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e857f97 | 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 | 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 json
import math
from .rawvideo_util import RawVideoExtractor
class ActivityNet_DataLoader(Dataset):
def __init__(
self,
subset,
data_path,
features_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"]
video_id_path_dict = {}
video_id_path_dict["train"] = os.path.join(self.data_path, "train_ids.json")
video_id_path_dict["val"] = os.path.join(self.data_path, "val_ids.json")
video_json_path_dict = {}
video_json_path_dict["train"] = os.path.join(self.data_path, "train.json")
video_json_path_dict["val"] = os.path.join(self.data_path, "val_1.json")
pseudo_video_id_list, video_id_list = self._get_video_id_single(video_id_path_dict[self.subset])
pseudo_caption_dict = self._get_captions_single(video_json_path_dict[self.subset])
print("video id list: {}".format(len(video_id_list)))
print("pseudo caption dict: {}".format(len(pseudo_caption_dict.keys())))
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])[2:]
if video_id_ not in video_id_list:
continue
file_path_ = os.path.join(root, video_file)
video_dict[video_id_] = file_path_
self.video_dict = video_dict
print("video dict: {}".format(len(video_dict)))
self.pseudo_video_id_list = pseudo_video_id_list
self.video_id_list = video_id_list
self.pseudo_caption_dict = pseudo_caption_dict
# Get iterator video ids
self.video_id2idx_dict = {pseudo_video_id: id for id, pseudo_video_id in enumerate(self.pseudo_video_id_list)}
# Get all captions
self.iter2video_pairs_dict = {}
for pseudo_video_id, video_id in zip(self.pseudo_video_id_list, self.video_id_list):
if pseudo_video_id not in self.pseudo_caption_dict or video_id not in self.video_dict:
continue
caption = self.pseudo_caption_dict[pseudo_video_id]
n_caption = len(caption['start'])
for sub_id in range(n_caption):
self.iter2video_pairs_dict[len(self.iter2video_pairs_dict)] = (pseudo_video_id, sub_id)
self.rawVideoExtractor = RawVideoExtractor(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 len(self.iter2video_pairs_dict)
def _get_video_id_from_pseduo(self, pseudo_video_id):
video_id = pseudo_video_id[2:]
return video_id
def _get_video_id_single(self, path):
pseudo_video_id_list = []
video_id_list = []
print('Loading json: {}'.format(path))
with open(path, 'r') as f:
json_data = json.load(f)
for pseudo_video_id in json_data:
if pseudo_video_id in pseudo_video_id_list:
print("reduplicate.")
else:
video_id = self._get_video_id_from_pseduo(pseudo_video_id)
pseudo_video_id_list.append(pseudo_video_id)
video_id_list.append(video_id)
return pseudo_video_id_list, video_id_list
def _get_captions_single(self, path):
pseudo_caption_dict = {}
with open(path, 'r') as f:
json_data = json.load(f)
for pseudo_video_id, v_ in json_data.items():
pseudo_caption_dict[pseudo_video_id] = {}
duration = v_["duration"]
pseudo_caption_dict[pseudo_video_id]["start"] = np.array([0], dtype=object)
pseudo_caption_dict[pseudo_video_id]["end"] = np.array([int(math.ceil(float(duration)))], dtype=object)
pseudo_caption_dict[pseudo_video_id]["text"] = np.array([" ".join(v_["sentences"])], dtype=object)
return pseudo_caption_dict
def _get_text(self, pseudo_video_id, sub_id):
caption = self.pseudo_caption_dict[pseudo_video_id]
k = 1
r_ind = [sub_id]
starts = np.zeros(k, dtype=np.int64)
ends = np.zeros(k, dtype=np.int64)
pairs_text = np.zeros((k, self.max_words), dtype=np.int64)
pairs_mask = np.zeros((k, self.max_words), dtype=np.int64)
pairs_segment = np.zeros((k, self.max_words), dtype=np.int64)
for i in range(k):
# ind = r_ind[i]
# start_, end_ = caption['start'][ind], caption['end'][ind]
# words = self.tokenizer.tokenize(caption['text'][ind])
# starts[i], ends[i] = start_, end_
#
# 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)
ind = r_ind[i]
start_, end_ = caption['start'][ind], caption['end'][ind]
output = self.tokenizer(caption['text'][ind])
starts[i], ends[i] = start_, end_
input_ids = output[0].squeeze()
input_mask = output[1].squeeze()
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, starts, ends
def _get_rawvideo(self, idx, s, e):
video_mask = np.zeros((len(s), self.max_frames), dtype=np.int64)
max_video_length = [0] * len(s)
# Pair x L x T x 3 x H x W
video = np.zeros((len(s), self.max_frames, 1, 3,
self.rawVideoExtractor.size, self.rawVideoExtractor.size), dtype=np.float32)
video_path = self.video_dict[idx]
try:
for i in range(len(s)):
start_time = int(s[i])
end_time = int(e[i])
start_time = start_time if start_time >= 0. else 0.
end_time = end_time if end_time >= 0. else 0.
if start_time > end_time:
start_time, end_time = end_time, start_time
elif start_time == end_time:
end_time = end_time + 1
# Should be optimized by gathering all asking of this video
raw_video_data = self.rawVideoExtractor.get_video_data(video_path, start_time, end_time)
raw_video_data = raw_video_data['video']
# print('raw_video_data', raw_video_data.shape)
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)
# print('sample_indx', raw_video_slice.shape[0], sample_indx)
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: {}, start: {}, end: {}".format(video_path, idx, start_time, end_time))
except Exception as excep:
print("video path: {} error. video id: {}, start: {}, end: {}, Error: {}".format(video_path, idx, s, e, excep))
raise excep
for i, v_length in enumerate(max_video_length):
video_mask[i][:v_length] = [1] * v_length
return video, video_mask
def __getitem__(self, feature_idx):
pseudo_video_id, sub_id = self.iter2video_pairs_dict[feature_idx]
idx = self.video_id2idx_dict[pseudo_video_id]
pairs_text, pairs_mask, pairs_segment, starts, ends = self._get_text(pseudo_video_id, sub_id)
video, video_mask = self._get_rawvideo(self.video_id_list[idx], starts, ends)
return pairs_text, pairs_mask, pairs_segment, video, video_mask |