748 lines
26 KiB
Python
748 lines
26 KiB
Python
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from keras_core import backend
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from keras_core import initializers
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from keras_core import operations as ops
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from keras_core.api_export import keras_core_export
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from keras_core.metrics.metric import Metric
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from keras_core.metrics.metrics_utils import confusion_matrix
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class _IoUBase(Metric):
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"""Computes the confusion matrix for Intersection-Over-Union metrics.
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Formula:
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```python
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iou = true_positives / (true_positives + false_positives + false_negatives)
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```
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Intersection-Over-Union is a common evaluation metric for semantic image
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segmentation.
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From IoUs of individual classes, the MeanIoU can be computed as the mean of
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the individual IoUs.
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To compute IoUs, the predictions are accumulated in a confusion matrix,
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weighted by `sample_weight` and the metric is then calculated from it.
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If `sample_weight` is `None`, weights default to 1.
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Use `sample_weight` of 0 to mask values.
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Args:
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num_classes: The possible number of labels the prediction task can have.
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name: (Optional) string name of the metric instance.
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dtype: (Optional) data type of the metric result.
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ignore_class: Optional integer. The ID of a class to be ignored during
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metric computation. This is useful, for example, in segmentation
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problems featuring a "void" class (commonly -1 or 255) in
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segmentation maps. By default (`ignore_class=None`), all classes are
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considered.
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sparse_y_true: Whether labels are encoded using integers or
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dense floating point vectors. If `False`, the `argmax` function
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is used to determine each sample's most likely associated label.
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sparse_y_pred: Whether predictions are encoded using integers or
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dense floating point vectors. If `False`, the `argmax` function
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is used to determine each sample's most likely associated label.
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axis: (Optional) -1 is the dimension containing the logits.
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Defaults to `-1`.
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"""
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def __init__(
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self,
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num_classes,
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name=None,
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dtype=None,
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ignore_class=None,
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sparse_y_true=True,
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sparse_y_pred=True,
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axis=-1,
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):
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# defaulting to float32 to avoid issues with confusion matrix
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super().__init__(name=name, dtype=dtype or "float32")
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self.num_classes = num_classes
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self.ignore_class = ignore_class
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self.sparse_y_true = sparse_y_true
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self.sparse_y_pred = sparse_y_pred
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self.axis = axis
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self.total_cm = self.add_variable(
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name="total_confusion_matrix",
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shape=(num_classes, num_classes),
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initializer=initializers.Zeros(),
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)
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def update_state(self, y_true, y_pred, sample_weight=None):
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"""Accumulates the confusion matrix statistics.
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Args:
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y_true: The ground truth values.
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y_pred: The predicted values.
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sample_weight: Optional weighting of each example. Can
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be a `Tensor` whose rank is either 0, or the same as `y_true`,
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and must be broadcastable to `y_true`. Defaults to `1`.
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Returns:
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Update op.
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"""
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if not self.sparse_y_true:
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y_true = ops.argmax(y_true, axis=self.axis)
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if not self.sparse_y_pred:
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y_pred = ops.argmax(y_pred, axis=self.axis)
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y_true = ops.convert_to_tensor(y_true, dtype=self.dtype)
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y_pred = ops.convert_to_tensor(y_pred, dtype=self.dtype)
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# Flatten the input if its rank > 1.
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if len(y_pred.shape) > 1:
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y_pred = ops.reshape(y_pred, [-1])
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if len(y_true.shape) > 1:
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y_true = ops.reshape(y_true, [-1])
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if sample_weight is None:
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sample_weight = 1
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sample_weight = ops.convert_to_tensor(sample_weight, dtype=self.dtype)
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if len(sample_weight.shape) > 1:
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sample_weight = ops.reshape(sample_weight, [-1])
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sample_weight = ops.broadcast_to(sample_weight, y_true.shape)
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if self.ignore_class is not None:
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ignore_class = ops.convert_to_tensor(
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self.ignore_class, y_true.dtype
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)
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valid_mask = ops.not_equal(y_true, ignore_class)
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y_true = y_true[valid_mask]
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y_pred = y_pred[valid_mask]
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if sample_weight is not None:
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sample_weight = sample_weight[valid_mask]
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y_pred = ops.cast(y_pred, dtype=self.dtype)
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y_true = ops.cast(y_true, dtype=self.dtype)
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sample_weight = ops.cast(sample_weight, dtype=self.dtype)
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current_cm = confusion_matrix(
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y_true,
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y_pred,
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self.num_classes,
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weights=sample_weight,
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dtype="float32",
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)
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return self.total_cm.assign(self.total_cm + current_cm)
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def reset_state(self):
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self.total_cm.assign(
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ops.zeros(self.total_cm.shape, dtype=self.total_cm.dtype)
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)
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@keras_core_export("keras_core.metrics.IoU")
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class IoU(_IoUBase):
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"""Computes the Intersection-Over-Union metric for specific target classes.
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Formula:
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```python
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iou = true_positives / (true_positives + false_positives + false_negatives)
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```
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Intersection-Over-Union is a common evaluation metric for semantic image
|
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|
|
segmentation.
|
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|
|
|
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|
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To compute IoUs, the predictions are accumulated in a confusion matrix,
|
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|
|
weighted by `sample_weight` and the metric is then calculated from it.
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If `sample_weight` is `None`, weights default to 1.
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Use `sample_weight` of 0 to mask values.
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Note, this class first computes IoUs for all individual classes, then
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returns the mean of IoUs for the classes that are specified by
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`target_class_ids`. If `target_class_ids` has only one id value, the IoU of
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that specific class is returned.
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Args:
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num_classes: The possible number of labels the prediction task can have.
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target_class_ids: A tuple or list of target class ids for which the
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metric is returned. To compute IoU for a specific class, a list
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(or tuple) of a single id value should be provided.
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name: (Optional) string name of the metric instance.
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dtype: (Optional) data type of the metric result.
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ignore_class: Optional integer. The ID of a class to be ignored during
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metric computation. This is useful, for example, in segmentation
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|
|
problems featuring a "void" class (commonly -1 or 255) in
|
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|
|
segmentation maps. By default (`ignore_class=None`), all classes are
|
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|
|
considered.
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|
|
sparse_y_true: Whether labels are encoded using integers or
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|
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dense floating point vectors. If `False`, the `argmax` function
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|
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is used to determine each sample's most likely associated label.
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sparse_y_pred: Whether predictions are encoded using integers or
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|
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dense floating point vectors. If `False`, the `argmax` function
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|
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is used to determine each sample's most likely associated label.
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axis: (Optional) -1 is the dimension containing the logits.
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Defaults to `-1`.
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Examples:
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Standalone usage:
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>>> # cm = [[1, 1],
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>>> # [1, 1]]
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>>> # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1]
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>>> # iou = true_positives / (sum_row + sum_col - true_positives))
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>>> # iou = [0.33, 0.33]
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>>> m = keras_core.metrics.IoU(num_classes=2, target_class_ids=[0])
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>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1])
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>>> m.result()
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0.33333334
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>>> m.reset_state()
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>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1],
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... sample_weight=[0.3, 0.3, 0.3, 0.1])
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>>> # cm = [[0.3, 0.3],
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>>> # [0.3, 0.1]]
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>>> # sum_row = [0.6, 0.4], sum_col = [0.6, 0.4],
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>>> # true_positives = [0.3, 0.1]
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>>> # iou = [0.33, 0.14]
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>>> m.result()
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0.33333334
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Usage with `compile()` API:
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```python
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model.compile(
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optimizer='sgd',
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loss='mse',
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metrics=[keras_core.metrics.IoU(num_classes=2, target_class_ids=[0])])
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```
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"""
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def __init__(
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self,
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num_classes,
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target_class_ids,
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name=None,
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dtype=None,
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ignore_class=None,
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sparse_y_true=True,
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sparse_y_pred=True,
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axis=-1,
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):
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super().__init__(
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name=name,
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num_classes=num_classes,
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ignore_class=ignore_class,
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sparse_y_true=sparse_y_true,
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sparse_y_pred=sparse_y_pred,
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axis=axis,
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dtype=dtype,
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)
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if max(target_class_ids) >= num_classes:
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raise ValueError(
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f"Target class id {max(target_class_ids)} "
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"is out of range, which is "
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f"[{0}, {num_classes})."
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)
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self.target_class_ids = list(target_class_ids)
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def result(self):
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"""Compute the intersection-over-union via the confusion matrix."""
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sum_over_row = ops.cast(
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ops.sum(self.total_cm, axis=0), dtype=self.dtype
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)
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sum_over_col = ops.cast(
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ops.sum(self.total_cm, axis=1), dtype=self.dtype
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)
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true_positives = ops.cast(ops.diag(self.total_cm), dtype=self.dtype)
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# sum_over_row + sum_over_col =
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# 2 * true_positives + false_positives + false_negatives.
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denominator = sum_over_row + sum_over_col - true_positives
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target_class_ids = ops.convert_to_tensor(
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self.target_class_ids, dtype="int32"
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)
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# Only keep the target classes
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true_positives = ops.take_along_axis(
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true_positives, target_class_ids, axis=-1
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)
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denominator = ops.take_along_axis(
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denominator, target_class_ids, axis=-1
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)
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# If the denominator is 0, we need to ignore the class.
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num_valid_entries = ops.sum(
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ops.cast(ops.greater(denominator, 1e-9), dtype=self.dtype)
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)
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iou = ops.divide(true_positives, denominator + backend.epsilon())
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return ops.divide(
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ops.sum(iou, axis=self.axis), num_valid_entries + backend.epsilon()
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)
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def get_config(self):
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config = {
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"num_classes": self.num_classes,
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"target_class_ids": self.target_class_ids,
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"ignore_class": self.ignore_class,
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"sparse_y_true": self.sparse_y_true,
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"sparse_y_pred": self.sparse_y_pred,
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"axis": self.axis,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@keras_core_export("keras_core.metrics.BinaryIoU")
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class BinaryIoU(IoU):
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"""Computes the Intersection-Over-Union metric for class 0 and/or 1.
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Formula:
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```python
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iou = true_positives / (true_positives + false_positives + false_negatives)
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```
|
||
|
|
Intersection-Over-Union is a common evaluation metric for semantic image
|
||
|
|
segmentation.
|
||
|
|
|
||
|
|
To compute IoUs, the predictions are accumulated in a confusion matrix,
|
||
|
|
weighted by `sample_weight` and the metric is then calculated from it.
|
||
|
|
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|
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If `sample_weight` is `None`, weights default to 1.
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Use `sample_weight` of 0 to mask values.
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This class can be used to compute IoUs for a binary classification task
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where the predictions are provided as logits. First a `threshold` is applied
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to the predicted values such that those that are below the `threshold` are
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converted to class 0 and those that are above the `threshold` are converted
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to class 1.
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IoUs for classes 0 and 1 are then computed, the mean of IoUs for the classes
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that are specified by `target_class_ids` is returned.
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Note: with `threshold=0`, this metric has the same behavior as `IoU`.
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Args:
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target_class_ids: A tuple or list of target class ids for which the
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metric is returned. Options are `[0]`, `[1]`, or `[0, 1]`. With
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`[0]` (or `[1]`), the IoU metric for class 0 (or class 1,
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respectively) is returned. With `[0, 1]`, the mean of IoUs for the
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two classes is returned.
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threshold: A threshold that applies to the prediction logits to convert
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them to either predicted class 0 if the logit is below `threshold`
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or predicted class 1 if the logit is above `threshold`.
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||
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name: (Optional) string name of the metric instance.
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||
|
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dtype: (Optional) data type of the metric result.
|
||
|
|
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||
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Examples:
|
||
|
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Standalone usage:
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>>> m = keras_core.metrics.BinaryIoU(target_class_ids=[0, 1], threshold=0.3)
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>>> m.update_state([0, 1, 0, 1], [0.1, 0.2, 0.4, 0.7])
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>>> m.result()
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0.33333334
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>>> m.reset_state()
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>>> m.update_state([0, 1, 0, 1], [0.1, 0.2, 0.4, 0.7],
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... sample_weight=[0.2, 0.3, 0.4, 0.1])
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>>> # cm = [[0.2, 0.4],
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>>> # [0.3, 0.1]]
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>>> # sum_row = [0.6, 0.4], sum_col = [0.5, 0.5],
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>>> # true_positives = [0.2, 0.1]
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>>> # iou = [0.222, 0.125]
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|
>>> m.result()
|
||
|
|
0.17361112
|
||
|
|
|
||
|
|
Usage with `compile()` API:
|
||
|
|
|
||
|
|
```python
|
||
|
|
model.compile(
|
||
|
|
optimizer='sgd',
|
||
|
|
loss='mse',
|
||
|
|
metrics=[keras_core.metrics.BinaryIoU(
|
||
|
|
target_class_ids=[0],
|
||
|
|
threshold=0.5
|
||
|
|
)]
|
||
|
|
)
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
target_class_ids=(0, 1),
|
||
|
|
threshold=0.5,
|
||
|
|
name=None,
|
||
|
|
dtype=None,
|
||
|
|
):
|
||
|
|
super().__init__(
|
||
|
|
num_classes=2,
|
||
|
|
target_class_ids=target_class_ids,
|
||
|
|
name=name,
|
||
|
|
dtype=dtype,
|
||
|
|
)
|
||
|
|
self.threshold = threshold
|
||
|
|
|
||
|
|
def update_state(self, y_true, y_pred, sample_weight=None):
|
||
|
|
"""Accumulates the confusion matrix statistics.
|
||
|
|
|
||
|
|
Before the confusion matrix is updated, the predicted values are
|
||
|
|
thresholded to be:
|
||
|
|
0 for values that are smaller than the `threshold`
|
||
|
|
1 for values that are larger or equal to the `threshold`
|
||
|
|
|
||
|
|
Args:
|
||
|
|
y_true: The ground truth values.
|
||
|
|
y_pred: The predicted values.
|
||
|
|
sample_weight: Optional weighting of each example. Can
|
||
|
|
be a `Tensor` whose rank is either 0, or the same as `y_true`,
|
||
|
|
and must be broadcastable to `y_true`. Defaults to `1`.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Update op.
|
||
|
|
"""
|
||
|
|
y_true = ops.convert_to_tensor(y_true, dtype=self.dtype)
|
||
|
|
y_pred = ops.convert_to_tensor(y_pred, dtype=self.dtype)
|
||
|
|
y_pred = ops.cast(y_pred >= self.threshold, self.dtype)
|
||
|
|
return super().update_state(y_true, y_pred, sample_weight)
|
||
|
|
|
||
|
|
def get_config(self):
|
||
|
|
return {
|
||
|
|
"target_class_ids": self.target_class_ids,
|
||
|
|
"threshold": self.threshold,
|
||
|
|
"name": self.name,
|
||
|
|
"dtype": self._dtype,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
@keras_core_export("keras_core.metrics.MeanIoU")
|
||
|
|
class MeanIoU(IoU):
|
||
|
|
"""Computes the mean Intersection-Over-Union metric.
|
||
|
|
|
||
|
|
Formula:
|
||
|
|
|
||
|
|
```python
|
||
|
|
iou = true_positives / (true_positives + false_positives + false_negatives)
|
||
|
|
```
|
||
|
|
Intersection-Over-Union is a common evaluation metric for semantic image
|
||
|
|
segmentation.
|
||
|
|
|
||
|
|
To compute IoUs, the predictions are accumulated in a confusion matrix,
|
||
|
|
weighted by `sample_weight` and the metric is then calculated from it.
|
||
|
|
|
||
|
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
|
||
|
|
Note that this class first computes IoUs for all individual classes, then
|
||
|
|
returns the mean of these values.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
num_classes: The possible number of labels the prediction task can have.
|
||
|
|
This value must be provided, since a confusion matrix of dimension =
|
||
|
|
[num_classes, num_classes] will be allocated.
|
||
|
|
name: (Optional) string name of the metric instance.
|
||
|
|
dtype: (Optional) data type of the metric result.
|
||
|
|
ignore_class: Optional integer. The ID of a class to be ignored during
|
||
|
|
metric computation. This is useful, for example, in segmentation
|
||
|
|
problems featuring a "void" class (commonly -1 or 255) in
|
||
|
|
segmentation maps. By default (`ignore_class=None`), all classes are
|
||
|
|
considered.
|
||
|
|
sparse_y_true: Whether labels are encoded using integers or
|
||
|
|
dense floating point vectors. If `False`, the `argmax` function
|
||
|
|
is used to determine each sample's most likely associated label.
|
||
|
|
sparse_y_pred: Whether predictions are encoded using integers or
|
||
|
|
dense floating point vectors. If `False`, the `argmax` function
|
||
|
|
is used to determine each sample's most likely associated label.
|
||
|
|
axis: (Optional) The dimension containing the logits. Defaults to `-1`.
|
||
|
|
|
||
|
|
Examples:
|
||
|
|
|
||
|
|
Standalone usage:
|
||
|
|
|
||
|
|
>>> # cm = [[1, 1],
|
||
|
|
>>> # [1, 1]]
|
||
|
|
>>> # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1]
|
||
|
|
>>> # iou = true_positives / (sum_row + sum_col - true_positives))
|
||
|
|
>>> # result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33
|
||
|
|
>>> m = keras_core.metrics.MeanIoU(num_classes=2)
|
||
|
|
>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1])
|
||
|
|
>>> m.result()
|
||
|
|
0.33333334
|
||
|
|
|
||
|
|
>>> m.reset_state()
|
||
|
|
>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1],
|
||
|
|
... sample_weight=[0.3, 0.3, 0.3, 0.1])
|
||
|
|
>>> m.result().numpy()
|
||
|
|
0.23809525
|
||
|
|
|
||
|
|
Usage with `compile()` API:
|
||
|
|
|
||
|
|
```python
|
||
|
|
model.compile(
|
||
|
|
optimizer='sgd',
|
||
|
|
loss='mse',
|
||
|
|
metrics=[keras_core.metrics.MeanIoU(num_classes=2)])
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
num_classes,
|
||
|
|
name=None,
|
||
|
|
dtype=None,
|
||
|
|
ignore_class=None,
|
||
|
|
sparse_y_true=True,
|
||
|
|
sparse_y_pred=True,
|
||
|
|
axis=-1,
|
||
|
|
):
|
||
|
|
target_class_ids = list(range(num_classes))
|
||
|
|
super().__init__(
|
||
|
|
name=name,
|
||
|
|
num_classes=num_classes,
|
||
|
|
target_class_ids=target_class_ids,
|
||
|
|
axis=axis,
|
||
|
|
dtype=dtype,
|
||
|
|
ignore_class=ignore_class,
|
||
|
|
sparse_y_true=sparse_y_true,
|
||
|
|
sparse_y_pred=sparse_y_pred,
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_config(self):
|
||
|
|
return {
|
||
|
|
"num_classes": self.num_classes,
|
||
|
|
"name": self.name,
|
||
|
|
"dtype": self._dtype,
|
||
|
|
"ignore_class": self.ignore_class,
|
||
|
|
"sparse_y_true": self.sparse_y_true,
|
||
|
|
"sparse_y_pred": self.sparse_y_pred,
|
||
|
|
"axis": self.axis,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
@keras_core_export("keras_core.metrics.OneHotIoU")
|
||
|
|
class OneHotIoU(IoU):
|
||
|
|
"""Computes the Intersection-Over-Union metric for one-hot encoded labels.
|
||
|
|
|
||
|
|
Formula:
|
||
|
|
|
||
|
|
```python
|
||
|
|
iou = true_positives / (true_positives + false_positives + false_negatives)
|
||
|
|
```
|
||
|
|
Intersection-Over-Union is a common evaluation metric for semantic image
|
||
|
|
segmentation.
|
||
|
|
|
||
|
|
To compute IoUs, the predictions are accumulated in a confusion matrix,
|
||
|
|
weighted by `sample_weight` and the metric is then calculated from it.
|
||
|
|
|
||
|
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
|
||
|
|
This class can be used to compute IoU for multi-class classification tasks
|
||
|
|
where the labels are one-hot encoded (the last axis should have one
|
||
|
|
dimension per class). Note that the predictions should also have the same
|
||
|
|
shape. To compute the IoU, first the labels and predictions are converted
|
||
|
|
back into integer format by taking the argmax over the class axis. Then the
|
||
|
|
same computation steps as for the base `IoU` class apply.
|
||
|
|
|
||
|
|
Note, if there is only one channel in the labels and predictions, this class
|
||
|
|
is the same as class `IoU`. In this case, use `IoU` instead.
|
||
|
|
|
||
|
|
Also, make sure that `num_classes` is equal to the number of classes in the
|
||
|
|
data, to avoid a "labels out of bound" error when the confusion matrix is
|
||
|
|
computed.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
num_classes: The possible number of labels the prediction task can have.
|
||
|
|
target_class_ids: A tuple or list of target class ids for which the
|
||
|
|
metric is returned. To compute IoU for a specific class, a list
|
||
|
|
(or tuple) of a single id value should be provided.
|
||
|
|
name: (Optional) string name of the metric instance.
|
||
|
|
dtype: (Optional) data type of the metric result.
|
||
|
|
ignore_class: Optional integer. The ID of a class to be ignored during
|
||
|
|
metric computation. This is useful, for example, in segmentation
|
||
|
|
problems featuring a "void" class (commonly -1 or 255) in
|
||
|
|
segmentation maps. By default (`ignore_class=None`), all classes are
|
||
|
|
considered.
|
||
|
|
sparse_y_pred: Whether predictions are encoded using integers or
|
||
|
|
dense floating point vectors. If `False`, the `argmax` function
|
||
|
|
is used to determine each sample's most likely associated label.
|
||
|
|
axis: (Optional) The dimension containing the logits. Defaults to `-1`.
|
||
|
|
|
||
|
|
Examples:
|
||
|
|
|
||
|
|
Standalone usage:
|
||
|
|
|
||
|
|
>>> y_true = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 0, 0]])
|
||
|
|
>>> y_pred = np.array([[0.2, 0.3, 0.5], [0.1, 0.2, 0.7], [0.5, 0.3, 0.1],
|
||
|
|
... [0.1, 0.4, 0.5]])
|
||
|
|
>>> sample_weight = [0.1, 0.2, 0.3, 0.4]
|
||
|
|
>>> m = keras_core.metrics.OneHotIoU(num_classes=3, target_class_ids=[0, 2])
|
||
|
|
>>> m.update_state(
|
||
|
|
... y_true=y_true, y_pred=y_pred, sample_weight=sample_weight)
|
||
|
|
>>> # cm = [[0, 0, 0.2+0.4],
|
||
|
|
>>> # [0.3, 0, 0],
|
||
|
|
>>> # [0, 0, 0.1]]
|
||
|
|
>>> # sum_row = [0.3, 0, 0.7], sum_col = [0.6, 0.3, 0.1]
|
||
|
|
>>> # true_positives = [0, 0, 0.1]
|
||
|
|
>>> # single_iou = true_positives / (sum_row + sum_col - true_positives))
|
||
|
|
>>> # mean_iou = (0 / (0.3 + 0.6 - 0) + 0.1 / (0.7 + 0.1 - 0.1)) / 2
|
||
|
|
>>> m.result()
|
||
|
|
0.071
|
||
|
|
|
||
|
|
Usage with `compile()` API:
|
||
|
|
|
||
|
|
```python
|
||
|
|
model.compile(
|
||
|
|
optimizer='sgd',
|
||
|
|
loss='mse',
|
||
|
|
metrics=[keras_core.metrics.OneHotIoU(
|
||
|
|
num_classes=3,
|
||
|
|
target_class_id=[1]
|
||
|
|
)]
|
||
|
|
)
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
num_classes,
|
||
|
|
target_class_ids,
|
||
|
|
name=None,
|
||
|
|
dtype=None,
|
||
|
|
ignore_class=None,
|
||
|
|
sparse_y_pred=False,
|
||
|
|
axis=-1,
|
||
|
|
):
|
||
|
|
super().__init__(
|
||
|
|
num_classes=num_classes,
|
||
|
|
target_class_ids=target_class_ids,
|
||
|
|
name=name,
|
||
|
|
dtype=dtype,
|
||
|
|
ignore_class=ignore_class,
|
||
|
|
sparse_y_true=False,
|
||
|
|
sparse_y_pred=sparse_y_pred,
|
||
|
|
axis=axis,
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_config(self):
|
||
|
|
return {
|
||
|
|
"num_classes": self.num_classes,
|
||
|
|
"target_class_ids": self.target_class_ids,
|
||
|
|
"name": self.name,
|
||
|
|
"dtype": self._dtype,
|
||
|
|
"ignore_class": self.ignore_class,
|
||
|
|
"sparse_y_pred": self.sparse_y_pred,
|
||
|
|
"axis": self.axis,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
@keras_core_export("keras_core.metrics.OneHotMeanIoU")
|
||
|
|
class OneHotMeanIoU(MeanIoU):
|
||
|
|
"""Computes mean Intersection-Over-Union metric for one-hot encoded labels.
|
||
|
|
|
||
|
|
Formula:
|
||
|
|
|
||
|
|
```python
|
||
|
|
iou = true_positives / (true_positives + false_positives + false_negatives)
|
||
|
|
```
|
||
|
|
Intersection-Over-Union is a common evaluation metric for semantic image
|
||
|
|
segmentation.
|
||
|
|
|
||
|
|
To compute IoUs, the predictions are accumulated in a confusion matrix,
|
||
|
|
weighted by `sample_weight` and the metric is then calculated from it.
|
||
|
|
|
||
|
|
If `sample_weight` is `None`, weights default to 1.
|
||
|
|
Use `sample_weight` of 0 to mask values.
|
||
|
|
|
||
|
|
This class can be used to compute the mean IoU for multi-class
|
||
|
|
classification tasks where the labels are one-hot encoded (the last axis
|
||
|
|
should have one dimension per class). Note that the predictions should also
|
||
|
|
have the same shape. To compute the mean IoU, first the labels and
|
||
|
|
predictions are converted back into integer format by taking the argmax over
|
||
|
|
the class axis. Then the same computation steps as for the base `MeanIoU`
|
||
|
|
class apply.
|
||
|
|
|
||
|
|
Note, if there is only one channel in the labels and predictions, this class
|
||
|
|
is the same as class `MeanIoU`. In this case, use `MeanIoU` instead.
|
||
|
|
|
||
|
|
Also, make sure that `num_classes` is equal to the number of classes in the
|
||
|
|
data, to avoid a "labels out of bound" error when the confusion matrix is
|
||
|
|
computed.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
num_classes: The possible number of labels the prediction task can have.
|
||
|
|
name: (Optional) string name of the metric instance.
|
||
|
|
dtype: (Optional) data type of the metric result.
|
||
|
|
ignore_class: Optional integer. The ID of a class to be ignored during
|
||
|
|
metric computation. This is useful, for example, in segmentation
|
||
|
|
problems featuring a "void" class (commonly -1 or 255) in
|
||
|
|
segmentation maps. By default (`ignore_class=None`), all classes are
|
||
|
|
considered.
|
||
|
|
sparse_y_pred: Whether predictions are encoded using natural numbers or
|
||
|
|
probability distribution vectors. If `False`, the `argmax`
|
||
|
|
function will be used to determine each sample's most likely
|
||
|
|
associated label.
|
||
|
|
axis: (Optional) The dimension containing the logits. Defaults to `-1`.
|
||
|
|
|
||
|
|
Examples:
|
||
|
|
|
||
|
|
Standalone usage:
|
||
|
|
|
||
|
|
>>> y_true = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 0, 0]])
|
||
|
|
>>> y_pred = np.array([[0.2, 0.3, 0.5], [0.1, 0.2, 0.7], [0.5, 0.3, 0.1],
|
||
|
|
... [0.1, 0.4, 0.5]])
|
||
|
|
>>> sample_weight = [0.1, 0.2, 0.3, 0.4]
|
||
|
|
>>> m = keras_core.metrics.OneHotMeanIoU(num_classes=3)
|
||
|
|
>>> m.update_state(
|
||
|
|
... y_true=y_true, y_pred=y_pred, sample_weight=sample_weight)
|
||
|
|
>>> # cm = [[0, 0, 0.2+0.4],
|
||
|
|
>>> # [0.3, 0, 0],
|
||
|
|
>>> # [0, 0, 0.1]]
|
||
|
|
>>> # sum_row = [0.3, 0, 0.7], sum_col = [0.6, 0.3, 0.1]
|
||
|
|
>>> # true_positives = [0, 0, 0.1]
|
||
|
|
>>> # single_iou = true_positives / (sum_row + sum_col - true_positives))
|
||
|
|
>>> # mean_iou = (0 + 0 + 0.1 / (0.7 + 0.1 - 0.1)) / 3
|
||
|
|
>>> m.result()
|
||
|
|
0.048
|
||
|
|
|
||
|
|
Usage with `compile()` API:
|
||
|
|
|
||
|
|
```python
|
||
|
|
model.compile(
|
||
|
|
optimizer='sgd',
|
||
|
|
loss='mse',
|
||
|
|
metrics=[keras_core.metrics.OneHotMeanIoU(num_classes=3)])
|
||
|
|
```
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
num_classes,
|
||
|
|
name=None,
|
||
|
|
dtype=None,
|
||
|
|
ignore_class=None,
|
||
|
|
sparse_y_pred=False,
|
||
|
|
axis=-1,
|
||
|
|
):
|
||
|
|
super().__init__(
|
||
|
|
num_classes=num_classes,
|
||
|
|
axis=axis,
|
||
|
|
name=name,
|
||
|
|
dtype=dtype,
|
||
|
|
ignore_class=ignore_class,
|
||
|
|
sparse_y_true=False,
|
||
|
|
sparse_y_pred=sparse_y_pred,
|
||
|
|
)
|
||
|
|
|
||
|
|
def get_config(self):
|
||
|
|
return {
|
||
|
|
"num_classes": self.num_classes,
|
||
|
|
"name": self.name,
|
||
|
|
"dtype": self._dtype,
|
||
|
|
"ignore_class": self.ignore_class,
|
||
|
|
"sparse_y_pred": self.sparse_y_pred,
|
||
|
|
"axis": self.axis,
|
||
|
|
}
|