File size: 7,930 Bytes
a3c8a6a | 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 | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import os
from torch.utils.data import Dataset
import numpy as np
import pickle
from dataloaders.tubeletvideo_util import TubeletVideoExtractor
class MSVD_DataLoader(Dataset):
"""MSVD dataset loader."""
def __init__(
self,
subset,
data_path,
features_path,
csv_path,
tokenizer,
max_words=30,
feature_framerate=1.0,
max_frames=100,
image_resolution=224,
frame_order=0,
slice_framepos=0,
):
self.data_path = data_path
self.features_path = features_path
self.feature_framerate = feature_framerate
self.max_words = max_words
self.max_frames = max_frames
self.tokenizer = tokenizer
# 0: ordinary order; 1: reverse order; 2: random order.
self.frame_order = frame_order
assert self.frame_order in [0, 1, 2]
# 0: cut from head frames; 1: cut from tail frames; 2: extract frames uniformly.
self.slice_framepos = slice_framepos
assert self.slice_framepos in [0, 1, 2]
self.subset = subset
assert self.subset in ["train", "val", "test"]
video_id_path_dict = {}
video_id_path_dict["train"] = os.path.join(self.data_path, "train_list.txt")
video_id_path_dict["val"] = os.path.join(self.data_path, "val_list.txt")
video_id_path_dict["test"] = os.path.join(self.data_path, "test_list.txt")
caption_file = os.path.join(self.data_path, "raw-captions.pkl")
with open(video_id_path_dict[self.subset], 'r') as fp:
video_ids = [itm.strip() for itm in fp.readlines()]
with open(caption_file, 'rb') as f:
captions = pickle.load(f)
video_dict = {}
for root, dub_dir, video_files in os.walk(self.features_path):
for video_file in video_files:
video_id_ = ".".join(video_file.split(".")[:-1])
if video_id_ not in video_ids:
continue
file_path_ = os.path.join(root, video_file)
video_dict[video_id_] = file_path_
self.video_dict = video_dict
self.sample_len = 0
self.sentences_dict = {}
self.cut_off_points = []
for video_id in video_ids:
assert video_id in captions
for cap in captions[video_id]:
cap_txt = " ".join(cap)
self.sentences_dict[len(self.sentences_dict)] = (video_id, cap_txt)
self.cut_off_points.append(len(self.sentences_dict))
## below variables are used to multi-sentences retrieval
# self.cut_off_points: used to tag the label when calculate the metric
# self.sentence_num: used to cut the sentence representation
# self.video_num: used to cut the video representation
self.multi_sentence_per_video = True # !!! important tag for eval
if self.subset == "val" or self.subset == "test":
self.sentence_num = len(self.sentences_dict)
self.video_num = len(video_ids)
assert len(self.cut_off_points) == self.video_num
print("For {}, sentence number: {}".format(self.subset, self.sentence_num))
print("For {}, video number: {}".format(self.subset, self.video_num))
print("Video number: {}".format(len(self.video_dict)))
print("Total Paire: {}".format(len(self.sentences_dict)))
self.sample_len = len(self.sentences_dict)
self.rawVideoExtractor = TubeletVideoExtractor(
csv_path=csv_path,
framerate=feature_framerate, size=image_resolution)
self.SPECIAL_TOKEN = {"CLS_TOKEN": "<|startoftext|>", "SEP_TOKEN": "<|endoftext|>",
"MASK_TOKEN": "[MASK]", "UNK_TOKEN": "[UNK]", "PAD_TOKEN": "[PAD]"}
def __len__(self):
return self.sample_len
def _get_text(self, video_id, caption):
k = 1
choice_video_ids = [video_id]
pairs_text = np.zeros((k, self.max_words), dtype=np.long)
pairs_mask = np.zeros((k, self.max_words), dtype=np.long)
pairs_segment = np.zeros((k, self.max_words), dtype=np.long)
for i, video_id in enumerate(choice_video_ids):
words = self.tokenizer.tokenize(caption)
words = [self.SPECIAL_TOKEN["CLS_TOKEN"]] + words
total_length_with_CLS = self.max_words - 1
if len(words) > total_length_with_CLS:
words = words[:total_length_with_CLS]
words = words + [self.SPECIAL_TOKEN["SEP_TOKEN"]]
input_ids = self.tokenizer.convert_tokens_to_ids(words)
input_mask = [1] * len(input_ids)
segment_ids = [0] * len(input_ids)
while len(input_ids) < self.max_words:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == self.max_words
assert len(input_mask) == self.max_words
assert len(segment_ids) == self.max_words
pairs_text[i] = np.array(input_ids)
pairs_mask[i] = np.array(input_mask)
pairs_segment[i] = np.array(segment_ids)
return pairs_text, pairs_mask, pairs_segment, choice_video_ids
def _get_rawvideo(self, choice_video_ids):
video_mask = np.zeros((len(choice_video_ids), self.max_frames), dtype=np.long)
max_video_length = [0] * len(choice_video_ids)
# Pair x L x T x 3 x H x W
video = np.zeros((len(choice_video_ids), self.max_frames, 1, 3,
self.rawVideoExtractor.size, self.rawVideoExtractor.size), dtype=np.float)
for i, video_id in enumerate(choice_video_ids):
video_path = self.video_dict[video_id]
raw_video_data = self.rawVideoExtractor.get_video_data(video_path)
raw_video_data = raw_video_data['video']
if len(raw_video_data.shape) > 3:
raw_video_data_clip = raw_video_data
# L x T x 3 x H x W
raw_video_slice = self.rawVideoExtractor.process_raw_data(raw_video_data_clip)
if self.max_frames < raw_video_slice.shape[0]:
if self.slice_framepos == 0:
video_slice = raw_video_slice[:self.max_frames, ...]
elif self.slice_framepos == 1:
video_slice = raw_video_slice[-self.max_frames:, ...]
else:
sample_indx = np.linspace(0, raw_video_slice.shape[0] - 1, num=self.max_frames, dtype=int)
video_slice = raw_video_slice[sample_indx, ...]
else:
video_slice = raw_video_slice
video_slice = self.rawVideoExtractor.process_frame_order(video_slice, frame_order=self.frame_order)
slice_len = video_slice.shape[0]
max_video_length[i] = max_video_length[i] if max_video_length[i] > slice_len else slice_len
if slice_len < 1:
pass
else:
video[i][:slice_len, ...] = video_slice
else:
print("video path: {} error. video id: {}".format(video_path, video_id))
for i, v_length in enumerate(max_video_length):
video_mask[i][:v_length] = [1] * v_length
return video, video_mask
def __getitem__(self, idx):
video_id, caption = self.sentences_dict[idx]
pairs_text, pairs_mask, pairs_segment, choice_video_ids = self._get_text(video_id, caption)
video, video_mask = self._get_rawvideo(choice_video_ids)
return pairs_text, pairs_mask, pairs_segment, video, video_mask
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