def __getitem__(self, index):
cap_id = self.cap_ids[index]
video_id = getVideoId(cap_id)
# video
frame_list = self.video2frames[video_id]
frame_vecs = []
for frame_id in frame_list:
frame_vecs.append(self.visual_feat.read_one(frame_id))
frames_tensor = torch.Tensor(frame_vecs)
# text
caption = self.captions[cap_id]
if self.bow2vec is not None:
cap_bow = self.bow2vec.mapping(caption)
if cap_bow is None:
cap_bow = torch.zeros(self.bow2vec.ndims)
else:
cap_bow = torch.Tensor(cap_bow)
else:
cap_bow = None
if self.vocab is not None:
tokens = clean_str(caption)
caption = []
caption.append(self.vocab('<start>'))
caption.extend([self.vocab(token) for token in tokens])
caption.append(self.vocab('<end>'))
cap_tensor = torch.Tensor(caption)
else:
cap_tensor = None
return frames_tensor, cap_tensor, cap_bow, index, cap_id, video_id
def preprocess(self, query, clear):
if clear:
words = clean_str(query)
else:
words = query.strip().split()
#print words
return words
def __getitem__(self, index):
cap_id = self.cap_ids[index]
video_id = getVideoId(cap_id)
# video
frame_list = self.video2frames[video_id]
frame_vecs = []
for frame_id in frame_list:
frame_vecs.append(self.visual_feat.read_one(frame_id))
frames_tensor = torch.Tensor(frame_vecs)
# text
caption = self.captions[cap_id]
if self.bow2vec is not None:
cap_bow = self.bow2vec.mapping(caption)
if cap_bow is None:
cap_bow = torch.zeros(self.bow2vec.ndims)
else:
cap_bow = torch.Tensor(cap_bow)
else:
cap_bow = None
if self.vocab is not None:
tokens = clean_str(caption)
caption = []
caption.append(self.vocab('<start>'))
caption.extend([self.vocab(token) for token in tokens])
caption.append(self.vocab('<end>'))
cap_tensor = torch.Tensor(caption)
else:
cap_tensor = None
if self.tag_path is not None:
vid_tag_str = self.vid2tags[video_id] # string representation
tag_list = [
self.tag2idx[word[0]] for word in vid_tag_str
if word[0] in self.tag2idx
] # index representation
score_list = [word[1] for word in vid_tag_str]
tag_one_hot = torch.zeros(
self.tag_vocab_size
) # build zero vector of tag vocabulary that is used to represent tags by one-hot
for idx, tag_idx in enumerate(tag_list):
tag_one_hot[tag_idx] = score_list[idx] # one-hot
else:
tag_one_hot = torch.zeros(self.tag_vocab_size)
# print tag_one_hot
vid_tag = torch.Tensor(np.array(tag_one_hot))
# print ('%s:' %video_id, vid_tag)
return frames_tensor, cap_tensor, cap_bow, index, cap_id, video_id, vid_tag
def __getitem__(self, index):
cap_id = self.cap_ids[index]
caption = self.captions[cap_id]
if self.bow2vec is not None:
cap_bow = self.bow2vec.mapping(caption)
if cap_bow is None:
cap_bow = torch.zeros(self.bow2vec.ndims)
else:
cap_bow = torch.Tensor(cap_bow)
else:
cap_bow = None
if self.vocab is not None:
tokens = clean_str(caption)
caption = []
caption.append(self.vocab('<start>'))
caption.extend([self.vocab(token) for token in tokens])
caption.append(self.vocab('<end>'))
cap_tensor = torch.Tensor(caption)
else:
cap_tensor = None
return cap_tensor, cap_bow, index, cap_id
def dataLoadedVideoText_one(video2frames, video_id, visual_feats, query,
bow2vec, vocab, tokenizer, options):
data = []
videos = []
frame_list = video2frames[video_id]
frame_vecs = []
for frame_id in frame_list:
# visual_feats.read_one(frame_id)
if options.do_visual_feas_norm:
frame_vecs.append(do_L2_norm(visual_feats.read_one(frame_id)))
else:
frame_vecs.append(visual_feats.read_one(frame_id))
# Text encoding
cap_tensors = []
cap_bows = []
caption_text = query[:]
caption_text = ' '.join(clean_str(caption_text))
caption_text = text2Berttext(caption_text, tokenizer)
caption_text = caption_text.encode("utf-8")
if bow2vec is not None:
cap_bow = bow2vec.mapping(caption_text)
if cap_bow is None:
cap_bow = torch.zeros(bow2vec.ndims)
else:
cap_bow = torch.Tensor(cap_bow)
else:
cap_bow = None
if vocab is not None:
tokens = clean_str(caption_text)
caption = []
caption.append(vocab('<start>'))
caption.extend([vocab(token) for token in tokens])
caption.append(vocab('<end>'))
cap_tensor = torch.Tensor(caption)
else:
cap_tensor = None
# BERT
caption_text = query[:]
caption_text = ' '.join(clean_str(query))
marked_text = "[CLS] " + caption_text + " [SEP]"
# print marked_text
tokenized_text = tokenizer.tokenize(marked_text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [1] * len(tokenized_text)
# Convert inputs to PyTorch tensors
tokens_tensor = torch.tensor(indexed_tokens)
segments_tensors = torch.tensor(segments_ids)
caption_text = caption_text.encode("utf-8")
data.append([
torch.Tensor(frame_vecs), cap_tensor, cap_bow, tokens_tensor,
segments_tensors, caption_text
])
return data
def __getitem__(self, index):
cap_id = self.cap_ids[index]
video_id = getVideoId(cap_id)
# video
frame_list = self.video2frames[video_id]
frame_vecs = []
for frame_id in frame_list:
# l_2 normalize
if (self.do_visual_feas_norm):
frame_vecs.append(
do_L2_norm(self.visual_feat.read_one(frame_id)))
else:
frame_vecs.append(self.visual_feat.read_one(frame_id))
frames_tensor = torch.Tensor(frame_vecs)
# text
# print video_id
cap_text = self.captions[cap_id]
caption_text = cap_text[:]
caption_text = ' '.join(clean_str(caption_text))
caption_text = text2Berttext(caption_text, self.tokenizer)
caption_text = caption_text.encode("utf-8")
if self.bow2vec is not None:
cap_bow = self.bow2vec.mapping(caption_text)
if cap_bow is None:
cap_bow = torch.zeros(self.bow2vec.ndims)
else:
cap_bow = torch.Tensor(cap_bow)
else:
cap_bow = None
if self.vocab is not None:
tokens = clean_str(caption_text)
caption = []
caption.append(self.vocab('<start>'))
caption.extend([self.vocab(token) for token in tokens])
caption.append(self.vocab('<end>'))
cap_tensor = torch.Tensor(caption)
else:
cap_tensor = None
# BERT
caption_text = cap_text[:]
caption_text = ' '.join(clean_str(caption_text))
marked_text = "[CLS] " + caption_text + " [SEP]"
# print marked_text
tokenized_text = self.tokenizer.tokenize(marked_text)
indexed_tokens = self.tokenizer.convert_tokens_to_ids(tokenized_text)
# tmptext = self.tokenizer.convert_tokens_to_string(tokenized_text)
segments_ids = [1] * len(tokenized_text)
# Convert inputs to PyTorch tensors
tokens_tensor = torch.tensor(indexed_tokens)
segments_tensors = torch.tensor(segments_ids)
# caption_text = cap_text[:]
# caption_text = text2Berttext(caption_text, self.tokenizer)
caption_text = caption_text.encode("utf-8")
# print tokens_tensor.shape
# print caption.__len__()
return frames_tensor, cap_tensor, cap_bow, index, cap_id, video_id, tokens_tensor, segments_tensors, caption_text
