import os
import init_path
import misc.config as cfg
from misc.utils import concat_feat, get_dataloader, make_cuda, make_variable
from misc.writer import RecordWriter
from models import PCAWrapper, inception_v3
if __name__ == '__main__':
# init Inception v3 model
model = make_cuda(
inception_v3(pretrained=True, transform_input=True, extract_feat=True))
model.eval()
# init PCA model
pca = PCAWrapper(n_components=cfg.n_components,
batch_size=cfg.pca_batch_size)
pca.load_params(filepath=cfg.pca_model)
# data loader for frames in ingle video
data_loader = get_dataloader(dataset="VideoFrame",
path=cfg.video_file,
num_frames=cfg.num_frames,
batch_size=cfg.batch_size)
# init writer
writer = RecordWriter(filepath="data/test.tfrecord", level="frame")
# extract features by inception_v3
feats = None
for step, frames in enumerate(data_loader):
print("extracting feature [{}/{}]".format(step + 1, len(data_loader)))
"""Main script for Youtube-8M feature extractor."""
import misc.config as cfg
from misc.utils import concat_feat, get_dataloader, make_cuda, make_variable
from models import PCAWrapper, inception_v3
if __name__ == '__main__':
# init models and data loader
model = make_cuda(
inception_v3(pretrained=True, transform_input=True, extract_feat=True))
pca = PCAWrapper(n_components=cfg.n_components)
model.eval()
# data loader for frames in ingle video
# data_loader = get_dataloader(dataset="VideoFrame",
# path=cfg.video_file,
# num_frames=cfg.num_frames,
# batch_size=cfg.batch_size)
# data loader for frames decoded from several videos
data_loader = get_dataloader(dataset="FrameImage",
path=cfg.frame_root,
batch_size=cfg.batch_size)
# extract features by inception_v3
feats = None
for step, frames in enumerate(data_loader):
print("extracting feature [{}/{}]".format(step + 1, len(data_loader)))
feat = model(make_variable(frames))
feats = concat_feat(feats, feat.data.cpu())
# recude dimensions by PCA
"""Train PCA for Youtube-8M feature extractor."""
import os
import torch
import init_path
import misc.config as cfg
from misc.utils import concat_feat_var
from models import PCAWrapper
if __name__ == '__main__':
# init models and data loader
pca = PCAWrapper(n_components=cfg.n_components,
batch_size=cfg.pca_batch_size)
if os.path.exists(cfg.inception_v3_feats_path.format("total")):
inception_v3_feats = torch.load(
cfg.inception_v3_feats_path.format("total"))
else:
# get inception_v3 feats list
feats_list = os.listdir(cfg.inception_v3_feats_root)
feats_list = [
v for v in feats_list
if os.path.splitext(v)[1] in cfg.inception_v3_feats_ext
]
# load inception_v3 feats
inception_v3_feats = None
for step, feat_file in enumerate(feats_list):