Spaces:
Sleeping
Sleeping
| # Copyright (c) 2025 Bytedance Ltd. and/or its affiliates | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import torch | |
| from torch import is_tensor | |
| from torch.utils.data import Dataset | |
| from torch.nn.utils.rnn import pad_sequence | |
| import numpy as np | |
| class SkeletonData(Dataset): | |
| """ | |
| A PyTorch Dataset to load and process skeleton data. | |
| """ | |
| def __init__(self, data, args, is_training): | |
| self.data = data | |
| self.input_pc_num = args.input_pc_num | |
| self.is_training = is_training | |
| self.hier_order = args.hier_order | |
| print(f"[Dataset] Created from {len(self.data)} entries") | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, idx): | |
| data = self.data[idx] | |
| joints = data['joints'] | |
| bones = data['bones'] | |
| vertices = data['vertices'] | |
| pc_normal = data['pc_w_norm'] | |
| indices = np.random.choice(pc_normal.shape[0], self.input_pc_num, replace=False) | |
| pc_normal = pc_normal[indices, :] | |
| pc_coor = pc_normal[:, :3] | |
| normal = pc_normal[:, 3:] | |
| if np.linalg.norm(normal, axis=1, keepdims=True).min() < 0.99: | |
| print("normal reroll") | |
| return self.__getitem__(np.random.randint(0, len(self.data))) | |
| data_dict = {} | |
| # normalize normal | |
| normal = normal / np.linalg.norm(normal, axis=1, keepdims=True) | |
| # scale to -0.5 to 0.5 | |
| bounds = np.array([vertices.min(axis=0), vertices.max(axis=0)]) | |
| vertices = vertices - (bounds[0] + bounds[1])[None, :] / 2 | |
| vertices = vertices / ((bounds[1] - bounds[0]).max() + 1e-5) | |
| joints = joints - (bounds[0] + bounds[1])[None, :] / 2 | |
| joints = joints / ((bounds[1] - bounds[0]).max() + 1e-5) | |
| joints = joints.clip(-0.5, 0.5) | |
| data_dict['joints'] = torch.from_numpy(np.asarray(joints).astype(np.float16)) | |
| data_dict['bones'] = torch.from_numpy(np.asarray(bones).astype(np.int64)) | |
| pc_coor = pc_coor / np.abs(pc_coor).max() * 0.9995 | |
| data_dict['pc_normal'] = torch.from_numpy(np.concatenate([pc_coor, normal], axis=-1).astype(np.float16)) | |
| data_dict['vertices'] = torch.from_numpy(data['vertices'].astype(np.float16)) | |
| data_dict['faces'] = torch.from_numpy(data['faces'].astype(np.int64)) | |
| data_dict['uuid'] = data['uuid'] | |
| data_dict['root_index'] = str(data['root_index']) | |
| return data_dict | |
| def load(cls, args, is_training=True): | |
| loaded_data = np.load(args.dataset_path, allow_pickle=True) | |
| data = [] | |
| for item in loaded_data["arr_0"]: | |
| data.append(item) | |
| print(f"[Dataset] Loaded {len(data)} entries") | |
| return cls(data, args, is_training) | |