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unitorch.cli.models¤

Classification¤

Tip

core/postprocess/classification/binary_score in configuration file to use the postprocess function.

Postprocess the classification outputs for binary classification with scores.

Parameters:

Name Type Description Default
outputs ClassificationOutputs

Outputs from the classification model.

 required

Returns:

Name Type Description
WriterOutputs

Processed outputs with scores.
Source code in src/unitorch/cli/models/classification_utils.py 
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@register_process("core/postprocess/classification/binary_score")
def _binary_score(
    self,
    outputs: ClassificationOutputs,
):
    """
    Postprocess the classification outputs for binary classification with scores.

    Args:
        outputs (ClassificationOutputs): Outputs from the classification model.

    Returns:
        WriterOutputs: Processed outputs with scores.
    """
    assert outputs.outputs.dim() == 2

    results = outputs.to_pandas()
    assert results.shape[0] == 0 or results.shape[0] == outputs.outputs.shape[0]

    outputs = outputs.outputs.numpy()
    if self.act_fn is not None:
        outputs = self.act_fn(outputs)

    if outputs.ndim == 2:
        pscore = outputs[:, 1] if outputs.shape[-1] > 1 else outputs[:, 0]
        results["pscore"] = pscore.tolist()
    else:
        results["pscore"] = outputs.tolist()
    return WriterOutputs(results)

Tip

core/postprocess/classification/score in configuration file to use the postprocess function.

Postprocess the classification outputs for multi-classes classification with scores.

Parameters:

Name Type Description Default
outputs ClassificationOutputs

Outputs from the classification model.

 required

Returns:

Name Type Description
WriterOutputs

Processed outputs with scores and predicted classes.
Source code in src/unitorch/cli/models/classification_utils.py 
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@register_process("core/postprocess/classification/score")
def _classifier_score(
    self,
    outputs: ClassificationOutputs,
):
    """
    Postprocess the classification outputs for multi-classes classification with scores.

    Args:
        outputs (ClassificationOutputs): Outputs from the classification model.

    Returns:
        WriterOutputs: Processed outputs with scores and predicted classes.
    """
    assert outputs.outputs.dim() == 2

    results = outputs.to_pandas()
    assert results.shape[0] == 0 or results.shape[0] == outputs.outputs.shape[0]
    outputs = outputs.outputs.numpy()
    if self.act_fn is not None:
        outputs = self.act_fn(outputs)

    results["pscore"] = outputs.max(-1)
    results["pclass"] = outputs.argmax(-1)
    if self.return_scores:
        results["scores"] = outputs.tolist()
    return WriterOutputs(results)

Tip

core/postprocess/classification/embedding in configuration file to use the postprocess function.

Postprocess the embedding outputs.

Parameters:

Name Type Description Default
outputs EmbeddingOutputs

Outputs from the embedding model.

 required

Returns:

Name Type Description
WriterOutputs

Processed outputs with embeddings.
Source code in src/unitorch/cli/models/classification_utils.py 
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@register_process("core/postprocess/classification/embedding")
def _embedding(
    self,
    outputs: EmbeddingOutputs,
):
    """
    Postprocess the embedding outputs.

    Args:
        outputs (EmbeddingOutputs): Outputs from the embedding model.

    Returns:
        WriterOutputs: Processed outputs with embeddings.
    """
    results = outputs.to_pandas()
    assert results.shape[0] == 0 or results.shape[0] == outputs.embedding.shape[0]

    embedding = outputs.embedding.numpy()
    if embedding.ndim > 2:
        embedding = embedding.reshape(embedding.size(0), -1)
    results["embedding"] = embedding.tolist()

    embedding1 = outputs.embedding1.numpy()
    if embedding1.size > 0:
        if embedding1.ndim > 2:
            embedding1 = embedding1.reshape(embedding1.size(0), -1)
        results["embedding1"] = embedding1.tolist()

    embedding2 = outputs.embedding2.numpy()
    if embedding2.size > 0:
        if embedding2.ndim > 2:
            embedding2 = embedding2.reshape(embedding2.size(0), -1)
        results["embedding2"] = embedding2.tolist()

    embedding3 = outputs.embedding3.numpy()
    if embedding3.size > 0:
        if embedding3.ndim > 2:
            embedding3 = embedding3.reshape(embedding3.size(0), -1)
        results["embedding3"] = embedding3.tolist()

    embedding4 = outputs.embedding4.numpy()
    if embedding4.size > 0:
        if embedding4.ndim > 2:
            embedding4 = embedding4.reshape(embedding4.size(0), -1)
        results["embedding4"] = embedding4.tolist()

    return WriterOutputs(results)

Tip

core/postprocess/classification/embedding/string in configuration file to use the postprocess function.

Postprocess the embedding outputs as string representations.

Parameters:

Name Type Description Default
outputs EmbeddingOutputs

Outputs from the embedding model.

 required

Returns:

Name Type Description
WriterOutputs

Processed outputs with string representations of embeddings.
Source code in src/unitorch/cli/models/classification_utils.py 
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@register_process("core/postprocess/classification/embedding/string")
def _embedding_string(
    self,
    outputs: EmbeddingOutputs,
):
    """
    Postprocess the embedding outputs as string representations.

    Args:
        outputs (EmbeddingOutputs): Outputs from the embedding model.

    Returns:
        WriterOutputs: Processed outputs with string representations of embeddings.
    """
    results = outputs.to_pandas()
    assert results.shape[0] == 0 or results.shape[0] == outputs.embedding.shape[0]

    embedding = outputs.embedding.numpy()
    if embedding.ndim > 2:
        embedding = embedding.reshape(embedding.size(0), -1)
    results["embedding"] = embedding.tolist()
    results["embedding"] = results["embedding"].map(
        lambda x: " ".join([str(i) for i in x])
    )

    embedding1 = outputs.embedding1.numpy()
    if embedding1.size > 0:
        if embedding1.ndim > 2:
            embedding1 = embedding1.reshape(embedding1.size(0), -1)
        results["embedding1"] = embedding1.tolist()
        results["embedding1"] = results["embedding1"].map(
            lambda x: " ".join([str(i) for i in x])
        )

    embedding2 = outputs.embedding2.numpy()
    if embedding2.size > 0:
        if embedding2.ndim > 2:
            embedding2 = embedding2.reshape(embedding2.size(0), -1)
        results["embedding2"] = embedding2.tolist()
        results["embedding2"] = results["embedding2"].map(
            lambda x: " ".join([str(i) for i in x])
        )

    embedding3 = outputs.embedding3.numpy()
    if embedding3.size > 0:
        if embedding3.ndim > 2:
            embedding3 = embedding3.reshape(embedding3.size(0), -1)
        results["embedding3"] = embedding3.tolist()
        results["embedding3"] = results["embedding3"].map(
            lambda x: " ".join([str(i) for i in x])
        )

    embedding4 = outputs.embedding4.numpy()
    if embedding4.size > 0:
        if embedding4.ndim > 2:
            embedding4 = embedding4.reshape(embedding4.size(0), -1)
        results["embedding4"] = embedding4.tolist()
        results["embedding4"] = results["embedding4"].map(
            lambda x: " ".join([str(i) for i in x])
        )

    return WriterOutputs(results)