何时在Conv2d（）中使用填充以及何时进行ReflectionPad2d（）Pytorch

debugcn 发表于 Dev

大卫·S

我有两个等效的PyTorch模型（我认为），它们之间的唯一区别是填充：

import torch
import torch.nn as nn

i = torch.arange(9, dtype=torch.float).reshape(1,1,3,3)
# First model:
model1 = nn.Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode='reflection')
# tensor([[[[-0.6095, -0.0321,  2.2022],
#           [ 0.1018,  1.7650,  5.5392],
#           [ 1.7988,  3.9165,  5.6506]]]], grad_fn=<MkldnnConvolutionBackward>)

# Second model:
model2 = nn.Sequential(nn.ReflectionPad2d((1, 1, 1, 1)),
             nn.Conv2d(1, 1, kernel_size=3))
# tensor([[[[1.4751, 1.5513, 2.6566],
#           [4.0281, 4.1043, 5.2096],
#           [2.6149, 2.6911, 3.7964]]]], grad_fn=<MkldnnConvolutionBackward>)

我想知道为什么以及当您使用这两种方法时，两者的输出是不同的，但是正如我所看到的，它们应该是相同的，因为填充是反射类型。

希望能对您有所帮助。

编辑

@Ash说完之后，我想检查一下权重是否有影响，所以我将所有权重固定为相同的值，但是这两种方法之间仍然存在差异：

import torch
import torch.nn as nn

i = torch.arange(9, dtype=torch.float).reshape(1,1,3,3)
# First model:
model1 = nn.Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), padding_mode='reflection')
model1.weight.data = torch.full(model1.weight.data.shape, 0.4)
print(model1(i))
print(model1.weight)
# tensor([[[[ 3.4411,  6.2411,  5.0412],
#           [ 8.6411, 14.6411, 11.0412],
#           [ 8.2411, 13.4411,  9.8412]]]], grad_fn=<MkldnnConvolutionBackward>)
# Parameter containing:
# tensor([[[[0.4000, 0.4000, 0.4000],
#           [0.4000, 0.4000, 0.4000],
#           [0.4000, 0.4000, 0.4000]]]], requires_grad=True)

# Second model:
model2 = [nn.ReflectionPad2d((1, 1, 1, 1)),
             nn.Conv2d(1, 1, kernel_size=3)]
model2[1].weight.data = torch.full(model2[1].weight.data.shape, 0.4)
model2 = nn.Sequential(*model2)
print(model2(i))
print(model2[1].weight)
# tensor([[[[ 9.8926, 11.0926, 12.2926],
#           [13.4926, 14.6926, 15.8926],
#           [17.0926, 18.2926, 19.4926]]]], grad_fn=<MkldnnConvolutionBackward>)
# Parameter containing:
# tensor([[[[0.4000, 0.4000, 0.4000],
#           [0.4000, 0.4000, 0.4000],
#           [0.4000, 0.4000, 0.4000]]]], requires_grad=True)

灰