def train_experiment(device, engine=None): with TemporaryDirectory() as logdir: # sample data num_samples, num_features, num_classes = int(1e4), int(1e1), 4 X = torch.rand(num_samples, num_features) y = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32) # pytorch loaders dataset = TensorDataset(X, y) loader = DataLoader(dataset, batch_size=32, num_workers=1) loaders = {"train": loader, "valid": loader} # model, criterion, optimizer, scheduler model = torch.nn.Linear(num_features, num_classes) criterion = torch.nn.BCEWithLogitsLoss() optimizer = torch.optim.Adam(model.parameters()) scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2]) # model training runner = dl.SupervisedRunner(input_key="features", output_key="logits", target_key="targets", loss_key="loss") callbacks = [ dl.BatchTransformCallback( transform="F.sigmoid", scope="on_batch_end", input_key="logits", output_key="scores", ), dl.MultilabelAccuracyCallback(input_key="scores", target_key="targets", threshold=0.5), dl.MultilabelPrecisionRecallF1SupportCallback( input_key="scores", target_key="targets", num_classes=num_classes), ] if SETTINGS.amp_required and (engine is None or not isinstance( engine, (dl.AMPEngine, dl.DataParallelAMPEngine, dl.DistributedDataParallelAMPEngine), )): callbacks.append( dl.AUCCallback(input_key="scores", target_key="targets")) runner.train( engine=engine or dl.DeviceEngine(device), model=model, criterion=criterion, optimizer=optimizer, scheduler=scheduler, loaders=loaders, logdir=logdir, num_epochs=1, valid_loader="valid", valid_metric="accuracy", minimize_valid_metric=False, verbose=False, callbacks=callbacks, )
def train_experiment(device): with TemporaryDirectory() as logdir: # sample data num_samples, num_features, num_classes = int(1e4), int(1e1), 4 X = torch.rand(num_samples, num_features) y = (torch.rand(num_samples, num_classes) > 0.5).to(torch.float32) # pytorch loaders dataset = TensorDataset(X, y) loader = DataLoader(dataset, batch_size=32, num_workers=1) loaders = {"train": loader, "valid": loader} # model, criterion, optimizer, scheduler model = torch.nn.Linear(num_features, num_classes) criterion = torch.nn.BCEWithLogitsLoss() optimizer = torch.optim.Adam(model.parameters()) scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2]) # model training runner = dl.SupervisedRunner(input_key="features", output_key="logits", target_key="targets", loss_key="loss") runner.train( engine=dl.DeviceEngine(device), model=model, criterion=criterion, optimizer=optimizer, scheduler=scheduler, loaders=loaders, logdir=logdir, num_epochs=1, valid_loader="valid", valid_metric="accuracy", minimize_valid_metric=False, verbose=False, callbacks=[ dl.AUCCallback(input_key="logits", target_key="targets"), dl.MultilabelAccuracyCallback(input_key="logits", target_key="targets", threshold=0.5), ], )