"validate_test_split": args.validate_test_split, "augment": False, "shuffle": False, "seed": args.random_seed } validation_generator = DataGenerator("validate", args.data_path, **validation_data_params) if (hvd.rank() == 0): validation_generator.print_info() # Fit the model # Do at least 3 steps for training and validation steps_per_epoch = max( 3, training_generator.get_length() // (args.bz * hvd.size())) validation_steps = max( 3, 3 * training_generator.get_length() // (args.bz * hvd.size())) unet_model.model.fit_generator( training_generator, steps_per_epoch=steps_per_epoch, epochs=args.epochs, verbose=verbose, validation_data=validation_generator, #validation_steps=validation_steps, callbacks=callbacks, max_queue_size=1, #args.num_prefetched_batches, workers=1, #args.num_data_loaders, use_multiprocessing=True)
"validate_test_split": args.validate_test_split, "augment": False, "shuffle": False, "seed": args.random_seed } validation_generator = DataGenerator("validate", args.data_path, **validation_data_params) if (hvd.rank() == 0): validation_generator.print_info() # Fit the model # Do at least 3 steps for training and validation steps_per_epoch = max( 3, training_generator.get_length() // (args.bz * hvd.size())) validation_steps = max( 3, validation_generator.get_length() // (args.bz * hvd.size())) unet_model.model.fit_generator(training_generator, steps_per_epoch=steps_per_epoch, epochs=args.epochs, verbose=verbose, validation_data=validation_generator, validation_steps=validation_steps, callbacks=callbacks, max_queue_size=args.num_prefetched_batches, workers=args.num_data_loaders, use_multiprocessing=False)