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train_identity.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
from models import incep_resnetV1, incep_resnetV1_multigpu
from models import Hopenet
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.prevD = prevD
self.prevG = prevG
self.criterion = criterion
self.plot_update_interval = args.plot_update_interval
self.ngpu = args.ngpu
self.cuda = args.cuda
self.device = torch.device("cuda" if (self.cuda and torch.cuda.is_available()) else "cpu")
self.facenet = incep_resnetV1_multigpu(3, 128, 0, self.ngpu)
self.facenet = self.facenet.to(self.device)
facenet_model = torch.load('models/facenet.pth')
self.facenet.load_state_dict(facenet_model)
self.identity_lambda = args.identity_lambda
self.cos_sim = torch.nn.CosineSimilarity(dim=1)
self.pose_bin = 66
self.hopenet = Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], self.pose_bin).to(self.device)
hopenet_model = torch.load('models/hopenet_robust_alpha1.pkl')
self.hopenet.load_state_dict(hopenet_model)
self.pose_lambda = args.pose_lambda
self.port = args.port
self.env = args.env
self.dir_save = args.save
self.dataset_fraction = args.dataset_fraction
self.len_dataset = 0
self.wgan = args.wgan
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.noise_z = self.nz - 128 - 3*66
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_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
# for classification
self.test_input = torch.FloatTensor(self.batch_size,self.nchannels,self.resolution_high,self.resolution_wide).to(self.device)
self.noise = torch.FloatTensor(self.batch_size, self.noise_z).normal_(0, 1).to(self.device)
self.fixed_noise = torch.FloatTensor(self.batch_size, self.nz).normal_(0, 1).to(self.device)
self.epsilon = torch.randn(self.batch_size, self.nz).to(self.device)
# Initialize optimizer
self.optimizerG = self.initialize_optimizer(self.modelG[0], lr=self.lr_gen, optim_method='RMSprop')
self.optimizerD = self.initialize_optimizer(self.modelD[0], lr=self.lr_dis, optim_method='RMSprop', weight_decay=1e-2*self.lr_dis)
self.schedulerG = optim.lr_scheduler.ReduceLROnPlateau(self.optimizerG, factor=self.lr_decay, patience=self.scheduler_patience , min_lr=1e-3*self.lr_gen)
self.schedulerD = optim.lr_scheduler.ReduceLROnPlateau(self.optimizerD, factor=self.lr_decay, patience=self.scheduler_patience , min_lr=1e-3*self.lr_dis)
# logging training
self.log_loss_train = plugins.Logger(args.logs, 'TrainLogger.txt')
self.params_loss_train = ['Loss_D0', 'Loss_G0', 'Score_D0', 'Score_D1', 'Score_D0_G0', 'Score_D0_G1', 'Score_D1_G1', 'Disc_Diff', 'Disc_Acc', 'Iden_Loss', 'Pose_Loss']
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', 'Score_D0', 'Score_D1', 'Score_D0_G0', 'Score_D0_G1', 'Score_D1_G1', 'Disc_Diff', 'Disc_Acc', 'Iden_Loss', 'Pose_Loss']
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}},
'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}},
# 'Score_D1':{'dtype':'scalar','vtype':'plot', 'win': 'loss_D1', 'layout': {'windows': ['Score_D1', 'Score_D1_G1'], 'id': 0}},
# 'Score_D1_G1':{'dtype':'scalar','vtype':'plot', 'win': 'loss_D1', 'layout': {'windows': ['Score_D1', 'Score_D1_G1'], 'id': 1}},
'netD_norm_w':{'dtype':'scalar','vtype':'plot', 'win': 'netd_norm'},
'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'},
'Iden_Loss':{'dtype':'scalar','vtype':'plot', 'win': 'iden_loss'},
'Pose_Loss':{'dtype':'scalar','vtype':'plot', 'win': 'pose_loss'},
'Real': {'dtype': output_dtype, 'vtype': output_vtype, 'win': 'real'},
'Fakes': {'dtype': output_dtype, 'vtype': output_vtype, 'win': 'fakes_normal'},
'Fakes_Previous': {'dtype': output_dtype, 'vtype': output_vtype, 'win': 'fakes_prev'},
}
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': {'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)
# 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])
# define a zero tensor
self.t_zero = torch.zeros(1)
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
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)
else:
raise(Exception("Unknown Optimization Method"))
return optimizer
def setup_stage(self, stage, dataloader=None):
if stage == 0:
self.optimizerD = self.initialize_optimizer(self.modelD[0], lr=self.lr_dis, optim_method=self.optim_method, weight_decay=1e-2*self.lr_dis)
self.optimizerG = self.initialize_optimizer(self.modelG[0], lr=self.lr_gen, optim_method=self.optim_method)
# self.margin *= 2
self.acc_margin = 0
else:
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, weight_decay=1e-2*self.lr_dis)
self.optimizerG = self.initialize_optimizer(self.modelG[0], lr=self.lr_gen, optim_method=self.optim_method)
self.gogan_optim_set = True
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.modelD[1].eval()
# self.modelG[1].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))
self.acc_margin = (self.marker_high + self.marker_low)/2
# print("Margin: {}".format(self.margin))
# self.margin = self.margin / 2.0
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.to(self.device)
interpolates = alpha * real_data + ((1 - alpha) * fake_data)
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()).to(self.device),
create_graph=True, retain_graph=True, only_inputs=True)[0]
gradients = gradients.view(batch_size, -1)
# gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
gradient_norm = torch.sqrt(torch.sum(gradients ** 2, dim=1) + 1e-12)
gradient_penalty = ((gradient_norm -1) ** 2).mean()
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 stage == 0:
############################
# Train Margin GAN/WGAN
############################
i = 0
avg_mean_scores_D0 = 0.0
avg_mean_scores_D0_G0 = 0.0
avg_mean_scores_D0_G1 = 0.0
feat_loss = torch.zeros(1)
pose_loss = torch.zeros(1)
lossG = 0
while i < self.len_dataset:
############################
# Update Discriminator Network
############################
self.modelD[0].train()
# self.modelG[0].eval()
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
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].to(self.device)
batch_size = input.size(0)
self.modelD[0].zero_grad()
self.modelG[0].zero_grad()
# train with real
if self.add_noise:
self.epsilon.data.resize_(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](input, self.extra_layer, self.extra_layer_gamma)
acc += torch.sum((scores_D0 > self.acc_margin).float())
# train with fake
input_x = torch.nn.functional.upsample(input, size=160, mode='bilinear')
input_iden = self.facenet(input_x)[0].detach()
input_iden = (input_iden - input_iden.mean(1, keepdim=True)) / input_iden.std(1, keepdim=True)
input_pose = self.hopenet(input_x)
input_pose = torch.cat(input_pose, dim=1).detach()
input_pose = (input_pose - input_pose.mean(1, keepdim=True)) / input_pose.std(1, keepdim=True)
self.noise.resize_(batch_size, self.noise_z, 1, 1).normal_(0, 1)
latent = torch.cat((self.noise, input_iden.unsqueeze(-1).unsqueeze(-1), input_pose.unsqueeze(-1).unsqueeze(-1)), 1)
fake_G0 = self.modelG[0](latent).detach()
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)
acc += torch.sum((scores_D0_G0 <= 0).float())
disc_acc = float(acc)*50 / batch_size
self.noise.resize_(batch_size, self.nz, 1, 1).normal_(0, 1)
fake_G1 = self.modelG[0](self.noise).detach()
if self.add_noise:
self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
dis_input = fake_G0 + 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_G1 <= 0).float())
disc_acc = float(acc)*100 / (3*batch_size)
# Compute loss and do backward()
if self.wgan:
errD0 = ((scores_D0_G0 + scores_D0_G1)/2 - scores_D0).mean()
else:
errD0 = self.criterion.hinge_loss(scores_D0, 1)
errD0 += self.criterion.hinge_loss(scores_D0_G0, -1)
errD0 /= 2
# self.schedulerD.step(errD0.data[0])
if self.gp:
gradient_penalty = self.calc_gradient_penalty(input, fake_G0, batch_size)
net_error = errD0 + self.gp_lambda * gradient_penalty
else:
net_error = errD0
gradient_penalty = self.t_zero
try:
net_error.backward()
except Exception as e:
print(e)
self.optimizerD.step()
self.extra_layer_gamma = min(self.extra_layer_gamma + 1/(2*self.len_dataset), 1)
mean_scores_D0 = scores_D0.median().item()
mean_scores_D0_G0 = scores_D0_G0.median().item()
lossD = errD0.item()
if j < d_iterations:
# Bookkeeping
losses_train = {}
losses_train['Loss_D0'] = lossD
losses_train['Loss_G0'] = lossG
losses_train['Score_D0'] = mean_scores_D0
losses_train['Score_D0_G0'] = mean_scores_D0_G0
losses_train['Score_D0_G1'] = 0
losses_train['Disc_Diff'] = 0
losses_train['Disc_Acc'] = disc_acc
# losses_train['Clamp'] = self.clamp_upper
losses_train['Score_D0_G1'] = 0
losses_train['Iden_Loss'] = feat_loss.item()
losses_train['Pose_Loss'] = pose_loss.item()
# losses_train['Score_D1_G1'] = 0
# losses_train['Score_D1'] = 0
self.monitor_train.update(losses_train, batch_size)
print('Stage %d: [%d/%d][%d/%d] Loss_D: %.3f Loss_G: %.3f Score_D0: %.3f Score_D0_G0: %.3f Disc_Acc %.3f GP %.3f Iden_Loss %.3f Pose_Loss %.3f'
% (stage, epoch, self.stage_epochs[stage], i, self.len_dataset, lossD, lossG, mean_scores_D0, mean_scores_D0_G0, disc_acc, gradient_penalty.item(), feat_loss.item(), pose_loss.item()))
if i % self.plot_update_interval == 0:
# Compute MSE
losses_train['netD_norm_w'] = self.get_model_norm(self.modelD[0])[0]
losses_train['Gradient_Penalty'] = gradient_penalty.item()
losses_train['Real'] = input.cpu()
losses_train['Fakes'] = fake_G0.detach().cpu()
losses_train['Fakes_Previous'] = torch.zeros(fake_G0.size())
self.visualizer_train.update(losses_train)
############################
# Update Generator Network
############################
# self.modelD[0].eval()
self.modelG[0].train()
self.modelG[0].zero_grad()
fake_G1 = self.modelG[0](self.noise)
if self.add_noise:
self.epsilon.data.resize_(self.input.size()).normal_(0, self.noise_var)
dis_input = fake_G0 + 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)
fake_G0 = self.modelG[0](latent)
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_x = torch.nn.functional.upsample(fake_G0, size=160, mode='bilinear')
fake_iden = self.facenet(fake_x)[0]
fake_iden = (fake_iden - fake_iden.mean(1, keepdim=True)) / fake_iden.std(1, keepdim=True)
# feat_loss = torch.norm(input_iden - fake_iden, dim=1).mean()
feat_loss = 1 - self.cos_sim(input_iden, fake_iden).mean()
fake_pose = self.hopenet(fake_x)
fake_pose = torch.cat(fake_pose, dim=1)
fake_pose = (fake_pose - fake_pose.mean(1, keepdim=True)) / fake_pose.std(1, keepdim=True)
pose_loss = torch.norm(input_pose - fake_pose, dim=1).mean()
lossG = (- scores_D0_G0.mean() - scores_D0_G1.mean())/2
if self.wgan:
if epoch < 0:
errG0 = lossG
else:
errG0 = lossG + self.identity_lambda*feat_loss + self.pose_lambda*pose_loss
else:
errG0 = self.criterion.hinge_loss(scores_D0_G0, 1) + self.identity_lambda*feat_loss + self.pose_lambda*pose_loss
try:
errG0.backward()
except Exception as e:
print(e)
self.optimizerG.step()
self.giterations += 1
# Bookkeeping
losses_train = {}
losses_train['Loss_D0'] = lossD
losses_train['Loss_G0'] = lossG.item()
losses_train['Score_D0'] = mean_scores_D0
# losses_train['Score_D1'] = 0
losses_train['Score_D0_G0'] = mean_scores_D0_G0
losses_train['Score_D0_G1'] = 0
# losses_train['Score_D1_G1'] = 0
losses_train['Disc_Diff'] = 0
losses_train['Disc_Acc'] = disc_acc
# losses_train['Clamp'] = self.clamp_upper
losses_train['Iden_Loss'] = feat_loss.item()
losses_train['Pose_Loss'] = pose_loss.item()
self.monitor_train.update(losses_train, batch_size)
print('Stage %d: [%d/%d][%d/%d] Loss_D: %.3f Loss_G: %.3f Score_D0: %.3f Score_D0_G0: %.3f Disc_Acc %.3f GP %.3f Iden_Loss %.3f Pose_Loss %.3f'
% (stage, epoch, self.stage_epochs[stage], i, self.len_dataset, lossD, lossG.item(), mean_scores_D0, mean_scores_D0_G0, disc_acc, gradient_penalty.item(), feat_loss.item(), pose_loss.item()))
if i % self.plot_update_interval == 0:
losses_train['netD_norm_w'] = self.get_model_norm(self.modelD[0])[0]
losses_train['Gradient_Penalty'] = gradient_penalty.item()
losses_train['Real'] = input.cpu()
losses_train['Fakes'] = fake_G0.detach().cpu()
losses_train['Fakes_Previous'] = torch.zeros(fake_G0.size())
self.visualizer_train.update(losses_train)
if i % 250 == 0:
try:
fake_normal_z = self.modelG[0](self.fixed_noise)
if len(input.size()) < 3:
fig = plt.figure()
plt.hist(input.squeeze().cpu().numpy(), bins=60)
fig.savefig("{}/stage_{}_epoch_{}_real.png".format(self.dir_save, 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.dir_save, stage, i), dpi=fig.dpi)
else:
vutils.save_image(fake_normal_z.data, '%s/fake_samples_stage_%03d_epoch_%03d_normal_z.png' % (self.args.save, stage, epoch), normalize=True)
vutils.save_image(input, '%s/real_samples.png' % self.args.save, normalize=True)
except Exception as e:
print(e)
if epoch == self.stage_epochs[stage]-1:
self.margin = (scores_D0.mean() - scores_D0_G0.mean()).data
else:
############################
# Train GAN
############################
i = 0
avg_mean_scores_D0 = 0.0
avg_mean_scores_D0_G0 = 0.0
avg_mean_scores_D0_G1 = 0.0
feat_loss = torch.zeros(1)
pose_loss = torch.zeros(1)
lossD = torch.zeros(1)
lossG = torch.zeros(1)
while i < self.len_dataset:
############################
# Update Discriminator Network
############################
self.modelD[0].train()
# self.modelG[0].eval()
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
while j < d_iterations and i < self.len_dataset:
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].to(self.device)
batch_size = input.size(0)
self.modelD[0].zero_grad()
self.modelG[0].zero_grad()
# train with real
if self.add_noise:
self.epsilon.data.resize_(input.size()).normal_(0, self.noise_var)
dis_input = input + self.epsilon
scores_D0 = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
else:
scores_D0 = self.modelD[0](input, self.extra_layer, self.extra_layer_gamma)
acc += torch.sum((scores_D0 > self.acc_margin).float())
# train with conditioned fake
with torch.no_grad():
input_x = torch.nn.functional.upsample(input, size=160, mode='bilinear')
input_iden = self.facenet(input_x)[0].detach()
input_iden = (input_iden - input_iden.mean(1, keepdim=True)) / input_iden.std(1, keepdim=True)
input_pose = self.hopenet(input_x)
input_pose = torch.cat(input_pose, dim=1).detach()
input_pose = (input_pose - input_pose.mean(1, keepdim=True)) / input_pose.std(1, keepdim=True)
self.noise.resize_(batch_size, self.noise_z, 1, 1).normal_(0, 1)
latent = torch.cat((self.noise, input_iden.unsqueeze(-1).unsqueeze(-1), input_pose.unsqueeze(-1).unsqueeze(-1)), 1)
with torch.no_grad():
fake_G0 = self.modelG[0](latent).detach()
if self.add_noise:
self.epsilon.data.resize_(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)
acc += torch.sum((scores_D0_G0 <= self.acc_margin).float())
with torch.no_grad():
fake_G1 = self.modelG[1](latent).detach()
if self.add_noise:
self.epsilon.data.resize_(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)
# # train with fake from random
# self.noise.data.resize_(batch_size, self.nz, 1, 1).normal_(0, 1)
# with torch.no_grad():
# fake_G0_random = self.modelG[0](self.noise).detach()
# fake_G0_random.requires_grad = True
# if self.add_noise:
# self.epsilon.data.resize_(input.size()).normal_(0, self.noise_var)
# dis_input = fake_G0_random + self.epsilon
# scores_D0_G0_random = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
# else:
# scores_D0_G0_random = self.modelD[0](fake_G0_random, self.extra_layer, self.extra_layer_gamma)
# with torch.no_grad():
# fake_G1_random = self.modelG[1](self.noise).detach()
# fake_G1_random.requires_grad = True
# if self.add_noise:
# self.epsilon.data.resize_(input.size()).normal_(0, self.noise_var)
# dis_input = fake_G1_random + self.epsilon
# scores_D0_G1_random = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
# else:
# scores_D0_G1_random = self.modelD[0](fake_G1_random, self.extra_layer, self.extra_layer_gamma)
# acc += torch.sum((scores_D0_G0_random <= self.acc_margin).float())
disc_acc = float(acc)*100 / (2*batch_size)
# Compute loss and do backward()
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])
# errD0 += self.criterion.rankerD([scores_D0, scores_D0_G0_random, scores_D0_G1_random])
# net_error = self.rank_error_weight*(errD0) + self.disc_diff_weight*disc_abs_diff
net_error = ((scores_D0_G1 + scores_D0_G0)/2 - scores_D0).mean() + errD0
if self.gp:
gradient_penalty = self.calc_gradient_penalty(input, fake_G0, batch_size)
net_error += self.gp_lambda * gradient_penalty
else:
gradient_penalty = self.t_zero
net_error.backward()
self.optimizerD.step()
self.extra_layer_gamma = min(self.extra_layer_gamma + 1/(2*self.len_dataset), 1)
mean_scores_D0 = scores_D0.median().item()
mean_scores_D0_G0 = scores_D0_G0.median().item()
mean_scores_D0_G1 = scores_D0_G1.median().item()
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
# else:
# self.d_iter = self.d_iter_init
lossD = errD0.item()#/2
if j < d_iterations:
# Bookkeeping
losses_train = {}
losses_train['Loss_D0'] = lossD
losses_train['Loss_G0'] = lossG
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['Disc_Diff'] = disc_abs_diff.item()
losses_train['Disc_Acc'] = disc_acc
losses_train['Iden_Loss'] = feat_loss.item()
losses_train['Pose_Loss'] = pose_loss.item()
# 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 Score_D0: %.3f Score_D0_G0: %.3f Score_D0_G1: %.3f Disc_Acc %.3f Disc_Diff %.3f GP %.3f Iden_Loss %.3f Pose_Loss %.3f'
% (stage, epoch, self.stage_epochs[stage], i, self.len_dataset, lossD, lossG, mean_scores_D0, mean_scores_D0_G0, mean_scores_D0_G1, disc_acc, disc_abs_diff.item(), gradient_penalty.item(), feat_loss.item(), pose_loss.item()))
if i % self.plot_update_interval == 0:
losses_train['netD_norm_w'] = self.get_model_norm(self.modelD[0])[0]
losses_train['Gradient_Penalty'] = gradient_penalty.item()
losses_train['Real'] = input.cpu()
losses_train['Fakes'] = fake_G0.detach().cpu()
losses_train['Fakes_Previous'] = fake_G1.detach().cpu()
self.visualizer_train.update(losses_train)
############################
# 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()
net_error = 0
# if self.add_noise:
# self.epsilon.data.resize_(input.size()).normal_(0, self.noise_var)
# dis_input = input + self.epsilon
# scores_D0 = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
# else:
# scores_D0 = self.modelD[0](input, self.extra_layer, self.extra_layer_gamma)
# fake_G0_random = self.modelG[0](self.noise)
# if self.add_noise:
# self.epsilon.data.resize_(input.size()).normal_(0, self.noise_var)
# dis_input = fake_G0_random + self.epsilon
# scores_D0_G0_random = self.modelD[0](dis_input, self.extra_layer, self.extra_layer_gamma)
# else:
# scores_D0_G0_random = self.modelD[0](fake_G0_random, self.extra_layer, self.extra_layer_gamma)
fake_G0 = self.modelG[0](latent)
if self.add_noise:
self.epsilon.data.resize_(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_x = torch.nn.functional.upsample(fake_G0, size=160, mode='bilinear')
fake_iden = self.facenet(fake_x)[0]
fake_iden = (fake_iden - fake_iden.mean(1, keepdim=True)) / fake_iden.std(1, keepdim=True)
# feat_loss = torch.norm(input_iden - fake_iden, dim=1).mean()
feat_loss = 1 - self.cos_sim(input_iden, fake_iden).mean()
fake_pose = self.hopenet(fake_x)
fake_pose = torch.cat(fake_pose, dim=1)
fake_pose = (fake_pose - fake_pose.mean(1, keepdim=True)) / fake_pose.std(1, keepdim=True)
pose_loss = torch.norm(input_pose - fake_pose, dim=1).mean()
# compute error and backpropagate
self.gen_gamma = min(self.gen_gamma + 1/(2*self.len_dataset), 1)
# errG0 = self.criterion.rankerG([scores_D0, scores_D0_G0])
errG0 = - scores_D0_G0.mean()
# net_error = self.gen_gamma * errG0 + self.identity_lambda*feat_loss + self.pose_lambda*pose_loss# - scores_D0_G0_random.mean()
net_error = errG0 + self.identity_lambda*feat_loss + self.pose_lambda*pose_loss
net_error.backward()
self.optimizerG.step()
self.giterations += 1
mean_scores_D0 = scores_D0.median().item()
mean_scores_D0_G0 = scores_D0_G0.median().item()
mean_scores_D0_G1 = scores_D0_G1.median().item()
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
# else:
# self.g_iter = self.g_iter_init
lossG = errG0.item()
if j < self.g_iter:
i += 1
# Bookkeeping
losses_train = {}
losses_train['Loss_D0'] = lossD
losses_train['Loss_G0'] = lossG
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['Disc_Diff'] = disc_abs_diff.item()
losses_train['Disc_Acc'] = disc_acc
losses_train['Iden_Loss'] = feat_loss.item()
losses_train['Pose_Loss'] = pose_loss.item()
# 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 Score_D0: %.3f Score_D0_G0: %.3f Disc_Acc %.3f Disc_Diff %.3f GP %.3f Iden_Loss %.3f Pose_Loss %.3f'
% (stage, epoch, self.stage_epochs[stage], i, self.len_dataset, lossD, lossG, mean_scores_D0, mean_scores_D0_G0, disc_acc, disc_abs_diff.item(), gradient_penalty.item(), feat_loss.item(), pose_loss.item()))
if i % self.plot_update_interval == 0:
losses_train['netD_norm_w'] = self.get_model_norm(self.modelD[0])[0]
losses_train['Gradient_Penalty'] = gradient_penalty.item()
losses_train['Real'] = input.cpu()
losses_train['Fakes'] = fake_G0.detach().cpu()
losses_train['Fakes_Previous'] = fake_G1.detach().cpu()
self.visualizer_train.update(losses_train)
input = data_iter.next()[0].to(self.device)
batch_size = input.size(0)
with torch.no_grad():
input_x = torch.nn.functional.upsample(input, size=160, mode='bilinear')
input_iden = self.facenet(input_x)[0].detach()
input_iden = (input_iden - input_iden.mean(1, keepdim=True)) / input_iden.std(1, keepdim=True)
input_pose = self.hopenet(input_x)
input_pose = torch.cat(input_pose, dim=1).detach()
input_pose = (input_pose - input_pose.mean(1, keepdim=True)) / input_pose.std(1, keepdim=True)
self.noise.resize_(batch_size, self.noise_z, 1, 1).normal_(0, 1)
latent = torch.cat((self.noise, input_iden.unsqueeze(-1).unsqueeze(-1), input_pose.unsqueeze(-1).unsqueeze(-1)), 1)
self.noise.data.resize_(batch_size, self.nz, 1, 1).normal_(0, 1)
# Bookkeeping
losses_train = {}
losses_train['Loss_D0'] = lossD
losses_train['Loss_G0'] = lossG
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['Disc_Diff'] = disc_abs_diff.item()
losses_train['Disc_Acc'] = disc_acc
losses_train['Iden_Loss'] = feat_loss.item()
losses_train['Pose_Loss'] = pose_loss.item()
# 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 Score_D0: %.3f Score_D0_G0: %.3f Score_D0_G1: %.3f Disc_Acc %.3f Disc_Diff %.3f GP %.3f Iden_Loss %.3f Pose_Loss %.3f'
% (stage, epoch, self.stage_epochs[stage], i, self.len_dataset, lossD, lossG, mean_scores_D0, mean_scores_D0_G0, mean_scores_D0_G1, disc_acc, disc_abs_diff.item(), gradient_penalty.item(), feat_loss.item(), pose_loss.item()))
if i % self.plot_update_interval == 0:
losses_train['netD_norm_w'] = self.get_model_norm(self.modelD[0])[0]
losses_train['Gradient_Penalty'] = gradient_penalty.item()
losses_train['Real'] = input.cpu()
losses_train['Fakes'] = fake_G0.detach().cpu()
losses_train['Fakes_Previous'] = fake_G1.detach().cpu()
self.visualizer_train.update(losses_train)
if i % 250 == 0 or i == self.len_dataset:
fake_normal_z = self.modelG[0](self.fixed_noise)
fake_encoder_z = fake_G0
if len(input.size()) < 3:
fig = plt.figure()
plt.hist(input.squeeze().cpu().numpy(), bins=60)
fig.savefig("{}/stage_{}_epoch_{}_real.png".format(self.args.save, 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.args.save, 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.args.save, stage, i), dpi=fig.dpi)
else:
vutils.save_image(fake_normal_z.data, '%s/fake_samples_stage_%03d_epoch_%03d_normal_z.png' % (self.args.save, stage, epoch), normalize=True)
vutils.save_image(fake_encoder_z.data, '%s/fake_samples_stage_%03d_epoch_%03d_encoder_z.png' % (self.args.save, stage, epoch), normalize=True)
vutils.save_image(input, '%s/real_samples.png' % self.args.save, normalize=True)
if True:
self.disc_diff_weight = self.disc_diff_weight_init
elif disc_abs_diff.item() < 5:
self.disc_diff_weight = (self.marker_high - self.marker_low)*0.2 #self.disc_diff_weight_init/1000
elif disc_abs_diff.item() < 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[stage], 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[1].eval()
# self.modelD[0].eval()
# self.modelD[1].eval()
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.noise.data.resize_(batch_size, self.nz, 1, 1).normal_(0, 1)
fake_G0 = self.modelG[0](self.noise)
fake_G1 = self.modelG[1](self.noise)
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.mean().item()
test_scores['Test_Score_D0_G0'] = score_D0_G0.mean().item()
test_scores['Test_Score_D0_G1'] = score_D0_G1.mean().item()
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.mean().item(), score_D0_G0.mean().item(), score_D0_G1.mean().item(), ssim_score, psnr_score, disc_acc))
if i == len(dataloader)-2:
try:
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.dir_save, 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.dir_save, stage, i), dpi=fig.dpi)
else:
vutils.save_image(fake_encoder_z.data, '%s/val_fake_samples_stage_%03d_encoder_z.png' % (self.args.save, stage), normalize=True)
vutils.save_image(input, '%s/val_real_samples.png' % self.args.save, normalize=True)
except Exception as e:
print(e)
avg_mean_scores_D0 = epoch_score_D0.median()
avg_mean_scores_D0_G0 = epoch_score_D0_G0.median()
avg_mean_scores_D0_G1 = epoch_score_D0_G1.median()
test_scores = {}
test_scores['Test_Score_D0'] = avg_mean_scores_D0
test_scores['Test_Score_D0_G0'] = avg_mean_scores_D0_G0
test_scores['Test_Score_D0_G1'] = avg_mean_scores_D0_G1
test_scores['SSIM'] = epoch_ssim_score/(num_batches-1)
test_scores['PSNR'] = epoch_psnr_score/(num_batches-1)
test_scores['Test_Disc_Acc'] = epoch_disc_acc / (num_batches-1)
test_scores['Test_Real'] = self.test_input.data.cpu()
test_scores['Test_Fakes'] = fake_G0.data.cpu()
self.visualizer_test.update(test_scores)
# Check for Discriminator Saturation
if avg_mean_scores_D0_G0 > avg_mean_scores_D0 - (avg_mean_scores_D0 - avg_mean_scores_D0_G1)/4:
self.update_opt_flag = True
else:
self.update_opt_flag = False
try:
loss = self.monitor_test.getvalues()
self.log_loss_test.update(loss)
except Exception as e:
print("Error while logging test loss")
print(e)
if stage == 1 and epoch_disc_acc / (num_batches) >= 80: #96
return True
else:
return False
def get_model_norm(self, model):
norm = torch.zeros(1)
param_list = list(model.main.parameters())
for l in param_list:
norm += torch.norm(l.data)
return norm
def update_opt_disc(self):
# Increase Discriminator Capacity When Generator Becomes Strong
if self.add_capacity:
self.extra_layer = min(self.extra_layer + 1, 5)
print("One layer added to the Discriminator")
elif self.add_clamp:
self.clamp_upper += 0.0002
self.clamp_lower -= 0.0002
self.lr_dis *= 1.003
self.lr_gen *= 1.003
if self.lr_dis > 10*self.lr_vae:
self.lr_dis = 10*self.lr_vae
if self.lr_gen > 10*self.lr_vae:
self.lr_gen = 10*self.lr_vae
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)
print("Clamping increased to: Upper {} Lower {} LR_Disc {} LR_Gen {}".format(self.clamp_upper, self.clamp_lower, self.lr_dis, self.lr_gen))
def optimize_discriminator(self, stage, epoch, dataloader):
############################
# Optimize Discriminator Network
############################
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))
i = 0