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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models import Accuracy, build_loss
def test_ce_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.))
def test_varifocal_loss():
# only sigmoid version of VarifocalLoss is implemented
with pytest.raises(AssertionError):
loss_cfg = dict(
type='VarifocalLoss', use_sigmoid=False, loss_weight=1.0)
build_loss(loss_cfg)
# test that alpha should be greater than 0
with pytest.raises(AssertionError):
loss_cfg = dict(
type='VarifocalLoss',
alpha=-0.75,
gamma=2.0,
use_sigmoid=True,
loss_weight=1.0)
build_loss(loss_cfg)
# test that pred and target should be of the same size
loss_cls_cfg = dict(
type='VarifocalLoss',
use_sigmoid=True,
alpha=0.75,
gamma=2.0,
iou_weighted=True,
reduction='mean',
loss_weight=1.0)
loss_cls = build_loss(loss_cls_cfg)
with pytest.raises(AssertionError):
fake_pred = torch.Tensor([[100.0, -100.0]])
fake_target = torch.Tensor([[1.0]])
loss_cls(fake_pred, fake_target)
# test the calculation
loss_cls = build_loss(loss_cls_cfg)
fake_pred = torch.Tensor([[100.0, -100.0]])
fake_target = torch.Tensor([[1.0, 0.0]])
assert torch.allclose(loss_cls(fake_pred, fake_target), torch.tensor(0.0))
# test the loss with weights
loss_cls = build_loss(loss_cls_cfg)
fake_pred = torch.Tensor([[0.0, 100.0]])
fake_target = torch.Tensor([[1.0, 1.0]])
fake_weight = torch.Tensor([0.0, 1.0])
assert torch.allclose(
loss_cls(fake_pred, fake_target, fake_weight), torch.tensor(0.0))
def test_kd_loss():
# test that temperature should be greater than 1
with pytest.raises(AssertionError):
loss_cfg = dict(
type='KnowledgeDistillationKLDivLoss', loss_weight=1.0, T=0.5)
build_loss(loss_cfg)
# test that pred and target should be of the same size
loss_cls_cfg = dict(
type='KnowledgeDistillationKLDivLoss', loss_weight=1.0, T=1)
loss_cls = build_loss(loss_cls_cfg)
with pytest.raises(AssertionError):
fake_pred = torch.Tensor([[100, -100]])
fake_label = torch.Tensor([1]).long()
loss_cls(fake_pred, fake_label)
# test the calculation
loss_cls = build_loss(loss_cls_cfg)
fake_pred = torch.Tensor([[100.0, 100.0]])
fake_target = torch.Tensor([[1.0, 1.0]])
assert torch.allclose(loss_cls(fake_pred, fake_target), torch.tensor(0.0))
# test the loss with weights
loss_cls = build_loss(loss_cls_cfg)
fake_pred = torch.Tensor([[100.0, -100.0], [100.0, 100.0]])
fake_target = torch.Tensor([[1.0, 0.0], [1.0, 1.0]])
fake_weight = torch.Tensor([0.0, 1.0])
assert torch.allclose(
loss_cls(fake_pred, fake_target, fake_weight), torch.tensor(0.0))
def test_seesaw_loss():
# only softmax version of Seesaw Loss is implemented
with pytest.raises(AssertionError):
loss_cfg = dict(type='SeesawLoss', use_sigmoid=True, loss_weight=1.0)
build_loss(loss_cfg)
# test that cls_score.size(-1) == num_classes + 2
loss_cls_cfg = dict(
type='SeesawLoss', p=0.0, q=0.0, loss_weight=1.0, num_classes=2)
loss_cls = build_loss(loss_cls_cfg)
# the length of fake_pred should be num_classes + 2 = 4
with pytest.raises(AssertionError):
fake_pred = torch.Tensor([[-100, 100]])
fake_label = torch.Tensor([1]).long()
loss_cls(fake_pred, fake_label)
# the length of fake_pred should be num_classes + 2 = 4
with pytest.raises(AssertionError):
fake_pred = torch.Tensor([[-100, 100, -100]])
fake_label = torch.Tensor([1]).long()
loss_cls(fake_pred, fake_label)
# test the calculation without p and q
loss_cls_cfg = dict(
type='SeesawLoss', p=0.0, q=0.0, loss_weight=1.0, num_classes=2)
loss_cls = build_loss(loss_cls_cfg)
fake_pred = torch.Tensor([[-100, 100, -100, 100]])
fake_label = torch.Tensor([1]).long()
loss = loss_cls(fake_pred, fake_label)
assert torch.allclose(loss['loss_cls_objectness'], torch.tensor(200.))
assert torch.allclose(loss['loss_cls_classes'], torch.tensor(0.))
# test the calculation with p and without q
loss_cls_cfg = dict(
type='SeesawLoss', p=1.0, q=0.0, loss_weight=1.0, num_classes=2)
loss_cls = build_loss(loss_cls_cfg)
fake_pred = torch.Tensor([[-100, 100, -100, 100]])
fake_label = torch.Tensor([0]).long()
loss_cls.cum_samples[0] = torch.exp(torch.Tensor([20]))
loss = loss_cls(fake_pred, fake_label)
assert torch.allclose(loss['loss_cls_objectness'], torch.tensor(200.))
assert torch.allclose(loss['loss_cls_classes'], torch.tensor(180.))
# test the calculation with q and without p
loss_cls_cfg = dict(
type='SeesawLoss', p=0.0, q=1.0, loss_weight=1.0, num_classes=2)
loss_cls = build_loss(loss_cls_cfg)
fake_pred = torch.Tensor([[-100, 100, -100, 100]])
fake_label = torch.Tensor([0]).long()
loss = loss_cls(fake_pred, fake_label)
assert torch.allclose(loss['loss_cls_objectness'], torch.tensor(200.))
assert torch.allclose(loss['loss_cls_classes'],
torch.tensor(200.) + torch.tensor(100.).log())
# test the others
loss_cls_cfg = dict(
type='SeesawLoss',
p=0.0,
q=1.0,
loss_weight=1.0,
num_classes=2,
return_dict=False)
loss_cls = build_loss(loss_cls_cfg)
fake_pred = torch.Tensor([[100, -100, 100, -100]])
fake_label = torch.Tensor([0]).long()
loss = loss_cls(fake_pred, fake_label)
acc = loss_cls.get_accuracy(fake_pred, fake_label)
act = loss_cls.get_activation(fake_pred)
assert torch.allclose(loss, torch.tensor(0.))
assert torch.allclose(acc['acc_objectness'], torch.tensor(100.))
assert torch.allclose(acc['acc_classes'], torch.tensor(100.))
assert torch.allclose(act, torch.tensor([1., 0., 0.]))
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)
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