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trellis/datasets/sparse_feat2render.py

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+import os
+from PIL import Image
+import json
+import numpy as np
+import pandas as pd
+import torch
+import utils3d.torch
+from ..modules.sparse.basic import SparseTensor
+from .components import StandardDatasetBase
+
+
+class SparseFeat2Render(StandardDatasetBase):
+    """
+    SparseFeat2Render dataset.
+    
+    Args:
+        roots (str): paths to the dataset
+        image_size (int): size of the image
+        model (str): model name
+        resolution (int): resolution of the data
+        min_aesthetic_score (float): minimum aesthetic score
+        max_num_voxels (int): maximum number of voxels
+    """
+    def __init__(
+        self,
+        roots: str,
+        image_size: int,
+        model: str = 'dinov2_vitl14_reg',
+        resolution: int = 64,
+        min_aesthetic_score: float = 5.0,
+        max_num_voxels: int = 32768,
+    ):
+        self.image_size = image_size
+        self.model = model
+        self.resolution = resolution
+        self.min_aesthetic_score = min_aesthetic_score
+        self.max_num_voxels = max_num_voxels
+        self.value_range = (0, 1)
+        
+        super().__init__(roots)
+        
+    def filter_metadata(self, metadata):
+        stats = {}
+        metadata = metadata[metadata[f'feature_{self.model}']]
+        stats['With features'] = len(metadata)
+        metadata = metadata[metadata['aesthetic_score'] >= self.min_aesthetic_score]
+        stats[f'Aesthetic score >= {self.min_aesthetic_score}'] = len(metadata)
+        metadata = metadata[metadata['num_voxels'] <= self.max_num_voxels]
+        stats[f'Num voxels <= {self.max_num_voxels}'] = len(metadata)
+        return metadata, stats
+
+    def _get_image(self, root, instance):
+        with open(os.path.join(root, 'renders', instance, 'transforms.json')) as f:
+            metadata = json.load(f)
+        n_views = len(metadata['frames'])
+        view = np.random.randint(n_views)
+        metadata = metadata['frames'][view]
+        fov = metadata['camera_angle_x']
+        intrinsics = utils3d.torch.intrinsics_from_fov_xy(torch.tensor(fov), torch.tensor(fov))
+        c2w = torch.tensor(metadata['transform_matrix'])
+        c2w[:3, 1:3] *= -1
+        extrinsics = torch.inverse(c2w)
+
+        image_path = os.path.join(root, 'renders', instance, metadata['file_path'])
+        image = Image.open(image_path)
+        alpha = image.getchannel(3)
+        image = image.convert('RGB')
+        image = image.resize((self.image_size, self.image_size), Image.Resampling.LANCZOS)
+        alpha = alpha.resize((self.image_size, self.image_size), Image.Resampling.LANCZOS)
+        image = torch.tensor(np.array(image)).permute(2, 0, 1).float() / 255.0
+        alpha = torch.tensor(np.array(alpha)).float() / 255.0
+        
+        return {
+            'image': image,
+            'alpha': alpha,
+            'extrinsics': extrinsics,
+            'intrinsics': intrinsics,
+        }
+    
+    def _get_feat(self, root, instance):
+        DATA_RESOLUTION = 64
+        feats_path = os.path.join(root, 'features', self.model, f'{instance}.npz')
+        feats = np.load(feats_path, allow_pickle=True)
+        coords = torch.tensor(feats['indices']).int()
+        feats = torch.tensor(feats['patchtokens']).float()
+        
+        if self.resolution != DATA_RESOLUTION:
+            factor = DATA_RESOLUTION // self.resolution
+            coords = coords // factor
+            coords, idx = coords.unique(return_inverse=True, dim=0)
+            feats = torch.scatter_reduce(
+                torch.zeros(coords.shape[0], feats.shape[1], device=feats.device),
+                dim=0,
+                index=idx.unsqueeze(-1).expand(-1, feats.shape[1]),
+                src=feats,
+                reduce='mean'
+            )
+        
+        return {
+            'coords': coords,
+            'feats': feats,
+        }
+
+    @torch.no_grad()
+    def visualize_sample(self, sample: dict):
+        return sample['image']
+
+    @staticmethod
+    def collate_fn(batch):
+        pack = {}
+        coords = []
+        for i, b in enumerate(batch):
+            coords.append(torch.cat([torch.full((b['coords'].shape[0], 1), i, dtype=torch.int32), b['coords']], dim=-1))
+        coords = torch.cat(coords)
+        feats = torch.cat([b['feats'] for b in batch])
+        pack['feats'] = SparseTensor(
+            coords=coords,
+            feats=feats,
+        )
+        
+        pack['image'] = torch.stack([b['image'] for b in batch])
+        pack['alpha'] = torch.stack([b['alpha'] for b in batch])
+        pack['extrinsics'] = torch.stack([b['extrinsics'] for b in batch])
+        pack['intrinsics'] = torch.stack([b['intrinsics'] for b in batch])
+
+        return pack
+
+    def get_instance(self, root, instance):
+        image = self._get_image(root, instance)
+        feat = self._get_feat(root, instance)
+        return {
+            **image,
+            **feat,
+        }