Is there any classification code?

[JamesQFreeman]

[ctlin001]

What I'm doing is replace the FC layers to my classification layers, although the performance was not good. Happy to discuss more if you are interested in.

[JasperHG90]

I used this:

class MedicalNet(nn.Module):

  def __init__(self, path_to_weights, device):
    super(MedicalNet, self).__init__()
    self.model = resnet34(sample_input_D=1, sample_input_H=112, sample_input_W=112, num_seg_classes=2)
    self.model.conv_seg = nn.Sequential(
        nn.AdaptiveMaxPool3d(output_size=(1, 1, 1)),
        nn.Flatten(start_dim=1),
        nn.Dropout(0.1)
    )
    net_dict = self.model.state_dict()
    pretrained_weights = torch.load(path_to_weights, map_location=torch.device(device))
    pretrain_dict = {
        k.replace("module.", ""): v for k, v in pretrained_weights['state_dict'].items() if k.replace("module.", "") in net_dict.keys()
      }
    net_dict.update(pretrain_dict)
    self.model.load_state_dict(net_dict)
    self.fc = nn.Linear(512, 1)

  def forward(self, x):
    features = self.model(x)
    return self.fc(features)

Then:

model = MedicalNet(path_to_weights="pretrain/resnet_34.pth", device=device)

for param_name, param in model.named_parameters():
  if param_name.startswith("conv_seg"):
    param.requires_grad = True
  else:
    param.requires_grad = False

[Batush123]

Hi @JasperHG90 Do you have the code for training classification? because in train.py there are some parts that connected to the segmentation for example masks etc

[JasperHG90]

@Batush123 I'm not entirely sure what you're asking for. Are you asking me what my input data & training loop look like?

[wizofe]

Hi @JasperHG90, I am looking at a similar problem and I would be glad if you could share your code including the data/training loop, if possible. Thanks!

[alexcla99]

Hello @JasperHG90, same here, would you be able to share your data/training loop? Thank you very much :)