trainVal4d.py

"""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 data import create_dataset2
from models import create_model
from util.visualizer import Visualizer
import numpy as np

if __name__ == '__main__':
    print('run till here 0')
    opt = TrainOptions().parse()   # get training options
    print('run till here 1')
    dataset = create_dataset(opt)  # create a dataset given opt.dataset_mode and other options
    dataset2 = create_dataset2(opt) #create dataset for validation
    dataset_size = len(dataset)    # get the number of images in the dataset.
    dataset2_size = len(dataset2) #get the number of images in validation dataset
    print('The number of training images = %d' % dataset_size)
    print('The number of validation images = %d' % dataset2_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

    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.eval()  #For going to validation
         for k, data2 in enumerate(dataset2):
             model.set_input(data2)
             model.test()
             model.compute_loss_only()

         model.train()
         model.update_learning_rate()    # update learning rates in the beginning of every epoch.
         for i, data in enumerate(dataset):  # inner loop within one epoch
             #print("i:%d",i)
             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
             #model.test()
             #if (i==190):
             #   visuals = model.get_current_visuals()
             #   print(visuals['real_B'])
             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)

             
             #if (i==259):
             #    print(data['A'])
             #    print(data['C'])
             #    model.print_values()
                 #print(model.fake_B)
                 #np.save('./datasets/testO/A.npy',data['A'].numpy())
                 #np.save('./datasets/testO/B.npy',data['B'].numpy())

             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))