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engine.py
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engine.py
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import torch
import util.util as util
import models
import time
import os
import sys
from os.path import join
from util.visualizer import Visualizer
class Engine(object):
def __init__(self, opt):
self.opt = opt
self.writer = None
self.visualizer = None
self.model = None
self.best_val_loss = 1e6
self.__setup()
def __setup(self):
self.basedir = join('checkpoints', self.opt.name)
if not os.path.exists(self.basedir):
os.mkdir(self.basedir)
opt = self.opt
"""Model"""
self.model = models.__dict__[self.opt.model]()
self.model.initialize(opt)
if not opt.no_log:
self.writer = util.get_summary_writer(os.path.join(self.basedir, 'logs'))
self.visualizer = Visualizer(opt)
def train(self, train_loader, **kwargs):
print('\nEpoch: %d' % self.epoch)
avg_meters = util.AverageMeters()
opt = self.opt
model = self.model
epoch = self.epoch
epoch_start_time = time.time()
for i, data in enumerate(train_loader):
iter_start_time = time.time()
iterations = self.iterations
model.set_input(data, mode='train')
model.optimize_parameters(**kwargs)
errors = model.get_current_errors()
avg_meters.update(errors)
util.progress_bar(i, len(train_loader), str(avg_meters))
if not opt.no_log:
util.write_loss(self.writer, 'train', avg_meters, iterations)
if iterations % opt.display_freq == 0 and opt.display_id != 0:
save_result = iterations % opt.update_html_freq == 0
self.visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
if iterations % opt.print_freq == 0 and opt.display_id != 0:
t = (time.time() - iter_start_time)
self.iterations += 1
self.epoch += 1
if not self.opt.no_log:
if self.epoch % opt.save_epoch_freq == 0:
print('saving the model at epoch %d, iters %d' %
(self.epoch, self.iterations))
model.save()
print('saving the latest model at the end of epoch %d, iters %d' %
(self.epoch, self.iterations))
model.save(label='latest')
print('Time Taken: %d sec' %
(time.time() - epoch_start_time))
# model.update_learning_rate()
train_loader.reset()
def eval(self, val_loader, dataset_name, savedir=None, loss_key=None, **kwargs):
avg_meters = util.AverageMeters()
model = self.model
opt = self.opt
with torch.no_grad():
for i, data in enumerate(val_loader):
index = model.eval(data, savedir=savedir, **kwargs)
avg_meters.update(index)
util.progress_bar(i, len(val_loader), str(avg_meters))
if not opt.no_log:
util.write_loss(self.writer, join('eval', dataset_name), avg_meters, self.epoch)
if loss_key is not None:
val_loss = avg_meters[loss_key]
if val_loss < self.best_val_loss:
self.best_val_loss = val_loss
print('saving the best model at the end of epoch %d, iters %d' %
(self.epoch, self.iterations))
model.save(label='best_{}_{}'.format(loss_key, dataset_name))
return avg_meters
def test(self, test_loader, savedir=None, **kwargs):
model = self.model
opt = self.opt
with torch.no_grad():
for i, data in enumerate(test_loader):
model.test(data, savedir=savedir, **kwargs)
util.progress_bar(i, len(test_loader))
@property
def iterations(self):
return self.model.iterations
@iterations.setter
def iterations(self, i):
self.model.iterations = i
@property
def epoch(self):
return self.model.epoch
@epoch.setter
def epoch(self, e):
self.model.epoch = e