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