Attention layers net initialization

Sofia: <p>Hi guys, I have trouble with the following. I have images (3-channel) and corresponding masks (1-channel) which contains areas/pixels where I would like my classifier to focus on. The mask is passed through a simple CNN. It’s only purpose is to abstractify the mask (I do not intend to train it) in the same size as the corresponding classification CNN. The mask features need to be added to each image feature map. I made a simple schematic which hopefully makes this explanation clear.</p>
<p><img src="https://discuss.pytorch.org/uploads/default/original/3X/9/d/9dde5983e7a8eba2e1dfd480b914914cf7a7380f.jpeg" alt="Addition_schematic" data-base62-sha1="mwzlnws4w6VbxfbYsLghuPWQYXR" width="531" height="441"></p>
<p>In my attempt I try to avoid explicitly stating all additions manually because I want to be able to dynamically specify the amount of layers. A mwp can be found below, however, I encounter the issue that I am only able to add attention to the first conv block. I am running out of CUDA memory once I try to add attention to the second and last/classification block (see commented out sections). I think that the reason for this is that I am unintentionally also training the attention blocks. Is there a better and more efficient method of achieving my goal? I have tried to declare the “attentionbranch” in a seperate class, however I am not sure how to add the intermediate mask features to the image features in a single forward pass.</p>
<p>Any pointers are appreciated! Thanks!</p>
<pre><code class="lang-auto">    import torch
    import torch.nn as nn

    class BasicDiscriminator(nn.Module):
       
        def __init__(self, opt):
            super(BasicDiscriminator, self).__init__()
            isize = opt.isize
            nz = opt.nz
            nc = opt.nc
            ngf = opt.ngf
            ndf = opt.ndf
            n_extra_layers = opt.extralayers

            feat1 = nn.Sequential()
            feat2 = nn.Sequential()
            clas = nn.Sequential()

            att1 = nn.Sequential()
            att2 = nn.Sequential()
            clas2 = nn.Sequential()

            # input is nc x isize x isize
            feat1.add_module('initial-conv-{0}-{1}'.format(nc, ndf), nn.Conv2d(nc, ndf, 4, 2, 1, bias=False))
            feat1.add_module('initial-relu-{0}'.format(ndf), nn.LeakyReLU(0.2, inplace=True))

            # init Attention model
            att1.add_module('attention-conv2d-{0}-{1}'.format(1, ndf), nn.Conv2d(1, ndf, 4, 2, 1, bias=True))

            csize, cndf = isize / 2, ndf

            # Extra layers
            for t in range(n_extra_layers):
                feat2.add_module('extra-layers-{0}-{1}-conv'.format(t, cndf),
                                nn.Conv2d(cndf, cndf, 3, 1, 1, bias=False))
                feat2.add_module('extra-layers-{0}-{1}-batchnorm'.format(t, cndf),
                                nn.BatchNorm2d(cndf))
                feat2.add_module('extra-layers-{0}-{1}-relu'.format(t, cndf),
                                nn.LeakyReLU(0.2, inplace=True))
                att2.add_module('attention_extra_layers-conv2d-{0}-{1}'.format(t, ndf),
                                nn.Conv2d(cndf, cndf, 3, 1, 1, bias=True))

            while csize &gt; 4:
                in_feat = cndf
                out_feat = cndf * 2
                feat2.add_module('pyramid-{0}-{1}-conv'.format(in_feat, out_feat),
                                nn.Conv2d(in_feat, out_feat, 4, 2, 1, bias=False))
                feat2.add_module('pyramid-{0}-batchnorm'.format(out_feat),
                                nn.BatchNorm2d(out_feat))
                feat2.add_module('pyramid-{0}-relu'.format(out_feat),
                                nn.LeakyReLU(0.2, inplace=True))
                att2.add_module('attention-pyramid-conv2d-{0}-{1}'.format(1, out_feat),
                                nn.Conv2d(in_feat, out_feat, stride=2, kernel_size=3, padding=1, bias=True))
                cndf = cndf * 2
                csize = csize / 2

            feat2.add_module('final-{0}-{1}-conv'.format(cndf, 1),
                                nn.Conv2d(cndf, nz, 4, 1, 0, bias=False))
            att2.add_module('attention-final-{0}-{1}-conv'.format(cndf, 1),
                            nn.Conv2d(cndf, nz, 4, 1, 0, bias=True))

            clas.add_module('classifier', nn.Conv2d(nz, 1, 3, 1, 1, bias=False))
            clas.add_module('Sigmoid', nn.Sigmoid())

            clas2.add_module('attention-classifier', nn.Conv2d(nz, 1, 3, 1, 1, bias=False))

            self.feat1 = feat1
            self.feat2 = feat2
            self.clas = clas

            self.att1 = att1
            self.att2 = att2
            self.clas2 = clas2

        def forward(self, input, mask):
            # Add attention to initial conv block
            feat1 = input
            for layer in self.feat1:
                # add attention to conv layer only
                if isinstance(layer, nn.Conv2d):
                    feat1 = layer(feat1)
                    attention1 = self.att1(mask) # extract features of att block
                    feat1 = feat1 + attention1
                else:
                    feat1 = layer(feat1)

            # # add attention to second conv block
            # feat2 = feat1
            # attention2 = attention1
            # counter = 0
            # for layer in self.feat2:
            #     if isinstance(layer, nn.Conv2d):
            #         feat2 = layer(feat2)
            #         attention2 = self.att2[counter](attention2)
            #         feat2 = feat2 + attention2
            #         counter += 1
            #     else:
            #         feat2 = layer(feat2)

            # # add attention to classifying block
            # clas = feat2
            # for layer in self.clas:
            #     if isinstance(layer, nn.Conv2d):
            #         clas = layer(clas)
            #         clas2 = self.clas2(attention2)
            #         clas = clas + clas2
            #     else:
            #         clas = layer(clas)

            feat2 = self.feat2(feat1)
            clas = self.clas(feat2)

            clas = clas.view(-1, 1).squeeze(1)
            return clas, feat2
</code></pre>

Kushaj: <p>For the layers/modules that you do not want to train, you can set their <code>requires_grad=False</code> which would also save you GPU memory. To make your modules non-trainable you can use <code>{model}.{submodule_name}.requires_grad_(False)</code>.</p>

Sofia: <p>Thank you that seems to work!</p>