# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from mmcv import ConfigDict
class BaseTestDataset:
@classmethod
def setup_class(cls):
# prefix path
cls.data_prefix = osp.normpath(
osp.join(osp.dirname(__file__), '../../data'))
cls.ann_file_prefix = osp.join(cls.data_prefix, 'annotations')
# annotations path
cls.action_ann_file = osp.join(cls.ann_file_prefix,
'action_test_anno.json')
cls.audio_feature_ann_file = osp.join(cls.ann_file_prefix,
'audio_feature_test_list.txt')
cls.audio_ann_file = osp.join(cls.ann_file_prefix,
'audio_test_list.txt')
cls.frame_ann_file_multi_label = osp.join(
cls.ann_file_prefix, 'rawframe_test_list_multi_label.txt')
cls.frame_ann_file_with_offset = osp.join(
cls.ann_file_prefix, 'rawframe_test_list_with_offset.txt')
cls.frame_ann_file = osp.join(cls.ann_file_prefix,
'rawframe_test_list.txt')
cls.hvu_frame_ann_file = osp.join(cls.ann_file_prefix,
'hvu_frame_test_anno.json')
cls.hvu_video_ann_file = osp.join(cls.ann_file_prefix,
'hvu_video_test_anno.json')
cls.hvu_video_eval_ann_file = osp.join(
cls.ann_file_prefix, 'hvu_video_eval_test_anno.json')
cls.proposal_ann_file = osp.join(cls.ann_file_prefix,
'proposal_test_list.txt')
cls.proposal_norm_ann_file = osp.join(cls.ann_file_prefix,
'proposal_normalized_list.txt')
cls.rawvideo_test_anno_json = osp.join(cls.ann_file_prefix,
'rawvideo_test_anno.json')
cls.rawvideo_test_anno_txt = osp.join(cls.ann_file_prefix,
'rawvideo_test_anno.txt')
cls.video_ann_file = osp.join(cls.ann_file_prefix,
'video_test_list.txt')
cls.video_ann_file_multi_label = osp.join(
cls.ann_file_prefix, 'video_test_list_multi_label.txt')
cls.pose_ann_file = osp.join(cls.ann_file_prefix, 'sample.pkl')
# pipeline configuration
cls.action_pipeline = []
cls.audio_feature_pipeline = [
dict(type='LoadAudioFeature'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=2,
num_clips=1),
dict(type='AudioFeatureSelector')
]
cls.audio_pipeline = [
dict(type='AudioDecodeInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=2,
num_clips=1),
dict(type='AudioDecode')
]
cls.frame_pipeline = [
dict(
type='SampleFrames',
clip_len=32,
frame_interval=2,
num_clips=1),
dict(type='RawFrameDecode', io_backend='disk')
]
cls.proposal_pipeline = [
dict(
type='SampleProposalFrames',
clip_len=1,
body_segments=5,
aug_segments=(2, 2),
aug_ratio=0.5),
dict(type='RawFrameDecode', io_backend='disk')
]
cls.proposal_test_pipeline = [
dict(
type='SampleProposalFrames',
clip_len=1,
body_segments=5,
aug_segments=(2, 2),
aug_ratio=0.5,
mode='test'),
dict(type='RawFrameDecode', io_backend='disk')
]
cls.proposal_train_cfg = ConfigDict(
dict(
ssn=dict(
assigner=dict(
positive_iou_threshold=0.7,
background_iou_threshold=0.01,
incomplete_iou_threshold=0.5,
background_coverage_threshold=0.02,
incomplete_overlap_threshold=0.01),
sampler=dict(
num_per_video=8,
positive_ratio=1,
background_ratio=1,
incomplete_ratio=6,
add_gt_as_proposals=True),
loss_weight=dict(
comp_loss_weight=0.1, reg_loss_weight=0.1),
debug=False)))
cls.proposal_test_cfg = ConfigDict(
dict(
ssn=dict(
sampler=dict(test_interval=6, batch_size=16),
evaluater=dict(
top_k=2000,
nms=0.2,
softmax_before_filter=True,
cls_top_k=2))))
cls.proposal_test_cfg_topall = ConfigDict(
dict(
ssn=dict(
sampler=dict(test_interval=6, batch_size=16),
evaluater=dict(
top_k=-1,
nms=0.2,
softmax_before_filter=True,
cls_top_k=2))))
cls.rawvideo_pipeline = []
cls.video_pipeline = [
dict(type='OpenCVInit'),
dict(
type='SampleFrames',
clip_len=32,
frame_interval=2,
num_clips=1),
dict(type='OpenCVDecode')
]
cls.hvu_categories = [
'action', 'attribute', 'concept', 'event', 'object', 'scene'
]
cls.hvu_category_nums = [739, 117, 291, 69, 1679, 248]
cls.hvu_categories_for_eval = ['action', 'scene', 'object']
cls.hvu_category_nums_for_eval = [3, 3, 3]
cls.filename_tmpl = 'img_{:05d}.jpg'
Datasets
Models
