-
Notifications
You must be signed in to change notification settings - Fork 36
/
data_loader.py
171 lines (140 loc) · 5.75 KB
/
data_loader.py
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
import os
import glob
import numpy as np
import torch
from PIL import Image
from data_parser import JpegDataset,JpegDataset_test
from torchvision.transforms import *
IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG']
def default_loader(path):
return Image.open(path).convert('RGB')
class VideoFolder(torch.utils.data.Dataset):
def __init__(self, root, csv_file_input, csv_file_labels, clip_size,
nclips, step_size, is_val, transform=None,
loader=default_loader):
self.dataset_object = JpegDataset(csv_file_input, csv_file_labels, root)
self.csv_data = self.dataset_object.csv_data
self.classes = self.dataset_object.classes
self.classes_dict = self.dataset_object.classes_dict
self.root = root
self.transform = transform
self.loader = loader
self.clip_size = clip_size
self.nclips = nclips
self.step_size = step_size
self.is_val = is_val
def __getitem__(self, index):
item = self.csv_data[index]
img_paths = self.get_frame_names(item.path)
imgs = []
for img_path in img_paths:
img = self.loader(img_path)
img = self.transform(img)
imgs.append(torch.unsqueeze(img, 0))
target_idx = self.classes_dict[item.label]
# format data to torch
data = torch.cat(imgs)
data = data.permute(1, 0, 2, 3)
return (data, target_idx)
def __len__(self):
return len(self.csv_data)
def get_frame_names(self, path):
frame_names = []
for ext in IMG_EXTENSIONS:
frame_names.extend(glob.glob(os.path.join(path, "*" + ext)))
frame_names = list(sorted(frame_names))
num_frames = len(frame_names)
# set number of necessary frames
if self.nclips > -1:
num_frames_necessary = self.clip_size * self.nclips * self.step_size
else:
num_frames_necessary = num_frames
# pick frames
offset = 0
if num_frames_necessary > num_frames:
# pad last frame if video is shorter than necessary
frame_names += [frame_names[-1]] * (num_frames_necessary - num_frames)
elif num_frames_necessary < num_frames:
# If there are more frames, then sample starting offset
diff = (num_frames - num_frames_necessary)
# Temporal augmentation
if not self.is_val:
offset = np.random.randint(0, diff)
frame_names = frame_names[offset:num_frames_necessary +
offset:self.step_size]
return frame_names
class VideoFolder_test(torch.utils.data.Dataset):
def __init__(self, root, csv_file_input, clip_size,
nclips, step_size, is_val, transform=None,
loader=default_loader):
self.dataset_object = JpegDataset_test(csv_file_input, root)
self.csv_data = self.dataset_object.csv_data
self.root = root
self.transform = transform
self.loader = loader
self.clip_size = clip_size
self.nclips = nclips
self.step_size = step_size
self.is_val = is_val
def __getitem__(self, index):
item = self.csv_data[index]
img_paths = self.get_frame_names(item.path)
imgs = []
for img_path in img_paths:
img = self.loader(img_path)
img = self.transform(img)
imgs.append(torch.unsqueeze(img, 0))
# format data to torch
data = torch.cat(imgs)
data = data.permute(1, 0, 2, 3)
return (data, -1)
def __len__(self):
return len(self.csv_data)
def get_frame_names(self, path):
frame_names = []
for ext in IMG_EXTENSIONS:
frame_names.extend(glob.glob(os.path.join(path, "*" + ext)))
frame_names = list(sorted(frame_names))
num_frames = len(frame_names)
# set number of necessary frames
if self.nclips > -1:
num_frames_necessary = self.clip_size * self.nclips * self.step_size
else:
num_frames_necessary = num_frames
# pick frames
offset = 0
if num_frames_necessary > num_frames:
# pad last frame if video is shorter than necessary
frame_names += [frame_names[-1]] * (num_frames_necessary - num_frames)
elif num_frames_necessary < num_frames:
# If there are more frames, then sample starting offset
diff = (num_frames - num_frames_necessary)
# Temporal augmentation
if not self.is_val:
offset = np.random.randint(0, diff)
frame_names = frame_names[offset:num_frames_necessary +
offset:self.step_size]
return frame_names
if __name__ == '__main__':
transform = Compose([
CenterCrop(84),
ToTensor(),
# Normalize(
# mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])
])
loader = VideoFolder(root="/hdd/20bn-datasets/20bn-jester-v1/",
csv_file_input="csv_files/jester-v1-validation.csv",
csv_file_labels="csv_files/jester-v1-labels.csv",
clip_size=18,
nclips=1,
step_size=2,
is_val=False,
transform=transform,
loader=default_loader)
# data_item, target_idx = loader[0]
# save_images_for_debug("input_images", data_item.unsqueeze(0))
train_loader = torch.utils.data.DataLoader(
loader,
batch_size=10, shuffle=False,
num_workers=5, pin_memory=True)