REVA-QCAV/eval.py

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