Create dataset.py

data/dataset.py

@@ -0,0 +1,202 @@
+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