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