mirror of
https://github.com/open-mmlab/mmclassification.git
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b30f79ea4c
* add mytrain.py for test * test before layers * test attr in layers * test classifier * delete mytrain.py * set cal_acc in ClsHead defaults to False * set cal_acc defaults to False * use *args, **kwargs instead * change bs16 to 3 in test_image_classifier_vit * fix some comments * change cal_acc=True * test LinearClsHead
51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
import torch
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from mmcls.models.heads import (ClsHead, LinearClsHead, MultiLabelClsHead,
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MultiLabelLinearClsHead)
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def test_cls_head():
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# test ClsHead with cal_acc=False
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head = ClsHead()
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fake_cls_score = torch.rand(4, 3)
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fake_gt_label = torch.randint(0, 2, (4, ))
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losses = head.loss(fake_cls_score, fake_gt_label)
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assert losses['loss'].item() > 0
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# test ClsHead with cal_acc=True
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head = ClsHead(cal_acc=True)
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fake_cls_score = torch.rand(4, 3)
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fake_gt_label = torch.randint(0, 2, (4, ))
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losses = head.loss(fake_cls_score, fake_gt_label)
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assert losses['loss'].item() > 0
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# test LinearClsHead
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head = LinearClsHead(10, 100)
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fake_cls_score = torch.rand(4, 10)
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fake_gt_label = torch.randint(0, 10, (4, ))
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losses = head.loss(fake_cls_score, fake_gt_label)
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assert losses['loss'].item() > 0
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def test_multilabel_head():
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head = MultiLabelClsHead()
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fake_cls_score = torch.rand(4, 3)
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fake_gt_label = torch.randint(0, 2, (4, 3))
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losses = head.loss(fake_cls_score, fake_gt_label)
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assert losses['loss'].item() > 0
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def test_multilabel_linear_head():
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head = MultiLabelLinearClsHead(3, 5)
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fake_cls_score = torch.rand(4, 3)
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fake_gt_label = torch.randint(0, 2, (4, 3))
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head.init_weights()
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losses = head.loss(fake_cls_score, fake_gt_label)
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assert losses['loss'].item() > 0
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