class ASPP(nn.Module): def __init__(self, in_channel=512, depth=256): super(ASPP,self).__init__() # global average pooling : init nn.AdaptiveAvgPool2d ;also forward torch.mean(,,keep_dim=True) self.mean = nn.AdaptiveAvgPool2d((1, 1)) self.conv = nn.Conv2d(in_channel, depth, 1, 1) # k=1 s=1 no pad self.atrous_block1 = nn.Conv2d(in_channel, depth, 1, 1) self.atrous_block6 = nn.Conv2d(in_channel, depth, 3, 1, padding=6, dilation=6) self.atrous_block12 = nn.Conv2d(in_channel, depth, 3, 1, padding=12, dilation=12) self.atrous_block18 = nn.Conv2d(in_channel, depth, 3, 1, padding=18, dilation=18) self.conv_1x1_output = nn.Conv2d(depth * 5, depth, 1, 1) def forward(self, x): size = x.shape[2:] image_features = self.mean(x) image_features = self.conv(image_features) image_features = F.upsample(image_features, size=size, mode='bilinear') atrous_block1 = self.atrous_block1(x) atrous_block6 = self.atrous_block6(x) atrous_block12 = self.atrous_block12(x) atrous_block18 = self.atrous_block18(x) net = self.conv_1x1_output(torch.cat([image_features, atrous_block1, atrous_block6, atrous_block12, atrous_block18], dim=1)) return net