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Running
on
Zero
Running
on
Zero
| import torch | |
| from torch import nn | |
| from beartype import beartype | |
| from miche.encode import load_model | |
| # helper functions | |
| def exists(val): | |
| return val is not None | |
| def default(*values): | |
| for value in values: | |
| if exists(value): | |
| return value | |
| return None | |
| # point-cloud encoder from Michelangelo | |
| class PointConditioner(torch.nn.Module): | |
| def __init__( | |
| self, | |
| *, | |
| dim_latent = None, | |
| model_name = 'miche-256-feature', | |
| cond_dim = 768, | |
| freeze = True, | |
| ): | |
| super().__init__() | |
| # open-source version of miche | |
| if model_name == 'miche-256-feature': | |
| ckpt_path = None | |
| config_path = 'miche/shapevae-256.yaml' | |
| self.feature_dim = 1024 # embedding dimension | |
| self.cond_length = 257 # length of embedding | |
| self.point_encoder = load_model(ckpt_path=ckpt_path, config_path=config_path) | |
| # additional layers to connect miche and GPT | |
| self.cond_head_proj = nn.Linear(cond_dim, self.feature_dim) | |
| self.cond_proj = nn.Linear(cond_dim, self.feature_dim) | |
| else: | |
| raise NotImplementedError | |
| # whether to finetuen point-cloud encoder | |
| if freeze: | |
| for parameter in self.point_encoder.parameters(): | |
| parameter.requires_grad = False | |
| self.freeze = freeze | |
| self.model_name = model_name | |
| self.dim_latent = default(dim_latent, self.feature_dim) | |
| self.register_buffer('_device_param', torch.tensor(0.), persistent = False) | |
| def device(self): | |
| return next(self.buffers()).device | |
| def embed_pc(self, pc_normal): | |
| # encode point cloud to embeddings | |
| if self.model_name == 'miche-256-feature': | |
| point_feature = self.point_encoder.encode_latents(pc_normal) | |
| pc_embed_head = self.cond_head_proj(point_feature[:, 0:1]) | |
| pc_embed = self.cond_proj(point_feature[:, 1:]) | |
| pc_embed = torch.cat([pc_embed_head, pc_embed], dim=1) | |
| return pc_embed | |
| def forward( | |
| self, | |
| pc = None, | |
| pc_embeds = None, | |
| ): | |
| if pc_embeds is None: | |
| pc_embeds = self.embed_pc(pc.to(next(self.buffers()).dtype)) | |
| assert not torch.any(torch.isnan(pc_embeds)), 'NAN values in pc embedings' | |
| return pc_embeds | |