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datasets.py
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from torchvision import datasets
import os
import numpy as np
import random
import torch
from torch.utils.data import Dataset
from torchvision.datasets.folder import default_loader
class TripletFolder(datasets.ImageFolder):
def __init__(self, root, transform):
super(TripletFolder, self).__init__(root, transform)
targets = np.asarray([s[1] for s in self.samples])
self.targets = targets
cams = []
for s in self.samples:
cams.append(self._get_cam_id(s[0]))
self.cams = np.asarray(cams)
def _get_cam_id(self, path):
camera_id = []
filename = os.path.basename(path)
camera_id = filename.split('c')[1][0]
# camera_id = filename.split('_')[2][0:2]
return int(camera_id) - 1
def _get_pos_sample(self, target, index):
pos_index = np.argwhere(self.targets == target)
pos_index = pos_index.flatten()
pos_index = np.setdiff1d(pos_index, index)
rand = random.randint(0, len(pos_index) - 1)
return self.samples[pos_index[rand]]
def _get_neg_sample(self, target):
neg_index = np.argwhere(self.targets != target)
neg_index = neg_index.flatten()
rand = random.randint(0, len(neg_index) - 1)
return self.samples[neg_index[rand]]
def __getitem__(self, index):
path, target = self.samples[index]
cam = self.cams[index]
# pos_path, neg_path
pos_path = self._get_pos_sample(target, index)
neg_path = self._get_neg_sample(target)
sample = self.loader(path)
pos = self.loader(pos_path[0])
neg = self.loader(neg_path[0])
if self.transform is not None:
sample = self.transform(sample)
pos = self.transform(pos)
neg = self.transform(neg)
if self.target_transform is not None:
target = self.target_transform(target)
return sample, target, pos, neg
class SiameseDataset(datasets.ImageFolder):
"""
Train: For each sample creates randomly a positive or a negative pair
Test: Creates fixed pairs for testing
"""
def __init__(self, root, transform):
super(SiameseDataset, self).__init__(root, transform)
self.labels = np.array(self.imgs)[:, 1]
self.data = np.array(self.imgs)[:, 0]
self.labels_set = set(self.labels)
self.label_to_indices = {label: np.where(self.labels == label)[0]
for label in self.labels_set}
# flag = []
# soft_label = []
# len_sub_label = 10
# i = 0
# for d in self.data:
# cnt = 0
# sub_soft_label = np.zeros((len_sub_label,)).astype(int)
# sub_soft_label.fill(-1)
# file_name = os.path.split(d)[-1]
# class_name = file_name.split('_')[0]
# # flag 1 means real; 0 means fake
# if len(class_name) == 4:
# flag.append(1)
# for c in self.class_to_idx.keys():
# if class_name in c[:4] or class_name in c[-4:]:
# sub_soft_label[cnt] = self.class_to_idx[c]
# cnt += 1
# if cnt >= len_sub_label:
# print('cnt %d >= len_sub_label %d' % (cnt, len_sub_label))
# exit()
# else:
# flag.append(0)
# class_name1 = class_name[:4]
# class_name2 = class_name[-4:]
# for c in self.class_to_idx.keys():
# if class_name1 in c[:4] or class_name2 in c[-4:]:
# sub_soft_label[cnt] = self.class_to_idx[c]
# cnt += 1
# if cnt >= len_sub_label:
# print('cnt %d >= len_sub_label %d' % (cnt, len_sub_label))
# exit()
# sub_soft_label = np.asarray(sub_soft_label)
# soft_label.append(sub_soft_label)
# i += 1
# if i % 2000 == 0:
# print('i = %6d calculate soft label' % i)
# self.flag = np.asarray(flag)
# self.soft_label = np.asarray(soft_label)
# self.soft_label = soft_label
cams = []
for s in self.samples:
cams.append(self._get_cam_id(s[0]))
self.cams = np.asarray(cams)
def _get_cam_id(self, path):
filename = os.path.basename(path)
camera_id = filename.split('c')[1][0]
return int(camera_id) - 1
def __getitem__(self, index):
siamese_target = np.random.randint(0, 2)
img1, label1 = self.data[index], self.labels[index].item()
# flag1, softlabel1 = self.flag[index], self.soft_label[index]
if siamese_target == 1:
siamese_index = index
while siamese_index == index:
siamese_index = np.random.choice(self.label_to_indices[label1])
else:
siamese_label = np.random.choice(list(self.labels_set - set([label1])))
siamese_index = np.random.choice(self.label_to_indices[siamese_label])
img2, label2 = self.data[siamese_index], self.labels[siamese_index].item()
# flag2, softlabel2 = self.flag[siamese_index], self.soft_label[siamese_index]
img1 = default_loader(img1)
img2 = default_loader(img2)
if self.transform is not None:
img1 = self.transform(img1)
img2 = self.transform(img2)
# return (img1, img2), siamese_target, (int(label1), int(label2)), (flag1, flag2), (softlabel1, softlabel2)
return (img1, img2), siamese_target, (int(label1), int(label2))
def __len__(self):
return len(self.imgs)
class GcnDataset(datasets.ImageFolder):
"""
Train: For each sample creates randomly 4 images
Test: Creates fixed pairs for testing
"""
def __init__(self, root, transform, img_num=4):
super(GcnDataset, self).__init__(root, transform)
self.labels = np.array(self.imgs)[:, 1].astype(int)
self.data = np.array(self.imgs)[:, 0]
self.labels_set = set(self.labels)
self.label_to_indices = {label: np.where(self.labels == label)[0]
for label in self.labels_set}
cams = []
for s in self.imgs:
cams.append(self._get_cam_id(s[0]))
self.cams = np.asarray(cams)
self.img_num = img_num
def _get_cam_id(self, path):
filename = os.path.basename(path)
camera_id = filename.split('c')[1][0]
return int(camera_id) - 1
def __getitem__(self, index):
img_num = self.img_num
label = self.labels[index].item()
img, label = self.__getimgs_bylabel__(label, img_num)
return img, label
def __len__(self):
return len(self.imgs)
def __getimgs_bylabel__(self, label, img_num):
if len(self.label_to_indices[label]) >= img_num:
index = np.random.choice(self.label_to_indices[label], size=img_num, replace=False)
else:
index1 = np.random.choice(self.label_to_indices[label], size=len(self.label_to_indices[label]), replace=False)
index2 = np.random.choice(self.label_to_indices[label], size=img_num - len(self.label_to_indices[label]),
replace=True)
index = np.concatenate((index1, index2))
for i in range(img_num):
img_temp = (self.data[index[i]])
label_temp = (self.labels[index[i]])
if type(label_temp) not in (tuple, list):
label_temp = (label_temp,)
label_temp = torch.LongTensor(label_temp)
img_temp = default_loader(img_temp)
if self.transform is not None:
img_temp = self.transform(img_temp)
img_temp = img_temp.unsqueeze(0)
if i == 0:
img = img_temp
label = label_temp
else:
img = torch.cat((img, img_temp), 0)
label = torch.cat((label, label_temp), 0)
return img, label