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train.py
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"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import sys
from collections import OrderedDict
from options.train_options import TrainOptions
import data
from util.iter_counter import IterationCounter
from util.visualizer import Visualizer
from trainers.pix2pix_trainer import Pix2PixTrainer
import numpy as np
from models.networks.sync_batchnorm import DataParallelWithCallback
from util.explanation_utils import explanation_hook, get_explanation, explanation_hook_cifar
# parse options
opt = TrainOptions().parse()
# print options to help debugging
print(' '.join(sys.argv))
# load the dataset
dataloader = data.create_dataloader(opt)
class Pix2PixTrainer():
"""
Trainer creates the model and optimizers, and uses them to
updates the weights of the network while reporting losses
and the latest visuals to visualize the progress in training.
"""
def __init__(self, opt):
self.opt = opt
if self.opt.dual:
from models.pix2pix_dualmodel import Pix2PixModel
elif self.opt.dual_segspade:
from models.pix2pix_dual_segspademodel import Pix2PixModel
elif opt.box_unpair:
from models.pix2pix_dualunpair import Pix2PixModel
else:
from models.pix2pix_model import Pix2PixModel
self.pix2pix_model = Pix2PixModel(opt)
self.netG = self.pix2pix_model.netG
self.discriminator = self.pix2pix_model.netD
if len(opt.gpu_ids) > 0:
self.pix2pix_model = DataParallelWithCallback(self.pix2pix_model,
device_ids=opt.gpu_ids)
self.pix2pix_model_on_one_gpu = self.pix2pix_model.module
else:
self.pix2pix_model_on_one_gpu = self.pix2pix_model
self.generated = None
if opt.isTrain:
self.optimizer_G, self.optimizer_D = \
self.pix2pix_model_on_one_gpu.create_optimizers(opt)
self.old_lr = opt.lr
# self.d_out = None
self.explanationType = 'shap'
def run_generator_one_step(self, data, local_explainable):
self.optimizer_G.zero_grad()
g_losses, generated = self.pix2pix_model(data, mode='generator')
import numpy as np
d_o1 = self.run_discriminator_one_step(data)
# print("do1", d_o1)
d_o1 = np.array(d_o1)
if local_explainable:
get_explanation(generated_data=generated, discriminator=self.discriminator, prediction=d_o1,
XAItype=self.explanationType, trained_data=data, data_type="abc")
g_loss = sum(g_losses.values()).mean()
g_loss.backward()
self.optimizer_G.step()
self.g_losses = g_losses
self.generated = generated
def run_discriminator_one_step(self, data):
self.optimizer_D.zero_grad()
d_losses, d_out = self.pix2pix_model(data, mode='discriminator')
d_loss = sum(d_losses.values()).mean()
d_loss.backward()
self.optimizer_D.step()
self.d_losses = d_losses
# self.d_out = d_out
return d_out
def get_latest_losses(self):
return {**self.g_losses, **self.d_losses}
def get_latest_generated(self):
return self.generated
def update_learning_rate(self, epoch):
self.update_learning_rate(epoch)
def save(self, epoch):
self.pix2pix_model_on_one_gpu.save(epoch)
##################################################################
# Helper functions
##################################################################
def update_learning_rate(self, epoch):
if epoch > self.opt.niter:
lrd = self.opt.lr / self.opt.niter_decay
new_lr = self.old_lr - lrd
else:
new_lr = self.old_lr
if new_lr != self.old_lr:
if self.opt.no_TTUR:
new_lr_G = new_lr
new_lr_D = new_lr
else:
new_lr_G = new_lr / 2
new_lr_D = new_lr * 2
for param_group in self.optimizer_D.param_groups:
param_group['lr'] = new_lr_D
for param_group in self.optimizer_G.param_groups:
param_group['lr'] = new_lr_G
print('update learning rate: %f -> %f' % (self.old_lr, new_lr))
self.old_lr = new_lr
# create trainer for our model
trainer = Pix2PixTrainer(opt)
# create tool for counting iterations
iter_counter = IterationCounter(opt, len(dataloader))
# create tool for visualization
visualizer = Visualizer(opt)
# seq_len_total = 0
# for i, data_i in enumerate(dataloader, start=iter_counter.epoch_iter):
# print(i)
# print(data_i['retrival_label_list'].shape)
# # if i == 1:
# # break
# exit()
# label_tensor = data_i['label']
# label_np = label_tensor.data.cpu().numpy()[0]
# label_seq = np.unique(label_np)
# seq_len_total += len(label_seq)
# break
# print(seq_len_total/float(i))
explanationSwitch = (len(iter_counter.training_epochs()) + 1) / 2 if len(iter_counter.training_epochs()) % 2 == 1 else len(iter_counter.training_epochs()) / 2
for epoch in iter_counter.training_epochs():
iter_counter.record_epoch_start(epoch)
for i, data_i in enumerate(dataloader, start=iter_counter.epoch_iter):
# local_explainable=True
iter_counter.record_one_iteration()
# if (epoch - 1) == explanationSwitch:
trainer.netG.out.register_backward_hook(explanation_hook)
local_explainable = True
# Training
# train generator
if i % opt.D_steps_per_G == 0:
trainer.run_generator_one_step(data_i, local_explainable)
# train discriminator
trainer.run_discriminator_one_step(data_i)
# Visualizations
if iter_counter.needs_printing():
losses = trainer.get_latest_losses()
visualizer.print_current_errors(epoch, iter_counter.epoch_iter,
losses, iter_counter.time_per_iter)
visualizer.plot_current_errors(losses, iter_counter.total_steps_so_far)
if iter_counter.needs_displaying():
visuals = OrderedDict([('input_label', data_i['label']),
('synthesized_image', trainer.get_latest_generated()),
('real_image', data_i['image'])])
visualizer.display_current_results(visuals, epoch, iter_counter.total_steps_so_far)
if iter_counter.needs_saving():
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, iter_counter.total_steps_so_far))
trainer.save('latest')
iter_counter.record_current_iter()
trainer.update_learning_rate(epoch)
iter_counter.record_epoch_end()
if epoch % opt.save_epoch_freq == 0 or \
epoch == iter_counter.total_epochs:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, iter_counter.total_steps_so_far))
trainer.save('latest')
trainer.save(epoch)
print('Training was successfully finished.')