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dcaea4a | 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 | from __future__ import annotations
import torch
from torch import nn
class JEncoder(nn.Module):
def __init__(self, latent_dim: int = 32) -> None:
super().__init__()
self.network = nn.Sequential(
nn.Linear(64, 96),
nn.LayerNorm(96),
nn.GELU(),
nn.Linear(96, 64),
nn.LayerNorm(64),
nn.GELU(),
nn.Linear(64, latent_dim),
)
def forward(self, images: torch.Tensor) -> torch.Tensor:
return self.network(images.flatten(1))
class JPredictor(nn.Module):
def __init__(self, latent_dim: int = 32) -> None:
super().__init__()
self.network = nn.Sequential(
nn.Linear(latent_dim, 64),
nn.GELU(),
nn.Linear(64, latent_dim),
)
def forward(self, embeddings: torch.Tensor) -> torch.Tensor:
return self.network(embeddings)
def parameter_count(module: nn.Module) -> int:
return sum(parameter.numel() for parameter in module.parameters())
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