|
|
| import platform
|
|
|
| import numpy as np
|
| import pytest
|
| import torch
|
| from mmcv.transforms import to_tensor
|
| from mmengine.structures import InstanceData
|
|
|
| from mmaction.registry import MODELS
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| from mmaction.structures import ActionDataSample
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| from mmaction.testing import get_localizer_cfg
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| from mmaction.utils import register_all_modules
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|
|
| register_all_modules()
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|
|
|
|
| def get_localization_data_sample():
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| gt_bbox = np.array([[0.1, 0.3], [0.375, 0.625]])
|
| data_sample = ActionDataSample()
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| instance_data = InstanceData()
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| instance_data['gt_bbox'] = to_tensor(gt_bbox)
|
| data_sample.gt_instances = instance_data
|
| data_sample.set_metainfo(
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| dict(
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| video_name='v_test',
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| duration_second=100,
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| duration_frame=960,
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| feature_frame=960))
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| return data_sample
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|
|
|
|
| @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
|
| def test_bmn_loss():
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| model_cfg = get_localizer_cfg(
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| 'bmn/bmn_2xb8-400x100-9e_activitynet-feature.py')
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|
|
| if 0 and torch.cuda.is_available():
|
| raw_feature = [torch.rand(400, 100).cuda()]
|
| data_samples = [get_localization_data_sample()]
|
| localizer_bmn = MODELS.build(model_cfg.model).cuda()
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| losses = localizer_bmn(raw_feature, data_samples, mode='loss')
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| assert isinstance(losses, dict)
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|
|
| else:
|
| raw_feature = [torch.rand(400, 100)]
|
| data_samples = [get_localization_data_sample()]
|
| localizer_bmn = MODELS.build(model_cfg.model)
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| losses = localizer_bmn(raw_feature, data_samples, mode='loss')
|
| assert isinstance(losses, dict)
|
|
|
|
|
| @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
|
| def test_bmn_predict():
|
| model_cfg = get_localizer_cfg(
|
| 'bmn/bmn_2xb8-400x100-9e_activitynet-feature.py')
|
|
|
| if 0 and torch.cuda.is_available():
|
| localizer_bmn = MODELS.build(model_cfg.model).cuda()
|
| data_samples = [get_localization_data_sample()]
|
|
|
| with torch.no_grad():
|
| one_raw_feature = [torch.rand(400, 100).cuda()]
|
| localizer_bmn(one_raw_feature, data_samples, mode='predict')
|
| else:
|
| localizer_bmn = MODELS.build(model_cfg.model)
|
| data_samples = [get_localization_data_sample()]
|
| with torch.no_grad():
|
| one_raw_feature = [torch.rand(400, 100)]
|
| localizer_bmn(one_raw_feature, data_samples, mode='predict')
|
|
|
|
|
| @pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
|
| def test_bmn_tensor():
|
| model_cfg = get_localizer_cfg(
|
| 'bmn/bmn_2xb8-400x100-9e_activitynet-feature.py')
|
|
|
| if 0 and torch.cuda.is_available():
|
| localizer_bmn = MODELS.build(model_cfg.model).cuda()
|
|
|
| with torch.no_grad():
|
| one_raw_feature = [torch.rand(400, 100).cuda()]
|
| localizer_bmn(one_raw_feature, data_samples=None, mode='tensor')
|
| else:
|
| localizer_bmn = MODELS.build(model_cfg.model)
|
| with torch.no_grad():
|
| one_raw_feature = [torch.rand(400, 100)]
|
| localizer_bmn(one_raw_feature, data_samples=None, mode='tensor')
|
|
|
