about the size of prototype.

[ShifuShen]

Thanks for your great work and code sharing. Here is one confusing I concerted.

I saw you define the objective_vectors in models/adaptation_modelv2.py with a size of 256 self.objective_vectors = torch.zeros([self.class_numbers, 256]) self.objective_vectors_num = torch.zeros([self.class_numbers])

but I found that the channel of feature maps is 2048 in layer4. self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, BatchNorm=BatchNorm) self.layer5 = self._make_pred_layer(Classifier_Module2, 2048, [6, 12, 18, 24], [6, 12, 18, 24], num_classes)

and in your command, you just use the output feature of layer4 as out['feat'] ` def forward(self, x, ssl=False, lbl=None): , , h, w = x.size() x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) if self.bn_clr: x = self.bn_pretrain(x)

    out = self.layer5(x, get_feat=True)
    # out = dict()
    # out['feat'] = x
    # x = self.layer5(x)

    # if not ssl:
    #     x = nn.functional.upsample(x, (h, w), mode='bilinear', align_corners=True)
    #     if lbl is not None:
    #         self.loss = self.CrossEntropy2d(x, lbl)    
    # out['out'] = x
    return out`

so....which is the correct size of prototype and if it's 256 how to get the feature~

[tudragon154203]

Hi ShifuShen,

I don't belong to the author team, but I might know the answer for your confusion.

The feature is extracted right before the last layer of Classifier_Module2 in deeplabv2.py. If you look at the forward function of Classifier_Module2, you might see that: out['feat'] is input of layer nn.Conv2d(256, num_classes, kernel_size=1, padding=0, dilation=1, bias=False)

So it has 256 kernels in the end.

[ShifuShen]

Hi ShifuShen,

I don't belong to the author team, but I might know the answer for your confusion.

The feature is extracted right before the last layer of Classifier_Module2 in deeplabv2.py. If you look at the forward function of Classifier_Module2, you might see that: out['feat'] is input of layer nn.Conv2d(256, num_classes, kernel_size=1, padding=0, dilation=1, bias=False)

So it has 256 kernels in the end.

Got it Thanks ~