Yes ptrblck, that’s what I got stuck with. The latent dimension is [Batch-size, 256, 2, 2] and my input has 2x2 which I am not sure how to give it as input. Secondly, if suppose I still consider keeping the feature extraction layers in my Resnet model, then how should I go about changing the input shape, would it be in bottleneck class or resnet class for resnet-50?
class Bottleneck(nn.Module):
expansion = 4

def __init__(self, in_planes, planes, stride=1):
    super(Bottleneck, self).__init__()
    self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
    self.bn2 = nn.BatchNorm2d(planes)
    self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
    self.bn3 = nn.BatchNorm2d(self.expansion*planes)

    self.shortcut = nn.Sequential()
    if stride != 1 or in_planes != self.expansion*planes:
        self.shortcut = nn.Sequential(
            nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(self.expansion*planes)
        )

def forward(self, x):
    out = F.relu(self.bn1(self.conv1(x)))
    out = F.relu(self.bn2(self.conv2(out)))
    out = self.bn3(self.conv3(out))
    out += self.shortcut(x)
    out = F.relu(out)
    return out

class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
    super(ResNet, self).__init__()
    self.in_planes = 64

    self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
    self.bn1 = nn.BatchNorm2d(64)
    self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
    self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
    self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
    self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
    self.linear = nn.Linear(512*block.expansion, num_classes)

def _make_layer(self, block, planes, num_blocks, stride):
    strides = [stride] + [1]*(num_blocks-1)
    layers = []
    for stride in strides:
        layers.append(block(self.in_planes, planes, stride))
        self.in_planes = planes * block.expansion
    return nn.Sequential(*layers)

def forward(self, x):
    out = F.relu(self.bn1(self.conv1(x)))
    out = self.layer1(out)
    out = self.layer2(out)
    out = self.layer3(out)
    out = self.layer4(out)
    out = F.avg_pool2d(out, 4)
    out = out.view(out.size(0), -1)
    out = self.linear(out)
    return out

def ResNet18():
    return ResNet(BasicBlock, [2,2,2,2])

def ResNet34():
    return ResNet(BasicBlock, [3,4,6,3])

def ResNet50():
    return ResNet(Bottleneck, [3,4,6,3])