Source code for quantizeml.analysis.tools.metrics

#!/usr/bin/env python
# ******************************************************************************
# Copyright 2024 Brainchip Holdings Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
__all__ = ["SMAPE", "Saturation", "print_metric_table"]

from collections import defaultdict
import warnings
import numpy as np
import keras




[docs]
class SMAPE(keras.metrics.Metric):
    """Compute the Symmetric Mean Absolute Percentage Error (SMAPE) as:

    >>> mean(abs(x - y) / (abs(x) + abs(y)))

    Reference: https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error

    Args:
        name (str, optional): name of the metric. Defaults to "smape".
    """

    def __init__(self, name="smape", **kwargs):
        super().__init__(name=name, **kwargs)
        self.error = self.add_weight(name='error', initializer='zeros', dtype="float64")
        self.count = self.add_weight(name='count', initializer='zeros', dtype="float64")

    def update_state(self, y_true, y_pred):
        assert y_true.shape == y_pred.shape
        total = y_true.size

        # Skip values that undefine the metric
        mask = (y_true == 0) & (y_pred == 0)
        y_true = y_true[~mask]
        y_pred = y_pred[~mask]

        # Compute smape
        smape = np.sum(np.abs(y_true - y_pred) / (np.abs(y_true) + np.abs(y_pred)))
        self.error.assign_add(smape)

        # Update the metric count.
        # Note here we take into account the set of values y_true = y_pred = 0,
        # since the error they contribute is zero.
        self.count.assign_add(total)

    def result(self):
        if self.count == 0:
            return 0.0
        return self.error / self.count

    def reset_states(self):
        self.error.assign(0.0)
        self.count.assign(0.0)






[docs]
class Saturation(keras.metrics.Metric):
    """Returns the percentage of saturating values.

    We consider a value saturated if it is one of {min_value, max_value}

    Args:
        min_value (np.ndarray, optional): the minimum of values.
            If not provided, it is inferred from the values type. Defaults to None.
        max_value (np.ndarray, optional): the maximum of values.
            If not provided, it is inferred from the values type. Defaults to None.
    """

    def __init__(self, name="saturation", min_value=None, max_value=None, **kwargs):
        super().__init__(name=name, **kwargs)
        self._min_value = min_value
        self._max_value = max_value
        self.total = self.add_weight(name='total', initializer='zeros', dtype="int64")
        self.count = self.add_weight(name='count', initializer='zeros', dtype="int64")

    @property
    def min_value(self):
        if self._min_value is None:
            iinfo = np.iinfo(self.dtype) if np.issubdtype(self.dtype, np.integer) else None
            assert iinfo, f"Unknown minimum value for data type {self.dtype}"
            return iinfo.min
        return self._min_value

    @property
    def max_value(self):
        if self._max_value is None:
            iinfo = np.iinfo(self.dtype) if np.issubdtype(self.dtype, np.integer) else None
            assert iinfo, f"Unknown maximum value for data type {self.dtype}"
            return iinfo.max
        return self._max_value

    def update_state(self, values):
        if np.any(values > self.max_value) or np.any(values < self.min_value):
            warnings.warn(f"Saturation is not accurate: there are values outside of range "
                          f"[{self.min_value}, {self.max_value}].")
        sat_values = np.sum((values == self.min_value) | (values == self.max_value))
        self.total.assign_add(sat_values)
        self.count.assign_add(values.size)

    def result(self):
        count = self.count if self.count != 0 else 1
        return 100 * (self.total / count)

    def reset_states(self):
        self.total.assign(0)
        self.count.assign(0)

    def get_config(self):
        config = super().get_config()
        config.update({"min_value": self._min_value, "max_value": self._max_value})
        return config