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datasets.py
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datasets.py
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import glob
import random
import os
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
def to_rgb(image):
rgb_image = Image.new("RGB", image.size)
rgb_image.paste(image)
return rgb_image
class ImageDataset(Dataset):
def __init__(self, root, transforms_=None, unaligned=False, mode="train"):
self.transform = transforms.Compose(transforms_)
self.unaligned = unaligned
self.files_A = sorted(glob.glob(os.path.join(root, "%s/A" % mode) + "/*.*"))
self.files_B = sorted(glob.glob(os.path.join(root, "%s/B" % mode) + "/*.*"))
def __getitem__(self, index):
image_A = Image.open(self.files_A[index % len(self.files_A)])
if self.unaligned:
image_B = Image.open(self.files_B[random.randint(0, len(self.files_B) - 1)])
else:
image_B = Image.open(self.files_B[index % len(self.files_B)])
# Convert grayscale images to rgb
if image_A.mode != "RGB":
image_A = to_rgb(image_A)
if image_B.mode != "RGB":
image_B = to_rgb(image_B)
item_A = self.transform(image_A)
item_B = self.transform(image_B)
return {"A": item_A, "B": item_B}
def __len__(self):
return max(len(self.files_A), len(self.files_B))