mindspore.nn.ReflectionPad2d

class mindspore.nn.ReflectionPad2d(padding)[源代码]

根据 padding 对输入 x 进行填充。

参数:

  • padding (union[int, tuple]) - 填充大小, 如果输入为int, 则对所有边界进行相同大小的填充; 如果是tuple,则顺序为 \((pad_{left}, pad_{right}, pad_{up}, pad_{down})\)

输入:

  • x (Tensor) - 输入Tensor, shape为 \((C, H_{in}, W_{in})\)\((N, C, H_{in}, W_{in})\)

输出:

Tensor,填充后的Tensor, shape为 \((C, H_{out}, W_{out})\)\((N, C, H_{out}, W_{out})\) 。其中 \(H_{out} = H_{in} + pad_{up} + pad_{down}\)\(W_{out} = W_{in} + pad_{left} + pad_{right}\)

异常:

  • TypeError - padding 不是tuple或int。

  • TypeError - padding 中存在不是int的元素。

  • ValueError - padding 是tuple,且长度不能被2整除。

  • ValueError - padding 是tuple,且存在负数。

  • ValueError - padding 是tuple,且长度和tensor的维度不匹配。

支持平台:

Ascend GPU CPU

样例:

>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore.nn import ReflectionPad2d
>>> x = Tensor(np.array([[[0, 1, 2], [3, 4, 5], [6, 7, 8]]]).astype(np.float32))
>>> # x has shape (1, 3, 3)
>>> padding = (1, 1, 2, 0)
>>> pad2d = ReflectionPad2d(padding)
>>> # The first dimension of x remains the same.
>>> # The second dimension of x: H_out = H_in + pad_up + pad_down = 3 + 1 + 1 = 5
>>> # The third dimension of x: W_out = W_in + pad_left + pad_right = 3 + 2 + 0 = 5
>>> out = pad2d(x)
>>> # The shape of out is (1, 5, 5)
>>> print(out)
[[[7. 6. 7. 8. 7.]
  [4. 3. 4. 5. 4.]
  [1. 0. 1. 2. 1.]
  [4. 3. 4. 5. 4.]
  [7. 6. 7. 8. 7.]]]