File size: 1,716 Bytes
9ade7a4 | 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 | from __future__ import annotations
import torch
from torch import nn
class PocketDenoiser(nn.Module):
def __init__(self, diffusion_steps: int = 50) -> None:
super().__init__()
self.diffusion_steps = diffusion_steps
self.time_embedding = nn.Embedding(diffusion_steps, 24)
self.label_embedding = nn.Embedding(11, 24)
self.network = nn.Sequential(
nn.Linear(64 + 24 + 24, 160),
nn.GELU(),
nn.Linear(160, 160),
nn.GELU(),
nn.Linear(160, 64),
)
def forward(
self,
noisy_pixels: torch.Tensor,
timesteps: torch.Tensor,
labels: torch.Tensor,
) -> torch.Tensor:
features = torch.cat(
[
noisy_pixels,
self.time_embedding(timesteps),
self.label_embedding(labels),
],
dim=1,
)
return self.network(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())
|