Comments (2)
I have used the following in the training file. You can then write the generalized_energy_distance function (as explained in the original paper) to properly evaluate the network. In addition, you need to supply all four masks from the dataloader. Good luck!
iou_score = 0
ged_score = 0
for val_step, (patch, _, _, masks, _) in enumerate(val_loader):
masks = torch.squeeze(masks,0)
patch = patch.to(device)
net.forward(patch, segm=None)
num_preds = 4
predictions = []
for i in range(num_preds):
mask_pred = net.sample(testing=True)
mask_pred = (torch.sigmoid(mask_pred) > 0.5).float()
mask_pred = torch.squeeze(mask_pred, 0)
predictions.append(mask_pred)
predictions = torch.cat(predictions, 0)
iou_score_iter, ged_score_iter = generalized_energy_distance_iou(predictions, masks)
iou_score += iou_score_iter
ged_score += ged_score_iter
ged = ged_score/len(val_dataset)
iou = iou_score/len(val_dataset)
from probabilistic-unet-pytorch.
Thank you!
from probabilistic-unet-pytorch.
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from probabilistic-unet-pytorch.