def test_create_labels_tensor_for_minibatch() -> None:
"""
Test to make sure labels tensor is created as expected for minibatch
"""
sequences = [
ClassificationItemSequence(id=x,
items=_create_scalar_items(length=i + 1))
for i, x in enumerate(["A", "B"])
]
labels = ClassificationItemSequence.create_labels_tensor_for_minibatch(
sequences, target_indices=[0, 1, 2])
assert torch.allclose(labels,
torch.tensor([[[1.0], [np.nan], [np.nan]],
[[1.0], [1.0], [np.nan]]]),
equal_nan=True)
labels = ClassificationItemSequence.create_labels_tensor_for_minibatch(
sequences, target_indices=[0, 1])
assert torch.allclose(labels,
torch.tensor([[[1.0], [np.nan]], [[1.0], [1.0]]]),
equal_nan=True)
labels = ClassificationItemSequence.create_labels_tensor_for_minibatch(
sequences, target_indices=[0])
assert torch.equal(labels, torch.tensor([[[1.0]], [[1.0]]]))
def get_scalar_model_inputs_and_labels(
model_config: ScalarModelBase, model: torch.nn.Module,
sample: Dict[str, Any]) -> ScalarModelInputsAndLabels:
"""
For a model that predicts scalars, gets the model input tensors from a sample returned by the data loader.
:param model_config: The configuration object for the model.
:param model: The instantiated PyTorch model.
:param sample: A training sample, as returned by a PyTorch data loader (dictionary mapping from field name to value)
:return: An instance of ScalarModelInputsAndLabels, containing the list of model input tensors,
label tensor, subject IDs, and the data item reconstructed from the data loader output
"""
if isinstance(model, DataParallelModel):
model = model.get_module()
if isinstance(model_config, SequenceModelBase):
sequence_model: DeviceAwareModule[List[ClassificationItemSequence],
torch.Tensor] = model # type: ignore
sequences = ClassificationItemSequence.from_minibatch(sample)
subject_ids = [x.id for x in sequences]
labels = ClassificationItemSequence.create_labels_tensor_for_minibatch(
sequences=sequences,
target_indices=model_config.get_target_indices())
model_inputs = sequence_model.get_input_tensors(sequences)
return ScalarModelInputsAndLabels[List[ClassificationItemSequence],
torch.Tensor](
model_inputs=model_inputs,
labels=labels,
subject_ids=subject_ids,
data_item=sequences)
else:
scalar_model: DeviceAwareModule[ScalarItem,
torch.Tensor] = model # type: ignore
scalar_item = ScalarItem.from_dict(sample)
subject_ids = [str(x.id) for x in scalar_item.metadata] # type: ignore
model_inputs = scalar_model.get_input_tensors(scalar_item)
return ScalarModelInputsAndLabels[ScalarItem, torch.Tensor](
model_inputs=model_inputs,
labels=scalar_item.label,
subject_ids=subject_ids,
data_item=scalar_item)
def get_scalar_model_inputs_and_labels(model: torch.nn.Module,
target_indices: List[int],
sample: Dict[str, Any]) -> ScalarModelInputsAndLabels:
"""
For a model that predicts scalars, gets the model input tensors from a sample returned by the data loader.
:param model: The instantiated PyTorch model.
:param target_indices: If this list is non-empty, assume that the model is a sequence model, and build the
model inputs and labels for a model that predicts those specific positions in the sequence. If the list is empty,
assume that the model is a normal (non-sequence) model.
:param sample: A training sample, as returned by a PyTorch data loader (dictionary mapping from field name to value)
:return: An instance of ScalarModelInputsAndLabels, containing the list of model input tensors,
label tensor, subject IDs, and the data item reconstructed from the data loader output
"""
if target_indices:
sequence_model: DeviceAwareModule[List[ClassificationItemSequence], torch.Tensor] = model # type: ignore
sequences = ClassificationItemSequence.from_minibatch(sample)
subject_ids = [x.id for x in sequences]
labels = ClassificationItemSequence.create_labels_tensor_for_minibatch(
sequences=sequences,
target_indices=target_indices
)
model_inputs = sequence_model.get_input_tensors(sequences)
return ScalarModelInputsAndLabels[List[ClassificationItemSequence]](
model_inputs=model_inputs,
labels=labels,
subject_ids=subject_ids,
data_item=sequences
)
else:
scalar_model: DeviceAwareModule[ScalarItem, torch.Tensor] = model # type: ignore
scalar_item = ScalarItem.from_dict(sample)
subject_ids = [str(x.id) for x in scalar_item.metadata] # type: ignore
model_inputs = scalar_model.get_input_tensors(scalar_item)
return ScalarModelInputsAndLabels[ScalarItem](
model_inputs=model_inputs,
labels=scalar_item.label,
subject_ids=subject_ids,
data_item=scalar_item
)