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import torch |
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from addict import Dict |
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from mmpose.models.detectors import GestureRecognizer |
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def test_gesture_recognizer_forward(): |
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model_cfg = dict( |
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type='GestureRecognizer', |
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pretrained=None, |
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modality=['rgb', 'depth'], |
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backbone=dict( |
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rgb=dict( |
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type='I3D', |
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in_channels=3, |
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expansion=0.25, |
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), |
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depth=dict( |
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type='I3D', |
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in_channels=1, |
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expansion=0.25, |
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), |
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), |
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cls_head=dict( |
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type='MultiModalSSAHead', |
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num_classes=25, |
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avg_pool_kernel=(1, 2, 2), |
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in_channels=256), |
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train_cfg=dict( |
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beta=2, |
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lambda_=1e-3, |
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ssa_start_epoch=10, |
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), |
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test_cfg=dict(), |
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) |
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detector = GestureRecognizer(model_cfg['backbone'], None, |
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model_cfg['cls_head'], model_cfg['train_cfg'], |
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model_cfg['test_cfg'], model_cfg['modality'], |
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model_cfg['pretrained']) |
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detector.set_train_epoch(11) |
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video = [torch.randn(1, 3, 16, 112, 112), torch.randn(1, 1, 16, 112, 112)] |
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labels = torch.tensor([1]).long() |
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img_metas = Dict() |
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img_metas.data = dict(modality=['rgb', 'depth']) |
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losses = detector.forward(video, labels, img_metas, return_loss=True) |
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assert isinstance(losses, dict) |
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assert 'ssa_loss' in losses |
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with torch.no_grad(): |
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_ = detector.forward( |
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video, labels, img_metas=img_metas, return_loss=False) |
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