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data_loader.py
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data_loader.py
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import re
import torch
from torch.utils.data import Dataset
from PIL import Image
import os
import os.path as osp
import glob
from augmentations import *
class CelebAMaskHQ(Dataset):
"""The data reading method modified from the original paper"""
def __init__(self, img_path, label_path, resize=512, transform=None, mode=True):
self.img_path = img_path
self.label_path = label_path
self.train_dataset = []
self.test_dataset = []
self.transform = transform
self.mode = mode
self.resize = resize
self.preprocess()
if mode:
self.num_images = len(self.train_dataset)
else:
self.num_images = len(self.test_dataset)
def preprocess(self):
for i in range(len([name for name in os.listdir(self.img_path) if osp.isfile(osp.join(self.img_path, name))])):
img_path = osp.join(self.img_path, str(i)+'.jpg')
label_path = osp.join(self.label_path, str(i)+'.png')
if self.mode:
self.train_dataset.append([img_path, label_path])
else:
self.test_dataset.append([img_path, label_path])
def __getitem__(self, index):
dataset = self.train_dataset if self.mode == True else self.test_dataset
img_path, label_path = dataset[index]
# Uniform image size
image = Image.open(img_path).convert("RGB")
label = Image.open(label_path).convert("L")
# Image resized to the same dimension
image, label = Compose(
[FreeScale(self.resize)])(image, label)
if self.mode: # train mode
if self.transform is not None:
image, label = self.transform(image, label)
# Convert it to pytorch style
image = img_transform(image)
mask = mask_transform(label)
edge = mask_transform(Image.fromarray(edge_contour(np.asarray(label))))
return image, mask, edge
else:
image = img_transform(image)
mask = mask_transform(label)
return image, mask
def __len__(self):
return self.num_images
class CustomDataLoader:
def __init__(self, img_path, label_path, image_size, batch_size, num_workers, transform=None, mode=True):
self.img_path = img_path
self.label_path = label_path
self.imsize = image_size
self.batch = batch_size
self.num_workers = num_workers
self.mode = mode
self.transform = transform
def loader(self):
dataset = CelebAMaskHQ(
self.img_path, self.label_path, self.imsize, self.transform, self.mode)
if self.mode == True:
loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=self.batch,
shuffle=True,
num_workers=self.num_workers,
drop_last=True,
pin_memory=True)
return loader
loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=self.batch,
shuffle=False,
num_workers=self.num_workers,
drop_last=True,
pin_memory=True)
return loader
class CelebAMaskHQ_Unseen(Dataset):
def __init__(self, img_path, resize=512, transform=None):
self.img_path = img_path
self.dataset = []
self.transform = transform
self.resize = resize
self.preprocess()
self.num_images = len(self.dataset)
def preprocess(self):
def custom_sort(item):
match = re.findall(r'(\d+)', item)
if match and len(match) >= 2:
return int(match[0]), int(match[1])
return 0, 0
self.dataset.extend(glob.glob(osp.join(self.img_path, "*.jpg")))
self.dataset = sorted(self.dataset, key=custom_sort)
def __getitem__(self, index):
img_path = self.dataset[index]
# Uniform image size
image = Image.open(img_path).convert("RGB")
# Image resized to the same dimension
image = image.resize((self.resize, self.resize), Image.BILINEAR)
image = img_transform(image)
return image
def __len__(self):
return self.num_images
class Unseen_DataLoader:
def __init__(self, img_path, image_size, batch_size, num_workers, transform=None):
self.img_path = img_path
self.imsize = image_size
self.batch = batch_size
self.num_workers = num_workers
self.transform = transform
def loader(self):
dataset = CelebAMaskHQ_Unseen(
self.img_path, self.imsize, self.transform)
loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=self.batch,
shuffle=False,
num_workers=self.num_workers,
drop_last=True,
pin_memory=True)
return loader