File size: 5,860 Bytes
9dd581e | 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 | from __future__ import annotations
import torch
from torch import nn
from torch.nn import functional as F
class VectorQuantizedAutoencoder(nn.Module):
def __init__(
self,
codebook_size: int = 32,
embedding_dimensions: int = 16,
) -> None:
super().__init__()
self.codebook_size = codebook_size
self.embedding_dimensions = embedding_dimensions
self.encoder = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=4, stride=2, padding=1),
nn.SiLU(),
nn.Conv2d(32, embedding_dimensions, kernel_size=3, padding=1),
)
self.codebook = nn.Embedding(codebook_size, embedding_dimensions)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(
embedding_dimensions,
32,
kernel_size=4,
stride=2,
padding=1,
),
nn.SiLU(),
nn.Conv2d(32, 1, kernel_size=3, padding=1),
nn.Sigmoid(),
)
nn.init.uniform_(
self.codebook.weight,
-1 / codebook_size,
1 / codebook_size,
)
def encode(self, pixels: torch.Tensor) -> torch.Tensor:
return self.encoder(pixels)
def quantize(
self,
encoded: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
channels_last = encoded.permute(0, 2, 3, 1).contiguous()
flat = channels_last.reshape(-1, self.embedding_dimensions)
distances = (
flat.square().sum(1, keepdim=True)
+ self.codebook.weight.square().sum(1)
- 2 * flat @ self.codebook.weight.t()
)
indices = distances.argmin(dim=1)
quantized = self.codebook(indices).reshape(channels_last.shape)
quantized = quantized.permute(0, 3, 1, 2).contiguous()
straight_through = encoded + (quantized - encoded).detach()
return straight_through, indices.reshape(encoded.shape[0], 4, 4)
def decode(self, latent: torch.Tensor) -> torch.Tensor:
return self.decoder(latent)
def decode_indices(self, indices: torch.Tensor) -> torch.Tensor:
quantized = self.codebook(indices)
quantized = quantized.permute(0, 3, 1, 2).contiguous()
return self.decode(quantized)
def forward(
self,
pixels: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
encoded = self.encode(pixels)
quantized_st, indices = self.quantize(encoded)
quantized = self.codebook(indices).permute(0, 3, 1, 2).contiguous()
reconstruction = self.decode(quantized_st)
codebook_loss = F.mse_loss(quantized, encoded.detach())
commitment_loss = F.mse_loss(encoded, quantized.detach())
return reconstruction, indices, codebook_loss, commitment_loss
class ConditionalCodePrior(nn.Module):
def __init__(
self,
codebook_size: int = 32,
token_dimensions: int = 32,
hidden_dimensions: int = 64,
) -> None:
super().__init__()
self.codebook_size = codebook_size
self.start_token = codebook_size
self.token_embedding = nn.Embedding(codebook_size + 1, token_dimensions)
self.label_embedding = nn.Embedding(10, 16)
self.position_embedding = nn.Embedding(16, 16)
self.recurrent = nn.GRU(
token_dimensions + 32,
hidden_dimensions,
batch_first=True,
)
self.output = nn.Linear(hidden_dimensions, codebook_size)
def forward(self, input_tokens: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
batch, length = input_tokens.shape
positions = torch.arange(length, device=input_tokens.device)
token_features = self.token_embedding(input_tokens)
condition = self.label_embedding(labels)[:, None, :].expand(batch, length, -1)
position = self.position_embedding(positions)[None, :, :].expand(batch, -1, -1)
hidden, _ = self.recurrent(
torch.cat([token_features, condition, position], dim=2)
)
return self.output(hidden)
@torch.inference_mode()
def generate(
self,
labels: torch.Tensor,
*,
seed: int,
temperature: float = 1.0,
) -> torch.Tensor:
generator = torch.Generator(device=labels.device).manual_seed(seed)
sequence = torch.full(
(len(labels), 1),
self.start_token,
dtype=torch.long,
device=labels.device,
)
for _ in range(16):
logits = self(sequence, labels)[:, -1]
if temperature <= 0.05:
token = logits.argmax(dim=1, keepdim=True)
else:
probabilities = torch.softmax(logits / temperature, dim=1)
token = torch.multinomial(
probabilities,
1,
generator=generator,
)
sequence = torch.cat([sequence, token], dim=1)
return sequence[:, 1:].reshape(-1, 4, 4)
class TinyVisionJudge(nn.Module):
def __init__(self) -> None:
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(1, 8, kernel_size=3, padding=1),
nn.GELU(),
nn.Conv2d(8, 8, kernel_size=3, padding=1, groups=8),
nn.GELU(),
nn.Conv2d(8, 12, kernel_size=1),
nn.GELU(),
nn.MaxPool2d(2),
)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(12 * 4 * 4, 10),
)
def forward(self, pixels: torch.Tensor) -> torch.Tensor:
return self.classifier(self.features(pixels))
def parameter_count(model: nn.Module) -> int:
return sum(parameter.numel() for parameter in model.parameters())
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