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import platform
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import numpy as np
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import pytest
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import torch
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from mmcv.transforms import to_tensor
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from mmengine.structures import InstanceData
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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]])
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data_sample = ActionDataSample()
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instance_data = InstanceData()
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instance_data['gt_bbox'] = to_tensor(gt_bbox)
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data_sample.gt_instances = instance_data
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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')
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def test_tem():
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model_cfg = get_localizer_cfg(
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'bsn/bsn_tem_1xb16-400x100-20e_activitynet-feature.py')
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localizer_tem = MODELS.build(model_cfg.model)
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raw_feature = torch.rand(8, 400, 100)
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data_samples = [get_localization_data_sample()] * 8
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losses = localizer_tem(raw_feature, data_samples, mode='loss')
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assert isinstance(losses, dict)
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with torch.no_grad():
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for one_raw_feature in raw_feature:
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one_raw_feature = one_raw_feature.reshape(1, 400, 100)
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data_samples = [get_localization_data_sample()]
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localizer_tem(one_raw_feature, data_samples, mode='predict')
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