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Update evaluate.py (Optimize Dice computation by removing one-hot encoding) #528

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Update evaluate.py (Optimize Dice computation by removing one-hot encoding) #528

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17 changes: 8 additions & 9 deletions evaluate.py
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Original file line number Diff line number Diff line change
@@ -1,15 +1,14 @@
import torch
import torch.nn.functional as F
from tqdm import tqdm

from utils.dice_score import multiclass_dice_coeff, dice_coeff


@torch.inference_mode()
def evaluate(net, dataloader, device, amp):
net.eval()
num_val_batches = len(dataloader)
dice_score = 0
dice_score = 0.0

# iterate over the validation set
with torch.autocast(device.type if device.type != 'mps' else 'cpu', enabled=amp):
Expand All @@ -24,17 +23,17 @@ def evaluate(net, dataloader, device, amp):
mask_pred = net(image)

if net.n_classes == 1:
# binary segmentation
assert mask_true.min() >= 0 and mask_true.max() <= 1, 'True mask indices should be in [0, 1]'
mask_pred = (F.sigmoid(mask_pred) > 0.5).float()
# compute the Dice score
mask_pred = (torch.sigmoid(mask_pred) > 0.5).float()
dice_score += dice_coeff(mask_pred, mask_true, reduce_batch_first=False)
else:
# multi-class segmentation: compute Dice directly on class indices
assert mask_true.min() >= 0 and mask_true.max() < net.n_classes, 'True mask indices should be in [0, n_classes['
# convert to one-hot format
mask_true = F.one_hot(mask_true, net.n_classes).permute(0, 3, 1, 2).float()
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)
mask_pred_indices = mask_pred.argmax(dim=1)
# ignore background class (0) if desired
dice_score += multiclass_dice_coeff(mask_pred_indices, mask_true, ignore_index=0)

net.train()
return dice_score / max(num_val_batches, 1)