def test_upload(): # Make dirs os.makedirs("tmp/publish_dir", exist_ok=True) populate_wham_dir("tmp/wham") # Dataset and NN train_set = WhamDataset("tmp/wham", task="sep_clean") model = ConvTasNet(n_src=2, n_repeats=2, n_blocks=2, bn_chan=16, hid_chan=4, skip_chan=8, n_filters=32) # Save publishable model_conf = model.serialize() model_conf.update(train_set.get_infos()) save_publishable("tmp/publish_dir", model_conf, metrics={}, train_conf={}) # Upload token = os.getenv("ACCESS_TOKEN") if token: # ACESS_TOKEN is not available on forks. zen, current = upload_publishable( "tmp/publish_dir", uploader="Manuel Pariente", affiliation="INRIA", use_sandbox=True, unit_test=True, # Remove this argument and monkeypatch `input()` git_username="******", ) # Assert metadata is correct meta = current.json()["metadata"] assert meta["creators"][0]["name"] == "Manuel Pariente" assert meta["creators"][0]["affiliation"] == "INRIA" assert "asteroid-models" in [ d["identifier"] for d in meta["communities"] ] # Clean up zen.remove_deposition(current.json()["id"]) shutil.rmtree("tmp/wham")
def test_upload(): # Make dirs os.makedirs('tmp/publish_dir', exist_ok=True) populate_wham_dir('tmp/wham') # Dataset and NN train_set = WhamDataset('tmp/wham', task='sep_clean') model = ConvTasNet(n_src=2, n_repeats=2, n_blocks=2, bn_chan=16, hid_chan=4, skip_chan=8, n_filters=32) # Save publishable model_conf = model.serialize() model_conf.update(train_set.get_infos()) save_publishable('tmp/publish_dir', model_conf, metrics={}, train_conf={}) # if False: # Upload zen, current = upload_publishable( 'tmp/publish_dir', uploader="Manuel Pariente", affiliation="INRIA", use_sandbox=True, unit_test=True, # Remove this argument and monkeypatch `input()` ) # Assert metadata is correct meta = current.json()['metadata'] assert meta['creators'][0]['name'] == "Manuel Pariente" assert meta['creators'][0]['affiliation'] == "INRIA" assert 'asteroid-models' in [d['identifier'] for d in meta['communities']] # Clean up zen.remove_deposition(current.json()['id']) shutil.rmtree('tmp/wham')
def main(conf): train_set = WhamDataset( conf["data"]["train_dir"], conf["data"]["task"], sample_rate=conf["data"]["sample_rate"], segment=conf["data"]["segment"], nondefault_nsrc=conf["data"]["nondefault_nsrc"], ) val_set = WhamDataset( conf["data"]["valid_dir"], conf["data"]["task"], sample_rate=conf["data"]["sample_rate"], nondefault_nsrc=conf["data"]["nondefault_nsrc"], ) train_loader = DataLoader( train_set, shuffle=True, batch_size=conf["training"]["batch_size"], num_workers=conf["training"]["num_workers"], drop_last=True, ) val_loader = DataLoader( val_set, shuffle=False, batch_size=conf["training"]["batch_size"], num_workers=conf["training"]["num_workers"], drop_last=True, ) # Update number of source values (It depends on the task) conf["masknet"].update({"n_src": train_set.n_src}) model = DPTNet(**conf["filterbank"], **conf["masknet"]) optimizer = make_optimizer(model.parameters(), **conf["optim"]) from asteroid.engine.schedulers import DPTNetScheduler schedulers = { "scheduler": DPTNetScheduler(optimizer, len(train_loader) // conf["training"]["batch_size"], 64), "interval": "step", } # Just after instantiating, save the args. Easy loading in the future. exp_dir = conf["main_args"]["exp_dir"] os.makedirs(exp_dir, exist_ok=True) conf_path = os.path.join(exp_dir, "conf.yml") with open(conf_path, "w") as outfile: yaml.safe_dump(conf, outfile) # Define Loss function. loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from="pw_mtx") system = System( model=model, loss_func=loss_func, optimizer=optimizer, scheduler=schedulers, train_loader=train_loader, val_loader=val_loader, config=conf, ) # Define callbacks checkpoint_dir = os.path.join(exp_dir, "checkpoints/") checkpoint = ModelCheckpoint(checkpoint_dir, monitor="val_loss", mode="min", save_top_k=5, verbose=True) early_stopping = False if conf["training"]["early_stop"]: early_stopping = EarlyStopping(monitor="val_loss", patience=30, verbose=True) # Don't ask GPU if they are not available. gpus = -1 if torch.cuda.is_available() else None trainer = pl.Trainer( max_epochs=conf["training"]["epochs"], checkpoint_callback=checkpoint, early_stop_callback=early_stopping, default_root_dir=exp_dir, gpus=gpus, distributed_backend="ddp", gradient_clip_val=conf["training"]["gradient_clipping"], ) trainer.fit(system) best_k = {k: v.item() for k, v in checkpoint.best_k_models.items()} with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f: json.dump(best_k, f, indent=0) state_dict = torch.load(checkpoint.best_model_path) system.load_state_dict(state_dict=state_dict["state_dict"]) system.cpu() to_save = system.model.serialize() to_save.update(train_set.get_infos()) torch.save(to_save, os.path.join(exp_dir, "best_model.pth"))
def main(conf): train_set = WhamDataset(conf['data']['train_dir'], conf['data']['task'], sample_rate=conf['data']['sample_rate'], nondefault_nsrc=conf['data']['nondefault_nsrc']) val_set = WhamDataset(conf['data']['valid_dir'], conf['data']['task'], sample_rate=conf['data']['sample_rate'], nondefault_nsrc=conf['data']['nondefault_nsrc']) train_loader = DataLoader(train_set, shuffle=True, batch_size=conf['training']['batch_size'], num_workers=conf['training']['num_workers'], drop_last=True) val_loader = DataLoader(val_set, shuffle=False, batch_size=conf['training']['batch_size'], num_workers=conf['training']['num_workers'], drop_last=True) # Update number of source values (It depends on the task) conf['masknet'].update({'n_src': train_set.n_src}) # Define model and optimizer model = ConvTasNet(**conf['filterbank'], **conf['masknet']) optimizer = make_optimizer(model.parameters(), **conf['optim']) # Define scheduler scheduler = None if conf['training']['half_lr']: scheduler = ReduceLROnPlateau(optimizer=optimizer, factor=0.5, patience=5) # Just after instantiating, save the args. Easy loading in the future. exp_dir = conf['main_args']['exp_dir'] os.makedirs(exp_dir, exist_ok=True) conf_path = os.path.join(exp_dir, 'conf.yml') with open(conf_path, 'w') as outfile: yaml.safe_dump(conf, outfile) # Define Loss function. loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from='pw_mtx') system = System(model=model, loss_func=loss_func, optimizer=optimizer, train_loader=train_loader, val_loader=val_loader, scheduler=scheduler, config=conf) # Define callbacks checkpoint_dir = os.path.join(exp_dir, 'checkpoints/') checkpoint = ModelCheckpoint(checkpoint_dir, monitor='val_loss', mode='min', save_top_k=5, verbose=1) early_stopping = False if conf['training']['early_stop']: early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1) # Don't ask GPU if they are not available. gpus = -1 if torch.cuda.is_available() else None trainer = pl.Trainer( max_epochs=conf['training']['epochs'], checkpoint_callback=checkpoint, early_stop_callback=early_stopping, default_save_path=exp_dir, gpus=gpus, distributed_backend='dp', train_percent_check=1.0, # Useful for fast experiment gradient_clip_val=5.) trainer.fit(system) best_k = {k: v.item() for k, v in checkpoint.best_k_models.items()} with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f: json.dump(best_k, f, indent=0) # Save best model (next PL version will make this easier) best_path = [b for b, v in best_k.items() if v == min(best_k.values())][0] state_dict = torch.load(best_path) system.load_state_dict(state_dict=state_dict['state_dict']) system.cpu() to_save = system.model.serialize() to_save.update(train_set.get_infos()) torch.save(to_save, os.path.join(exp_dir, 'best_model.pth'))
def main(conf): assert (conf["training"]["batch_size"] % 2 == 0), "Batch size must be divisible by two to run this recipe" train_set = WhamDataset( conf["data"]["train_dir"], "sep_clean", sample_rate=conf["data"]["sample_rate"], segment=conf["data"]["segment"], nondefault_nsrc=None, ) val_set = WhamDataset( conf["data"]["valid_dir"], "sep_clean", sample_rate=conf["data"]["sample_rate"], nondefault_nsrc=None, ) train_loader = DataLoader( train_set, shuffle=True, batch_size=conf["training"]["batch_size"], num_workers=conf["training"]["num_workers"], drop_last=True, ) val_loader = DataLoader( val_set, shuffle=False, batch_size=conf["training"]["batch_size"], num_workers=conf["training"]["num_workers"], drop_last=True, ) model = DPRNNTasNet(**conf["filterbank"], **conf["masknet"], sample_rate=conf["data"]["sample_rate"]) optimizer = make_optimizer(model.parameters(), **conf["optim"]) # Define scheduler scheduler = None if conf["training"]["half_lr"]: scheduler = ReduceLROnPlateau(optimizer=optimizer, factor=0.5, patience=5) # Just after instantiating, save the args. Easy loading in the future. exp_dir = conf["main_args"]["exp_dir"] os.makedirs(exp_dir, exist_ok=True) conf_path = os.path.join(exp_dir, "conf.yml") with open(conf_path, "w") as outfile: yaml.safe_dump(conf, outfile) # Define Loss function. loss_func = { "pit": PITLossWrapper(pairwise_neg_sisdr, pit_from="pw_mtx"), "mixit": MixITLossWrapper(pairwise_neg_sisdr, generalized=True), } system = MixITSystem( model=model, loss_func=loss_func, optimizer=optimizer, train_loader=train_loader, val_loader=val_loader, scheduler=scheduler, config=conf, ) # Define callbacks callbacks = [] checkpoint_dir = os.path.join(exp_dir, "checkpoints/") checkpoint = ModelCheckpoint(checkpoint_dir, monitor="val_loss", mode="min", save_top_k=5, verbose=True) callbacks.append(checkpoint) if conf["training"]["early_stop"]: callbacks.append( EarlyStopping(monitor="val_loss", mode="min", patience=30, verbose=True)) # Don't ask GPU if they are not available. gpus = -1 if torch.cuda.is_available() else None distributed_backend = "ddp" if torch.cuda.is_available() else None trainer = pl.Trainer( max_epochs=conf["training"]["epochs"], callbacks=callbacks, default_root_dir=exp_dir, gpus=gpus, distributed_backend=distributed_backend, gradient_clip_val=conf["training"]["gradient_clipping"], ) trainer.fit(system) best_k = {k: v.item() for k, v in checkpoint.best_k_models.items()} with open(os.path.join(exp_dir, "best_k_models.json"), "w") as f: json.dump(best_k, f, indent=0) state_dict = torch.load(checkpoint.best_model_path) system.load_state_dict(state_dict=state_dict["state_dict"]) system.cpu() to_save = system.model.serialize() to_save.update(train_set.get_infos()) torch.save(to_save, os.path.join(exp_dir, "best_model.pth"))
def main(conf): train_set = WhamDataset( conf["data"]["train_dir"], conf["data"]["task"], sample_rate=conf["data"]["sample_rate"], segment=conf["data"]["segment"], nondefault_nsrc=conf["data"]["nondefault_nsrc"], ) val_set = WhamDataset( conf["data"]["valid_dir"], conf["data"]["task"], sample_rate=conf["data"]["sample_rate"], nondefault_nsrc=conf["data"]["nondefault_nsrc"], ) train_loader = DataLoader( train_set, shuffle=True, batch_size=conf["training"]["batch_size"], num_workers=conf["training"]["num_workers"], drop_last=True, ) val_loader = DataLoader( val_set, shuffle=False, batch_size=conf["training"]["batch_size"], num_workers=conf["training"]["num_workers"], drop_last=True, ) # Update number of source values (It depends on the task) conf["masknet"].update({"n_src": train_set.n_src}) model = DPRNNTasNet(**conf["filterbank"], **conf["masknet"]) optimizer = make_optimizer(model.parameters(), **conf["optim"]) # Define scheduler scheduler = None if conf["training"]["half_lr"]: scheduler = ReduceLROnPlateau(optimizer=optimizer, factor=0.5, patience=5) # Just after instantiating, save the args. Easy loading in the future. exp_dir = conf["main_args"]["exp_dir"] os.makedirs(exp_dir, exist_ok=True) conf_path = os.path.join(exp_dir, "conf.yml") with open(conf_path, "w") as outfile: yaml.safe_dump(conf, outfile) # Define Loss function. loss_func = PITLossWrapper(pairwise_neg_sisdr, pit_from="pw_mtx") system = System( model=model, loss_func=loss_func, optimizer=optimizer, train_loader=train_loader, val_loader=val_loader, scheduler=scheduler, config=conf, ) # Define callbacks checkpoint_dir = os.path.join(exp_dir, 'checkpoints/') checkpoint = ModelCheckpoint(checkpoint_dir, monitor='val_loss', verbose=True, mode='min', save_top_k=5) early_stopping = False if conf["training"]["early_stop"]: early_stopping = EarlyStopping(monitor="val_loss", patience=30, verbose=1) # Don't ask GPU if they are not available. gpus = -1 if torch.cuda.is_available() else None trainer = pl.Trainer( max_epochs=conf['training']['epochs'], checkpoint_callback=checkpoint, early_stop_callback=early_stopping, default_root_dir=exp_dir, gpus=gpus, distributed_backend='ddp', gradient_clip_val=conf['training']["gradient_clipping"]) trainer.fit(system) state_dict = torch.load(checkpoint.best_model_path) system.load_state_dict(state_dict=state_dict['state_dict']) system.cpu() to_save = system.model.serialize() to_save.update(train_set.get_infos()) torch.save(to_save, os.path.join(exp_dir, "best_model.pth"))