLU(),
nn.Conv2d(
input_dim, output_dim, kernel_size=3, stride=stride, padding=padding
),
nn.BatchNorm2d(output_dim),
nn.ReLU(),
nn.Conv2d(output_dim, output_dim, kernel_size=3, padding=1),
)
self.conv_skip = nn.Sequential(
nn.Conv2d(input_dim, output_dim, kernel_size=3, stride=stride, padding=1),
nn.BatchNorm2d(output_dim),
)
def forward(self, x):
return self.conv_block(x) + self.conv_skip(x)
class Upsample(nn.Module):
def __init__(self, input_dim, output_dim, kernel, stride):
super(Upsample, self).__init__()
self.upsample = nn.ConvTranspose2d(
input_dim, output_dim, kernel_size=kernel, stride=stride
)
def forward(self, x):
return self.upsample(x)
class Squeeze_Excite_Block(nn.Module):
def __init__(self, channel, reduction=16):
super(Squeeze_Excite_Block, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channel, channel // reduction, bias=False),
nn.ReLU(inplace=True),
nn.Linear(channel // reduction, channel, bias=False),
nn.Sigmoid(),
)
def forward(self, x):
b, c, _, _ = x.size()
y = self.avg_pool(x).view(b, c)
y = self.fc(y).view(b, c, 1, 1)
return x * y.expand_as(x)
class ASPP(nn.Module):
def __init__(self, in_dims, out_dims, rate=[6, 12, 18]):
super(ASPP, self).__init__()
self.aspp_block1 = nn.Sequential(
nn.Conv2d(
in_dims, out_dims, 3, stride=1, padding=rate[0], dilation=rate[0]
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(out_dims),
)
self.aspp_block2 = nn.Sequential(
nn.Conv2d(
in_dims, out_dims, 3, stride=1, padding=rate[1], dilation=rate[1]
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(out_dims),
)
self.aspp_block3 = nn.Sequential(
nn.Conv2d(
in_dims, out_dims, 3, stride=1, padding=rate[2], dilation=rate[2]
),
nn.ReLU(inplace=True),
nn.BatchNorm2d(out_dims),
)
self.output = nn.Conv2d(len(rate) * out_dims, out_dims, 1)
self._init_weights()
def forward(self, x):
x1 = self.aspp_block1(x)
x2 = self.aspp_block2(x)
x3 = self.aspp_block3(x)
out = torch.cat([x1, x2, x3], dim=1)
return self.output(out)
def _init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
class Upsample_(nn.Module):
def __init__(self, scale=2):
super(Upsample_, self).__init__()
self.upsample = nn.Upsample(mode="bilinear", scale_factor=scale)
def forward(self, x):
return self.upsample(x)
class AttentionBlock(nn.Module):
def __init__(self, input_encoder, input_decoder, output_dim):
super(AttentionBlock, self).__init__()
self.conv_encoder = nn.Sequential(
nn.BatchNorm2d(input_encoder),
nn.ReLU(),
nn.Conv2d(input_encoder, output_dim, 3, padding=1),
nn.MaxPool2d(2, 2),
)
self.conv_decoder = nn.Sequential(
nn.BatchNorm2d(input_decoder),
nn.ReLU(),
nn.Conv2d(input_decoder, output_dim, 3, padding=1),
)
self.conv_attn = nn.Sequential(
nn.BatchNorm2d(output_dim),
nn.ReLU(),
nn.Conv2d(output_dim, 1, 1),
)
def forward(self, x1, x2):
out = self.conv_encoder(x1) + self.conv_decoder(x2)
out = self.conv_attn(out)
return out * x2 |