nade=args.nade,
conditioning=args.conditioning,
double_conditioning=args.double_conditioning,
num_layers=args.num_layers,
dropout=dropout,
input_dropout=input_dropout,
input_dropout_token=input_dropout_token,
lr=lr,
reduction_flag=reduction_flag,
gpu_ids=gpu_ids,
suffix=args.suffix,
mixup=mixup,
scheduled_training=scheduled_training)
model.load_overfit(device=device)
model.to(device)
model = model.eval()
# Dir for writing generated files
writing_dir = f'{os.getcwd()}/generation'
if not os.path.isdir(writing_dir):
os.makedirs(writing_dir)
# Create server
server = OrchestraServer(args.in_port,
args.out_port,
args.ip,
args.ip_client,
model=model,
subdivision=subdivision,
writing_dir=writing_dir)
def main(args):
"""
:param args:
:return:
"""
dropout = 0.
input_dropout = 0.
input_dropout_token = 0.
mixup = False
scheduled_training = 0.
group_instrument_per_section = False
reduction_flag = False
lr = 1.
cpc_config_name = None
subdivision = args.subdivision
# Use all gpus available
gpu_ids = [int(gpu) for gpu in range(torch.cuda.device_count())]
print(f'Using GPUs {gpu_ids}')
if len(gpu_ids) == 0:
device = 'cpu'
else:
device = 'cuda'
# Get dataset
dataset_manager = DatasetManager()
dataset, processor_decoder, processor_encoder, processor_encodencoder = \
dataset_import.get_dataset(dataset_manager, args.dataset_type, args.subdivision, args.sequence_size,
args.velocity_quantization, args.max_transposition,
args.num_heads, args.per_head_dim, args.local_position_embedding_dim,
args.block_attention,
group_instrument_per_section, args.nade, cpc_config_name, args.double_conditioning,
args.instrument_presence_in_encoder)
# Load model
model = Transformer(dataset=dataset,
data_processor_encodencoder=processor_encodencoder,
data_processor_encoder=processor_encoder,
data_processor_decoder=processor_decoder,
num_heads=args.num_heads,
per_head_dim=args.per_head_dim,
position_ff_dim=args.position_ff_dim,
enc_dec_conditioning=args.enc_dec_conditioning,
hierarchical_encoding=args.hierarchical,
block_attention=args.block_attention,
nade=args.nade,
conditioning=args.conditioning,
double_conditioning=args.double_conditioning,
num_layers=args.num_layers,
dropout=dropout,
input_dropout=input_dropout,
input_dropout_token=input_dropout_token,
lr=lr,
reduction_flag=reduction_flag,
gpu_ids=gpu_ids,
suffix=args.suffix,
mixup=mixup,
scheduled_training=scheduled_training)
model.load_overfit(device=device)
model.to(device)
model = model.eval()
# Dir for writing generated files
writing_dir = f'{os.getcwd()}/generation'
if not os.path.isdir(writing_dir):
os.makedirs(writing_dir)
# Create server
server_address = (args.ip, args.port)
server = OrchestraServer(server_address, model, subdivision, writing_dir)
print(f'[Server listening to {args.ip} on port {args.port}]')
server.serve_forever()