| from typing import * |
| from abc import abstractmethod |
| import os |
| import json |
| import torch |
| import numpy as np |
| import pandas as pd |
| from PIL import Image |
| from torch.utils.data import Dataset |
|
|
|
|
| class StandardDatasetBase(Dataset): |
| """ |
| Base class for standard datasets. |
| |
| Args: |
| roots (str): paths to the dataset |
| """ |
|
|
| def __init__(self, |
| roots: str, |
| ): |
| super().__init__() |
| self.roots = roots.split(',') |
| self.instances = [] |
| self.metadata = pd.DataFrame() |
| |
| self._stats = {} |
| for root in self.roots: |
| key = os.path.basename(root) |
| self._stats[key] = {} |
| metadata = pd.read_csv(os.path.join(root, 'metadata.csv')) |
| self._stats[key]['Total'] = len(metadata) |
| metadata, stats = self.filter_metadata(metadata) |
| self._stats[key].update(stats) |
| self.instances.extend([(root, sha256) for sha256 in metadata['sha256'].values]) |
| metadata.set_index('sha256', inplace=True) |
| self.metadata = pd.concat([self.metadata, metadata]) |
| |
| @abstractmethod |
| def filter_metadata(self, metadata: pd.DataFrame) -> Tuple[pd.DataFrame, Dict[str, int]]: |
| pass |
| |
| @abstractmethod |
| def get_instance(self, root: str, instance: str) -> Dict[str, Any]: |
| pass |
| |
| def __len__(self): |
| return len(self.instances) |
|
|
| def __getitem__(self, index) -> Dict[str, Any]: |
| try: |
| root, instance = self.instances[index] |
| return self.get_instance(root, instance) |
| except Exception as e: |
| print(e) |
| return self.__getitem__(np.random.randint(0, len(self))) |
| |
| def __str__(self): |
| lines = [] |
| lines.append(self.__class__.__name__) |
| lines.append(f' - Total instances: {len(self)}') |
| lines.append(f' - Sources:') |
| for key, stats in self._stats.items(): |
| lines.append(f' - {key}:') |
| for k, v in stats.items(): |
| lines.append(f' - {k}: {v}') |
| return '\n'.join(lines) |
|
|
|
|
| class TextConditionedMixin: |
| def __init__(self, roots, **kwargs): |
| super().__init__(roots, **kwargs) |
| self.captions = {} |
| for instance in self.instances: |
| sha256 = instance[1] |
| self.captions[sha256] = json.loads(self.metadata.loc[sha256]['captions']) |
| |
| def filter_metadata(self, metadata): |
| metadata, stats = super().filter_metadata(metadata) |
| metadata = metadata[metadata['captions'].notna()] |
| stats['With captions'] = len(metadata) |
| return metadata, stats |
| |
| def get_instance(self, root, instance): |
| pack = super().get_instance(root, instance) |
| text = np.random.choice(self.captions[instance]) |
| pack['cond'] = text |
| return pack |
| |
| |
| class ImageConditionedMixin: |
| def __init__(self, roots, *, image_size=518, **kwargs): |
| self.image_size = image_size |
| super().__init__(roots, **kwargs) |
| |
| def filter_metadata(self, metadata): |
| metadata, stats = super().filter_metadata(metadata) |
| metadata = metadata[metadata[f'cond_rendered']] |
| stats['Cond rendered'] = len(metadata) |
| return metadata, stats |
| |
| def get_instance(self, root, instance): |
| pack = super().get_instance(root, instance) |
| |
| image_root = os.path.join(root, 'renders_cond', instance) |
| with open(os.path.join(image_root, 'transforms.json')) as f: |
| metadata = json.load(f) |
| n_views = len(metadata['frames']) |
| view = np.random.randint(n_views) |
| metadata = metadata['frames'][view] |
|
|
| image_path = os.path.join(image_root, metadata['file_path']) |
| image = Image.open(image_path) |
|
|
| alpha = np.array(image.getchannel(3)) |
| bbox = np.array(alpha).nonzero() |
| bbox = [bbox[1].min(), bbox[0].min(), bbox[1].max(), bbox[0].max()] |
| center = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2] |
| hsize = max(bbox[2] - bbox[0], bbox[3] - bbox[1]) / 2 |
| aug_size_ratio = 1.2 |
| aug_hsize = hsize * aug_size_ratio |
| aug_center_offset = [0, 0] |
| aug_center = [center[0] + aug_center_offset[0], center[1] + aug_center_offset[1]] |
| aug_bbox = [int(aug_center[0] - aug_hsize), int(aug_center[1] - aug_hsize), int(aug_center[0] + aug_hsize), int(aug_center[1] + aug_hsize)] |
| image = image.crop(aug_bbox) |
|
|
| image = image.resize((self.image_size, self.image_size), Image.Resampling.LANCZOS) |
| alpha = image.getchannel(3) |
| image = image.convert('RGB') |
| image = torch.tensor(np.array(image)).permute(2, 0, 1).float() / 255.0 |
| alpha = torch.tensor(np.array(alpha)).float() / 255.0 |
| image = image * alpha.unsqueeze(0) |
| pack['cond'] = image |
| |
| return pack |
| |
