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a4e88c8 | 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 | """FAE with CNN spatial pooling for token reduction.
Encoder: CNN downsample (24×24 → H'×W') + self-attention + project to latent_dim
Decoder: project up + ViT layers at compressed resolution + CNN upsample (H'×W' → 24×24)
pool_factor=2: 576 → 144 tokens (s2)
pool_factor=4: 576 → 36 tokens (s4)
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import sys, os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from utils import RMSNorm
from models.feature_decoder import RotaryPositionalEmbedding2D, ViTDecoderBlock
class CNNDownsample(nn.Module):
"""Spatial downsampling with strided convolutions.
Each layer does 2x downsample. Stacks log2(pool_factor) layers.
"""
def __init__(self, dim, pool_factor):
super().__init__()
assert pool_factor in (2, 4), f"pool_factor must be 2 or 4, got {pool_factor}"
num_layers = int(math.log2(pool_factor))
layers = []
for _ in range(num_layers):
layers.extend([
nn.Conv2d(dim, dim, kernel_size=3, stride=2, padding=1),
nn.GELU(),
])
self.net = nn.Sequential(*layers)
def forward(self, x):
"""x: [B, C, H, W] → [B, C, H/pf, W/pf]"""
return self.net(x)
class CNNUpsample(nn.Module):
"""Spatial upsampling with transposed convolutions.
Each layer does 2x upsample. Stacks log2(pool_factor) layers.
"""
def __init__(self, dim, pool_factor):
super().__init__()
assert pool_factor in (2, 4), f"pool_factor must be 2 or 4, got {pool_factor}"
num_layers = int(math.log2(pool_factor))
layers = []
for _ in range(num_layers):
layers.extend([
nn.ConvTranspose2d(dim, dim, kernel_size=4, stride=2, padding=1),
nn.GELU(),
])
self.net = nn.Sequential(*layers)
def forward(self, x):
"""x: [B, C, H', W'] → [B, C, H'*pf, W'*pf]"""
return self.net(x)
class FAESpatialEncoder(nn.Module):
"""FAE Encoder with CNN spatial pooling.
Input: [B, 576, embed_dim]
Output: [B, N_compressed, latent_dim]
where N_compressed = (24/pool_factor)^2
"""
def __init__(self, embed_dim=1152, latent_dim=32, num_heads=16,
pool_factor=2, grid_size=24, use_vae=True):
super().__init__()
self.embed_dim = embed_dim
self.latent_dim = latent_dim
self.pool_factor = pool_factor
self.grid_size = grid_size
self.compressed_grid = grid_size // pool_factor
self.use_vae = use_vae
# CNN spatial downsampling
self.downsample = CNNDownsample(embed_dim, pool_factor)
# Self-attention at compressed resolution (pre-norm)
self.norm1 = RMSNorm(embed_dim)
self.self_attn = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True)
# SwiGLU FFN
self.norm2 = RMSNorm(embed_dim)
ffn_dim = int(embed_dim * 2.7)
self.w1 = nn.Linear(embed_dim, ffn_dim, bias=False)
self.w2 = nn.Linear(ffn_dim, embed_dim, bias=False)
self.w3 = nn.Linear(embed_dim, ffn_dim, bias=False)
# Per-token projection to latent dim
self.proj = nn.Linear(embed_dim, latent_dim)
# VAE heads
if use_vae:
self.mu_head = nn.Linear(latent_dim, latent_dim)
self.logvar_head = nn.Linear(latent_dim, latent_dim)
def forward(self, x):
"""
Args:
x: [B, N, embed_dim] where N = grid_size^2 = 576
Returns:
z_sample: [B, N_compressed, latent_dim]
mu, logvar: same shape
"""
B, N, D = x.shape
# Reshape to 2D and downsample
x = x.transpose(1, 2).reshape(B, D, self.grid_size, self.grid_size)
x = self.downsample(x) # [B, D, H', W']
x = x.flatten(2).transpose(1, 2) # [B, N_compressed, D]
# Self-attention
normed = self.norm1(x)
x = x + self.self_attn(normed, normed, normed)[0]
# SwiGLU FFN
h = self.norm2(x)
x = x + self.w2(F.silu(self.w1(h)) * self.w3(h))
# Project to latent
z = self.proj(x)
if not self.use_vae:
return z, z, torch.zeros_like(z)
mu = self.mu_head(z)
logvar = self.logvar_head(z)
if self.training:
std = torch.exp(0.5 * logvar)
z_sample = mu + std * torch.randn_like(std)
else:
z_sample = mu
return z_sample, mu, logvar
class FAESpatialDecoder(nn.Module):
"""FAE Decoder with CNN spatial upsampling.
Input: [B, N_compressed, latent_dim]
Output: [B, 576, output_dim]
ViT layers operate at compressed resolution, then CNN upsamples.
"""
def __init__(self, latent_dim=32, output_dim=1152, num_layers=6,
num_heads=16, ffn_mult=2.7, pool_factor=2, grid_size=24):
super().__init__()
self.output_dim = output_dim
self.pool_factor = pool_factor
self.grid_size = grid_size
self.compressed_grid = grid_size // pool_factor
# Project latent up to full dim
self.input_proj = nn.Linear(latent_dim, output_dim)
# RoPE at compressed grid resolution
head_dim = output_dim // num_heads
self.rope = RotaryPositionalEmbedding2D(head_dim, grid_size=self.compressed_grid)
# Transformer layers at compressed resolution
self.layers = nn.ModuleList([
ViTDecoderBlock(output_dim, num_heads, ffn_mult)
for _ in range(num_layers)
])
self.pre_upsample_norm = RMSNorm(output_dim)
# CNN spatial upsampling
self.upsample = CNNUpsample(output_dim, pool_factor)
# Final projection after upsample (refine features)
self.final_norm = RMSNorm(output_dim)
def forward(self, z):
"""
Args:
z: [B, N_compressed, latent_dim]
Returns:
x_hat: [B, N_full, output_dim] where N_full = grid_size^2
"""
B = z.shape[0]
x = self.input_proj(z) # [B, N_compressed, output_dim]
rope_cos, rope_sin = self.rope(x.shape[1], x.device)
for layer in self.layers:
x = layer(x, rope_cos, rope_sin)
x = self.pre_upsample_norm(x)
# Reshape to 2D and upsample
x = x.transpose(1, 2).reshape(B, self.output_dim,
self.compressed_grid, self.compressed_grid)
x = self.upsample(x) # [B, output_dim, grid_size, grid_size]
x = x.flatten(2).transpose(1, 2) # [B, N_full, output_dim]
return self.final_norm(x)
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