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from pathlib import Path
from typing import Tuple
import torch
from torch import nn
import torch.nn.functional as F
class Dense(nn.Module):
def __init__(self, input_dim, output_dim, bias=True, activation=nn.LeakyReLU, **kwargs):
super().__init__()
self.fc = nn.Linear(input_dim, output_dim, bias=bias)
nn.init.xavier_uniform_(self.fc.weight)
nn.init.constant_(self.fc.bias, 0.0)
self.activation = activation(**kwargs) if activation is not None else None
def forward(self, x):
if self.activation is None:
return self.fc(x)
return self.activation(self.fc(x))
class Encoder(nn.Module):
def __init__(self, input_dim, *dims):
super().__init__()
dims = (input_dim,) + dims
self.layers = nn.Sequential(
*[Dense(dims[i], dims[i+1], negative_slope=0.4, inplace=True) for i in range(len(dims) - 1)]
)
def forward(self, x):
return self.layers(x)
class Decoder(nn.Module):
def __init__(self, output_dim, *dims):
super().__init__()
self.layers = nn.Sequential(
*[Dense(dims[i], dims[i + 1], negative_slope=0.4, inplace=True) for i in range(len(dims) - 1)]
+ [Dense(dims[-1], output_dim, activation=nn.Sigmoid)]
)
def forward(self, x):
return self.layers(x)
class Autoencoder(nn.Module):
def __init__(self, input_dim: int = 784, hidden_dims: Tuple[int] = (256, 64, 16, 4, 2)):
super().__init__()
self.encoder = Encoder(input_dim, *hidden_dims)
self.decoder = Decoder(input_dim, *reversed(hidden_dims))
self.input_dim = input_dim
self.hidden_dims = hidden_dims
def forward(self, x):
x = x.flatten(1)
latent = self.encoder(x)
recon = self.decoder(latent)
loss = F.mse_loss(recon, x)
return recon, latent, loss
class MessageModel:
def __init__(self, msg='hello, world'):
self.msg = msg
def __call__(self):
print(self.msg)
@classmethod
def from_pretrained(cls, path):
path = Path(path)
msg_file_path = path / 'message.txt'
assert msg_file_path.exists()
msg = msg_file_path.read_text()
return cls(msg)
def save_pretrained(self, path):
path = Path(path)
path.mkdir(exist_ok=True, parents=True)
msg_file_path = path / 'message.txt'
msg_file_path.write_text(self.msg)
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