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dataloader_finetune.py
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dataloader_finetune.py
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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
from autoaugment import CIFAR10Policy, ImageNetPolicy
from torchnet.meter import AUCMeter
import torch.nn.functional as F
from sklearn.metrics import confusion_matrix
from Asymmetric_Noise import *
## For plotting the logs
import wandb
wandb.init(project="noisy-label-project", entity="ryota170")
def unpickle(file):
import _pickle as cPickle
with open(file, 'rb') as fo:
dict = cPickle.load(fo, encoding='latin1')
return dict
transform_none_10 = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
]
)
transform_mnist_10 = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.), (0.2023, 0.1994, 0.2010)),
]
)
transform_none_100 = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.507, 0.487, 0.441), (0.267, 0.256, 0.276)),
]
)
transform_weak_10 = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
]
)
transform_weak_100 = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.507, 0.487, 0.441), (0.267, 0.256, 0.276)),
]
)
transform_strong_10 = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
CIFAR10Policy(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
]
)
transform_strong_100 = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
CIFAR10Policy(),
transforms.ToTensor(),
transforms.Normalize((0.507, 0.487, 0.441), (0.267, 0.256, 0.276)),
]
)
class cifar_dataset(Dataset):
def __init__(self, dataset, X_images, X_labels, U_images, root_dir, transform, mode, noise_file='', pred=[], probability=[], log=''):
self.transform = transform
self.mode = mode
self.class_ind = {}
# if self.noise_mode=='sup':
# ext = ''
# else:
# ext = 'rand'
if self.mode=='test':
if dataset=='cifar10':
test_dic = unpickle('%s/test_batch'%root_dir)
self.test_data = test_dic['data']
self.test_data = self.test_data.reshape((10000, 3, 32, 32))
self.test_data = self.test_data.transpose((0, 2, 3, 1))
self.test_label = test_dic['labels']
elif dataset=='cifar100':
test_dic = unpickle('%s/test'%root_dir)
self.test_data = test_dic['data']
self.test_data = self.test_data.reshape((10000, 3, 32, 32))
self.test_data = self.test_data.transpose((0, 2, 3, 1))
self.test_label = test_dic['fine_labels']
else:
train_data_labelled = X_images
train_label_labelled = X_labels
train_data_unlabelled = U_images
num_sample_labelled = np.shape(train_label_labelled)[0]
num_sample_unlabelled = np.shape(train_data_unlabelled)[0]
### Get number of classes and fix the orientation of dimension ###
# print(num_sample_labelled, num_sample_unlabelled)
train_data_labelled = np.array(train_data_labelled).reshape((num_sample_labelled, 32, 32,3))
train_data_unlabelled = np.array(train_data_unlabelled).reshape((num_sample_unlabelled, 32, 32,3))
class_name = np.unique(train_label_labelled)
# print("Class:", class_name)
# train_data = train_data.transpose((0, 2, 3, 1))
num_sample = num_sample_labelled + num_sample_unlabelled
if self.mode == 'labeled' or 'all':
self.train_data = train_data_labelled
self.noise_label = np.array(train_label_labelled)
self.probability = np.ones(num_sample_labelled)
else:
self.train_data = train_data_unlabelled
def __getitem__(self, index):
if self.mode=='labeled':
img, target, prob = 255*self.train_data[index], self.noise_label[index], self.probability[index]
img = img.astype(np.uint8)
image = Image.fromarray(img)
img1 = self.transform[0](image)
img2 = self.transform[1](image)
img3 = self.transform[2](image)
img4 = self.transform[3](image)
return img1, img2, img3, img4, target, prob
elif self.mode=='unlabeled':
img = 255*self.train_data[index]
img = img.astype(np.uint8)
image = Image.fromarray(img)
img1 = self.transform[0](image)
img2 = self.transform[1](image)
img3 = self.transform[2](image)
img4 = self.transform[3](image)
return img1, img2, img3, img4
elif self.mode=='all':
img, target = 255*self.train_data[index], self.noise_label[index]
img = img.astype(np.uint8)
img = Image.fromarray(img)
img = self.transform(img)
return img, target, index
elif self.mode=='test':
img, target = self.test_data[index], self.test_label[index]
img = img.astype(np.uint8)
img = Image.fromarray(img)
img = self.transform(img)
return img, target
def __len__(self):
if self.mode!='test':
return len(self.train_data)
else:
return len(self.test_data)
class cifar_dataloader():
def __init__(self, dataset, task_mode, r, noise_mode, batch_size, num_workers, root_dir, log, noise_file=''):
self.dataset = dataset
self.r = r
self.task_mode = task_mode
self.noise_mode = noise_mode
self.batch_size = batch_size
self.num_workers = num_workers
self.root_dir = root_dir
self.log = log
self.noise_file = noise_file
if self.dataset=='cifar10':
self.transforms = {
"warmup": transform_weak_10,
"unlabeled": [
transform_weak_10,
transform_weak_10,
transform_strong_10,
transform_strong_10
],
"labeled": [
transform_weak_10,
transform_weak_10,
transform_strong_10,
transform_strong_10
],
}
self.transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),(0.2023, 0.1994, 0.2010)),
])
elif self.dataset=='cifar100':
self.transforms = {
"warmup": transform_weak_100,
"unlabeled": [
transform_weak_100,
transform_weak_100,
transform_strong_100,
transform_strong_100
],
"labeled": [
transform_weak_100,
transform_weak_100,
transform_strong_100,
transform_strong_100
],
}
self.transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.507, 0.487, 0.441), (0.267, 0.256, 0.276)),
])
def run(self,sample_ratio, X_images, X_labels, U_images, mode, class_name=[], pred=[], prob=[]):
if mode=='train':
labeled_dataset = cifar_dataset(dataset=self.dataset, X_images = X_images, X_labels = X_labels, U_images = U_images, root_dir=self.root_dir, transform=self.transforms["labeled"], mode="labeled", noise_file=self.noise_file, pred=pred, probability=prob,log=self.log)
labeled_trainloader = DataLoader(
dataset=labeled_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers, drop_last=True)
unlabeled_dataset = cifar_dataset(dataset=self.dataset, X_images = X_images, X_labels = X_labels, U_images = U_images, root_dir=self.root_dir, transform=self.transforms["unlabeled"], mode="unlabeled", noise_file=self.noise_file, pred=pred)
unlabeled_trainloader = DataLoader(
dataset=unlabeled_dataset,
batch_size= self.batch_size,
shuffle=True,
num_workers=self.num_workers, drop_last =True)
return labeled_trainloader, unlabeled_trainloader
elif mode=='test':
test_dataset = cifar_dataset(dataset=self.dataset, X_images = [], X_labels =[], U_images = [], root_dir=self.root_dir, transform=self.transform_test, mode='test')
test_loader = DataLoader(
dataset=test_dataset,
batch_size=100,
shuffle=False,
num_workers=self.num_workers)
return test_loader