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train_distan.py
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train_distan.py
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### Copyright (C) 2020 Roy Or-El. All rights reserved.
### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
import time
import scipy # this is to prevent a potential error caused by importing torch before scipy (happens due to a bad combination of torch & scipy versions)
from collections import OrderedDict
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models_distan.models import create_model
import util.util as util
from util.visualizer import Visualizer
import os
import numpy as np
import torch
from pdb import set_trace as st
def train(opt):
iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
if opt.continue_train:
if opt.which_epoch == 'latest':
try:
start_epoch, epoch_iter = np.loadtxt(iter_path , delimiter=',', dtype=int)
except:
start_epoch, epoch_iter = 1, 0
else:
start_epoch, epoch_iter = int(opt.which_epoch), 0
print('Resuming from epoch %d at iteration %d' % (start_epoch, epoch_iter))
for update_point in opt.decay_epochs:
if start_epoch < update_point:
break
opt.lr *= opt.decay_gamma
else:
start_epoch, epoch_iter = 0, 0
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
model = create_model(opt)
visualizer = Visualizer(opt)
total_steps = (start_epoch) * dataset_size + epoch_iter
display_delta = total_steps % opt.display_freq
print_delta = total_steps % opt.print_freq
save_delta = total_steps % opt.save_latest_freq
bSize = opt.batchSize
#in case there's no display sample one image from each class to test after every epoch
if opt.display_id == 0:
dataset.dataset.set_sample_mode(True)
dataset.num_workers = 1
for i, data in enumerate(dataset):
if i*opt.batchSize >= opt.numClasses:
break
if i == 0:
sample_data = data
else:
for key, value in data.items():
if torch.is_tensor(data[key]):
sample_data[key] = torch.cat((sample_data[key], data[key]), 0)
else:
sample_data[key] = sample_data[key] + data[key]
dataset.num_workers = opt.nThreads
dataset.dataset.set_sample_mode(False)
for epoch in range(start_epoch, opt.epochs):
epoch_start_time = time.time()
if epoch != start_epoch:
epoch_iter = 0
for i, data in enumerate(dataset, start=epoch_iter):
iter_start_time = time.time()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
# whether to collect output images
save_fake = (total_steps % opt.display_freq == display_delta) and (opt.display_id > 0)
############## Network Pass ########################
#import ipdb; ipdb.set_trace()
model.set_inputs(data)
disc_losses = model.update_distan_D()
gen_losses, gen_in, gen_out, rec_out, cyc_out = model.update_distan_G(infer=save_fake)
loss_dict = dict(gen_losses, **disc_losses)
##################################################
############## Display results and errors ##########
### print out errors
if total_steps % opt.print_freq == print_delta:
errors = {k: v.item() if not (isinstance(v, float) or isinstance(v, int)) else v for k, v in loss_dict.items()}
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch+1, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
### display output images
if save_fake and opt.display_id > 0:
class_a_suffix = ' class {}'.format(data['A_class'][0])
class_b_suffix = ' class {}'.format(data['B_class'][0])
classes = None
visuals = OrderedDict()
visuals_A = OrderedDict([('real image' + class_a_suffix, util.tensor2im(gen_in.data[0]))])
visuals_B = OrderedDict([('real image' + class_b_suffix, util.tensor2im(gen_in.data[bSize]))])
A_out_vis = OrderedDict([('synthesized image' + class_b_suffix, util.tensor2im(gen_out.data[0]))])
B_out_vis = OrderedDict([('synthesized image' + class_a_suffix, util.tensor2im(gen_out.data[bSize]))])
if opt.lambda_rec > 0:
A_out_vis.update([('reconstructed image' + class_a_suffix, util.tensor2im(rec_out.data[0]))])
B_out_vis.update([('reconstructed image' + class_b_suffix, util.tensor2im(rec_out.data[bSize]))])
if opt.lambda_cyc > 0:
A_out_vis.update([('cycled image' + class_a_suffix, util.tensor2im(cyc_out.data[0]))])
B_out_vis.update([('cycled image' + class_b_suffix, util.tensor2im(cyc_out.data[bSize]))])
visuals_A.update(A_out_vis)
visuals_B.update(B_out_vis)
visuals.update(visuals_A)
visuals.update(visuals_B)
ncols = len(visuals_A)
visualizer.display_current_results(visuals, epoch, classes, ncols)
### save latest model
if total_steps % opt.save_latest_freq == save_delta:
print('saving the latest model (epoch %d, total_steps %d)' % (epoch+1, total_steps))
model.save('latest')
np.savetxt(iter_path, (epoch, epoch_iter), delimiter=',', fmt='%d')
if opt.display_id == 0:
model.eval()
visuals = model.inference(sample_data)
visualizer.save_matrix_image(visuals, 'latest')
model.train()
# end of epoch
iter_end_time = time.time()
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch+1, opt.epochs, time.time() - epoch_start_time))
### save model for this epoch
if (epoch+1) % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' % (epoch+1, total_steps))
model.save('latest')
model.save(epoch+1)
np.savetxt(iter_path, (epoch+1, 0), delimiter=',', fmt='%d')
if opt.display_id == 0:
model.eval()
visuals = model.inference(sample_data)
visualizer.save_matrix_image(visuals, epoch+1)
model.train()
### multiply learning rate by opt.decay_gamma after certain iterations
if (epoch+1) in opt.decay_epochs:
model.update_learning_rate()
if __name__ == "__main__":
opt = TrainOptions().parse()
train(opt)