def sub_forward(self, x)
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
print("ok")
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
print("ok1")
return x
def forward(self, x, labels=None, return_cam=False):
batch_size = x.shape[0]
x1 = self.sub_forward(x)
x1 = self.conv6(x1)
x1 = self.relu(x1)
x1 = self.conv7(x1)
x1 = self.relu(x1)
x2 = self.sub_forward(x)
x2 = self.conv8(x2)
x2 = self.relu(x2)
x2 = self.conv9(x2)
x2 = self.relu(x2)
x = x1 + x2
if return_cam:
normalized_feature_map = normalize_tensor(x.detach().clone())
cams = normalized_feature_map[range(batch_size), labels]
return cams
x = self.avgpool(x)
x = x.view(x.size(0), -1)
print("ok2")
return {'logits': x}
I am training a resnet model. I can not change the architecture of the model, nor can I reduce the batch size. Is there anything else I can do to avoid this error?
Output:
ok
ok1
ok
ok1
ok2
ok
It can be seen that it completes the first forward pass but fails in second forward pass
