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import json
import numpy as np
import torch
from torch.utils.data import Dataset
from torch.nn import functional as F
from .datasets import register_dataset
from .data_utils import truncate_feats
from IPython import embed
@register_dataset("vidf")
class VidF(Dataset):
def __init__(
self,
is_training, # if in training mode
split, # split, a tuple/list allowing concat of subsets
feat_folder, # folder for features
json_file, # json file for annotations
feat_stride, # temporal stride of the feats
num_frames, # number of frames for each feat
default_fps, # default fps
downsample_rate, # downsample rate for feats
max_seq_len, # maximum sequence length during training
trunc_thresh, # threshold for truncate an action segment
crop_ratio, # a tuple (e.g., (0.9, 1.0)) for random cropping
input_dim, # input feat dim
num_classes, # number of action categories
file_prefix, # feature file prefix if any
file_ext, # feature file extension if any
force_upsampling, # force to upsample to max_seq_len
**kwargs,
):
# file path
# embed()
# assert os.path.exists(feat_folder) and os.path.exists(json_file)
# assert isinstance(split, tuple) or isinstance(split, list)
# assert crop_ratio == None or len(crop_ratio) == 2
# self.feat_folder = feat_folder
# if file_prefix is not None:
# self.file_prefix = file_prefix
# else:
# self.file_prefix = ''
# self.file_ext = file_ext
# self.json_file = json_file
#
# # split / training mode
# self.split = split
# self.is_training = is_training
#
# # features meta info
# self.feat_stride = feat_stride
self.num_frames = num_frames
self.input_dim = input_dim
# self.default_fps = default_fps
# self.downsample_rate = downsample_rate
# self.max_seq_len = max_seq_len
# self.trunc_thresh = trunc_thresh
# self.num_classes = num_classes
# self.label_dict = None
# self.crop_ratio = crop_ratio
#
# # load database and select the subset
# dict_db, label_dict = self._load_json_db(self.json_file)
# assert len(label_dict) == num_classes
# self.data_list = dict_db
# self.label_dict = label_dict
# dataset specific attributes
self.db_attributes = {
'dataset_name': 'vidf',
'tiou_thresholds': np.linspace(0.3, 0.7, 5),
'empty_label_ids': [],
}
self.version = kwargs['version']
self.data_dir = f'/home/users/xxx/scratch/dataset/vidf/{self.version}'
assert os.path.exists(self.data_dir), 'Please specify data_dir'
self.split = split
if isinstance(self.split, str):
self.split = [self.split]
annotations = []
self.split = [s for s in self.split if "real" not in s] + [s for s in self.split if "real" in s]
for split_itm in self.split:
anno_file = open(
os.path.join(
self.data_dir,
"{}.txt".format(split_itm)
), 'r'
)
line_cnt = -1
tmp_annotations = []
for line in anno_file:
line_cnt += 1
# anno = line.split("##")[0]
# sent = sent.split('.\n')[0]
anno = line
if 'real' in split_itm:
vid, duration = anno.split(" ")
duration = float(duration)
pairs = []
else:
vid, duration, time_str = anno.split(" ")
duration = float(duration)
time_str = time_str.replace('\n', '')
pairs = [x.split('=') for x in time_str.split('+')]
time_list = []
start_list = []
end_list = []
for p in pairs:
# Check format
assert len(p) == 2, f"Invalid format: '{'='.join(p)}' is not in start=end format"
start_str, end_str = p
# Convert to float and assert valid
start = float(start_str)
end = min(float(end_str), duration)
time_list.append([start, end])
start_list.append(start)
end_list.append(end)
# if line_cnt % 2 == 1:
# time_list = []
# annotations.append(
# {'video': vid, 'times': time_list, 'duration': duration})
# continue
tmp_annotations.append(
{'video': vid, 'times': time_list, 'duration': duration})
anno_file.close()
assert 'real' not in split_itm
if 'real' in split_itm:
tmp_annotations_num_1 = int(len(tmp_annotations) * kwargs['real_ratio'])
tmp_annotations_num_2 = int(len(annotations))
tmp_annotations_num = min(tmp_annotations_num_1, tmp_annotations_num_2)
tmp_annotations = tmp_annotations[:tmp_annotations_num]
annotations += tmp_annotations
if 'train' in split[0]:
annot_num = kwargs['train_annot_num']
else:
annot_num = kwargs['test_annot_num']
if annot_num > 0:
indices = np.linspace(0, len(annotations) - 1, annot_num, dtype=int)
annotations = [annotations[i] for i in indices]
self.annotations = annotations
self.feature_type = 'clipL14'
def get_attributes(self):
return self.db_attributes
def _load_json_db(self, json_file):
# load database and select the subset
with open(json_file, 'r') as fid:
json_data = json.load(fid)
json_db = json_data['database']
# if label_dict is not available
if self.label_dict is None:
label_dict = {}
for key, value in json_db.items():
for act in value['annotations']:
label_dict[act['label']] = act['label_id']
dict_db = tuple()
for key, value in json_db.items():
if value['subset'].lower() not in self.split:
continue
# or does not have the feature file
feat_file = os.path.join(self.feat_folder,
self.file_prefix + key + self.file_ext)
if not os.path.exists(feat_file):
continue
# get fps if available
if self.default_fps is not None:
fps = self.default_fps
elif 'fps' in value:
fps = value['fps']
else:
assert False, "Unknown video FPS."
if 'duration' in value:
duration = value['duration']
else:
duration = 1e8
# get annotations if available
if ('annotations' in value) and (len(value['annotations']) > 0):
segments, labels = [], []
for act in value['annotations']:
segments.append(act['segment'])
labels.append([label_dict[act['label']]])
segments = np.asarray(segments, dtype=np.float32)
labels = np.squeeze(np.asarray(labels, dtype=np.int64), axis=1)
else:
segments = None
labels = None
dict_db += ({'id': key,
'fps' : fps,
'duration' : duration,
'segments' : segments,
'labels' : labels
}, )
return dict_db, label_dict
def __len__(self):
return len(self.annotations)
def __getitem__(self, idx):
# Example dimensions
C = self.input_dim # feature channels
# Generate feats: C x T
video_id = self.annotations[idx]['video'].split('.mp4')[0]
visual_input = self.get_video_features(video_id)
def average_to_fixed_length(visual_input, num_sample_clips):
num_clips = visual_input.shape[0]
idxs = torch.arange(0, num_sample_clips + 1, 1.0) / num_sample_clips * num_clips
idxs = torch.min(torch.round(idxs).long(), torch.tensor(num_clips - 1))
new_visual_input = []
for i in range(num_sample_clips):
s_idx, e_idx = idxs[i].item(), idxs[i + 1].item()
if s_idx < e_idx:
new_visual_input.append(torch.mean(visual_input[s_idx:e_idx], dim=0))
else:
new_visual_input.append(visual_input[s_idx])
new_visual_input = torch.stack(new_visual_input, dim=0)
return new_visual_input
visual_input = average_to_fixed_length(visual_input, self.num_frames)
feats = visual_input.permute(1, 0)
times = torch.tensor(self.annotations[idx]['times']) # (N, 2)
N = times.shape[0]
starts = times[:, 0] / self.annotations[idx]['duration'] * self.num_frames
ends = times[:, 1] / self.annotations[idx]['duration'] * self.num_frames
segments = torch.stack([starts, ends], dim=1)
labels = torch.zeros((N,)).long()
data_dict = {'video_id' : str(idx),
'feats' : feats, # C x T
'segments' : segments, # N x 2
'labels' : labels, # N
'feat_num_frames' : self.num_frames,
'duration' : self.annotations[idx]['duration'],
'gt_time' : self.annotations[idx]['times'],
}
return data_dict
def get_video_features(self, vid):
if 'clipL14' in self.feature_type:
features = np.load(os.path.join(self.data_dir, f'../feat/01a.2a_L14/{vid}.npy'))
features = torch.from_numpy(features).float()
return features
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