def _handle_batch(self, batch):
x, y = batch
x_noise = (x + torch.rand_like(x)).clamp_(0, 1)
y_hat, x_ = self.model(x_noise)
loss_clf = F.cross_entropy(y_hat, y)
iou = metrics.iou(x_, x)
loss_iou = 1 - iou
loss = loss_clf + loss_iou
accuracy01, accuracy03, accuracy05 = metrics.accuracy(
y_hat, y, topk=(1, 3, 5)
)
self.batch_metrics = {
"loss_clf": loss_clf,
"loss_iou": loss_iou,
"loss": loss,
"iou": iou,
"accuracy01": accuracy01,
"accuracy03": accuracy03,
"accuracy05": accuracy05,
}
if self.is_train_loader:
loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
def _handle_batch(self, batch):
# model train/valid step
x, y = batch
y_hat = self.model(x)
loss = LovaszLoss()(y_hat, y)
iou = metrics.iou(y_hat, y, threshold=0.5, activation=None)
self.batch_metrics.update(
{"loss": loss, "iou": iou}
)
if self.is_train_loader:
loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
def test_iou():
"""
Tests for catalyst.utils.metrics.iou metric.
"""
size = 4
half_size = size // 2
shape = (1, 1, size, size)
# check 0: one empty
empty = torch.zeros(shape)
full = torch.ones(shape)
assert metrics.iou(empty, full, activation="none").item() == 0
# check 0: no overlap
left = torch.ones(shape)
left[:, :, :, half_size:] = 0
right = torch.ones(shape)
right[:, :, :, :half_size] = 0
assert metrics.iou(left, right, activation="none").item() == 0
# check 1: both empty, both full, complete overlap
assert metrics.iou(empty, empty, activation="none") == 1
assert metrics.iou(full, full, activation="none") == 1
assert metrics.iou(left, left, activation="none") == 1
# check 0.5: half overlap
top_left = torch.zeros(shape)
top_left[:, :, :half_size, :half_size] = 1
assert metrics.iou(top_left, left, activation="none").item() == 0.5
# check multiclass: 0, 0, 1, 1, 1, 0.5
a = torch.cat([empty, left, empty, full, left, top_left], dim=1)
b = torch.cat([full, right, empty, full, left, left], dim=1)
ans = torch.Tensor([0, 0, 1, 1, 1, 0.5])
assert torch.all(
metrics.iou(a, b, classes=["dummy"], activation="none") == ans)