From 36d22d82aa202bb199967e9512281e9a53db42c9 Mon Sep 17 00:00:00 2001 From: Daniel Baumann Date: Sun, 7 Apr 2024 21:33:14 +0200 Subject: Adding upstream version 115.7.0esr. Signed-off-by: Daniel Baumann --- third_party/aom/av1/encoder/ml.c | 73 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 third_party/aom/av1/encoder/ml.c (limited to 'third_party/aom/av1/encoder/ml.c') diff --git a/third_party/aom/av1/encoder/ml.c b/third_party/aom/av1/encoder/ml.c new file mode 100644 index 0000000000..d21def43a8 --- /dev/null +++ b/third_party/aom/av1/encoder/ml.c @@ -0,0 +1,73 @@ +/* + * 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. + */ + +#include +#include + +#include "aom_dsp/aom_dsp_common.h" +#include "av1/encoder/ml.h" + +void av1_nn_predict(const float *features, const NN_CONFIG *nn_config, + float *output) { + int num_input_nodes = nn_config->num_inputs; + int buf_index = 0; + float buf[2][NN_MAX_NODES_PER_LAYER]; + const float *input_nodes = features; + + // Propagate hidden layers. + const int num_layers = nn_config->num_hidden_layers; + assert(num_layers <= NN_MAX_HIDDEN_LAYERS); + for (int layer = 0; layer < num_layers; ++layer) { + const float *weights = nn_config->weights[layer]; + const float *bias = nn_config->bias[layer]; + float *output_nodes = buf[buf_index]; + const int num_output_nodes = nn_config->num_hidden_nodes[layer]; + assert(num_output_nodes < NN_MAX_NODES_PER_LAYER); + for (int node = 0; node < num_output_nodes; ++node) { + float val = 0.0f; + for (int i = 0; i < num_input_nodes; ++i) + val += weights[i] * input_nodes[i]; + val += bias[node]; + // ReLU as activation function. + val = val > 0.0f ? val : 0.0f; // Could use AOMMAX(). + output_nodes[node] = val; + weights += num_input_nodes; + } + num_input_nodes = num_output_nodes; + input_nodes = output_nodes; + buf_index = 1 - buf_index; + } + + // Final output layer. + const float *weights = nn_config->weights[num_layers]; + for (int node = 0; node < nn_config->num_outputs; ++node) { + const float *bias = nn_config->bias[num_layers]; + float val = 0.0f; + for (int i = 0; i < num_input_nodes; ++i) + val += weights[i] * input_nodes[i]; + output[node] = val + bias[node]; + weights += num_input_nodes; + } +} + +void av1_nn_softmax(const float *input, float *output, int n) { + // Softmax function is invariant to adding the same constant + // to all input values, so we subtract the maximum input to avoid + // possible overflow. + float max_inp = input[0]; + for (int i = 1; i < n; i++) max_inp = AOMMAX(max_inp, input[i]); + float sum_out = 0.0f; + for (int i = 0; i < n; i++) { + output[i] = (float)exp(input[i] - max_inp); + sum_out += output[i]; + } + for (int i = 0; i < n; i++) output[i] /= sum_out; +} -- cgit v1.2.3