REVA-QCAV/eval.py

30 lines
882 B
Python
Raw Normal View History

import torch
import torch.nn.functional as F
from tqdm import tqdm
from dice_loss import dice_coeff
2017-08-19 08:59:51 +00:00
def eval_net(net, loader, device, n_val):
"""Evaluation without the densecrf with the dice coefficient"""
net.eval()
2017-08-19 08:59:51 +00:00
tot = 0
for i, b in tqdm(enumerate(loader), desc='Validation round', unit='img'):
imgs = b['image']
true_masks = b['mask']
2017-08-19 08:59:51 +00:00
imgs = imgs.to(device=device, dtype=torch.float32)
true_masks = true_masks.to(device=device, dtype=torch.float32)
2017-08-19 08:59:51 +00:00
mask_pred = net(imgs)
2017-08-19 08:59:51 +00:00
for true_mask in true_masks:
mask_pred = (mask_pred > 0.5).float()
if net.n_classes > 1:
tot += F.cross_entropy(mask_pred.unsqueeze(dim=0), true_mask.unsqueeze(dim=0)).item()
else:
tot += dice_coeff(mask_pred, true_mask.squeeze(dim=1)).item()
return tot / n_val