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# Copyright (c) OpenMMLab. All rights reserved.
# Copyright (c) OpenMMLab. All rights reserved.
import platform
import pytest
import torch
from mmengine.structures import InstanceData
from mmaction.registry import MODELS
from mmaction.structures import ActionDataSample
from mmaction.testing import get_localizer_cfg
from mmaction.utils import register_all_modules
register_all_modules()
def get_localization_data_sample():
bsp_feature = torch.rand(100, 32)
reference_temporal_iou = torch.rand(100)
data_sample = ActionDataSample()
instance_data = InstanceData()
instance_data['bsp_feature'] = bsp_feature
instance_data['reference_temporal_iou'] = reference_temporal_iou
data_sample.gt_instances = instance_data
return data_sample
@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
def test_pem():
model_cfg = get_localizer_cfg(
'bsn/bsn_pem_1xb16-400x100-20e_activitynet-feature.py')
localizer_pem = MODELS.build(model_cfg.model)
raw_features = [torch.rand(100, 32)] * 8
data_samples = [get_localization_data_sample()] * 8
losses = localizer_pem(raw_features, data_samples, mode='loss')
assert isinstance(losses, dict)
# Test forward predict
tmin = torch.rand(100)
tmax = torch.rand(100)
tmin_score = torch.rand(100)
tmax_score = torch.rand(100)
video_meta = dict(
video_name='v_test',
duration_second=100,
duration_frame=1000,
annotations=[{
'segment': [0.3, 0.6],
'label': 'Rock climbing'
}],
feature_frame=900)
with torch.no_grad():
raw_feature = [torch.rand(100, 32)]
data_sample = get_localization_data_sample()
data_sample.set_metainfo(video_meta)
gt_instances = data_sample.gt_instances
gt_instances['tmin'] = tmin
gt_instances['tmax'] = tmax
gt_instances['tmin_score'] = tmin_score
gt_instances['tmax_score'] = tmax_score
data_samples = [data_sample]
localizer_pem(raw_feature, data_samples, mode='predict')
# Test forward tensor
with torch.no_grad():
raw_feature = [torch.rand(100, 32)]
localizer_pem(raw_feature, data_samples=None, mode='tensor')
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