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import os |
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from PIL import Image |
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import json |
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import numpy as np |
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import pandas as pd |
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import torch |
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import utils3d.torch |
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from ..modules.sparse.basic import SparseTensor |
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from .components import StandardDatasetBase |
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class SparseFeat2Render(StandardDatasetBase): |
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""" |
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SparseFeat2Render dataset. |
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Args: |
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roots (str): paths to the dataset |
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image_size (int): size of the image |
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model (str): model name |
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resolution (int): resolution of the data |
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min_aesthetic_score (float): minimum aesthetic score |
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max_num_voxels (int): maximum number of voxels |
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""" |
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def __init__( |
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self, |
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roots: str, |
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image_size: int, |
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model: str = 'dinov2_vitl14_reg', |
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resolution: int = 64, |
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min_aesthetic_score: float = 5.0, |
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max_num_voxels: int = 32768, |
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): |
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self.image_size = image_size |
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self.model = model |
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self.resolution = resolution |
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self.min_aesthetic_score = min_aesthetic_score |
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self.max_num_voxels = max_num_voxels |
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self.value_range = (0, 1) |
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super().__init__(roots) |
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def filter_metadata(self, metadata): |
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stats = {} |
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metadata = metadata[metadata[f'feature_{self.model}']] |
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stats['With features'] = len(metadata) |
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metadata = metadata[metadata['aesthetic_score'] >= self.min_aesthetic_score] |
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stats[f'Aesthetic score >= {self.min_aesthetic_score}'] = len(metadata) |
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metadata = metadata[metadata['num_voxels'] <= self.max_num_voxels] |
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stats[f'Num voxels <= {self.max_num_voxels}'] = len(metadata) |
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return metadata, stats |
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def _get_image(self, root, instance): |
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with open(os.path.join(root, 'renders', instance, '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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fov = metadata['camera_angle_x'] |
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intrinsics = utils3d.torch.intrinsics_from_fov_xy(torch.tensor(fov), torch.tensor(fov)) |
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c2w = torch.tensor(metadata['transform_matrix']) |
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c2w[:3, 1:3] *= -1 |
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extrinsics = torch.inverse(c2w) |
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image_path = os.path.join(root, 'renders', instance, metadata['file_path']) |
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image = Image.open(image_path) |
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alpha = image.getchannel(3) |
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image = image.convert('RGB') |
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image = image.resize((self.image_size, self.image_size), Image.Resampling.LANCZOS) |
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alpha = alpha.resize((self.image_size, self.image_size), Image.Resampling.LANCZOS) |
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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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return { |
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'image': image, |
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'alpha': alpha, |
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'extrinsics': extrinsics, |
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'intrinsics': intrinsics, |
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} |
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def _get_feat(self, root, instance): |
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DATA_RESOLUTION = 64 |
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feats_path = os.path.join(root, 'features', self.model, f'{instance}.npz') |
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feats = np.load(feats_path, allow_pickle=True) |
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coords = torch.tensor(feats['indices']).int() |
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feats = torch.tensor(feats['patchtokens']).float() |
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if self.resolution != DATA_RESOLUTION: |
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factor = DATA_RESOLUTION // self.resolution |
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coords = coords // factor |
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coords, idx = coords.unique(return_inverse=True, dim=0) |
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feats = torch.scatter_reduce( |
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torch.zeros(coords.shape[0], feats.shape[1], device=feats.device), |
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dim=0, |
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index=idx.unsqueeze(-1).expand(-1, feats.shape[1]), |
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src=feats, |
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reduce='mean' |
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) |
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return { |
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'coords': coords, |
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'feats': feats, |
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} |
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@torch.no_grad() |
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def visualize_sample(self, sample: dict): |
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return sample['image'] |
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@staticmethod |
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def collate_fn(batch): |
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pack = {} |
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coords = [] |
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for i, b in enumerate(batch): |
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coords.append(torch.cat([torch.full((b['coords'].shape[0], 1), i, dtype=torch.int32), b['coords']], dim=-1)) |
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coords = torch.cat(coords) |
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feats = torch.cat([b['feats'] for b in batch]) |
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pack['feats'] = SparseTensor( |
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coords=coords, |
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feats=feats, |
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) |
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pack['image'] = torch.stack([b['image'] for b in batch]) |
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pack['alpha'] = torch.stack([b['alpha'] for b in batch]) |
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pack['extrinsics'] = torch.stack([b['extrinsics'] for b in batch]) |
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pack['intrinsics'] = torch.stack([b['intrinsics'] for b in batch]) |
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return pack |
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def get_instance(self, root, instance): |
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image = self._get_image(root, instance) |
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feat = self._get_feat(root, instance) |
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return { |
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**image, |
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**feat, |
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} |
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