pytorch 中pad函数toch.nn.functional.pad()的用法
padding操作是给图像外围加像素点。
为了实际说明操作过程，这里我们使用一张实际的图片来做一下处理。
这张图片是大小是(256,256)，使用pad来给它加上一个黑色的边框。具体代码如下：
importtorch.nn,functionalasF
importtorch
fromPILimportImage
im=Image.open("heibai.jpg",'r')
X=torch.Tensor(np.asarray(im))
print("shape:",X.shape)
dim=(10,10,10,10)
X=F.pad(X,dim,"constant",value=0)
padX=X.data.numpy()
padim=Image.fromarray(padX)
padim=padim.convert("RGB")#这里必须转为RGB不然会
padim.save("padded.jpg","jpeg")
padim.show()
print("shape:",padX.shape)
输出：
shape:torch.Size([256,256]) shape:(276,276)
可以看出给原图四个方向给加上10维度的0，维度变为256+10+10得到的图像如下：
我们在举几个简单例子：
x=np.asarray([[[1,2],[1,2]]]) X=torch.Tensor(x) print(X.shape) pad_dims=( 2,2, 2,2, 1,1, ) X=F.pad(X,pad_dims,"constant") print(X.shape) print(X)
输出：
torch.Size([1,2,2]) torch.Size([3,6,6]) tensor([[[0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.]], [[0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,1.,2.,0.,0.], [0.,0.,1.,2.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.]], [[0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.]]])
可以知若pid_sim为(2,2,2,2,1,1)则原维度变化是2+2+2=6,1+1+1=3.也就是第一个(2,2)pad的是最后一个维度，第二个（2,2）pad是倒数第二个维度，第三个（1,1）pad是第一个维度。
再举一个四维度的，但是只pad三个维度：
x=np.asarray([[[[1,2],[1,2]]]]) X=torch.Tensor(x)#(1,2,2) print(X.shape) pad_dims=( 2,2, 2,2, 1,1, ) X=F.pad(X,pad_dims,"constant")#(1,1,12,12) print(X.shape) print(X)
输出：
torch.Size([1,1,2,2]) torch.Size([1,3,6,6]) tensor([[[[0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.]], [[0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,1.,2.,0.,0.], [0.,0.,1.,2.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.]], [[0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.]]]])
再举一个四维度的，pad四个维度：
x=np.asarray([[[[1,2],[1,2]]]]) X=torch.Tensor(x)#(1,2,2) print(X.shape) pad_dims=( 2,2, 2,2, 1,1, 2,2 ) X=F.pad(X,pad_dims,"constant")#(1,1,12,12) print(X.shape) print(X)
输出：
torch.Size([1,1,2,2]) torch.Size([5,3,6,6]) tensor([[[[0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.]], [[0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.]], [[0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.], [0.,0.,0.,0.,0.,0.]]], .........太多了
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