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3718945 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 | import torch.nn as nn
import torch
from timm.models.vision_transformer import Mlp, DropPath
#from utils import timestep_embedding
import math
# --------------------------------------------------------
# Timestep embedding
# References:
# Point-E: https://github.com/openai/point-e
# --------------------------------------------------------
def timestep_embedding(timesteps, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=timesteps.device)
args = timesteps[:, None].to(timesteps.dtype) * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def init_linear(l, stddev):
nn.init.normal_(l.weight, std=stddev)
if l.bias is not None:
nn.init.constant_(l.bias, 0.0)
class MLP(nn.Module):
def __init__(self, *,
width: int,
init_scale: float):
super().__init__()
self.width = width
self.c_fc = nn.Linear(width, width * 4)
self.c_proj = nn.Linear(width * 4, width)
self.gelu = nn.GELU()
init_linear(self.c_fc, init_scale)
init_linear(self.c_proj, init_scale)
def forward(self, x):
return self.c_proj(self.gelu(self.c_fc(x)))
# Code borrow from: https://github.com/facebookresearch/PointInfinity/blob/main/modules.py
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# RIN: https://arxiv.org/pdf/2212.11972
# --------------------------------------------------------
class CrossAttention(nn.Module):
def __init__(
self,
dim,
kv_dim=None,
num_heads=16,
qkv_bias=False,
attn_drop=0.,
proj_drop=0.,
):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = head_dim ** -0.5
kv_dim = dim if not kv_dim else kv_dim
self.wq = nn.Linear(dim, dim, bias=qkv_bias)
self.wk = nn.Linear(kv_dim, dim, bias=qkv_bias)
self.wv = nn.Linear(kv_dim, dim, bias=qkv_bias)
self.attn_drop_rate = attn_drop
self.attn_drop = nn.Dropout(self.attn_drop_rate)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x_q, x_kv):
B, N_q, C = x_q.shape
B, N_kv, _ = x_kv.shape
# [B, N_q, C] -> [B, N_q, H, C/H] -> [B, H, N_q, C/H]
q = self.wq(x_q).reshape(B, N_q, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
# [B, N_kv, C] -> [B, N_kv, H, C/H] -> [B, H, N_kv, C/H]
k = self.wk(x_kv).reshape(B, N_kv, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
# [B, N_kv, C] -> [B, N_kv, H, C/H] -> [B, H, N_kv, C/H]
v = self.wv(x_kv).reshape(B, N_kv, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
# [B, H, N_q, C/H] @ [B, H, C/H, N_kv] -> [B, H, N_q, N_kv]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
# [B, H, N_q, N_kv] @ [B, H, N_kv, C/H] -> [B, H, N_q, C/H]
x = attn @ v
# [B, H, N_q, C/H] -> [B, N_q, C]
x = x.transpose(1, 2).reshape(B, N_q, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Compute_Block(nn.Module):
def __init__(self, z_dim, num_heads=16, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm_z1 = norm_layer(z_dim)
self.attn = CrossAttention(
z_dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm_z2 = norm_layer(z_dim)
mlp_hidden_dim = int(z_dim * mlp_ratio)
self.mlp = Mlp(in_features=z_dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, z):
zn = self.norm_z1(z)
z = z + self.drop_path(self.attn(zn, zn))
z = z + self.drop_path(self.mlp(self.norm_z2(z)))
return z
class Read_Block(nn.Module):
def __init__(self, z_dim, x_dim, num_heads=16, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm_x = norm_layer(x_dim)
self.norm_z1 = norm_layer(z_dim)
self.attn = CrossAttention(
z_dim, x_dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm_z2 = norm_layer(z_dim)
mlp_hidden_dim = int(z_dim * mlp_ratio)
self.mlp = Mlp(in_features=z_dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, z, x):
z = z + self.drop_path(self.attn(self.norm_z1(z), self.norm_x(x)))
z = z + self.drop_path(self.mlp(self.norm_z2(z)))
return z
class Write_Block(nn.Module):
def __init__(self, z_dim, x_dim, num_heads=16, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm_z = norm_layer(z_dim)
self.norm_x1 = norm_layer(x_dim)
self.attn = CrossAttention(
x_dim, z_dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm_x2 = norm_layer(x_dim)
mlp_hidden_dim = int(x_dim * mlp_ratio)
self.mlp = Mlp(in_features=x_dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, z, x):
x = x + self.drop_path(self.attn(self.norm_x1(x), self.norm_z(z)))
x = x + self.drop_path(self.mlp(self.norm_x2(x)))
return x
class RCW_Block(nn.Module):
def __init__(self, z_dim, x_dim, num_compute_layers=4, num_heads=16,
mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.read = Read_Block(z_dim, x_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop,
attn_drop=attn_drop, drop_path=drop_path, act_layer=act_layer, norm_layer=norm_layer)
self.write = Write_Block(z_dim, x_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop,
attn_drop=attn_drop, drop_path=drop_path, act_layer=act_layer, norm_layer=norm_layer)
self.compute = nn.ModuleList([
Compute_Block(z_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop,
attn_drop=attn_drop, drop_path=drop_path, act_layer=act_layer, norm_layer=norm_layer)
for _ in range(num_compute_layers)
])
def forward(self, z, x):
z = self.read(z, x)
for layer in self.compute:
z = layer(z)
x = self.write(z, x)
return z, x
class Denoiser_backbone(nn.Module):
def __init__(self, input_channels=3, output_channels=3,
num_z=256, z_dim=768, x_dim=512,
num_blocks=6, num_compute_layers=4, num_heads=8,
mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.num_z = num_z
self.z_dim = z_dim
# input blocks
self.input_proj = nn.Linear(input_channels, x_dim)
self.ln_pre = nn.LayerNorm(x_dim)
#self.z_init = nn.Parameter(torch.zeros(1, num_z, z_dim))
# timestep embedding
mlp_hidden_dim = int(z_dim * mlp_ratio)
self.time_embed = Mlp(in_features=z_dim, hidden_features=mlp_hidden_dim)
# RCW blocks
self.latent_mlp = Mlp(in_features=z_dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.ln_latent = nn.LayerNorm(z_dim)
self.blocks = nn.ModuleList([
RCW_Block(z_dim, x_dim, num_compute_layers=num_compute_layers,
num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
drop=drop, attn_drop=attn_drop, drop_path=drop_path,
act_layer=act_layer, norm_layer=norm_layer)
for _ in range(num_blocks)
])
# output blocks
self.ln_post = nn.LayerNorm(x_dim)
self.output_proj = nn.Linear(x_dim, output_channels)
self.initialize_weights()
def initialize_weights(self):
# initialize nn.Linear and nn.LayerNorm
self.apply(self._init_weights)
nn.init.constant_(self.ln_latent.weight, 0)
nn.init.constant_(self.ln_latent.bias, 0)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x, cond, t):
"""
Forward pass of the model.
Parameters:
x: [B, num_x, C_in]
t: [B]
cond: [B, num_cond, C_latent]
prev_latent: [B, num_cond + 1, C_latent]
Returns:
x_denoised: [B, num_x, C_out]
z: [B, num_cond + 1, C_latent]
"""
B, num_x, _ = x.shape
num_cond = cond.shape[1]
#assert num_x == self.num_x
# timestep embedding, [B, 1, z_dim]
t_embed = self.time_embed(timestep_embedding(t, self.z_dim)).unsqueeze(1)
# project x -> [B, num_x, C_x]
x = self.input_proj(x)
x = self.ln_pre(x)
# latent self-conditioning
#z = self.z_init.repeat(B, 1, 1) # [B, num_z, z_dim]
z = torch.cat([cond, t_embed], dim=1) # [B, num_z + num_cond + 1, z_dim]
#prev_latent = prev_latent + self.latent_mlp(prev_latent.detach())
#z = z # + self.ln_latent(prev_latent)
# compute
for blk in self.blocks:
z, x = blk(z, x)
# output proj
x = self.ln_post(x)
x_denoised = self.output_proj(x)
return x_denoised, z |