mindspore.ops.Conv2D

class mindspore.ops.Conv2D(out_channel, kernel_size, mode=1, pad_mode='valid', pad=0, stride=1, dilation=1, group=1, data_format='NCHW')[source]

2D convolution layer.

Applies a 2D convolution over an input tensor which is typically of shape \((N, C_{in}, H_{in}, W_{in})\), where \(N\) is batch size, \(C\) is channel number, \(H\) is height, \(W\) is width, \(X_i\) is the \(i^{th}\) input value and \(b_i\) indicates the deviation value of the \(i^{th}\) input value. For each batch of shape \((C_{in}, H_{in}, W_{in})\), the formula is defined as:

\[out_j = \sum_{i=0}^{C_{in} - 1} ccor(W_{ij}, X_i) + b_j,\]

where \(ccor\) is the cross correlation operator, \(C_{in}\) is the input channel number, \(j\) ranges from \(0\) to \(C_{out} - 1\), \(W_{ij}\) corresponds to the \(i\)-th channel of the \(j\)-th filter and \(out_{j}\) corresponds to the \(j\)-th channel of the output. \(W_{ij}\) is a slice of kernel and it has shape \((\text{kernel_size[0]}, \text{kernel_size[1]})\), where \(\text{kernel_size[0]}\) and \(\text{kernel_size[1]}\) are the height and width of the convolution kernel. The full kernel has shape \((C_{out}, C_{in} // \text{group}, \text{kernel_size[0]}, \text{kernel_size[1]})\), where group is the group number to split the input in the channel dimension.

If the ‘pad_mode’ is set to be “valid”, the output height and width will be \(\left \lfloor{1 + \frac{H_{in} + \text{padding[0]} + \text{padding[1]} - \text{kernel_size[0]} - (\text{kernel_size[0]} - 1) \times (\text{dilation[0]} - 1) }{\text{stride[0]}}} \right \rfloor\) and \(\left \lfloor{1 + \frac{W_{in} + \text{padding[2]} + \text{padding[3]} - \text{kernel_size[1]} - (\text{kernel_size[1]} - 1) \times (\text{dilation[1]} - 1) }{\text{stride[1]}}} \right \rfloor\) respectively. Where \(dialtion\) is Spacing between kernel elements, \(stride\) is The step length of each step, \(padding\) is zero-padding added to both sides of the input.

The first introduction can be found in paper Gradient Based Learning Applied to Document Recognition. More detailed introduction can be found here: http://cs231n.github.io/convolutional-networks/.

Parameters

  • out_channel (int) – The number of output channel \(C_{out}\).

  • kernel_size (Union[int, tuple[int]]) – The data type is int or a tuple of 2 integers. Specifies the height and width of the 2D convolution window. Single int means the value is for both the height and the width of the kernel. A tuple of 2 ints means the first value is for the height and the other is for the width of the kernel.

  • mode (int) – Modes for different convolutions. 0 Math convolutiuon, 1 cross-correlation convolution , 2 deconvolution, 3 depthwise convolution. Default: 1.

  • pad_mode (str) –

    Specifies padding mode. The optional values are “same”, “valid”, “pad”. Default: “valid”.

    • same: Adopts the way of completion. The height and width of the output will be the same as the input x. The total number of padding will be calculated in horizontal and vertical directions and evenly distributed to top and bottom, left and right if possible. Otherwise, the last extra padding will be done from the bottom and the right side. If this mode is set, pad must be 0.

    • valid: Adopts the way of discarding. The possible largest height and width of output will be returned without padding. Extra pixels will be discarded. If this mode is set, pad must be 0.

    • pad: Implicit paddings on both sides of the input x. The number of pad will be padded to the input Tensor borders. pad must be greater than or equal to 0.

  • pad (Union(int, tuple[int])) – Implicit paddings on both sides of the input x. If pad is one integer, the paddings of top, bottom, left and right are the same, equal to pad. If pad is a tuple with four integers, the paddings of top, bottom, left and right will be equal to pad[0], pad[1], pad[2], and pad[3] accordingly. Default: 0.

  • stride (Union(int, tuple[int])) – The distance of kernel moving, an int number that represents the height and width of movement are both strides, or a tuple of two int numbers that represent height and width of movement respectively. Default: 1.

  • dilation (Union(int, tuple[int])) – The data type is int or a tuple of 2 integers. Specifies the dilation rate to use for dilated convolution. If set to be \(k > 1\), there will be \(k - 1\) pixels skipped for each sampling location. Its value must be greater or equal to 1 and bounded by the height and width of the input x. Default: 1.

  • group (int) – Splits input into groups. Default: 1.

  • data_format (str) – The optional value for data format, is ‘NHWC’ or ‘NCHW’. Default: “NCHW”.

Inputs:

  • x (Tensor) - Tensor of shape \((N, C_{in}, H_{in}, W_{in})\).

  • weight (Tensor) - Set size of kernel is \((\text{kernel_size[0]}, \text{kernel_size[1]})\), then the shape is \((C_{out}, C_{in}, \text{kernel_size[0]}, \text{kernel_size[1]})\).

Outputs:

Tensor, the value that applied 2D convolution. The shape is \((N, C_{out}, H_{out}, W_{out})\).

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 pad 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

>>> x = Tensor(np.ones([10, 32, 32, 32]), mindspore.float32)
>>> weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32)
>>> conv2d = ops.Conv2D(out_channel=32, kernel_size=3)
>>> output = conv2d(x, weight)
>>> print(output.shape)
(10, 32, 30, 30)