from pathlib import Path
from typing import Optional
from PIL import Image
from PIL.ImageOps import exif_transpose
from torch.utils.data import Dataset
from torchvision import transforms
import json
import random
from facenet_pytorch import MTCNN
import torch
from utils.utils import extract_faces_and_landmarks, REFERNCE_FACIAL_POINTS_RELATIVE
def load_image(image_path: str) -> Image:
image = Image.open(image_path)
image = exif_transpose(image)
if not image.mode == "RGB":
image = image.convert("RGB")
return image
class ImageDataset(Dataset):
"""
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
It pre-processes the images.
"""
def __init__(
self,
instance_data_root,
instance_prompt,
metadata_path: Optional[str] = None,
prompt_in_filename=False,
use_only_vanilla_for_encoder=False,
concept_placeholder='a face',
size=1024,
center_crop=False,
aug_images=False,
use_only_decoder_prompts=False,
crop_head_for_encoder_image=False,
random_target_prob=0.0,
):
self.mtcnn = MTCNN(device='cuda:0')
self.mtcnn.forward = self.mtcnn.detect
resize_factor = 1.3
self.resized_reference_points = REFERNCE_FACIAL_POINTS_RELATIVE / resize_factor + (resize_factor - 1) / (2 * resize_factor)
self.size = size
self.center_crop = center_crop
self.concept_placeholder = concept_placeholder
self.prompt_in_filename = prompt_in_filename
self.aug_images = aug_images
self.instance_prompt = instance_prompt
self.custom_instance_prompts = None
self.name_to_label = None
self.crop_head_for_encoder_image = crop_head_for_encoder_image
self.random_target_prob = random_target_prob
self.use_only_decoder_prompts = use_only_decoder_prompts
self.instance_data_root = Path(instance_data_root)
if not self.instance_data_root.exists():
raise ValueError(f"Instance images root {self.instance_data_root} doesn't exist.")
if metadata_path is not None:
with open(metadata_path, 'r') as f:
self.name_to_label = json.load(f) # dict of filename: label
# Create a reversed mapping
self.label_to_names = {}
for name, label in self.name_to_label.items():
if use_only_vanilla_for_encoder and 'vanilla' not in name:
continue
if label not in self.label_to_names:
self.label_to_names[label] = []
self.label_to_names[label].append(name)
self.all_paths = [self.instance_data_root / filename for filename in self.name_to_label.keys()]
# Verify all paths exist
n_all_paths = len(self.all_paths)
self.all_paths = [path for path in self.all_paths if path.exists()]
print(f'Found {len(self.all_paths)} out of {n_all_paths} paths.')
else:
self.all_paths = [path for path in list(Path(instance_data_root).glob('**/*')) if
path.suffix.lower() in [".png", ".jpg", ".jpeg"]]
# Sort by name so that order for validation remains the same across runs
self.all_paths = sorted(self.all_paths, key=lambda x: x.stem)
self.custom_instance_prompts = None
self._length = len(self.all_paths)
self.class_data_root = None
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
if self.prompt_in_filename:
self.prompts_set = set([self._path_to_prompt(path) for path in self.all_paths])
else:
self.prompts_set = set([self.instance_prompt])
if self.aug_images:
self.aug_transforms = transforms.Compose(
[
transforms.RandomResizedCrop(size, scale=(0.8, 1.0), ratio=(1.0, 1.0)),
transforms.RandomHorizontalFlip(p=0.5)
]
)
def __len__(self):
return self._length
def _path_to_prompt(self, path):
# Remove the extension and seed
split_path = path.stem.split('_')
while split_path[-1].isnumeric():
split_path = split_path[:-1]
prompt = ' '.join(split_path)
# Replace placeholder in prompt with training placeholder
prompt = prompt.replace('conceptname', self.concept_placeholder)
return prompt
def __getitem__(self, index):
example = {}
instance_path = self.all_paths[index]
instance_image = load_image(instance_path)
example["instance_images"] = self.image_transforms(instance_image)
if self.prompt_in_filename:
example["instance_prompt"] = self._path_to_prompt(instance_path)
else:
example["instance_prompt"] = self.instance_prompt
if self.name_to_label is None:
# If no labels, simply take the same image but with different augmentation
example["encoder_images"] = self.aug_transforms(example["instance_images"]) if self.aug_images else example["instance_images"]
example["encoder_prompt"] = example["instance_prompt"]
else:
# Randomly select another image with the same label
instance_name = str(instance_path.relative_to(self.instance_data_root))
instance_label = self.name_to_label[instance_name]
label_set = set(self.label_to_names[instance_label])
if len(label_set) == 1:
# We are not supposed to have only one image per label, but just in case
encoder_image_name = instance_name
print(f'WARNING: Only one image for label {instance_label}.')
else:
encoder_image_name = random.choice(list(label_set - {instance_name}))
encoder_image = load_image(self.instance_data_root / encoder_image_name)
example["encoder_images"] = self.image_transforms(encoder_image)
if self.prompt_in_filename:
example["encoder_prompt"] = self._path_to_prompt(self.instance_data_root / encoder_image_name)
else:
example["encoder_prompt"] = self.instance_prompt
if self.crop_head_for_encoder_image:
example["encoder_images"] = extract_faces_and_landmarks(example["encoder_images"][None], self.size, self.mtcnn, self.resized_reference_points)[0][0]
example["encoder_prompt"] = example["encoder_prompt"].format(placeholder="<ph>")
example["instance_prompt"] = example["instance_prompt"].format(placeholder="<s*>")
if random.random() < self.random_target_prob:
random_path = random.choice(self.all_paths)
random_image = load_image(random_path)
example["instance_images"] = self.image_transforms(random_image)
if self.prompt_in_filename:
example["instance_prompt"] = self._path_to_prompt(random_path)
if self.use_only_decoder_prompts:
example["encoder_prompt"] = example["instance_prompt"]
return example
def collate_fn(examples, with_prior_preservation=False):
pixel_values = [example["instance_images"] for example in examples]
encoder_pixel_values = [example["encoder_images"] for example in examples]
prompts = [example["instance_prompt"] for example in examples]
encoder_prompts = [example["encoder_prompt"] for example in examples]
if with_prior_preservation:
raise NotImplementedError("Prior preservation not implemented.")
pixel_values = torch.stack(pixel_values)
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
encoder_pixel_values = torch.stack(encoder_pixel_values)
encoder_pixel_values = encoder_pixel_values.to(memory_format=torch.contiguous_format).float()
batch = {"pixel_values": pixel_values, "encoder_pixel_values": encoder_pixel_values,
"prompts": prompts, "encoder_prompts": encoder_prompts}
return batch