train.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
import importlib
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
from util.gan_logger import logger
from models.base_model import BaseModel

def find_model_using_name(model_name):
    """Import the module "models/[model_name]_model.py".

    In the file, the class called DatasetNameModel() will
    be instantiated. It has to be a subclass of BaseModel,
    and it is case-insensitive.
    """
    model_filename = "models." + model_name + "_model"
    modellib = importlib.import_module(model_filename)
    model = None
    target_model_name = model_name.replace('_', '') + 'model'
    for name, cls in modellib.__dict__.items():
        if name.lower() == target_model_name.lower() \
           and issubclass(cls, BaseModel):
            model = cls

    if model is None:
        print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name))
        exit(0)

    return model


def create_model(opt):
    """Create a model given the option.

    This function warps the class CustomDatasetDataLoader.
    This is the main interface between this package and 'train.py'/'test.py'

    Example:
        >>> from models import create_model
        >>> model = create_model(opt)
    """
    print("what's the model type", opt.model_type)

    if opt.model_type == 'default':
        use_model = 'cycle_gan'

    elif opt.model_type == 'shallow':
        use_model = 'cyclegansa_shallow'
        
    elif opt.model_type == 'deep':
        use_model = 'cyclegansa_deep'
        
    else:
        raise ValueError("Unknown model type: %s" % opt.model_type)

    
    model = find_model_using_name(use_model)
#     model = find_model_using_name("cyclegansa_shallow")
    instance = model(opt)
    print("model [%s] was created" % type(instance).__name__)
    return instance


if __name__ == '__main__':
    # python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan --gpu_ids -1
    opt = TrainOptions().parse()   # get training options
#     opt.dataroot = "datasets/mnist/"
#     opt.name = "mnist_cyclegan_default"
#     opt.model = f"cycle_gan_{opt.model_type}"
    opt.use_wandb = False
    print(" opt.gpu_ids !!!!", opt.gpu_ids )

    opt.display_id = 0
    opt.epoch_count = 80 # for using pretrain model
    
    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)
    

    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.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
            print(f"idx:{i} ts:{iter_start_time}")

            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, stage="Basic")
                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
                logger.info('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
            logger.info('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
            model.save_networks('latest')
            model.save_networks(epoch)

        logger.info('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))