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train.py
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# train.py
import torch
import torchvision
import torchvision.utils as vutils
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.autograd as autograd
import copy
import matplotlib.pyplot as plt
from skimage.measure import compare_ssim, compare_psnr
import time
import os
import numpy as np
import plugins
class Trainer():
def __init__(self, args, modelD, modelG, Encoder, criterion, prevD, prevG):
self.args = args
self.modelD = [modelD for i in range(2)]
self.modelG = [modelG for i in range(2)]
self.Encoder = Encoder
self.prevD = prevD
self.prevG = prevG
self.criterion = criterion
self.plot_update_interval = args.plot_update_interval
self.port = args.port
self.env = args.env
self.result_path = args.result_path
self.save_path = args.save
self.log_path = args.logs
self.dataset_fraction = args.dataset_fraction
self.len_dataset = 0
self.cuda = args.cuda
self.nepochs = args.nepochs
self.stage_epochs = args.stage_epochs
self.start_stage = args.start_stage
self.nchannels = args.nchannels
self.batch_size = args.batch_size
self.resolution_high = args.resolution_high
self.resolution_wide = args.resolution_wide
self.nz = args.nz
self.gp = args.gp
self.gp_lambda = args.gp_lambda
self.scheduler_patience = args.scheduler_patience
self.scheduler_maxlen = args.scheduler_maxlen
self.weight_gan_final = args.weight_gan_final
self.weight_vae_init = args.weight_vae_init
self.weight_kld = args.weight_kld
self.margin = args.margin
self.acc_margin = args.margin
self.disc_diff_weight_init = args.disc_diff_weight
self.disc_diff_weight = self.disc_diff_weight_init
self.num_stages = args.num_stages
self.counter_disc_opt = 0
self.adaptive_iter = args.adaptive_iter
self.lr_vae = args.learning_rate_vae
self.lr_dis = args.learning_rate_dis
self.lr_gen = args.learning_rate_gen
self.lr_decay = args.learning_rate_decay
self.momentum = args.momentum
self.adam_beta1 = args.adam_beta1
self.adam_beta2 = args.adam_beta2
self.weight_decay = args.weight_decay
self.optim_method = args.optim_method
self.vae_loss_type = args.vae_loss_type
# for classification
self.input = Variable(torch.FloatTensor(self.batch_size,self.nchannels,self.resolution_high,self.resolution_wide), requires_grad=True)
self.test_input = Variable(torch.FloatTensor(self.batch_size,self.nchannels,self.resolution_high,self.resolution_wide), volatile=True)
self.fixed_noise = Variable(torch.FloatTensor(self.batch_size, self.nz).normal_(0, 1), volatile=True)
self.epsilon = Variable(torch.randn(self.batch_size, self.nz), requires_grad=False)
if args.cuda:
self.input = self.input.cuda()
self.test_input = self.test_input.cuda()
self.fixed_noise = self.fixed_noise.cuda()
self.epsilon = self.epsilon.cuda()
# Initialize optimizer
self.optimizerE = self.initialize_optimizer(self.Encoder, lr=self.lr_vae, optim_method='Adam')
self.optimizerG = self.initialize_optimizer(self.modelG[0], lr=self.lr_vae, optim_method='Adam')
self.optimizerD = self.initialize_optimizer(self.modelD[0], lr=self.lr_dis, optim_method='Adam', weight_decay=100)
# self.schedulerE = optim.lr_scheduler.ReduceLROnPlateau(self.optimizerE, factor=self.lr_decay, patience=self.scheduler_patience, min_lr=1e-3*self.lr_vae)
# self.schedulerG = optim.lr_scheduler.ReduceLROnPlateau(self.optimizerG, factor=self.lr_decay, patience=self.scheduler_patience, min_lr=1e-3*self.lr_vae)
# self.schedulerD = optim.lr_scheduler.ReduceLROnPlateau(self.optimizerD, factor=self.lr_decay, patience=self.scheduler_patience, min_lr=1e-3*self.lr_vae)
# Automatic scheduler
self.schedulerE = plugins.AutomaticLRScheduler(self.optimizerE, maxlen=self.scheduler_maxlen, factor=self.lr_decay, patience=self.scheduler_patience)
self.schedulerG = plugins.AutomaticLRScheduler(self.optimizerG, maxlen=self.scheduler_maxlen, factor=self.lr_decay, patience=self.scheduler_patience)
self.schedulerD = plugins.AutomaticLRScheduler(self.optimizerD, maxlen=self.scheduler_maxlen, factor=self.lr_decay, patience=self.scheduler_patience)
# logging training
self.log_loss_train = plugins.Logger(args.logs, 'TrainLogger.txt')
self.params_loss_train = ['Loss_D0', 'Loss_G0', 'MSE', 'KLD', 'Corr_Loss', 'Mean', 'Sigma', 'Score_D0', 'Score_D0_G0', 'Score_D0_G1', 'Score_D0_Normal_G0', 'Score_D0_Normal_G1', 'Disc_Diff', 'Disc_Acc', 'Clamp']
self.log_loss_train.register(self.params_loss_train)
self.log_loss_test = plugins.Logger(args.logs, 'TestLogger.txt')
self.params_loss_test = ['SSIM', 'PSNR', 'Test_Score_D0', 'Test_Score_D0_G0', 'Test_Score_D0_G1', 'Test_Disc_Acc']
self.log_loss_test.register(self.params_loss_test)
# monitor training
self.monitor_train = plugins.Monitor()
self.params_monitor_train = ['Loss_D0', 'Loss_G0', 'MSE', 'KLD', 'Corr_Loss', 'Mean', 'Sigma', 'Score_D0', 'Score_D0_G0', 'Score_D0_G1', 'Score_D0_Normal_G0', 'Score_D0_Normal_G1', 'Disc_Diff', 'Disc_Acc', 'Clamp']
self.monitor_train.register(self.params_monitor_train)
self.monitor_test = plugins.Monitor()
self.params_monitor_test = ['SSIM', 'PSNR', 'Test_Score_D0', 'Test_Score_D0_G0', 'Test_Score_D0_G1', 'Test_Disc_Acc']
self.monitor_test.register(self.params_monitor_test)
# Define visualizer plot type for given dataset
if args.net_type == 'gmm':
self.plot_update_interval = 300
if self.args.gmm_dim == 1:
output_dtype, output_vtype = 'vector', 'histogram'
elif self.args.gmm_dim == 2:
output_dtype, output_vtype = 'vector', 'scatter'
else:
output_dtype, output_vtype = 'images', 'images'
self.fixed_noise = self.fixed_noise.unsqueeze(-1).unsqueeze(-1)
# visualize training
self.visualizer_train = plugins.HourGlassVisualizer(port=self.port, env=self.env, title='Train')
self.params_visualizer_train = {
'Loss_D0':{'dtype':'scalar', 'vtype':'plot', 'win': 'loss_gan', 'layout': {'windows': ['Loss_D0', 'Loss_G0'], 'id': 0}},
'Loss_G0':{'dtype':'scalar','vtype':'plot', 'win': 'loss_gan', 'layout': {'windows': ['Loss_D0', 'Loss_G0'], 'id': 1}},
'MSE':{'dtype':'scalar','vtype':'plot', 'win': 'enc_losses', 'layout': {'windows': ['MSE', 'KLD', 'Corr_Loss'], 'id': 0}},
'KLD':{'dtype':'scalar','vtype':'plot', 'win': 'enc_losses', 'layout': {'windows': ['MSE', 'KLD', 'Corr_Loss'], 'id': 1}},
'Corr_Loss':{'dtype':'scalar','vtype':'plot', 'win': 'enc_losses', 'layout': {'windows': ['MSE', 'KLD', 'Corr_Loss'], 'id': 2}},
'Mean':{'dtype':'scalar','vtype':'plot', 'win': 'norm_params', 'layout': {'windows': ['Mean', 'Sigma'], 'id': 0}},
'Sigma':{'dtype':'scalar','vtype':'plot', 'win': 'norm_params', 'layout': {'windows': ['Mean', 'Sigma'], 'id': 1}},
'Score_D0':{'dtype':'scalar','vtype':'plot', 'win': 'loss_D0', 'layout': {'windows': ['Score_D0', 'Score_D0_G0', 'Score_D0_G1'], 'id': 0}},
'Score_D0_G0':{'dtype':'scalar','vtype':'plot', 'win': 'loss_D0', 'layout': {'windows': ['Score_D0', 'Score_D0_G0', 'Score_D0_G1'], 'id': 1}},
'Score_D0_G1':{'dtype':'scalar','vtype':'plot', 'win': 'loss_D0', 'layout': {'windows': ['Score_D0', 'Score_D0_G0', 'Score_D0_G1'], 'id': 2}},
'netD_norm_w':{'dtype':'scalar','vtype':'plot', 'win': 'netd_norm'},
'Score_D0_Normal_G0':{'dtype':'scalar','vtype':'plot', 'win': 'normal_losses', 'layout': {'windows': ['Score_D0_Normal_G0', 'Score_D0_Normal_G1'], 'id': 0}},
'Score_D0_Normal_G1':{'dtype':'scalar','vtype':'plot', 'win': 'normal_losses', 'layout': {'windows': ['Score_D0_Normal_G0', 'Score_D0_Normal_G1'], 'id': 1}},
'Learning_Rate_E':{'dtype':'scalar','vtype':'plot', 'win': 'lr_E'},
'Learning_Rate_G':{'dtype':'scalar','vtype':'plot', 'win': 'lr_G'},
'Learning_Rate_D':{'dtype':'scalar','vtype':'plot', 'win': 'lr_D'},
'Gradient_Penalty':{'dtype':'scalar','vtype':'plot', 'win': 'gp'},
'Disc_Diff':{'dtype':'scalar','vtype':'plot', 'win': 'disc_diff'},
'Disc_Acc':{'dtype':'scalar','vtype':'plot', 'win': 'disc_acc'},
'Clamp':{'dtype':'scalar','vtype':'plot', 'win': 'clamp'},
'Real': {'dtype': output_dtype, 'vtype': output_vtype, 'win': 'real'},
'Fakes_Encoder': {'dtype': output_dtype, 'vtype': output_vtype, 'win': 'fakes_enc'},
'Fakes_Normal': {'dtype': output_dtype, 'vtype': output_vtype, 'win': 'fakes_normal'},
'Fakes_Previous': {'dtype': output_dtype, 'vtype': output_vtype, 'win': 'fakes_prev'},
# 'Corr_Output': {'dtype': output_dtype, 'vtype': output_vtype, 'win': 'corr'},
}
self.visualizer_train.register(self.params_visualizer_train)
self.visualizer_test = plugins.HourGlassVisualizer(port=self.port, env=self.env, title='Test')
self.params_visualizer_test = {
'Test_Score_D0':{'dtype':'scalar', 'vtype':'plot', 'win':'test_disc_scores', 'layout':{'windows': ['Test_Score_D0', 'Test_Score_D0_G0', 'Test_Score_D0_G1'], 'id':0}},
'Test_Score_D0_G0':{'dtype':'scalar', 'vtype':'plot', 'win':'test_disc_scores', 'layout':{'windows': ['Test_Score_D0', 'Test_Score_D0_G0', 'Test_Score_D0_G1'], 'id':1}},
'Test_Score_D0_G1':{'dtype':'scalar', 'vtype':'plot', 'win':'test_disc_scores', 'layout':{'windows': ['Test_Score_D0', 'Test_Score_D0_G0', 'Test_Score_D0_G1'], 'id':2}},
'SSIM':{'dtype':'scalar', 'vtype':'plot', 'win':'ssim_scores'},
'PSNR':{'dtype':'scalar', 'vtype':'plot', 'win':'psnr_scores'},
'Test_Real': {'dtype': output_dtype, 'vtype': output_vtype, 'win': 'real_test'},
'Test_Fakes_Encoder': {'dtype': output_dtype, 'vtype': output_vtype, 'win': 'fakes_enc_test'},
'Test_Disc_Acc': {'dtype':'scalar','vtype':'plot', 'win': 'test_disc_acc'},
}
self.visualizer_test.register(self.params_visualizer_test)
# impulse = self.criterion.impulse[0,...].data.cpu().numpy()
# print(impulse)
# self.visualizer_train.viz.image(impulse, opts=dict(title='ground truth g'))
# self.visualizer_train.viz.surf(X=impulse.tolist(), opts=dict(colormap='Hot', title='ground truth g'))
# display training progress
self.print_train = '[%d/%d][%d/%d] '
for item in self.params_loss_train:
self.print_train = self.print_train + item + " %.3f "
self.print_test = '[%d/%d][%d/%d] '
for item in self.params_loss_test:
self.print_test = self.print_test + item + " %.3f "
self.giterations = 0
self.d_iter_init = args.d_iter
self.d_iter = self.d_iter_init
self.g_iter_init = args.g_iter
self.g_iter = self.g_iter_init
self.clamp_lower = args.clamp_lower
self.clamp_upper = args.clamp_upper
self.marker_high = args.margin
self.marker_low = - args.margin
print('Discriminator:', self.modelD[0])
print('Generator:', self.modelG[0])
print('Encoder:', self.Encoder)
# define a zero tensor
self.t_zero = Variable(torch.zeros(1), requires_grad=True)
self.update_opt_flag = False #False
self.update_opt_counter = 0 # -2
self.disc_optimize = args.disc_optimize
self.add_capacity = args.add_capacity
self.add_clamp = args.add_clamp
self.add_noise = args.add_noise
self.noise_var = args.noise_var
self.extra_layer = 0
self.extra_layer_gamma = 0
self.gen_gamma = args.gen_gamma
self.gp_norm = args.gp_norm
self.rank_error_weight = args.rank_weight
self.gogan_optim_set = False
self.optimize_mse = args.optimize_mse
self.weight_mse = args.weight_mse
def initialize_optimizer(self, model, lr, optim_method='RMSprop', weight_decay=None):
if weight_decay is None:
weight_decay = self.weight_decay
if optim_method == 'Adam':
optimizer = optim.Adam(model.parameters(), lr=lr, betas=(self.adam_beta1, self.adam_beta2), weight_decay=weight_decay)
elif optim_method == 'RMSprop':
optimizer = optim.RMSprop(model.parameters(), lr=lr, momentum=self.momentum, weight_decay=weight_decay)
elif optim_method == 'SGD':
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=self.momentum, weight_decay=weight_decay)
else:
raise(Exception("Unknown Optimization Method"))
return optimizer
def model_train(self):
self.modelD[0].train()
self.modelG[0].train()
self.Encoder.train()
def setup_stage(self, stage, dataloader=None):
if stage == 0:
self.weight_vae, self.weight_gan = self.weight_vae_init, 0
self.optimizerE = self.initialize_optimizer(self.Encoder, lr=self.lr_vae, optim_method='Adam') #Adam
self.optimizerG = self.initialize_optimizer(self.modelG[0], lr=self.lr_vae, optim_method=self.optim_method) #RMSprop
# self.schedulerE = optim.lr_scheduler.ReduceLROnPlateau(self.optimizerE, factor=self.lr_decay, patience=self.scheduler_patience, min_lr=1e-3*self.lr_vae)
# self.schedulerG = optim.lr_scheduler.ReduceLROnPlateau(self.optimizerG, factor=self.lr_decay, patience=self.scheduler_patience, min_lr=1e-3*self.lr_gen)
self.schedulerE = plugins.AutomaticLRScheduler(self.optimizerE, maxlen=self.scheduler_maxlen, factor=self.lr_decay, patience=self.scheduler_patience)
self.schedulerG = plugins.AutomaticLRScheduler(self.optimizerG, maxlen=self.scheduler_maxlen, factor=self.lr_decay, patience=self.scheduler_patience)
elif stage == 1:
self.weight_vae, self.weight_gan = 0, self.weight_gan_final
self.optimizerD = self.initialize_optimizer(self.modelD[0], lr=self.lr_vae, optim_method='Adam', weight_decay=self.args.stage1_weight_decay)
self.schedulerD = plugins.AutomaticLRScheduler(self.optimizerD, maxlen=self.scheduler_maxlen, factor=self.lr_decay, patience=self.scheduler_patience)
self.criterion.set_margin(self.margin)
print("Margin: {}".format(self.margin))
else:
# if stage == 2 and ((not self.gp) or (self.gp and self.start_stage == stage)):
if stage > self.start_stage:
self.gen_gamma = 0
if not self.gogan_optim_set:
self.optimizerD = self.initialize_optimizer(self.modelD[0], lr=self.lr_dis, optim_method=self.optim_method)
self.optimizerG = self.initialize_optimizer(self.modelG[0], lr=self.lr_gen, optim_method=self.optim_method)
self.gogan_optim_set = True
self.margin *= 2
self.weight_vae, self.weight_gan = 0, self.weight_gan_final
self.criterion.set_margin(self.margin)
self.disc_diff_weight = self.disc_diff_weight_init
# Initialize previous stage models and set them to eval mode
if stage == self.start_stage and self.prevD is not None:
print("Loading previous discriminator")
self.modelD[1] = self.prevD
else:
self.modelD[1] = copy.deepcopy(self.modelD[0])
self.prevD = None
if stage == self.start_stage and self.prevG is not None:
print("Loading previous generator")
self.modelG[1] = self.prevG
else:
self.modelG[1] = copy.deepcopy(self.modelG[0])
self.prevG = None
# self.modelG[1].eval()
# self.modelD[1].eval()
self.Encoder.eval()
for p in self.modelD[1].parameters():
p.requires_grad = False
for p in self.modelG[1].parameters():
p.requires_grad = False
self.marker_high, self.marker_low = self.compute_markers(dataloader)
print("Marker High: {}, Marker Low: {}".format(self.marker_high, self.marker_low))
print("Margin: {}".format(self.margin))
self.margin = self.margin / 2.0
print("Loss Weightage: VAE: {}, GAN: {}".format(self.weight_vae, self.weight_gan))
def calc_gradient_penalty(self, real_data, fake_data, batch_size):
alpha = torch.rand(batch_size, 1)
while len(alpha.size()) < len(real_data.size()):
alpha = alpha.unsqueeze(-1)
alpha = alpha.expand(real_data.size())
alpha = alpha.cuda() if self.cuda else alpha
interpolates = alpha * real_data.data + ((1 - alpha) * fake_data.data)
if self.cuda:
interpolates = interpolates.cuda()
interpolates = autograd.Variable(interpolates, requires_grad=True)
disc_interpolates = self.modelD[0](interpolates, self.extra_layer, self.extra_layer_gamma)
gradients = autograd.grad(outputs=disc_interpolates, inputs=interpolates,
grad_outputs=torch.ones(disc_interpolates.size()).cuda() if self.cuda else torch.ones(
disc_interpolates.size()),
create_graph=True, retain_graph=True, only_inputs=True)[0]
gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * self.gp_lambda
return gradient_penalty
def train(self, stage, epoch, dataloader):
self.monitor_train.reset()
data_iter = iter(dataloader)
self.len_dataset = int(len(dataloader) * self.dataset_fraction)
if epoch == 0:
print("Length of Dataset: {}".format(self.len_dataset))
if self.args.net_type == 'gmm':
self.visualizer_train.reset('Real')
self.visualizer_train.reset('Fakes_Encoder')
self.visualizer_train.reset('Fakes_Normal')
# Optimize Discriminator if needed
# while (stage == 2 and epoch == 0 and self.update_opt_counter == -1) or self.update_opt_flag:
# if self.update_opt_counter % 5 == 0:
# self.update_opt_disc()
# print("Training Discriminator Till optimality")
# self.update_opt_flag = self.optimize_discriminator(stage, epoch, dataloader)
# self.update_opt_counter += 1
if stage == 2 and self.disc_optimize:
self.optimize_discriminator(stage, epoch, dataloader)
self.disc_optimize = False
if self.update_opt_flag:
if self.update_opt_counter % 3 == 0:
self.update_opt_disc()
self.update_opt_counter += 1
if stage == 0:
############################
# Train VAE
############################
self.Encoder.train()
self.modelG[0].train()
i = 0
while i < self.len_dataset:
input = data_iter.next()[0]
i += 1
batch_size = input.size(0)
self.input.data.resize_(input.size()).copy_(input)
# zero grad
self.Encoder.zero_grad()
self.modelG[0].zero_grad()
# get latents
self.epsilon.data.resize_(batch_size, self.nz).normal_(0, 1)
noise_mu, noise_logvar = self.Encoder(self.input)
noise_sigma = torch.exp(torch.mul(noise_logvar, 0.5))
latents = noise_mu + torch.mul(noise_sigma, self.epsilon)
while(len(latents.size()) < len(self.input.size())):
latents = latents.unsqueeze(-1)
# compute vae loss
fake = self.modelG[0](latents)
loss_mse = self.criterion.diff_loss(fake, self.input, type=self.vae_loss_type)
# loss_correlation, corr = self.criterion.correlation_loss(self.input, fake)
loss_correlation = self.t_zero
loss_kld = self.criterion.kl_divergence(noise_mu, noise_logvar)
net_error = self.weight_vae * loss_mse + self.weight_kld * loss_kld
# net_error = self.weight_kld * loss_kld + 1*loss_correlation
try:
net_error.backward()
except Exception as e:
print(e)
self.optimizerE.step()
self.optimizerG.step()
self.schedulerE.step(loss_kld.data[0])
self.schedulerG.step(loss_mse.data[0])
# Bookkeeping
losses_train = {}
losses_train['Loss_D0'] = 0
losses_train['Loss_G0'] = 0
losses_train['MSE'] = loss_mse.data[0]
losses_train['KLD'] = loss_kld.data[0]
losses_train['Corr_Loss'] = loss_correlation.data[0]
losses_train['Mean'] = noise_mu.mean().data[0]
losses_train['Sigma'] = noise_sigma.mean().data[0]
losses_train['Score_D0'] = 0
losses_train['Score_D0_G0'] = 0
losses_train['Score_D0_G1'] = 0
losses_train['Score_D0_Normal_G0'] = 0
losses_train['Score_D0_Normal_G1'] = 0
losses_train['Disc_Diff'] = 0
losses_train['Disc_Acc'] = 0
losses_train['Clamp'] = self.clamp_upper
self.monitor_train.update(losses_train, batch_size)
print('Stage %d: [%d/%d][%d/%d] Loss_D0: %.3f Loss_G0: %.3f MSE: %.3f KLD: %.3f Corr_Loss %.3f Score_D0: %.3f Score_D0_G0: %.3f Score_D0_G1: %.3f Disc_Acc %.3f'
% (stage, epoch, self.stage_epochs, i, self.len_dataset, 0, 0, loss_mse.data[0], loss_kld.data[0], loss_correlation.data[0], 0, 0, 0, 0))
if i % self.plot_update_interval == 0:
losses_train['netD_norm_w'] = self.get_model_norm(self.modelD[0])
losses_train['Gradient_Penalty'] = 0
losses_train['Learning_Rate_E'] = self.optimizerE.param_groups[0]['lr']
losses_train['Learning_Rate_G'] = self.optimizerG.param_groups[0]['lr']
losses_train['Learning_Rate_D'] = self.lr_dis
losses_train['Real'] = self.input.data.cpu()
losses_train['Fakes_Encoder'] = fake.data.cpu()
losses_train['Fakes_Previous'] = torch.zeros(fake.size())
losses_train['Fakes_Normal'] = self.modelG[0](self.fixed_noise).data.cpu()
# losses_train['Corr_Output'] = corr.unsqueeze(1).data.cpu()
# losses_train['Corr_Output'] = torch.zeros(fake.size())
self.visualizer_train.update(losses_train)
if i % 250 == 0:
try:
fake_normal_z = self.modelG[0](self.fixed_noise)
fake_encoder_z = self.modelG[0](latents)
if len(self.input.size()) < 3:
fig = plt.figure()
plt.hist(self.input.squeeze().data.cpu().numpy(), bins=60)
fig.savefig("{}/stage_{}_epoch_{}_real.png".format(self.save_path, stage, i), dpi=fig.dpi)
fig = plt.figure()
plt.hist(fake_encoder_z.squeeze().data.cpu().numpy(), bins=60)
fig.savefig("{}/stage_{}_epoch_{}_fake_enc.png".format(self.save_path, stage, i), dpi=fig.dpi)
fig = plt.figure()
plt.hist(fake_normal_z.squeeze().data.cpu().numpy(), bins=60)
fig.savefig("{}/stage_{}_epoch_{}_fake_norm.png".format(self.save_path, stage, i), dpi=fig.dpi)
else:
vutils.save_image(fake_normal_z.data, '%s/fake_samples_stage_%03d_normal_z.png' % (self.save_path, stage), normalize=True)
vutils.save_image(fake_encoder_z.data, '%s/fake_samples_stage_%03d_encoder_z.png' % (self.save_path, stage), normalize=True)
vutils.save_image(input, '%s/real_samples.png' % self.save_path, normalize=True)
except Exception as e:
print(e)
elif stage == 1:
################################################
# Train Discriminator till optimality
################################################
# self.Encoder.eval()
# self.modelG[0].eval()
self.modelD[0].train()
i = 0
while i < self.len_dataset:
acc = 0.0
# clamp parameters to a cube
if not self.gp:
for p in self.modelD[0].parameters():
p.data.clamp_(self.clamp_lower, self.clamp_upper)
input = data_iter.next()[0]
i += 1
batch_size = input.size(0)
self.input.data.resize_(input.size()).copy_(input)
self.modelD[0].zero_grad()
# train with real
if self.add_noise:
self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
dis_input = self.input + self.epsilon
scores_D0 = self.modelD[0](dis_input)
else:
scores_D0 = self.modelD[0](self.input)
acc += torch.sum((scores_D0 > self.acc_margin).float())
# train with fake
self.epsilon.data.resize_(batch_size, self.nz).normal_(0, 1)
noise_mu, noise_logvar = self.Encoder(self.input)
noise_sigma = torch.exp(torch.mul(noise_logvar, 0.5))
latents = noise_mu + torch.mul(noise_sigma, self.epsilon)
latents = latents.detach()
while(len(latents.size()) < len(self.input.size())):
latents = latents.unsqueeze(-1)
fake = self.modelG[0](latents).detach()
if self.add_noise:
self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
dis_input = fake + self.epsilon
scores_D0_G0 = self.modelD[0](dis_input)
else:
scores_D0_G0 = self.modelD[0](fake)
acc += torch.sum((scores_D0_G0 <= 0).float())
disc_acc = float(acc)*50 / batch_size
errD0 = self.criterion.hinge_loss(scores_D0, 1)
errD0 += self.criterion.hinge_loss(scores_D0_G0, -1)
errD0 /= 2
net_error = errD0
gradient_penalty = self.t_zero
# errD0 = self.criterion.rankerD([scores_D0, scores_D0_G0])
if self.gp:
gradient_penalty = self.calc_gradient_penalty(self.input, fake, batch_size)
net_error = errD0 + gradient_penalty
try:
net_error.backward()
except Exception as e:
print(e)
self.optimizerD.step()
self.schedulerD.step(errD0.data[0])
# Bookkeeping
losses_train = {}
losses_train['Loss_D0'] = errD0.data[0]
losses_train['Loss_G0'] = 0
losses_train['MSE'] = 0
losses_train['KLD'] = 0
losses_train['Corr_Loss'] = 0
losses_train['Mean'] = noise_mu.mean().data[0]
losses_train['Sigma'] = noise_sigma.mean().data[0]
losses_train['Score_D0'] = scores_D0.median().data[0]
losses_train['Score_D0_G0'] = scores_D0_G0.median().data[0]
losses_train['Score_D0_G1'] = 0
losses_train['Score_D0_Normal_G0'] = 0
losses_train['Score_D0_Normal_G1'] = 0
losses_train['Disc_Diff'] = 0
losses_train['Disc_Acc'] = disc_acc
losses_train['Clamp'] = self.clamp_upper
self.monitor_train.update(losses_train, batch_size)
print('Stage %d: [%d/%d][%d/%d] Loss_D0: %.3f Loss_G0: %.3f MSE: %.3f KLD: %.3f Score_D0: %.3f Score_D0_G0: %.3f Score_D0_G1: %.3f Disc_Diff %.3f Disc_Acc %.3f'
% (stage, epoch, self.stage_epochs, i, self.len_dataset, errD0.data[0], 0, 0, 0, scores_D0.median().data[0], scores_D0_G0.median().data[0], 0, 0, disc_acc))
if i % self.plot_update_interval == 0:
losses_train['netD_norm_w'] = self.get_model_norm(self.modelD[0])[0]
losses_train['Learning_Rate_E'] = self.optimizerE.param_groups[0]['lr']
losses_train['Learning_Rate_G'] = self.optimizerG.param_groups[0]['lr']
losses_train['Learning_Rate_D'] = self.optimizerD.param_groups[0]['lr']
losses_train['Gradient_Penalty'] = gradient_penalty.data[0]
losses_train['Real'] = self.input.data.cpu()
losses_train['Fakes_Encoder'] = fake.data.cpu()
losses_train['Fakes_Previous'] = torch.zeros(fake.size())
losses_train['Fakes_Normal'] = self.modelG[0](self.fixed_noise).data.cpu()
# losses_train['Corr_Output'] = torch.zeros(fake.size())
self.visualizer_train.update(losses_train)
else:
############################
# Train GAN
############################
# if epoch == 1:
# for param_group in self.optimizerD.param_groups:
# param_group['lr'] = 2*self.lr_dis
i = 0
avg_mean_scores_D0 = 0.0
avg_mean_scores_D0_G0 = 0.0
avg_mean_scores_D0_G1 = 0.0
while i < self.len_dataset:
############################
# Update Discriminator Network
############################
# iter_start = time.time()
self.modelD[0].train()
# self.modelG[0].eval()
# if self.giterations < 10 or self.giterations % (self.len_dataset/5) == 0:
if epoch !=0 and self.giterations % (self.len_dataset/5) == 0:
d_iterations = self.len_dataset/10
else:
d_iterations = self.d_iter
j=0
lossD = 0
lossG = 0
while j < d_iterations and i < self.len_dataset:
lossG = 0
acc = 0.0
j += 1
i += 1
# clamp parameters to a cube
if not self.gp:
for p in self.modelD[0].parameters():
p.data.clamp_(self.clamp_lower, self.clamp_upper)
input = data_iter.next()[0]
batch_size = input.size(0)
self.input.data.resize_(input.size()).copy_(input)
self.modelD[0].zero_grad()
self.modelG[0].zero_grad()
# train with real
if self.add_noise:
self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
dis_input = self.input + self.epsilon
scores_D0 = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
else:
scores_D0 = self.modelD[0](self.input, self.extra_layer, self.extra_layer_gamma)
# if self.add_noise:
# self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
# dis_input = self.input + self.epsilon
# scores_D1 = self.modelD[1](dis_input, self.extra_layer-1)
# else:
# scores_D1 = self.modelD[1](self.input, self.extra_layer-1)
acc += torch.sum((scores_D0 > self.acc_margin).float())
# train with fake
self.epsilon.data.resize_(batch_size, self.nz).normal_(0, 1)
noise_mu, noise_logvar = self.Encoder(self.input)
noise_sigma = torch.exp(torch.mul(noise_logvar, 0.5))
latents = noise_mu + torch.mul(noise_sigma, self.epsilon)
latents = latents.detach()
while(len(latents.size()) < len(input.size())):
latents = latents.unsqueeze(-1)
latents = latents.detach()
fake_G0 = self.modelG[0](latents).detach()
fake_G0.requires_grad = True
if self.add_noise:
self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
dis_input = fake_G0 + self.epsilon
scores_D0_G0 = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
else:
scores_D0_G0 = self.modelD[0](fake_G0, self.extra_layer, self.extra_layer_gamma)
fake_G1 = self.modelG[1](latents).detach()
# if self.add_noise:
# self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
# dis_input = fake_G1 + self.epsilon
# scores_D1_G1 = self.modelD[1](dis_input, self.extra_layer-1)
# else:
# scores_D1_G1 = self.modelD[1](fake_G1, self.extra_layer-1)
fake_G1.requires_grad = True
if self.add_noise:
self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
dis_input = fake_G1 + self.epsilon
scores_D0_G1 = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
else:
scores_D0_G1 = self.modelD[0](fake_G1, self.extra_layer, self.extra_layer_gamma)
acc += torch.sum((scores_D0_G0 <= 0).float())
disc_acc = float(acc)*50 / batch_size
# Compute loss and do backward()
# disc_abs_diff = torch.pow(scores_D0 - self.marker_high, 2).mean() + torch.pow(scores_D0_G1 - self.marker_low, 2).mean()
# disc_abs_diff = torch.norm(scores_D0 - self.marker_high) + torch.norm(scores_D0_G1 - self.marker_low)
# disc_abs_diff = torch.abs(scores_D0 - self.marker_high).mean() + torch.abs(scores_D0_G1 - self.marker_low).mean()
disc_abs_diff = self.criterion.hinge_loss(scores_D0, 1, self.marker_high)
disc_abs_diff += self.criterion.hinge_loss(scores_D0_G1, -1, -self.marker_low)
errD0 = self.criterion.rankerD([scores_D0, scores_D0_G0, scores_D0_G1])
net_error = self.rank_error_weight*errD0 + self.disc_diff_weight*disc_abs_diff
if self.gp:
gradient_penalty = self.calc_gradient_penalty(self.input, fake_G0, batch_size)
net_error += gradient_penalty
else:
gradient_penalty = self.t_zero
try:
net_error.backward()
except Exception as e:
print(e)
self.optimizerD.step()
if self.optimize_mse:
self.modelG[0].zero_grad()
latents.requires_grad = True
fake_G0 = self.modelG[0](latents)
loss_mse = self.criterion.diff_loss(fake_G0, self.input, type=self.vae_loss_type)
mse_error = self.weight_mse * loss_mse
try:
mse_error.backward()
self.optimizerG.step()
except Exception as e:
print(e)
else:
loss_mse = self.t_zero
self.extra_layer_gamma = min(self.extra_layer_gamma + 1/(2*self.len_dataset), 1)
mean_scores_D0 = scores_D0.median().data[0]
mean_scores_D0_G0 = scores_D0_G0.median().data[0]
mean_scores_D0_G1 = scores_D0_G1.median().data[0]
# avg_mean_scores_D0 = (avg_mean_scores_D0*(i-1) + mean_scores_D0) / i
# avg_mean_scores_D0_G0 = (avg_mean_scores_D0_G0*(i-1) + mean_scores_D0_G0) / i
# avg_mean_scores_D0_G1 = (avg_mean_scores_D0_G1*(i-1) + mean_scores_D0_G1) / i
avg_mean_scores_D0 = avg_mean_scores_D0 * 0.2 + mean_scores_D0 * 0.8
avg_mean_scores_D0_G0 = avg_mean_scores_D0_G0 * 0.2 + mean_scores_D0_G0 * 0.8
avg_mean_scores_D0_G1 = avg_mean_scores_D0_G1 * 0.2 + mean_scores_D0_G1 * 0.8
if self.adaptive_iter:
if avg_mean_scores_D0_G0 > avg_mean_scores_D0 - (avg_mean_scores_D0 - avg_mean_scores_D0_G1)/3:
self.d_iter += 1
# elif avg_mean_scores_D0_G0 <= avg_mean_scores_D0 - (avg_mean_scores_D0 - avg_mean_scores_D0_G1)/2:
else:
self.d_iter = self.d_iter_init
lossD = errD0.data[0]
if j < d_iterations:
# Bookkeeping
losses_train = {}
losses_train['Loss_D0'] = lossD
losses_train['Loss_G0'] = lossG
losses_train['MSE'] = loss_mse.data[0]
losses_train['KLD'] = 0
losses_train['Corr_Loss'] = 0
losses_train['Mean'] = noise_mu.mean().data[0]
losses_train['Sigma'] = noise_sigma.mean().data[0]
losses_train['Score_D0'] = mean_scores_D0
losses_train['Score_D0_G0'] = mean_scores_D0_G0
losses_train['Score_D0_G1'] = mean_scores_D0_G1
losses_train['Score_D0_Normal_G0'] = self.t_zero.data[0]
losses_train['Score_D0_Normal_G1'] = self.t_zero.data[0]
losses_train['Disc_Diff'] = disc_abs_diff.data[0]
losses_train['Disc_Acc'] = disc_acc
losses_train['Clamp'] = self.clamp_upper
self.monitor_train.update(losses_train, batch_size)
print('Stage %d: [%d/%d][%d/%d] Loss_D: %.3f Loss_G: %.3f MSE %.3f Score_D0: %.3f Score_D0_G0: %.3f Score_D0_G1: %.3f Disc_Diff %.3f Disc_Acc %.3f Gamma %.3f'
% (stage, epoch, self.stage_epochs, i, self.len_dataset, lossD, lossG, loss_mse.data[0], mean_scores_D0, mean_scores_D0_G0, mean_scores_D0_G1, disc_abs_diff.data[0], disc_acc, self.gen_gamma))
if i % self.plot_update_interval == 0:
losses_train['netD_norm_w'] = self.get_model_norm(self.modelD[0])[0]
losses_train['Learning_Rate_E'] = self.optimizerE.param_groups[0]['lr']
losses_train['Learning_Rate_G'] = self.optimizerG.param_groups[0]['lr']
losses_train['Learning_Rate_D'] = self.optimizerD.param_groups[0]['lr']
losses_train['Gradient_Penalty'] = gradient_penalty.data[0]
losses_train['Real'] = self.input.data.cpu()
losses_train['Fakes_Encoder'] = fake_G0.data.cpu()
losses_train['Fakes_Previous'] = fake_G1.data.cpu()
losses_train['Fakes_Normal'] = self.modelG[0](self.fixed_noise).data.cpu()
# losses_train['Corr_Output'] = torch.zeros(fake_G0.size())
self.visualizer_train.update(losses_train)
torch.save(self.modelD[0].state_dict(), '%s/stage_%d_epoch_%d_netD.pth' % (self.save_path, stage, 3*int(epoch/3)))
############################
# Update Generator Network
############################
j = 0
while j < self.g_iter and i < self.len_dataset:
j += 1
# self.modelD[0].eval()
self.modelG[0].train()
self.modelG[0].zero_grad()
self.modelD[0].zero_grad()
if self.add_noise:
self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
dis_input = self.input + self.epsilon
scores_D0 = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
else:
scores_D0 = self.modelD[0](self.input, self.extra_layer, self.extra_layer_gamma)
fake_G1 = self.modelG[1](latents)
if self.add_noise:
self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
dis_input = fake_G1 + self.epsilon
scores_D0_G1 = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
else:
scores_D0_G1 = self.modelD[0](fake_G1, self.extra_layer, self.extra_layer_gamma)
latents.requires_grad = True
fake_G0 = self.modelG[0](latents)
if self.add_noise:
self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
dis_input = fake_G0 + self.epsilon
scores_D0_G0 = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
else:
scores_D0_G0 = self.modelD[0](fake_G0, self.extra_layer, self.extra_layer_gamma)
self.gen_gamma = min(self.gen_gamma + 1/(2*self.len_dataset), 1)
errG0 = self.criterion.rankerG([scores_D0, scores_D0_G0, scores_D0_G1])
net_error = self.gen_gamma * errG0
try:
net_error.backward()
except Exception as e:
print(e)
self.optimizerG.step()
self.giterations += 1
mean_scores_D0 = scores_D0.median().data[0]
mean_scores_D0_G0 = scores_D0_G0.median().data[0]
mean_scores_D0_G1 = scores_D0_G1.median().data[0]
avg_mean_scores_D0 = avg_mean_scores_D0 * 0.2 + mean_scores_D0 * 0.8
avg_mean_scores_D0_G0 = avg_mean_scores_D0_G0 * 0.2 + mean_scores_D0_G0 * 0.8
avg_mean_scores_D0_G1 = avg_mean_scores_D0_G1 * 0.2 + mean_scores_D0_G1 * 0.8
if self.gen_gamma > 0.5 and self.adaptive_iter:
if avg_mean_scores_D0_G0 < avg_mean_scores_D0 - 2*(avg_mean_scores_D0 - avg_mean_scores_D0_G1)/3:
self.g_iter += 1
# elif avg_mean_scores_D0_G0 >= avg_mean_scores_D0 - (avg_mean_scores_D0 - avg_mean_scores_D0_G1)/2:
else:
self.g_iter = self.g_iter_init
lossG = errG0.data[0]
if j < self.g_iter:
i += 1
# Bookkeeping
losses_train = {}
losses_train['Loss_D0'] = lossD
losses_train['Loss_G0'] = lossG
losses_train['MSE'] = loss_mse.data[0]
losses_train['KLD'] = 0
losses_train['Corr_Loss'] = 0
losses_train['Mean'] = noise_mu.mean().data[0]
losses_train['Sigma'] = noise_sigma.mean().data[0]
losses_train['Score_D0'] = mean_scores_D0
losses_train['Score_D0_G0'] = mean_scores_D0_G0
losses_train['Score_D0_G1'] = mean_scores_D0_G1
losses_train['Score_D0_Normal_G0'] = self.t_zero.data[0]
losses_train['Score_D0_Normal_G1'] = self.t_zero.data[0]
losses_train['Disc_Diff'] = disc_abs_diff.data[0]
losses_train['Disc_Acc'] = disc_acc
losses_train['Clamp'] = self.clamp_upper
self.monitor_train.update(losses_train, batch_size)
print('Stage %d: [%d/%d][%d/%d] Loss_D: %.3f Loss_G: %.3f MSE %.3f Score_D0: %.3f Score_D0_G0: %.3f Score_D0_G1: %.3f Disc_Diff %.3f Disc_Acc %.3f Gamma %.3f'
% (stage, epoch, self.stage_epochs, i, self.len_dataset, lossD, lossG, loss_mse.data[0], mean_scores_D0, mean_scores_D0_G0, mean_scores_D0_G1, disc_abs_diff.data[0], disc_acc, self.gen_gamma))
if i % self.plot_update_interval == 0:
losses_train['netD_norm_w'] = self.get_model_norm(self.modelD[0])[0]
losses_train['Learning_Rate_E'] = self.optimizerE.param_groups[0]['lr']
losses_train['Learning_Rate_G'] = self.optimizerG.param_groups[0]['lr']
losses_train['Learning_Rate_D'] = self.optimizerD.param_groups[0]['lr']
losses_train['Gradient_Penalty'] = gradient_penalty.data[0]
losses_train['Real'] = self.input.data.cpu()
losses_train['Fakes_Encoder'] = fake_G0.data.cpu()
losses_train['Fakes_Previous'] = fake_G1.data.cpu()
losses_train['Fakes_Normal'] = self.modelG[0](self.fixed_noise).data.cpu()
# losses_train['Corr_Output'] = torch.zeros(fake_G0.size())
self.visualizer_train.update(losses_train)
input = data_iter.next()[0]
batch_size = input.size(0)
self.input.data.resize_(input.size()).copy_(input)
self.epsilon.data.resize_(batch_size, self.nz).normal_(0, 1)
noise_mu, noise_logvar = self.Encoder(self.input)
noise_sigma = torch.exp(torch.mul(noise_logvar, 0.5))
latents = noise_mu + torch.mul(noise_sigma, self.epsilon)
latents = latents.detach()
while(len(latents.size()) < len(input.size())):
latents = latents.unsqueeze(-1)
latents = latents.detach()
lossD = 0
# Bookkeeping
losses_train = {}
losses_train['Loss_D0'] = lossD
losses_train['Loss_G0'] = lossG
losses_train['MSE'] = loss_mse.data[0]
losses_train['KLD'] = 0
losses_train['Corr_Loss'] = 0
losses_train['Mean'] = noise_mu.mean().data[0]
losses_train['Sigma'] = noise_sigma.mean().data[0]
losses_train['Score_D0'] = mean_scores_D0
losses_train['Score_D0_G0'] = mean_scores_D0_G0
losses_train['Score_D0_G1'] = mean_scores_D0_G1
losses_train['Score_D0_Normal_G0'] = self.t_zero.data[0]
losses_train['Score_D0_Normal_G1'] = self.t_zero.data[0]
losses_train['Disc_Diff'] = disc_abs_diff.data[0]
losses_train['Disc_Acc'] = disc_acc
losses_train['Clamp'] = self.clamp_upper
self.monitor_train.update(losses_train, batch_size)
print('Stage %d: [%d/%d][%d/%d] Loss_D: %.3f Loss_G: %.3f MSE %.3f Score_D0: %.3f Score_D0_G0: %.3f Score_D0_G1: %.3f Disc_Diff %.3f Disc_Acc %.3f Gamma %.3f'
% (stage, epoch, self.stage_epochs, i, self.len_dataset, lossD, lossG, loss_mse.data[0], mean_scores_D0, mean_scores_D0_G0, mean_scores_D0_G1, disc_abs_diff.data[0], disc_acc, self.gen_gamma))
if i % self.plot_update_interval == 0:
# Compute MSE
losses_train['netD_norm_w'] = self.get_model_norm(self.modelD[0])[0]
losses_train['Learning_Rate_E'] = self.optimizerE.param_groups[0]['lr']
losses_train['Learning_Rate_G'] = self.optimizerG.param_groups[0]['lr']
losses_train['Learning_Rate_D'] = self.optimizerD.param_groups[0]['lr']
losses_train['Gradient_Penalty'] = gradient_penalty.data[0]
losses_train['Real'] = self.input.data.cpu()
losses_train['Fakes_Encoder'] = fake_G0.data.cpu()
losses_train['Fakes_Previous'] = fake_G1.data.cpu()
losses_train['Fakes_Normal'] = self.modelG[0](self.fixed_noise).data.cpu()
# losses_train['Corr_Output'] = torch.zeros(fake_G0.size())
self.visualizer_train.update(losses_train)
if i % 250 == 0 or i == self.len_dataset:
try:
fake_normal_z = self.modelG[0](self.fixed_noise)
fake_encoder_z = fake_G0
if len(self.input.size()) < 3:
fig = plt.figure()
plt.hist(input.squeeze().cpu().numpy(), bins=60)
fig.savefig("{}/stage_{}_epoch_{}_real.png".format(self.save_path, stage, i), dpi=fig.dpi)
fig = plt.figure()
plt.hist(fake_encoder_z.squeeze().data.cpu().numpy(), bins=60)
fig.savefig("{}/stage_{}_epoch_{}_fake_enc.png".format(self.save_path, stage, i), dpi=fig.dpi)
fig = plt.figure()
plt.hist(fake_normal_z.squeeze().data.cpu().numpy(), bins=60)
fig.savefig("{}/stage_{}_epoch_{}_fake_norm.png".format(self.save_path, stage, i), dpi=fig.dpi)
else:
vutils.save_image(fake_normal_z.data, '%s/fake_samples_stage_%03d_epoch_%03d_normal_z.png' % (self.save_path, stage, epoch), normalize=True)
vutils.save_image(fake_encoder_z.data, '%s/fake_samples_stage_%03d_epoch_%03d_encoder_z.png' % (self.save_path, stage, epoch), normalize=True)
vutils.save_image(input, '%s/real_samples.png' % self.save_path, normalize=True)
except Exception as e:
print(e)
if True:
self.disc_diff_weight = self.disc_diff_weight_init
elif disc_abs_diff.data[0] < 5:
self.disc_diff_weight = (self.marker_high - self.marker_low)*0.2 #self.disc_diff_weight_init/1000
elif disc_abs_diff.data[0] < 10:
self.disc_diff_weight = (self.marker_high - self.marker_low)*10 #self.disc_diff_weight_init/500
else:
self.disc_diff_weight = self.disc_diff_weight_init
print('Stage %d: [%d/%d] Avg_Scores_D0 %.3f Avg_Scores_D0_G0 %.3f Avg_Scores_D0_G1 %.3f' % (stage, epoch, self.stage_epochs, avg_mean_scores_D0, avg_mean_scores_D0_G0, avg_mean_scores_D0_G1))
try:
loss = self.monitor_train.getvalues()
self.log_loss_train.update(loss)
except Exception as e:
print("Error while logging loss")
print(e)
def test(self, stage, epoch, dataloader):
self.monitor_test.reset()
data_iter = iter(dataloader)
# switch to eval mode
# self.modelG[0].eval()
self.modelG[0].zero_grad()
# self.modelG[1].eval()
self.modelG[1].zero_grad()
# self.modelD[0].eval()
self.modelD[0].zero_grad()
# self.modelD[1].eval()
self.modelD[1].zero_grad()
# self.Encoder.eval()
self.Encoder.zero_grad()
self.t_zero = Variable(torch.zeros(1))
epoch_score_D0 = torch.Tensor([]).cuda()
epoch_score_D0_G0 = torch.Tensor([]).cuda()
epoch_score_D0_G1 = torch.Tensor([]).cuda()
epoch_ssim_score = 0.0
epoch_psnr_score = 0.0
epoch_disc_acc = 0.0
num_batches = len(dataloader)
i = 0
while i < len(dataloader):
############################
# Evaluate Network
############################
acc = 0.0
# Get real data
input = data_iter.next()[0]
i += 1
batch_size = input.size(0)
self.test_input.data.resize_(input.size()).copy_(input)
# Generate fake data
self.epsilon.data.resize_(batch_size, self.nz).normal_(0, 1)
noise_mu, noise_logvar = self.Encoder(self.test_input)
noise_sigma = torch.exp(torch.mul(noise_logvar, 0.5))
latents = noise_mu + torch.mul(noise_sigma, self.epsilon)
while(len(latents.size()) < len(self.test_input.size())):
latents = latents.unsqueeze(-1)
fake_G0 = self.modelG[0](latents)
fake_G1 = self.modelG[1](latents)
score_D0 = self.modelD[0](self.test_input, self.extra_layer, self.extra_layer_gamma)
score_D0_G0 = self.modelD[0](fake_G0, self.extra_layer, self.extra_layer_gamma)
score_D0_G1 = self.modelD[0](fake_G1, self.extra_layer, self.extra_layer_gamma)
ssim_score, psnr_score = 0.0, 0.0
data_range = input.max() - input.min()
acc += torch.sum((score_D0 > 0).float())
acc += torch.sum((score_D0_G0 <= 0).float())
disc_acc = float(acc)*50/batch_size
if self.args.net_type != 'gmm':
compare_real = input.permute(0,2,3,1)
compare_fake = fake_G0.permute(0,2,3,1)
for j in range(batch_size):
ssim_score += compare_ssim(compare_real[j,...].cpu().numpy(), compare_fake[j,...].data.cpu().numpy(), data_range=data_range, multichannel=True)
psnr_score += compare_psnr(compare_real[j,...].cpu().numpy(), compare_fake[j,...].data.cpu().numpy(), data_range=data_range)
epoch_score_D0 = torch.cat((epoch_score_D0, score_D0.data))
epoch_score_D0_G0 = torch.cat((epoch_score_D0_G0, score_D0_G0.data))
epoch_score_D0_G1 = torch.cat((epoch_score_D0_G1, score_D0_G1.data))
epoch_ssim_score += ssim_score/batch_size
epoch_psnr_score += psnr_score/batch_size
epoch_disc_acc += disc_acc
# Bookkeeping
test_scores = {}
test_scores['Test_Score_D0'] = score_D0.median().data[0]
test_scores['Test_Score_D0_G0'] = score_D0_G0.median().data[0]
test_scores['Test_Score_D0_G1'] = score_D0_G1.median().data[0]
test_scores['SSIM'] = ssim_score/batch_size
test_scores['PSNR'] = psnr_score/batch_size
test_scores['Test_Disc_Acc'] = disc_acc
self.monitor_test.update(test_scores, batch_size)
print('Test: [%d/%d][%d/%d] Score_D0: %.3f Score_D0_G0: %.3f Score_D0_G1: %.3f SSIM: %.3f PSNR: %.3f Disc_Acc: %.3f'
% (epoch, self.stage_epochs, i, len(dataloader), score_D0.median().data[0], score_D0_G0.median().data[0], score_D0_G1.median().data[0], ssim_score/batch_size, psnr_score/batch_size, disc_acc))
# if (i % int(len(dataloader)*0.25)) == 0:
# test_scores['Test_Real'] = self.test_input.data.cpu()
# test_scores['Test_Fakes_Encoder'] = fake_G0.data.cpu()
# self.visualizer_test.update(test_scores)
if i == len(dataloader)-2:
try: