78 lines
4.3 KiB
Python
78 lines
4.3 KiB
Python
|
import time
|
||
|
|
import torch
|
||
|
|
from options.train_options import TrainOptions
|
||
|
|
from data import create_dataset
|
||
|
|
from models import create_model
|
||
|
|
from util.visualizer import Visualizer
|
||
|
|
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
model = create_model(opt) # create a model given opt.model and other options
|
||
|
|
print('The number of training images = %d' % dataset_size)
|
||
|
|
|
||
|
|
visualizer = Visualizer(opt) # create a visualizer that display/save images and plots
|
||
|
|
opt.visualizer = visualizer
|
||
|
|
total_iters = 0 # the total number of training iterations
|
||
|
|
|
||
|
|
optimize_time = 0.1
|
||
|
|
|
||
|
|
times = []
|
||
|
|
for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 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
|
||
|
|
|
||
|
|
dataset.set_epoch(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
|
||
|
|
|
||
|
|
batch_size = data["A0"].size(0)
|
||
|
|
total_iters += batch_size
|
||
|
|
epoch_iter += batch_size
|
||
|
|
if len(opt.gpu_ids) > 0:
|
||
|
|
torch.cuda.synchronize()
|
||
|
|
optimize_start_time = time.time()
|
||
|
|
if epoch == opt.epoch_count and i == 0:
|
||
|
|
# model.data_dependent_initialize(data)
|
||
|
|
model.setup(opt) # regular setup: load and print networks; create schedulers
|
||
|
|
model.parallelize()
|
||
|
|
model.set_input(data) # unpack data from dataset and apply preprocessing
|
||
|
|
model.optimize_parameters() # calculate loss functions, get gradients, update network weights
|
||
|
|
if len(opt.gpu_ids) > 0:
|
||
|
|
torch.cuda.synchronize()
|
||
|
|
optimize_time = (time.time() - optimize_start_time) / batch_size * 0.005 + 0.995 * optimize_time
|
||
|
|
|
||
|
|
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()
|
||
|
|
visualizer.print_current_losses(epoch, epoch_iter, losses, optimize_time, t_data)
|
||
|
|
if opt.display_id is None or 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))
|
||
|
|
print(opt.name) # it's useful to occasionally show the experiment name on console
|
||
|
|
save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
|
||
|
|
model.save_networks(save_suffix)
|
||
|
|
|
||
|
|
iter_data_time = time.time()
|
||
|
|
|
||
|
|
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))
|
||
|
|
model.update_learning_rate() # update learning rates at the end of every epoch.
|