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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmengine import dump, list_from_file, load
from mmengine.config import Config, DictAction
from mmengine.runner import Runner
def parse_args():
parser = argparse.ArgumentParser(
description='MMAction2 feature extraction')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('output_prefix', type=str, help='output prefix')
parser.add_argument(
'--video-list', type=str, default=None, help='video file list')
parser.add_argument(
'--video-root', type=str, default=None, help='video root directory')
parser.add_argument(
'--spatial-type',
type=str,
default='avg',
choices=['avg', 'max', 'keep'],
help='Pooling type in spatial dimension')
parser.add_argument(
'--temporal-type',
type=str,
default='avg',
choices=['avg', 'max', 'keep'],
help='Pooling type in temporal dimension')
parser.add_argument(
'--long-video-mode',
action='store_true',
help='Perform long video inference to get a feature list from a video')
parser.add_argument(
'--clip-interval',
type=int,
default=None,
help='Clip interval for Clip interval of adjacent center of sampled '
'clips, used for long video inference')
parser.add_argument(
'--frame-interval',
type=int,
default=None,
help='Temporal interval of adjacent sampled frames, used for long '
'video long video inference')
parser.add_argument(
'--multi-view',
action='store_true',
help='Perform multi view inference')
parser.add_argument(
'--dump-score',
action='store_true',
help='Dump predict scores rather than features')
parser.add_argument(
'--cfg-options',
nargs='+',
action=DictAction,
help='override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file. If the value to '
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
'Note that the quotation marks are necessary and that no white space '
'is allowed.')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
default='none',
help='job launcher')
parser.add_argument('--local_rank', '--local-rank', type=int, default=0)
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
return args
def merge_args(cfg, args):
"""Merge CLI arguments to config."""
test_pipeline = cfg.test_dataloader.dataset.pipeline
# -------------------- Feature Head --------------------
if not args.dump_score:
backbone_type2name = dict(
ResNet3dSlowFast='slowfast',
MobileNetV2TSM='tsm',
ResNetTSM='tsm',
)
if cfg.model.type == 'RecognizerGCN':
backbone_name = 'gcn'
else:
backbone_name = backbone_type2name.get(cfg.model.backbone.type)
num_segments = None
if backbone_name == 'tsm':
for idx, transform in enumerate(test_pipeline):
if transform.type == 'UntrimmedSampleFrames':
clip_len = transform['clip_len']
continue
elif transform.type == 'SampleFrames':
clip_len = transform['num_clips']
num_segments = cfg.model.backbone.get('num_segments', 8)
assert num_segments == clip_len, \
f'num_segments and clip length must same for TSM, but got ' \
f'num_segments {num_segments} clip_len {clip_len}'
if cfg.model.test_cfg is not None:
max_testing_views = cfg.model.test_cfg.get(
'max_testing_views', num_segments)
assert max_testing_views % num_segments == 0, \
'tsm needs to infer with batchsize of multiple ' \
'of num_segments.'
spatial_type = None if args.spatial_type == 'keep' else \
args.spatial_type
temporal_type = None if args.temporal_type == 'keep' else \
args.temporal_type
feature_head = dict(
type='FeatureHead',
spatial_type=spatial_type,
temporal_type=temporal_type,
backbone_name=backbone_name,
num_segments=num_segments)
cfg.model.cls_head = feature_head
# ---------------------- multiple view ----------------------
if not args.multi_view:
# average features among multiple views
cfg.model.cls_head['average_clips'] = 'score'
if cfg.model.type == 'Recognizer3D':
for idx, transform in enumerate(test_pipeline):
if transform.type == 'SampleFrames':
test_pipeline[idx]['num_clips'] = 1
for idx, transform in enumerate(test_pipeline):
if transform.type == 'SampleFrames':
test_pipeline[idx]['twice_sample'] = False
# if transform.type in ['ThreeCrop', 'TenCrop']:
if transform.type == 'TenCrop':
test_pipeline[idx].type = 'CenterCrop'
# -------------------- pipeline settings --------------------
# assign video list and video root
if args.video_list is not None:
cfg.test_dataloader.dataset.ann_file = args.video_list
if args.video_root is not None:
if cfg.test_dataloader.dataset.type == 'VideoDataset':
cfg.test_dataloader.dataset.data_prefix = dict(
video=args.video_root)
elif cfg.test_dataloader.dataset.type == 'RawframeDataset':
cfg.test_dataloader.dataset.data_prefix = dict(img=args.video_root)
args.video_list = cfg.test_dataloader.dataset.ann_file
args.video_root = cfg.test_dataloader.dataset.data_prefix
# use UntrimmedSampleFrames for long video inference
if args.long_video_mode:
# preserve features of multiple clips
cfg.model.cls_head['average_clips'] = None
cfg.test_dataloader.batch_size = 1
is_recognizer2d = (cfg.model.type == 'Recognizer2D')
frame_interval = args.frame_interval
for idx, transform in enumerate(test_pipeline):
if transform.type == 'UntrimmedSampleFrames':
clip_len = transform['clip_len']
continue
# replace SampleFrame by UntrimmedSampleFrames
elif transform.type in ['SampleFrames', 'UniformSample']:
assert args.clip_interval is not None, \
'please specify clip interval for long video inference'
if is_recognizer2d:
# clip_len of UntrimmedSampleFrames is same as
# num_clips for 2D Recognizer.
clip_len = transform['num_clips']
else:
clip_len = transform['clip_len']
if frame_interval is None:
# take frame_interval of SampleFrames as default
frame_interval = transform.get('frame_interval')
assert frame_interval is not None, \
'please specify frame interval for long video ' \
'inference when use UniformSample or 2D Recognizer'
sample_cfgs = dict(
type='UntrimmedSampleFrames',
clip_len=clip_len,
clip_interval=args.clip_interval,
frame_interval=frame_interval)
test_pipeline[idx] = sample_cfgs
continue
# flow input will stack all frames
if cfg.test_dataloader.dataset.get('modality') == 'Flow':
clip_len = 1
if is_recognizer2d:
from mmaction.models import ActionDataPreprocessor
from mmaction.registry import MODELS
@MODELS.register_module()
class LongVideoDataPreprocessor(ActionDataPreprocessor):
"""DataPreprocessor for 2D recognizer to infer on long video.
Which would stack the num_clips to batch dimension, to preserve
feature of each clip (no average among clips)
"""
def __init__(self, num_frames=8, **kwargs) -> None:
super().__init__(**kwargs)
self.num_frames = num_frames
def preprocess(self, inputs, data_samples, training=False):
batch_inputs, data_samples = super().preprocess(
inputs, data_samples, training)
# [N*M, T, C, H, W]
nclip_batch_inputs = batch_inputs.view(
(-1, self.num_frames) + batch_inputs.shape[2:])
# data_samples = data_samples * \
# nclip_batch_inputs.shape[0]
return nclip_batch_inputs, data_samples
preprocessor_cfg = cfg.model.data_preprocessor
preprocessor_cfg.type = 'LongVideoDataPreprocessor'
preprocessor_cfg['num_frames'] = clip_len
# -------------------- Dump predictions --------------------
args.dump = osp.join(args.output_prefix, 'total_feats.pkl')
dump_metric = dict(type='DumpResults', out_file_path=args.dump)
cfg.test_evaluator = [dump_metric]
cfg.work_dir = osp.join(args.output_prefix, 'work_dir')
return cfg
def split_feats(args):
total_feats = load(args.dump)
if args.dump_score:
total_feats = [sample['pred_scores']['item'] for sample in total_feats]
video_list = list_from_file(args.video_list)
video_list = [line.split(' ')[0] for line in video_list]
for video_name, feature in zip(video_list, total_feats):
dump(feature, osp.join(args.output_prefix, video_name + '.pkl'))
os.remove(args.dump)
def main():
args = parse_args()
# load config
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
cfg = merge_args(cfg, args)
cfg.launcher = args.launcher
cfg.load_from = args.checkpoint
# build the runner from config
runner = Runner.from_cfg(cfg)
# start testing
runner.test()
split_feats(args)
if __name__ == '__main__':
main()
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