mindspore.nn.Conv1dTranspose

class mindspore.nn.Conv1dTranspose(in_channels, out_channels, kernel_size, stride=1, pad_mode='same', padding=0, dilation=1, group=1, has_bias=False, weight_init=None, bias_init=None, dtype=mstype.float32)[source]

Calculates a 1D transposed convolution, which can be regarded as Conv1d for the gradient of the input, also called deconvolution (although it is not an actual deconvolution).

The input is typically of shape \((N, C_{in}, L_{in})\), where \(N\) is batch size, \(C_{in}\) is a number of channels and \(L_{in}\) is a length of sequence.

When Conv1d and ConvTranspose1d are initialized with the same parameters, and pad_mode is set to ‘pad’, \(dilation * (kernel\_size - 1) - padding\) amount of zero will be paded to both sizes of input, they are inverses of each other in regard to the input and output shapes in this case. However, when stride > 1, Conv1d maps multiple input shapes to the same output shape. Deconvolutional network can refer to Deconvolutional Networks.

Parameters

  • in_channels (int) – The channel number of the input tensor of the Conv1dTranspose layer.

  • out_channels (int) – The channel number of the output tensor of the Conv1dTranspose layer.

  • kernel_size (int) – Specifies the width of the 1D convolution kernel.

  • stride (int) – The movement stride of the 1D convolution kernel. Default: 1 .

  • pad_mode (str, optional) –

    Specifies the padding mode with a padding value of 0. It can be set to: "same" , "valid" or "pad" . Default: "same" .

    • "same": Pad the input at the begin and end so that the shape of input and output are the same when stride is set to 1. The amount of padding to is calculated by the operator internally. If the amount is even, it is uniformly distributed around the input, if it is odd, the excess padding is goes to the right side. If this mode is set, padding must be 0.

    • "valid": No padding is applied to the input, and the output returns the maximum possible length. Extra pixels that could not complete a full stride will be discarded. If this mode is set, padding must be 0.

    • "pad": Pad the input with a specified amount. In this mode, the amount of padding at the begin and end is determined by the padding parameter. If this mode is set, padding must be greater than or equal to 0.

  • padding (int) – The number of padding on both sides of input. The value should be greater than or equal to 0. Default: 0 .

  • dilation (int) – Dilation size of 1D convolution kernel. If \(k > 1\), the kernel is sampled every k elements. The value of k is in range of [1, L]. Default: 1 .

  • group (int) – Splits filter into groups, in_channels and out_channels must be divisible by group. When group > 1, the Ascend platform is not supported yet. Default: 1 .

  • has_bias (bool) – Whether the Conv1dTranspose layer has a bias parameter. Default: False.

  • weight_init (Union[Tensor, str, Initializer, numbers.Number]) – Initialization method of weight parameter. It can be a Tensor, a string, an Initializer or a numbers.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: None , weight will be initialized using HeUniform.

  • bias_init (Union[Tensor, str, Initializer, numbers.Number]) – Initialization method of bias parameter. Available initialization methods are the same as ‘weight_init’. Refer to the values of Initializer for more details. Default: None , bias will be initialized using Uniform.

  • dtype (mindspore.dtype) – Dtype of Parameters. Default: mstype.float32 .

Inputs:

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

Outputs:

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

pad_mode is 'same': \(L_{out} = \frac{ L_{in} + \text{stride} - 1 }{ \text{stride} }\)

pad_mode is 'valid': \(L_{out} = (L_{in} - 1) \times \text{stride} + \text{dilation} \times (\text{kernel_size} - 1) + 1\)

pad_mode is 'pad': \(L_{out} = (L_{in} - 1) \times \text{stride} - 2 \times \text{padding} + \text{dilation} \times (\text{kernel_size} - 1) + 1\)

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

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> net = nn.Conv1dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad')
>>> x = Tensor(np.ones([1, 3, 50]), mindspore.float32)
>>> output = net(x).shape
>>> print(output)
(1, 64, 53)