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65 lines
2.2 KiB
Python
65 lines
2.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from copy import deepcopy
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from unittest import TestCase
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import torch
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from mmengine import ConfigDict
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from mmcls.models import AverageClsScoreTTA, ImageClassifier
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from mmcls.registry import MODELS
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from mmcls.structures import ClsDataSample
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class TestAverageClsScoreTTA(TestCase):
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DEFAULT_ARGS = dict(
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type='AverageClsScoreTTA',
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module=dict(
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type='ImageClassifier',
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backbone=dict(type='ResNet', depth=18),
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neck=dict(type='GlobalAveragePooling'),
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head=dict(
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type='LinearClsHead',
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num_classes=10,
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in_channels=512,
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loss=dict(type='CrossEntropyLoss'))))
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def test_initialize(self):
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model: AverageClsScoreTTA = MODELS.build(self.DEFAULT_ARGS)
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self.assertIsInstance(model.module, ImageClassifier)
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def test_forward(self):
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inputs = torch.rand(1, 3, 224, 224)
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model: AverageClsScoreTTA = MODELS.build(self.DEFAULT_ARGS)
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# The forward of TTA model should not be called.
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with self.assertRaisesRegex(NotImplementedError, 'will not be called'):
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model(inputs)
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def test_test_step(self):
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cfg = ConfigDict(deepcopy(self.DEFAULT_ARGS))
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cfg.module.data_preprocessor = dict(
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mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5])
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model: AverageClsScoreTTA = MODELS.build(cfg)
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img1 = torch.randint(0, 256, (1, 3, 224, 224))
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img2 = torch.randint(0, 256, (1, 3, 224, 224))
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data1 = {
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'inputs': img1,
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'data_samples': [ClsDataSample().set_gt_label(1)]
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}
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data2 = {
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'inputs': img2,
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'data_samples': [ClsDataSample().set_gt_label(1)]
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}
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data_tta = {
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'inputs': [img1, img2],
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'data_samples': [[ClsDataSample().set_gt_label(1)],
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[ClsDataSample().set_gt_label(1)]]
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}
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score1 = model.module.test_step(data1)[0].pred_label.score
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score2 = model.module.test_step(data2)[0].pred_label.score
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score_tta = model.test_step(data_tta)[0].pred_label.score
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torch.testing.assert_allclose(score_tta, (score1 + score2) / 2)
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