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import torch |
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import torch.nn as nn |
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from typing import Optional |
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from einops import repeat |
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import math |
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from michelangelo.models.modules import checkpoint |
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from michelangelo.models.modules.embedder import FourierEmbedder |
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from michelangelo.models.modules.distributions import DiagonalGaussianDistribution |
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from michelangelo.models.modules.transformer_blocks import ( |
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ResidualCrossAttentionBlock, |
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Transformer |
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) |
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from .tsal_base import ShapeAsLatentModule |
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class CrossAttentionEncoder(nn.Module): |
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def __init__(self, *, |
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device: Optional[torch.device], |
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dtype: Optional[torch.dtype], |
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num_latents: int, |
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fourier_embedder: FourierEmbedder, |
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point_feats: int, |
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width: int, |
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heads: int, |
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layers: int, |
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init_scale: float = 0.25, |
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qkv_bias: bool = True, |
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flash: bool = False, |
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use_ln_post: bool = False, |
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use_checkpoint: bool = False): |
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super().__init__() |
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self.use_checkpoint = use_checkpoint |
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self.num_latents = num_latents |
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self.query = nn.Parameter(torch.randn((num_latents, width), device=device, dtype=dtype) * 0.02) |
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self.fourier_embedder = fourier_embedder |
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self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width, device=device, dtype=dtype) |
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self.cross_attn = ResidualCrossAttentionBlock( |
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device=device, |
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dtype=dtype, |
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width=width, |
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heads=heads, |
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init_scale=init_scale, |
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qkv_bias=qkv_bias, |
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flash=flash, |
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) |
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self.self_attn = Transformer( |
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device=device, |
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dtype=dtype, |
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n_ctx=num_latents, |
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width=width, |
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layers=layers, |
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heads=heads, |
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init_scale=init_scale, |
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qkv_bias=qkv_bias, |
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flash=flash, |
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use_checkpoint=False |
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) |
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if use_ln_post: |
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self.ln_post = nn.LayerNorm(width, dtype=dtype, device=device) |
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else: |
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self.ln_post = None |
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def _forward(self, pc, feats): |
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""" |
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Args: |
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pc (torch.FloatTensor): [B, N, 3] |
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feats (torch.FloatTensor or None): [B, N, C] |
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Returns: |
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""" |
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bs = pc.shape[0] |
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data = self.fourier_embedder(pc) |
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if feats is not None: |
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data = torch.cat([data, feats], dim=-1) |
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data = self.input_proj(data) |
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query = repeat(self.query, "m c -> b m c", b=bs) |
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latents = self.cross_attn(query, data) |
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latents = self.self_attn(latents) |
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if self.ln_post is not None: |
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latents = self.ln_post(latents) |
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return latents, pc |
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def forward(self, pc: torch.FloatTensor, feats: Optional[torch.FloatTensor] = None): |
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""" |
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Args: |
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pc (torch.FloatTensor): [B, N, 3] |
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feats (torch.FloatTensor or None): [B, N, C] |
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Returns: |
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dict |
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""" |
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return checkpoint(self._forward, (pc, feats), self.parameters(), self.use_checkpoint) |
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class CrossAttentionDecoder(nn.Module): |
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def __init__(self, *, |
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device: Optional[torch.device], |
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dtype: Optional[torch.dtype], |
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num_latents: int, |
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out_channels: int, |
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fourier_embedder: FourierEmbedder, |
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width: int, |
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heads: int, |
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init_scale: float = 0.25, |
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qkv_bias: bool = True, |
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flash: bool = False, |
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use_checkpoint: bool = False): |
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super().__init__() |
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self.use_checkpoint = use_checkpoint |
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self.fourier_embedder = fourier_embedder |
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self.query_proj = nn.Linear(self.fourier_embedder.out_dim, width, device=device, dtype=dtype) |
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self.cross_attn_decoder = ResidualCrossAttentionBlock( |
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device=device, |
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dtype=dtype, |
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n_data=num_latents, |
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width=width, |
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heads=heads, |
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init_scale=init_scale, |
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qkv_bias=qkv_bias, |
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flash=flash |
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) |
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self.ln_post = nn.LayerNorm(width, device=device, dtype=dtype) |
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self.output_proj = nn.Linear(width, out_channels, device=device, dtype=dtype) |
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def _forward(self, queries: torch.FloatTensor, latents: torch.FloatTensor): |
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queries = self.query_proj(self.fourier_embedder(queries)) |
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x = self.cross_attn_decoder(queries, latents) |
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x = self.ln_post(x) |
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x = self.output_proj(x) |
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return x |
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def forward(self, queries: torch.FloatTensor, latents: torch.FloatTensor): |
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return checkpoint(self._forward, (queries, latents), self.parameters(), self.use_checkpoint) |
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class ShapeAsLatentPerceiver(ShapeAsLatentModule): |
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def __init__(self, *, |
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device: Optional[torch.device], |
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dtype: Optional[torch.dtype], |
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num_latents: int, |
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point_feats: int = 0, |
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embed_dim: int = 0, |
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num_freqs: int = 8, |
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include_pi: bool = True, |
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width: int, |
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heads: int, |
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num_encoder_layers: int, |
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num_decoder_layers: int, |
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init_scale: float = 0.25, |
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qkv_bias: bool = True, |
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flash: bool = False, |
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use_ln_post: bool = False, |
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use_checkpoint: bool = False): |
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super().__init__() |
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self.use_checkpoint = use_checkpoint |
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self.num_latents = num_latents |
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self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi) |
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init_scale = init_scale * math.sqrt(1.0 / width) |
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self.encoder = CrossAttentionEncoder( |
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device=device, |
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dtype=dtype, |
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fourier_embedder=self.fourier_embedder, |
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num_latents=num_latents, |
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point_feats=point_feats, |
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width=width, |
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heads=heads, |
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layers=num_encoder_layers, |
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init_scale=init_scale, |
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qkv_bias=qkv_bias, |
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flash=flash, |
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use_ln_post=use_ln_post, |
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use_checkpoint=use_checkpoint |
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) |
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self.embed_dim = embed_dim |
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if embed_dim > 0: |
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self.pre_kl = nn.Linear(width, embed_dim * 2, device=device, dtype=dtype) |
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self.post_kl = nn.Linear(embed_dim, width, device=device, dtype=dtype) |
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self.latent_shape = (num_latents, embed_dim) |
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else: |
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self.latent_shape = (num_latents, width) |
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self.transformer = Transformer( |
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device=device, |
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dtype=dtype, |
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n_ctx=num_latents, |
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width=width, |
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layers=num_decoder_layers, |
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heads=heads, |
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init_scale=init_scale, |
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qkv_bias=qkv_bias, |
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flash=flash, |
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use_checkpoint=use_checkpoint |
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) |
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self.geo_decoder = CrossAttentionDecoder( |
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device=device, |
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dtype=dtype, |
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fourier_embedder=self.fourier_embedder, |
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out_channels=1, |
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num_latents=num_latents, |
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width=width, |
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heads=heads, |
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init_scale=init_scale, |
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qkv_bias=qkv_bias, |
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flash=flash, |
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use_checkpoint=use_checkpoint |
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) |
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def encode(self, |
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pc: torch.FloatTensor, |
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feats: Optional[torch.FloatTensor] = None, |
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sample_posterior: bool = True): |
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""" |
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Args: |
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pc (torch.FloatTensor): [B, N, 3] |
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feats (torch.FloatTensor or None): [B, N, C] |
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sample_posterior (bool): |
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Returns: |
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latents (torch.FloatTensor) |
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center_pos (torch.FloatTensor or None): |
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posterior (DiagonalGaussianDistribution or None): |
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""" |
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latents, center_pos = self.encoder(pc, feats) |
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posterior = None |
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if self.embed_dim > 0: |
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moments = self.pre_kl(latents) |
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posterior = DiagonalGaussianDistribution(moments, feat_dim=-1) |
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if sample_posterior: |
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latents = posterior.sample() |
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else: |
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latents = posterior.mode() |
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return latents, center_pos, posterior |
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def decode(self, latents: torch.FloatTensor): |
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latents = self.post_kl(latents) |
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return self.transformer(latents) |
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def query_geometry(self, queries: torch.FloatTensor, latents: torch.FloatTensor): |
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logits = self.geo_decoder(queries, latents).squeeze(-1) |
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return logits |
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def forward(self, |
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pc: torch.FloatTensor, |
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feats: torch.FloatTensor, |
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volume_queries: torch.FloatTensor, |
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sample_posterior: bool = True): |
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""" |
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Args: |
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pc (torch.FloatTensor): [B, N, 3] |
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feats (torch.FloatTensor or None): [B, N, C] |
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volume_queries (torch.FloatTensor): [B, P, 3] |
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sample_posterior (bool): |
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Returns: |
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logits (torch.FloatTensor): [B, P] |
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center_pos (torch.FloatTensor): [B, M, 3] |
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posterior (DiagonalGaussianDistribution or None). |
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""" |
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latents, center_pos, posterior = self.encode(pc, feats, sample_posterior=sample_posterior) |
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latents = self.decode(latents) |
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logits = self.query_geometry(volume_queries, latents) |
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return logits, center_pos, posterior |
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class AlignedShapeLatentPerceiver(ShapeAsLatentPerceiver): |
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def __init__(self, *, |
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device: Optional[torch.device], |
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dtype: Optional[torch.dtype], |
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num_latents: int, |
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point_feats: int = 0, |
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embed_dim: int = 0, |
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num_freqs: int = 8, |
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include_pi: bool = True, |
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width: int, |
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heads: int, |
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num_encoder_layers: int, |
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num_decoder_layers: int, |
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init_scale: float = 0.25, |
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qkv_bias: bool = True, |
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flash: bool = False, |
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use_ln_post: bool = False, |
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use_checkpoint: bool = False): |
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super().__init__( |
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device=device, |
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dtype=dtype, |
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num_latents=1 + num_latents, |
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point_feats=point_feats, |
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embed_dim=embed_dim, |
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num_freqs=num_freqs, |
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include_pi=include_pi, |
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width=width, |
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heads=heads, |
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num_encoder_layers=num_encoder_layers, |
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num_decoder_layers=num_decoder_layers, |
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init_scale=init_scale, |
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qkv_bias=qkv_bias, |
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flash=flash, |
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use_ln_post=use_ln_post, |
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use_checkpoint=use_checkpoint |
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) |
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self.width = width |
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def encode(self, |
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pc: torch.FloatTensor, |
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feats: Optional[torch.FloatTensor] = None, |
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sample_posterior: bool = True): |
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""" |
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Args: |
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pc (torch.FloatTensor): [B, N, 3] |
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feats (torch.FloatTensor or None): [B, N, c] |
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sample_posterior (bool): |
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Returns: |
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shape_embed (torch.FloatTensor) |
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kl_embed (torch.FloatTensor): |
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posterior (DiagonalGaussianDistribution or None): |
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""" |
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shape_embed, latents = self.encode_latents(pc, feats) |
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kl_embed, posterior = self.encode_kl_embed(latents, sample_posterior) |
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return shape_embed, kl_embed, posterior |
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def encode_latents(self, |
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pc: torch.FloatTensor, |
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feats: Optional[torch.FloatTensor] = None): |
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x, _ = self.encoder(pc, feats) |
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shape_embed = x[:, 0] |
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latents = x[:, 1:] |
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return shape_embed, latents |
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def encode_kl_embed(self, latents: torch.FloatTensor, sample_posterior: bool = True): |
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posterior = None |
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if self.embed_dim > 0: |
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moments = self.pre_kl(latents) |
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posterior = DiagonalGaussianDistribution(moments, feat_dim=-1) |
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if sample_posterior: |
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kl_embed = posterior.sample() |
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else: |
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kl_embed = posterior.mode() |
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else: |
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kl_embed = latents |
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return kl_embed, posterior |
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def forward(self, |
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pc: torch.FloatTensor, |
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feats: torch.FloatTensor, |
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volume_queries: torch.FloatTensor, |
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sample_posterior: bool = True): |
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""" |
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Args: |
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pc (torch.FloatTensor): [B, N, 3] |
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feats (torch.FloatTensor or None): [B, N, C] |
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volume_queries (torch.FloatTensor): [B, P, 3] |
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sample_posterior (bool): |
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Returns: |
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shape_embed (torch.FloatTensor): [B, projection_dim] |
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logits (torch.FloatTensor): [B, M] |
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posterior (DiagonalGaussianDistribution or None). |
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""" |
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shape_embed, kl_embed, posterior = self.encode(pc, feats, sample_posterior=sample_posterior) |
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latents = self.decode(kl_embed) |
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logits = self.query_geometry(volume_queries, latents) |
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return shape_embed, logits, posterior |
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