-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathCreate_MultiModal_Dataset.py
126 lines (110 loc) · 5.07 KB
/
Create_MultiModal_Dataset.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
import os
import glob
import matplotlib.pyplot as plt
import numpy as np
import torch
from torchvision import datasets, transforms
# from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM
from PIL import Image
import json
class Multimodel_Dateset(torch.utils.data.Dataset):
def __init__(self, data_path, transforms):
self.transforms = transforms
self.img_data_path = data_path
if "drone" in os.path.basename(self.img_data_path):
self.text_path = os.path.join(os.path.dirname(data_path), "text_drone")
elif "satellite" in os.path.basename(self.img_data_path):
self.text_path = os.path.join(os.path.dirname(data_path), "text_satellite")
self.tensor = torch.load(os.path.join(self.text_path, "satellite.pth"))
img_list = glob.glob(os.path.join(data_path, "*"))
self.classes = os.listdir(data_path)
self.img_names = []
for imgs in img_list:
self.img_names += glob.glob(os.path.join(imgs, '*'))
len_img = len(glob.glob(os.path.join(imgs, '*')))
self.labels = range(len(img_list))
img_arr = np.array(self.labels).reshape(1, -1)
img_arr = np.repeat(img_arr, len_img).tolist()
self.imgs = list(zip(self.img_names, img_arr))
# print(imgs[:10])
# for img_dir in img_list:
# for img_file in glob.glob(os.path.join(img_dir, "*")):
def __len__(self):
return len(self.img_names)
def __getitem__(self, item):
img = self.img_names[item]
# text = self.text[os.path.basename(self.img_names[item])]
if "drone" in os.path.basename(self.img_data_path):
name = os.path.basename(img).split('.')[0] + '.pth'
text = torch.load(os.path.join(self.text_path, name)).cpu()
elif "satellite" in os.path.basename(self.img_data_path):
text = self.tensor.cpu()
# print(text.device)
label = self.labels[self.classes.index(os.path.basename(os.path.dirname(img)))]
# print(img, label)
img = Image.open(img).convert('RGB')
img = self.transforms(img)
return img, text, label
class Multimodel_Dateset_flip(torch.utils.data.Dataset):
def __init__(self, data_path, transforms, gap):
self.transforms = transforms
self.img_data_path = data_path
self.gap = gap
if "drone" in os.path.basename(self.img_data_path):
self.text_path = os.path.join(os.path.dirname(data_path), "text_drone")
elif "satellite" in os.path.basename(self.img_data_path):
self.text_path = os.path.join(os.path.dirname(data_path), "text_satellite")
self.tensor = torch.load(os.path.join(self.text_path, "satellite.pth"))
img_list = glob.glob(os.path.join(data_path, "*"))
self.classes = os.listdir(data_path)
self.img_names = []
for imgs in img_list:
self.img_names += glob.glob(os.path.join(imgs, '*'))
len_img = len(glob.glob(os.path.join(imgs, '*')))
self.labels = range(len(img_list))
img_arr = np.array(self.labels).reshape(1, -1)
img_arr = np.repeat(img_arr, len_img).tolist()
self.imgs = list(zip(self.img_names, img_arr))
# print(imgs[:10])
# for img_dir in img_list:
# for img_file in glob.glob(os.path.join(img_dir, "*")):
def __len__(self):
return len(self.img_names)
def __getitem__(self, item):
img = self.img_names[item]
# text = self.text[os.path.basename(self.img_names[item])]
if "drone" in os.path.basename(self.img_data_path):
name = os.path.basename(img).split('.')[0] + '.pth'
text = torch.load(os.path.join(self.text_path, name)).cpu()
elif "satellite" in os.path.basename(self.img_data_path):
text = self.tensor.cpu()
# print(text.device)
label = self.labels[self.classes.index(os.path.basename(os.path.dirname(img)))]
# print(img, label)
img = Image.open(img).convert('RGB')
height = img.height
flip = img.crop((0, 0, self.gap, height))
img = img.crop((0, 0, height - self.gap, height))
flip = flip.transpose(Image.FLIP_LEFT_RIGHT)
joint = Image.new("RGB", (height, height))
joint.paste(flip, (0, 0, self.gap, height))
joint.paste(img, (self.gap, 0, height, height))
img = self.transforms(joint)
# plt.figure("black")
# plt.imshow(joint)
# plt.show()
return img, text, label
if __name__ == "__main__":
path = "/home/sues/media/disk2/University-Release-MultiModel/University-Release/test/gallery_satellite"
print(os.path.basename(path))
transforms = transforms.Compose([
transforms.Resize((384, 384), interpolation=3),
transforms.ToTensor(),
])
dataset = Multimodel_Dateset(path, transforms=transforms)
loader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=False)
for img, text, label in loader:
print(img.shape)
print(text.shape)
print(label)
break