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28e129b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 | """Reference with Ivo's implementation"""
import argparse
import logging
import os
from os import path, mkdir
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from video_loader import VideoIter
from utils import register_logger, get_torch_device
import transforms_video
from torch.utils.data import DataLoader
from torchvision.transforms import transforms
# Video Swin Transformer related repository
from mmcv import Config
from mmaction.models import build_model
from mmcv.runner import load_checkpoint
import warnings
warnings.filterwarnings("ignore", message="The pts_unit 'pts' gives wrong results. Please use pts_unit 'sec'.")
warnings.filterwarnings('ignore', message='No handlers found: "aten::pad". Skipped.')
def get_args():
parser = argparse.ArgumentParser(description="VST Feature Extractor Parser")
# I/O
parser.add_argument('--dataset_path', default='test_videos',
help="path to dataset")
parser.add_argument('--save_dir', type=str, default="features",
help="set output root for the features.")
# extraction params
parser.add_argument('--model_type', default='swinB',
type=str,
help="type of feature extractor")
parser.add_argument('--pretrained_3d',
default='/media/DataDrive/yiling/models/VST_finetune/hflip_speed_120_2d/best_top1_acc_epoch_15.pth',
type=str,
help="load default 3D pretrained feature extractor model.")
parser.add_argument('--clip_length', type=int, default=8,
help="define the length of each input sample.")
parser.add_argument('--frame_interval', type=int, default=1,
help="define the sampling interval between frames.")
parser.add_argument('--use_splits', type=bool, default=False,
help="use full anomalous data or splits, only applicable of Split Dataset of UCF-CRIME and VAD")
parser.add_argument('--batch_size', type=int, default=8, help="batch size")
# running cfg
parser.add_argument('--num_workers', type=int, default=0,
help="define the number of workers used for loading the videos")
parser.add_argument('--seed', type=int, default=None, help='random seed')
parser.add_argument('--log_every', type=int, default=10,
help="log the writing of clips every n steps.")
parser.add_argument('--log_file', type=str,
help="set logging file.")
parser.add_argument('--gpu', type=int, default=0, help="gpu id")
return parser.parse_args()
def set_random_seed(seed=42):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
def to_segments(data, num=32):
"""
These code is taken from:
https://github.com/rajanjitenpatel/C3D_feature_extraction/blob/b5894fa06d43aa62b3b64e85b07feb0853e7011a/extract_C3D_feature.py#L805
:param data: list of features of a certain video
:return: list of 32 segments
"""
data = np.array(data)
Segments_Features = []
thirty2_shots = np.round(np.linspace(0, len(data) - 1, num=num + 1)).astype(int)
for ss, ee in zip(thirty2_shots[:-1], thirty2_shots[1:]):
if ss == ee:
temp_vect = data[min(ss, data.shape[0] - 1), :]
else:
temp_vect = data[ss:ee, :].mean(axis=0)
temp_vect = temp_vect / np.linalg.norm(temp_vect)
if np.linalg.norm == 0:
logging.error("Feature norm is 0")
exit()
if len(temp_vect) != 0:
Segments_Features.append(temp_vect.tolist())
return Segments_Features
class FeaturesWriter:
def __init__(self, num_videos, chunk_size=16):
"""
Initialize a FeaturesWriter instance.
Args:
num_videos (int): Total number of videos to process.
chunk_size (int, optional): Chunk size for writing features, and not used. Defaults to 16.
"""
self.path = None
self.dir = None
self.data = None
self.chunk_size = chunk_size
self.num_videos = num_videos
self.dump_count = 0
def _init_video(self, video_name, dir):
self.path = path.join(dir, f"{video_name}.txt")
self.dir = dir
self.data = dict()
def has_video(self):
return self.data is not None
def dump(self):
logging.info(f'{self.dump_count} / {self.num_videos}: Dumping {self.path}')
self.dump_count += 1
if not path.exists(self.dir):
os.mkdir(self.dir)
features = to_segments([self.data[key] for key in sorted(self.data)])
with open(self.path, 'w') as fp:
for d in features:
d = [str(x) for x in d]
fp.write(' '.join(d) + '\n')
def _is_new_video(self, video_name, dir):
new_path = path.join(dir, f"{video_name}.txt")
if self.path != new_path and self.path is not None:
return True
return False
def store(self, feature, idx):
self.data[idx] = list(feature)
def write(self, feature, video_name, idx, dir):
if not self.has_video():
self._init_video(video_name, dir)
if self._is_new_video(video_name, dir):
self.dump()
self._init_video(video_name, dir)
self.store(feature, idx)
def get_features_loader(dataset_path, clip_length, frame_interval, batch_size, num_workers, save_dir, use_splits):
"""
Get the data loader for extracting video features.
Args:
dataset_path (str): Path to the videos.
clip_length (int): Length of each input sample.
frame_interval (int): Sampling interval between frames.
batch_size (int): Batch size.
num_workers (int): Number of workers used for loading videos.
save_dir (str): Directory to save features.
use_splits (bool): Whether to use full anomalous data or splits.
Returns:
data_loader (VideoIter): Video data loader.
data_iter (DataLoader): Torch data loader for video features extraction.
"""
mean = [0.400, 0.388, 0.372] # VAD mean and std in RGB
std = [0.247, 0.245, 0.243]
size = 224
resize = size, size
crop = size
res = transforms.Compose([
transforms_video.ToTensorVideo(),
transforms_video.ResizeVideo(resize),
transforms_video.CenterCropVideo(crop),
transforms_video.NormalizeVideo(mean=mean, std=std)
])
if os.path.exists(save_dir):
proc_v = []
for root, dirs, files in os.walk(save_dir):
for file in files:
file_path = os.path.join(root, file)
relative_path = os.path.relpath(file_path, save_dir)
proc_v.append(relative_path)
proc_v = [v.split(".")[0] for v in proc_v]
if len(proc_v) > 0:
logging.info(
f"[Data] Already {len(proc_v)} files have been processed"
)
data_loader = VideoIter(
dataset_path=dataset_path,
proc_video=proc_v,
clip_length=clip_length,
frame_stride=frame_interval,
video_transform=res,
use_splits=use_splits,
return_label=False,
)
data_iter = torch.utils.data.DataLoader(
data_loader,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True,
)
return data_loader, data_iter
def load_VST(checkpoint, device):
"""load pretrained VST"""
config = 'utils/swin_config/recognition/swin/swin_base_patch244_window877_kinetics400_22k_VAD.py'
cfg = Config.fromfile(config)
model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.get('test_cfg'))
load_checkpoint(model, checkpoint, map_location='cpu')
return model.to(device)
def main():
args = get_args()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
torch.cuda.set_device(args.gpu)
device = get_torch_device()
register_logger(log_file=args.log_file)
if args.seed is not None:
set_random_seed(args.seed)
cudnn.benchmark = True
feature_path = os.path.join(args.save_dir, 'L'+str(args.clip_length))
if not path.exists(feature_path):
mkdir(feature_path)
data_loader, data_iter = get_features_loader(args.dataset_path,
args.clip_length,
args.frame_interval,
args.batch_size,
args.num_workers,
feature_path,
args.use_splits, )
if data_loader.video_count == 0:
return
model = load_VST(args.pretrained_3d, device)
features_writer = FeaturesWriter(num_videos=data_loader.video_count)
loop_i = 0
# Perform feature extraction on the dataset
with torch.no_grad():
for data, clip_idxs, dirs, vid_names in data_iter: # 1 batch
outputs = model.extract_feat(data.to(device))
outputs = outputs.mean(dim=[2, 3, 4])
outputs = outputs.detach().cpu().numpy()
for i, (dir, vid_name, clip_idx) in enumerate(zip(dirs, vid_names, clip_idxs)):
if loop_i == 0:
logging.info(
f"Video {features_writer.dump_count} / {features_writer.num_videos} : Writing clip {clip_idx} of video {vid_name}")
loop_i += 1
loop_i %= args.log_every
dir = path.join(feature_path, dir)
features_writer.write(feature=outputs[i],
video_name=vid_name,
idx=clip_idx,
dir=dir, )
# Dump the remaining features to files
features_writer.dump()
if __name__ == "__main__":
main()
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