File size: 3,027 Bytes
f3a1fe5 | 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 | from __future__ import annotations
import math
import torch
from torch import nn
class ConditionalGenerator(nn.Module):
def __init__(self, noise_dimensions: int = 32, embedding_dimensions: int = 16) -> None:
super().__init__()
self.noise_dimensions = noise_dimensions
self.label_embedding = nn.Embedding(10, embedding_dimensions)
self.network = nn.Sequential(
nn.Linear(noise_dimensions + embedding_dimensions, 128),
nn.LayerNorm(128),
nn.SiLU(),
nn.Linear(128, 128),
nn.LayerNorm(128),
nn.SiLU(),
nn.Linear(128, 64),
nn.Sigmoid(),
)
def forward(self, noise: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
condition = self.label_embedding(labels)
return self.network(torch.cat([noise, condition], dim=1))
@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)
noise = torch.randn(
len(labels),
self.noise_dimensions,
generator=generator,
device=labels.device,
)
return self(noise * temperature, labels)
class ProjectionCritic(nn.Module):
def __init__(self, feature_dimensions: int = 64) -> None:
super().__init__()
self.features = nn.Sequential(
nn.Linear(64, 128),
nn.LeakyReLU(0.2),
nn.Linear(128, feature_dimensions),
nn.LeakyReLU(0.2),
)
self.score = nn.Linear(feature_dimensions, 1)
self.label_projection = nn.Embedding(10, feature_dimensions)
self.classifier = nn.Linear(feature_dimensions, 10)
def forward(
self,
pixels: torch.Tensor,
labels: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
features = self.features(pixels)
projection = (features * self.label_projection(labels)).sum(dim=1)
projection = projection / math.sqrt(features.shape[1])
score = self.score(features).squeeze(1) + projection
return score, self.classifier(features)
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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