REVA-QCAV/evaluate.py

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import torch
import torch.nn.functional as F
from tqdm import tqdm
from utils.dice_score import multiclass_dice_coeff, dice_coeff
def evaluate(net, dataloader, device):
net.eval()
num_val_batches = len(dataloader)
dice_score = 0
# iterate over the validation set
for batch in tqdm(dataloader, total=num_val_batches, desc="Validation round", unit="batch", leave=False):
image, mask_true = batch["image"], batch["mask"]
# move images and labels to correct device and type
image = image.to(device=device, dtype=torch.float32)
mask_true = mask_true.to(device=device, dtype=torch.long)
mask_true = F.one_hot(mask_true, net.n_classes).permute(0, 3, 1, 2).float()
with torch.no_grad():
# predict the mask
mask_pred = net(image)
# convert to one-hot format
if net.n_classes == 1:
mask_pred = (F.sigmoid(mask_pred) > 0.5).float()
# compute the Dice score
dice_score += dice_coeff(mask_pred, mask_true, reduce_batch_first=False)
else:
mask_pred = F.one_hot(mask_pred.argmax(dim=1), net.n_classes).permute(0, 3, 1, 2).float()
# compute the Dice score, ignoring background
dice_score += multiclass_dice_coeff(
mask_pred[:, 1:, ...], mask_true[:, 1:, ...], reduce_batch_first=False
)
net.train()
# Fixes a potential division by zero error
if num_val_batches == 0:
return dice_score
return dice_score / num_val_batches