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objdetmetrics

objdetmetrics

Module for object detection metrics for integration into model training

Classes

AvgNegPredObjDet 

AvgNegPredObjDet(name='avg_neg_pred')

Negative Classification Values for object detection

Initializes the AvgNegPredObjDet with the given name

Source code in niceml/dlframeworks/keras/metrics/objdetmetrics.py 
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def __init__(self, name: str = "avg_neg_pred"):
    """Initializes the AvgNegPredObjDet with the given name"""
    self.__name__ = name
Functions
__call__ 
__call__(y_true, y_pred)

Call method is used as a default interface for the metric

Source code in niceml/dlframeworks/keras/metrics/objdetmetrics.py 
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def __call__(self, y_true, y_pred):
    """Call method is used as a default interface for the metric"""
    y_pred = tf.cast(y_pred, dtype=tf.float32)

    cls_predictions = y_pred[:, :, 4:]
    cls_labels = y_true[:, :, 5:]
    ignore_mask = tf.cast(
        tf.equal(y_true[:, :, 4], IGNORE_MASK_VALUE), dtype=tf.float32
    )
    not_ignore_mask = tf.cast(
        tf.expand_dims(tf.equal(ignore_mask, 0.0), axis=2), dtype=tf.float32
    )

    probs = tf.nn.sigmoid(cls_predictions)
    negative_count_mask = tf.cast(
        tf.logical_and(tf.equal(cls_labels, 0.0), tf.equal(not_ignore_mask, 1.0)),
        dtype=tf.float32,
    )
    probs = tf.where(tf.equal(negative_count_mask, 1.0), probs, 0.0)
    probs = tf.reduce_sum(probs, axis=[-1, -2])
    avg_probs = tf.math.divide_no_nan(
        probs, tf.reduce_sum(negative_count_mask, axis=[-1, -2])
    )
    return avg_probs

AvgNegTargetCountObjDet 

AvgNegTargetCountObjDet(name='avg_neg_target_count')

Average negative target count for one image in object detection

Initializes the AvgNegTargetCountObjDet

Source code in niceml/dlframeworks/keras/metrics/objdetmetrics.py 
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def __init__(self, name: str = "avg_neg_target_count"):
    """Initializes the AvgNegTargetCountObjDet"""
    self.__name__ = name
Functions
__call__ 
__call__(y_true, y_pred)

Call method is used as a default interface for the metric

Source code in niceml/dlframeworks/keras/metrics/objdetmetrics.py 
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def __call__(self, y_true, y_pred):
    """Call method is used as a default interface for the metric"""
    negative_mask = tf.cast(
        tf.equal(y_true[:, :, 4], NEGATIVE_MASK_VALUE), dtype=tf.float32
    )
    negative_mask_count = tf.reduce_sum(negative_mask, axis=-1)

    return negative_mask_count

AvgPosPredObjDet 

AvgPosPredObjDet(name='avg_pos_pred')

Positive Classification Values for object detection

Initializes the AvgPosPredObjDet with the given name

Source code in niceml/dlframeworks/keras/metrics/objdetmetrics.py 
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def __init__(self, name: str = "avg_pos_pred"):
    """Initializes the AvgPosPredObjDet with the given name"""
    self.__name__ = name
Functions
__call__ 
__call__(y_true, y_pred)

Call method is used as a default interface for the metric

Source code in niceml/dlframeworks/keras/metrics/objdetmetrics.py 
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def __call__(self, y_true, y_pred):
    """Call method is used as a default interface for the metric"""
    y_pred = tf.cast(y_pred, dtype=tf.float32)

    cls_predictions = y_pred[:, :, 4:]
    cls_labels = y_true[:, :, 5:]

    probs = tf.nn.sigmoid(cls_predictions)
    probs = tf.where(tf.equal(cls_labels, 1.0), probs, 0.0)
    probs = tf.reduce_sum(probs, axis=[-1, -2])
    avg_probs = tf.math.divide_no_nan(
        probs, tf.reduce_sum(cls_labels, axis=[-1, -2])
    )
    return avg_probs

AvgPosTargetCountObjDet 

AvgPosTargetCountObjDet(name='avg_pos_target_count')

Average positive target count for one image in object detection

Initializes the AvgPosTargetCountObjDet

Source code in niceml/dlframeworks/keras/metrics/objdetmetrics.py 
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def __init__(self, name: str = "avg_pos_target_count"):
    """Initializes the AvgPosTargetCountObjDet"""
    self.__name__ = name
Functions
__call__ 
__call__(y_true, y_pred)

Call method is used as a default interface for the metric

Source code in niceml/dlframeworks/keras/metrics/objdetmetrics.py 
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def __call__(self, y_true, y_pred):
    """Call method is used as a default interface for the metric"""
    positive_mask = tf.cast(
        tf.equal(y_true[:, :, 4], POSITIVE_MASK_VALUE), dtype=tf.float32
    )
    positive_mask_count = tf.reduce_sum(positive_mask, axis=-1)

    return positive_mask_count