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