118 lines
4.5 KiB
Python
118 lines
4.5 KiB
Python
import os
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import copy
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from typing import Optional, Union
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import numpy as np
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from torch.utils.data import Dataset
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from .pipelines import Compose
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from .builder import DATASETS
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from motiondiff_modules.mogen.core.evaluation import build_evaluator
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@DATASETS.register_module()
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class BaseMotionDataset(Dataset):
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"""Base motion dataset.
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Args:
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data_prefix (str): the prefix of data path.
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pipeline (list): a list of dict, where each element represents
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a operation defined in `mogen.datasets.pipelines`.
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ann_file (str | None, optional): the annotation file. When ann_file is
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str, the subclass is expected to read from the ann_file. When
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ann_file is None, the subclass is expected to read according
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to data_prefix.
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test_mode (bool): in train mode or test mode. Default: None.
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dataset_name (str | None, optional): the name of dataset. It is used
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to identify the type of evaluation metric. Default: None.
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"""
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def __init__(self,
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data_prefix: str,
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pipeline: list,
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dataset_name: Optional[Union[str, None]] = None,
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fixed_length: Optional[Union[int, None]] = None,
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ann_file: Optional[Union[str, None]] = None,
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motion_dir: Optional[Union[str, None]] = None,
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eval_cfg: Optional[Union[dict, None]] = None,
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test_mode: Optional[bool] = False):
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super(BaseMotionDataset, self).__init__()
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self.data_prefix = data_prefix
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self.pipeline = Compose(pipeline)
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self.dataset_name = dataset_name
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self.fixed_length = fixed_length
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self.ann_file = os.path.join(data_prefix, 'datasets', dataset_name, ann_file)
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self.motion_dir = os.path.join(data_prefix, 'datasets', dataset_name, motion_dir)
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self.eval_cfg = copy.deepcopy(eval_cfg)
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self.test_mode = test_mode
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self.load_annotations()
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if self.test_mode:
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self.prepare_evaluation()
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def load_anno(self, name):
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motion_path = os.path.join(self.motion_dir, name + '.npy')
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motion_data = np.load(motion_path)
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return {'motion': motion_data}
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def load_annotations(self):
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"""Load annotations from ``ann_file`` to ``data_infos``"""
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self.data_infos = []
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for line in open(self.ann_file, 'r').readlines():
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line = line.strip()
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self.data_infos.append(self.load_anno(line))
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def prepare_data(self, idx: int):
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""""Prepare raw data for the f'{idx'}-th data."""
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results = copy.deepcopy(self.data_infos[idx])
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results['dataset_name'] = self.dataset_name
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results['sample_idx'] = idx
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return self.pipeline(results)
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def __len__(self):
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"""Return the length of current dataset."""
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if self.test_mode:
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return len(self.eval_indexes)
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elif self.fixed_length is not None:
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return self.fixed_length
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return len(self.data_infos)
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def __getitem__(self, idx: int):
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"""Prepare data for the ``idx``-th data.
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As for video dataset, we can first parse raw data for each frame. Then
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we combine annotations from all frames. This interface is used to
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simplify the logic of video dataset and other special datasets.
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"""
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if self.test_mode:
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idx = self.eval_indexes[idx]
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elif self.fixed_length is not None:
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idx = idx % len(self.data_infos)
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return self.prepare_data(idx)
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def prepare_evaluation(self):
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self.evaluators = []
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self.eval_indexes = []
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for _ in range(self.eval_cfg['replication_times']):
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eval_indexes = np.arange(len(self.data_infos))
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if self.eval_cfg.get('shuffle_indexes', False):
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np.random.shuffle(eval_indexes)
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self.eval_indexes.append(eval_indexes)
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for metric in self.eval_cfg['metrics']:
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evaluator, self.eval_indexes = build_evaluator(
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metric, self.eval_cfg, len(self.data_infos), self.eval_indexes)
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self.evaluators.append(evaluator)
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self.eval_indexes = np.concatenate(self.eval_indexes)
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def evaluate(self, results, work_dir, logger=None):
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metrics = {}
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device = results[0]['motion'].device
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for evaluator in self.evaluators:
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evaluator.to_device(device)
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metrics.update(evaluator.evaluate(results))
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if logger is not None:
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logger.info(metrics)
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return metrics
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