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33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
import torch
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import torch.nn.functional as F
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from tqdm import tqdm
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from dice_loss import dice_coeff
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def eval_net(net, loader, device, n_val):
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"""Evaluation without the densecrf with the dice coefficient"""
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net.eval()
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tot = 0
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with tqdm(total=n_val, desc='Validation round', unit='img', leave=False) as pbar:
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for batch in loader:
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imgs = batch['image']
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true_masks = batch['mask']
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imgs = imgs.to(device=device, dtype=torch.float32)
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mask_type = torch.float32 if net.n_classes == 1 else torch.long
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true_masks = true_masks.to(device=device, dtype=mask_type)
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mask_pred = net(imgs)
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for true_mask, pred in zip(true_masks, mask_pred):
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pred = (pred > 0.5).float()
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if net.n_classes > 1:
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tot += F.cross_entropy(pred.unsqueeze(dim=0), true_mask.unsqueeze(dim=0)).item()
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else:
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tot += dice_coeff(pred, true_mask.squeeze(dim=1)).item()
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pbar.update(imgs.shape[0])
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return tot / n_val
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