Python class

ConvTranspose1d

`ConvTranspose1d`

class max.nn.ConvTranspose1d(length, in_channels, out_channels, dtype, stride=1, padding=0, dilation=1, output_padding=0, device=None, has_bias=False, permute=False, name=None)

source

Bases: Module

A 1D transposed convolution operator over an input image composed of several input planes.

When called, ConvTranspose1d accepts a TensorValue of shape (batch, length, in_channels) and returns a TensorValue of shape (batch, new_length, out_channels). If permute=True, the input and output follow PyTorch channel-first layout: (batch, in_channels, length) and (batch, out_channels, new_length).

conv = nn.ConvTranspose1d(
    in_channels,
    out_channels,
    kernel_size,
    stride,
    padding,
    output_padding,
    has_bias=False,
    name="conv3d_weight",
    device=DeviceRef.GPU(),
)

Initializes the ConvTranspose1d layer with weights and optional bias.

Parameters:

length (int) – The length of the convolution kernel.
in_channels (int) – Number of channels in the input image.
out_channels (int) – Number of channels produced by the convolution.
dtype (DType) – The data type for weights and bias.
stride (tuple[int, int]) – Stride of the convolution. Default: 1.
padding (tuple[int, int, int, int]) – Padding added to input. Default: 0.
dilation (tuple[int, int]) – Spacing between kernel elements. Default: 1.
output_padding (tuple[int, int]) – Additional size added to output shape. Default: 0.
device (DeviceRef | None) – The target device for computation.
has_bias (bool) – When True, adds a bias vector. Default: False.
permute (bool) – Whether to permute weights between PyTorch and MAX format.
name (str | None) – Base name for weights.

`bias`

bias: Weight | None = None

source

The optional bias vector stored on CPU with shape (out_channels,). Model init moves the bias to device if present.

`device`

device: DeviceRef | None

source

The device where matrix operations are performed.

`dilation`

dilation: tuple[int, int]

source

Not implemented yet. Assuming dilation = 1 for now.

`output_padding`

output_padding: tuple[int, int]

source

Type:: Additional size added to one side of the output shape. Default

`padding`

padding: tuple[int, int, int, int]

source

Controls the amount of padding applied before and after the input for depth, height, and width dimensions.

`permute`

permute: bool

source

bool controls whether self.weight is permuted from PyTorch order to max order. PyTorch order is: (in_channels, out_channels, kernel_length) Max API order: (kernel_length, out_channels, in_channels).

`stride`

stride: tuple[int, int]

source

Controls the stride for the cross-correlation.

`weight`

weight: Weight

source

The weight matrix stored on CPU with shape (kernel_length, out_channels, in_channels). Model init moves the weight to device.