mirror of
https://github.com/Laurent2916/REVA-QCAV.git
synced 2024-09-19 11:35:28 +00:00
33 lines
1 KiB
Python
33 lines
1 KiB
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):
|
|
"""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
|
|
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
|