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experiment.py
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import os
import math
import torch
import numpy as np
from torch import optim
from models import BaseVAE, VDE
from 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
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):
t_0, labels = batch
self.curr_device = t_0.device
results = self.forward(t_0, 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):
t_0, labels = batch
self.curr_device = t_0.device
results = self.forward(t_0, labels=labels)
val_loss = self.model.loss_function(*results,
M_N=1.0, # t_0.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()
def test_step(self, batch, batch_idx, optimizer_idx=0):
t_0, labels = batch
self.curr_device = t_0.device
results = self.forward(t_0, labels=labels)
test_loss = self.model.loss_function(*results,
M_N=1.0, # t_0.shape[0]/ self.num_test_imgs,
optimizer_idx=optimizer_idx,
batch_idx=batch_idx)
self.log_dict({f"test_{key}": val.item() for key, val in test_loss.items()}, sync_dist=True)
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)
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
class VDEXperiment(pl.LightningModule):
def __init__(self,
vae_model: VDE,
edges: np.ndarray,
params: dict) -> None:
super(VDEXperiment, self).__init__()
self.model = vae_model
self.edges = edges
self.params = params
self.curr_device = None
self.hold_graph = False
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):
t_0, t_1 = batch
self.curr_device = t_0.device
results = self.forward(t_0)
# results = results.append(t_1)
train_loss = self.model.loss_function(results,
t_1,
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):
t_0, t_1 = batch
self.curr_device = t_0.device
results = self.forward(t_0)
# print("Results t0 from forward pass: ", type(results))
# print(len(results))
# print(results[0].size())
# print(results[1].size())
# print(results[2].size())
# print(results[3].size())
# results = results.append(t_1)
# print("Results t0 + t1 from forward pass: ", type(results))
# print(len(results))
val_loss = self.model.loss_function(results,
t_1,
M_N=1.0, # t_0.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:
pass
def test_step(self, batch, batch_idx, optimizer_idx=0):
t_0, t_1 = batch
self.curr_device = t_0.device
results = self.forward(t_0)
# results = results.append(t_1)
test_loss = self.model.loss_function(results,
t_1,
M_N=1.0, # t_0.shape[0]/ self.num_test_imgs,
optimizer_idx=optimizer_idx,
batch_idx=batch_idx)
self.log_dict({f"test_{key}": val.item() for key, val in test_loss.items()}, sync_dist=True)
def predict_step(self, batch, batch_idx, optimizer_idx=0):
t_0, t_1 = batch
self.curr_device = t_0.device
# print("t_0: ", t_0.size())
results = self.model.check_latent(t_0)
return results
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
if __name__ == "__main__":
print("This is the experiment.py file.")