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train_lt.py
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"""General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model').
It first creates model, dataset, and visualizer given the option.
It then does standard network training. During the training, it also visualize/save the images, print/save the loss plot, and save models.
The script supports continue/resume training. Use '--continue_train' to resume your previous training.
Example:
Train a CycleGAN model:
python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
Train a pix2pix model:
python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
See options/base_options.py and options/train_options.py for more training options.
See training and test tips at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md
See frequently asked questions at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md
"""
import time
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
from util.util import mkdir
import os
if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
dataset_size = len(dataset) # get the number of images in the dataset.
print('The number of training images = %d' % dataset_size)
model = create_model(opt) # create a model given opt.model and other options
model.setup(opt) # regular setup: load and print networks; create schedulers
visualizer = Visualizer(opt) # create a visualizer that display/save images and plots
total_iters = 0 # the total number of training iterations
#pretrain G_A and G_B
print("Start Pretraining of G_A and G_B\n")
model.save_dir = model.save_dir_pre
visualizer.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web_pretrain')
visualizer.img_dir = os.path.join(visualizer.web_dir, 'images')
mkdir(visualizer.img_dir)
for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay_1 + 1): # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
epoch_start_time = time.time() # timer for entire epoch
iter_data_time = time.time() # timer for data loading per iteration
epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch
visualizer.reset() # reset the visualizer: make sure it saves the results to HTML at least once every epoch
model.update_learning_rate() # update learning rates in the beginning of every epoch.
for i, data in enumerate(dataset): # inner loop within one epoch
iter_start_time = time.time() # timer for computation per iteration
if total_iters % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
#
total_iters += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data) # unpack data from dataset and apply preprocessing
model.optimize_parameters() # calculate loss functions, get gradients, update network weights
#
if total_iters % opt.display_freq == 0: # display images on visdom and save images to a HTML file
save_result = total_iters % opt.update_html_freq == 0
model.compute_visuals()
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
#
if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk
losses = model.get_current_losses()
t_comp = (time.time() - iter_start_time) / opt.batch_size
visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
#if opt.display_id > 0:
# visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
#
if total_iters % opt.save_latest_freq == 0: # cache our latest model every <save_latest_freq> iterations
print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
model.save_networks(save_suffix)
#
iter_data_time = time.time()
#
#if i==1: break
#
if epoch % opt.save_epoch_freq == 0: # cache our model every <save_epoch_freq> epochs
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
model.save_networks('latest')
model.save_networks(epoch)
#
print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))
#
#do lithotwin
"""
print("Start LithoTwin\n")
model.save_dir = model.save_dir_post
visualizer.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web_posttrain')
visualizer.img_dir = os.path.join(visualizer.web_dir, 'images')
mkdir(visualizer.img_dir)
model.reset_optimizer()
#model.reset_learning_rate()
model.setup(opt) # regular setup: load and print networks; create schedulers
for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay_2 + 1): # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
epoch_start_time = time.time() # timer for entire epoch
iter_data_time = time.time() # timer for data loading per iteration
epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch
visualizer.reset() # reset the visualizer: make sure it saves the results to HTML at least once every epoch
model.update_learning_rate() # update learning rates in the beginning of every epoch.
for i, data in enumerate(dataset): # inner loop within one epoch
iter_start_time = time.time() # timer for computation per iteration
if total_iters % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
total_iters += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data, postrain=True) # unpack data from dataset and apply preprocessing
model.optimize_parameters_postrain() # calculate loss functions, get gradients, update network weights()
if total_iters % opt.display_freq == 0: # display images on visdom and save images to a HTML file
save_result = total_iters % opt.update_html_freq == 0
model.compute_visuals()
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk
losses = model.get_current_losses()
t_comp = (time.time() - iter_start_time) / opt.batch_size
visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
if opt.display_id > 0:
visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
if total_iters % opt.save_latest_freq == 0: # cache our latest model every <save_latest_freq> iterations
print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
model.save_networks(save_suffix)
iter_data_time = time.time()
#if i==2: break
if epoch % opt.save_epoch_freq == 0: # cache our model every <save_epoch_freq> epochs
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
model.save_networks('latest')
model.save_networks(epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))
"""