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from unittest.mock import MagicMock
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import torch
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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_skeletongcn_cfg
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from mmaction.utils import register_all_modules
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def train_test_step(cfg, input_shape):
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recognizer = MODELS.build(cfg.model)
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num_classes = cfg.model.cls_head.num_classes
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data_batch = {
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'inputs': [torch.randn(input_shape)],
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'data_samples': [ActionDataSample().set_gt_label(2)]
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}
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optim_wrapper = MagicMock()
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loss_vars = recognizer.train_step(data_batch, optim_wrapper)
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assert 'loss' in loss_vars
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assert 'loss_cls' in loss_vars
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optim_wrapper.update_params.assert_called_once()
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with torch.no_grad():
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predictions = recognizer.test_step(data_batch)
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score = predictions[0].pred_score
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assert len(predictions) == 1
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assert score.shape == torch.Size([num_classes])
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assert torch.min(score) >= 0
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assert torch.max(score) <= 1
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recognizer.cls_head.average_clips = None
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num_clips = 3
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input_shape = (num_clips, *input_shape[1:])
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data_batch['inputs'] = [torch.randn(input_shape)]
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with torch.no_grad():
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predictions = recognizer.test_step(data_batch)
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score = predictions[0].pred_score
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assert len(predictions) == 1
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assert score.shape == torch.Size([num_clips, num_classes])
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return loss_vars, predictions
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def test_stgcn():
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register_all_modules()
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config = get_skeletongcn_cfg(
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'stgcn/stgcn_8xb16-joint-u100-80e_ntu60-xsub-keypoint-2d.py')
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input_shape = (1, 2, 30, 17, 3)
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train_test_step(config, input_shape=input_shape)
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def test_agcn():
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register_all_modules()
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config = get_skeletongcn_cfg(
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'2s-agcn/2s-agcn_8xb16-joint-u100-80e_ntu60-xsub-keypoint-2d.py')
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input_shape = (1, 2, 30, 17, 3)
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train_test_step(config, input_shape=input_shape)
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def test_stgcn_plusplus():
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register_all_modules()
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config = get_skeletongcn_cfg(
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'stgcnpp/stgcnpp_8xb16-joint-u100-80e_ntu60-xsub-keypoint-2d.py')
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input_shape = (1, 2, 30, 17, 3)
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train_test_step(config, input_shape=input_shape)
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