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train_mean_std.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
import matplotlib.pyplot as plt
import torch
from options.train_options import TrainOptions
from data import create_dataset, get_source_and_target, get_source_and_target_mean_std
from models import create_model
# from util.visualizer import Visualizer
import os
from tifffile import imsave
def plot_results(realA, realB, fakeA, fakeB, recA, recB, save_path, idx):
fig, ax = plt.subplots(2, 3, figsize=(16, 8))
realA = (realA - realA.min()) / (realA.max() - realA.min())
realB = (realB - realB.min()) / (realB.max() - realB.min())
fakeA = (fakeA - fakeA.min()) / (fakeA.max() - fakeA.min())
fakeB = (fakeB - fakeB.min()) / (fakeB.max() - fakeB.min())
recA = (recA - recA.min()) / (recA.max() - recA.min())
recB = (recB - recB.min()) / (recB.max() - recB.min())
ax[0, 0].imshow(realA.transpose((1, 2, 0)))
ax[0, 0].set_title('Real Mat_19/Ace_20', fontsize=18)
ax[0, 1].imshow(fakeB.transpose((1, 2, 0)))
ax[0, 1].set_title('Mat_19/Ace_20 as WBC', fontsize=18)
# ax[0, 1].imshow(fakeA.transpose((1, 2, 0)))
# ax[0, 1].set_title('Fake Source', fontsize=18)
ax[0, 2].imshow(recA.transpose((1, 2, 0)))
ax[0, 2].set_title('Reconstructed Mat_19/Ace_20', fontsize=18)
ax[1, 0].imshow(realB.transpose((1, 2, 0)))
ax[1, 0].set_title('Real WBC', fontsize=18)
ax[1, 1].imshow(fakeA.transpose((1, 2, 0)))
ax[1, 1].set_title('WBC as Mat_19/Ace_20', fontsize=18)
# ax[1, 1].imshow(fakeB.transpose((1, 2, 0)))
# ax[1, 1].set_title('Fake Target', fontsize=18)
ax[1, 2].imshow(recB.transpose((1, 2, 0)))
ax[1, 2].set_title('Reconstructed WBC', fontsize=18)
plt.savefig(os.path.join(save_path, '{}.png'.format(idx)))
plt.close()
def save_it(visuals, save_path):
real_As = visuals['real_A'].cpu().numpy()
real_Bs = visuals['real_B'].cpu().numpy()
fake_As = visuals['fake_A'].cpu().numpy()
fake_Bs = visuals['fake_B'].cpu().numpy()
rec_As = visuals['rec_A'].cpu().numpy()
rec_Bs = visuals['rec_B'].cpu().numpy()
for i in range(real_As.shape[0]):
plot_results(real_As[i], real_Bs[i], fake_As[i], fake_Bs[i], rec_As[i], rec_Bs[i], save_path, i)
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)
dataset, val = get_source_and_target_mean_std('metadata.csv', batch_size=opt.batch_size, num_workers=opt.num_threads)
dataset_size = len(dataset.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
log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
with open(log_name, "a") as log_file:
now = time.strftime("%c")
log_file.write('================ Training Loss (%s) ================\n' % now)
total_iters = 0 # the total number of training iterations
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
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
message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, epoch_iter, t_comp, t_data)
for k, v in losses.items():
message += '%s: %.3f ' % (k, v)
print(message) # print the message
with open(log_name, "a") as log_file:
log_file.write('%s\n' % message) # save the message
# 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 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)
b = next(iter(val))
model.set_input(data)
with torch.no_grad():
model.forward()
visuals = model.get_current_visuals()
os.makedirs('figures_mean_std/epoch_{}'.format(epoch), exist_ok=True)
save_it(visuals, 'figures_mean_std/epoch_{}'.format(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))