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Add unittest for BaseDataset (#15) 

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* add unittest for basedataset

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Tao Gong 2022-02-15 20:17:47 +08:00 committed by GitHub
parent 6ba7ac3a8e
commit 100ede73e6
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tests/data/annotations/dummy_annotation.json 100644
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{
"metadata":
{
"dataset_type": "test_dataset",
"task_name": "test_task"
},
"data_infos":
[
{
"img_path": "test_img.jpg",
"height": 604,
"width": 640,
"instances":
[
{
"bbox": [0, 0, 10, 20],
"bbox_label": 1,
"mask": [[0,0],[0,10],[10,20],[20,0]],
"extra_anns": [1,2,3]
},
{
"bbox": [10, 10, 110, 120],
"bbox_label": 2,
"mask": [[10,10],[10,110],[110,120],[120,10]],
"extra_anns": [4,5,6]
}
]
},
{
"img_path": "gray.jpg",
"height": 288,
"width": 512,
"instances":
[
{
"bbox": [0, 0, 10, 20],
"bbox_label": 1,
"mask": [[0,0],[0,10],[10,20],[20,0]],
"extra_anns": [1,2,3]
},
{
"bbox": [10, 10, 110, 120],
"bbox_label": 2,
"mask": [[10,10],[10,110],[110,120],[120,10]],
"extra_anns": [4,5,6]
}
]
}
]
}

50
tests/data/annotations/wrong_annotation.json 100644
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{
"meta":
{
"dataset_type": "test_dataset",
"task_name": "test_task"
},
"data":
[
{
"img_path": "test_img.jpg",
"height": 604,
"width": 640,
"instances":
[
{
"bbox": [0, 0, 10, 20],
"bbox_label": 1,
"mask": [[0,0],[0,10],[10,20],[20,0]],
"extra_anns": [1,2,3]
},
{
"bbox": [10, 10, 110, 120],
"bbox_label": 2,
"mask": [[10,10],[10,110],[110,120],[120,10]],
"extra_anns": [4,5,6]
}
]
},
{
"img_path": "gray.jpg",
"height": 288,
"width": 512,
"instances":
[
{
"bbox": [0, 0, 10, 20],
"bbox_label": 1,
"mask": [[0,0],[0,10],[10,20],[20,0]],
"extra_anns": [1,2,3]
},
{
"bbox": [10, 10, 110, 120],
"bbox_label": 2,
"mask": [[10,10],[10,110],[110,120],[120,10]],
"extra_anns": [4,5,6]
}
]
}
]
}

0
tests/data/imgs/gray.jpg 100644
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tests/data/imgs/test_img.jpg 100644
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386
tests/test_data/test_base_dataset.py 100644
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import os.path as osp
from unittest.mock import MagicMock
import pytest
import torch
from mmengine.data import (BaseDataset, ClassBalancedDataset, ConcatDataset,
RepeatDataset)
class TestBaseDataset:
def __init__(self):
self.base_dataset = BaseDataset
self.data_info = dict(filename='test_img.jpg', height=604, width=640)
self.base_dataset.parse_annotations = MagicMock(
return_value=self.data_info)
self.imgs = torch.rand((2, 3, 32, 32))
self.base_dataset.pipeline = MagicMock(
return_value=dict(imgs=self.imgs))
def test_init(self):
# test the instantiation of self.base_dataset
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json')
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
assert hasattr(dataset, 'data_address')
# test the instantiation of self.base_dataset with
# `serialize_data=False`
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json',
serialize_data=False)
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
assert not hasattr(dataset, 'data_address')
# test the instantiation of self.base_dataset with lazy init
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json',
lazy_init=True)
assert not dataset._fully_initialized
assert not hasattr(dataset, 'data_infos')
# test the instantiation of self.base_dataset when the ann_file is
# wrong
with pytest.raises(ValueError):
self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/wrong_annotation.json')
# test the instantiation of self.base_dataset when `parse_annotations`
# return `list[dict]`
self.base_dataset.parse_annotations = MagicMock(
return_value=[self.data_info,
self.data_info.copy()])
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json')
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
assert hasattr(dataset, 'data_address')
assert len(dataset) == 4
assert dataset[0] == dict(imgs=self.imgs)
assert dataset.get_data_info(0) == self.data_info
# set self.base_dataset to initial state
self.__init__()
def test_meta(self):
# test dataset.meta with setting the meta from annotation file as the
# meta of self.base_dataset
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json')
assert dataset.meta == dict(
dataset_type='test_dataset', task_name='test_task')
# test dataset.meta with setting META in self.base_dataset
dataset_type = 'new_dataset'
self.base_dataset.META = dict(
dataset_type=dataset_type, classes=('dog', 'cat'))
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json')
assert dataset.meta == dict(
dataset_type=dataset_type,
task_name='test_task',
classes=('dog', 'cat'))
# test dataset.meta with passing meta into self.base_dataset
meta = dict(classes=('dog', ))
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json',
meta=meta)
assert self.base_dataset.META == dict(
dataset_type=dataset_type, classes=('dog', 'cat'))
assert dataset.meta == dict(
dataset_type=dataset_type,
task_name='test_task',
classes=('dog', ))
# reset `base_dataset.META`, the `dataset.meta` should not change
self.base_dataset.META['classes'] = ('dog', 'cat', 'fish')
assert self.base_dataset.META == dict(
dataset_type=dataset_type, classes=('dog', 'cat', 'fish'))
assert dataset.meta == dict(
dataset_type=dataset_type,
task_name='test_task',
classes=('dog', ))
# test dataset.meta with passing meta into self.base_dataset and
# lazy_init is True
meta = dict(classes=('dog', ))
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json',
meta=meta,
lazy_init=True)
# 'task_name' not in dataset.meta
assert dataset.meta == dict(
dataset_type=dataset_type, classes=('dog', ))
# test whether self.base_dataset.META is changed when a customize
# dataset inherit self.base_dataset
# test reset META in ToyDataset.
class ToyDataset(self.base_dataset):
META = dict(xxx='xxx')
assert ToyDataset.META == dict(xxx='xxx')
assert self.base_dataset.META == dict(
dataset_type=dataset_type, classes=('dog', 'cat', 'fish'))
# test update META in ToyDataset.
class ToyDataset(self.base_dataset):
self.base_dataset.META['classes'] = ('bird', )
assert ToyDataset.META == dict(
dataset_type=dataset_type, classes=('bird', ))
assert self.base_dataset.META == dict(
dataset_type=dataset_type, classes=('dog', 'cat', 'fish'))
# set self.base_dataset to initial state
self.__init__()
@pytest.mark.parametrize('lazy_init', [True, False])
def test_length(self, lazy_init):
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json',
lazy_init=lazy_init)
if not lazy_init:
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
assert len(dataset) == 2
else:
# test `__len__()` when lazy_init is True
assert not dataset._fully_initialized
assert not hasattr(dataset, 'data_infos')
# call `full_init()` automatically
assert len(dataset) == 2
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
@pytest.mark.parametrize('lazy_init', [True, False])
def test_getitem(self, lazy_init):
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json',
lazy_init=lazy_init)
if not lazy_init:
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
assert dataset[0] == dict(imgs=self.imgs)
else:
# test `__getitem__()` when lazy_init is True
assert not dataset._fully_initialized
assert not hasattr(dataset, 'data_infos')
# call `full_init()` automatically
assert dataset[0] == dict(imgs=self.imgs)
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
@pytest.mark.parametrize('lazy_init', [True, False])
def test_get_data_info(self, lazy_init):
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json',
lazy_init=lazy_init)
if not lazy_init:
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
assert dataset.get_data_info(0) == self.data_info
else:
# test `get_data_info()` when lazy_init is True
assert not dataset._fully_initialized
assert not hasattr(dataset, 'data_infos')
# call `full_init()` automatically
assert dataset.get_data_info(0) == self.data_info
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
@pytest.mark.parametrize('lazy_init', [True, False])
def test_full_init(self, lazy_init):
dataset = self.base_dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json',
lazy_init=lazy_init)
if not lazy_init:
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
assert len(dataset) == 2
assert dataset[0] == dict(imgs=self.imgs)
assert dataset.get_data_info(0) == self.data_info
else:
# test `full_init()` when lazy_init is True
assert not dataset._fully_initialized
assert not hasattr(dataset, 'data_infos')
# call `full_init()` manually
dataset.full_init()
assert dataset._fully_initialized
assert hasattr(dataset, 'data_infos')
assert len(dataset) == 2
assert dataset[0] == dict(imgs=self.imgs)
assert dataset.get_data_info(0) == self.data_info
class TestConcatDataset:
def __init__(self):
dataset = BaseDataset
# create dataset_a
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))
self.dataset_a = dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json')
# create dataset_b
data_info = dict(filename='gray.jpg', height=288, width=512)
dataset.parse_annotations = MagicMock(return_value=data_info)
imgs = torch.rand((2, 3, 32, 32))
dataset.pipeline = MagicMock(return_value=dict(imgs=imgs))
self.dataset_b = dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json',
meta=dict(classes=('dog', 'cat')))
# test init
self.cat_datasets = ConcatDataset(
datasets=[self.dataset_a, self.dataset_b])
def test_meta(self):
assert self.cat_datasets.meta == self.dataset_a.meta
# meta of self.cat_datasets is from the first dataset when
# concatnating datasets with different metas.
assert self.cat_datasets.meta != self.dataset_b.meta
def test_length(self):
assert len(self.cat_datasets) == (
len(self.dataset_a) + len(self.dataset_b))
def test_getitem(self):
assert self.cat_datasets[0] == self.dataset_a[0]
assert self.cat_datasets[0] != self.dataset_b[0]
assert self.cat_datasets[-1] == self.dataset_b[-1]
assert self.cat_datasets[-1] != self.dataset_a[-1]
def test_get_data_info(self):
assert self.cat_datasets.get_data_info(
0) == self.dataset_a.get_data_info(0)
assert self.cat_datasets.get_data_info(
0) != self.dataset_b.get_data_info(0)
assert self.cat_datasets.get_data_info(
-1) == self.dataset_b.get_data_info(-1)
assert self.cat_datasets.get_data_info(
-1) != self.dataset_a[-1].get_data_info(-1)
class TestRepeatDataset:
def __init__(self):
dataset = BaseDataset
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))
self.dataset = dataset(
data_root=osp.join(osp.dirname(__file__), '../data/'),
data_prefix=dict(img='imgs'),
ann_file='annotations/dummy_annotation.json')
self.repeat_times = 5
# test init
self.repeat_datasets = RepeatDataset(
dataset=self.dataset, times=self.repeat_times)
def test_meta(self):
assert self.repeat_datasets.meta == self.dataset.meta
def test_length(self):
assert len(
self.repeat_datasets) == len(self.dataset) * self.repeat_times
def test_getitem(self):
for i in range(self.repeat_times):
assert self.repeat_datasets[len(self.dataset) *
i] == self.dataset[0]
def test_get_data_info(self):
for i in range(self.repeat_times):
assert self.repeat_datasets.get_data_info(
len(self.dataset) * i) == self.dataset.get_data_info(0)
class TestClassBalancedDataset:
def __init__(self):
dataset = BaseDataset
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])