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dataloader_load_by_epoch.py
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dataloader_load_by_epoch.py
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import numpy as np
import torch.utils.data as data_utils
from torchvision import transforms
import os
import cv2
from PIL import Image
path_mean = [0.6185205578804016, 0.3677789568901062, 0.7136943936347961]
path_std = [0.23521704971790314, 0.2494743913412094, 0.17246422171592712]
class PerSlideBags(data_utils.Dataset):
def __init__(self, root, train=True, transform=None):
self.root = root
self.train = train
self.files = os.listdir(self.root)
self.transform = transform
self.num_examples = len(self.files)
self._tensor = transforms.ToTensor()
def __len__(self):
return len(self.files)
def __getitem__(self, index):
img = cv2.imread(self.root + self.files[index], cv2.IMREAD_UNCHANGED)
# print(self.root)
img = Image.fromarray(img)
# print(str(index))
if self.transform is not None:
img = self.transform(img)
# img = self._tensor(img)
return img
class ALLSlideBags(data_utils.Dataset):
def __init__(self, root, seed=1, train=True, positive='P', bag_length=50):
self.train = train
self.root = root
self.positive = positive
self.r = np.random.RandomState(seed)
self.bag_length = bag_length
if self.train:
self.all_train_bags_list, self.all_train_labels_list = self._create_allbags()
else:
self.all_test_bags_list, self.all_test_labels_list = self._create_allbags()
def _create_perbags(self, path):
if self.train:
dataset = PerSlideBags(root=path,
train=True,
transform=transforms.Compose([
transforms.ColorJitter(brightness=0.5,
contrast=0.5,
saturation=0.5,
hue=0.2),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.RandomRotation(40),
transforms.ToTensor(),
transforms.Normalize(mean=path_mean, std=path_std)]))
loader = data_utils.DataLoader(dataset=dataset, batch_size=self.bag_length, shuffle=True)
else:
dataset = PerSlideBags(root=path,
train=False,
transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize(mean=path_mean, std=path_std)]))
loader = data_utils.DataLoader(dataset=dataset, batch_size=self.bag_length, shuffle=True)
for batch_idx, batch_data in enumerate(loader):
all_imgs_perbag = batch_data
if batch_idx == 0:
break
per_bags_list = []
per_bags_list.append(all_imgs_perbag)
# print(self.root)
return per_bags_list
def _create_allbags(self):
train_bags_list = []
train_labels_list = []
for filename_class in os.listdir(self.root):
for filename_slide in os.listdir(self.root + filename_class + '/'):
# print('uploading '+filename_slide + ' ' + str(self.bag_length) + ' Per Slide ')
path_perbags = self.root + filename_class + '/' + filename_slide + '/'
label_bags = filename_class == self.positive
train_labels_list.append(label_bags)
train_bags_list.append(self._create_perbags(path_perbags))
all_bags_list = [b for a in train_bags_list for b in a]
all_labels_list = [val for val in train_labels_list]
return all_bags_list, all_labels_list
def __len__(self):
if self.train:
return len(self.all_train_bags_list)
else:
return len(self.all_test_bags_list)
def __getitem__(self, index):
if self.train:
bag = self.all_train_bags_list[index]
label = self.all_train_labels_list[index]
else:
bag = self.all_test_bags_list[index]
label = self.all_test_labels_list[index]
return bag, label
if __name__ == "__main__":
train_loader = data_utils.DataLoader(ALLSlideBags(
seed=1,
root='./train_dataset/',
bag_length=10,
train=True),
batch_size=1,
shuffle=True)
test_loader = data_utils.DataLoader(ALLSlideBags(
seed=1,
root='./val_dataset/',
bag_length=10,
train=False),
batch_size=1,
shuffle=True)
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.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.max(len_bag_list_train), np.min(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.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.max(len_bag_list_test), np.min(len_bag_list_test)))