mindspore.ops.Conv2DTranspose
- class mindspore.ops.Conv2DTranspose(out_channel, kernel_size, pad_mode='valid', pad=0, pad_list=None, mode=1, stride=1, dilation=1, group=1, data_format='NCHW')[源代码]
Compute a 2D transposed convolution, which is also known as a deconvolution (although it is not an actual deconvolution).
- Parameters
out_channel (int) – The dimensionality of the output space.
kernel_size (Union[int, tuple[int]]) – The size of the convolution window.
pad_mode (str) – Modes to fill padding. It could be “valid”, “same”, or “pad”. Default: “valid”.
pad (Union[int, tuple[int]]) – The pad value to be filled. Default: 0. If pad is an integer, the paddings of top, bottom, left and right are the same, equal to pad. If pad is a tuple of four integers, the padding of top, bottom, left and right equal to pad[0], pad[1], pad[2], and pad[3] correspondingly.
pad_list (Union[str, None]) – The pad list like (top, bottom, left, right). Default: None.
mode (int) – Modes for different convolutions. The value is currently not used. Default: 1.
stride (Union[int. tuple[int]]) – The stride to be applied to the convolution filter. Default: 1.
dilation (Union[int. tuple[int]]) – Specifies the dilation rate to be used for the dilated convolution. Default: 1.
group (int) – Splits input into groups. Default: 1.
data_format (str) – The format of input and output data. It should be ‘NHWC’ or ‘NCHW’, default is ‘NCHW’.
- Inputs:
dout (Tensor) - the gradients with respect to the output of the convolution. The shape conforms to the default data_format \((N, C_{out}, H_{out}, W_{out})\).
weight (Tensor) - Set size of kernel is \((K_1, K_2)\), then the shape is \((C_{out}, C_{in}, K_1, K_2)\).
input_size (Tensor) - A tuple describes the shape of the input which conforms to the format \((N, C_{in}, H_{in}, W_{in})\).
- Outputs:
Tensor, the gradients with respect to the input of convolution. It has the same shape as the input.
- Raises
TypeError – If kernel_size, stride, pad or dilation is neither an int nor a tuple.
TypeError – If out_channel or group is not an int.
ValueError – If kernel_size, stride or dilation is less than 1.
ValueError – If pad_mode is not one of ‘same’, ‘valid’ or ‘pad’.
ValueError – If padding is a tuple whose length is not equal to 4.
ValueError – If pad_mode it not equal to ‘pad’ and pad is not equal to (0, 0, 0, 0).
ValueError – If data_format is neither ‘NCHW’ nor ‘NHWC’.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> dout = Tensor(np.ones([10, 32, 30, 30]), mindspore.float32) >>> weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32) >>> x = Tensor(np.ones([10, 32, 32, 32])) >>> conv2d_transpose_input = ops.Conv2DTranspose(out_channel=32, kernel_size=3) >>> output = conv2d_transpose_input(dout, weight, ops.shape(x)) >>> print(output.shape) (10, 32, 32, 32)