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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["A"].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.
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