Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,662 Bytes
dada74e |
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 |
"""Building blocks for TiTok.
Copyright (2024) Bytedance Ltd. and/or its affiliates
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Reference:
https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/transformer.py
"""
import torch
import torch.nn as nn
from collections import OrderedDict
class ResidualAttentionBlock(nn.Module):
def __init__(
self,
d_model,
n_head,
mlp_ratio = 4.0,
act_layer = nn.GELU,
norm_layer = nn.LayerNorm
):
super().__init__()
self.ln_1 = norm_layer(d_model)
self.attn = nn.MultiheadAttention(d_model, n_head)
self.mlp_ratio = mlp_ratio
# optionally we can disable the FFN
if mlp_ratio > 0:
self.ln_2 = norm_layer(d_model)
mlp_width = int(d_model * mlp_ratio)
self.mlp = nn.Sequential(OrderedDict([
("c_fc", nn.Linear(d_model, mlp_width)),
("gelu", act_layer()),
("c_proj", nn.Linear(mlp_width, d_model))
]))
def attention(
self,
x: torch.Tensor
):
return self.attn(x, x, x, need_weights=False)[0]
def forward(
self,
x: torch.Tensor,
):
attn_output = self.attention(x=self.ln_1(x))
x = x + attn_output
if self.mlp_ratio > 0:
x = x + self.mlp(self.ln_2(x))
return x
def _expand_token(token, batch_size: int):
return token.unsqueeze(0).expand(batch_size, -1, -1)
class TiTokEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.image_size = config.dataset.preprocessing.crop_size
self.patch_size = config.model.vq_model.vit_enc_patch_size
self.grid_size = self.image_size // self.patch_size
self.model_size = config.model.vq_model.vit_enc_model_size
self.num_latent_tokens = config.model.vq_model.num_latent_tokens
self.token_size = config.model.vq_model.token_size
self.width = {
"small": 512,
"base": 768,
"large": 1024,
}[self.model_size]
self.num_layers = {
"small": 8,
"base": 12,
"large": 24,
}[self.model_size]
self.num_heads = {
"small": 8,
"base": 12,
"large": 16,
}[self.model_size]
self.patch_embed = nn.Conv2d(
in_channels=3, out_channels=self.width,
kernel_size=self.patch_size, stride=self.patch_size, bias=True)
scale = self.width ** -0.5
self.class_embedding = nn.Parameter(scale * torch.randn(1, self.width))
self.positional_embedding = nn.Parameter(
scale * torch.randn(self.grid_size ** 2 + 1, self.width))
self.latent_token_positional_embedding = nn.Parameter(
scale * torch.randn(self.num_latent_tokens, self.width))
self.ln_pre = nn.LayerNorm(self.width)
self.transformer = nn.ModuleList()
for i in range(self.num_layers):
self.transformer.append(ResidualAttentionBlock(
self.width, self.num_heads, mlp_ratio=4.0
))
self.ln_post = nn.LayerNorm(self.width)
self.conv_out = nn.Conv2d(self.width, self.token_size, kernel_size=1, bias=True)
def forward(self, pixel_values, latent_tokens):
batch_size = pixel_values.shape[0]
x = pixel_values
x = self.patch_embed(x)
x = x.reshape(x.shape[0], x.shape[1], -1)
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
# class embeddings and positional embeddings
x = torch.cat([_expand_token(self.class_embedding, x.shape[0]).to(x.dtype), x], dim=1)
x = x + self.positional_embedding.to(x.dtype) # shape = [*, grid ** 2 + 1, width]
latent_tokens = _expand_token(latent_tokens, x.shape[0]).to(x.dtype)
latent_tokens = latent_tokens + self.latent_token_positional_embedding.to(x.dtype)
x = torch.cat([x, latent_tokens], dim=1)
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
for i in range(self.num_layers):
x = self.transformer[i](x)
x = x.permute(1, 0, 2) # LND -> NLD
latent_tokens = x[:, 1+self.grid_size**2:]
latent_tokens = self.ln_post(latent_tokens)
# fake 2D shape
latent_tokens = latent_tokens.reshape(batch_size, self.width, self.num_latent_tokens, 1)
latent_tokens = self.conv_out(latent_tokens)
latent_tokens = latent_tokens.reshape(batch_size, self.token_size, 1, self.num_latent_tokens)
return latent_tokens
class TiTokDecoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.image_size = config.dataset.preprocessing.crop_size
self.patch_size = config.model.vq_model.vit_dec_patch_size
self.grid_size = self.image_size // self.patch_size
self.model_size = config.model.vq_model.vit_dec_model_size
self.num_latent_tokens = config.model.vq_model.num_latent_tokens
self.token_size = config.model.vq_model.token_size
self.width = {
"small": 512,
"base": 768,
"large": 1024,
}[self.model_size]
self.num_layers = {
"small": 8,
"base": 12,
"large": 24,
}[self.model_size]
self.num_heads = {
"small": 8,
"base": 12,
"large": 16,
}[self.model_size]
self.decoder_embed = nn.Linear(
self.token_size, self.width, bias=True)
scale = self.width ** -0.5
self.class_embedding = nn.Parameter(scale * torch.randn(1, self.width))
self.positional_embedding = nn.Parameter(
scale * torch.randn(self.grid_size ** 2 + 1, self.width))
# add mask token and query pos embed
self.mask_token = nn.Parameter(scale * torch.randn(1, 1, self.width))
self.latent_token_positional_embedding = nn.Parameter(
scale * torch.randn(self.num_latent_tokens, self.width))
self.ln_pre = nn.LayerNorm(self.width)
self.transformer = nn.ModuleList()
for i in range(self.num_layers):
self.transformer.append(ResidualAttentionBlock(
self.width, self.num_heads, mlp_ratio=4.0
))
self.ln_post = nn.LayerNorm(self.width)
self.ffn = nn.Sequential(
nn.Conv2d(self.width, 2 * self.width, 1, padding=0, bias=True),
nn.Tanh(),
nn.Conv2d(2 * self.width, 1024, 1, padding=0, bias=True),
)
self.conv_out = nn.Identity()
def forward(self, z_quantized):
N, C, H, W = z_quantized.shape
assert H == 1 and W == self.num_latent_tokens, f"{H}, {W}, {self.num_latent_tokens}"
x = z_quantized.reshape(N, C*H, W).permute(0, 2, 1) # NLD
x = self.decoder_embed(x)
batchsize, seq_len, _ = x.shape
mask_tokens = self.mask_token.repeat(batchsize, self.grid_size**2, 1).to(x.dtype)
mask_tokens = torch.cat([_expand_token(self.class_embedding, mask_tokens.shape[0]).to(mask_tokens.dtype),
mask_tokens], dim=1)
mask_tokens = mask_tokens + self.positional_embedding.to(mask_tokens.dtype)
x = x + self.latent_token_positional_embedding[:seq_len]
x = torch.cat([mask_tokens, x], dim=1)
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
for i in range(self.num_layers):
x = self.transformer[i](x)
x = x.permute(1, 0, 2) # LND -> NLD
x = x[:, 1:1+self.grid_size**2] # remove cls embed
x = self.ln_post(x)
# N L D -> N D H W
x = x.permute(0, 2, 1).reshape(batchsize, self.width, self.grid_size, self.grid_size)
x = self.ffn(x.contiguous())
x = self.conv_out(x)
return x |