5 changed files with 866 additions and 109 deletions
@ -0,0 +1,383 @@ |
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import math |
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import torch |
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import torch.nn as nn |
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from dataclasses import dataclass |
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from typing import Dict, Optional, Tuple, Union |
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|
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from diffusers.configuration_utils import ConfigMixin, register_to_config |
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from diffusers.utils import BaseOutput, is_torch_version |
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from diffusers.utils.accelerate_utils import apply_forward_hook |
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from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor |
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from diffusers.models.modeling_utils import ModelMixin |
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from diffusers.models.autoencoders.vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder |
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from diffusers.models.unets.unet_1d_blocks import ResConvBlock, SelfAttention1d, get_down_block, get_up_block, Upsample1d |
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from diffusers.models.attention_processor import SpatialNorm |
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class Decoder1D(nn.Module): |
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def __init__( |
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self, |
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in_channels=3, |
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out_channels=3, |
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up_block_types=("UpDecoderBlock2D",), |
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block_out_channels=(64,), |
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layers_per_block=2, |
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norm_num_groups=32, |
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act_fn="silu", |
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norm_type="group", # group, spatial |
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): |
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''' |
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这是第一阶段的解码器，用于处理B-rep特征 |
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包含三个主要部分： |
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conv_in: 输入卷积层，处理初始特征 |
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mid_block: 中间处理块 |
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up_blocks: 上采样块序列 |
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支持梯度检查点功能（gradient checkpointing）以节省内存 |
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输出维度: [B, C, L] |
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# NOTE: |
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1. 移除了分片(slicing)和平铺(tiling)功能 |
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2. 直接使用mode()而不是sample()获取潜在向量 |
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3. 简化了编码过程，只保留核心功能 |
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4. 返回确定性的潜在向量而不是分布 |
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''' |
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super().__init__() |
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self.layers_per_block = layers_per_block |
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self.conv_in = nn.Conv1d( |
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in_channels, |
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block_out_channels[-1], |
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kernel_size=3, |
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stride=1, |
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padding=1, |
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) |
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self.mid_block = None |
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self.up_blocks = nn.ModuleList([]) |
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temb_channels = in_channels if norm_type == "spatial" else None |
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# mid |
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self.mid_block = UNetMidBlock1D( |
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in_channels=block_out_channels[-1], |
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mid_channels=block_out_channels[-1], |
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) |
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|
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# up |
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reversed_block_out_channels = list(reversed(block_out_channels)) |
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output_channel = reversed_block_out_channels[0] |
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for i, up_block_type in enumerate(up_block_types): |
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prev_output_channel = output_channel |
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output_channel = reversed_block_out_channels[i] |
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is_final_block = i == len(block_out_channels) - 1 |
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up_block = UpBlock1D( |
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in_channels=prev_output_channel, |
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out_channels=output_channel, |
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) |
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self.up_blocks.append(up_block) |
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prev_output_channel = output_channel |
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|
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# out |
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if norm_type == "spatial": |
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self.conv_norm_out = SpatialNorm(block_out_channels[0], temb_channels) |
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else: |
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self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6) |
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self.conv_act = nn.SiLU() |
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self.conv_out = nn.Conv1d(block_out_channels[0], out_channels, 3, padding=1) |
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self.gradient_checkpointing = False |
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def forward(self, z, latent_embeds=None): |
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sample = z |
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sample = self.conv_in(sample) |
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if self.training and self.gradient_checkpointing: |
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def create_custom_forward(module): |
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def custom_forward(*inputs): |
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return module(*inputs) |
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return custom_forward |
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if is_torch_version(">=", "1.11.0"): |
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# middle |
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sample = torch.utils.checkpoint.checkpoint( |
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create_custom_forward(self.mid_block), sample, latent_embeds, use_reentrant=False |
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) |
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# sample = sample.to(upscale_dtype) |
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# up |
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for up_block in self.up_blocks: |
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sample = torch.utils.checkpoint.checkpoint( |
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create_custom_forward(up_block), sample, latent_embeds, use_reentrant=False |
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) |
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else: |
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# middle |
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sample = torch.utils.checkpoint.checkpoint( |
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create_custom_forward(self.mid_block), sample, latent_embeds |
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) |
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# sample = sample.to(upscale_dtype) |
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# up |
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for up_block in self.up_blocks: |
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sample = torch.utils.checkpoint.checkpoint(create_custom_forward(up_block), sample, latent_embeds) |
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else: |
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# middle |
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sample = self.mid_block(sample, latent_embeds) |
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# sample = sample.to(upscale_dtype) |
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# up |
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for up_block in self.up_blocks: |
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sample = up_block(sample, latent_embeds) |
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# post-process |
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if latent_embeds is None: |
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sample = self.conv_norm_out(sample) |
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else: |
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sample = self.conv_norm_out(sample, latent_embeds) |
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sample = self.conv_act(sample) |
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sample = self.conv_out(sample) |
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return sample |
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class SDFDecoder(nn.Module): |
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def __init__( |
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self, |
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latent_size, |
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dims, |
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dropout=None, |
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dropout_prob=0.0, |
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norm_layers=(), |
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latent_in=(), |
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weight_norm=False, |
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xyz_in_all=None, |
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use_tanh=False, |
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latent_dropout=False, |
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): |
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''' |
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这是第二阶段的解码器，用于生成SDF值 |
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使用多层MLP结构 |
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特点： |
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支持在不同层注入latent信息（通过latent_in参数） |
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可以在每层添加xyz坐标（通过xyz_in_all参数） |
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支持权重归一化和dropout |
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输入维度: [N, latent_size+3] |
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输出维度: [N, 1] |
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''' |
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super(SDFDecoder, self).__init__() |
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def make_sequence(): |
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return [] |
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dims = [latent_size + 3] + dims + [1] |
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self.num_layers = len(dims) |
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self.norm_layers = norm_layers |
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self.latent_in = latent_in |
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self.latent_dropout = latent_dropout |
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if self.latent_dropout: |
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self.lat_dp = nn.Dropout(0.2) |
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self.xyz_in_all = xyz_in_all |
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self.weight_norm = weight_norm |
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for layer in range(0, self.num_layers - 1): |
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if layer + 1 in latent_in: |
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out_dim = dims[layer + 1] - dims[0] |
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else: |
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out_dim = dims[layer + 1] |
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if self.xyz_in_all and layer != self.num_layers - 2: |
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out_dim -= 3 |
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if weight_norm and layer in self.norm_layers: |
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setattr( |
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self, |
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"lin" + str(layer), |
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nn.utils.weight_norm(nn.Linear(dims[layer], out_dim)), |
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) |
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else: |
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setattr(self, "lin" + str(layer), nn.Linear(dims[layer], out_dim)) |
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if ( |
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(not weight_norm) |
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and self.norm_layers is not None |
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and layer in self.norm_layers |
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): |
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setattr(self, "bn" + str(layer), nn.LayerNorm(out_dim)) |
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self.use_tanh = use_tanh |
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if use_tanh: |
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self.tanh = nn.Tanh() |
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self.relu = nn.ReLU() |
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self.dropout_prob = dropout_prob |
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self.dropout = dropout |
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self.th = nn.Tanh() |
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# input: N x (L+3) |
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def forward(self, input): |
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xyz = input[:, -3:] |
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if input.shape[1] > 3 and self.latent_dropout: |
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latent_vecs = input[:, :-3] |
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latent_vecs = F.dropout(latent_vecs, p=0.2, training=self.training) |
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x = torch.cat([latent_vecs, xyz], 1) |
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else: |
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x = input |
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for layer in range(0, self.num_layers - 1): |
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lin = getattr(self, "lin" + str(layer)) |
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if layer in self.latent_in: |
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x = torch.cat([x, input], 1) |
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elif layer != 0 and self.xyz_in_all: |
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x = torch.cat([x, xyz], 1) |
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x = lin(x) |
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# last layer Tanh |
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if layer == self.num_layers - 2 and self.use_tanh: |
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x = self.tanh(x) |
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if layer < self.num_layers - 2: |
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if ( |
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self.norm_layers is not None |
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and layer in self.norm_layers |
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and not self.weight_norm |
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): |
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bn = getattr(self, "bn" + str(layer)) |
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x = bn(x) |
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x = self.relu(x) |
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if self.dropout is not None and layer in self.dropout: |
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x = F.dropout(x, p=self.dropout_prob, training=self.training) |
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if hasattr(self, "th"): |
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x = self.th(x) |
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return x |
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class BRep2SdfDecoder(nn.Module): |
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def __init__( |
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self, |
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latent_size=256, |
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feature_dims=[512, 512, 256, 128], # 特征解码器维度 |
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sdf_dims=[512, 512, 512, 512], # SDF解码器维度 |
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up_block_types=("UpDecoderBlock2D",), |
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layers_per_block=2, |
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norm_num_groups=32, |
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norm_type="group", |
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dropout=None, |
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dropout_prob=0.0, |
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norm_layers=(), |
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latent_in=(), |
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weight_norm=False, |
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xyz_in_all=True, |
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use_tanh=True, |
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): |
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super().__init__() |
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# 1. 特征解码器 (使用Decoder1D结构) |
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self.feature_decoder = Decoder1D( |
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in_channels=latent_size, |
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out_channels=feature_dims[-1], |
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up_block_types=up_block_types, |
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block_out_channels=feature_dims, |
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layers_per_block=layers_per_block, |
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norm_num_groups=norm_num_groups, |
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norm_type=norm_type |
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) |
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# 2. SDF解码器 (使用原始Decoder结构) |
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self.sdf_decoder = SDFDecoder( |
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latent_size=feature_dims[-1], # 使用特征解码器的输出维度 |
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dims=sdf_dims, |
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dropout=dropout, |
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dropout_prob=dropout_prob, |
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norm_layers=norm_layers, |
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latent_in=latent_in, |
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weight_norm=weight_norm, |
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xyz_in_all=xyz_in_all, |
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use_tanh=use_tanh, |
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latent_dropout=False |
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) |
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# 3. 特征转换层 (将特征解码器的输出转换为SDF解码器需要的格式) |
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self.feature_transform = nn.Sequential( |
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nn.Linear(feature_dims[-1], feature_dims[-1]), |
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nn.LayerNorm(feature_dims[-1]), |
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nn.SiLU() |
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) |
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def forward(self, latent, query_points, latent_embeds=None): |
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""" |
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Args: |
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latent: [B, C, L] B-rep特征 |
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query_points: [B, N, 3] 查询点 |
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latent_embeds: 可选的条件嵌入 |
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Returns: |
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sdf: [B, N, 1] SDF值 |
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""" |
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# 1. 特征解码 |
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features = self.feature_decoder(latent, latent_embeds) # [B, C, L] |
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# 2. 特征转换 |
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B, C, L = features.shape |
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features = features.permute(0, 2, 1) # [B, L, C] |
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features = self.feature_transform(features) # [B, L, C] |
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# 3. 准备SDF解码器输入 |
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_, N, _ = query_points.shape |
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features = features.unsqueeze(1).expand(-1, N, -1, -1) # [B, N, L, C] |
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query_points = query_points.unsqueeze(2).expand(-1, -1, L, -1) # [B, N, L, 3] |
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# 4. 合并特征和坐标 |
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sdf_input = torch.cat([ |
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features.reshape(B*N*L, -1), # [B*N*L, C] |
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query_points.reshape(B*N*L, -1) # [B*N*L, 3] |
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], dim=-1) |
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# 5. SDF生成 |
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sdf = self.sdf_decoder(sdf_input) # [B*N*L, 1] |
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sdf = sdf.reshape(B, N, L, 1) # [B, N, L, 1] |
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# 6. 聚合多尺度SDF |
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sdf = sdf.mean(dim=2) # [B, N, 1] |
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return sdf |
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# 使用示例 |
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if __name__ == "__main__": |
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# 创建模型 |
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decoder = BRepDecoder( |
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latent_size=256, |
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feature_dims=[512, 256, 128, 64], |
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sdf_dims=[512, 512, 512, 512], |
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up_block_types=("UpDecoderBlock2D",), |
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layers_per_block=2, |
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norm_num_groups=32, |
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dropout=None, |
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dropout_prob=0.0, |
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norm_layers=[0, 1, 2, 3], |
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latent_in=[4], |
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weight_norm=True, |
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xyz_in_all=True, |
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use_tanh=True |
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) |
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# 测试数据 |
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batch_size = 4 |
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seq_len = 32 |
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num_points = 1000 |
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latent = torch.randn(batch_size, 256, seq_len) |
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query_points = torch.randn(batch_size, num_points, 3) |
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latent_embeds = torch.randn(batch_size, 256) |
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# 前向传播 |
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sdf = decoder(latent, query_points, latent_embeds) |
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print(f"Input latent shape: {latent.shape}") |
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print(f"Query points shape: {query_points.shape}") |
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print(f"Output SDF shape: {sdf.shape}") |
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@ -1,109 +0,0 @@ |
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#!/usr/bin/env python3 |
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# Copyright 2004-present Facebook. All Rights Reserved. |
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import torch.nn as nn |
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import torch |
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import torch.nn.functional as F |
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|
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|
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class Decoder(nn.Module): |
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def __init__( |
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self, |
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latent_size, |
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dims, |
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dropout=None, |
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dropout_prob=0.0, |
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norm_layers=(), |
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latent_in=(), |
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weight_norm=False, |
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xyz_in_all=None, |
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use_tanh=False, |
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latent_dropout=False, |
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): |
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super(Decoder, self).__init__() |
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|
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def make_sequence(): |
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return [] |
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|
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dims = [latent_size + 3] + dims + [1] |
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self.num_layers = len(dims) |
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self.norm_layers = norm_layers |
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self.latent_in = latent_in |
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self.latent_dropout = latent_dropout |
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if self.latent_dropout: |
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self.lat_dp = nn.Dropout(0.2) |
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|
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self.xyz_in_all = xyz_in_all |
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self.weight_norm = weight_norm |
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for layer in range(0, self.num_layers - 1): |
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if layer + 1 in latent_in: |
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out_dim = dims[layer + 1] - dims[0] |
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else: |
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out_dim = dims[layer + 1] |
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if self.xyz_in_all and layer != self.num_layers - 2: |
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out_dim -= 3 |
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|
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if weight_norm and layer in self.norm_layers: |
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setattr( |
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self, |
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"lin" + str(layer), |
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nn.utils.weight_norm(nn.Linear(dims[layer], out_dim)), |
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) |
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else: |
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setattr(self, "lin" + str(layer), nn.Linear(dims[layer], out_dim)) |
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|
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if ( |
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(not weight_norm) |
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and self.norm_layers is not None |
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and layer in self.norm_layers |
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): |
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setattr(self, "bn" + str(layer), nn.LayerNorm(out_dim)) |
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|
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self.use_tanh = use_tanh |
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if use_tanh: |
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self.tanh = nn.Tanh() |
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self.relu = nn.ReLU() |
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|
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self.dropout_prob = dropout_prob |
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self.dropout = dropout |
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self.th = nn.Tanh() |
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# input: N x (L+3) |
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def forward(self, input): |
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xyz = input[:, -3:] |
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|
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if input.shape[1] > 3 and self.latent_dropout: |
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latent_vecs = input[:, :-3] |
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latent_vecs = F.dropout(latent_vecs, p=0.2, training=self.training) |
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x = torch.cat([latent_vecs, xyz], 1) |
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else: |
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x = input |
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|
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for layer in range(0, self.num_layers - 1): |
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lin = getattr(self, "lin" + str(layer)) |
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if layer in self.latent_in: |
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x = torch.cat([x, input], 1) |
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elif layer != 0 and self.xyz_in_all: |
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x = torch.cat([x, xyz], 1) |
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x = lin(x) |
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# last layer Tanh |
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if layer == self.num_layers - 2 and self.use_tanh: |
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x = self.tanh(x) |
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if layer < self.num_layers - 2: |
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if ( |
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self.norm_layers is not None |
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and layer in self.norm_layers |
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and not self.weight_norm |
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): |
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bn = getattr(self, "bn" + str(layer)) |
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x = bn(x) |
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x = self.relu(x) |
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if self.dropout is not None and layer in self.dropout: |
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x = F.dropout(x, p=self.dropout_prob, training=self.training) |
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|
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if hasattr(self, "th"): |
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x = self.th(x) |
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return x |
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@ -0,0 +1,356 @@ |
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import math |
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import torch |
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import torch.nn as nn |
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from dataclasses import dataclass |
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from typing import Dict, Optional, Tuple, Union |
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|
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from diffusers.configuration_utils import ConfigMixin, register_to_config |
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from diffusers.utils import BaseOutput, is_torch_version |
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from diffusers.utils.accelerate_utils import apply_forward_hook |
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from diffusers.models.attention_processor import AttentionProcessor, AttnProcessor |
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from diffusers.models.modeling_utils import ModelMixin |
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from diffusers.models.autoencoders.vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder |
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from diffusers.models.unets.unet_1d_blocks import ResConvBlock, SelfAttention1d, get_down_block, get_up_block, Upsample1d |
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from diffusers.models.attention_processor import SpatialNorm |
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|
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''' |
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# NOTE: |
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移除了分片(slicing)和平铺(tiling)功能 |
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直接使用mode()而不是sample()获取潜在向量 |
|||
简化了编码过程，只保留核心功能 |
|||
返回确定性的潜在向量而不是分布 |
|||
''' |
|||
|
|||
# 1. 基础网络组件 |
|||
class Embedder(nn.Module): |
|||
def __init__(self, vocab_size, d_model): |
|||
super().__init__() |
|||
self.embed = nn.Embedding(vocab_size, d_model) |
|||
self._init_embeddings() |
|||
|
|||
def _init_embeddings(self): |
|||
nn.init.kaiming_normal_(self.embed.weight, mode="fan_in") |
|||
|
|||
def forward(self, x): |
|||
return self.embed(x) |
|||
|
|||
|
|||
class UpBlock1D(nn.Module): |
|||
def __init__(self, in_channels, out_channels, mid_channels=None): |
|||
super().__init__() |
|||
mid_channels = in_channels if mid_channels is None else mid_channels |
|||
|
|||
resnets = [ |
|||
ResConvBlock(in_channels, mid_channels, mid_channels), |
|||
ResConvBlock(mid_channels, mid_channels, mid_channels), |
|||
ResConvBlock(mid_channels, mid_channels, out_channels), |
|||
] |
|||
|
|||
self.resnets = nn.ModuleList(resnets) |
|||
self.up = Upsample1d(kernel="cubic") |
|||
|
|||
def forward(self, hidden_states, temb=None): |
|||
for resnet in self.resnets: |
|||
hidden_states = resnet(hidden_states) |
|||
hidden_states = self.up(hidden_states) |
|||
return hidden_states |
|||
|
|||
|
|||
class UNetMidBlock1D(nn.Module): |
|||
def __init__(self, mid_channels: int, in_channels: int, out_channels: Optional[int] = None): |
|||
super().__init__() |
|||
|
|||
out_channels = in_channels if out_channels is None else out_channels |
|||
|
|||
# there is always at least one resnet |
|||
resnets = [ |
|||
ResConvBlock(in_channels, mid_channels, mid_channels), |
|||
ResConvBlock(mid_channels, mid_channels, mid_channels), |
|||
ResConvBlock(mid_channels, mid_channels, mid_channels), |
|||
ResConvBlock(mid_channels, mid_channels, mid_channels), |
|||
ResConvBlock(mid_channels, mid_channels, mid_channels), |
|||
ResConvBlock(mid_channels, mid_channels, out_channels), |
|||
] |
|||
attentions = [ |
|||
SelfAttention1d(mid_channels, mid_channels // 32), |
|||
SelfAttention1d(mid_channels, mid_channels // 32), |
|||
SelfAttention1d(mid_channels, mid_channels // 32), |
|||
SelfAttention1d(mid_channels, mid_channels // 32), |
|||
SelfAttention1d(mid_channels, mid_channels // 32), |
|||
SelfAttention1d(out_channels, out_channels // 32), |
|||
] |
|||
|
|||
self.attentions = nn.ModuleList(attentions) |
|||
self.resnets = nn.ModuleList(resnets) |
|||
|
|||
def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: |
|||
for attn, resnet in zip(self.attentions, self.resnets): |
|||
hidden_states = resnet(hidden_states) |
|||
hidden_states = attn(hidden_states) |
|||
|
|||
return hidden_states |
|||
|
|||
|
|||
class Encoder1D(nn.Module): |
|||
def __init__( |
|||
self, |
|||
in_channels=3, |
|||
out_channels=3, |
|||
down_block_types=("DownEncoderBlock1D",), |
|||
block_out_channels=(64,), |
|||
layers_per_block=2, |
|||
norm_num_groups=32, |
|||
act_fn="silu", |
|||
double_z=True, |
|||
): |
|||
super().__init__() |
|||
self.layers_per_block = layers_per_block |
|||
|
|||
self.conv_in = torch.nn.Conv1d( |
|||
in_channels, |
|||
block_out_channels[0], |
|||
kernel_size=3, |
|||
stride=1, |
|||
padding=1, |
|||
) |
|||
|
|||
self.mid_block = None |
|||
self.down_blocks = nn.ModuleList([]) |
|||
|
|||
# down |
|||
output_channel = block_out_channels[0] |
|||
for i, down_block_type in enumerate(down_block_types): |
|||
input_channel = output_channel |
|||
output_channel = block_out_channels[i] |
|||
is_final_block = i == len(block_out_channels) - 1 |
|||
|
|||
down_block = get_down_block( |
|||
down_block_type, |
|||
num_layers=self.layers_per_block, |
|||
in_channels=input_channel, |
|||
out_channels=output_channel, |
|||
add_downsample=not is_final_block, |
|||
temb_channels=None, |
|||
) |
|||
self.down_blocks.append(down_block) |
|||
|
|||
# mid |
|||
self.mid_block = UNetMidBlock1D( |
|||
in_channels=block_out_channels[-1], |
|||
mid_channels=block_out_channels[-1], |
|||
) |
|||
|
|||
# out |
|||
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6) |
|||
self.conv_act = nn.SiLU() |
|||
|
|||
conv_out_channels = 2 * out_channels if double_z else out_channels |
|||
self.conv_out = nn.Conv1d(block_out_channels[-1], conv_out_channels, 3, padding=1) |
|||
|
|||
self.gradient_checkpointing = False |
|||
|
|||
def forward(self, x): |
|||
sample = x |
|||
sample = self.conv_in(sample) |
|||
|
|||
if self.training and self.gradient_checkpointing: |
|||
|
|||
def create_custom_forward(module): |
|||
def custom_forward(*inputs): |
|||
return module(*inputs) |
|||
|
|||
return custom_forward |
|||
|
|||
# down |
|||
if is_torch_version(">=", "1.11.0"): |
|||
for down_block in self.down_blocks: |
|||
sample = torch.utils.checkpoint.checkpoint( |
|||
create_custom_forward(down_block), sample, use_reentrant=False |
|||
) |
|||
|
|||
# middle |
|||
sample = torch.utils.checkpoint.checkpoint( |
|||
create_custom_forward(self.mid_block), sample, use_reentrant=False |
|||
) |
|||
else: |
|||
for down_block in self.down_blocks: |
|||
sample = torch.utils.checkpoint.checkpoint(create_custom_forward(down_block), sample) |
|||
# middle |
|||
sample = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block), sample) |
|||
|
|||
else: |
|||
# down |
|||
for down_block in self.down_blocks: |
|||
sample = down_block(sample)[0] |
|||
|
|||
# middle |
|||
sample = self.mid_block(sample) |
|||
|
|||
# post-process |
|||
sample = self.conv_norm_out(sample) |
|||
sample = self.conv_act(sample) |
|||
sample = self.conv_out(sample) |
|||
return sample |
|||
|
|||
# 2. B-rep特征处理 |
|||
class BRepFeatureExtractor: |
|||
def __init__(self, config): |
|||
self.encoder = Encoder1D( |
|||
in_channels=config.in_channels, # 根据特征维度设置 |
|||
out_channels=config.out_channels, |
|||
block_out_channels=config.block_out_channels, |
|||
layers_per_block=config.layers_per_block |
|||
) |
|||
|
|||
def extract_face_features(self, face): |
|||
"""提取面的特征""" |
|||
features = [] |
|||
try: |
|||
# 基本几何特征 |
|||
center = face.center_point |
|||
normal = face.normal_vector |
|||
|
|||
# 边界特征 |
|||
bounds = face.bounds() |
|||
|
|||
# 曲面类型特征 |
|||
surface_type = face.surface_type |
|||
|
|||
# 组合特征 |
|||
feature = np.concatenate([ |
|||
center, # [3] |
|||
normal, # [3] |
|||
bounds.flatten(), |
|||
[surface_type] # 可以用one-hot编码 |
|||
]) |
|||
features.append(feature) |
|||
|
|||
except Exception as e: |
|||
print(f"Error extracting face features: {e}") |
|||
|
|||
return np.array(features) |
|||
|
|||
def extract_edge_features(self, edge): |
|||
"""提取边的特征""" |
|||
features = [] |
|||
try: |
|||
# 采样点 |
|||
points = self.sample_points_on_edge(edge) |
|||
|
|||
for point in points: |
|||
# 位置 |
|||
pos = point.coordinates |
|||
# 切向量 |
|||
tangent = point.tangent |
|||
# 曲率 |
|||
curvature = point.curvature |
|||
|
|||
point_feature = np.concatenate([ |
|||
pos, # [3] |
|||
tangent, # [3] |
|||
[curvature] # [1] |
|||
]) |
|||
features.append(point_feature) |
|||
|
|||
except Exception as e: |
|||
print(f"Error extracting edge features: {e}") |
|||
|
|||
return np.array(features) |
|||
|
|||
@staticmethod |
|||
def sample_points_on_edge(edge, num_points=32): |
|||
"""在边上均匀采样点""" |
|||
points = [] |
|||
try: |
|||
length = edge.length() |
|||
for i in range(num_points): |
|||
t = i / (num_points - 1) |
|||
point = edge.point_at(t * length) |
|||
points.append(point) |
|||
except Exception as e: |
|||
print(f"Error sampling points: {e}") |
|||
return points |
|||
|
|||
class BRepDataProcessor: |
|||
def __init__(self, feature_extractor): |
|||
self.feature_extractor = feature_extractor |
|||
|
|||
def process_brep(self, brep_model): |
|||
"""处理单个B-rep模型""" |
|||
try: |
|||
# 1. 提取面特征 |
|||
face_features = [] |
|||
for face in brep_model.faces: |
|||
feat = self.feature_extractor.extract_face_features(face) |
|||
face_features.append(feat) |
|||
|
|||
# 2. 提取边特征 |
|||
edge_features = [] |
|||
for edge in brep_model.edges: |
|||
feat = self.feature_extractor.extract_edge_features(edge) |
|||
edge_features.append(feat) |
|||
|
|||
# 3. 组织数据结构 |
|||
return { |
|||
'face_features': torch.tensor(face_features), |
|||
'edge_features': torch.tensor(edge_features), |
|||
'topology': self.extract_topology(brep_model) |
|||
} |
|||
|
|||
except Exception as e: |
|||
print(f"Error processing B-rep: {e}") |
|||
return None |
|||
|
|||
def extract_topology(self, brep_model): |
|||
"""提取拓扑关系""" |
|||
# 面-边关系矩阵 |
|||
face_edge_adj = np.zeros((len(brep_model.faces), len(brep_model.edges))) |
|||
# 填充邻接关系 |
|||
for i, face in enumerate(brep_model.faces): |
|||
for j, edge in enumerate(brep_model.edges): |
|||
if edge in face.edges: |
|||
face_edge_adj[i,j] = 1 |
|||
return face_edge_adj |
|||
|
|||
# 3. 主编码器 |
|||
class BRepEncoder: |
|||
def __init__(self, config): |
|||
self.processor = BRepDataProcessor( |
|||
BRepFeatureExtractor(config) |
|||
) |
|||
self.encoder = Encoder1D(**config.encoder_params) |
|||
|
|||
def encode(self, brep_model): |
|||
"""编码B-rep模型""" |
|||
try: |
|||
# 1. 处理原始数据 |
|||
processed_data = self.processor.process_brep(brep_model) |
|||
if processed_data is None: |
|||
return None |
|||
|
|||
# 2. 特征编码 |
|||
face_features = self.encoder(processed_data['face_features']) |
|||
edge_features = self.encoder(processed_data['edge_features']) |
|||
|
|||
# 3. 组合特征 |
|||
combined_features = self.combine_features( |
|||
face_features, |
|||
edge_features, |
|||
processed_data['topology'] |
|||
) |
|||
|
|||
return combined_features |
|||
|
|||
except Exception as e: |
|||
print(f"Error encoding B-rep: {e}") |
|||
return None |
|||
|
|||
def combine_features(self, face_features, edge_features, topology): |
|||
"""组合不同类型的特征""" |
|||
# 可以使用图神经网络或者注意力机制来组合特征 |
|||
combined = torch.cat([ |
|||
face_features.mean(dim=1), # 全局面特征 |
|||
edge_features.mean(dim=1), # 全局边特征 |
|||
topology.flatten() # 拓扑信息 |
|||
], dim=-1) |
|||
return combined |
|||
@ -0,0 +1,127 @@ |
|||
import torch |
|||
import torch.nn as nn |
|||
from encoder import BRepEncoder |
|||
from decoder import BRep2SdfDecoder |
|||
|
|||
class BRep2SDF(nn.Module): |
|||
def __init__( |
|||
self, |
|||
# 编码器参数 |
|||
in_channels=3, |
|||
latent_size=256, |
|||
encoder_block_out_channels=(512, 256, 128, 64), |
|||
# 解码器参数 |
|||
decoder_feature_dims=(512, 256, 128, 64), |
|||
sdf_dims=(512, 512, 512, 512), |
|||
# 共享参数 |
|||
layers_per_block=2, |
|||
norm_num_groups=32, |
|||
# SDF特定参数 |
|||
dropout=None, |
|||
dropout_prob=0.0, |
|||
norm_layers=(0, 1, 2, 3), |
|||
latent_in=(4,), |
|||
weight_norm=True, |
|||
xyz_in_all=True, |
|||
use_tanh=True, |
|||
): |
|||
super().__init__() |
|||
|
|||
# 1. 编码器配置 |
|||
encoder_config = type('Config', (), { |
|||
'in_channels': in_channels, |
|||
'out_channels': latent_size, |
|||
'block_out_channels': encoder_block_out_channels, |
|||
'layers_per_block': layers_per_block, |
|||
'norm_num_groups': norm_num_groups, |
|||
'encoder_params': { |
|||
'in_channels': in_channels, |
|||
'out_channels': latent_size, |
|||
'block_out_channels': encoder_block_out_channels, |
|||
'layers_per_block': layers_per_block, |
|||
'norm_num_groups': norm_num_groups, |
|||
} |
|||
})() |
|||
|
|||
# 2. 解码器配置 |
|||
decoder_config = { |
|||
'latent_size': latent_size, |
|||
'feature_dims': decoder_feature_dims, |
|||
'sdf_dims': sdf_dims, |
|||
'layers_per_block': layers_per_block, |
|||
'norm_num_groups': norm_num_groups, |
|||
'dropout': dropout, |
|||
'dropout_prob': dropout_prob, |
|||
'norm_layers': norm_layers, |
|||
'latent_in': latent_in, |
|||
'weight_norm': weight_norm, |
|||
'xyz_in_all': xyz_in_all, |
|||
'use_tanh': use_tanh, |
|||
} |
|||
|
|||
# 3. 创建编码器和解码器 |
|||
self.encoder = BRepEncoder(encoder_config) |
|||
self.decoder = BRep2SdfDecoder(**decoder_config) |
|||
|
|||
def encode(self, brep_model): |
|||
"""编码B-rep模型为潜在特征""" |
|||
return self.encoder.encode(brep_model) |
|||
|
|||
def decode(self, latent, query_points, latent_embeds=None): |
|||
"""从潜在特征解码SDF值""" |
|||
return self.decoder(latent, query_points, latent_embeds) |
|||
|
|||
def forward(self, brep_model, query_points): |
|||
"""完整的前向传播过程""" |
|||
# 1. 编码B-rep模型 |
|||
latent = self.encode(brep_model) |
|||
if latent is None: |
|||
return None |
|||
|
|||
# 2. 解码SDF值 |
|||
sdf = self.decode(latent, query_points) |
|||
return sdf |
|||
|
|||
# 使用示例 |
|||
if __name__ == "__main__": |
|||
# 创建模型 |
|||
model = BRep2SDF( |
|||
in_channels=3, |
|||
latent_size=256, |
|||
encoder_block_out_channels=(512, 256, 128, 64), |
|||
decoder_feature_dims=(512, 256, 128, 64), |
|||
sdf_dims=(512, 512, 512, 512), |
|||
layers_per_block=2, |
|||
norm_num_groups=32, |
|||
) |
|||
|
|||
# 测试数据 |
|||
batch_size = 4 |
|||
seq_len = 32 |
|||
num_points = 1000 |
|||
|
|||
# 模拟B-rep模型数据 |
|||
class MockBRep: |
|||
def __init__(self): |
|||
self.faces = [MockFace() for _ in range(10)] |
|||
self.edges = [MockEdge() for _ in range(20)] |
|||
|
|||
class MockFace: |
|||
def __init__(self): |
|||
self.center_point = torch.randn(3) |
|||
self.normal_vector = torch.randn(3) |
|||
self.surface_type = 0 |
|||
self.edges = [] |
|||
|
|||
class MockEdge: |
|||
def __init__(self): |
|||
self.length = lambda: 1.0 |
|||
self.point_at = lambda t: torch.randn(3) |
|||
|
|||
brep_model = MockBRep() |
|||
query_points = torch.randn(batch_size, num_points, 3) |
|||
|
|||
# 前向传播 |
|||
sdf = model(brep_model, query_points) |
|||
if sdf is not None: |
|||
print(f"Output SDF shape: {sdf.shape}") |
|||
Loading…
Reference in new issue