patch_first_conv

tiatoolbox . models . architecture . timm_efficientnet . patch_first_conv

patch_first_conv(model, new_in_channels, default_in_channels=3, *, pretrained=True)

Update the first convolution layer for a new input channel size.

This function updates the first convolutional layer of a model to handle arbitrary input channels. It optionally reuses pretrained weights or initializes weights randomly.

Parameters:

• model (nn.Module) – The neural network model whose first convolution layer will be patched.

• new_in_channels (int) – Number of input channels for the new first layer.

• default_in_channels (int) – Original number of input channels. Defaults to 3.

• pretrained (bool) – Whether to reuse pretrained weights. Defaults to True.

Return type:

None

Notes

• If new_in_channels == 1 or 2 → reuse original weights.

• If new_in_channels > 3 → initialize weights using Kaiming normal.

Example

>>> patch_first_conv(model, new_in_channels=1, pretrained=True)