Custom Weights

CustomWeights

Bases: PlainResNetInference

Custom Weight Classifier that inherits from PlainResNetInference. This classifier can load any model that was based on the PytorchWildlife finetuning tool.

Source code in PytorchWildlife/models/classification/resnet_base/custom_weights.py

class CustomWeights(PlainResNetInference):
    """
    Custom Weight Classifier that inherits from PlainResNetInference.
    This classifier can load any model that was based on the PytorchWildlife finetuning tool.
    """

    # Image size for the classifier
    IMAGE_SIZE = 224


    def __init__(self, weights=None, class_names=None, device="cpu"):
        """
        Initialize the CustomWeights Classifier.

        Args:
            weights (str, optional): Path to the model weights. Defaults to None.
            class_names (list[str]): List of class names for the classifier.
            device (str, optional): Device for model inference. Defaults to "cpu".
        """
        self.CLASS_NAMES = class_names
        self.num_cls = len(self.CLASS_NAMES)
        super(CustomWeights, self).__init__(weights=weights, device=device,
                                                   num_cls=self.num_cls, num_layers=50, url=None)

    def results_generation(self, logits: torch.Tensor, img_ids: list[str], id_strip: str = None) -> list[dict]:
        """
        Generate results for classification.

        Args:
            logits (torch.Tensor): Output tensor from the model.
            img_ids (list[str]): List of image identifiers.
            id_strip (str): Stripping string for better image ID saving.

        Returns:
            list[dict]: List of dictionaries containing image ID, prediction, and confidence score.
        """

        probs = torch.softmax(logits, dim=1)
        preds = probs.argmax(dim=1)
        confs = probs.max(dim=1)[0]
        confidences = probs[0].tolist()
        result = [[self.CLASS_NAMES[i], confidence] for i, confidence in enumerate(confidences)]

        results = []
        for pred, img_id, conf in zip(preds, img_ids, confs):
            r = {"img_id": str(img_id).strip(id_strip)}
            r["prediction"] = self.CLASS_NAMES[pred.item()]
            r["class_id"] = pred.item()
            r["confidence"] = conf.item()
            r["all_confidences"] = result
            results.append(r)

        return results

__init__(weights=None, class_names=None, device='cpu')

Initialize the CustomWeights Classifier.

Parameters:

Name	Type	Description	Default
weights	str	Path to the model weights. Defaults to None.	None
class_names	list[str]	List of class names for the classifier.	None
device	str	Device for model inference. Defaults to "cpu".	'cpu'

Source code in PytorchWildlife/models/classification/resnet_base/custom_weights.py

def __init__(self, weights=None, class_names=None, device="cpu"):
    """
    Initialize the CustomWeights Classifier.

    Args:
        weights (str, optional): Path to the model weights. Defaults to None.
        class_names (list[str]): List of class names for the classifier.
        device (str, optional): Device for model inference. Defaults to "cpu".
    """
    self.CLASS_NAMES = class_names
    self.num_cls = len(self.CLASS_NAMES)
    super(CustomWeights, self).__init__(weights=weights, device=device,
                                               num_cls=self.num_cls, num_layers=50, url=None)

results_generation(logits, img_ids, id_strip=None)

Generate results for classification.

Parameters:

Name	Type	Description	Default
logits	Tensor	Output tensor from the model.	required
img_ids	list[str]	List of image identifiers.	required
id_strip	str	Stripping string for better image ID saving.	None

Returns:

Type	Description
list[dict]	list[dict]: List of dictionaries containing image ID, prediction, and confidence score.

Source code in PytorchWildlife/models/classification/resnet_base/custom_weights.py

def results_generation(self, logits: torch.Tensor, img_ids: list[str], id_strip: str = None) -> list[dict]:
    """
    Generate results for classification.

    Args:
        logits (torch.Tensor): Output tensor from the model.
        img_ids (list[str]): List of image identifiers.
        id_strip (str): Stripping string for better image ID saving.

    Returns:
        list[dict]: List of dictionaries containing image ID, prediction, and confidence score.
    """

    probs = torch.softmax(logits, dim=1)
    preds = probs.argmax(dim=1)
    confs = probs.max(dim=1)[0]
    confidences = probs[0].tolist()
    result = [[self.CLASS_NAMES[i], confidence] for i, confidence in enumerate(confidences)]

    results = []
    for pred, img_id, conf in zip(preds, img_ids, confs):
        r = {"img_id": str(img_id).strip(id_strip)}
        r["prediction"] = self.CLASS_NAMES[pred.item()]
        r["class_id"] = pred.item()
        r["confidence"] = conf.item()
        r["all_confidences"] = result
        results.append(r)

    return results