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mindspore.ops.Conv2DBackpropInput

class mindspore.ops.Conv2DBackpropInput(*args, **kwargs)

Computes the gradients of convolution with respect to the input.

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.

• mode (int) – Modes for different convolutions. 0 Math convolutiuon, 1 cross-correlation convolution , 2 deconvolution, 3 depthwise convolution. 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 write 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 $(extk{s}_w,extk{s}_h)$, where :math:` ext{ks_w}` and :math:` ext{ks_h}` are the height and width of the convolution kernel, then the shape is $(C_{out},C_{in},extk{s}_w,extk{s}_h)$.

• 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’, ‘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’ not ‘NHWC’.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore.common import dtype as mstype
>>> import mindspore.ops.functional as F
>>> import mindspore.ops as ops
>>> dout = Tensor(np.ones([10, 32, 30, 30]), mstype.float32)
>>> weight = Tensor(np.ones([32, 32, 3, 3]), mstype.float32)
>>> input_x = Tensor(np.ones([10, 32, 32, 32]))
>>> conv2d_backprop_input = ops.Conv2DBackpropInput(out_channel=32, kernel_size=3)
>>> output = conv2d_backprop_input(dout, weight, F.shape(input_x))
>>> print(output.shape)
(10, 32, 32, 32)