File size: 4,779 Bytes
11f6a98 |
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 |
import torch
from torch import nn
from collections import OrderedDict
import logging
logger = logging.getLogger(__name__)
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
if self.weight.dtype != x.dtype:
orig_type = x.dtype
ret = super().forward(x.type(self.weight.dtype))
return ret.type(orig_type)
else:
return super().forward(x)
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlock(nn.Module):
def __init__(
self,
d_model: int,
n_head: int,
attn_mask: torch.Tensor = None,
):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = LayerNorm(d_model)
self.mlp = nn.Sequential(
OrderedDict(
[
(
"c_fc",
nn.Linear(d_model, d_model * 4),
),
("gelu", QuickGELU()),
(
"c_proj",
nn.Linear(d_model * 4, d_model),
),
]
)
)
self.ln_2 = LayerNorm(d_model)
self.attn_mask = attn_mask
def attention(self, x: torch.Tensor):
self.attn_mask = (
self.attn_mask.to(dtype=x.dtype, device=x.device)
if self.attn_mask is not None
else None
)
return self.attn(
x,
x,
x,
need_weights=False,
attn_mask=self.attn_mask,
)[0]
def forward(self, x: torch.Tensor):
x = x + self.attention(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class Transformer(nn.Module):
def __init__(
self,
width: int,
layers: int,
heads: int,
attn_mask: torch.Tensor = None,
):
super().__init__()
self.width = width
self.layers = layers
self.resblocks = nn.Sequential(
*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]
)
def forward(self, x: torch.Tensor):
return self.resblocks(x)
class ConditionalViT(nn.Module):
def __init__(
self,
input_resolution: int,
patch_size: int,
width: int,
layers: int,
heads: int,
output_dim: int,
n_categories: int,
):
super().__init__()
self.input_resolution = input_resolution
self.output_dim = output_dim
self.conv1 = nn.Conv2d(
in_channels=3,
out_channels=width,
kernel_size=patch_size,
stride=patch_size,
bias=False,
)
scale = width**-0.5
self.class_embedding = nn.Parameter(scale * torch.randn(width))
self.n_categories = n_categories
self.c_embedding = nn.Embedding(self.n_categories, width)
self.c_pos_embedding = nn.Parameter(scale * torch.randn(1, width))
self.positional_embedding = nn.Parameter(
scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)
)
self.ln_pre = LayerNorm(width)
self.transformer = Transformer(width, layers, heads)
self.ln_post = LayerNorm(width)
self.logit_scale = torch.nn.Parameter(torch.ones([]) * 4.6052)
self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
def forward(self, imgs: torch.Tensor, c: torch.Tensor = None):
"""
imgs : Batch of images
c : category indices.
"""
x = self.conv1(imgs) # shape = [*, width, grid, grid]
# shape = [*, width, grid ** 2]
x = x.reshape(x.shape[0], x.shape[1], -1)
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
# [CLS, grid] + maybe Categories.
tokens = [self.class_embedding.tile(x.shape[0], 1, 1), x] # NLD
pos_embed = [self.positional_embedding] # LD
if c is not None: # If c is None, we don't add the token
tokens += [self.c_embedding(c).unsqueeze(1)] # ND -> N1D
pos_embed += [self.c_pos_embedding] # 1D
# shape = [*, grid ** 2 + 1|2, width] = N(L|L+1)D
x = torch.cat(tokens, dim=1)
pos_embed = torch.cat(pos_embed, dim=0).unsqueeze(0) # 1(L|L+1)D
x = x + pos_embed
x = self.ln_pre(x)
x = x.permute(1, 0, 2) # NLD -> LND
x = self.transformer(x)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.ln_post(x[:, 0, :])
x = x @ self.proj
return x
|