mindspore.nn.Conv3d

class mindspore.nn.Conv3d(in_channels, out_channels, kernel_size, stride=1, pad_mode='same', padding=0, dilation=1, group=1, has_bias=False, weight_init='normal', bias_init='zeros', data_format='NCDHW')[source]

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. 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 \(ccor\) is the cross-correlation operator.

If the ‘pad_mode’ is set to be “valid”, the output depth, height and width will be \(\left \lfloor{1 + \frac{D_{in} + \text{padding[0]} + \text{padding[1]} - \text{kernel_size[0]} - (\text{kernel_size[0]} - 1) \times (\text{dilation[0]} - 1) }{\text{stride[0]}}} \right \rfloor\) and \(\left \lfloor{1 + \frac{H_{in} + \text{padding[2]} + \text{padding[3]} - \text{kernel_size[1]} - (\text{kernel_size[1]} - 1) \times (\text{dilation[1]} - 1) }{\text{stride[1]}}} \right \rfloor\) and \(\left \lfloor{1 + \frac{W_{in} + \text{padding[4]} + \text{padding[5]} - \text{kernel_size[2]} - (\text{kernel_size[2]} - 1) \times (\text{dilation[2]} - 1) }{\text{stride[2]}}} \right \rfloor\) respectively.

Parameters

  • in_channels (int) – The number of input channel \(C_{in}\).

  • out_channels (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 the width of the kernel. A tuple of 3 ints means the first value is for the depth, second value is for height and the other is for the width of the kernel.

  • 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”, “pad”. Default: “same”.

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

    • pad: Implicit paddings on both sides of the input in depth, height, width. The number of padding will be padded to the input Tensor borders. padding must be greater than or equal to 0.

  • padding (Union(int, tuple[int])) – Implicit paddings on both sides of the input. The data type is int or a tuple of 6 integers. Default: 0. If padding is an integer, the paddings of head, tail, top, bottom, left and right are the same, equal to padding. If paddings is a tuple of six integers, the padding of head, tail, top, bottom, left and right equal to padding[0], padding[1], padding[2], padding[3], padding[4] and padding[5] correspondingly.

  • dilation (Union[int, tuple[int]]) – The data type is int or a tuple of 3 integers : math:(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 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. 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.

  • has_bias (bool) – Specifies whether the layer uses a bias vector. Default: False.

  • weight_init (Union[Tensor, str, Initializer, numbers.Number]) – Initializer for the convolution kernel. It can be a Tensor, a string, an Initializer or a number. When a string is specified, values from ‘TruncatedNormal’, ‘Normal’, ‘Uniform’, ‘HeUniform’ and ‘XavierUniform’ distributions as well as constant ‘One’ and ‘Zero’ distributions are possible. Alias ‘xavier_uniform’, ‘he_uniform’, ‘ones’ and ‘zeros’ are acceptable. Uppercase and lowercase are both acceptable. Refer to the values of Initializer for more details. Default: ‘normal’.

  • bias_init (Union[Tensor, str, Initializer, numbers.Number]) – Initializer for the bias vector. Possible Initializer and string are the same as ‘weight_init’. Refer to the values of Initializer for more details. Default: ‘zeros’.

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

Inputs:

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

Outputs:

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

Raises

  • TypeError – If in_channels, out_channels or group is not an int.

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

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

  • ValueError – If padding is less than 0.

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

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

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

  • ValueError – If data_format is not ‘NCDHW’.

Supported Platforms:

Ascend

Examples

>>> input = Tensor(np.ones([16, 3, 10, 32, 32]), mindspore.float32)
>>> conv3d = nn.Conv3d(in_channels=3, out_channels=32, kernel_size=(4, 3, 3))
>>> output = conv3d(input)
>>> print(output.shape)
(16, 32, 10, 32, 32)