mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-11-08 22:42:02 +00:00
32 lines
1,012 B
Python
32 lines
1,012 B
Python
import torch
|
|
import torch.nn.functional as F
|
|
from tqdm import tqdm
|
|
|
|
from dice_loss import dice_coeff
|
|
|
|
|
|
def eval_net(net, loader, device, n_val):
|
|
"""Evaluation without the densecrf with the dice coefficient"""
|
|
net.eval()
|
|
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
|