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32 lines
906 B
Python
32 lines
906 B
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, dataset, 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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for i, b in tqdm(enumerate(dataset), total=n_val, desc='Validation round', unit='img'):
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img = b[0]
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true_mask = b[1]
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img = torch.from_numpy(img).unsqueeze(0)
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true_mask = torch.from_numpy(true_mask).unsqueeze(0)
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img = img.to(device=device)
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true_mask = true_mask.to(device=device)
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mask_pred = net(img).squeeze(dim=0)
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mask_pred = (mask_pred > 0.5).float()
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if net.n_classes > 1:
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tot += F.cross_entropy(mask_pred.unsqueeze(dim=0), true_mask.unsqueeze(dim=0)).item()
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else:
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tot += dice_coeff(mask_pred, true_mask.squeeze(dim=1)).item()
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return tot / n_val
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