Spaces:
Build error
Build error
File size: 6,744 Bytes
daf0288 |
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 |
import torch
from torch import nn, Tensor, einsum
from typing import Optional, Tuple
import math
from functools import partial
from collections import OrderedDict
import torch.nn.functional as F
from einops import rearrange
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def eval_decorator(fn):
def inner(model, *args, **kwargs):
was_training = model.training
model.eval()
out = fn(model, *args, **kwargs)
model.train(was_training)
return out
return inner
class ResBlock(nn.Module):
def __init__(self, chan_in, hidden_size, chan_out):
super().__init__()
self.net = nn.Sequential(
nn.Conv2d(chan_in, hidden_size, 3, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_size, hidden_size, 3, padding=1),
nn.ReLU(),
nn.Conv2d(hidden_size, chan_out, 1),
)
def forward(self, x):
return self.net(x) + x
class BasicVAE(nn.Module):
def get_codebook_indices(self, images):
raise NotImplementedError()
def decode(self, img_seq):
raise NotImplementedError()
def get_codebook_probs(self, img_seq):
raise NotImplementedError()
def get_image_tokens_size(self):
pass
def get_image_size(self):
pass
class DiscreteVAE(BasicVAE):
def __init__(
self,
image_size: Tuple[int, int] = [256, 256], # input image size
codebook_tokens: int = 512, # codebook vocab size
codebook_dim: int = 512, # codebook embedding dimension
num_layers: int = 3, # layers of resnet blocks in encoder/decoder
hidden_dim: int = 64, # dimension in resnet blocks
channels: int = 3, # input channels
smooth_l1_loss: bool = False, # prevents exploding gradients
temperature: float = 0.9, # tau in gumbel softmax
straight_through: bool = False, # if True, the returned samples will be discretized as one-hot vectors, but will be differentiated as if it is the soft sample in autograd
kl_div_loss_weight: float = 0.0,
):
super().__init__()
assert num_layers >= 1, "number of layers must be greater than or equal to 1"
self.image_size = image_size
self.codebook_tokens = codebook_tokens
self.num_layers = num_layers
self.temperature = temperature
self.straight_through = straight_through
self.codebook = nn.Embedding(codebook_tokens, codebook_dim)
encoder_layers = list()
decoder_layers = list()
encoder_in = channels
decoder_in = codebook_dim
for _ in range(num_layers):
encoder_layers.append(
nn.Sequential(
nn.Conv2d(encoder_in, hidden_dim, 4, stride=2, padding=1), nn.ReLU()
)
)
encoder_layers.append(
ResBlock(
chan_in=hidden_dim, hidden_size=hidden_dim, chan_out=hidden_dim
)
)
encoder_in = hidden_dim
decoder_layers.append(
nn.Sequential(
nn.ConvTranspose2d(decoder_in, hidden_dim, 4, stride=2, padding=1),
nn.ReLU(),
)
)
decoder_layers.append(
ResBlock(
chan_in=hidden_dim, hidden_size=hidden_dim, chan_out=hidden_dim
)
)
decoder_in = hidden_dim
encoder_layers.append(nn.Conv2d(hidden_dim, codebook_tokens, 1))
decoder_layers.append(nn.Conv2d(hidden_dim, channels, 1))
self.encoder = nn.Sequential(*encoder_layers)
self.decoder = nn.Sequential(*decoder_layers)
self.loss_fn = F.smooth_l1_loss if smooth_l1_loss else F.mse_loss
self.kl_div_loss_weight = kl_div_loss_weight
def get_image_size(self):
return self.image_size
def get_image_tokens_size(self) -> int:
ds_ratio = math.pow(2, self.num_layers)
return int((self.image_size[0] // ds_ratio) * (self.image_size[1] // ds_ratio))
@torch.no_grad()
@eval_decorator
def get_codebook_indices(self, images: Tensor):
logits = self.forward(images, return_logits=True)
codebook_indices = logits.argmax(dim=1)
return codebook_indices
@torch.no_grad()
@eval_decorator
def get_codebook_probs(self, images: Tensor):
logits = self.forward(images, return_logits=True)
return nn.Softmax(dim=1)(logits)
def decode(self, img_seq: Tensor):
image_embeds = self.codebook(img_seq)
image_embeds = image_embeds.permute((0, 3, 1, 2)).contiguous()
# image_embeds = rearrange(image_embeds, "b h w d -> b d h w", h=h, w=w)
images = self.decoder(image_embeds)
return images
def forward(
self,
img: Tensor,
return_loss: bool = False,
return_recons: bool = False,
return_logits: bool = False,
temp=None,
) -> Tuple[Tensor, Optional[Tensor]]:
assert (
img.shape[-1] == self.image_size[0] and img.shape[-2] == self.image_size[1]
), f"input must have the correct image size {self.image_size}"
logits = self.encoder(img)
if return_logits:
return logits # return logits for getting hard image indices for DALL-E training
temp = default(temp, self.temperature)
soft_one_hot = F.gumbel_softmax(
logits, tau=temp, dim=1, hard=self.straight_through
)
sampled = einsum(
"b n h w, n d -> b d h w", soft_one_hot, self.codebook.weight
).contiguous()
out = self.decoder(sampled)
if not return_loss:
return out
# reconstruction loss
recon_loss = self.loss_fn(img, out)
# kl divergence
logits = rearrange(logits, "b n h w -> b (h w) n").contiguous()
qy = F.softmax(logits, dim=-1)
log_qy = torch.log(qy + 1e-10)
log_uniform = torch.log(
torch.tensor([1.0 / self.codebook_tokens], device=img.device)
)
kl_div = F.kl_div(log_uniform, log_qy, None, None, "batchmean", log_target=True)
loss = recon_loss + (kl_div * self.kl_div_loss_weight)
if not return_recons:
return loss
return loss, out
if __name__ == "__main__":
input = torch.rand(1, 3, 256, 256)
model = DiscreteVAE()
loss, output = model(input, return_loss=True, return_recons=True)
print(model)
print(model.get_image_tokens_size())
print(model.get_codebook_indices(input).shape)
print(loss, output.shape, output.max(), output.min())
|