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import os
import torch
from PIL import Image
from torchvision import transforms
import torchvision
import json
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
class SquarePad:
def __call__(self, image):
max_wh = max(image.size)
p_left, p_top = [(max_wh - s) // 2 for s in image.size]
p_right, p_bottom = [max_wh - (s+pad) for s, pad in zip(image.size, [p_left, p_top])]
padding = (p_left, p_top, p_right, p_bottom)
return transforms.functional.pad(image, padding, padding_mode = 'edge')
class BaseDataset(Dataset):
def __init__(self, img_size, dataset_path, inpainter):
self.IMAGENET_MEAN = [0.485, 0.456, 0.406]
self.IMAGENET_STD = [0.229, 0.224, 0.225]
self.img_size = img_size
self.dataset_path = dataset_path
self.inpainter = inpainter
self.json_path = os.path.join(dataset_path, 'DFDS_V2/DFDS_V2.0_2Percent.json')
# self.json_path = os.path.join(dataset_path, 'DFDS_V2.0_2Percent.json')
self.data = self.load_json()
self.data_train = self.data[0:500]
self.rgb_transform = transforms.Compose([
SquarePad(),
transforms.Resize((img_size, img_size), interpolation = transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize(mean = self.IMAGENET_MEAN, std = self.IMAGENET_STD)
])
def load_json(self):
with open(self.json_path, 'r') as file:
data = json.load(file)
return data
class TrainDataset(BaseDataset):
def __init__(self, img_size, dataset_path):
super().__init__(img_size = img_size, dataset_path = dataset_path, inpainter = None)
self.gt_transform = transforms.Compose([
SquarePad(),
transforms.Resize((img_size, img_size), interpolation = transforms.InterpolationMode.BICUBIC),
transforms.ToTensor()]
)
self.img_paths_pos, self.img_paths_neg, self.mask_paths_neg = self.load_dataset()
def load_dataset(self):
positive_imgs = [os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data_train]
negative_imgs = [os.path.join(self.dataset_path, data['masks'][0]['inpainters']['SD1_Inpaint'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['inpainters']['SD1.5_Inpaint'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['inpainters']['SD2_Inpaint'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['inpainters']['SDXL_Inpaint'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['inpainters']['SD3_Inpaint'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['inpainters']['SD3.5_Inpaint'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['inpainters']['kadinsky2.2_Inpaint'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['inpainters']['kadinsky3.1_Inpaint'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['inpainters']['FLUX_SHNELL_Inpaint'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['inpainters']['FLUX_DEV_FILL'].lstrip('/')) for data in self.data_train]
negative_masks = [os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data_train] + \
[os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data_train]
return positive_imgs, negative_imgs, negative_masks
def __len__(self):
return len(self.data_train) * 3
def __getitem__(self, idx):
img_path_pos, img_path_neg, gt_neg = self.img_paths_pos[idx], self.img_paths_neg[idx], self.mask_paths_neg[idx]
img_pos = Image.open(img_path_pos.replace('/Open_V7/','')).convert('RGB')
img_neg = Image.open(img_path_neg.replace('/Open_V7/','')).convert('RGB')
rgb_pos = self.rgb_transform(img_pos)
rgb_neg = self.rgb_transform(img_neg)
gt_pos = torch.zeros([1, img_pos.size[1], img_pos.size[0]])
gt_pos = torchvision.transforms.functional.to_pil_image(gt_pos)
gt_pos = self.gt_transform(gt_pos)
gt_neg = Image.open(gt_neg.replace('/Open_V7/','')).convert('L')
gt_neg = self.gt_transform(gt_neg)
gt_neg = torch.where(gt_neg > 0.5, 1., .0)
return rgb_pos, gt_pos, rgb_neg, gt_neg
class TestDataset(BaseDataset):
def __init__(self, img_size, dataset_path, inpainter):
super().__init__(img_size = img_size, dataset_path = dataset_path, inpainter = inpainter)
self.gt_transform = transforms.Compose([
transforms.ToTensor()])
self.img_paths, self.mask_paths, self.labels = self.load_dataset()
def load_dataset(self):
positive_imgs = [os.path.join(self.dataset_path, data['base_image_location'].lstrip('/')) for data in self.data[500:600]]
positive_masks = [None for data in self.data[500:600]]
negative_imgs = [os.path.join(self.dataset_path, data['masks'][0]['inpainters'][self.inpainter].lstrip('/')) for data in self.data[600:700]]
negative_masks = [os.path.join(self.dataset_path, data['masks'][0]['edited_mask_location'].lstrip('/')) for data in self.data[600:700]]
labels = [0.0 for data in self.data[500:600]] + [1.0 for data in self.data[600:700]]
return positive_imgs + negative_imgs, positive_masks + negative_masks, labels
def __len__(self):
return len(self.data[500:700])
def __getitem__(self, idx):
img_path, gt, label = self.img_paths[idx], self.mask_paths[idx], self.labels[idx]
img = Image.open(img_path.replace('/Open_V7/','').replace('data/', 'data/DFDS_V2/')).convert('RGB')
rgb = self.rgb_transform(img)
if gt == None:
gt = torch.zeros(
[1, img.size[1], img.size[0]])
else:
gt = Image.open(gt.replace('/Open_V7/','').replace('data/', 'data/DFDS_V2/')).convert('L')
gt = self.gt_transform(gt)
gt = torch.where(gt > 0.5, 1., .0)
return rgb, label, gt, img_path
def get_data_loader(split, img_size, batch_size, dataset_path, inpainter = None):
if split == 'train':
dataset = TrainDataset(img_size, dataset_path)
data_loader = DataLoader(dataset = dataset, batch_size = batch_size, shuffle = True, num_workers = 8, drop_last = True, pin_memory = False)
elif split == 'test':
dataset = TestDataset(img_size, dataset_path, inpainter)
data_loader = DataLoader(dataset = dataset, batch_size = batch_size, shuffle = False, num_workers = 8, drop_last = False, pin_memory = False)
return data_loader |