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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)

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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).

from max.driver import Accelerator, CPU, accelerator_count
from max.dtype import DType
from max.graph import DeviceRef
from max.nn import ConvTranspose1d

device = Accelerator() if accelerator_count() > 0 else CPU()
device_ref = DeviceRef.from_device(device)

conv = ConvTranspose1d(
    length=3,
    in_channels=64,
    out_channels=128,
    dtype=DType.float32,
    stride=1,
    padding=0,
    output_padding=0,
    has_bias=False,
    name="conv_transpose1d_weight",
    device=device_ref,
)

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

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

device

device: DeviceRef | None

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The device where matrix operations are performed.

dilation

dilation: tuple[int, int]

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Not implemented yet. Assuming dilation = 1 for now.

output_padding

output_padding: tuple[int, int]

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Additional size added to one side of the output shape. Default: 0

padding

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

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

permute

permute: bool

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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]

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Controls the stride for the cross-correlation.

weight

weight: Weight

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