Add unittest for BaseDataset (#15)
* tmp commit * add unittest for basedataset * fix lint * resolve all comments * update * update * update * update * update * update * update * resolve comments * resolve comments * update * resolve comments * resolve commentspull/23/head
Tao Gong
GitHub
parent
6ba7ac3a8e
commit
100ede73e6
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{
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"metadata":
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{
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"dataset_type": "test_dataset",
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"task_name": "test_task"
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},
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"data_infos":
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[
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{
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"img_path": "test_img.jpg",
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"height": 604,
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"width": 640,
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"instances":
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[
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{
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"bbox": [0, 0, 10, 20],
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"bbox_label": 1,
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"mask": [[0,0],[0,10],[10,20],[20,0]],
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"extra_anns": [1,2,3]
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},
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{
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"bbox": [10, 10, 110, 120],
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"bbox_label": 2,
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"mask": [[10,10],[10,110],[110,120],[120,10]],
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"extra_anns": [4,5,6]
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}
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]
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},
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{
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"img_path": "gray.jpg",
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"height": 288,
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"width": 512,
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"instances":
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[
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{
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"bbox": [0, 0, 10, 20],
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"bbox_label": 1,
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"mask": [[0,0],[0,10],[10,20],[20,0]],
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"extra_anns": [1,2,3]
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},
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{
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"bbox": [10, 10, 110, 120],
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"bbox_label": 2,
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"mask": [[10,10],[10,110],[110,120],[120,10]],
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"extra_anns": [4,5,6]
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}
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]
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}
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]
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}
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{
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"meta":
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{
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"dataset_type": "test_dataset",
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"task_name": "test_task"
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},
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"data":
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[
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{
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"img_path": "test_img.jpg",
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"height": 604,
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"width": 640,
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"instances":
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[
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{
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"bbox": [0, 0, 10, 20],
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"bbox_label": 1,
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"mask": [[0,0],[0,10],[10,20],[20,0]],
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"extra_anns": [1,2,3]
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},
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{
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"bbox": [10, 10, 110, 120],
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"bbox_label": 2,
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"mask": [[10,10],[10,110],[110,120],[120,10]],
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"extra_anns": [4,5,6]
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}
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]
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},
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{
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"img_path": "gray.jpg",
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"height": 288,
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"width": 512,
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"instances":
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[
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{
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"bbox": [0, 0, 10, 20],
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"bbox_label": 1,
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"mask": [[0,0],[0,10],[10,20],[20,0]],
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"extra_anns": [1,2,3]
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},
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{
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"bbox": [10, 10, 110, 120],
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"bbox_label": 2,
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"mask": [[10,10],[10,110],[110,120],[120,10]],
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"extra_anns": [4,5,6]
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}
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]
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}
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]
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}
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import os.path as osp
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from unittest.mock import MagicMock
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import pytest
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import torch
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from mmengine.data import (BaseDataset, ClassBalancedDataset, ConcatDataset,
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RepeatDataset)
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class TestBaseDataset:
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def __init__(self):
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self.base_dataset = BaseDataset
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self.data_info = dict(filename='test_img.jpg', height=604, width=640)
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self.base_dataset.parse_annotations = MagicMock(
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return_value=self.data_info)
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self.imgs = torch.rand((2, 3, 32, 32))
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self.base_dataset.pipeline = MagicMock(
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return_value=dict(imgs=self.imgs))
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def test_init(self):
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# test the instantiation of self.base_dataset
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json')
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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assert hasattr(dataset, 'data_address')
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# test the instantiation of self.base_dataset with
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# `serialize_data=False`
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json',
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serialize_data=False)
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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assert not hasattr(dataset, 'data_address')
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# test the instantiation of self.base_dataset with lazy init
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json',
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lazy_init=True)
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assert not dataset._fully_initialized
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assert not hasattr(dataset, 'data_infos')
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# test the instantiation of self.base_dataset when the ann_file is
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# wrong
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with pytest.raises(ValueError):
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self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/wrong_annotation.json')
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# test the instantiation of self.base_dataset when `parse_annotations`
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# return `list[dict]`
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self.base_dataset.parse_annotations = MagicMock(
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return_value=[self.data_info,
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self.data_info.copy()])
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json')
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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assert hasattr(dataset, 'data_address')
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assert len(dataset) == 4
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assert dataset[0] == dict(imgs=self.imgs)
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assert dataset.get_data_info(0) == self.data_info
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# set self.base_dataset to initial state
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self.__init__()
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def test_meta(self):
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# test dataset.meta with setting the meta from annotation file as the
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# meta of self.base_dataset
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json')
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assert dataset.meta == dict(
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dataset_type='test_dataset', task_name='test_task')
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# test dataset.meta with setting META in self.base_dataset
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dataset_type = 'new_dataset'
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self.base_dataset.META = dict(
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dataset_type=dataset_type, classes=('dog', 'cat'))
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json')
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assert dataset.meta == dict(
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dataset_type=dataset_type,
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task_name='test_task',
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classes=('dog', 'cat'))
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# test dataset.meta with passing meta into self.base_dataset
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meta = dict(classes=('dog', ))
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json',
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meta=meta)
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assert self.base_dataset.META == dict(
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dataset_type=dataset_type, classes=('dog', 'cat'))
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assert dataset.meta == dict(
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dataset_type=dataset_type,
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task_name='test_task',
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classes=('dog', ))
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# reset `base_dataset.META`, the `dataset.meta` should not change
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self.base_dataset.META['classes'] = ('dog', 'cat', 'fish')
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assert self.base_dataset.META == dict(
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dataset_type=dataset_type, classes=('dog', 'cat', 'fish'))
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assert dataset.meta == dict(
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dataset_type=dataset_type,
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task_name='test_task',
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classes=('dog', ))
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# test dataset.meta with passing meta into self.base_dataset and
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# lazy_init is True
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meta = dict(classes=('dog', ))
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json',
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meta=meta,
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lazy_init=True)
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# 'task_name' not in dataset.meta
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assert dataset.meta == dict(
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dataset_type=dataset_type, classes=('dog', ))
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# test whether self.base_dataset.META is changed when a customize
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# dataset inherit self.base_dataset
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# test reset META in ToyDataset.
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class ToyDataset(self.base_dataset):
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META = dict(xxx='xxx')
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assert ToyDataset.META == dict(xxx='xxx')
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assert self.base_dataset.META == dict(
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dataset_type=dataset_type, classes=('dog', 'cat', 'fish'))
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# test update META in ToyDataset.
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class ToyDataset(self.base_dataset):
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self.base_dataset.META['classes'] = ('bird', )
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assert ToyDataset.META == dict(
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dataset_type=dataset_type, classes=('bird', ))
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assert self.base_dataset.META == dict(
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dataset_type=dataset_type, classes=('dog', 'cat', 'fish'))
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# set self.base_dataset to initial state
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self.__init__()
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@pytest.mark.parametrize('lazy_init', [True, False])
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def test_length(self, lazy_init):
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json',
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lazy_init=lazy_init)
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if not lazy_init:
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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assert len(dataset) == 2
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else:
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# test `__len__()` when lazy_init is True
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assert not dataset._fully_initialized
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assert not hasattr(dataset, 'data_infos')
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# call `full_init()` automatically
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assert len(dataset) == 2
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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@pytest.mark.parametrize('lazy_init', [True, False])
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def test_getitem(self, lazy_init):
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json',
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lazy_init=lazy_init)
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if not lazy_init:
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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assert dataset[0] == dict(imgs=self.imgs)
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else:
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# test `__getitem__()` when lazy_init is True
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assert not dataset._fully_initialized
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assert not hasattr(dataset, 'data_infos')
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# call `full_init()` automatically
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assert dataset[0] == dict(imgs=self.imgs)
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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@pytest.mark.parametrize('lazy_init', [True, False])
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def test_get_data_info(self, lazy_init):
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json',
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lazy_init=lazy_init)
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if not lazy_init:
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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assert dataset.get_data_info(0) == self.data_info
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else:
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# test `get_data_info()` when lazy_init is True
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assert not dataset._fully_initialized
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assert not hasattr(dataset, 'data_infos')
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# call `full_init()` automatically
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assert dataset.get_data_info(0) == self.data_info
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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@pytest.mark.parametrize('lazy_init', [True, False])
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def test_full_init(self, lazy_init):
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dataset = self.base_dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json',
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lazy_init=lazy_init)
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if not lazy_init:
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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assert len(dataset) == 2
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assert dataset[0] == dict(imgs=self.imgs)
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assert dataset.get_data_info(0) == self.data_info
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else:
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# test `full_init()` when lazy_init is True
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assert not dataset._fully_initialized
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assert not hasattr(dataset, 'data_infos')
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# call `full_init()` manually
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dataset.full_init()
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assert dataset._fully_initialized
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assert hasattr(dataset, 'data_infos')
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assert len(dataset) == 2
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assert dataset[0] == dict(imgs=self.imgs)
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assert dataset.get_data_info(0) == self.data_info
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class TestConcatDataset:
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def __init__(self):
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dataset = BaseDataset
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# create dataset_a
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data_info = dict(filename='test_img.jpg', height=604, width=640)
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dataset.parse_annotations = MagicMock(return_value=data_info)
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imgs = torch.rand((2, 3, 32, 32))
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dataset.pipeline = MagicMock(return_value=dict(imgs=imgs))
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self.dataset_a = dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json')
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# create dataset_b
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data_info = dict(filename='gray.jpg', height=288, width=512)
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dataset.parse_annotations = MagicMock(return_value=data_info)
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imgs = torch.rand((2, 3, 32, 32))
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dataset.pipeline = MagicMock(return_value=dict(imgs=imgs))
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self.dataset_b = dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json',
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meta=dict(classes=('dog', 'cat')))
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# test init
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self.cat_datasets = ConcatDataset(
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datasets=[self.dataset_a, self.dataset_b])
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def test_meta(self):
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assert self.cat_datasets.meta == self.dataset_a.meta
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# meta of self.cat_datasets is from the first dataset when
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# concatnating datasets with different metas.
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assert self.cat_datasets.meta != self.dataset_b.meta
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def test_length(self):
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assert len(self.cat_datasets) == (
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len(self.dataset_a) + len(self.dataset_b))
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def test_getitem(self):
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assert self.cat_datasets[0] == self.dataset_a[0]
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assert self.cat_datasets[0] != self.dataset_b[0]
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assert self.cat_datasets[-1] == self.dataset_b[-1]
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assert self.cat_datasets[-1] != self.dataset_a[-1]
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def test_get_data_info(self):
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assert self.cat_datasets.get_data_info(
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0) == self.dataset_a.get_data_info(0)
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assert self.cat_datasets.get_data_info(
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0) != self.dataset_b.get_data_info(0)
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assert self.cat_datasets.get_data_info(
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-1) == self.dataset_b.get_data_info(-1)
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assert self.cat_datasets.get_data_info(
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-1) != self.dataset_a[-1].get_data_info(-1)
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class TestRepeatDataset:
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def __init__(self):
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dataset = BaseDataset
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data_info = dict(filename='test_img.jpg', height=604, width=640)
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dataset.parse_annotations = MagicMock(return_value=data_info)
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imgs = torch.rand((2, 3, 32, 32))
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dataset.pipeline = MagicMock(return_value=dict(imgs=imgs))
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self.dataset = dataset(
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data_root=osp.join(osp.dirname(__file__), '../data/'),
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data_prefix=dict(img='imgs'),
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ann_file='annotations/dummy_annotation.json')
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self.repeat_times = 5
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# test init
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self.repeat_datasets = RepeatDataset(
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dataset=self.dataset, times=self.repeat_times)
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def test_meta(self):
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assert self.repeat_datasets.meta == self.dataset.meta
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def test_length(self):
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assert len(
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self.repeat_datasets) == len(self.dataset) * self.repeat_times
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def test_getitem(self):
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for i in range(self.repeat_times):
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assert self.repeat_datasets[len(self.dataset) *
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i] == self.dataset[0]
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def test_get_data_info(self):
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for i in range(self.repeat_times):
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assert self.repeat_datasets.get_data_info(
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len(self.dataset) * i) == self.dataset.get_data_info(0)
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class TestClassBalancedDataset:
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def __init__(self):
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dataset = BaseDataset
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data_info = dict(filename='test_img.jpg', height=604, width=640)
|
||||
dataset.parse_annotations = MagicMock(return_value=data_info)
|
||||
imgs = torch.rand((2, 3, 32, 32))
|
||||
dataset.pipeline = MagicMock(return_value=dict(imgs=imgs))
|
||||
dataset.get_cat_ids = MagicMock(return_value=[0])
|
||||
self.dataset = dataset(
|
||||
data_root=osp.join(osp.dirname(__file__), '../data/'),
|
||||
data_prefix=dict(img='imgs'),
|
||||
ann_file='annotations/dummy_annotation.json')
|
||||
|
||||
self.repeat_indices = [0, 0, 1, 1, 1]
|
||||
# test init
|
||||
self.cls_banlanced_datasets = ClassBalancedDataset(
|
||||
dataset=self.dataset, oversample_thr=1e-3)
|
||||
self.cls_banlanced_datasets.repeat_indices = self.repeat_indices
|
||||
|
||||
def test_meta(self):
|
||||
assert self.cls_banlanced_datasets.meta == self.dataset.meta
|
||||
|
||||
def test_length(self):
|
||||
assert len(self.cls_banlanced_datasets) == len(self.repeat_indices)
|
||||
|
||||
def test_getitem(self):
|
||||
for i in range(len(self.repeat_indices)):
|
||||
assert self.cls_banlanced_datasets[i] == self.dataset[
|
||||
self.repeat_indices[i]]
|
||||
|
||||
def test_get_data_info(self):
|
||||
for i in range(len(self.repeat_indices)):
|
||||
assert self.cls_banlanced_datasets.get_data_info(
|
||||
i) == self.dataset.get_data_info(self.repeat_indices[i])
|
||||
|
||||
def test_get_cat_ids(self):
|
||||
for i in range(len(self.repeat_indices)):
|
||||
assert self.cls_banlanced_datasets.get_cat_ids(
|
||||
i) == self.dataset.get_cat_ids(self.repeat_indices[i])
|
||||
Loading…
Reference in New Issue