File size: 7,784 Bytes
251713e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | """Stage 1: Unified Conv3d patchification for image, video, and 3D triplane inputs."""
from __future__ import annotations
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
class FourDPositionEmbedding(nn.Module):
"""Learned 4D position embedding for (t, x, y, z) coordinates."""
def __init__(self, dim: int, max_t: int = 16, max_x: int = 64,
max_y: int = 64, max_z: int = 64):
super().__init__()
self.embed_t = nn.Embedding(max_t, dim // 4)
self.embed_x = nn.Embedding(max_x, dim // 4)
self.embed_y = nn.Embedding(max_y, dim // 4)
self.embed_z = nn.Embedding(max_z, dim // 4)
self.proj = nn.Linear(dim, dim)
def forward(self, positions: torch.Tensor) -> torch.Tensor:
"""positions: (N, 4) or (B, N, 4) long tensor."""
t = positions[..., 0].clamp(0, self.embed_t.num_embeddings - 1)
x = positions[..., 1].clamp(0, self.embed_x.num_embeddings - 1)
y = positions[..., 2].clamp(0, self.embed_y.num_embeddings - 1)
z = positions[..., 3].clamp(0, self.embed_z.num_embeddings - 1)
pe = torch.cat([self.embed_t(t), self.embed_x(x),
self.embed_y(y), self.embed_z(z)], dim=-1)
return self.proj(pe)
class PatchifyEncoder(nn.Module):
"""Unified Conv3d patchification.
A single Conv3d handles all modalities. Images are temporally padded so the
Conv3d output is numerically equivalent to a Conv2d (causal zero-pad).
A learned 4D position embedding is added to every token before it leaves
this module — without it the downstream self-attention blocks are
permutation-invariant and cannot recover spatial / temporal layout.
"""
def __init__(
self,
embed_dim: int = 1152,
patch_size: int = 16,
t_patch: int = 2,
max_t: int = 16,
max_x: int = 64,
max_y: int = 64,
max_z: int = 64,
):
super().__init__()
self.embed_dim = embed_dim
self.patch_size = patch_size
self.t_patch = t_patch
self.proj = nn.Conv3d(
3, embed_dim,
kernel_size=(t_patch, patch_size, patch_size),
stride=(t_patch, patch_size, patch_size),
bias=True,
)
nn.init.xavier_uniform_(self.proj.weight.reshape(embed_dim, -1).T
.reshape(self.proj.weight.shape))
nn.init.zeros_(self.proj.bias)
self.pos_embed = FourDPositionEmbedding(
embed_dim, max_t=max_t, max_x=max_x, max_y=max_y, max_z=max_z,
)
# ------------------------------------------------------------------ #
# Position grid helpers #
# ------------------------------------------------------------------ #
@staticmethod
def _image_positions(Hp: int, Wp: int, device) -> torch.Tensor:
i = torch.arange(Hp, device=device)
j = torch.arange(Wp, device=device)
gi, gj = torch.meshgrid(i, j, indexing='ij')
pos = torch.zeros(Hp * Wp, 4, dtype=torch.long, device=device)
pos[:, 1] = gi.reshape(-1)
pos[:, 2] = gj.reshape(-1)
return pos # (N, 4)
@staticmethod
def _video_positions(Tp: int, Hp: int, Wp: int, device) -> torch.Tensor:
k = torch.arange(Tp, device=device)
i = torch.arange(Hp, device=device)
j = torch.arange(Wp, device=device)
gk, gi, gj = torch.meshgrid(k, i, j, indexing='ij')
pos = torch.zeros(Tp * Hp * Wp, 4, dtype=torch.long, device=device)
pos[:, 0] = gk.reshape(-1)
pos[:, 1] = gi.reshape(-1)
pos[:, 2] = gj.reshape(-1)
return pos # (N, 4)
# ------------------------------------------------------------------ #
# Modality-specific forward passes #
# ------------------------------------------------------------------ #
def _conv3d_image(self, x: torch.Tensor) -> Tuple[torch.Tensor, int, int]:
"""Apply Conv3d to a single-frame input with causal padding.
x: (B, 3, H, W) → tokens (B, D, 1, Hp, Wp)
"""
x = x.unsqueeze(2) # (B, 3, 1, H, W)
x = F.pad(x, (0, 0, 0, 0, 1, 0)) # zero-prepend 1 temporal frame
out = self.proj(x) # (B, D, 1, Hp, Wp)
return out, out.shape[3], out.shape[4]
def forward_image(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""x: (B, 3, H, W) → tokens (B, N, D), positions (N, 4), plane_ids (N,)"""
out, Hp, Wp = self._conv3d_image(x)
B, D = out.shape[0], out.shape[1]
tokens = out.permute(0, 2, 3, 4, 1).reshape(B, Hp * Wp, D)
pos = self._image_positions(Hp, Wp, x.device)
tokens = tokens + self.pos_embed(pos).unsqueeze(0)
plane_ids = torch.full((Hp * Wp,), -1, dtype=torch.long, device=x.device)
return tokens, pos, plane_ids
def forward_video(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""x: (B, 3, T, H, W) → tokens (B, N, D), positions (N, 4), plane_ids (N,)"""
out = self.proj(x) # (B, D, Tp, Hp, Wp)
B, D, Tp, Hp, Wp = out.shape
tokens = out.permute(0, 2, 3, 4, 1).reshape(B, Tp * Hp * Wp, D)
pos = self._video_positions(Tp, Hp, Wp, x.device)
tokens = tokens + self.pos_embed(pos).unsqueeze(0)
plane_ids = torch.full((Tp * Hp * Wp,), -1, dtype=torch.long, device=x.device)
return tokens, pos, plane_ids
def forward_threed(self, planes: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""planes: (B, 3, 3, S, S) — 3 planes each with 3 channels.
Returns tokens (B, 3*Np, D), positions (3*Np, 4), plane_ids (3*Np,).
"""
B = planes.shape[0]
xy = planes[:, 0] # (B, 3, S, S)
xz = planes[:, 1]
yz = planes[:, 2]
def _patchify(p):
out, Hp, Wp = self._conv3d_image(p)
return out.permute(0, 2, 3, 4, 1).reshape(B, Hp * Wp, self.embed_dim), Hp, Wp
tok_xy, Hp, Wp = _patchify(xy)
tok_xz, _, _ = _patchify(xz)
tok_yz, _, _ = _patchify(yz)
N_plane = Hp * Wp
device = planes.device
i = torch.arange(Hp, device=device)
j = torch.arange(Wp, device=device)
gi, gj = torch.meshgrid(i, j, indexing='ij')
fi = gi.reshape(-1)
fj = gj.reshape(-1)
z0 = torch.zeros(N_plane, dtype=torch.long, device=device)
# XY: (0, x, y, 0), XZ: (0, x, 0, z), YZ: (0, 0, y, z)
pos_xy = torch.stack([z0, fi, fj, z0], dim=-1)
pos_xz = torch.stack([z0, fi, z0, fj], dim=-1)
pos_yz = torch.stack([z0, z0, fi, fj], dim=-1)
positions = torch.cat([pos_xy, pos_xz, pos_yz], dim=0) # (3*Np, 4)
plane_ids = torch.cat([
torch.zeros(N_plane, dtype=torch.long, device=device),
torch.ones(N_plane, dtype=torch.long, device=device),
torch.full((N_plane,), 2, dtype=torch.long, device=device),
]) # (3*Np,)
tokens = torch.cat([tok_xy, tok_xz, tok_yz], dim=1) # (B, 3*Np, D)
tokens = tokens + self.pos_embed(positions).unsqueeze(0)
return tokens, positions, plane_ids
def forward(self, x: torch.Tensor, modality: str) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
if modality == 'image':
return self.forward_image(x)
elif modality == 'video':
return self.forward_video(x)
elif modality == 'threed':
return self.forward_threed(x)
else:
raise ValueError(f"Unknown modality: {modality}")
|