def main(args): lr_train_patch_size = args.patch_size layers_to_extract = [5, 9] hr_train_patch_size = lr_train_patch_size * args.scale if args.model == 'rdn': model = RDN(arch_params={'C':4, 'D':3, 'G':64, 'G0':64, 'T':10, 'x':args.scale}, patch_size=lr_train_patch_size) else: model = RRDN(arch_params={'C':4, 'D':3, 'G':64, 'G0':64, 'T':10, 'x':args.scale}, patch_size=lr_train_patch_size) f_ext = Cut_VGG19(patch_size=hr_train_patch_size, layers_to_extract=layers_to_extract) discr = Discriminator(patch_size=hr_train_patch_size, kernel_size=3) loss_weights = { 'generator': 0.0, 'feature_extractor': 0.0833, 'discriminator': 0.01, } trainer = Trainer( generator=model, discriminator=discr, feature_extractor=f_ext, lr_train_dir='low_res/training/images', hr_train_dir='high_res/training/images', lr_valid_dir='low_res/validation/images', hr_valid_dir='high_res/validation/images', loss_weights=loss_weights, dataname=args.name, logs_dir='./logs', weights_dir='./weights', weights_generator=None, weights_discriminator=None, n_validation=40, lr_decay_frequency=30, lr_decay_factor=0.5, ) trainer.train(epochs=args.num_epochs, steps_per_epoch=args.epoch_steps, batch_size,args.batch_size)
learning_rate = { 'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30 } flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5} trainer = Trainer( generator=rrdn, discriminator=discr, feature_extractor=f_ext, lr_train_dir='/home/sandeeppanku/Public/Code/superres/data/trainImageslr', hr_train_dir='/home/sandeeppanku/Public/Code/superres/data/trainImageshr', lr_valid_dir='/home/sandeeppanku/Public/Code/superres/data/testImageslr', hr_valid_dir='/home/sandeeppanku/Public/Code/superres/data/testImageshr', loss_weights=loss_weights, learning_rate=learning_rate, flatness=flatness, log_dirs=log_dirs, weights_generator=None, weights_discriminator=None, n_validation=40, ) trainer.train(epochs=80, steps_per_epoch=500, batch_size=16, monitored_metrics={'val_PSNR_Y': 'max'}) ##################################################################################################################
learning_rate = { 'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30 } flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5} trainer = Trainer( generator=rdn, discriminator=discr, feature_extractor=f_ext, hr_train_dir='galaxy_zoo/individuals_2blend_train/', lr_train_dir='galaxy_zoo/merged_2blend_train/', hr_valid_dir='galaxy_zoo/individuals_2blend_valid/', lr_valid_dir='galaxy_zoo/merged_2blend_valid/', loss_weights=loss_weights, learning_rate=learning_rate, flatness=flatness, dataname='div2k', log_dirs=log_dirs, weights_generator=None, weights_discriminator=None, n_validation=5, #to be modified ) trainer.train(epochs=200, steps_per_epoch=5000, batch_size=16, monitored_metrics={'val_generator_PSNR_Y': 'max'})
learning_rate = { 'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30 } flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5} trainer = Trainer( generator=rrdn, discriminator=discr, feature_extractor=f_ext, hr_train_dir='images/face_train/', lr_train_dir='images/musk_train/', hr_valid_dir='images/face_valid/', lr_valid_dir='images/musk_valid/', loss_weights=loss_weights, learning_rate=learning_rate, flatness=flatness, dataname='div2k', log_dirs=log_dirs, weights_generator=None, weights_discriminator=None, n_validation=5, #to be modified ) trainer.train(epochs=200, steps_per_epoch=1000, batch_size=4, monitored_metrics={'val_generator_PSNR_Y': 'max'})
'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30 } flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5} trainer = Trainer( generator=rrdn, discriminator=discr, feature_extractor=f_ext, lr_train_dir='training_lr/3x/', hr_train_dir='training/', lr_valid_dir='training_lr/3x/', hr_valid_dir='training/', loss_weights=loss_weights, learning_rate=learning_rate, flatness=flatness, dataname='training', log_dirs=log_dirs, weights_generator=None, weights_discriminator=None, n_validation=291, ) trainer.train( epochs=100, steps_per_epoch=500, batch_size=16, monitored_metrics={'val_generator_PSNR_Y': 'max'} )
'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30 } flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5} trainer = Trainer( generator=rrdn, discriminator=discr, feature_extractor=f_ext, lr_train_dir='data/DIV2K/DIV2K_train_LR_bicubic/X2', hr_train_dir='data/DIV2K/DIV2K_train_HR', lr_valid_dir='data/DIV2K/DIV2K_valid_LR_bicubic/X2', hr_valid_dir='data/DIV2K/DIV2K_valid_HR', loss_weights=loss_weights, learning_rate=learning_rate, flatness=flatness, dataname='image_dataset', log_dirs=log_dirs, weights_generator=None, weights_discriminator=None, n_validation=40, ) trainer.train(epochs=80, steps_per_epoch=500, batch_size=16, monitored_metrics={'val_PSNR_Y': 'max'}) rrdn.model.save_weights('ex.hdf5')
} flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5} adam_optimizer = {'beta1': 0.9, 'beta2': 0.999, 'epsilon': None} trainer = Trainer( generator=rrdn, discriminator=discr, feature_extractor=f_ext, lr_train_dir='/media/jhonatan/Data/h6f86gl(2)/final/lr_train', hr_train_dir='/media/jhonatan/Data/h6f86gl(2)/final/hr_train', lr_valid_dir='/media/jhonatan/Data/h6f86gl(2)/final/lr_val', hr_valid_dir='/media/jhonatan/Data/h6f86gl(2)/final/hr_val', loss_weights=loss_weights, losses=losses, learning_rate=learning_rate, flatness=flatness, log_dirs=log_dirs, adam_optimizer=adam_optimizer, metrics={'generator': 'PSNR_Y'}, dataname='aolp', weights_generator=None, weights_discriminator=None, n_validation=40, ) trainer.train(epochs=100, steps_per_epoch=20, batch_size=4, monitored_metrics={'val_generator_loss': 'min'})
learning_rate = { 'initial_value': 0.0004, 'decay_factor': 0.5, 'decay_frequency': 30 } flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5} trainer = Trainer( generator=rrdn, discriminator=discr, feature_extractor=f_ext, lr_train_dir='train/low', hr_train_dir='train/high', lr_valid_dir='test/low', hr_valid_dir='test/high', loss_weights=loss_weights, learning_rate=learning_rate, flatness=flatness, dataname='image_dataset', log_dirs=log_dirs, weights_generator=None, weights_discriminator=None, n_validation=40, ) trainer.train(epochs=1, steps_per_epoch=11, batch_size=16, monitored_metrics={'val_PSNR_Y': 'max'})
'decay_factor': 0.5, 'decay_frequency': 30 } flatness = {'min': 0.0, 'max': 0.15, 'increase': 0.01, 'increase_frequency': 5} trainer = Trainer( generator=rrdn, discriminator=discr, feature_extractor=f_ext, lr_train_dir= '/home/minhhoang/image-super-resolution/data/training/div2k/DIV2K_train_LR_bicubic/X2/', hr_train_dir= '/home/minhhoang/image-super-resolution/data/training/div2k/DIV2K_train_HR/', lr_valid_dir= '/home/minhhoang/image-super-resolution/data/training/div2k/DIV2K_train_LR_bicubic/X2/', hr_valid_dir= '/home/minhhoang/image-super-resolution/data/training/div2k/DIV2K_train_HR/', loss_weights=loss_weights, learning_rate=learning_rate, flatness=flatness, dataname='div2k', log_dirs=log_dirs, weights_generator=None, weights_discriminator=None, n_validation=40, ) trainer.train(epochs=1, steps_per_epoch=20, batch_size=4, monitored_metrics={'val_generator_PSNR_Y': 'max'})