def get_loggers(self):
return {
"console": dl.ConsoleLogger(),
"csv": dl.CSVLogger(logdir=self._logdir),
"tensorboard": dl.TensorboardLogger(logdir=self._logdir),
"mlflow": dl.MLflowLogger(experiment=self._name),
}
def get_loggers(self):
loggers = {
"console": dl.ConsoleLogger(),
"csv": dl.CSVLogger(logdir=self._logdir),
"tensorboard": dl.TensorboardLogger(logdir=self._logdir),
}
return loggers
def get_loggers(self):
loggers = {
"console": dl.ConsoleLogger(),
"csv": dl.CSVLogger(logdir=self._logdir),
"tensorboard": dl.TensorboardLogger(logdir=self._logdir),
}
if SETTINGS.mlflow_required:
loggers["mlflow"] = dl.MLflowLogger(experiment=self.name)
if SETTINGS.wandb_required:
loggers["wandb"] = dl.WandbLogger(project="catalyst_test", name=self.name)
if SETTINGS.neptune_required:
loggers["neptune"] = dl.NeptuneLogger(
base_namespace="catalyst-tests",
api_token="ANONYMOUS",
project="common/catalyst-integration",
)
return loggers
def get_loggers(self):
return {
"console": dl.ConsoleLogger(),
"csv": dl.CSVLogger(logdir=self._logdir)
}
def train_experiment(device):
with TemporaryDirectory() as logdir:
# sample data
num_samples, num_features, num_classes1, num_classes2 = int(1e4), int(
1e1), 4, 10
X = torch.rand(num_samples, num_features)
y1 = (torch.rand(num_samples, ) * num_classes1).to(torch.int64)
y2 = (torch.rand(num_samples, ) * num_classes2).to(torch.int64)
# pytorch loaders
dataset = TensorDataset(X, y1, y2)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}
class CustomModule(nn.Module):
def __init__(self, in_features: int, out_features1: int,
out_features2: int):
super().__init__()
self.shared = nn.Linear(in_features, 128)
self.head1 = nn.Linear(128, out_features1)
self.head2 = nn.Linear(128, out_features2)
def forward(self, x):
x = self.shared(x)
y1 = self.head1(x)
y2 = self.head2(x)
return y1, y2
# model, criterion, optimizer, scheduler
model = CustomModule(num_features, num_classes1, num_classes2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [2])
class CustomRunner(dl.Runner):
def handle_batch(self, batch):
x, y1, y2 = batch
y1_hat, y2_hat = self.model(x)
self.batch = {
"features": x,
"logits1": y1_hat,
"logits2": y2_hat,
"targets1": y1,
"targets2": y2,
}
# model training
runner = CustomRunner()
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
num_epochs=1,
verbose=False,
callbacks=[
dl.CriterionCallback(metric_key="loss1",
input_key="logits1",
target_key="targets1"),
dl.CriterionCallback(metric_key="loss2",
input_key="logits2",
target_key="targets2"),
dl.MetricAggregationCallback(prefix="loss",
metrics=["loss1", "loss2"],
mode="mean"),
dl.OptimizerCallback(metric_key="loss"),
dl.SchedulerCallback(),
dl.AccuracyCallback(
input_key="logits1",
target_key="targets1",
num_classes=num_classes1,
prefix="one_",
),
dl.AccuracyCallback(
input_key="logits2",
target_key="targets2",
num_classes=num_classes2,
prefix="two_",
),
dl.ConfusionMatrixCallback(
input_key="logits1",
target_key="targets1",
num_classes=num_classes1,
prefix="one_cm",
),
# catalyst[ml] required
dl.ConfusionMatrixCallback(
input_key="logits2",
target_key="targets2",
num_classes=num_classes2,
prefix="two_cm",
),
# catalyst[ml] required
dl.CheckpointCallback(
"./logs/one",
loader_key="valid",
metric_key="one_accuracy",
minimize=False,
save_n_best=1,
),
dl.CheckpointCallback(
"./logs/two",
loader_key="valid",
metric_key="two_accuracy03",
minimize=False,
save_n_best=3,
),
],
loggers={
"console": dl.ConsoleLogger(),
"tb": dl.TensorboardLogger("./logs/tb")
},
)
def train_experiment(engine=None):
with TemporaryDirectory() as logdir:
# sample data
num_samples, num_features, num_classes1, num_classes2 = int(1e4), int(
1e1), 4, 10
X = torch.rand(num_samples, num_features)
y1 = (torch.rand(num_samples) * num_classes1).to(torch.int64)
y2 = (torch.rand(num_samples) * num_classes2).to(torch.int64)
# pytorch loaders
dataset = TensorDataset(X, y1, y2)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}
# model, criterion, optimizer, scheduler
model = CustomModule(num_features, num_classes1, num_classes2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())
scheduler = optim.lr_scheduler.MultiStepLR(optimizer, [2])
callbacks = [
dl.CriterionCallback(metric_key="loss1",
input_key="logits1",
target_key="targets1"),
dl.CriterionCallback(metric_key="loss2",
input_key="logits2",
target_key="targets2"),
dl.MetricAggregationCallback(metric_key="loss",
metrics=["loss1", "loss2"],
mode="mean"),
dl.BackwardCallback(metric_key="loss"),
dl.OptimizerCallback(metric_key="loss"),
dl.SchedulerCallback(),
dl.AccuracyCallback(
input_key="logits1",
target_key="targets1",
num_classes=num_classes1,
prefix="one_",
),
dl.AccuracyCallback(
input_key="logits2",
target_key="targets2",
num_classes=num_classes2,
prefix="two_",
),
dl.CheckpointCallback(
"./logs/one",
loader_key="valid",
metric_key="one_accuracy01",
minimize=False,
topk=1,
),
dl.CheckpointCallback(
"./logs/two",
loader_key="valid",
metric_key="two_accuracy03",
minimize=False,
topk=3,
),
]
if SETTINGS.ml_required:
# catalyst[ml] required
callbacks.append(
dl.ConfusionMatrixCallback(
input_key="logits1",
target_key="targets1",
num_classes=num_classes1,
prefix="one_cm",
))
# catalyst[ml] required
callbacks.append(
dl.ConfusionMatrixCallback(
input_key="logits2",
target_key="targets2",
num_classes=num_classes2,
prefix="two_cm",
))
# model training
runner = CustomRunner()
runner.train(
engine=engine,
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
num_epochs=1,
verbose=False,
callbacks=callbacks,
loggers={
"console": dl.ConsoleLogger(),
"tb": dl.TensorboardLogger("./logs/tb"),
},
)
