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6163604 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['ASPP']
class ASPPModule(nn.Module):
def __init__(self, inplanes, planes, kernel_size, padding, dilation):
super(ASPPModule, self).__init__()
self.atrous_conv = nn.Conv2d(
inplanes, planes, kernel_size=kernel_size, stride=1, padding=padding, dilation=dilation, bias=False
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x): # skipcq: PYL-W0221
x = self.atrous_conv(x)
x = self.relu(x)
return x
class ASPP(nn.Module):
def __init__(self, inplanes: int, output_stride: int, output_features: int, dropout=0.5):
super(ASPP, self).__init__()
if output_stride == 32:
dilations = [1, 3, 6, 9]
elif output_stride == 16:
dilations = [1, 6, 12, 18]
elif output_stride == 8:
dilations = [1, 12, 24, 36]
else:
raise NotImplementedError
self.aspp1 = ASPPModule(inplanes, output_features, 1, padding=0, dilation=dilations[0])
self.aspp2 = ASPPModule(inplanes, output_features, 3, padding=dilations[1], dilation=dilations[1])
self.aspp3 = ASPPModule(inplanes, output_features, 3, padding=dilations[2], dilation=dilations[2])
self.aspp4 = ASPPModule(inplanes, output_features, 3, padding=dilations[3], dilation=dilations[3])
self.global_avg_pool = nn.Sequential(
nn.AdaptiveAvgPool2d((1, 1)),
nn.Conv2d(inplanes, output_features, 1, stride=1, bias=False),
nn.ReLU(inplace=True),
)
self.conv1 = nn.Conv2d(output_features * 5, output_features, 1, bias=False)
self.relu1 = nn.ReLU(inplace=True)
self.dropout = nn.Dropout(dropout)
def forward(self, x): # skipcq: PYL-W0221
x1 = self.aspp1(x)
x2 = self.aspp2(x)
x3 = self.aspp3(x)
x4 = self.aspp4(x)
x5 = self.global_avg_pool(x)
x5 = F.interpolate(x5, size=x4.size()[2:], mode="bilinear", align_corners=False)
x = torch.cat((x1, x2, x3, x4, x5), dim=1)
x = self.conv1(x)
x = self.relu1(x)
return self.dropout(x) |