torch.nn.ConvTranspose2d() 卷积: 蓝色为输入,蓝色上的阴影为卷积核(kernel),绿色为输出,蓝色边缘的白色框为padding 反卷积: 卷积和反卷积函数中的in_channels与out_channels以及kernel_size的含义相同。 需要注意的是padding和stride和conv2d不同,padding不是蓝色的留白,是kernel像图像中心移动的单位。如下当...
upsample(input=x, scale_factor=2, mode='bilinear') return self.conv(p) Example #27Source File: resnet_v1.py From pytorch-FPN with MIT License 5 votes def __init__(self, planes=256): super(BuildBlock, self).__init__() # Top-down layers, use nn.ConvTranspose2d to replace nn....
conv1 = nn.Conv2d(in_channels, in_channels // 4, 1) self.norm1 = nn.BatchNorm2d(in_channels // 4) self.relu1 = nonlinearity(inplace=True) if scale: # B, C/4, H, W -> B, C/4, H, W if is_deconv: self.upscale = nn.ConvTranspose2d(in_channels // 4, in_channels /...