There is such a model (ResNet18 + GAT):
class AntispoofModel(nn.Module):
def __init__(self, device="cpu", **kwargs):
super().__init__()
resnet = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
self.resnet = nn.Sequential(*[i for i in list(resnet.children())[:-2]]).to(device)
for ch in self.resnet.children():
for param in ch.parameters():
param.requires_grad = False
self.gat = GAT(**kwargs).to(device)
self.device = device
self.adj = torch.tensor(grid_to_graph(7, 7, return_as=np.ndarray)).to(device)
def forward(self, x):
x = self.resnet(x.to(self.device))
x = x.view(-1, 49, 512)
#adj = torch.stack([self.adj for i in range(x.shape[0])]).to(self.device)
x = self.gat(x, self.adj)
return torch.sigmoid(x)
I’m trying to do the same thing, but without GAT, for example I rewrite code like:
class AntispoofModel(nn.Module):
def __init__(self, device="cpu", **kwargs):
super().__init__()
model_resnet = ResNet(BasicBlock, [2, 2, 2, 2])
self.conv1 = model_resnet.conv1
self.bn1 = model_resnet.bn1
self.relu = model_resnet.relu
self.maxpool = model_resnet.maxpool
self.layer1 = model_resnet.layer1
self.layer2 = model_resnet.layer2
self.layer3 = model_resnet.layer3
self.layer4 = model_resnet.layer4
self.avgpool = nn.AdaptiveAvgPool2d(output_size=1)
def forward(self, x):
feature = self.conv1(x)
feature = self.bn1(feature)
feature = self.relu(feature)
feature = self.maxpool(feature)
feature = self.layer1(feature)
feature = self.layer2(feature)
feature = self.layer3(feature)
feature = self.layer4(feature)
out = self.avgpool(feature) #[,512,1,1]
out = out.view(out.size(0), -1) #[,512]
return out
But I have Using a target size (torch.Size([64, 1])) that is different to the input size (torch.Size([64, 512])) is deprecated. Please ensure they have the same size.
I understand what it means. But I don’t understand how to fix it…