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solver.py
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solver.py
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import torch
import torch.nn as nn
import torch.optim as optim
import os
import numpy as np
from sklearn.metrics import accuracy_score
import sampler
class Solver:
def __init__(self, args, test_dataloader):
self.args = args
self.test_dataloader = test_dataloader
self.bce_loss = nn.BCELoss()
self.mse_loss = nn.MSELoss()
self.ce_loss = nn.CrossEntropyLoss()
self.sampler = sampler.AdversarySampler(self.args.budget)
def read_data(self, dataloader, labels=True):
if labels:
while True:
for img, label, _ in dataloader:
yield img, label
else:
while True:
for img, _, _ in dataloader:
yield img
def train(self, querry_dataloader, task_model, vae, discriminator, unlabeled_dataloader):
labeled_data = self.read_data(querry_dataloader)
unlabeled_data = self.read_data(unlabeled_dataloader, labels=False)
optim_vae = optim.Adam(vae.parameters(), lr=5e-4)
optim_task_model = optim.Adam(task_model.parameters(), lr=5e-4)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=5e-4)
vae.train()
discriminator.train()
task_model.train()
if self.args.cuda:
vae = vae.cuda()
discriminator = discriminator.cuda()
task_model = task_model.cuda()
change_lr_iter = self.args.train_iterations // 25
for iter_count in range(self.args.train_iterations):
if iter_count is not 0 and iter_count % change_lr_iter == 0:
for param in optim_vae.param_groups:
param['lr'] = param['lr'] * 0.9
for param in optim_task_model.param_groups:
param['lr'] = param['lr'] * 0.9
for param in optim_discriminator.param_groups:
param['lr'] = param['lr'] * 0.9
labeled_imgs, labels = next(labeled_data)
unlabeled_imgs = next(unlabeled_data)
if self.args.cuda:
labeled_imgs = labeled_imgs.cuda()
unlabeled_imgs = unlabeled_imgs.cuda()
labels = labels.cuda()
# task_model step
preds = task_model(labeled_imgs)
task_loss = self.ce_loss(preds, labels)
optim_task_model.zero_grad()
task_loss.backward()
optim_task_model.step()
# 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))
if self.args.cuda:
lab_real_preds = lab_real_preds.cuda()
unlab_real_preds = unlab_real_preds.cuda()
dsc_loss = self.bce_loss(labeled_preds, lab_real_preds) + \
self.bce_loss(unlabeled_preds, unlab_real_preds)
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)
if self.args.cuda:
labeled_imgs = labeled_imgs.cuda()
unlabeled_imgs = unlabeled_imgs.cuda()
labels = labels.cuda()
# 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))
if self.args.cuda:
lab_real_preds = lab_real_preds.cuda()
unlab_fake_preds = unlab_fake_preds.cuda()
dsc_loss = self.bce_loss(labeled_preds, lab_real_preds) + \
self.bce_loss(unlabeled_preds, unlab_fake_preds)
optim_discriminator.zero_grad()
dsc_loss.backward()
optim_discriminator.step()
# 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)
if self.args.cuda:
labeled_imgs = labeled_imgs.cuda()
unlabeled_imgs = unlabeled_imgs.cuda()
labels = labels.cuda()
if iter_count % 1000 == 0:
print('Current training iteration: {}'.format(iter_count))
print('Current task model loss: {:.4f}'.format(task_loss.item()))
print('Current vae model loss: {:.4f}'.format(total_vae_loss.item()))
print('Current discriminator model loss: {:.4f}'.format(dsc_loss.item()))
final_accuracy = self.test(task_model)
return final_accuracy, vae, discriminator
def sample_for_labeling(self, vae, discriminator, unlabeled_dataloader):
querry_indices = self.sampler.sample(vae,
discriminator,
unlabeled_dataloader,
self.args.cuda)
return querry_indices
def test(self, task_model):
task_model.eval()
total, correct = 0, 0
for imgs, labels in self.test_dataloader:
if self.args.cuda:
imgs = imgs.cuda()
with torch.no_grad():
preds = task_model(imgs)
preds = torch.argmax(preds, dim=1).cpu().numpy()
correct += accuracy_score(labels, preds, normalize=False)
total += imgs.size(0)
return correct / total * 100
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