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
X = feats.numpy()
pca.fit(X)
X_ = pca.transform(X)
print("reduce X {} to X_ {}".format(X.shape, X_.shape))
# sabe PCA params
pca.save_params(filepath=cfg.pca_model)
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)))
feat = model(make_variable(frames))
feat_np = feat.data.cpu().numpy()
# recude dimensions by PCA
feat_ = pca.transform(feat_np)
feats = concat_feat(feats, feat_)
# write features into TFRecord
vid = os.path.splitext(os.path.basename(cfg.video_file))[0]
writer.write(vid=vid, feat_rgb=feats)
writer.close()