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import numpy as np
import pytest
import torch
from mmseg.models.losses import Accuracy, reduce_loss, weight_reduce_loss
def test_utils():
loss = torch.rand(1, 3, 4, 4)
weight = torch.zeros(1, 3, 4, 4)
weight[:, :, :2, :2] = 1
# test reduce_loss()
reduced = reduce_loss(loss, 'none')
assert reduced is loss
reduced = reduce_loss(loss, 'mean')
np.testing.assert_almost_equal(reduced.numpy(), loss.mean())
reduced = reduce_loss(loss, 'sum')
np.testing.assert_almost_equal(reduced.numpy(), loss.sum())
# test weight_reduce_loss()
reduced = weight_reduce_loss(loss, weight=None, reduction='none')
assert reduced is loss
reduced = weight_reduce_loss(loss, weight=weight, reduction='mean')
target = (loss * weight).mean()
np.testing.assert_almost_equal(reduced.numpy(), target)
reduced = weight_reduce_loss(loss, weight=weight, reduction='sum')
np.testing.assert_almost_equal(reduced.numpy(), (loss * weight).sum())
with pytest.raises(AssertionError):
weight_wrong = weight[0, 0, ...]
weight_reduce_loss(loss, weight=weight_wrong, reduction='mean')
with pytest.raises(AssertionError):
weight_wrong = weight[:, 0:2, ...]
weight_reduce_loss(loss, weight=weight_wrong, reduction='mean')
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.))
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)
# TODO test use_mask
def test_accuracy():
# test for empty pred
pred = torch.empty(0, 4)
label = torch.empty(0)
accuracy = Accuracy(topk=1)
acc = accuracy(pred, label)
assert acc.item() == 0
pred = torch.Tensor([[0.2, 0.3, 0.6, 0.5], [0.1, 0.1, 0.2, 0.6],
[0.9, 0.0, 0.0, 0.1], [0.4, 0.7, 0.1, 0.1],
[0.0, 0.0, 0.99, 0]])
# test for top1
true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
accuracy = Accuracy(topk=1)
acc = accuracy(pred, true_label)
assert acc.item() == 100
# test for top1 with score thresh=0.8
true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
accuracy = Accuracy(topk=1, thresh=0.8)
acc = accuracy(pred, true_label)
assert acc.item() == 40
# test for top2
accuracy = Accuracy(topk=2)
label = torch.Tensor([3, 2, 0, 0, 2]).long()
acc = accuracy(pred, label)
assert acc.item() == 100
# test for both top1 and top2
accuracy = Accuracy(topk=(1, 2))
true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
acc = accuracy(pred, true_label)
for a in acc:
assert a.item() == 100
# topk is larger than pred class number
with pytest.raises(AssertionError):
accuracy = Accuracy(topk=5)
accuracy(pred, true_label)
# wrong topk type
with pytest.raises(AssertionError):
accuracy = Accuracy(topk='wrong type')
accuracy(pred, true_label)
# label size is larger than required
with pytest.raises(AssertionError):
label = torch.Tensor([2, 3, 0, 1, 2, 0]).long() # size mismatch
accuracy = Accuracy()
accuracy(pred, label)
# wrong pred dimension
with pytest.raises(AssertionError):
accuracy = Accuracy()
accuracy(pred[:, :, None], true_label)
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 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)
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