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import platform
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import pytest
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import torch
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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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bsp_feature = torch.rand(100, 32)
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reference_temporal_iou = torch.rand(100)
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data_sample = ActionDataSample()
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instance_data = InstanceData()
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instance_data['bsp_feature'] = bsp_feature
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instance_data['reference_temporal_iou'] = reference_temporal_iou
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data_sample.gt_instances = instance_data
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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_pem():
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model_cfg = get_localizer_cfg(
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'bsn/bsn_pem_1xb16-400x100-20e_activitynet-feature.py')
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localizer_pem = MODELS.build(model_cfg.model)
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raw_features = [torch.rand(100, 32)] * 8
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data_samples = [get_localization_data_sample()] * 8
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losses = localizer_pem(raw_features, data_samples, mode='loss')
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assert isinstance(losses, dict)
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tmin = torch.rand(100)
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tmax = torch.rand(100)
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tmin_score = torch.rand(100)
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tmax_score = torch.rand(100)
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video_meta = dict(
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video_name='v_test',
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duration_second=100,
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duration_frame=1000,
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annotations=[{
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'segment': [0.3, 0.6],
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'label': 'Rock climbing'
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}],
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feature_frame=900)
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with torch.no_grad():
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raw_feature = [torch.rand(100, 32)]
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data_sample = get_localization_data_sample()
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data_sample.set_metainfo(video_meta)
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gt_instances = data_sample.gt_instances
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gt_instances['tmin'] = tmin
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gt_instances['tmax'] = tmax
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gt_instances['tmin_score'] = tmin_score
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gt_instances['tmax_score'] = tmax_score
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data_samples = [data_sample]
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localizer_pem(raw_feature, data_samples, mode='predict')
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with torch.no_grad():
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raw_feature = [torch.rand(100, 32)]
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localizer_pem(raw_feature, data_samples=None, mode='tensor')
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