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from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as transforms
import random
import numpy as np
from PIL import Image
import json
import torch
import os
import matplotlib
def unpickle(file):
fo = open(file, 'rb').read()
size = 64 * 64 * 3 + 1
for i in range(50000):
arr = np.fromstring(fo[i * size:(i + 1) * size], dtype=np.uint8)
lab = np.identity(10)[arr[0]]
img = arr[1:].reshape((3, 64, 64)).transpose((1, 2, 0))
return img, lab
class animal_dataset(Dataset):
def __init__(self, root, transform, mode, pred=[], path=[], probability=[], num_class=10):
self.root = root
self.transform = transform
self.mode = mode
self.train_dir = root + '/training/'
self.test_dir = root + '/testing/'
train_imgs = os.listdir(self.train_dir)
test_imgs = os.listdir(self.test_dir)
self.test_data = []
self.test_labels = []
noise_file1 = './training_batch.json'
noise_file2 = './testing_batch.json'
if mode == 'test':
if os.path.exists(noise_file2):
dict = json.load(open(noise_file2, "r"))
self.test_labels = dict['data']
self.test_data = dict['label']
else:
for img in test_imgs:
self.test_data.append(self.test_dir+img)
self.test_labels.append(int(img[0]))
dicts = {}
dicts['data'] = self.test_data
dicts['label'] = self.test_labels
# json.dump(dicts, open(noise_file2, "w"))
else:
if os.path.exists(noise_file1):
dict = json.load(open(noise_file1, "r"))
train_data = dict['data']
train_labels = dict['label']
else:
train_data = []
train_labels = {}
for img in train_imgs:
img_path = self.train_dir+img
train_data.append(img_path)
train_labels[img_path] = (int(img[0]))
dicts = {}
dicts['data'] = train_data
dicts['label'] = train_labels
# json.dump(dicts, open(noise_file1, "w"))
if self.mode == "all":
self.train_data = train_data
self.train_labels = train_labels
elif self.mode == "labeled":
pred_idx = pred.nonzero()[0]
train_img = path
self.train_data = [train_img[i] for i in pred_idx]
self.probability = probability[pred_idx]
# self.train_labels = train_labels[pred_idx]
print("%s data has a size of %d" % (self.mode, len(self.train_data)))
self.train_labels = train_labels
elif self.mode == "unlabeled":
pred_idx = (1 - pred).nonzero()[0]
train_img = path
self.train_data = [train_img[i] for i in pred_idx]
self.probability = probability[pred_idx]
# self.train_labels = train_labels[pred_idx]
print("%s data has a size of %d" % (self.mode, len(self.train_data)))
self.train_labels = train_labels
def __getitem__(self, index):
if self.mode == 'labeled':
img_path = self.train_data[index]
target = self.train_labels[img_path]
prob = self.probability[index]
image = Image.open(img_path).convert('RGB')
img1 = self.transform(image)
img2 = self.transform(image)
return img1, img2, target, prob
elif self.mode == 'unlabeled':
img_path = self.train_data[index]
image = Image.open(img_path).convert('RGB')
img1 = self.transform(image)
img2 = self.transform(image)
return img1, img2
elif self.mode == 'all':
img_path = self.train_data[index]
target = self.train_labels[img_path]
image = Image.open(img_path).convert('RGB')
img = self.transform(image)
return img, target,img_path
elif self.mode == 'test':
img_path = self.test_data[index]
target = self.test_labels[index]
image = Image.open(img_path).convert('RGB')
img = self.transform(image)
return img, target
def __len__(self):
if self.mode == 'test':
return len(self.test_data)
else:
return len(self.train_data)
class animal_dataloader():
def __init__(self, root='E:/2_Dataset_All/Animal-10N', batch_size=32, num_workers=0):
self.batch_size = batch_size
self.num_workers = num_workers
self.root = root
self.transform_train = transforms.Compose([
transforms.Resize(64),
transforms.RandomCrop(64),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.6959, 0.6537, 0.6371), (0.3113, 0.3192, 0.3214)),
])
self.transform_test = transforms.Compose([
# transforms.Resize(64),
# transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize((0.6959, 0.6537, 0.6371), (0.3113, 0.3192, 0.3214)),
])
def run(self, mode, pred=[], prob=[], paths=[]):
if mode == 'warmup':
warmup_dataset = animal_dataset(self.root, transform=self.transform_train, mode='all')
warmup_loader = DataLoader(
dataset=warmup_dataset,
batch_size=self.batch_size * 2,
shuffle=True,
num_workers=self.num_workers,
pin_memory=True)
return warmup_loader
elif mode == 'train':
labeled_dataset = animal_dataset(self.root, transform=self.transform_train, mode='labeled', pred=pred, path=paths,
probability=prob)
labeled_loader = DataLoader(
dataset=labeled_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers,
pin_memory=True)
unlabeled_dataset = animal_dataset(self.root, transform=self.transform_train, mode='unlabeled', pred=pred,path=paths,
probability=prob)
unlabeled_loader = DataLoader(
dataset=unlabeled_dataset,
batch_size=int(self.batch_size),
shuffle=True,
num_workers=self.num_workers,
pin_memory=True)
return labeled_loader, unlabeled_loader
elif mode == 'eval_train':
eval_dataset = animal_dataset(self.root, transform=self.transform_test, mode='all')
eval_loader = DataLoader(
dataset=eval_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
pin_memory=True)
return eval_loader
elif mode == 'test':
test_dataset = animal_dataset(self.root, transform=self.transform_test, mode='test')
test_loader = DataLoader(
dataset=test_dataset,
batch_size=1000,
shuffle=False,
num_workers=self.num_workers,
pin_memory=True)
return test_loader
# if __name__ == '__main__':
# loader = animal_dataloader()
# train_loader = loader.run('warmup')
# import matplotlib.pyplot as plt
# for batch_idx, (inputs, labels, idx, img_path) in enumerate(train_loader):
# print(img_path[0])
# plt.figure(dpi=300)
# # plt.imshow(inputs[0])
# plt.imshow(inputs[0].reshape(64, 64, 3))
# plt.show()
# plt.close()
# print(inputs.shape())
# print(idx)
# print(labels, len(labels))
# # print(train_loader.dataset.__len__()) |