mindspore.ops.Conv3D

class mindspore.ops.Conv3D(out_channel, kernel_size, mode=1, pad_mode='valid', pad=0, stride=1, dilation=1, group=1, data_format='NCDHW')

3D convolution layer.

Applies a 3D convolution over an input tensor which is typically of shape \((N, C_{in}, D_{in}, H_{in}, W_{in})\) and output shape \((N, C_{out}, D_{out}, H_{out}, W_{out})\). Where \(N\) is batch size, \(C\) is channel number, \(D\) is depth, \(H\) is height, \(W\) is width. the formula is defined as:

\[\operatorname{out}\left(N_{i}, C_{\text {out}_j}\right)=\operatorname{bias}\left(C_{\text {out}_j}\right)+ \sum_{k=0}^{C_{in}-1} ccor(\text {weight}\left(C_{\text {out}_j}, k\right), \operatorname{input}\left(N_{i}, k\right))\]

where \(k\) is kernel, \(ccor\) is the cross-correlation operator.

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

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 3 integers. Specifies the depth, height and width of the 3D convolution window. Single int means the value is for the depth, height and width of the kernel. A tuple of 3 ints means the first value is for the depth, height and the other is for the width of the kernel.

• mode (int) – Modes for different convolutions. It is currently not used. Default: 1.

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

• pad_mode (str) –

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

  • same: Adopts the way of completion. The depth, height and width of the output will be equal to the input x divided by stride. The padding will be evenly calculated in head and tail, top and bottom, left and right directions possiblily. Otherwise, the last extra padding will be calculated from the tail, bottom and the right side. If this mode is set, pad must be 0.

  • valid: Adopts the way of discarding. The possible largest depth, 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 in depth, height and width. 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])) – The pad value to be filled. Default: 0. If pad is an integer, the paddings of head, tail, top, bottom, left and right are the same, equal to pad. If pad is a tuple of six integers, the padding of head, tail, top, bottom, left and right equal to pad[0], pad[1], pad[2], pad[3], pad[4] and pad[5] correspondingly.

• dilation (Union[int, tuple[int]]) – The data type is int or a tuple of 3 integers \((dilation_d, dilation_h, dilation_w)\). Currently, dilation on depth only supports the case of 1. Specifies the dilation rate to use for dilated convolution. If set \(k > 1\), there will be \(k - 1\) pixels skipped for each sampling location. Its value must be greater than or equal to 1 and bounded by the height and width of the input. Default: 1.

• group (int) – Splits filter into groups, in_channels and out_channels must be divisible by the number of groups. Default: 1. Only 1 is currently supported.

• data_format (str) – The optional value for data format. Currently only support “NCDHW”.

Inputs:

• x (Tensor) - Tensor of shape \((N, C_{in}, D_{in}, H_{in}, W_{in})\). Currently input data type only support float16 and float32.

• weight (Tensor) - Set size of kernel is \((k_d, K_h, K_w)\), then the shape is \((C_{out}, C_{in}/groups, k_d, K_h, K_w)\). Currently weight data type only support float16 and float32.

• bias (Tensor) - Tensor of shape \(C_{in}\). Currently, only support none.

Outputs:

Tensor, the value that applied 3D convolution. The shape is \((N, C_{out}, D_{out}, H_{out}, W_{out})\).

Raises

• TypeError – If out_channel or group is not an int.

• TypeError – If kernel_size, stride, pad or dilation is neither an int nor a tuple.

• ValueError – If out_channel, kernel_size, stride or dilation is less than 1.

• ValueError – If pad is less than 0.

• ValueError – If pad_mode is not one of ‘same’, ‘valid’ or ‘pad’.

• ValueError – If pad is a tuple whose length is not equal to 6.

• ValueError – If pad_mode is not equal to ‘pad’ and pad is not equal to (0, 0, 0, 0, 0, 0).

• ValueError – If data_format is not ‘NCDHW’.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.ones([16, 3, 10, 32, 32]), mindspore.float16)
>>> weight = Tensor(np.ones([32, 3, 4, 3, 3]), mindspore.float16)
>>> conv3d = ops.Conv3D(out_channel=32, kernel_size=(4, 3, 3))
>>> output = conv3d(x, weight)
>>> print(output.shape)
(16, 32, 7, 30, 30)