mirror of https://github.com/alibaba/EasyCV.git
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import random
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from abc import ABCMeta, abstractmethod
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from torch.utils.data import Dataset
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from easycv.utils.registry import build_from_cfg
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from ..builder import build_datasource
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from ..registry import PIPELINES
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from .pipelines.transforms import Compose
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class BaseDataset(Dataset, metaclass=ABCMeta):
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"""Base Dataset
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"""
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def __init__(self, data_source, pipeline, profiling=False):
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self.data_source = build_datasource(data_source)
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pipeline = [build_from_cfg(p, PIPELINES) for p in pipeline]
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self.pipeline = Compose(pipeline, profiling=profiling)
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def __len__(self):
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return len(self.data_source)
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def _rand_another(self, idx):
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"""Randomly get another item.
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Returns:
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int: Another index of item.
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"""
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idx = random.randint(0, len(self) - 1)
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return idx
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@abstractmethod
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def __getitem__(self, idx):
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pass
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@abstractmethod
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def evaluate(self, results, evaluators, logger=None, **kwargs):
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pass
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def visualize(self, results, **kwargs):
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"""Visulaize the model output results on validation data.
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Returns: A dictionary
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If add image visualization, return dict containing
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images: List of visulaized images.
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img_metas: List of length number of test images,
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dict of image meta info, containing filename, img_shape,
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origin_img_shape, scale_factor and so on.
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"""
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return {}
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