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import os
import json
import datetime
import torchvision
import numpy as np
import torch
from omegaconf import OmegaConf
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision.datasets import ImageFolder
from torchvision import transforms
from torchvision.transforms.functional import hflip
from accelerate.logging import get_logger
from safetensors.torch import load_file
from .sampler_utils import get_train_sampler
logger = get_logger(__name__, log_level="INFO")
def center_crop_arr(pil_image, image_size):
"""
Center cropping implementation from ADM.
https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
"""
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.Resampling.BOX
)
scale = image_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.Resampling.BICUBIC
)
arr = np.array(pil_image)
crop_y = (arr.shape[0] - image_size) // 2
crop_x = (arr.shape[1] - image_size) // 2
return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size])
class ImagenetDictWrapper(Dataset):
def __init__(self, dataset):
super().__init__()
self.dataset = dataset
def __getitem__(self, i):
x, y = self.dataset[i]
return {"image": x, "label": y}
def __len__(self):
return len(self.dataset)
class ImagenetLatentDataset(Dataset):
def __init__(self, latent_dir, image_dir, image_size):
super().__init__()
self.RandomHorizontalFlipProb = 0.5
self.transform = transforms.Compose([
transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, image_size)),
transforms.Lambda(lambda pil_image: (pil_image, hflip(pil_image))),
transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])), # returns a 4D tensor
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
self.dataset = []
for class_folder in os.listdir(image_dir):
if os.path.isfile(os.path.join(image_dir, class_folder)):
continue
latent_class_folder = os.path.join(latent_dir, class_folder)
image_class_folder = os.path.join(image_dir, class_folder)
for file in os.listdir(image_class_folder):
self.dataset.append(
dict(
latent=os.path.join(latent_class_folder, file.split('.')[0]+'.safetensors'),
image=os.path.join(image_class_folder, file)
)
)
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
data_item = dict()
data = load_file(self.dataset[idx]['latent'])
image = self.transform(Image.open(self.dataset[idx]['image']).convert("RGB"))
if torch.rand(1) < self.RandomHorizontalFlipProb:
data_item['latent'] = data['latent'][0]
data_item['image'] = image[0]
else:
data_item['latent'] = data['latent'][1]
data_item['image'] = image[1]
data_item['label'] = data['label']
return data_item
class C2ILoader():
def __init__(self, data_config):
super().__init__()
self.batch_size = data_config.dataloader.batch_size
self.num_workers = data_config.dataloader.num_workers
self.data_type = data_config.data_type
if data_config.data_type == 'image':
self.train_dataset = ImagenetDictWrapper(**OmegaConf.to_container(data_config.dataset))
elif data_config.data_type == 'latent':
self.train_dataset = ImagenetLatentDataset(**OmegaConf.to_container(data_config.dataset))
else:
raise NotImplementedError
self.test_dataset = None
self.val_dataset = None
def train_len(self):
return len(self.train_dataset)
def train_dataloader(self, rank, world_size, global_batch_size, max_steps, resume_steps, seed):
sampler = get_train_sampler(
self.train_dataset, rank, world_size, global_batch_size, max_steps, resume_steps, seed
)
return DataLoader(
self.train_dataset,
batch_size=self.batch_size,
sampler=sampler,
num_workers=self.num_workers,
pin_memory=True,
drop_last=True,
prefetch_factor=2,
)
def test_dataloader(self):
return None
def val_dataloader(self):
return DataLoader(
self.train_dataset,
batch_size=self.batch_size,
shuffle=self.shuffle,
num_workers=self.num_workers,
pin_memory=True,
drop_last=True
)
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