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gan_trainer.py
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gan_trainer.py
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import copy
import os
import math
import torch
import torch.nn as nn
import torch.nn.init as init
from torch.optim import lr_scheduler
from balagan import BalaGAN
from utils import get_model_list
def update_average(model_tgt, model_src, beta=0.999):
with torch.no_grad():
param_dict_src = dict(model_src.named_parameters())
for p_name, p_tgt in model_tgt.named_parameters():
p_src = param_dict_src[p_name]
assert(p_src is not p_tgt)
p_tgt.copy_(beta*p_tgt + (1. - beta)*p_src)
class GANTrainer(nn.Module):
def __init__(self, config):
super(GANTrainer, self).__init__()
self.config = config
self.model = BalaGAN(config)
lr_gen = config['gan']['lr_gen']
lr_dis = config['gan']['lr_dis']
dis_params = list(self.model.dis.parameters())
gen_params = list(self.model.gen.parameters())
self.dis_opt = torch.optim.RMSprop(
[p for p in dis_params if p.requires_grad], lr=lr_gen, weight_decay=config['gan']['weight_decay'])
self.gen_opt = torch.optim.RMSprop(
[p for p in gen_params if p.requires_grad], lr=lr_dis, weight_decay=config['gan']['weight_decay'])
self.dis_scheduler = get_scheduler(self.dis_opt, config)
self.gen_scheduler = get_scheduler(self.gen_opt, config)
self.apply(weights_init(config['gan']['init']))
self.model.gen_test = copy.deepcopy(self.model.gen)
def gen_update(self, source_data, target_data, hp):
self.gen_opt.zero_grad()
al, ad, xr, cr, sr, ac = self.model(source_data, target_data, hp, 'gen_update')
self.loss_gen_total = torch.mean(al)
self.loss_gen_recon_x = torch.mean(xr)
self.loss_gen_recon_c = torch.mean(cr)
self.loss_gen_recon_s = torch.mean(sr)
self.loss_gen_adv = torch.mean(ad)
self.accuracy_gen_adv = torch.mean(ac)
self.gen_opt.step()
update_average(self.model.gen_test, self.model.gen)
return self.accuracy_gen_adv.item()
def dis_update(self, source_data, target_data, hp):
self.dis_opt.zero_grad()
al, lfa, lre, reg, acc, l_cls = self.model(source_data, target_data, hp, 'dis_update')
self.loss_dis_total = torch.mean(al)
self.loss_dis_fake_adv = torch.mean(lfa)
self.loss_dis_real_adv = torch.mean(lre)
self.loss_dis_reg = torch.mean(reg)
self.accuracy_dis_adv = torch.mean(acc)
self.loss_dis_cls = torch.mean(l_cls)
self.dis_opt.step()
return self.accuracy_dis_adv.item()
def test(self, source_data, target_data):
return self.model.test(source_data, target_data)
def resume(self, checkpoint_dir, hp):
last_model_name = get_model_list(checkpoint_dir, "gen")
if last_model_name:
state_dict = torch.load(last_model_name)
self.model.gen.load_state_dict(state_dict['gen'])
self.model.gen_test.load_state_dict(state_dict['gen_test'])
iterations = int(last_model_name[-11:-3])
last_model_name = get_model_list(checkpoint_dir, "dis")
state_dict = torch.load(last_model_name)
self.model.dis.load_state_dict(state_dict['dis'])
last_opt_name = get_model_list(checkpoint_dir, "optimizer")
state_dict = torch.load(last_opt_name)
self.dis_opt.load_state_dict(state_dict['dis'])
self.gen_opt.load_state_dict(state_dict['gen'])
self.dis_scheduler = get_scheduler(self.dis_opt, hp, iterations)
self.gen_scheduler = get_scheduler(self.gen_opt, hp, iterations)
else:
iterations = 0
print(f'Resume GAN from iteration {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))
dis_name = os.path.join(snapshot_dir, 'dis_%08d.pt' % (iterations))
opt_name = os.path.join(snapshot_dir, 'optimizer_%08d.pt' % (iterations))
torch.save({'gen': self.model.gen.state_dict(),
'gen_test': self.model.gen_test.state_dict()}, gen_name)
torch.save({'dis': self.model.dis.state_dict()}, dis_name)
torch.save({'gen': self.gen_opt.state_dict(),
'dis': self.dis_opt.state_dict()}, opt_name)
def load_ckpt(self, ckpt_name):
state_dict = torch.load(ckpt_name)
self.model.gen.load_state_dict(state_dict['gen'])
self.model.gen_test.load_state_dict(state_dict['gen_test'])
def load_dis_pt(self, pt_name):
state_dic = torch.load(pt_name)
self.model.dis.load_state_dict(state_dic['dis'])
def translate(self, source_data, target_data):
return self.model.translate(source_data, target_data)
def forward(self, *inputs):
print('Forward function not implemented.')
pass
def get_scheduler(optimizer, hp, it=-1):
if 'lr_policy' not in hp or hp['lr_policy'] == 'constant':
scheduler = None # constant scheduler
elif hp['lr_policy'] == 'step':
scheduler = lr_scheduler.StepLR(optimizer, step_size=hp['step_size'],
gamma=hp['gamma'], last_epoch=it)
else:
return NotImplementedError('%s not implemented', hp['lr_policy'])
return scheduler
def weights_init(init_type='gaussian'):
def init_fun(m):
classname = m.__class__.__name__
if (classname.find('Conv') == 0 or classname.find(
'Linear') == 0) and hasattr(m, 'weight'):
if init_type == 'gaussian':
init.normal_(m.weight.data, 0.0, 0.02)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=math.sqrt(2))
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=math.sqrt(2))
elif init_type == 'default':
pass
else:
assert 0, "Unsupported initialization: {}".format(init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
return init_fun