MaskedConv2d¶
- class torch_uncertainty.layers.MaskedConv2d(in_channels, out_channels, kernel_size, num_estimators, scale, stride=1, padding=0, dilation=1, groups=1, bias=True, device=None, dtype=None)[source]¶
Masksembles-style Conv2d layer.
- Parameters:
in_channels (int) – Number of channels in the input image.
out_channels (int) – Number of channels produced by the convolution.
kernel_size (int or tuple) – Size of the convolving kernel.
num_estimators (int) – Number of estimators in the ensemble.
scale (float) – The scale parameter for the masks.
stride (int or tuple, optional) – Stride of the convolution. Defaults to
1.padding (int, tuple or str, optional) – Padding added to all four sides of the input. Defaults to
0.dilation (int or tuple, optional) – Spacing between kernel elements. Defaults to
1.groups (int, optional) – Number of blocked connexions from input channels to output channels for each estimator. Defaults to
1.bias (bool, optional) – If
True, adds a learnable bias to the output. Defaults toTrue.device (Any, optional) – The desired device of returned tensor. Defaults to
None.dtype (Any, optional) – The desired data type of returned tensor. Defaults to
None.
Warning
Be sure to apply a repeat on the batch at the start of the training if you use MaskedConv2d.
- Reference:
Masksembles for Uncertainty Estimation, Nikita Durasov, Timur Bagautdinov, Pierre Baque, Pascal Fua.