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VAAL_solver.py
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VAAL_solver.py
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import torch
import torch.nn as nn
import torch.optim as optim
from pathlib import Path
import os
import numpy as np
from sklearn.metrics import accuracy_score
import wandb
import sampler
import copy
from torch.autograd import grad as torch_grad
from torch.autograd import Variable
from torch import autograd
class VAAL_Solver:
def __init__(self, args, test_dataloader):
self.args = args
self.test_dataloader = test_dataloader
self.device = torch.device('cuda:'+args.gpu_id if torch.cuda.is_available() else 'cpu')
self.bce_loss = nn.BCELoss()
self.mse_loss = nn.MSELoss()
self.ce_loss = nn.CrossEntropyLoss()
self.dir_checkpoint = Path('./checkpoints/')
self.sampler = sampler.AdversarySampler(self.args.budget)
self.gp_weight =1
def read_data(self, dataloader, labels=True):
if labels:
while True:
for img, label, aux in dataloader:
yield img, label
else:
while True:
for img, _, _ in dataloader:
yield img
def _gradient_penalty(self, disc, real_data, generated_data):
batch_size = real_data.size()[0]
# Calculate interpolation
alpha = torch.rand(batch_size, 1)
alpha = alpha.expand_as(real_data)
#print (alpha.shape,real_data.shape,generated_data.shape)
alpha = alpha.cuda()
interpolated = alpha * real_data.data + (1 - alpha) * generated_data.data
interpolated = Variable(interpolated, requires_grad=True)
interpolated = interpolated.cuda()
# Calculate probability of interpolated examples
prob_interpolated = disc(interpolated)
# Calculate gradients of probabilities with respect to examples
gradients = torch_grad(outputs=prob_interpolated, inputs=interpolated,
grad_outputs=torch.ones(prob_interpolated.size()).cuda(),
create_graph=True, retain_graph=True)[0]
# Gradients have shape (batch_size, num_channels, img_width, img_height),
# so flatten to easily take norm per example in batch
gradients = gradients.view(batch_size, -1)
#self.losses['gradient_norm'].append(gradients.norm(2, dim=1).mean().data[0])
# Derivatives of the gradient close to 0 can cause problems because of
# the square root, so manually calculate norm and add epsilon
gradients_norm = torch.sqrt(torch.sum(gradients ** 2, dim=1) + 1e-12)
# Return gradient penalty
return self.gp_weight * ((gradients_norm - 1) ** 2).mean()
def train(self, current_split, querry_dataloader, val_dataloader, vae, discriminator, unlabeled_dataloader,wandb_log = None):
self.args.train_iterations = (self.args.num_images* self.args.query_train_epochs) // self.args.batch_size
if not (os.path.exists(str(self.dir_checkpoint)+'/'+self.args.expt + '/'+ 'vae_checkpoint'+str(current_split)+'.pth') and os.path.exists(str(self.dir_checkpoint)+'/'+self.args.expt + '/'+ 'discriminator_checkpoint'+str(current_split)+'.pth')):
labeled_data = self.read_data(querry_dataloader)
unlabeled_data = self.read_data(unlabeled_dataloader, labels=False)
optim_vae = optim.Adam(vae.parameters(), lr=self.args.alpha1)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=self.args.alpha2)
vae.train()
discriminator.train()
for iter_count in range(self.args.train_iterations):
labeled_imgs, labels = next(labeled_data)
unlabeled_imgs = next(unlabeled_data)
labeled_imgs = labeled_imgs.to(device=self.args.device, dtype=torch.float32)
unlabeled_imgs = unlabeled_imgs.to(device=self.args.device, dtype=torch.float32)
labels = labels.to(device=self.args.device, dtype=torch.long)
# VAE step
for count in range(self.args.num_vae_steps):
recon, z, mu, logvar = vae(labeled_imgs)
unsup_loss = self.vae_loss(labeled_imgs, recon, mu, logvar, self.args.beta)
unlab_recon, unlab_z, unlab_mu, unlab_logvar = vae(unlabeled_imgs)
transductive_loss = self.vae_loss(unlabeled_imgs,
unlab_recon, unlab_mu, unlab_logvar, self.args.beta)
labeled_preds = discriminator(mu)
unlabeled_preds = discriminator(unlab_mu)
lab_real_preds = torch.ones(labeled_imgs.size(0))
unlab_real_preds = torch.ones(unlabeled_imgs.size(0))
lab_real_preds = lab_real_preds.to(device=self.args.device)
unlab_real_preds = unlab_real_preds.to(device=self.args.device)
# change to GANGP
real_loss = labeled_preds.mean()
fake_loss = unlabeled_preds.mean()
dsc_loss = -fake_loss + real_loss
total_vae_loss = unsup_loss + transductive_loss + self.args.adversary_param * dsc_loss
optim_vae.zero_grad()
total_vae_loss.backward()
optim_vae.step()
# sample new batch if needed to train the adversarial network
if count < (self.args.num_vae_steps - 1):
labeled_imgs, _ = next(labeled_data)
unlabeled_imgs = next(unlabeled_data)
labeled_imgs = labeled_imgs.to(device=self.args.device, dtype=torch.float32)
unlabeled_imgs = unlabeled_imgs.to(device=self.args.device, dtype=torch.float32)
labels = labels.to(device=self.args.device, dtype=torch.long)
wandb_log.log({
'vae total train loss': total_vae_loss.item(),
'vae unsup_loss': unsup_loss.item(),
'vae transductive_loss': transductive_loss.item(),
'vae adverserial loss': dsc_loss.item(), })
if (iter_count%100 == 0):
wandb_log.log({
'org images': wandb.Image(unlabeled_imgs[0].cpu()),
'reconstructed images': wandb.Image(unlab_recon[0].float().cpu())
})
# Discriminator step
for count in range(self.args.num_adv_steps):
with torch.no_grad():
_, _, mu, _ = vae(labeled_imgs)
_, _, unlab_mu, _ = vae(unlabeled_imgs)
labeled_preds = discriminator(mu)
unlabeled_preds = discriminator(unlab_mu)
lab_real_preds = torch.ones(labeled_imgs.size(0))
unlab_fake_preds = torch.zeros(unlabeled_imgs.size(0))
lab_real_preds = lab_real_preds.to(device=self.args.device)
unlab_fake_preds = unlab_fake_preds.to(device=self.args.device)
# change to GANGP
real_loss = labeled_preds.mean()
fake_loss = unlabeled_preds.mean()
gradient_penalty = self._gradient_penalty(discriminator, mu, unlab_mu)
dsc_loss = fake_loss - real_loss + gradient_penalty
optim_discriminator.zero_grad()
dsc_loss.backward()
optim_discriminator.step()
wandb_log.log({
'discriminator train loss': dsc_loss.item() })
# sample new batch if needed to train the adversarial network
if count < (self.args.num_adv_steps - 1):
labeled_imgs, _ = next(labeled_data)
unlabeled_imgs = next(unlabeled_data)
labeled_imgs = labeled_imgs.to(device=self.args.device, dtype=torch.float32)
unlabeled_imgs = unlabeled_imgs.to(device=self.args.device, dtype=torch.float32)
labels = labels.to(device=self.args.device, dtype=torch.long)
if iter_count % 100 == 0:
print('Current vae model loss: {:.4f}'.format(total_vae_loss.item()))
print('Current discriminator model loss: {:.4f}'.format(dsc_loss.item()))
Path(str(self.dir_checkpoint)+'/'+self.args.expt+'/VAAL').mkdir(parents=True, exist_ok=True)
torch.save(vae.state_dict(), str(self.dir_checkpoint)+'/'+self.args.expt + '/'+ 'vae_checkpoint'+str(current_split)+'.pth')
torch.save(discriminator.state_dict(), str(self.dir_checkpoint)+'/'+self.args.expt + '/'+ 'discriminator_checkpoint'+str(current_split)+'.pth')
else:
# load the checkpoint models
discriminator.load_state_dict(torch.load(str(self.dir_checkpoint)+'/'+self.args.expt + '/'+ 'discriminator_checkpoint'+str(current_split)+'.pth'))
vae.load_state_dict(torch.load(str(self.dir_checkpoint)+'/'+self.args.expt + '/'+ 'vae_checkpoint'+str(current_split)+'.pth'))
return vae, discriminator
def sample_for_labeling(self, vae, discriminator, unlabeled_dataloader, unlabeled_indices):
querry_indices = self.sampler.sample(vae,
discriminator,
unlabeled_dataloader, unlabeled_indices,
self.args.device)
return querry_indices
def vae_loss(self, x, recon, mu, logvar, beta):
MSE = self.mse_loss(recon, x)
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
KLD = KLD * beta
return MSE + KLD