def test_stats_step_batch_reduce(test_pred_batch, test_mask_batch):
pred_classes = test_pred_batch.argmax(dim=1)
assert pred_classes.shape == (4, 4, 4)
stats = func.statistics_step(pred_classes,
test_mask_batch,
num_classes=6,
ignore_index=255,
reduction=True)
assert len(stats) == 4
for s in stats:
assert s.size() == ()
def test_fscore_empty(test_mask_batch):
num_classes = 6
pred_classes = (test_mask_batch - 1) % num_classes
assert pred_classes.shape == (4, 4, 4)
tp, fp, tn, fn = func.statistics_step(pred_classes,
test_mask_batch,
num_classes=num_classes,
ignore_index=255,
reduction=False)
score = func.f1_score(tp, fp, fn, reduce=True)
assert score == 0.0
LOG.debug(score)
def test_fscore_best():
num_classes = 6
test_mask_batch = torch.randint(0, 6, size=(4, 4, 4))
pred_classes = test_mask_batch.clone()
assert pred_classes.shape == (4, 4, 4)
tp, fp, tn, fn = func.statistics_step(pred_classes,
test_mask_batch,
num_classes=num_classes,
ignore_index=255,
reduction=False)
score = func.f1_score(tp, fp, fn, reduce=False)
LOG.debug(score)
assert torch.all(score[:4] >= 0.99)
def test_fscore_batch_macro(test_pred_batch, test_mask_batch):
pred_classes = test_pred_batch.argmax(dim=1)
assert pred_classes.shape == (4, 4, 4)
tp, fp, tn, fn = func.statistics_step(pred_classes,
test_mask_batch,
num_classes=6,
ignore_index=255,
reduction=False)
fscore = func.f1_score(tp, fp, fn, reduce=True)
y_true, y_pred = func.valid_samples(255, test_mask_batch, pred_classes)
skl_prec = f1_score(y_true.cpu().numpy(),
y_pred.cpu().numpy(),
average="macro")
LOG.debug("sklearn: %s - custom: %s", str(skl_prec), str(fscore))
# sklearn does not account for empty classes
diff = abs(skl_prec - fscore.item())
assert diff <= EPS
def test_recall_batch(test_pred_batch, test_mask_batch):
pred_classes = test_pred_batch.argmax(dim=1)
assert pred_classes.shape == (4, 4, 4)
tp, fp, tn, fn = func.statistics_step(pred_classes,
test_mask_batch,
num_classes=6,
ignore_index=255,
reduction=False)
recall = func.recall_score(tp, fn, reduce=False)
y_true, y_pred = func.valid_samples(255, test_mask_batch, pred_classes)
skl_prec = recall_score(y_true.cpu().numpy(),
y_pred.cpu().numpy(),
average=None)
LOG.debug("sklearn: %s - custom: %s", str(skl_prec), str(recall))
# sklearn does not account for empty classes
for i in range(len(skl_prec)):
diff = abs(skl_prec[i] - recall[i].item())
assert diff <= EPS
def update(self, pred: torch.Tensor, target: torch.Tensor) -> None:
"""Updates the statistics by including the provided predictions and targets.
:param pred: prediction batch, yet to be tansformed into indices, with size [B, C, H, W]
:type pred: torch.Tensor
:param target: true targets, provided as indices, size [B, H, W]
:type target: torch.Tensor
"""
# assume 0=batch size, 1=classes, 2, 3 = dims
indices = pred.argmax(dim=1) if len(pred.shape) > 3 else pred
tp, fp, tn, fn = func.statistics_step(pred=indices,
target=target,
num_classes=self.num_classes,
ignore_index=self.ignore_index,
reduction=self.is_micro)
self.tp += tp
self.fp += fp
self.tn += tn
self.fn += fn
def test_miou_batch_macro(big_rand_batch):
num_classes = 6
noise_data = torch.randint(0, num_classes, size=big_rand_batch.size())
# generate a random mask in 0-1, use it with a threshold to substitute a percent of values
# from the batch with noise data
change_mask = torch.rand_like(big_rand_batch, dtype=torch.float)
pred_batch = torch.where(change_mask >= 0.1, big_rand_batch, noise_data)
tp, fp, tn, fn = func.statistics_step(pred_batch,
big_rand_batch,
num_classes=6,
ignore_index=255,
reduction=False)
miou = func.iou_from_statistics(tp=tp, fp=fp, fn=fn, reduce=True)
y_true, y_pred = func.valid_samples(255, big_rand_batch, pred_batch)
skl_iou = jaccard_score(y_true.cpu().numpy(),
y_pred.cpu().numpy(),
average="macro")
LOG.debug("sklearn: %s - custom: %s", str(skl_iou), str(miou))
assert miou.size() == ()
diff = abs(skl_iou - miou.item())
assert diff <= EPS
