|
|
|
@ -121,7 +121,9 @@ class BRepSDFDataset(Dataset): |
|
|
|
|
|
|
|
# 解包处理后的特征 |
|
|
|
edge_ncs, edge_pos, edge_mask, surf_ncs, surf_pos, vertex_pos = brep_features |
|
|
|
|
|
|
|
sdf_points = sdf_data[:, :3] |
|
|
|
sdf_values = sdf_data[:, 3:] |
|
|
|
|
|
|
|
# 构建返回字典 |
|
|
|
return { |
|
|
|
'name': name, |
|
|
|
@ -131,8 +133,8 @@ class BRepSDFDataset(Dataset): |
|
|
|
'surf_ncs': surf_ncs, # [max_face, 100, 3] |
|
|
|
'surf_pos': surf_pos, # [max_face, 6] |
|
|
|
'vertex_pos': vertex_pos, # [max_face, max_edge, 6] |
|
|
|
'points': sdf_data[:, :3], # [num_queries, 3] 所有点的xyz坐标 |
|
|
|
'sdf': sdf_data[:, 3:] # [num_queries, 1] 所有点的sdf值 |
|
|
|
'points': sdf_points, # [num_queries, 3] 所有点的xyz坐标 |
|
|
|
'sdf': sdf_values # [num_queries, 1] 所有点的sdf值 |
|
|
|
} |
|
|
|
|
|
|
|
except Exception as e: |
|
|
|
@ -161,115 +163,6 @@ class BRepSDFDataset(Dataset): |
|
|
|
raise |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def _load_brep_file(self, brep_path): |
|
|
|
"""加载B-rep特征文件""" |
|
|
|
try: |
|
|
|
# 1. 加载原始数据 |
|
|
|
with open(brep_path, 'rb') as f: |
|
|
|
raw_data = pickle.load(f) |
|
|
|
|
|
|
|
brep_data = {} |
|
|
|
|
|
|
|
# 2. 处理几何数据（不等长序列） |
|
|
|
geom_keys = ['surf_wcs', 'surf_ncs', 'edge_wcs', 'edge_ncs'] |
|
|
|
for key in geom_keys: |
|
|
|
if key in raw_data: |
|
|
|
try: |
|
|
|
# 确保数据是列表 |
|
|
|
if not isinstance(raw_data[key], list): |
|
|
|
raise ValueError(f"{key} is not a list") |
|
|
|
|
|
|
|
# 转换每个元素为张量 |
|
|
|
tensors = [] |
|
|
|
for i, x in enumerate(raw_data[key]): |
|
|
|
try: |
|
|
|
# 先转换为numpy数组 |
|
|
|
arr = np.array(x, dtype=np.float32) |
|
|
|
# 再转换为张量 |
|
|
|
tensor = torch.from_numpy(arr) |
|
|
|
tensors.append(tensor) |
|
|
|
except Exception as e: |
|
|
|
logger.error(f"Error converting {key}[{i}]:") |
|
|
|
logger.error(f" Data type: {type(x)}") |
|
|
|
if isinstance(x, np.ndarray): |
|
|
|
logger.error(f" Shape: {x.shape}") |
|
|
|
logger.error(f" dtype: {x.dtype}") |
|
|
|
raise ValueError(f"Failed to convert {key}[{i}]: {str(e)}") |
|
|
|
|
|
|
|
brep_data[key] = tensors |
|
|
|
|
|
|
|
except Exception as e: |
|
|
|
logger.error(f"Error processing {key}:") |
|
|
|
logger.error(f" Raw data type: {type(raw_data[key])}") |
|
|
|
raise ValueError(f"Failed to process {key}: {str(e)}") |
|
|
|
|
|
|
|
# 3. 处理固定形状的数据 |
|
|
|
fixed_keys = ['corner_wcs', 'corner_unique', 'surf_bbox_wcs', 'edge_bbox_wcs'] |
|
|
|
for key in fixed_keys: |
|
|
|
if key in raw_data: |
|
|
|
try: |
|
|
|
# 直接从原始数据转换 |
|
|
|
arr = np.array(raw_data[key], dtype=np.float32) |
|
|
|
brep_data[key] = torch.from_numpy(arr) |
|
|
|
except Exception as e: |
|
|
|
logger.error(f"Error converting fixed shape data {key}:") |
|
|
|
logger.error(f" Raw data type: {type(raw_data[key])}") |
|
|
|
if isinstance(raw_data[key], np.ndarray): |
|
|
|
logger.error(f" Shape: {raw_data[key].shape}") |
|
|
|
logger.error(f" dtype: {raw_data[key].dtype}") |
|
|
|
raise ValueError(f"Failed to convert {key}: {str(e)}") |
|
|
|
|
|
|
|
# 4. 处理邻接矩阵 |
|
|
|
adj_keys = ['edgeFace_adj', 'edgeCorner_adj', 'faceEdge_adj'] |
|
|
|
for key in adj_keys: |
|
|
|
if key in raw_data: |
|
|
|
try: |
|
|
|
# 转换为整型数组 |
|
|
|
arr = np.array(raw_data[key], dtype=np.int32) |
|
|
|
brep_data[key] = torch.from_numpy(arr) |
|
|
|
except Exception as e: |
|
|
|
logger.error(f"Error converting adjacency matrix {key}:") |
|
|
|
logger.error(f" Raw data type: {type(raw_data[key])}") |
|
|
|
if isinstance(raw_data[key], np.ndarray): |
|
|
|
logger.error(f" Shape: {raw_data[key].shape}") |
|
|
|
logger.error(f" dtype: {raw_data[key].dtype}") |
|
|
|
raise ValueError(f"Failed to convert {key}: {str(e)}") |
|
|
|
|
|
|
|
# 5. 验证必要的键是否存在 |
|
|
|
required_keys = {'surf_wcs', 'edge_wcs', 'corner_wcs'} |
|
|
|
missing_keys = required_keys - set(brep_data.keys()) |
|
|
|
if missing_keys: |
|
|
|
raise ValueError(f"Missing required keys: {missing_keys}") |
|
|
|
|
|
|
|
# 6. 使用process_brep_data处理数据 |
|
|
|
try: |
|
|
|
features = process_brep_data( |
|
|
|
data=brep_data, |
|
|
|
max_face=self.max_face, |
|
|
|
max_edge=self.max_edge, |
|
|
|
bbox_scaled=self.bbox_scaled |
|
|
|
) |
|
|
|
return features |
|
|
|
|
|
|
|
except Exception as e: |
|
|
|
logger.error("Error in process_brep_data:") |
|
|
|
logger.error(f" Error message: {str(e)}") |
|
|
|
# 打印数据形状信息 |
|
|
|
logger.error("\nInput data shapes:") |
|
|
|
for key, value in brep_data.items(): |
|
|
|
if isinstance(value, list): |
|
|
|
shapes = [t.shape for t in value] |
|
|
|
logger.error(f" {key}: list of tensors with shapes {shapes}") |
|
|
|
elif isinstance(value, torch.Tensor): |
|
|
|
logger.error(f" {key}: tensor of shape {value.shape}") |
|
|
|
raise |
|
|
|
|
|
|
|
except Exception as e: |
|
|
|
logger.error(f"\nError loading B-rep file: {brep_path}") |
|
|
|
logger.error(f"Error message: {str(e)}") |
|
|
|
raise |
|
|
|
|
|
|
|
def _load_sdf_file(self, sdf_path): |
|
|
|
"""加载和处理SDF数据，并进行随机采样""" |
|
|
|
try: |
|
|
|
@ -321,52 +214,7 @@ class BRepSDFDataset(Dataset): |
|
|
|
logger.error(f"Error type: {type(e).__name__}") |
|
|
|
logger.error(f"Error message: {str(e)}") |
|
|
|
raise |
|
|
|
@staticmethod |
|
|
|
def collate_fn(batch): |
|
|
|
"""自定义批处理函数""" |
|
|
|
# 收集所有样本的名称 |
|
|
|
names = [item['name'] for item in batch] |
|
|
|
|
|
|
|
# 处理固定大小的张量数据 |
|
|
|
tensor_keys = ['edge_ncs', 'edge_pos', 'edge_mask', |
|
|
|
'surf_ncs', 'surf_pos', 'vertex_pos'] |
|
|
|
tensors = { |
|
|
|
key: torch.stack([item[key] for item in batch]) |
|
|
|
for key in tensor_keys |
|
|
|
} |
|
|
|
|
|
|
|
# 处理变长的SDF数据 |
|
|
|
sdf_data = [item['sdf'] for item in batch] |
|
|
|
max_sdf_len = max(sdf.size(0) for sdf in sdf_data) |
|
|
|
|
|
|
|
# 填充SDF数据 |
|
|
|
padded_sdfs = [] |
|
|
|
sdf_masks = [] |
|
|
|
for sdf in sdf_data: |
|
|
|
pad_len = max_sdf_len - sdf.size(0) |
|
|
|
if pad_len > 0: |
|
|
|
padding = torch.zeros(pad_len, sdf.size(1), |
|
|
|
dtype=sdf.dtype, device=sdf.device) |
|
|
|
padded_sdf = torch.cat([sdf, padding], dim=0) |
|
|
|
mask = torch.cat([ |
|
|
|
torch.ones(sdf.size(0), dtype=torch.bool), |
|
|
|
torch.zeros(pad_len, dtype=torch.bool) |
|
|
|
]) |
|
|
|
else: |
|
|
|
padded_sdf = sdf |
|
|
|
mask = torch.ones(sdf.size(0), dtype=torch.bool) |
|
|
|
padded_sdfs.append(padded_sdf) |
|
|
|
sdf_masks.append(mask) |
|
|
|
|
|
|
|
# 合并所有数据 |
|
|
|
batch_data = { |
|
|
|
'name': names, |
|
|
|
'sdf': torch.stack(padded_sdfs), |
|
|
|
'sdf_mask': torch.stack(sdf_masks), |
|
|
|
**tensors |
|
|
|
} |
|
|
|
|
|
|
|
return batch_data |
|
|
|
|
|
|
|
|
|
|
|
def test_dataset(): |
|
|
|
"""测试数据集功能""" |
|
|
|
@ -381,6 +229,7 @@ def test_dataset(): |
|
|
|
max_edge = config.data.max_edge |
|
|
|
num_edge_points = config.model.num_edge_points |
|
|
|
num_surf_points = config.model.num_surf_points |
|
|
|
num_query_points = config.data.num_query_points |
|
|
|
|
|
|
|
# 定义预期的数据维度，使用配置中的参数 |
|
|
|
expected_shapes = { |
|
|
|
@ -390,7 +239,8 @@ def test_dataset(): |
|
|
|
'surf_ncs': (max_face, num_surf_points, 3), |
|
|
|
'surf_pos': (max_face, 6), |
|
|
|
'vertex_pos': (max_face, max_edge, 2, 3), |
|
|
|
'sdf': (2097152, 4) |
|
|
|
'points': (num_query_points, 3), |
|
|
|
'sdf': (num_query_points, 1) |
|
|
|
} |
|
|
|
|
|
|
|
logger.info("="*50) |
|
|
|
@ -424,6 +274,7 @@ def test_dataset(): |
|
|
|
logger.info(f" Expected: {expected_shape}") |
|
|
|
logger.info(f" Match: {shape_match}") |
|
|
|
logger.info(f" dtype: {value.dtype}") |
|
|
|
logger.info(f" grad: {value.requires_grad}") |
|
|
|
|
|
|
|
if not shape_match: |
|
|
|
logger.warning(f" Shape mismatch for {key}!") |
|
|
|
|
