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dataloader_clothing1M.py
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dataloader_clothing1M.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
class clothing_dataset(Dataset):
def __init__(self, root, transform, mode, num_samples=0, pred=[], probability=[], paths=[], num_class=14):
self.root = root
self.transform = transform
self.mode = mode
self.train_labels = {}
self.test_labels = {}
self.val_labels = {}
with open('%s/noisy_label_kv.txt'%self.root,'r') as f:
lines = f.read().splitlines()
for l in lines:
entry = l.split()
img_path = '%s/'%self.root+entry[0][7:]
self.train_labels[img_path] = int(entry[1])
with open('%s/clean_label_kv.txt'%self.root,'r') as f:
lines = f.read().splitlines()
for l in lines:
entry = l.split()
img_path = '%s/'%self.root+entry[0][7:]
self.test_labels[img_path] = int(entry[1])
if mode == 'all':
train_imgs=[]
with open('%s/noisy_train_key_list.txt'%self.root,'r') as f:
lines = f.read().splitlines()
for l in lines:
img_path = '%s/'%self.root+l[7:]
train_imgs.append(img_path)
random.shuffle(train_imgs)
class_num = torch.zeros(num_class)
self.train_imgs = []
for impath in train_imgs:
label = self.train_labels[impath]
if class_num[label]<(num_samples/14) and len(self.train_imgs)<num_samples:
self.train_imgs.append(impath)
class_num[label]+=1
random.shuffle(self.train_imgs)
elif self.mode == "labeled":
train_imgs = paths
pred_idx = pred.nonzero()[0]
self.train_imgs = [train_imgs[i] for i in pred_idx]
self.probability = [probability[i] for i in pred_idx]
print("%s data has a size of %d"%(self.mode,len(self.train_imgs)))
elif self.mode == "unlabeled":
train_imgs = paths
pred_idx = (1-pred).nonzero()[0]
self.train_imgs = [train_imgs[i] for i in pred_idx]
self.probability = [probability[i] for i in pred_idx]
print("%s data has a size of %d"%(self.mode,len(self.train_imgs)))
elif mode=='test':
self.test_imgs = []
with open('%s/clean_test_key_list.txt'%self.root,'r') as f:
lines = f.read().splitlines()
for l in lines:
img_path = '%s/'%self.root+l[7:]
self.test_imgs.append(img_path)
elif mode=='val':
self.val_imgs = []
with open('%s/clean_val_key_list.txt'%self.root,'r') as f:
lines = f.read().splitlines()
for l in lines:
img_path = '%s/'%self.root+l[7:]
self.val_imgs.append(img_path)
def __getitem__(self, index):
if self.mode=='labeled':
img_path = self.train_imgs[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_imgs[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_imgs[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_imgs[index]
target = self.test_labels[img_path]
image = Image.open(img_path).convert('RGB')
img = self.transform(image)
return img, target
elif self.mode=='val':
img_path = self.val_imgs[index]
target = self.test_labels[img_path]
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_imgs)
if self.mode=='val':
return len(self.val_imgs)
else:
return len(self.train_imgs)
class clothing_dataloader():
def __init__(self, root, batch_size, num_batches, num_workers):
self.batch_size = batch_size
self.num_workers = num_workers
self.num_batches = num_batches
self.root = root
self.transform_train = transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
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(256),
transforms.CenterCrop(224),
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 = clothing_dataset(self.root,transform=self.transform_train, mode='all',num_samples=self.num_batches*self.batch_size*2)
warmup_loader = DataLoader(
dataset=warmup_dataset,
batch_size=self.batch_size*2,
shuffle=True,
num_workers=self.num_workers)
return warmup_loader
elif mode=='train':
labeled_dataset = clothing_dataset(self.root,transform=self.transform_train, mode='labeled',pred=pred, probability=prob,paths=paths)
labeled_loader = DataLoader(
dataset=labeled_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=self.num_workers)
unlabeled_dataset = clothing_dataset(self.root,transform=self.transform_train, mode='unlabeled',pred=pred, probability=prob,paths=paths)
unlabeled_loader = DataLoader(
dataset=unlabeled_dataset,
batch_size=int(self.batch_size),
shuffle=True,
num_workers=self.num_workers)
return labeled_loader,unlabeled_loader
elif mode=='eval_train':
eval_dataset = clothing_dataset(self.root,transform=self.transform_test, mode='all',num_samples=self.num_batches*self.batch_size)
eval_loader = DataLoader(
dataset=eval_dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers)
return eval_loader
elif mode=='test':
test_dataset = clothing_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)
return test_loader
elif mode=='val':
val_dataset = clothing_dataset(self.root,transform=self.transform_test, mode='val')
val_loader = DataLoader(
dataset=val_dataset,
batch_size=1000,
shuffle=False,
num_workers=self.num_workers)
return val_loader