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all_loss['val_cls'] = all_loss.pop('cls')
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all_loss['val_patch'] = all_loss.pop('patch')
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all_loss['val_recon'] = all_loss.pop('recon')
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all_loss['val_loss'] = all_loss.pop('loss')
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# logging
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torch.cuda.synchronize()
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metric_logger.update(val_loss=all_loss['val_loss'].item())
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for key, value in all_loss.items():
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metric_logger.update(**{key: value.item()})
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metric_logger.synchronize_between_processes()
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print("Averaged validation stats:", metric_logger)
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return_dict = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
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return return_dict
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# <FILESEP>
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import os
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import logging as log
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from keras.callbacks import EarlyStopping, ModelCheckpoint
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# define training strategy
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def train_model(model, dataset):
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log.info("training model (train on %d samples, validate on %d) ..." % ( \
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len(dataset.Y_train),
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len(dataset.Y_val) ) )
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loss = 'binary_crossentropy'
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optimizer = 'adam'
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metrics = ['accuracy']
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model.compile(loss = loss, optimizer = optimizer, metrics = metrics)
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checkpath = os.path.join(os.path.dirname(os.path.abspath(__file__)), "checkpoints")
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os.makedirs(checkpath, exist_ok=True)
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checkpath = os.path.join(checkpath, 'model-epoch{epoch:03d}-acc{val_acc:03f}.h5')
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# this will stop the training when the validation accuracy will stop changing
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stopper = EarlyStopping(monitor = 'val_acc', min_delta=0.0001, patience = 5, mode = 'auto')
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# this will take snapshots of the best performing epoch
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saver = ModelCheckpoint(checkpath, save_best_only=True, verbose=1, monitor='val_loss', mode='min')
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# start training
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return model.fit( dataset.X_train, dataset.Y_train,
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batch_size = 64,
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epochs = 50,
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verbose = 2,
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validation_data = (dataset.X_val, dataset.Y_val),
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callbacks = [saver, stopper])
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# <FILESEP>
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from experiments.whole_song_gen import WholeSongGeneration
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import torch
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from argparse import ArgumentParser
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DEFAULT_FRM_MODEL_FOLDER = 'results_default/frm---/v-default'
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DEFAULT_CTP_MODEL_FOLDER = 'results_default/ctp-a-b-/v-default'
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DEFAULT_LSH_MODEL_FOLDER = 'results_default/lsh-a-b-/v-default'
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DEFAULT_ACC_MODEL_FOLDER = 'results_default/acc-a-b-/v-default'
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DEFAULT_DEMO_DIR = 'demo'
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def init_parser():
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parser = ArgumentParser(description='inference a whole-song generation experiment')
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parser.add_argument(
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"--demo_dir",
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default=DEFAULT_DEMO_DIR,
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help='directory in which to generated samples'
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)
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parser.add_argument("--mpath0", default=DEFAULT_FRM_MODEL_FOLDER, help="Form generation model path")
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parser.add_argument("--mid0", default='default', help="Form generation model id")
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parser.add_argument("--mpath1", default=DEFAULT_CTP_MODEL_FOLDER, help="Counterpoint generation model path")
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parser.add_argument("--mid1", default='default', help="Counterpoint generation model id")
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parser.add_argument("--mpath2", default=DEFAULT_LSH_MODEL_FOLDER, help="Lead Sheet generation model path")
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parser.add_argument("--mid2", default='default', help="Lead Sheet generation model id")
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parser.add_argument("--mpath3", default=DEFAULT_ACC_MODEL_FOLDER, help="Accompaniment generation model path")
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parser.add_argument("--mid3", default='default', help="Accompaniment generation model id")
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parser.add_argument("--nsample", default=1, type=int, help="Number of generated samples")
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parser.add_argument("--pstring", help="Specify phrase structure. If specified, key must be specified.")
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parser.add_argument("--nbpm", default=4, type=int, help="Number of beats per measure")
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parser.add_argument("--key", default=0, type=int, help="Tonic of the key (0 - 11)")
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parser.add_argument('--minor', action='store_false', help="Whether to generated in minor key.")
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parser.add_argument('--debug', action='store_true', help="Whether to use a toy dataset")
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return parser
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if __name__ == '__main__':
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parser = init_parser()
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