mirror of
https://github.com/open-mmlab/mmclassification.git
synced 2025-06-03 21:53:55 +08:00
6916f33d56
* add asymmetric loss * minor change * fix docstring * do not apply sum over classes and fix docstring * fix docstring * fix weight shape * fix weight shape * add reference * fix linkting issue Co-authored-by: Y. Xiong <xiongyuxy@gmail.com>
86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
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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# test ce_loss
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cls_score = torch.Tensor([[100, -100]])
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label = torch.Tensor([1]).long()
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weight = torch.tensor(0.5)
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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(200.))
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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(100.))
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# test bce_loss
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cls_score = torch.Tensor([[100, -100], [100, -100]])
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label = torch.Tensor([[1, 0], [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='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(50.))
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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(25.))
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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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