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34 lines
1 KiB
Python
34 lines
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):
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"""Evaluation without the densecrf with the dice coefficient"""
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net.eval()
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mask_type = torch.float32 if net.n_classes == 1 else torch.long
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n_val = len(loader) # the number of batch
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tot = 0
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with tqdm(total=n_val, desc='Validation round', unit='batch', leave=False) as pbar:
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for batch in loader:
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imgs, true_masks = batch['image'], batch['mask']
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imgs = imgs.to(device=device, dtype=torch.float32)
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true_masks = true_masks.to(device=device, dtype=mask_type)
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with torch.no_grad():
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mask_pred = net(imgs)
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if net.n_classes > 1:
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tot += F.cross_entropy(mask_pred, true_masks).item()
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else:
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pred = torch.sigmoid(mask_pred)
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pred = (pred > 0.5).float()
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tot += dice_coeff(pred, true_masks).item()
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pbar.update()
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net.train()
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return tot / n_val
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