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27 lines
658 B
Python
27 lines
658 B
Python
import torch
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import torch.nn.functional as F
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from dice_loss import dice_coeff
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def eval_net(net, dataset, gpu=False):
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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 enumerate(dataset):
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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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if gpu:
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img = img.cuda()
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true_mask = true_mask.cuda()
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mask_pred = net(img)[0]
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mask_pred = (F.sigmoid(mask_pred) > 0.5).float()
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tot += dice_coeff(mask_pred, true_mask).item()
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return tot / i
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