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trainer.py
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"""
modified from: Multimodal Unsupervised Image-to-Image Translation
https://github.com/NVlabs/MUNIT
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)..
"""
from networks import MsImageDis, MaskGen, MaskGenOld
from utils import weights_init, get_model_list, get_scheduler
import torch
import torch.nn as nn
import os
class MUSIC_Trainer(nn.Module):
def __init__(self, hyperparameters):
super(MUSIC_Trainer, self).__init__()
lr = hyperparameters['lr']
old_flag = hyperparameters['old_flag']
# Initiate the networks
if old_flag == 1:
self.gen = MaskGenOld(hyperparameters['input_dim_a'], hyperparameters['gen']) # auto-encoder for domain a
self.style_dim = hyperparameters['gen']['style_dim']
self.s_a = torch.randn(8, self.style_dim, 1, 1).cuda()
self.s_b = torch.randn(8, self.style_dim, 1, 1).cuda()
else:
self.gen = MaskGen(hyperparameters['input_dim_a'], hyperparameters['gen']) # auto-encoder for domain a
self.dis_a = MsImageDis(hyperparameters['input_dim_a'], hyperparameters['dis']) # discriminator for domain a
self.dis_b = MsImageDis(hyperparameters['input_dim_b'], hyperparameters['dis']) # discriminator for domain b
self.instancenorm = nn.InstanceNorm2d(512, affine=False)
try:
enhance = hyperparameters['enhance']
except KeyError:
enhance = None
self.enhance = enhance
# Setup the optimizers
beta1 = hyperparameters['beta1']
beta2 = hyperparameters['beta2']
dis_params = list(self.dis_a.parameters()) + list(self.dis_b.parameters())
gen_params = list(self.gen.parameters())
self.dis_opt = torch.optim.Adam([p for p in dis_params if p.requires_grad],
lr=lr, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])
self.gen_opt = torch.optim.Adam([p for p in gen_params if p.requires_grad],
lr=lr, betas=(beta1, beta2), weight_decay=hyperparameters['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters)
# Network weight initialization
self.apply(weights_init(hyperparameters['init']))
self.dis_a.apply(weights_init('gaussian'))
self.dis_b.apply(weights_init('gaussian'))
def recon_criterion(self, input, target):
return torch.mean(torch.abs(input - target))
def masking(self, mask, img):
if self.enhance:
mask = mask ** self.enhance
img = img * 0.5 + 0.5
masked_image = mask * img
masked_image = (masked_image - 0.5) * 2
return masked_image
def scaled_sum(self, input_1, input_2):
input_1 = input_1 * 0.5 + 0.5
input_2 = input_2 * 0.5 + 0.5
sum_output = input_1 + input_2
sum_output = torch.clamp(sum_output, 0, 1) # added at 3.yaml
sum_output = (sum_output - 0.5) * 2
return sum_output
def scaled_sub(self, input_1, input_2):
input_1 = input_1 * 0.5 + 0.5
input_2 = input_2 * 0.5 + 0.5
sub_output = input_1 - input_2
sub_output = torch.clamp(sub_output, 0, 1) # added at 3.yaml
sub_output = (sub_output - 0.5) * 2
return sub_output
def forward(self, x_b):
self.eval()
x_ba_mask = self.gen.decode(self.gen.encode(x_b))
x_ba = self.masking(x_ba_mask, x_b)
self.train()
return x_ba
def gen_update(self, x_a, x_b, hyperparameters):
self.gen_opt.zero_grad()
# encode-decode
x_ba_mask = self.gen.decode(self.gen.encode(x_b))
x_aa_mask = self.gen.decode(self.gen.encode(x_a))
x_ba = self.masking(x_ba_mask, x_b)
x_aa = self.masking(x_aa_mask, x_a)
# encode again
x_t = self.scaled_sub(x_b, x_ba)
x_b_new = self.scaled_sum(x_t, x_a)
x_b_new_mask = self.gen.decode(self.gen.encode(x_b_new))
x_ba_new = self.masking(x_b_new_mask, x_b_new)
x_t_new = self.scaled_sub(x_b_new, x_ba_new)
# decode twice
x_baa_mask = self.gen.decode(self.gen.encode(x_ba))
x_baa = self.masking(x_baa_mask, x_ba)
# reconstruction loss
self.loss_gen_recon_x_aa = self.recon_criterion(x_aa, x_a)
self.loss_gen_recon_x_t = self.recon_criterion(x_t_new, x_t)
self.loss_gen_recon_x_ba_new = self.recon_criterion(x_ba_new, x_a)
self.loss_gen_recon_x_baa = self.recon_criterion(x_baa, x_ba)
# GAN loss
self.loss_gen_adv_a = self.dis_a.calc_gen_loss(x_ba)
self.loss_gen_adv_b = self.dis_b.calc_gen_loss(x_b_new)
# total loss
self.loss_gen_total = hyperparameters['gan_w_a'] * self.loss_gen_adv_a + \
hyperparameters['gan_w_b'] * self.loss_gen_adv_b + \
hyperparameters['a2a_w'] * self.loss_gen_recon_x_aa + \
hyperparameters['x_t_w'] * self.loss_gen_recon_x_t + \
hyperparameters['recon_w'] * self.loss_gen_recon_x_ba_new + \
hyperparameters['DTN_w'] * self.loss_gen_recon_x_baa
self.loss_gen_total.backward()
self.gen_opt.step()
def sample(self, x_a, x_b):
self.eval()
x_ba, x_aa, x_ba_new = [], [], []
x_t, x_t_new = [], []
x_baa, x_b_new = [], []
for i in range(x_a.size(0)):
x_ba.append(self.masking(self.gen.decode(self.gen.encode(x_b[i].unsqueeze(0))), x_b[i].unsqueeze(0)))
x_aa.append(self.masking(self.gen.decode(self.gen.encode(x_a[i].unsqueeze(0))), x_a[i].unsqueeze(0)))
x_t.append(self.scaled_sub(x_b[i], x_ba[i]))
x_b_new.append(self.scaled_sum(x_t[i], x_a[i].unsqueeze(0)))
x_ba_new.append(self.masking(self.gen.decode(self.gen.encode(x_b_new[i])), x_b_new[i]))
x_t_new.append(self.scaled_sub(x_b_new[i], x_ba_new[i]))
x_baa.append(self.masking(self.gen.decode(self.gen.encode(x_ba[i])), x_ba[i]))
x_ba, x_aa, x_ba_new = torch.cat(x_ba), torch.cat(x_aa), torch.cat(x_ba_new)
x_t, x_t_new = torch.cat(x_t), torch.cat(x_t_new)
x_baa, x_b_new = torch.cat(x_baa), torch.cat(x_b_new)
self.train()
return x_b, x_ba, x_baa, x_a, x_ba_new, x_aa, x_t, x_t_new, x_b_new
def dis_update(self, x_a, x_b, hyperparameters):
self.dis_opt.zero_grad()
x_ba = self.masking(self.gen.decode(self.gen.encode(x_b)), x_b)
x_t = self.scaled_sub(x_b, x_ba)
x_b_new = self.scaled_sum(x_t, x_a)
# D loss
self.loss_dis_a = self.dis_a.calc_dis_loss(x_ba.detach(), x_a)
self.loss_dis_b = self.dis_b.calc_dis_loss(x_b_new.detach(), x_b)
self.loss_dis_total = hyperparameters['gan_w_a'] * self.loss_dis_a + hyperparameters['gan_w_b'] * self.loss_dis_b
self.loss_dis_total.backward()
self.dis_opt.step()
def update_learning_rate(self):
if self.dis_scheduler is not None:
self.dis_scheduler.step()
if self.gen_scheduler is not None:
self.gen_scheduler.step()
def resume(self, checkpoint_dir, hyperparameters):
# Load generators
last_model_name = get_model_list(checkpoint_dir, "gen")
state_dict = torch.load(last_model_name)
self.gen.load_state_dict(state_dict['gen'])
iterations = int(last_model_name[-11:-3])
# Load discriminators
last_model_name = get_model_list(checkpoint_dir, "dis")
state_dict = torch.load(last_model_name)
self.dis_a.load_state_dict(state_dict['a'])
self.dis_b.load_state_dict(state_dict['b'])
# Load optimizers
state_dict = torch.load(os.path.join(checkpoint_dir, 'optimizer.pt'))
self.dis_opt.load_state_dict(state_dict['dis'])
self.gen_opt.load_state_dict(state_dict['gen'])
# Reinitilize schedulers
self.dis_scheduler = get_scheduler(self.dis_opt, hyperparameters, iterations)
self.gen_scheduler = get_scheduler(self.gen_opt, hyperparameters, iterations)
print('Resume from iteration %d' % iterations)
return iterations
def save(self, snapshot_dir, iterations):
# Save generators, discriminators, and optimizers
gen_name = os.path.join(snapshot_dir, 'gen_%08d.pt' % (iterations + 1))
dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % (iterations + 1))
opt_name = os.path.join(snapshot_dir, 'optimizer.pt')
torch.save({'gen': self.gen.state_dict()}, gen_name)
torch.save({'a': self.dis_a.state_dict(), 'b': self.dis_b.state_dict()}, dis_name)
torch.save({'gen': self.gen_opt.state_dict(), 'dis': self.dis_opt.state_dict()}, opt_name)