wehub-resource-sync
75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# pylint: disable=invalid-name
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# ruff: noqa: RUF005
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"""NLLLoss in python"""
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import numpy as np
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def nll_loss(predictions, targets, weights, reduction="mean", ignore_index=-100):
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"""nll_loss operator implemented in numpy.
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output{n, i_1, i_2, ..., i_k} = -p * w
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where t = target{n, i_1, i_2, ..., i_k}
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p = predictions{n, t, i_1, i_2, i_k}
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w = weights{n, i_1, i_2, ..., i_k} if t != ignore_index else 0
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result = reduction(output)
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Parameters
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----------
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predictions : numpy.ndarray
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(k+2)-D with shape (N, C, d_1, d_2, ..., d_k),
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where C is the number of target classes
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targets : numpy.ndarray
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(k+1)-D with shape (N, d_1, d_2, ..., d_k)
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The target value of the input.
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weights : numpy.ndarray
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1-D with shape (C,)
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The weight of each target value.
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reduction : string
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The reduction method to apply to output.
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Can be "mean", "sum" or "none".
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ignore_index : int
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The target value to ignore.
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Returns
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-------
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output : numpy.ndarray
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a scalar if the reduction type is "mean" or "sum",
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otherwise the same shape as `target`.
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"""
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res = np.zeros(targets.shape)
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weight_sum = 0.0
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for index in np.ndindex(targets.shape):
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class_id = targets[index]
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if class_id != ignore_index:
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index_list = list(index)
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pred_index = tuple(index_list[:1] + [class_id] + index_list[1:])
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res[index] = -predictions[pred_index] * weights[class_id]
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weight_sum += weights[class_id]
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if reduction == "mean":
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return np.sum(res) / weight_sum
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if reduction == "sum":
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return np.sum(res)
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return res
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