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@ -121,6 +121,8 @@ class BRepSDFDataset(Dataset): |
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# 解包处理后的特征 |
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edge_ncs, edge_pos, edge_mask, surf_ncs, surf_pos, vertex_pos = brep_features |
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sdf_points = sdf_data[:, :3] |
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sdf_values = sdf_data[:, 3:] |
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# 构建返回字典 |
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return { |
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@ -131,8 +133,8 @@ class BRepSDFDataset(Dataset): |
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'surf_ncs': surf_ncs, # [max_face, 100, 3] |
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'surf_pos': surf_pos, # [max_face, 6] |
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'vertex_pos': vertex_pos, # [max_face, max_edge, 6] |
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'points': sdf_data[:, :3], # [num_queries, 3] 所有点的xyz坐标 |
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'sdf': sdf_data[:, 3:] # [num_queries, 1] 所有点的sdf值 |
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'points': sdf_points, # [num_queries, 3] 所有点的xyz坐标 |
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'sdf': sdf_values # [num_queries, 1] 所有点的sdf值 |
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} |
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except Exception as e: |
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@ -161,115 +163,6 @@ class BRepSDFDataset(Dataset): |
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raise |
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def _load_brep_file(self, brep_path): |
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"""加载B-rep特征文件""" |
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try: |
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# 1. 加载原始数据 |
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with open(brep_path, 'rb') as f: |
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raw_data = pickle.load(f) |
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brep_data = {} |
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# 2. 处理几何数据（不等长序列） |
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geom_keys = ['surf_wcs', 'surf_ncs', 'edge_wcs', 'edge_ncs'] |
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for key in geom_keys: |
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if key in raw_data: |
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try: |
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# 确保数据是列表 |
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if not isinstance(raw_data[key], list): |
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raise ValueError(f"{key} is not a list") |
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# 转换每个元素为张量 |
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tensors = [] |
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for i, x in enumerate(raw_data[key]): |
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try: |
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# 先转换为numpy数组 |
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arr = np.array(x, dtype=np.float32) |
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# 再转换为张量 |
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tensor = torch.from_numpy(arr) |
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tensors.append(tensor) |
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except Exception as e: |
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logger.error(f"Error converting {key}[{i}]:") |
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logger.error(f" Data type: {type(x)}") |
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if isinstance(x, np.ndarray): |
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logger.error(f" Shape: {x.shape}") |
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logger.error(f" dtype: {x.dtype}") |
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raise ValueError(f"Failed to convert {key}[{i}]: {str(e)}") |
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brep_data[key] = tensors |
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except Exception as e: |
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logger.error(f"Error processing {key}:") |
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logger.error(f" Raw data type: {type(raw_data[key])}") |
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raise ValueError(f"Failed to process {key}: {str(e)}") |
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# 3. 处理固定形状的数据 |
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fixed_keys = ['corner_wcs', 'corner_unique', 'surf_bbox_wcs', 'edge_bbox_wcs'] |
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for key in fixed_keys: |
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if key in raw_data: |
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try: |
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# 直接从原始数据转换 |
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arr = np.array(raw_data[key], dtype=np.float32) |
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brep_data[key] = torch.from_numpy(arr) |
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except Exception as e: |
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logger.error(f"Error converting fixed shape data {key}:") |
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logger.error(f" Raw data type: {type(raw_data[key])}") |
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if isinstance(raw_data[key], np.ndarray): |
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logger.error(f" Shape: {raw_data[key].shape}") |
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logger.error(f" dtype: {raw_data[key].dtype}") |
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raise ValueError(f"Failed to convert {key}: {str(e)}") |
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# 4. 处理邻接矩阵 |
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adj_keys = ['edgeFace_adj', 'edgeCorner_adj', 'faceEdge_adj'] |
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for key in adj_keys: |
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if key in raw_data: |
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try: |
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# 转换为整型数组 |
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arr = np.array(raw_data[key], dtype=np.int32) |
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brep_data[key] = torch.from_numpy(arr) |
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except Exception as e: |
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logger.error(f"Error converting adjacency matrix {key}:") |
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logger.error(f" Raw data type: {type(raw_data[key])}") |
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if isinstance(raw_data[key], np.ndarray): |
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logger.error(f" Shape: {raw_data[key].shape}") |
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logger.error(f" dtype: {raw_data[key].dtype}") |
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raise ValueError(f"Failed to convert {key}: {str(e)}") |
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# 5. 验证必要的键是否存在 |
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required_keys = {'surf_wcs', 'edge_wcs', 'corner_wcs'} |
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missing_keys = required_keys - set(brep_data.keys()) |
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if missing_keys: |
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raise ValueError(f"Missing required keys: {missing_keys}") |
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# 6. 使用process_brep_data处理数据 |
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try: |
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features = process_brep_data( |
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data=brep_data, |
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max_face=self.max_face, |
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max_edge=self.max_edge, |
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bbox_scaled=self.bbox_scaled |
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) |
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return features |
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except Exception as e: |
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logger.error("Error in process_brep_data:") |
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logger.error(f" Error message: {str(e)}") |
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# 打印数据形状信息 |
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logger.error("\nInput data shapes:") |
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for key, value in brep_data.items(): |
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if isinstance(value, list): |
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shapes = [t.shape for t in value] |
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logger.error(f" {key}: list of tensors with shapes {shapes}") |
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elif isinstance(value, torch.Tensor): |
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logger.error(f" {key}: tensor of shape {value.shape}") |
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raise |
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except Exception as e: |
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logger.error(f"\nError loading B-rep file: {brep_path}") |
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logger.error(f"Error message: {str(e)}") |
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raise |
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def _load_sdf_file(self, sdf_path): |
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"""加载和处理SDF数据，并进行随机采样""" |
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try: |
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@ -321,52 +214,7 @@ class BRepSDFDataset(Dataset): |
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logger.error(f"Error type: {type(e).__name__}") |
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logger.error(f"Error message: {str(e)}") |
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raise |
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@staticmethod |
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def collate_fn(batch): |
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"""自定义批处理函数""" |
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# 收集所有样本的名称 |
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names = [item['name'] for item in batch] |
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# 处理固定大小的张量数据 |
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tensor_keys = ['edge_ncs', 'edge_pos', 'edge_mask', |
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'surf_ncs', 'surf_pos', 'vertex_pos'] |
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tensors = { |
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key: torch.stack([item[key] for item in batch]) |
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for key in tensor_keys |
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} |
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# 处理变长的SDF数据 |
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sdf_data = [item['sdf'] for item in batch] |
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max_sdf_len = max(sdf.size(0) for sdf in sdf_data) |
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# 填充SDF数据 |
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padded_sdfs = [] |
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sdf_masks = [] |
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for sdf in sdf_data: |
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pad_len = max_sdf_len - sdf.size(0) |
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if pad_len > 0: |
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padding = torch.zeros(pad_len, sdf.size(1), |
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dtype=sdf.dtype, device=sdf.device) |
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padded_sdf = torch.cat([sdf, padding], dim=0) |
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mask = torch.cat([ |
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torch.ones(sdf.size(0), dtype=torch.bool), |
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torch.zeros(pad_len, dtype=torch.bool) |
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]) |
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else: |
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padded_sdf = sdf |
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mask = torch.ones(sdf.size(0), dtype=torch.bool) |
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padded_sdfs.append(padded_sdf) |
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sdf_masks.append(mask) |
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# 合并所有数据 |
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batch_data = { |
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'name': names, |
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'sdf': torch.stack(padded_sdfs), |
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'sdf_mask': torch.stack(sdf_masks), |
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**tensors |
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} |
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return batch_data |
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def test_dataset(): |
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"""测试数据集功能""" |
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@ -381,6 +229,7 @@ def test_dataset(): |
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max_edge = config.data.max_edge |
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num_edge_points = config.model.num_edge_points |
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num_surf_points = config.model.num_surf_points |
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num_query_points = config.data.num_query_points |
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# 定义预期的数据维度，使用配置中的参数 |
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expected_shapes = { |
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@ -390,7 +239,8 @@ def test_dataset(): |
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'surf_ncs': (max_face, num_surf_points, 3), |
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'surf_pos': (max_face, 6), |
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'vertex_pos': (max_face, max_edge, 2, 3), |
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'sdf': (2097152, 4) |
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'points': (num_query_points, 3), |
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'sdf': (num_query_points, 1) |
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} |
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logger.info("="*50) |
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@ -424,6 +274,7 @@ def test_dataset(): |
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logger.info(f" Expected: {expected_shape}") |
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logger.info(f" Match: {shape_match}") |
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logger.info(f" dtype: {value.dtype}") |
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logger.info(f" grad: {value.requires_grad}") |
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if not shape_match: |
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logger.warning(f" Shape mismatch for {key}!") |
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