mindspore.nn.ReflectionPad2d
- class mindspore.nn.ReflectionPad2d(padding)[source]
Using a given padding to do reflection pad the given tensor.
- Parameters
padding (union[int, tuple]) – The padding size to pad the input tensor. If padding is an integer: all directions will be padded with the same size. If padding is a tuple: uses \((pad_{left}, pad_{right}, pad_{up}, pad_{down})\) to pad.
- Inputs:
x (Tensor) - 3D or 4D, shape: \((C, H_{in}, W_{out})\) or \((N, C, H_{out}, W_{out})\).
- Outputs:
Tensor, after padding. Shape: \((C, H_{out}, W_{out})\) or \((N, C, H_{out}, W_{out})\), where \(H_{out} = H_{in} + pad_{up} + pad_{down}\), \(W_{out} = W_{in} + pad_{left} + pad_{right}\).
- Raises
TypeError – If ‘padding’ is not a tuple or int.
TypeError – If there is an element in ‘padding’ that is not int.
ValueError – If the length of ‘padding’ is not divisible by 2.
ValueError – If there is an element in ‘padding’ that is negative.
ValueError – If the there is a dimension mismatch between the padding and the tensor.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> 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.]]]