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500 lines
15 KiB
500 lines
15 KiB
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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import pytest
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from mmcv import Config
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from numpy.testing import assert_almost_equal
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from mmpose.datasets import DATASETS
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from tests.utils.data_utils import convert_db_to_output
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def test_animal_horse10_dataset():
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dataset = 'AnimalHorse10Dataset'
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dataset_class = DATASETS.get(dataset)
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/horse10.py').dataset_info
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channel_cfg = dict(
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num_output_channels=22,
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dataset_joints=22,
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dataset_channel=[
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[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
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18, 19, 21
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],
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],
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inference_channel=[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
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19, 21
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])
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data_cfg = dict(
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image_size=[256, 256],
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heatmap_size=[64, 64],
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num_output_channels=channel_cfg['num_output_channels'],
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num_joints=channel_cfg['dataset_joints'],
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dataset_channel=channel_cfg['dataset_channel'],
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inference_channel=channel_cfg['inference_channel'])
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# Test
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data_cfg_copy = copy.deepcopy(data_cfg)
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_ = dataset_class(
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ann_file='tests/data/horse10/test_horse10.json',
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img_prefix='tests/data/horse10/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=True)
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custom_dataset = dataset_class(
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ann_file='tests/data/horse10/test_horse10.json',
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img_prefix='tests/data/horse10/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=False)
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assert custom_dataset.dataset_name == 'horse10'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 3
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_ = custom_dataset[0]
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results = convert_db_to_output(custom_dataset.db)
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infos = custom_dataset.evaluate(results, metric=['PCK'])
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assert_almost_equal(infos['PCK'], 1.0)
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with pytest.raises(KeyError):
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infos = custom_dataset.evaluate(results, metric='mAP')
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def test_animal_fly_dataset():
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dataset = 'AnimalFlyDataset'
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dataset_class = DATASETS.get(dataset)
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/fly.py').dataset_info
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channel_cfg = dict(
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num_output_channels=32,
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dataset_joints=32,
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dataset_channel=[
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[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
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18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
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],
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],
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inference_channel=[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
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19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
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])
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data_cfg = dict(
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image_size=[192, 192],
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heatmap_size=[48, 48],
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num_output_channels=channel_cfg['num_output_channels'],
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num_joints=channel_cfg['dataset_joints'],
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dataset_channel=channel_cfg['dataset_channel'],
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inference_channel=channel_cfg['inference_channel'])
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# Test
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data_cfg_copy = copy.deepcopy(data_cfg)
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_ = dataset_class(
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ann_file='tests/data/fly/test_fly.json',
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img_prefix='tests/data/fly/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=True)
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custom_dataset = dataset_class(
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ann_file='tests/data/fly/test_fly.json',
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img_prefix='tests/data/fly/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=False)
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assert custom_dataset.dataset_name == 'fly'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 2
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_ = custom_dataset[0]
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results = convert_db_to_output(custom_dataset.db)
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infos = custom_dataset.evaluate(results, metric=['PCK'])
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assert_almost_equal(infos['PCK'], 1.0)
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with pytest.raises(KeyError):
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infos = custom_dataset.evaluate(results, metric='mAP')
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def test_animal_locust_dataset():
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dataset = 'AnimalLocustDataset'
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dataset_class = DATASETS.get(dataset)
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/locust.py').dataset_info
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channel_cfg = dict(
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num_output_channels=35,
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dataset_joints=35,
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dataset_channel=[
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[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
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18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
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34
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],
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],
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inference_channel=[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
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19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34
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])
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data_cfg = dict(
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image_size=[160, 160],
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heatmap_size=[40, 40],
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num_output_channels=channel_cfg['num_output_channels'],
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num_joints=channel_cfg['dataset_joints'],
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dataset_channel=channel_cfg['dataset_channel'],
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inference_channel=channel_cfg['inference_channel'])
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# Test
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data_cfg_copy = copy.deepcopy(data_cfg)
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_ = dataset_class(
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ann_file='tests/data/locust/test_locust.json',
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img_prefix='tests/data/locust/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=True)
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custom_dataset = dataset_class(
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ann_file='tests/data/locust/test_locust.json',
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img_prefix='tests/data/locust/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=False)
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assert custom_dataset.dataset_name == 'locust'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 2
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_ = custom_dataset[0]
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results = convert_db_to_output(custom_dataset.db)
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infos = custom_dataset.evaluate(results, metric=['PCK'])
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assert_almost_equal(infos['PCK'], 1.0)
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with pytest.raises(KeyError):
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infos = custom_dataset.evaluate(results, metric='mAP')
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def test_animal_zebra_dataset():
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dataset = 'AnimalZebraDataset'
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dataset_class = DATASETS.get(dataset)
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/zebra.py').dataset_info
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channel_cfg = dict(
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num_output_channels=9,
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dataset_joints=9,
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dataset_channel=[
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[0, 1, 2, 3, 4, 5, 6, 7, 8],
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],
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inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8])
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data_cfg = dict(
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image_size=[160, 160],
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heatmap_size=[40, 40],
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num_output_channels=channel_cfg['num_output_channels'],
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num_joints=channel_cfg['dataset_joints'],
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dataset_channel=channel_cfg['dataset_channel'],
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inference_channel=channel_cfg['inference_channel'])
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# Test
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data_cfg_copy = copy.deepcopy(data_cfg)
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_ = dataset_class(
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ann_file='tests/data/zebra/test_zebra.json',
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img_prefix='tests/data/zebra/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=True)
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custom_dataset = dataset_class(
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ann_file='tests/data/zebra/test_zebra.json',
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img_prefix='tests/data/zebra/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=False)
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assert custom_dataset.dataset_name == 'zebra'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 2
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_ = custom_dataset[0]
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results = convert_db_to_output(custom_dataset.db)
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infos = custom_dataset.evaluate(results, metric=['PCK'])
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assert_almost_equal(infos['PCK'], 1.0)
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with pytest.raises(KeyError):
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infos = custom_dataset.evaluate(results, metric='mAP')
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def test_animal_ATRW_dataset():
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dataset = 'AnimalATRWDataset'
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dataset_class = DATASETS.get(dataset)
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/atrw.py').dataset_info
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channel_cfg = dict(
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num_output_channels=15,
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dataset_joints=15,
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dataset_channel=[
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
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],
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inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
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data_cfg = dict(
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image_size=[256, 256],
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heatmap_size=[64, 64],
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num_output_channels=channel_cfg['num_output_channels'],
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num_joints=channel_cfg['dataset_joints'],
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dataset_channel=channel_cfg['dataset_channel'],
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inference_channel=channel_cfg['inference_channel'],
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soft_nms=False,
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nms_thr=1.0,
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oks_thr=0.9,
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vis_thr=0.2,
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use_gt_bbox=True,
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det_bbox_thr=0.0,
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bbox_file='',
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)
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# Test
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data_cfg_copy = copy.deepcopy(data_cfg)
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_ = dataset_class(
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ann_file='tests/data/atrw/test_atrw.json',
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img_prefix='tests/data/atrw/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=True)
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custom_dataset = dataset_class(
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ann_file='tests/data/atrw/test_atrw.json',
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img_prefix='tests/data/atrw/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=False)
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assert custom_dataset.dataset_name == 'atrw'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 2
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_ = custom_dataset[0]
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results = convert_db_to_output(custom_dataset.db)
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infos = custom_dataset.evaluate(results, metric='mAP')
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assert_almost_equal(infos['AP'], 1.0)
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with pytest.raises(KeyError):
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infos = custom_dataset.evaluate(results, metric=['PCK'])
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def test_animal_Macaque_dataset():
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dataset = 'AnimalMacaqueDataset'
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dataset_class = DATASETS.get(dataset)
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/macaque.py').dataset_info
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channel_cfg = dict(
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num_output_channels=17,
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dataset_joints=17,
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dataset_channel=[
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
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],
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inference_channel=[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
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])
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data_cfg = dict(
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image_size=[192, 256],
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heatmap_size=[48, 64],
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num_output_channels=channel_cfg['num_output_channels'],
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num_joints=channel_cfg['dataset_joints'],
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dataset_channel=channel_cfg['dataset_channel'],
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inference_channel=channel_cfg['inference_channel'],
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soft_nms=False,
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nms_thr=1.0,
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oks_thr=0.9,
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vis_thr=0.2,
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use_gt_bbox=True,
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det_bbox_thr=0.0,
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bbox_file='',
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)
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# Test
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data_cfg_copy = copy.deepcopy(data_cfg)
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_ = dataset_class(
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ann_file='tests/data/macaque/test_macaque.json',
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img_prefix='tests/data/macaque/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=True)
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custom_dataset = dataset_class(
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ann_file='tests/data/macaque/test_macaque.json',
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img_prefix='tests/data/macaque/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=False)
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assert custom_dataset.dataset_name == 'macaque'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 2
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_ = custom_dataset[0]
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results = convert_db_to_output(custom_dataset.db)
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infos = custom_dataset.evaluate(results, metric='mAP')
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assert_almost_equal(infos['AP'], 1.0)
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with pytest.raises(KeyError):
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infos = custom_dataset.evaluate(results, metric=['PCK'])
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def test_animalpose_dataset():
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dataset = 'AnimalPoseDataset'
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dataset_class = DATASETS.get(dataset)
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/animalpose.py').dataset_info
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channel_cfg = dict(
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num_output_channels=20,
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dataset_joints=20,
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dataset_channel=[
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[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
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18, 19
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],
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],
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inference_channel=[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
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19
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])
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data_cfg = dict(
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image_size=[256, 256],
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heatmap_size=[64, 64],
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num_output_channels=channel_cfg['num_output_channels'],
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num_joints=channel_cfg['dataset_joints'],
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dataset_channel=channel_cfg['dataset_channel'],
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inference_channel=channel_cfg['inference_channel'],
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soft_nms=False,
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nms_thr=1.0,
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oks_thr=0.9,
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vis_thr=0.2,
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use_gt_bbox=True,
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det_bbox_thr=0.0,
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bbox_file='',
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)
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# Test
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data_cfg_copy = copy.deepcopy(data_cfg)
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_ = dataset_class(
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ann_file='tests/data/animalpose/test_animalpose.json',
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img_prefix='tests/data/animalpose/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=True)
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custom_dataset = dataset_class(
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ann_file='tests/data/animalpose/test_animalpose.json',
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img_prefix='tests/data/animalpose/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=False)
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assert custom_dataset.dataset_name == 'animalpose'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 2
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_ = custom_dataset[0]
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results = convert_db_to_output(custom_dataset.db)
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infos = custom_dataset.evaluate(results, metric='mAP')
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assert_almost_equal(infos['AP'], 1.0)
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with pytest.raises(KeyError):
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infos = custom_dataset.evaluate(results, metric=['PCK'])
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def test_ap10k_dataset():
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dataset = 'AnimalAP10KDataset'
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dataset_class = DATASETS.get(dataset)
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/ap10k.py').dataset_info
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channel_cfg = dict(
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num_output_channels=17,
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dataset_joints=17,
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dataset_channel=[
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
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],
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inference_channel=[
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
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])
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data_cfg = dict(
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image_size=[256, 256],
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heatmap_size=[64, 64],
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num_output_channels=channel_cfg['num_output_channels'],
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num_joints=channel_cfg['dataset_joints'],
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dataset_channel=channel_cfg['dataset_channel'],
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inference_channel=channel_cfg['inference_channel'],
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soft_nms=False,
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nms_thr=1.0,
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oks_thr=0.9,
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vis_thr=0.2,
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use_gt_bbox=True,
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det_bbox_thr=0.0,
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bbox_file='',
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)
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# Test
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data_cfg_copy = copy.deepcopy(data_cfg)
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_ = dataset_class(
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ann_file='tests/data/ap10k/test_ap10k.json',
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img_prefix='tests/data/ap10k/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=True)
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custom_dataset = dataset_class(
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ann_file='tests/data/ap10k/test_ap10k.json',
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img_prefix='tests/data/ap10k/',
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data_cfg=data_cfg_copy,
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dataset_info=dataset_info,
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pipeline=[],
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test_mode=False)
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assert custom_dataset.dataset_name == 'ap10k'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 2
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_ = custom_dataset[0]
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results = convert_db_to_output(custom_dataset.db)
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for output in results:
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# as there is only one box in each image for test
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output['bbox_ids'] = [0 for _ in range(len(output['bbox_ids']))]
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infos = custom_dataset.evaluate(results, metric='mAP')
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assert_almost_equal(infos['AP'], 1.0)
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with pytest.raises(KeyError):
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infos = custom_dataset.evaluate(results, metric=['PCK'])
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