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beta-VAE-XYS.py
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beta-VAE-XYS.py
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import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision
from torchvision import datasets, transforms
from skimage import io, transform
import numpy as np
from PIL import Image
from models import Rescale, betaVAE, betaVAEdSprite, betaVAEXYS, betaVAEXYS2, Bernoulli
from datasetXYS import load_dataset_XYS
use_cuda = True
def setting(nbr_epoch=100,offset=0,train=True,batch_size=32, evaluate=False):
size = 256
dataset = load_dataset_XYS(img_dim=size)
# Data loader
data_loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True)
# Model :
'''
frompath = True
z_dim = 4
img_dim = size
img_depth=3
conv_dim = 32
global use_cuda
net_depth = 5
beta = 5000e0
betavae = betaVAEXYS(beta=beta,net_depth=net_depth,z_dim=z_dim,img_dim=img_dim,img_depth=img_depth,conv_dim=conv_dim, use_cuda=use_cuda)
'''
frompath = True
z_dim = 10
img_dim = size
img_depth=3
conv_dim = 8#32
global use_cuda
net_depth = 5
beta = 1000e0
betavae = betaVAEXYS2(beta=beta,net_depth=net_depth,z_dim=z_dim,img_dim=img_dim,img_depth=img_depth,conv_dim=conv_dim, use_cuda=use_cuda)
print(betavae)
# Optim :
lr = 1e-4
optimizer = torch.optim.Adam( betavae.parameters(), lr=lr)
path = 'test--XYS--img{}-lr{}-beta{}-layers{}-z{}-conv{}'.format(img_dim,lr,beta,net_depth,z_dim,conv_dim)
if not os.path.exists( './beta-data/{}/'.format(path) ) :
os.mkdir('./beta-data/{}/'.format(path))
if not os.path.exists( './beta-data/{}/gen_images/'.format(path) ) :
os.mkdir('./beta-data/{}/gen_images/'.format(path))
if not os.path.exists( './beta-data/{}/reconst_images/'.format(path) ) :
os.mkdir('./beta-data/{}/reconst_images/'.format(path))
SAVE_PATH = './beta-data/{}'.format(path)
if frompath :
try :
betavae.load_state_dict( torch.load( os.path.join(SAVE_PATH,'weights')) )
print('NET LOADING : OK.')
except Exception as e :
print('EXCEPTION : NET LOADING : {}'.format(e) )
if train :
train_model(betavae,data_loader, optimizer, SAVE_PATH,path,nbr_epoch=nbr_epoch,batch_size=batch_size,offset=offset)
else :
if evaluate :
evaluate_disentanglement(betavae, dataset, nbr_epoch=nbr_epoch)
else :
query_XYS(betavae, data_loader,path)
def train_model(betavae,data_loader, optimizer, SAVE_PATH,path,nbr_epoch=100,batch_size=32, offset=0) :
global use_cuda
z_dim = betavae.z_dim
img_depth=betavae.img_depth
img_dim = betavae.img_dim
data_iter = iter(data_loader)
iter_per_epoch = len(data_loader)
# Debug :
# fixed inputs for debugging
fixed_z = Variable(torch.randn(45, z_dim))
if use_cuda :
fixed_z = fixed_z.cuda()
sample = next(data_iter)
fixed_x, _ = sample['image'], sample['landmarks']
fixed_x = fixed_x.view( (-1, img_depth, img_dim, img_dim) )
torchvision.utils.save_image(fixed_x.cpu(), './beta-data/{}/real_images.png'.format(path))
fixed_x = Variable(fixed_x.view(fixed_x.size(0), img_depth, img_dim, img_dim)).float()
if use_cuda :
fixed_x = fixed_x.cuda()
out = torch.zeros((1,1))
# variations over the latent variable :
sigma_mean = torch.ones((z_dim))
mu_mean = torch.zeros((z_dim))
best_loss = None
best_model_wts = betavae.state_dict()
for epoch in range(nbr_epoch):
# Save generated variable images :
nbr_steps = 8
mu_mean /= batch_size
sigma_mean /= batch_size
gen_images = torch.ones( (8, img_depth, img_dim, img_dim) )
for latent in range(z_dim) :
#var_z0 = torch.stack( [mu_mean]*nbr_steps, dim=0)
var_z0 = torch.zeros(nbr_steps, z_dim)
val = mu_mean[latent]-sigma_mean[latent]
step = 2.0*sigma_mean[latent]/nbr_steps
print(latent,mu_mean[latent],step)
for i in range(nbr_steps) :
var_z0[i] = mu_mean
var_z0[i][latent] = val
val += step
var_z0 = Variable(var_z0)
if use_cuda :
var_z0 = var_z0.cuda()
gen_images_latent = betavae.decoder(var_z0)
gen_images_latent = gen_images_latent.view(-1, img_depth, img_dim, img_dim).cpu().data
gen_images = torch.cat( [gen_images,gen_images_latent], dim=0)
#torchvision.utils.save_image(gen_images.data.cpu(),'./beta-data/{}/gen_images/dim{}/{}.png'.format(path,latent,(epoch+1)) )
torchvision.utils.save_image(gen_images,'./beta-data/{}/gen_images/{}.png'.format(path,(epoch+offset+1)) )
mu_mean = 0.0
sigma_mean = 0.0
epoch_loss = 0.0
for i, sample in enumerate(data_loader):
images = sample['image']
# Save the reconstructed images
if i % 100 == 0 :
reconst_images, _, _ = betavae(fixed_x)
reconst_images = reconst_images.view(-1, img_depth, img_dim, img_dim).cpu().data
orimg = fixed_x.cpu().data.view(-1, img_depth, img_dim, img_dim)
ri = torch.cat( [orimg, reconst_images], dim=2)
torchvision.utils.save_image(ri,'./beta-data/{}/reconst_images/{}.png'.format(path,(epoch+offset+1) ) )
images = Variable( (images.view(-1, img_depth,img_dim, img_dim) ) ).float()
if use_cuda :
images = images.cuda()
out, mu, log_var = betavae(images)
mu_mean += torch.mean(mu.data,dim=0)
sigma_mean += torch.mean( torch.sqrt( torch.exp(log_var.data) ), dim=0 )
# Compute :
#reconstruction loss :
reconst_loss = F.binary_cross_entropy( out, images, size_average=False)
#reconst_loss = nn.MultiLabelSoftMarginLoss()(input=out_logits, target=images)
#reconst_loss = F.binary_cross_entropy_with_logits( input=out, target=images, size_average=False)
#reconst_loss = F.binary_cross_entropy( Bernoulli(out).sample(), images, size_average=False)
#reconst_loss = torch.mean( (out.view(-1) - images.view(-1))**2 )
# expected log likelyhood :
expected_log_lik = torch.mean( Bernoulli( out.view((-1)) ).log_prob( images.view((-1)) ) )
#expected_log_lik = torch.mean( Bernoulli( out ).log_prob( images ) )
# kl divergence :
kl_divergence = 0.5 * torch.mean( torch.sum( (mu**2 + torch.exp(log_var) - log_var -1), dim=1) )
#kl_divergence = 0.5 * torch.sum( (mu**2 + torch.exp(log_var) - log_var -1) )
# ELBO :
elbo = expected_log_lik - betavae.beta * kl_divergence
# TOTAL LOSS :
total_loss = reconst_loss + betavae.beta*kl_divergence
#total_loss = reconst_loss
#total_loss = -elbo
# Backprop + Optimize :
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
del images
epoch_loss += total_loss.cpu().data[0]
if i % 10 == 0:
print ("Epoch[%d/%d], Step [%d/%d], Total Loss: %.4f, "
"Reconst Loss: %.4f, KL Div: %.7f, E[ |~| p(x|theta)]: %.7f "
%(epoch+1, 50, i+1, iter_per_epoch, total_loss.data[0],
reconst_loss.data[0], kl_divergence.data[0],expected_log_lik.exp().data[0]) )
if best_loss is None :
#first validation : let us set the initialization but not save it :
best_loss = epoch_loss
best_model_wts = betavae.state_dict()
# save best model weights :
torch.save( best_model_wts, os.path.join(SAVE_PATH,'weights') )
print('Model saved at : {}'.format(os.path.join(SAVE_PATH,'weights')) )
elif epoch_loss < best_loss:
best_loss = epoch_loss
best_model_wts = betavae.state_dict()
# save best model weights :
torch.save( best_model_wts, os.path.join(SAVE_PATH,'weights') )
print('Model saved at : {}'.format(os.path.join(SAVE_PATH,'weights')) )
def query_XYS(betavae,data_loader,path):
global use_cuda
z_dim = betavae.z_dim
img_depth=betavae.img_depth
img_dim = betavae.img_dim
data_iter = iter(data_loader)
iter_per_epoch = len(data_loader)
# Debug :
# fixed inputs for debugging
fixed_z = Variable(torch.randn(45, z_dim))
if use_cuda :
fixed_z = fixed_z.cuda()
sample = next(data_iter)
fixed_x, _ = sample['image'], sample['landmarks']
fixed_x = fixed_x.view( (-1, img_depth, img_dim, img_dim) )
torchvision.utils.save_image(fixed_x.cpu(), './beta-data/{}/real_images_query.png'.format(path))
fixed_x = Variable(fixed_x.view(fixed_x.size(0), img_depth, img_dim, img_dim)).float()
if use_cuda :
fixed_x = fixed_x.cuda()
# variations over the latent variable :
sigma_mean = 3.0*torch.ones((z_dim))
mu_mean = torch.zeros((z_dim))
# Save generated variable images :
nbr_steps = 8
gen_images = torch.ones( (8, img_depth, img_dim, img_dim) )
for latent in range(z_dim) :
#var_z0 = torch.stack( [mu_mean]*nbr_steps, dim=0)
var_z0 = torch.zeros(nbr_steps, z_dim)
val = mu_mean[latent]-sigma_mean[latent]
step = 2.0*sigma_mean[latent]/nbr_steps
print(latent,mu_mean[latent]-sigma_mean[latent],mu_mean[latent],mu_mean[latent]+sigma_mean[latent])
for i in range(nbr_steps) :
var_z0[i] = mu_mean
var_z0[i][latent] = val
val += step
var_z0 = Variable(var_z0)
if use_cuda :
var_z0 = var_z0.cuda()
gen_images_latent = betavae.decoder(var_z0)
gen_images_latent = gen_images_latent.view(-1, img_depth, img_dim, img_dim).cpu().data
gen_images = torch.cat( [gen_images,gen_images_latent], dim=0)
#torchvision.utils.save_image(gen_images.data.cpu(),'./beta-data/{}/gen_images/dim{}/{}.png'.format(path,latent,(epoch+1)) )
torchvision.utils.save_image(gen_images,'./beta-data/{}/gen_images/query.png'.format(path) )
reconst_images, _, _ = betavae(fixed_x)
reconst_images = reconst_images.view(-1, img_depth, img_dim, img_dim).cpu().data
orimg = fixed_x.cpu().data.view(-1, img_depth, img_dim, img_dim)
ri = torch.cat( [orimg, reconst_images], dim=2)
torchvision.utils.save_image(ri,'./beta-data/{}/reconst_images/query.png'.format(path ) )
def generateTarget(latent_dim=3,idx_latent=0, batch_size=8 ) :
target = torch.zeros( (1, latent_dim))
target[0,idx_latent] = 1.0
target = torch.cat( batch_size*[target], dim=0)
target = Variable(target)
global use_cuda
if use_cuda :
target = target.cuda()
return target
def generatePairs(index) :
nbrel = len(index)
# if not even :
if nbrel % 2 :
nbrel -= 1
print('NBR ELEMENT : {}'.format(nbrel) )
# ditch the last one...
id1 = []
id2 = []
for i in range( 0, nbrel, 2 ) :
id1.append( index[i] )
id2.append( index[i+1] )
return id1, id2
def evaluate_disentanglement(model,dataset,nbr_epoch=20) :
from datasetXYS import generateIDX, generateClassifier
global use_cuda
lr = 1e-4
indexes = generateIDX(dataset)
nbr_latent = len(indexes)
disentanglement_measure = [0.0] * nbr_latent
classifier = generateClassifier(input_dim=model.z_dim, output_dim=nbr_latent)
if use_cuda :
classifier = classifier.cuda()
optimizer = torch.optim.Adam( classifier.parameters(), lr=lr)
batch_size = 1
iteration_training = 0
cum_training_acc = 0.0
for phase in ['train','val'] :
if phase == 'val' :
nbrepoch = 1
else :
nbrepoch = nbr_epoch
for epoch in range(nbrepoch) :
cum_epoch_acc = 0.0
iteration_epoch = 0
for idx_latent in range(3) :
index = indexes[idx_latent]
nbr_classes = len(index)
iteration_latent = 0
cum_latent_acc = 0.0
for cl in range(nbr_classes) :
#print('CLASS : {} : nbr element = {}'.format(cl,len(index[cl]) ) )
index1, index2 = generatePairs(index[cl])
nbrel = len(index1)
print('')
#print('IDX LATENT : {} // NBR ELEMENT : {}'.format(idx_latent,nbrel))
for it in range(nbrel) :
sample1 = dataset[index1[it]]
img1 = sample1['image'].unsqueeze(0)
sample2 = dataset[index2[it]]
img2 = sample2['image'].unsqueeze(0)
#img1 = Variable( (img1.view(-1, model.img_depth, model.img_dim, model.img_dim) ) ).float()
#img2 = Variable( (img2.view(-1, model.img_depth, model.img_dim, model.img_dim) ) ).float()
img1 = Variable( img1 ).float()
img2 = Variable( img2 ).float()
if use_cuda :
img1 = img1.cuda()
img2 = img2.cuda()
_, mu1, log_var1 = model(img1)
_, mu2, log_var2 = model(img2)
z_diff = torch.abs(mu2-mu1)
#av_z_diff = z_diff/float(nbrel)
target = generateTarget(latent_dim=3, idx_latent=idx_latent, batch_size=1)
#logits = classifier(av_z_diff)
logits = classifier(z_diff)
# Accuracy :
acc = (logits.cpu().data.max(1)[1] == idx_latent)
acc = acc.numpy().mean()*100.0
cum_latent_acc = (cum_latent_acc*iteration_latent + acc)/(iteration_latent+1)
iteration_latent += 1
cum_epoch_acc = (cum_epoch_acc*iteration_epoch + acc)/(iteration_epoch+1)
iteration_epoch += 1
cum_training_acc = (cum_training_acc*iteration_training + acc)/(iteration_training+1)
iteration_training += 1
print('{} EPOCH : {} :: iteration {}/{} :: Cumulative Accuracy : {} // Cumulative Latent {} Accuracy : {}'.format(phase, epoch, it, nbrel, cum_epoch_acc, idx_latent, cum_latent_acc), end='\r')
# Loss :
loss = F.binary_cross_entropy( logits, target)
if phase == 'train' :
# Training :
loss.backward()
if iteration_latent % batch_size == 0 :
optimizer.step()
optimizer.zero_grad()
print('')
print('-'*20)
print('{} EPOCH : {}/{} :: Cumulative Accuracy : {} // Cumulative Latent {} Accuracy : {}'.format(phase, epoch, nbrepoch, cum_epoch_acc, idx_latent, cum_latent_acc))
print('-'*20)
if __name__ == '__main__' :
import argparse
parser = argparse.ArgumentParser(description='beta-VAE')
parser.add_argument('--train',action='store_true',default=False)
parser.add_argument('--query',action='store_true',default=False)
parser.add_argument('--evaluate',action='store_true',default=False)
parser.add_argument('--offset', type=int, default=0)
parser.add_argument('--batch', type=int, default=32)
parser.add_argument('--epoch', type=int, default=100)
args = parser.parse_args()
if args.train :
setting(offset=args.offset,batch_size=args.batch,train=True,nbr_epoch=args.epoch)
if args.query :
setting(train=False)
if args.evaluate :
setting(train=False,evaluate=True,nbr_epoch=args.epoch)