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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_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():
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raw_feature = [torch.rand(400, 100).cuda()]
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data_samples = [get_localization_data_sample()]
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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:
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raw_feature = [torch.rand(400, 100)]
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data_samples = [get_localization_data_sample()]
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localizer_bmn = MODELS.build(model_cfg.model)
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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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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_bmn_predict():
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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():
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localizer_bmn = MODELS.build(model_cfg.model).cuda()
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data_samples = [get_localization_data_sample()]
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with torch.no_grad():
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one_raw_feature = [torch.rand(400, 100).cuda()]
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localizer_bmn(one_raw_feature, data_samples, mode='predict')
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else:
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localizer_bmn = MODELS.build(model_cfg.model)
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data_samples = [get_localization_data_sample()]
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with torch.no_grad():
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one_raw_feature = [torch.rand(400, 100)]
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localizer_bmn(one_raw_feature, data_samples, mode='predict')
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_bmn_tensor():
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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():
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localizer_bmn = MODELS.build(model_cfg.model).cuda()
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with torch.no_grad():
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one_raw_feature = [torch.rand(400, 100).cuda()]
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localizer_bmn(one_raw_feature, data_samples=None, mode='tensor')
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else:
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localizer_bmn = MODELS.build(model_cfg.model)
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with torch.no_grad():
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one_raw_feature = [torch.rand(400, 100)]
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localizer_bmn(one_raw_feature, data_samples=None, mode='tensor')
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