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authorDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-07 19:33:14 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-07 19:33:14 +0000
commit36d22d82aa202bb199967e9512281e9a53db42c9 (patch)
tree105e8c98ddea1c1e4784a60a5a6410fa416be2de /third_party/aom/av1/encoder/ml.h
parentInitial commit. (diff)
downloadfirefox-esr-36d22d82aa202bb199967e9512281e9a53db42c9.tar.xz
firefox-esr-36d22d82aa202bb199967e9512281e9a53db42c9.zip
Adding upstream version 115.7.0esr.upstream/115.7.0esrupstream
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
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+/*
+ * Copyright (c) 2016, Alliance for Open Media. All rights reserved
+ *
+ * This source code is subject to the terms of the BSD 2 Clause License and
+ * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
+ * was not distributed with this source code in the LICENSE file, you can
+ * obtain it at www.aomedia.org/license/software. If the Alliance for Open
+ * Media Patent License 1.0 was not distributed with this source code in the
+ * PATENTS file, you can obtain it at www.aomedia.org/license/patent.
+ */
+
+#ifndef AOM_AV1_ENCODER_ML_H_
+#define AOM_AV1_ENCODER_ML_H_
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#define NN_MAX_HIDDEN_LAYERS 10
+#define NN_MAX_NODES_PER_LAYER 128
+
+typedef struct {
+ int num_inputs; // Number of input nodes, i.e. features.
+ int num_outputs; // Number of output nodes.
+ int num_hidden_layers; // Number of hidden layers, maximum 10.
+ // Number of nodes for each hidden layer.
+ int num_hidden_nodes[NN_MAX_HIDDEN_LAYERS];
+ // Weight parameters, indexed by layer.
+ const float *weights[NN_MAX_HIDDEN_LAYERS + 1];
+ // Bias parameters, indexed by layer.
+ const float *bias[NN_MAX_HIDDEN_LAYERS + 1];
+} NN_CONFIG;
+
+// Calculate prediction based on the given input features and neural net config.
+// Assume there are no more than NN_MAX_NODES_PER_LAYER nodes in each hidden
+// layer.
+void av1_nn_predict(const float *features, const NN_CONFIG *nn_config,
+ float *output);
+
+// Applies the softmax normalization function to the input
+// to get a valid probability distribution in the output:
+// output[i] = exp(input[i]) / sum_{k \in [0,n)}(exp(input[k]))
+void av1_nn_softmax(const float *input, float *output, int n);
+
+#ifdef __cplusplus
+} // extern "C"
+#endif
+
+#endif // AOM_AV1_ENCODER_ML_H_