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import torch
import numpy
def predict(mask, label, threshold=0.5, score_type='combined'):
with torch.no_grad():
if score_type == 'pixel':
score = torch.mean(mask, axis=(1, 2, 3))
elif score_type == 'binary':
score = label
else:
score = (torch.mean(mask, axis=(1, 2, 3)) + label) / 2
preds = (score > threshold).type(torch.FloatTensor)
return preds, score
def test_accuracy(model, test_dl):
acc = 0
total = len(test_dl.dataset)
for img, mask, label in test_dl:
net_mask, net_label = model(img)
preds, _ = predict(net_mask, net_label)
ac = (preds == label).type(torch.FloatTensor)
acc += torch.sum(ac).item()
return (acc / total) * 100
def test_loss(model, test_dl, loss_fn):
loss = 0
total = len(test_dl)
for img, mask, label in test_dl:
net_mask, net_label = model(img)
losses = loss_fn(net_mask, net_label, mask, label)
loss += torch.mean(losses).item()
return loss / total
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