File size: 8,040 Bytes
16906c1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 |
import torch
import torch.nn as nn
import pytorch_lightning as pl
import random
from torchvision.datasets import MNIST, FashionMNIST, CelebA
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.utils import save_image
from torch.optim import Adam
from torch.optim.lr_scheduler import ReduceLROnPlateau
import os
from typing import Optional
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.size(0), -1)
class Stack(nn.Module):
def __init__(self, channels, height, width):
super(Stack, self).__init__()
self.channels = channels
self.height = height
self.width = width
def forward(self, x):
return x.view(x.size(0), self.channels, self.height, self.width)
class VAE(pl.LightningModule):
def __init__(self, latent_size: int, hidden_size: int, alpha: int, lr: float,
batch_size: int,
dataset: Optional[str] = None,
save_images: Optional[bool] = None,
save_path: Optional[str] = None, **kwargs):
"""Init function for the VAE
Args:
latent_size (int): Latent Hidden Size
alpha (int): Hyperparameter to control the importance of
reconstruction loss vs KL-Divergence Loss
lr (float): Learning Rate, will not be used if auto_lr_find is used.
dataset (Optional[str]): Dataset to used
save_images (Optional[bool]): Boolean to decide whether to save images
save_path (Optional[str]): Path to save images
"""
super().__init__()
self.latent_size = latent_size
self.hidden_size = hidden_size
if save_images:
self.save_path = f'{save_path}/{kwargs["model_type"]}_images/'
self.save_hyperparameters()
self.save_images = save_images
self.lr = lr
self.batch_size = batch_size
self.encoder = nn.Sequential(
Flatten(),
nn.Linear(784, 392), nn.BatchNorm1d(392), nn.LeakyReLU(0.1),
nn.Linear(392, 196), nn.BatchNorm1d(196), nn.LeakyReLU(0.1),
nn.Linear(196, 128), nn.BatchNorm1d(128), nn.LeakyReLU(0.1),
nn.Linear(128, latent_size)
)
self.hidden2mu = nn.Linear(latent_size, latent_size)
self.hidden2log_var = nn.Linear(latent_size, latent_size)
self.alpha = alpha
self.decoder = nn.Sequential(
nn.Linear(latent_size, 128), nn.BatchNorm1d(128), nn.LeakyReLU(0.1),
nn.Linear(128, 196), nn.BatchNorm1d(196), nn.LeakyReLU(0.1),
nn.Linear(196, 392), nn.BatchNorm1d(392), nn.LeakyReLU(0.1),
nn.Linear(392, 784),
Stack(1, 28, 28),
nn.Tanh()
)
self.height = kwargs.get("height")
self.width = kwargs.get("width")
self.data_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Lambda(lambda x:2*x-1.)])
self.dataset = dataset
def encode(self, x):
hidden = self.encoder(x)
mu = self.hidden2mu(hidden)
log_var = self.hidden2log_var(hidden)
return mu, log_var
def decode(self, x):
x = self.decoder(x)
return x
def reparametrize(self, mu, log_var):
# Reparametrization Trick to allow gradients to backpropagate from the
# stochastic part of the model
sigma = torch.exp(0.5*log_var)
z = torch.randn_like(sigma)
return mu + sigma*z
def training_step(self, batch, batch_idx):
x, _ = batch
mu, log_var, x_out = self.forward(x)
kl_loss = (-0.5*(1+log_var - mu**2 -
torch.exp(log_var)).sum(dim=1)).mean(dim=0)
recon_loss_criterion = nn.MSELoss()
recon_loss = recon_loss_criterion(x, x_out)
# print(kl_loss.item(),recon_loss.item())
loss = recon_loss*self.alpha + kl_loss
self.log('train_loss', loss, on_step=False,
on_epoch=True, prog_bar=True)
return loss
def validation_step(self, batch, batch_idx):
x, _ = batch
mu, log_var, x_out = self.forward(x)
kl_loss = (-0.5*(1+log_var - mu**2 -
torch.exp(log_var)).sum(dim=1)).mean(dim=0)
recon_loss_criterion = nn.MSELoss()
recon_loss = recon_loss_criterion(x, x_out)
# print(kl_loss.item(),recon_loss.item())
loss = recon_loss*self.alpha + kl_loss
self.log('val_kl_loss', kl_loss, on_step=False, on_epoch=True)
self.log('val_recon_loss', recon_loss, on_step=False, on_epoch=True)
self.log('val_loss', loss, on_step=False, on_epoch=True)
# print(x.mean(),x_out.mean())
return x_out, loss
def validation_epoch_end(self, outputs):
if not self.save_images:
return
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
choice = random.choice(outputs)
output_sample = choice[0]
output_sample = output_sample.reshape(-1, 1, self.width, self.height)
# output_sample = self.scale_image(output_sample)
save_image(
output_sample,
f"{self.save_path}/epoch_{self.current_epoch+1}.png",
# value_range=(-1, 1)
)
def configure_optimizers(self):
optimizer = Adam(self.parameters(), lr=(self.lr or self.learning_rate))
lr_scheduler = ReduceLROnPlateau(optimizer,)
return {
"optimizer": optimizer, "lr_scheduler": lr_scheduler,
"monitor": "val_loss"
}
def forward(self, x):
mu, log_var = self.encode(x)
hidden = self.reparametrize(mu, log_var)
output = self.decode(hidden)
return mu, log_var, output
# Functions for dataloading
def train_dataloader(self):
if self.dataset == "mnist":
train_set = MNIST('data/', download=True,
train=True, transform=self.data_transform)
elif self.dataset == "fashion-mnist":
train_set = FashionMNIST(
'data/', download=True, train=True,
transform=self.data_transform)
elif self.dataset == "celeba":
train_set = CelebA('data/', download=False, split="train", transform=self.data_transform)
return DataLoader(train_set, batch_size=self.batch_size, shuffle=True)
def val_dataloader(self):
if self.dataset == "mnist":
val_set = MNIST('data/', download=True, train=False,
transform=self.data_transform)
elif self.dataset == "fashion-mnist":
val_set = FashionMNIST(
'data/', download=True, train=False,
transform=self.data_transform)
elif self.dataset == "celeba":
val_set = CelebA('data/', download=False, split="valid", transform=self.data_transform)
return DataLoader(val_set, batch_size=self.batch_size)
def scale_image(self, img):
out = (img + 1) / 2
return out
def interpolate(self, x1, x2):
assert x1.shape == x2.shape, "Inputs must be of the same shape"
if x1.dim() == 3:
x1 = x1.unsqueeze(0)
if x2.dim() == 3:
x2 = x2.unsqueeze(0)
if self.training:
raise Exception(
"This function should not be called when model is still "
"in training mode. Use model.eval() before calling the "
"function")
mu1, lv1 = self.encode(x1)
mu2, lv2 = self.encode(x2)
z1 = self.reparametrize(mu1, lv1)
z2 = self.reparametrize(mu2, lv2)
weights = torch.arange(0.1, 0.9, 0.1)
intermediate = [self.decode(z1)]
for wt in weights:
inter = (1.-wt)*z1 + wt*z2
intermediate.append(self.decode(inter))
intermediate.append(self.decode(z2))
out = torch.stack(intermediate, dim=0).squeeze(1)
return out, (mu1, lv1), (mu2, lv2)
|