253 lines
8.1 KiB
Python
253 lines
8.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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from mmcls.models import build_loss
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def test_asymmetric_loss():
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# test asymmetric_loss
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cls_score = torch.Tensor([[5, -5, 0], [5, -5, 0]])
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label = torch.Tensor([[1, 0, 1], [0, 1, 0]])
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weight = torch.tensor([0.5, 0.5])
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loss_cfg = dict(
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type='AsymmetricLoss',
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gamma_pos=1.0,
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gamma_neg=4.0,
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clip=0.05,
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reduction='mean',
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loss_weight=1.0)
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loss = build_loss(loss_cfg)
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assert torch.allclose(loss(cls_score, label), torch.tensor(3.80845 / 3))
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# test asymmetric_loss with weight
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assert torch.allclose(
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loss(cls_score, label, weight=weight), torch.tensor(3.80845 / 6))
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# test asymmetric_loss without clip
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loss_cfg = dict(
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type='AsymmetricLoss',
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gamma_pos=1.0,
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gamma_neg=4.0,
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clip=None,
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reduction='mean',
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loss_weight=1.0)
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loss = build_loss(loss_cfg)
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assert torch.allclose(loss(cls_score, label), torch.tensor(5.1186 / 3))
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def test_cross_entropy_loss():
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with pytest.raises(AssertionError):
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# use_sigmoid and use_soft could not be set simultaneously
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loss_cfg = dict(
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type='CrossEntropyLoss', use_sigmoid=True, use_soft=True)
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loss = build_loss(loss_cfg)
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# test ce_loss
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cls_score = torch.Tensor([[-1000, 1000], [100, -100]])
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label = torch.Tensor([0, 1]).long()
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class_weight = [0.3, 0.7] # class 0 : 0.3, class 1 : 0.7
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weight = torch.tensor([0.6, 0.4])
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# test ce_loss without class weight
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loss_cfg = dict(type='CrossEntropyLoss', reduction='mean', loss_weight=1.0)
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loss = build_loss(loss_cfg)
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assert torch.allclose(loss(cls_score, label), torch.tensor(1100.))
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# test ce_loss with weight
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assert torch.allclose(
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loss(cls_score, label, weight=weight), torch.tensor(640.))
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# test ce_loss with class weight
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loss_cfg = dict(
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type='CrossEntropyLoss',
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reduction='mean',
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loss_weight=1.0,
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class_weight=class_weight)
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loss = build_loss(loss_cfg)
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assert torch.allclose(loss(cls_score, label), torch.tensor(370.))
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# test ce_loss with weight
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assert torch.allclose(
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loss(cls_score, label, weight=weight), torch.tensor(208.))
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# test bce_loss
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cls_score = torch.Tensor([[-200, 100], [500, -1000], [300, -300]])
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label = torch.Tensor([[1, 0], [0, 1], [1, 0]])
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weight = torch.Tensor([0.6, 0.4, 0.5])
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class_weight = [0.1, 0.9] # class 0: 0.1, class 1: 0.9
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# test bce_loss without class weight
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loss_cfg = dict(
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type='CrossEntropyLoss',
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use_sigmoid=True,
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reduction='mean',
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loss_weight=1.0)
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loss = build_loss(loss_cfg)
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assert torch.allclose(loss(cls_score, label), torch.tensor(300.))
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# test ce_loss with weight
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assert torch.allclose(
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loss(cls_score, label, weight=weight), torch.tensor(130.))
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# test bce_loss with class weight
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loss_cfg = dict(
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type='CrossEntropyLoss',
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use_sigmoid=True,
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reduction='mean',
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loss_weight=1.0,
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class_weight=class_weight)
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loss = build_loss(loss_cfg)
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assert torch.allclose(loss(cls_score, label), torch.tensor(176.667))
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# test bce_loss with weight
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assert torch.allclose(
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loss(cls_score, label, weight=weight), torch.tensor(74.333))
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# test soft_ce_loss
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cls_score = torch.Tensor([[-1000, 1000], [100, -100]])
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label = torch.Tensor([[1.0, 0.0], [0.0, 1.0]])
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class_weight = [0.3, 0.7] # class 0 : 0.3, class 1 : 0.7
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weight = torch.tensor([0.6, 0.4])
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# test soft_ce_loss without class weight
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loss_cfg = dict(
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type='CrossEntropyLoss',
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use_soft=True,
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reduction='mean',
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loss_weight=1.0)
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loss = build_loss(loss_cfg)
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assert torch.allclose(loss(cls_score, label), torch.tensor(1100.))
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# test soft_ce_loss with weight
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assert torch.allclose(
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loss(cls_score, label, weight=weight), torch.tensor(640.))
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# test soft_ce_loss with class weight
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loss_cfg = dict(
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type='CrossEntropyLoss',
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use_soft=True,
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reduction='mean',
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loss_weight=1.0,
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class_weight=class_weight)
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loss = build_loss(loss_cfg)
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assert torch.allclose(loss(cls_score, label), torch.tensor(370.))
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# test soft_ce_loss with weight
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assert torch.allclose(
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loss(cls_score, label, weight=weight), torch.tensor(208.))
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def test_focal_loss():
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# test focal_loss
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cls_score = torch.Tensor([[5, -5, 0], [5, -5, 0]])
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label = torch.Tensor([[1, 0, 1], [0, 1, 0]])
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weight = torch.tensor([0.5, 0.5])
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loss_cfg = dict(
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type='FocalLoss',
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gamma=2.0,
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alpha=0.25,
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reduction='mean',
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loss_weight=1.0)
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loss = build_loss(loss_cfg)
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assert torch.allclose(loss(cls_score, label), torch.tensor(0.8522))
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# test focal_loss with weight
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assert torch.allclose(
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loss(cls_score, label, weight=weight), torch.tensor(0.8522 / 2))
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def test_label_smooth_loss():
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# test label_smooth_val assertion
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with pytest.raises(AssertionError):
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loss_cfg = dict(type='LabelSmoothLoss', label_smooth_val=1.0)
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build_loss(loss_cfg)
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with pytest.raises(AssertionError):
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loss_cfg = dict(type='LabelSmoothLoss', label_smooth_val='str')
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build_loss(loss_cfg)
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# test reduction assertion
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with pytest.raises(AssertionError):
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loss_cfg = dict(
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type='LabelSmoothLoss', label_smooth_val=0.1, reduction='unknown')
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build_loss(loss_cfg)
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# test mode assertion
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with pytest.raises(AssertionError):
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loss_cfg = dict(
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type='LabelSmoothLoss', label_smooth_val=0.1, mode='unknown')
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build_loss(loss_cfg)
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# test original mode label smooth loss
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cls_score = torch.tensor([[1., -1.]])
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label = torch.tensor([0])
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loss_cfg = dict(
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type='LabelSmoothLoss',
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label_smooth_val=0.1,
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mode='original',
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reduction='mean',
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loss_weight=1.0)
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loss = build_loss(loss_cfg)
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correct = 0.2269 # from timm
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assert loss(cls_score, label) - correct <= 0.0001
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# test classy_vision mode label smooth loss
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loss_cfg = dict(
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type='LabelSmoothLoss',
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label_smooth_val=0.1,
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mode='classy_vision',
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reduction='mean',
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loss_weight=1.0)
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loss = build_loss(loss_cfg)
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correct = 0.2178 # from ClassyVision
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assert loss(cls_score, label) - correct <= 0.0001
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# test multi_label mode label smooth loss
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cls_score = torch.tensor([[1., -1., 1]])
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label = torch.tensor([[1, 0, 1]])
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loss_cfg = dict(
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type='LabelSmoothLoss',
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label_smooth_val=0.1,
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mode='multi_label',
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reduction='mean',
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loss_weight=1.0)
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loss = build_loss(loss_cfg)
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smooth_label = torch.tensor([[0.9, 0.1, 0.9]])
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correct = torch.binary_cross_entropy_with_logits(cls_score,
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smooth_label).mean()
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assert torch.allclose(loss(cls_score, label), correct)
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# test label linear combination smooth loss
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cls_score = torch.tensor([[1., -1., 0.]])
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label1 = torch.tensor([[1., 0., 0.]])
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label2 = torch.tensor([[0., 0., 1.]])
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label_mix = label1 * 0.6 + label2 * 0.4
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loss_cfg = dict(
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type='LabelSmoothLoss',
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label_smooth_val=0.1,
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mode='original',
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reduction='mean',
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num_classes=3,
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loss_weight=1.0)
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loss = build_loss(loss_cfg)
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smooth_label1 = loss.original_smooth_label(label1)
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smooth_label2 = loss.original_smooth_label(label2)
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label_smooth_mix = smooth_label1 * 0.6 + smooth_label2 * 0.4
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correct = (-torch.log_softmax(cls_score, -1) * label_smooth_mix).sum()
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assert loss(cls_score, label_mix) - correct <= 0.0001
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# test label smooth loss with weight
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cls_score = torch.tensor([[1., -1.], [1., -1.]])
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label = torch.tensor([0, 1])
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weight = torch.tensor([0.5, 0.5])
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loss_cfg = dict(
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type='LabelSmoothLoss',
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reduction='mean',
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label_smooth_val=0.1,
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loss_weight=1.0)
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loss = build_loss(loss_cfg)
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assert torch.allclose(
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loss(cls_score, label, weight=weight),
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loss(cls_score, label) / 2)
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