def test_legacy_calculate_loss_without_sample_weight(self):
labels = torch.tensor([[1.0, 2.0, 3.0]])
outputs = torch.tensor([[1.0, 0.0, 2.0]])
def fn_minus(output, label, reduction=None):
losses = label - output
if reduction == 'none':
return losses
else:
return losses.mean()
def fn_add(output, label, reduction=None):
losses = label + output
if reduction == 'none':
return losses
else:
return losses.mean()
kwargs = dict(model=Mock(), optimizer=Mock(), feature_cols=[], sample_weights_col=None, validation=0)
model = to_lightning_module(loss_fns=[fn_minus], loss_weights=[1], label_cols=['a'], **kwargs)
loss = model._calculate_loss(outputs, labels)
assert loss == 1.0
labels = torch.tensor([[1.0, 2.0, 3.0], [1.0, 2.0, 4.0]])
outputs = torch.tensor([[1.0, 0.0, 2.0], [0.0, 0.0, 2.0]])
model = to_lightning_module(loss_fns=[fn_minus, fn_add], loss_weights=[0.2, 0.8], label_cols=['a', 'b'], **kwargs)
loss = model._calculate_loss(outputs, labels)
assert torch.isclose(loss, torch.tensor(2.6))
def _fit_on_prepared_data(self, backend, train_rows, val_rows, metadata, avg_row_size, dataset_idx=None):
self._check_params(metadata)
run_id = self.getRunId()
if run_id is None:
run_id = 'pytorch_' + str(int(time.time()))
model = self.getModel()
is_legacy = not isinstance(model, LightningModule)
if is_legacy:
# Legacy: convert params to LightningModule
model = to_lightning_module(model=self.getModel(),
optimizer=self._get_optimizer(),
loss_fns=self.getLoss(),
loss_weights=self.getLossWeights(),
feature_cols=self.getFeatureCols(),
label_cols=self.getLabelCols(),
sample_weights_col=self.getSampleWeightCol(),
validation=self.getValidation())
serialized_model = serialize_fn()(model)
# FIXME: checkpoint bytes should be loaded into serialized_model, same as Keras Estimator.
ckpt_bytes = self._read_checkpoint(run_id) if self._has_checkpoint(run_id) else None
trainer = remote.RemoteTrainer(self,
metadata=metadata,
ckpt_bytes=ckpt_bytes,
run_id=run_id,
dataset_idx=dataset_idx,
train_rows=train_rows,
val_rows=val_rows,
avg_row_size=avg_row_size,
is_legacy=is_legacy)
handle = backend.run(trainer, args=(serialized_model,), env={})
return self._create_model(handle, run_id, metadata)
