AnswerBun.com

How can I implement 2D CNN filter with channelwise-bound kernel weights?

Artificial Intelligence Asked by petsol on October 12, 2020

I would like to bind kernel parameters through channels/feature-maps for each filter. In a conv2d operation, each filter consists of HxWxC parameters I would like to have filters that have HxW parameters, but the same (HxWxC) form.

The scenario I have is that I have 4 gray pictures of bulb samples (yielding similar images from each side), which I overlay as channels, but a possible failure that needs to be detected might only appear on one side (a bulb has 4 images and a single classification). The rotation of the object when the picture is taken is arbitrary. Now I solve this by shuffling the channels at training, but it would be more efficient if I could just bind the kernel parameters. Pytorch and Tensorflow solutions are both welcome.

One Answer

Assuming you want HxWx1 kernel to perform convolution on hxwxc images.

Here's sample code which uses single channel kernel to operate on multichannel feature: maps

import torch
import torch.nn as nn
import torch.nn.functional as F


class model(nn.Module):
    def __init__(self, in_ch=4):
        super().__init__()
        self.in_ch  = in_ch
        # single channel kernel initialization
        self.kernel = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=1, stride=1)

    def forward(self, x):
        bs, ch, w, h = x.shape
        x   = x.view((bs, ch, 1, w, h))
        out = self.kernel(x[:, 0])
        # reusing of same kernel
        for i in range(1, self.in_ch):
            out = torch.cat((out, self.kernel(x[:, i])), 1)
        return out

net = model(4)
print(net)
inp = torch.randn((10, 4, 100, 100))
out = net(inp)
print(out.shape)

(The main hack is in the forward function)

Answered by Girish Dattatray Hegde on October 12, 2020

Add your own answers!

Related Questions

Ask a Question

Get help from others!

© 2022 AnswerBun.com. All rights reserved. Sites we Love: PCI Database, MenuIva, UKBizDB, Menu Kuliner, Sharing RPP