def prepare_training_loop()

in torchbenchmark/models/pytorch_CycleGAN_and_pix2pix/train_cyclegan.py [0:0]

• 40 lines of code
• 21 McCabe index (conditional complexity)

def prepare_training_loop(args):
    opt = TrainOptions().parse(args)   # 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
    model.setup(opt)               # regular setup: load and print networks; create schedulers
    visualizer = Visualizer(opt)   # create a visualizer that display/save images and plots

    def training_loop(niteration):
        total_iters = 0                # the total number of training iterations
        if niteration is None:
            niteration = opt.n_epochs + opt.n_epochs_decay + 1
        for epoch in range(opt.epoch_count, niteration):    # 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

            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
                    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
                    save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
                    model.save_networks(save_suffix)

                iter_data_time = time.time()
            
            model.update_learning_rate()                     # update learning rates at the end of every epoch.
    return training_loop