def test_ce_loss():
from mmseg.models import build_loss
# use_mask and use_sigmoid cannot be true at the same time
with pytest.raises(AssertionError):
loss_cfg = dict(
type='CrossEntropyLoss',
use_mask=True,
use_sigmoid=True,
loss_weight=1.0)
build_loss(loss_cfg)
# test loss with class weights
loss_cls_cfg = dict(
type='CrossEntropyLoss',
use_sigmoid=False,
class_weight=[0.8, 0.2],
loss_weight=1.0)
loss_cls = build_loss(loss_cls_cfg)
fake_pred = torch.Tensor([[100, -100]])
fake_label = torch.Tensor([1]).long()
assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.))
# test loss with class weights from file
import os
import tempfile
import mmcv
import numpy as np
tmp_file = tempfile.NamedTemporaryFile()
mmcv.dump([0.8, 0.2], f'{tmp_file.name}.pkl', 'pkl') # from pkl file
loss_cls_cfg = dict(
type='CrossEntropyLoss',
use_sigmoid=False,
class_weight=f'{tmp_file.name}.pkl',
loss_weight=1.0)
loss_cls = build_loss(loss_cls_cfg)
assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.))
np.save(f'{tmp_file.name}.npy', np.array([0.8, 0.2])) # from npy file
loss_cls_cfg = dict(
type='CrossEntropyLoss',
use_sigmoid=False,
class_weight=f'{tmp_file.name}.npy',
loss_weight=1.0)
loss_cls = build_loss(loss_cls_cfg)
assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.))
tmp_file.close()
os.remove(f'{tmp_file.name}.pkl')
os.remove(f'{tmp_file.name}.npy')
loss_cls_cfg = dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)
loss_cls = build_loss(loss_cls_cfg)
assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(200.))
loss_cls_cfg = dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)
loss_cls = build_loss(loss_cls_cfg)
assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(100.))
fake_pred = torch.full(size=(2, 21, 8, 8), fill_value=0.5)
fake_label = torch.ones(2, 8, 8).long()
assert torch.allclose(
loss_cls(fake_pred, fake_label), torch.tensor(0.9503), atol=1e-4)
fake_label[:, 0, 0] = 255
assert torch.allclose(
loss_cls(fake_pred, fake_label, ignore_index=255),
torch.tensor(0.9354),
atol=1e-4)
def test_ce_loss(use_sigmoid, reduction, avg_non_ignore, bce_input_same_dim):
from mmseg.models import build_loss
# use_mask and use_sigmoid cannot be true at the same time
with pytest.raises(AssertionError):
loss_cfg = dict(type='CrossEntropyLoss',
use_mask=True,
use_sigmoid=True,
loss_weight=1.0)
build_loss(loss_cfg)
# test loss with simple case for ce/bce
fake_pred = torch.Tensor([[100, -100]])
fake_label = torch.Tensor([1]).long()
loss_cls_cfg = dict(type='CrossEntropyLoss',
use_sigmoid=use_sigmoid,
loss_weight=1.0,
avg_non_ignore=avg_non_ignore,
loss_name='loss_ce')
loss_cls = build_loss(loss_cls_cfg)
if use_sigmoid:
assert torch.allclose(loss_cls(fake_pred, fake_label),
torch.tensor(100.))
else:
assert torch.allclose(loss_cls(fake_pred, fake_label),
torch.tensor(200.))
# test loss with complicated case for ce/bce
# when avg_non_ignore is False, `avg_factor` would not be calculated
fake_pred = torch.full(size=(2, 21, 8, 8), fill_value=0.5)
fake_label = torch.ones(2, 8, 8).long()
fake_label[:, 0, 0] = 255
fake_weight = None
# extra test bce loss when pred.shape == label.shape
if use_sigmoid and bce_input_same_dim:
fake_pred = torch.randn(2, 10).float()
fake_label = torch.rand(2, 10).float()
fake_weight = torch.rand(2, 10) # set weight in forward function
fake_label[0, [1, 2, 5, 7]] = 255 # set ignore_index
fake_label[1, [0, 5, 8, 9]] = 255
loss_cls = build_loss(loss_cls_cfg)
loss = loss_cls(fake_pred,
fake_label,
weight=fake_weight,
ignore_index=255)
if use_sigmoid:
if fake_pred.dim() != fake_label.dim():
fake_label, weight, valid_mask = _expand_onehot_labels(
labels=fake_label,
label_weights=None,
target_shape=fake_pred.shape,
ignore_index=255)
else:
# should mask out the ignored elements
valid_mask = ((fake_label >= 0) & (fake_label != 255)).float()
weight = valid_mask
torch_loss = torch.nn.functional.binary_cross_entropy_with_logits(
fake_pred,
fake_label.float(),
reduction='none',
weight=fake_weight)
if avg_non_ignore:
avg_factor = valid_mask.sum().item()
torch_loss = (torch_loss * weight).sum() / avg_factor
else:
torch_loss = (torch_loss * weight).mean()
else:
if avg_non_ignore:
torch_loss = torch.nn.functional.cross_entropy(fake_pred,
fake_label,
reduction='mean',
ignore_index=255)
else:
torch_loss = torch.nn.functional.cross_entropy(
fake_pred, fake_label, reduction='sum',
ignore_index=255) / fake_label.numel()
assert torch.allclose(loss, torch_loss)
if use_sigmoid:
# test loss with complicated case for ce/bce
# when avg_non_ignore is False, `avg_factor` would not be calculated
fake_pred = torch.full(size=(2, 21, 8, 8), fill_value=0.5)
fake_label = torch.ones(2, 8, 8).long()
fake_label[:, 0, 0] = 255
fake_weight = torch.rand(2, 8, 8)
loss_cls = build_loss(loss_cls_cfg)
loss = loss_cls(fake_pred,
fake_label,
weight=fake_weight,
ignore_index=255)
if use_sigmoid:
fake_label, weight, valid_mask = _expand_onehot_labels(
labels=fake_label,
label_weights=None,
target_shape=fake_pred.shape,
ignore_index=255)
torch_loss = torch.nn.functional.binary_cross_entropy_with_logits(
fake_pred,
fake_label.float(),
reduction='none',
weight=fake_weight.unsqueeze(1).expand(fake_pred.shape))
if avg_non_ignore:
avg_factor = valid_mask.sum().item()
torch_loss = (torch_loss * weight).sum() / avg_factor
else:
torch_loss = (torch_loss * weight).mean()
assert torch.allclose(loss, torch_loss)
# test loss with class weights from file
fake_pred = torch.Tensor([[100, -100]])
fake_label = torch.Tensor([1]).long()
import os
import tempfile
import mmcv
import numpy as np
tmp_file = tempfile.NamedTemporaryFile()
mmcv.dump([0.8, 0.2], f'{tmp_file.name}.pkl', 'pkl') # from pkl file
loss_cls_cfg = dict(type='CrossEntropyLoss',
use_sigmoid=False,
class_weight=f'{tmp_file.name}.pkl',
loss_weight=1.0,
loss_name='loss_ce')
loss_cls = build_loss(loss_cls_cfg)
assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.))
np.save(f'{tmp_file.name}.npy', np.array([0.8, 0.2])) # from npy file
loss_cls_cfg = dict(type='CrossEntropyLoss',
use_sigmoid=False,
class_weight=f'{tmp_file.name}.npy',
loss_weight=1.0,
loss_name='loss_ce')
loss_cls = build_loss(loss_cls_cfg)
assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.))
tmp_file.close()
os.remove(f'{tmp_file.name}.pkl')
os.remove(f'{tmp_file.name}.npy')
loss_cls_cfg = dict(type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0)
loss_cls = build_loss(loss_cls_cfg)
assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(200.))
# test `avg_non_ignore` without ignore index would not affect ce/bce loss
# when reduction='sum'/'none'/'mean'
loss_cls_cfg1 = dict(type='CrossEntropyLoss',
use_sigmoid=use_sigmoid,
reduction=reduction,
loss_weight=1.0,
avg_non_ignore=True)
loss_cls1 = build_loss(loss_cls_cfg1)
loss_cls_cfg2 = dict(type='CrossEntropyLoss',
use_sigmoid=use_sigmoid,
reduction=reduction,
loss_weight=1.0,
avg_non_ignore=False)
loss_cls2 = build_loss(loss_cls_cfg2)
assert torch.allclose(
loss_cls1(fake_pred, fake_label, ignore_index=255) / fake_pred.numel(),
loss_cls2(fake_pred, fake_label, ignore_index=255) / fake_pred.numel(),
atol=1e-4)
# test ce/bce loss with ignore index and class weight
# in 5-way classification
if use_sigmoid:
# test bce loss when pred.shape == or != label.shape
if bce_input_same_dim:
fake_pred = torch.randn(2, 10).float()
fake_label = torch.rand(2, 10).float()
class_weight = torch.rand(2, 10)
else:
fake_pred = torch.full(size=(2, 21, 8, 8), fill_value=0.5)
fake_label = torch.ones(2, 8, 8).long()
class_weight = torch.randn(2, 21, 8, 8)
fake_label, weight, valid_mask = _expand_onehot_labels(
labels=fake_label,
label_weights=None,
target_shape=fake_pred.shape,
ignore_index=-100)
torch_loss = torch.nn.functional.binary_cross_entropy_with_logits(
fake_pred,
fake_label.float(),
reduction='mean',
pos_weight=class_weight)
else:
fake_pred = torch.randn(2, 5, 10).float() # 5-way classification
fake_label = torch.randint(0, 5, (2, 10)).long()
class_weight = torch.rand(5)
class_weight /= class_weight.sum()
torch_loss = torch.nn.functional.cross_entropy(
fake_pred, fake_label, reduction='sum',
weight=class_weight) / fake_label.numel()
loss_cls_cfg = dict(type='CrossEntropyLoss',
use_sigmoid=use_sigmoid,
reduction='mean',
class_weight=class_weight,
loss_weight=1.0,
avg_non_ignore=avg_non_ignore)
loss_cls = build_loss(loss_cls_cfg)
# test cross entropy loss has name `loss_ce`
assert loss_cls.loss_name == 'loss_ce'
# test avg_non_ignore is in extra_repr
assert loss_cls.extra_repr() == f'avg_non_ignore={avg_non_ignore}'
loss = loss_cls(fake_pred, fake_label)
assert torch.allclose(loss, torch_loss)
fake_label[0, [1, 2, 5, 7]] = 10 # set ignore_index
fake_label[1, [0, 5, 8, 9]] = 10
loss = loss_cls(fake_pred, fake_label, ignore_index=10)
if use_sigmoid:
if avg_non_ignore:
torch_loss = torch.nn.functional.binary_cross_entropy_with_logits(
fake_pred[fake_label != 10],
fake_label[fake_label != 10].float(),
pos_weight=class_weight[fake_label != 10],
reduction='mean')
else:
torch_loss = torch.nn.functional.binary_cross_entropy_with_logits(
fake_pred[fake_label != 10],
fake_label[fake_label != 10].float(),
pos_weight=class_weight[fake_label != 10],
reduction='sum') / fake_label.numel()
else:
if avg_non_ignore:
torch_loss = torch.nn.functional.cross_entropy(
fake_pred,
fake_label,
ignore_index=10,
reduction='sum',
weight=class_weight) / fake_label[fake_label != 10].numel()
else:
torch_loss = torch.nn.functional.cross_entropy(
fake_pred,
fake_label,
ignore_index=10,
reduction='sum',
weight=class_weight) / fake_label.numel()
assert torch.allclose(loss, torch_loss)
def test_lovasz_loss():
from mmseg.models import build_loss
# loss_type should be 'binary' or 'multi_class'
with pytest.raises(AssertionError):
loss_cfg = dict(type='LovaszLoss',
loss_type='Binary',
reduction='none',
loss_weight=1.0)
build_loss(loss_cfg)
# reduction should be 'none' when per_image is False.
with pytest.raises(AssertionError):
loss_cfg = dict(type='LovaszLoss', loss_type='multi_class')
build_loss(loss_cfg)
# test lovasz loss with loss_type = 'multi_class' and per_image = False
loss_cfg = dict(type='LovaszLoss', reduction='none', loss_weight=1.0)
lovasz_loss = build_loss(loss_cfg)
logits = torch.rand(1, 3, 4, 4)
labels = (torch.rand(1, 4, 4) * 2).long()
lovasz_loss(logits, labels)
# test lovasz loss with loss_type = 'multi_class' and per_image = True
loss_cfg = dict(type='LovaszLoss',
per_image=True,
reduction='mean',
class_weight=[1.0, 2.0, 3.0],
loss_weight=1.0)
lovasz_loss = build_loss(loss_cfg)
logits = torch.rand(1, 3, 4, 4)
labels = (torch.rand(1, 4, 4) * 2).long()
lovasz_loss(logits, labels, ignore_index=None)
# test loss with class weights from file
import os
import tempfile
import mmcv
import numpy as np
tmp_file = tempfile.NamedTemporaryFile()
mmcv.dump([1.0, 2.0, 3.0], f'{tmp_file.name}.pkl', 'pkl') # from pkl file
loss_cfg = dict(type='LovaszLoss',
per_image=True,
reduction='mean',
class_weight=f'{tmp_file.name}.pkl',
loss_weight=1.0)
lovasz_loss = build_loss(loss_cfg)
lovasz_loss(logits, labels, ignore_index=None)
np.save(f'{tmp_file.name}.npy', np.array([1.0, 2.0, 3.0])) # from npy file
loss_cfg = dict(type='LovaszLoss',
per_image=True,
reduction='mean',
class_weight=f'{tmp_file.name}.npy',
loss_weight=1.0)
lovasz_loss = build_loss(loss_cfg)
lovasz_loss(logits, labels, ignore_index=None)
tmp_file.close()
os.remove(f'{tmp_file.name}.pkl')
os.remove(f'{tmp_file.name}.npy')
# test lovasz loss with loss_type = 'binary' and per_image = False
loss_cfg = dict(type='LovaszLoss',
loss_type='binary',
reduction='none',
loss_weight=1.0)
lovasz_loss = build_loss(loss_cfg)
logits = torch.rand(2, 4, 4)
labels = (torch.rand(2, 4, 4)).long()
lovasz_loss(logits, labels)
# test lovasz loss with loss_type = 'binary' and per_image = True
loss_cfg = dict(type='LovaszLoss',
loss_type='binary',
per_image=True,
reduction='mean',
loss_weight=1.0)
lovasz_loss = build_loss(loss_cfg)
logits = torch.rand(2, 4, 4)
labels = (torch.rand(2, 4, 4)).long()
lovasz_loss(logits, labels, ignore_index=None)
def test_dice_lose():
from mmseg.models import build_loss
# test dice loss with loss_type = 'multi_class'
loss_cfg = dict(type='DiceLoss',
reduction='none',
class_weight=[1.0, 2.0, 3.0],
loss_weight=1.0,
ignore_index=1,
loss_name='loss_dice')
dice_loss = build_loss(loss_cfg)
logits = torch.rand(8, 3, 4, 4)
labels = (torch.rand(8, 4, 4) * 3).long()
dice_loss(logits, labels)
# test loss with class weights from file
import os
import tempfile
import mmcv
import numpy as np
tmp_file = tempfile.NamedTemporaryFile()
mmcv.dump([1.0, 2.0, 3.0], f'{tmp_file.name}.pkl', 'pkl') # from pkl file
loss_cfg = dict(type='DiceLoss',
reduction='none',
class_weight=f'{tmp_file.name}.pkl',
loss_weight=1.0,
ignore_index=1,
loss_name='loss_dice')
dice_loss = build_loss(loss_cfg)
dice_loss(logits, labels, ignore_index=None)
np.save(f'{tmp_file.name}.npy', np.array([1.0, 2.0, 3.0])) # from npy file
loss_cfg = dict(type='DiceLoss',
reduction='none',
class_weight=f'{tmp_file.name}.pkl',
loss_weight=1.0,
ignore_index=1,
loss_name='loss_dice')
dice_loss = build_loss(loss_cfg)
dice_loss(logits, labels, ignore_index=None)
tmp_file.close()
os.remove(f'{tmp_file.name}.pkl')
os.remove(f'{tmp_file.name}.npy')
# test dice loss with loss_type = 'binary'
loss_cfg = dict(type='DiceLoss',
smooth=2,
exponent=3,
reduction='sum',
loss_weight=1.0,
ignore_index=0,
loss_name='loss_dice')
dice_loss = build_loss(loss_cfg)
logits = torch.rand(8, 2, 4, 4)
labels = (torch.rand(8, 4, 4) * 2).long()
dice_loss(logits, labels)
# test dice loss has name `loss_dice`
loss_cfg = dict(type='DiceLoss',
smooth=2,
exponent=3,
reduction='sum',
loss_weight=1.0,
ignore_index=0,
loss_name='loss_dice')
dice_loss = build_loss(loss_cfg)
assert dice_loss.loss_name == 'loss_dice'
