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experiment.py
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experiment.py
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import os
import math
import torch
from torch import optim
from models import BaseVAE
from models.types_ import *
from utils import data_loader
import pytorch_lightning as pl
from torchvision import transforms
import torchvision.utils as vutils
from torchvision.datasets import CelebA
from torch.utils.data import DataLoader
class VAEXperiment(pl.LightningModule):
def __init__(self,
vae_model: BaseVAE,
params: dict) -> None:
super(VAEXperiment, self).__init__()
self.model = vae_model
self.params = params
self.curr_device = None
self.hold_graph = False
self.image_sum = 0
self.image_sq_sum = 0
self.num_images = 0
try:
self.hold_graph = self.params['retain_first_backpass']
except:
pass
def forward(self, input: Tensor, **kwargs) -> Tensor:
return self.model(input, **kwargs)
def training_step(self, batch, batch_idx, optimizer_idx = 0):
real_img, labels = batch
self.curr_device = real_img.device
results = self.forward(real_img, labels = labels)
train_loss = self.model.loss_function(*results,
M_N = self.params['kld_weight'], #al_img.shape[0]/ self.num_train_imgs,
optimizer_idx=optimizer_idx,
batch_idx = batch_idx)
self.log_dict({key: val.item() for key, val in train_loss.items()}, sync_dist=True)
return train_loss['loss']
def validation_step(self, batch, batch_idx, optimizer_idx = 0):
real_img, labels = batch
self.curr_device = real_img.device
results = self.forward(real_img, labels = labels)
val_loss = self.model.loss_function(*results,
M_N = 1.0, #real_img.shape[0]/ self.num_val_imgs,
optimizer_idx = optimizer_idx,
batch_idx = batch_idx)
self.log_dict({f"val_{key}": val.item() for key, val in val_loss.items()}, sync_dist=True)
def on_validation_end(self) -> None:
self.sample_images()
mean_image = self.image_sum / self.num_images
std_image = (self.image_sq_sum / self.num_images - mean_image ** 2).sqrt() # variance = E[X^2] - (E[X])^2
# You can save or log these images as needed, e.g.,
self.logger.experiment.add_image('mean_image', mean_image, self.current_epoch)
self.logger.experiment.add_image('std_image', std_image, self.current_epoch)
# Optionally, you might want to clear the accumulators to free up memory
del self.image_sum
del self.image_sq_sum
torch.cuda.empty_cache() # If using GPU
self.plot_latent_space(self.model, self.trainer.datamodule.test_dataloader(), self.curr_device)
def plot_latent_space(vae_model, data_loader, device):
model.eval()
latent_vectors = []
labels = []
with torch.no_grad():
for images, lbls in data_loader:
images = images.to(device)
# Get latent vector
_, _, z, _ = vae_model(images) # Adjust this depending on your model's output
latent_vectors.append(z)
labels.append(lbls)
latent_vectors = torch.cat(latent_vectors, 0).cpu().numpy()
labels = torch.cat(labels, 0).cpu().numpy()
# Apply t-SNE
tsne = TSNE(n_components=2, random_state=0)
tsne_results = tsne.fit_transform(latent_vectors)
# Plot
plt.figure(figsize=(10, 6))
scatter = plt.scatter(tsne_results[:, 0], tsne_results[:, 1], c=labels, cmap='viridis', alpha=0.5)
plt.colorbar(scatter)
plt.show()
def sample_images(self):
# Get sample reconstruction image
test_input, test_label = next(iter(self.trainer.datamodule.test_dataloader()))
test_input = test_input.to(self.curr_device)
test_label = test_label.to(self.curr_device)
# test_input, test_label = batch
recons = self.model.generate(test_input, labels = test_label)
vutils.save_image(recons.data,
os.path.join(self.logger.log_dir ,
"Reconstructions",
f"recons_{self.logger.name}_Epoch_{self.current_epoch}.png"),
normalize=True,
nrow=12)
try:
samples = self.model.sample(144,
self.curr_device,
labels = test_label)
vutils.save_image(samples.cpu().data,
os.path.join(self.logger.log_dir ,
"Samples",
f"{self.logger.name}_Epoch_{self.current_epoch}.png"),
normalize=True,
nrow=12)
# Update accumulators for mean and std calculation
self.image_sum += samples.sum(dim=0)
self.image_sq_sum += (samples ** 2).sum(dim=0)
self.num_images += samples.size(0)
except Warning:
pass
def configure_optimizers(self):
optims = []
scheds = []
optimizer = optim.Adam(self.model.parameters(),
lr=self.params['LR'],
weight_decay=self.params['weight_decay'])
optims.append(optimizer)
# Check if more than 1 optimizer is required (Used for adversarial training)
try:
if self.params['LR_2'] is not None:
optimizer2 = optim.Adam(getattr(self.model,self.params['submodel']).parameters(),
lr=self.params['LR_2'])
optims.append(optimizer2)
except:
pass
try:
if self.params['scheduler_gamma'] is not None:
scheduler = optim.lr_scheduler.ExponentialLR(optims[0],
gamma = self.params['scheduler_gamma'])
scheds.append(scheduler)
# Check if another scheduler is required for the second optimizer
try:
if self.params['scheduler_gamma_2'] is not None:
scheduler2 = optim.lr_scheduler.ExponentialLR(optims[1],
gamma = self.params['scheduler_gamma_2'])
scheds.append(scheduler2)
except:
pass
return optims, scheds
except:
return optims