def gen(df): """ Wrapper around generator. Keras fit_generator requires looping generator. :param df: dataflow instance """ while True: for i in df.get_data(): yield i if __name__ == '__main__': # get the model model = get_training_model(weight_decay) # restore weights last_epoch = restore_weights(weights_best_file, model) # prepare generators curr_dir = os.path.dirname(__file__) annot_path_train = os.path.join(curr_dir, '../dataset/annotations/person_keypoints_train2017.json') img_dir_train = os.path.abspath(os.path.join(curr_dir, '../dataset/train2017/')) annot_path_val = os.path.join(curr_dir, '../dataset/annotations/person_keypoints_val2017.json') img_dir_val = os.path.abspath(os.path.join(curr_dir, '../dataset/val2017/')) # get dataflow of samples from training set and validation set (we use validation set for training as well)
:param df: dataflow instance """ while True: for i in df.get_data(): yield i if __name__ == '__main__': parser = argparse.ArgumentParser( description='Training codes for Keras Pose Estimation') parser.add_argument('--model', default='cmu', help='model name') args = parser.parse_args() # get the model if args.model == 'cmu': model = cmu_model.get_training_model(weight_decay) elif args.model == 'squeeze': model = squeeze_model.get_training_model(weight_decay) elif args.model == 'mobile': model = mobile_model.get_training_model(weight_decay) elif args.model == 'cmu-thin': model = cmu_thin_model.get_training_model(weight_decay) # restore weights last_epoch = restore_weights(weights_best_file, model) #last_epoch = restore_weights("../model/squeeze_imagenet.h5", model) print(model.summary()) # prepare generators curr_dir = os.path.dirname(__file__)