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https://github.com/Laurent2916/REVA-QCAV.git
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43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
import torch
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import torch.nn.functional as F
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from tqdm import tqdm
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from utils.dice_score import multiclass_dice_coeff, dice_coeff
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def evaluate(net, dataloader, device):
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net.eval()
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num_val_batches = len(dataloader)
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dice_score = 0
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# iterate over the validation set
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for batch in tqdm(dataloader, total=num_val_batches, desc='Validation round', unit='batch', leave=False):
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image, mask_true = batch['image'], batch['mask']
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# move images and labels to correct device and type
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image = image.to(device=device, dtype=torch.float32)
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mask_true = mask_true.to(device=device, dtype=torch.long)
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mask_true = F.one_hot(mask_true, net.n_classes).permute(0, 3, 1, 2).float()
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with torch.no_grad():
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# predict the mask
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mask_pred = net(image)
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# convert to one-hot format
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if net.n_classes == 1:
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mask_pred = (F.sigmoid(mask_pred) > 0.5).float()
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# compute the Dice score
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dice_score += dice_coeff(mask_pred, mask_true, reduce_batch_first=False)
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else:
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mask_pred = F.one_hot(mask_pred.argmax(dim=1), net.n_classes).permute(0, 3, 1, 2).float()
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# compute the Dice score, ignoring background
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dice_score += multiclass_dice_coeff(mask_pred[:, 1:, ...], mask_true[:, 1:, ...], reduce_batch_first=False)
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net.train()
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# Fixes a potential division by zero error
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if num_val_batches == 0:
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return dice_score
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return dice_score / num_val_batches
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