-
Notifications
You must be signed in to change notification settings - Fork 4
/
outfit_dataloader.py
124 lines (105 loc) · 4.11 KB
/
outfit_dataloader.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
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@author: ZuoXiang
@contact: zx_data@126.com
@file: outfit_dataloader.py
@time: 2020/4/28 17:25
@desc:
"""
import json
import torch
import numpy as np
from copy import deepcopy
from PIL import Image
from os.path import join
def default_image_loader(path):
im = Image.open(path)
if im.mode != 'RGBA':
im = im.convert('RGBA')
return im
class TripletImageLoader(torch.utils.data.Dataset):
def __init__(self, data_dir, is_train, split=0.99, transform=None, loader=default_image_loader, neg_num=1):
self.is_train = is_train
self.loader = loader
self.transform = transform
self.neg_num = neg_num
self.image_dir = join(data_dir, 'image')
self.label_path = join(data_dir, '4and5_items_train.json')
outfits_json, category2id, id2category = self.get_data(self.label_path)
self.category2id = category2id
self.id2category = id2category
pos_pairs = []
for outfit_id, items_list in outfits_json.items():
for item_id in items_list:
anchor_list = deepcopy(items_list)
anchor_list.remove(item_id)
pos_pairs.append([anchor_list, item_id])
split_num = int(len(pos_pairs) * split)
if is_train:
self.pos_pairs_train = pos_pairs[:split_num]
else:
self.pos_pairs_valid = pos_pairs[split_num:]
def sample_negative(self, item_id, item_type, neg_num):
item_out = item_id
candidate_sets = self.category2id[item_type]
count = 0
negative_items = []
while count < neg_num:
negative_index = np.random.choice(range(len(candidate_sets)))
item_out = candidate_sets[negative_index]
if item_out == item_id:
continue
negative_items.append(item_out)
count += 1
return negative_items
@staticmethod
def get_data(file_path):
outfits_json = dict()
category_id_dict = dict(tops=[],
bottoms=[],
shoes=[],
outerwear=[],
all_body=[],
bags=[],
jewellery=[],
accessories=[],
hats=[])
id_category_dict = dict()
with open(file_path, 'r') as f1:
outfits_info = json.load(f1)
for outfit_info in outfits_info:
outfit_id = outfit_info["set_id"]
outfits_json[outfit_id] = []
for item_info in outfit_info["items"]:
item_id = item_info["item_id"]
item_type = item_info["category"]
outfits_json[outfit_id].append(item_id)
category_id_dict[item_type].append(item_id)
id_category_dict[item_id] = item_type
return outfits_json, category_id_dict, id_category_dict
def load_image(self, image_id):
img_path = join(self.image_dir, '{}.png'.format(image_id))
img = self.loader(img_path)
if len(img.split()) != 4:
print(img_path)
if self.transform is not None:
img = self.transform(img)
return img
def __getitem__(self, index):
if self.is_train:
anchors_id, positive_id = self.pos_pairs_train[index]
else:
anchors_id, positive_id = self.pos_pairs_valid[index]
positive_type = self.id2category[positive_id]
negatives_id = self.sample_negative(positive_id, positive_type, neg_num=self.neg_num)
anchors_img = [self.load_image(anchor_id) for anchor_id in anchors_id]
anchors_img = torch.cat(anchors_img)
positive_img = self.load_image(positive_id)
negatives_img = [self.load_image(negative_id) for negative_id in negatives_id]
negatives_img = torch.cat(negatives_img)
return anchors_img, positive_img, negatives_img
def __len__(self):
if self.is_train:
return len(self.pos_pairs_train)
return len(self.pos_pairs_valid)