You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1022 lines
32 KiB
1022 lines
32 KiB
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import copy
|
|
from unittest.mock import MagicMock
|
|
|
|
import pytest
|
|
from mmcv import Config
|
|
from numpy.testing import assert_almost_equal
|
|
|
|
from mmpose.datasets import DATASETS
|
|
from tests.utils.data_utils import convert_db_to_output
|
|
|
|
|
|
def test_top_down_COCO_dataset():
|
|
dataset = 'TopDownCocoDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/coco.py').dataset_info
|
|
# test COCO datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=17,
|
|
dataset_joints=17,
|
|
dataset_channel=[
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
|
|
],
|
|
inference_channel=[
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
|
|
])
|
|
|
|
data_cfg = dict(
|
|
image_size=[192, 256],
|
|
heatmap_size=[48, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
soft_nms=False,
|
|
nms_thr=1.0,
|
|
oks_thr=0.9,
|
|
vis_thr=0.2,
|
|
use_gt_bbox=True,
|
|
det_bbox_thr=0.0,
|
|
bbox_file='tests/data/coco/test_coco_det_AP_H_56.json',
|
|
)
|
|
# Test det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
_ = dataset_class(
|
|
ann_file='tests/data/coco/test_coco.json',
|
|
img_prefix='tests/data/coco/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
_ = dataset_class(
|
|
ann_file='tests/data/coco/test_coco.json',
|
|
img_prefix='tests/data/coco/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
# Test gt bbox
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/coco/test_coco.json',
|
|
img_prefix='tests/data/coco/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'coco'
|
|
|
|
image_id = 785
|
|
assert image_id in custom_dataset.img_ids
|
|
assert len(custom_dataset.img_ids) == 4
|
|
_ = custom_dataset[0]
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
assert_almost_equal(infos['AP'], 1.0)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
|
|
def test_top_down_MHP_dataset():
|
|
dataset = 'TopDownMhpDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/mhp.py').dataset_info
|
|
# test MHP datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=16,
|
|
dataset_joints=16,
|
|
dataset_channel=[
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
|
|
],
|
|
inference_channel=[
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
|
|
])
|
|
|
|
data_cfg = dict(
|
|
image_size=[192, 256],
|
|
heatmap_size=[48, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
soft_nms=False,
|
|
nms_thr=1.0,
|
|
oks_thr=0.9,
|
|
vis_thr=0.2,
|
|
bbox_thr=1.0,
|
|
use_gt_bbox=True,
|
|
det_bbox_thr=0.0,
|
|
bbox_file='',
|
|
)
|
|
|
|
# Test det bbox
|
|
with pytest.raises(AssertionError):
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
|
|
_ = dataset_class(
|
|
ann_file='tests/data/mhp/test_mhp.json',
|
|
img_prefix='tests/data/mhp/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
# Test gt bbox
|
|
_ = dataset_class(
|
|
ann_file='tests/data/mhp/test_mhp.json',
|
|
img_prefix='tests/data/mhp/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/mhp/test_mhp.json',
|
|
img_prefix='tests/data/mhp/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'mhp'
|
|
|
|
image_id = 2889
|
|
assert image_id in custom_dataset.img_ids
|
|
assert len(custom_dataset.img_ids) == 2
|
|
_ = custom_dataset[0]
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
assert_almost_equal(infos['AP'], 1.0)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
|
|
def test_top_down_PoseTrack18_dataset():
|
|
dataset = 'TopDownPoseTrack18Dataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/posetrack18.py').dataset_info
|
|
# test PoseTrack datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=17,
|
|
dataset_joints=17,
|
|
dataset_channel=[
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
|
|
],
|
|
inference_channel=[
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
|
|
])
|
|
|
|
data_cfg = dict(
|
|
image_size=[192, 256],
|
|
heatmap_size=[48, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
soft_nms=False,
|
|
nms_thr=1.0,
|
|
oks_thr=0.9,
|
|
vis_thr=0.2,
|
|
use_gt_bbox=True,
|
|
det_bbox_thr=0.0,
|
|
bbox_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_human_detections.json',
|
|
)
|
|
# Test det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
_ = dataset_class(
|
|
ann_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_val.json',
|
|
img_prefix='tests/data/posetrack18/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
_ = dataset_class(
|
|
ann_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_val.json',
|
|
img_prefix='tests/data/posetrack18/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
# Test gt bbox
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_val.json',
|
|
img_prefix='tests/data/posetrack18/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'posetrack18'
|
|
|
|
image_id = 10128340000
|
|
assert image_id in custom_dataset.img_ids
|
|
assert len(custom_dataset.img_ids) == 3
|
|
assert len(custom_dataset) == 14
|
|
_ = custom_dataset[0]
|
|
|
|
# Test evaluate function, use gt bbox
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
assert_almost_equal(infos['Total AP'], 100)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
# Test evaluate function, use det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_val.json',
|
|
img_prefix='tests/data/posetrack18/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert len(custom_dataset) == 278
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
# since the det box input assume each keypoint position to be (0,0)
|
|
# the Total AP will be zero.
|
|
assert_almost_equal(infos['Total AP'], 0.)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
|
|
def test_top_down_PoseTrack18Video_dataset():
|
|
dataset = 'TopDownPoseTrack18VideoDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/posetrack18.py').dataset_info
|
|
# test PoseTrack18Video dataset
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=17,
|
|
dataset_joints=17,
|
|
dataset_channel=[
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
|
|
],
|
|
inference_channel=[
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
|
|
])
|
|
|
|
data_cfg = dict(
|
|
image_size=[288, 384],
|
|
heatmap_size=[72, 96],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
use_nms=True,
|
|
soft_nms=False,
|
|
nms_thr=1.0,
|
|
oks_thr=0.9,
|
|
vis_thr=0.2,
|
|
use_gt_bbox=True,
|
|
det_bbox_thr=0.0,
|
|
bbox_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_human_detections.json',
|
|
# frame-related arguments
|
|
frame_index_rand=True,
|
|
frame_index_range=[-2, 2],
|
|
num_adj_frames=1,
|
|
frame_indices_test=[-2, 2, -1, 1, 0],
|
|
frame_weight_train=(0.0, 1.0),
|
|
frame_weight_test=(0.3, 0.1, 0.25, 0.25, 0.1),
|
|
)
|
|
|
|
# Test value of dataset_info
|
|
with pytest.raises(ValueError):
|
|
_ = dataset_class(
|
|
ann_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_val.json',
|
|
img_prefix='tests/data/posetrack18/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=None,
|
|
test_mode=False)
|
|
|
|
# Test train mode (must use gt bbox)
|
|
with pytest.warns(UserWarning):
|
|
_ = dataset_class(
|
|
ann_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_val.json',
|
|
img_prefix='tests/data/posetrack18/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
# # Test gt bbox + test mode
|
|
with pytest.warns(UserWarning):
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_val.json',
|
|
img_prefix='tests/data/posetrack18/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'posetrack18'
|
|
assert custom_dataset.ph_fill_len == 6
|
|
|
|
image_id = 10128340000
|
|
assert image_id in custom_dataset.img_ids
|
|
assert len(custom_dataset.img_ids) == 3
|
|
assert len(custom_dataset) == 14
|
|
_ = custom_dataset[0]
|
|
|
|
# Test det bbox + test mode
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
with pytest.warns(UserWarning):
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_val.json',
|
|
img_prefix='tests/data/posetrack18/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.frame_indices_test == [-2, -1, 0, 1, 2]
|
|
assert len(custom_dataset) == 278
|
|
|
|
# Test non-random index
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['frame_index_rand'] = False
|
|
data_cfg_copy['frame_indices_train'] = [0, -1]
|
|
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_val.json',
|
|
img_prefix='tests/data/posetrack18/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
assert custom_dataset.frame_indices_train == [-1, 0]
|
|
|
|
# Test evaluate function, use gt bbox
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
assert_almost_equal(infos['Total AP'], 100)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
# Test evaluate function, use det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
with pytest.warns(UserWarning):
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/posetrack18/annotations/'
|
|
'test_posetrack18_val.json',
|
|
img_prefix='tests/data/posetrack18/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
# since the det box input assume each keypoint position to be (0,0),
|
|
# the Total AP will be zero.
|
|
assert_almost_equal(infos['Total AP'], 0)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
|
|
def test_top_down_CrowdPose_dataset():
|
|
dataset = 'TopDownCrowdPoseDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/crowdpose.py').dataset_info
|
|
# test CrowdPose datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=14,
|
|
dataset_joints=14,
|
|
dataset_channel=[
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
|
|
],
|
|
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
|
|
|
|
data_cfg = dict(
|
|
image_size=[192, 256],
|
|
heatmap_size=[48, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
soft_nms=False,
|
|
nms_thr=1.0,
|
|
oks_thr=0.9,
|
|
vis_thr=0.2,
|
|
use_gt_bbox=True,
|
|
det_bbox_thr=0.0,
|
|
bbox_file='tests/data/crowdpose/test_crowdpose_det_AP_40.json',
|
|
)
|
|
# Test det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
_ = dataset_class(
|
|
ann_file='tests/data/crowdpose/test_crowdpose.json',
|
|
img_prefix='tests/data/crowdpose/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
_ = dataset_class(
|
|
ann_file='tests/data/crowdpose/test_crowdpose.json',
|
|
img_prefix='tests/data/crowdpose/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
# Test gt bbox
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/crowdpose/test_crowdpose.json',
|
|
img_prefix='tests/data/crowdpose/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'crowdpose'
|
|
|
|
image_id = 103319
|
|
assert image_id in custom_dataset.img_ids
|
|
assert len(custom_dataset.img_ids) == 2
|
|
_ = custom_dataset[0]
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
assert_almost_equal(infos['AP'], 1.0)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
|
|
def test_top_down_COCO_wholebody_dataset():
|
|
dataset = 'TopDownCocoWholeBodyDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/coco_wholebody.py').dataset_info
|
|
# test COCO datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=133,
|
|
dataset_joints=133,
|
|
dataset_channel=[
|
|
list(range(133)),
|
|
],
|
|
inference_channel=list(range(133)))
|
|
|
|
data_cfg = dict(
|
|
image_size=[192, 256],
|
|
heatmap_size=[48, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
soft_nms=False,
|
|
nms_thr=1.0,
|
|
oks_thr=0.9,
|
|
vis_thr=0.2,
|
|
use_gt_bbox=True,
|
|
det_bbox_thr=0.0,
|
|
bbox_file='tests/data/coco/test_coco_det_AP_H_56.json',
|
|
)
|
|
# Test det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
_ = dataset_class(
|
|
ann_file='tests/data/coco/test_coco_wholebody.json',
|
|
img_prefix='tests/data/coco/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
_ = dataset_class(
|
|
ann_file='tests/data/coco/test_coco_wholebody.json',
|
|
img_prefix='tests/data/coco/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
# Test gt bbox
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/coco/test_coco_wholebody.json',
|
|
img_prefix='tests/data/coco/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'coco_wholebody'
|
|
|
|
image_id = 785
|
|
assert image_id in custom_dataset.img_ids
|
|
assert len(custom_dataset.img_ids) == 4
|
|
_ = custom_dataset[0]
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
assert_almost_equal(infos['AP'], 1.0)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
|
|
def test_top_down_halpe_dataset():
|
|
dataset = 'TopDownHalpeDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/halpe.py').dataset_info
|
|
# test Halpe datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=136,
|
|
dataset_joints=136,
|
|
dataset_channel=[
|
|
list(range(136)),
|
|
],
|
|
inference_channel=list(range(136)))
|
|
|
|
data_cfg = dict(
|
|
image_size=[192, 256],
|
|
heatmap_size=[48, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
soft_nms=False,
|
|
nms_thr=1.0,
|
|
oks_thr=0.9,
|
|
vis_thr=0.2,
|
|
use_gt_bbox=True,
|
|
det_bbox_thr=0.0,
|
|
bbox_file='tests/data/coco/test_coco_det_AP_H_56.json',
|
|
)
|
|
# Test det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
_ = dataset_class(
|
|
ann_file='tests/data/halpe/test_halpe.json',
|
|
img_prefix='tests/data/coco/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
_ = dataset_class(
|
|
ann_file='tests/data/halpe/test_halpe.json',
|
|
img_prefix='tests/data/coco/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
# Test gt bbox
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/halpe/test_halpe.json',
|
|
img_prefix='tests/data/coco/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'halpe'
|
|
|
|
image_id = 785
|
|
assert image_id in custom_dataset.img_ids
|
|
assert len(custom_dataset.img_ids) == 4
|
|
_ = custom_dataset[0]
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
assert_almost_equal(infos['AP'], 1.0)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
|
|
def test_top_down_OCHuman_dataset():
|
|
dataset = 'TopDownOCHumanDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/ochuman.py').dataset_info
|
|
# test OCHuman datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=17,
|
|
dataset_joints=17,
|
|
dataset_channel=[
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
|
|
],
|
|
inference_channel=[
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
|
|
])
|
|
|
|
data_cfg = dict(
|
|
image_size=[192, 256],
|
|
heatmap_size=[48, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
soft_nms=False,
|
|
nms_thr=1.0,
|
|
oks_thr=0.9,
|
|
vis_thr=0.2,
|
|
use_gt_bbox=True,
|
|
det_bbox_thr=0.0,
|
|
bbox_file='',
|
|
)
|
|
|
|
with pytest.raises(AssertionError):
|
|
# Test det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
_ = dataset_class(
|
|
ann_file='tests/data/ochuman/test_ochuman.json',
|
|
img_prefix='tests/data/ochuman/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
# Test gt bbox
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/ochuman/test_ochuman.json',
|
|
img_prefix='tests/data/ochuman/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'ochuman'
|
|
|
|
image_id = 1
|
|
assert image_id in custom_dataset.img_ids
|
|
assert len(custom_dataset.img_ids) == 3
|
|
_ = custom_dataset[0]
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
assert_almost_equal(infos['AP'], 1.0)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
|
|
def test_top_down_MPII_dataset():
|
|
dataset = 'TopDownMpiiDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/mpii.py').dataset_info
|
|
# test COCO datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=16,
|
|
dataset_joints=16,
|
|
dataset_channel=[
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
|
|
],
|
|
inference_channel=[
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
|
|
])
|
|
|
|
data_cfg = dict(
|
|
image_size=[256, 256],
|
|
heatmap_size=[64, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
)
|
|
|
|
# Test det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/mpii/test_mpii.json',
|
|
img_prefix='tests/data/mpii/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
)
|
|
|
|
assert len(custom_dataset) == 5
|
|
assert custom_dataset.dataset_name == 'mpii'
|
|
_ = custom_dataset[0]
|
|
|
|
|
|
def test_top_down_MPII_TRB_dataset():
|
|
dataset = 'TopDownMpiiTrbDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/mpii_trb.py').dataset_info
|
|
# test MPII TRB datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=40,
|
|
dataset_joints=40,
|
|
dataset_channel=[list(range(40))],
|
|
inference_channel=list(range(40)))
|
|
|
|
data_cfg = dict(
|
|
image_size=[256, 256],
|
|
heatmap_size=[64, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'])
|
|
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
_ = dataset_class(
|
|
ann_file='tests/data/mpii/test_mpii_trb.json',
|
|
img_prefix='tests/data/mpii/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/mpii/test_mpii_trb.json',
|
|
img_prefix='tests/data/mpii/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'mpii_trb'
|
|
_ = custom_dataset[0]
|
|
|
|
|
|
def test_top_down_AIC_dataset():
|
|
dataset = 'TopDownAicDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/aic.py').dataset_info
|
|
# test AIC datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=14,
|
|
dataset_joints=14,
|
|
dataset_channel=[
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
|
|
],
|
|
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
|
|
|
|
data_cfg = dict(
|
|
image_size=[192, 256],
|
|
heatmap_size=[48, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
soft_nms=False,
|
|
nms_thr=1.0,
|
|
oks_thr=0.9,
|
|
vis_thr=0.2,
|
|
use_gt_bbox=True,
|
|
det_bbox_thr=0.0,
|
|
bbox_file='')
|
|
|
|
with pytest.raises(AssertionError):
|
|
# Test det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
_ = dataset_class(
|
|
ann_file='tests/data/aic/test_aic.json',
|
|
img_prefix='tests/data/aic/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
_ = dataset_class(
|
|
ann_file='tests/data/aic/test_aic.json',
|
|
img_prefix='tests/data/aic/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
# Test gt bbox
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/aic/test_aic.json',
|
|
img_prefix='tests/data/aic/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'aic'
|
|
|
|
image_id = 1
|
|
assert image_id in custom_dataset.img_ids
|
|
assert len(custom_dataset.img_ids) == 3
|
|
_ = custom_dataset[0]
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='mAP')
|
|
assert_almost_equal(infos['AP'], 1.0)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='PCK')
|
|
|
|
|
|
def test_top_down_JHMDB_dataset():
|
|
dataset = 'TopDownJhmdbDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/jhmdb.py').dataset_info
|
|
# test JHMDB datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=15,
|
|
dataset_joints=15,
|
|
dataset_channel=[
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
|
|
],
|
|
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
|
|
|
|
data_cfg = dict(
|
|
image_size=[192, 256],
|
|
heatmap_size=[48, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'],
|
|
soft_nms=False,
|
|
nms_thr=1.0,
|
|
oks_thr=0.9,
|
|
vis_thr=0.2,
|
|
use_gt_bbox=True,
|
|
det_bbox_thr=0.0,
|
|
bbox_file='')
|
|
|
|
with pytest.raises(AssertionError):
|
|
# Test det bbox
|
|
data_cfg_copy = copy.deepcopy(data_cfg)
|
|
data_cfg_copy['use_gt_bbox'] = False
|
|
_ = dataset_class(
|
|
ann_file='tests/data/jhmdb/test_jhmdb_sub1.json',
|
|
img_prefix='tests/data/jhmdb/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
_ = dataset_class(
|
|
ann_file='tests/data/jhmdb/test_jhmdb_sub1.json',
|
|
img_prefix='tests/data/jhmdb/',
|
|
data_cfg=data_cfg_copy,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=False)
|
|
|
|
# Test gt bbox
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/jhmdb/test_jhmdb_sub1.json',
|
|
img_prefix='tests/data/jhmdb/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'jhmdb'
|
|
|
|
image_id = 2290001
|
|
assert image_id in custom_dataset.img_ids
|
|
assert len(custom_dataset.img_ids) == 3
|
|
_ = custom_dataset[0]
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric=['PCK'])
|
|
assert_almost_equal(infos['Mean PCK'], 1.0)
|
|
|
|
infos = custom_dataset.evaluate(results, metric=['tPCK'])
|
|
assert_almost_equal(infos['Mean tPCK'], 1.0)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='mAP')
|
|
|
|
|
|
def test_top_down_h36m_dataset():
|
|
dataset = 'TopDownH36MDataset'
|
|
dataset_info = Config.fromfile(
|
|
'configs/_base_/datasets/h36m.py').dataset_info
|
|
# test AIC datasets
|
|
dataset_class = DATASETS.get(dataset)
|
|
dataset_class.load_annotations = MagicMock()
|
|
dataset_class.coco = MagicMock()
|
|
|
|
channel_cfg = dict(
|
|
num_output_channels=17,
|
|
dataset_joints=17,
|
|
dataset_channel=[
|
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
|
|
],
|
|
inference_channel=[
|
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
|
|
])
|
|
|
|
data_cfg = dict(
|
|
image_size=[256, 256],
|
|
heatmap_size=[64, 64],
|
|
num_output_channels=channel_cfg['num_output_channels'],
|
|
num_joints=channel_cfg['dataset_joints'],
|
|
dataset_channel=channel_cfg['dataset_channel'],
|
|
inference_channel=channel_cfg['inference_channel'])
|
|
|
|
# Test gt bbox
|
|
custom_dataset = dataset_class(
|
|
ann_file='tests/data/h36m/h36m_coco.json',
|
|
img_prefix='tests/data/h36m/',
|
|
data_cfg=data_cfg,
|
|
pipeline=[],
|
|
dataset_info=dataset_info,
|
|
test_mode=True)
|
|
|
|
assert custom_dataset.test_mode is True
|
|
assert custom_dataset.dataset_name == 'h36m'
|
|
|
|
image_id = 1
|
|
assert image_id in custom_dataset.img_ids
|
|
_ = custom_dataset[0]
|
|
|
|
results = convert_db_to_output(custom_dataset.db)
|
|
infos = custom_dataset.evaluate(results, metric='EPE')
|
|
assert_almost_equal(infos['EPE'], 0.0)
|
|
|
|
with pytest.raises(KeyError):
|
|
_ = custom_dataset.evaluate(results, metric='AUC')
|
|
|