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dataloader.py
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dataloader.py
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"""Pytorch dataset object that loads MPII dataset as bags."""
import numpy as np
import cv2
import torch
import torch.utils.data as data_utils
from torchvision import datasets, transforms
class MnistBags(data_utils.Dataset):
def __init__(self, target_number=9, mean_bag_length=10, var_bag_length=2, num_bag=250, seed=1, train=True):
self.target_number = target_number
self.mean_bag_length = mean_bag_length
self.var_bag_length = var_bag_length
self.num_bag = num_bag
self.train = train
self.r = np.random.RandomState(seed)
self.num_in_train = 1400
self.num_in_test = 200
if self.train:
self.train_bags_list, self.train_labels_list = self._create_bags()
else:
self.test_bags_list, self.test_labels_list = self._create_bags()
def _create_bags(self):
if self.train:
loader = data_utils.DataLoader(datasets.MNIST('../datasets',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])),
batch_size=self.num_in_train,
shuffle=False)
else:
loader = data_utils.DataLoader(datasets.MNIST('../datasets',
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])),
batch_size=self.num_in_test,
shuffle=False)
for (batch_data, batch_labels) in loader:
all_imgs = batch_data
all_labels = batch_labels
bags_list = []
labels_list = []
for i in range(self.num_bag):
bag_length = np.int(self.r.normal(self.mean_bag_length, self.var_bag_length, 1))
if bag_length < 1:
bag_length = 1
if self.train:
indices = torch.LongTensor(self.r.randint(0, self.num_in_train, bag_length))
else:
indices = torch.LongTensor(self.r.randint(0, self.num_in_test, bag_length))
labels_in_bag = all_labels[indices]
labels_in_bag = labels_in_bag == self.target_number
bags_list.append(all_imgs[indices])
labels_list.append(labels_in_bag)
return bags_list, labels_list
def __len__(self):
if self.train:
return len(self.train_labels_list)
else:
return len(self.test_labels_list)
def __getitem__(self, index):
if self.train:
bag = self.train_bags_list[index]
label = [max(self.train_labels_list[index]), self.train_labels_list[index]]
else:
bag = self.test_bags_list[index]
label = [max(self.test_labels_list[index]), self.test_labels_list[index]]
return bag, label
if __name__ == "__main__":
train_loader = data_utils.DataLoader(MnistBags(target_number=9,
mean_bag_length=10,
var_bag_length=2,
num_bag=100,
seed=1,
train=True),
batch_size=1,
shuffle=True)
test_loader = data_utils.DataLoader(MnistBags(target_number=9,
mean_bag_length=10,
var_bag_length=2,
num_bag=100,
seed=1,
train=False),
batch_size=1,
shuffle=False)
len_bag_list_train = []
mnist_bags_train = 0
for batch_idx, (bag, label) in enumerate(train_loader):
len_bag_list_train.append(int(bag.squeeze(0).size()[0]))
mnist_bags_train += label[0].numpy()[0]
print('Number positive train bags: {}/{}\n'
'Number of instances per bag, mean: {}, max: {}, min {}\n'.format(
mnist_bags_train, len(train_loader),
np.mean(len_bag_list_train), np.min(len_bag_list_train), np.max(len_bag_list_train)))
len_bag_list_test = []
mnist_bags_test = 0
for batch_idx, (bag, label) in enumerate(test_loader):
len_bag_list_test.append(int(bag.squeeze(0).size()[0]))
mnist_bags_test += label[0].numpy()[0]
print('Number positive test bags: {}/{}\n'
'Number of instances per bag, mean: {}, max: {}, min {}\n'.format(
mnist_bags_test, len(test_loader),
np.mean(len_bag_list_test), np.min(len_bag_list_test), np.max(len_bag_list_test)))