mindspore.nn.Conv1d

class mindspore.nn.Conv1d(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')[source]

1D convolution layer.

Applies a 1D convolution over an input tensor which is typically of shape \((N, C_{in}, W_{in})\), where \(N\) is batch size and \(C_{in}\) is channel number. For each batch of shape \((C_{in}, W_{in})\), 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 \((\text{ks_w})\), where \(\text{ks_w}\) is the width of the convolution kernel. The full kernel has shape \((C_{out}, C_{in} // \text{group}, \text{ks_w})\), where group is the group number to split the input x in the channel dimension.

If the ‘pad_mode’ is set to be “valid”, the output width will be \(\left \lfloor{1 + \frac{W_{in} + 2 \times \text{padding} - \text{ks_w} - (\text{ks_w} - 1) \times (\text{dilation} - 1) }{\text{stride}}} \right \rfloor\) respectively.

The first introduction of convolution layer can be found in paper Gradient Based Learning Applied to Document Recognition.

Parameters

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

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

  • kernel_size (int) – The data type is int. Specifies the width of the 1D convolution window.

  • stride (int) – The distance of kernel moving, an int number that represents the width of movement. Default: 1.

  • pad_mode (str) –

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

    • same: Adopts the way of completion. The output width will be the same as the input x. The total number of padding will be calculated in the horizontal direction and evenly distributed to 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, padding must be 0.

    • valid: Adopts the way of discarding. The possible largest width of the 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 x. The number of padding will be padded to the input Tensor borders. padding must be greater than or equal to 0.

  • padding (int) – Implicit paddings on both sides of the input x. Default: 0.

  • dilation (int) – The data type is int. 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 filter into groups, in_ channels and out_channels must be divisible by the number of groups. Default: 1.

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

  • weight_init (Union[Tensor, str, Initializer, numbers.Number]) – An 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’.

Inputs:

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

Outputs:

Tensor of shape \((N, C_{out}, W_{out})\).

Raises

  • TypeError – If in_channels, out_channels, kernel_size, stride, padding or dilation is not an int.

  • ValueError – If in_channels, 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’.

Supported Platforms:

Ascend GPU CPU

Examples

>>> net = nn.Conv1d(120, 240, 4, has_bias=False, weight_init='normal')
>>> x = Tensor(np.ones([1, 120, 640]), mindspore.float32)
>>> output = net(x).shape
>>> print(output)
(1, 240, 640)