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train.py
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train.py
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import time
from options.train_options import TrainOptions
from data.custom_dataset_data_loader import CustomDatasetDataLoader
from models.models import ModelsFactory
from utils.tb_visualizer import TBVisualizer
from collections import OrderedDict
import os
class Train:
def __init__(self):
self._opt = TrainOptions().parse()
data_loader_train = CustomDatasetDataLoader(self._opt, is_for_train=True)
data_loader_test = CustomDatasetDataLoader(self._opt, is_for_train=False)
self._dataset_train = data_loader_train.load_data()
self._dataset_test = data_loader_test.load_data()
self._dataset_train_size = len(data_loader_train)
self._dataset_test_size = len(data_loader_test)
print('#train images = %d' % self._dataset_train_size)
print('#test images = %d' % self._dataset_test_size)
self._model = ModelsFactory.get_by_name(self._opt.model, self._opt)
self._tb_visualizer = TBVisualizer(self._opt)
self._train()
def _train(self):
self._total_steps = self._opt.load_epoch * self._dataset_train_size
self._iters_per_epoch = self._dataset_train_size / self._opt.batch_size
self._last_display_time = None
self._last_save_latest_time = None
self._last_print_time = time.time()
for i_epoch in range(self._opt.load_epoch + 1, self._opt.nepochs_no_decay + self._opt.nepochs_decay + 1):
epoch_start_time = time.time()
# train epoch
self._train_epoch(i_epoch)
# save model
print('saving the model at the end of epoch %d, iters %d' % (i_epoch, self._total_steps))
self._model.save(i_epoch)
# print epoch info
time_epoch = time.time() - epoch_start_time
print('End of epoch %d / %d \t Time Taken: %d sec (%d min or %d h)' %
(i_epoch, self._opt.nepochs_no_decay + self._opt.nepochs_decay, time_epoch,
time_epoch / 60, time_epoch / 3600))
# update learning rate
if i_epoch > self._opt.nepochs_no_decay:
self._model.update_learning_rate()
def _train_epoch(self, i_epoch):
epoch_iter = 0
self._model.set_train()
for i_train_batch, train_batch in enumerate(self._dataset_train):
iter_start_time = time.time()
# display flags
do_visuals = self._last_display_time is None or time.time() - self._last_display_time > self._opt.display_freq_s
do_print_terminal = time.time() - self._last_print_time > self._opt.print_freq_s or do_visuals
# train model
self._model.set_input(train_batch)
train_generator = ((i_train_batch+1) % self._opt.train_G_every_n_iterations == 0) or do_visuals
self._model.optimize_parameters(keep_data_for_visuals=do_visuals, train_generator=train_generator)
# update epoch info
self._total_steps += self._opt.batch_size
epoch_iter += self._opt.batch_size
# display terminal
if do_print_terminal:
self._display_terminal(iter_start_time, i_epoch, i_train_batch, do_visuals)
self._last_print_time = time.time()
# display visualizer
if do_visuals:
self._display_visualizer_train(self._total_steps)
self._display_visualizer_val(i_epoch, self._total_steps)
self._last_display_time = time.time()
# save model
if self._last_save_latest_time is None or time.time() - self._last_save_latest_time > self._opt.save_latest_freq_s:
print('saving the latest model (epoch %d, total_steps %d)' % (i_epoch, self._total_steps))
self._model.save(i_epoch)
self._last_save_latest_time = time.time()
def _display_terminal(self, iter_start_time, i_epoch, i_train_batch, visuals_flag):
errors = self._model.get_current_errors()
t = (time.time() - iter_start_time) / self._opt.batch_size
self._tb_visualizer.print_current_train_errors(i_epoch, i_train_batch, self._iters_per_epoch, errors, t, visuals_flag)
def _display_visualizer_train(self, total_steps):
self._tb_visualizer.display_current_results(self._model.get_current_visuals(), total_steps, is_train=True)
self._tb_visualizer.plot_scalars(self._model.get_current_errors(), total_steps, is_train=True)
self._tb_visualizer.plot_scalars(self._model.get_current_scalars(), total_steps, is_train=True)
def _display_visualizer_val(self, i_epoch, total_steps):
val_start_time = time.time()
# set model to eval
self._model.set_eval()
# evaluate self._opt.num_iters_validate epochs
val_errors = OrderedDict()
for i_val_batch, val_batch in enumerate(self._dataset_test):
if i_val_batch == self._opt.num_iters_validate:
break
# evaluate model
self._model.set_input(val_batch)
self._model.forward(keep_data_for_visuals=(i_val_batch == 0))
errors = self._model.get_current_errors()
# store current batch errors
for k, v in errors.iteritems():
if k in val_errors:
val_errors[k] += v
else:
val_errors[k] = v
# normalize errors
for k in val_errors.iterkeys():
val_errors[k] /= self._opt.num_iters_validate
# visualize
t = (time.time() - val_start_time)
self._tb_visualizer.print_current_validate_errors(i_epoch, val_errors, t)
self._tb_visualizer.plot_scalars(val_errors, total_steps, is_train=False)
self._tb_visualizer.display_current_results(self._model.get_current_visuals(), total_steps, is_train=False)
# set model back to train
self._model.set_train()
if __name__ == "__main__":
Train()