Move aom source to a sub-directory under media/libaom

There is no damned reason to treat this differently than any other media lib given its license and there never was.

1 files changed, 0 insertions, 73 deletions

diff --git a/third_party/aom/av1/encoder/ml.c b/third_party/aom/av1/encoder/ml.c
deleted file mode 100644
index d21def43a..000000000
--- a/third_party/aom/av1/encoder/ml.c
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * 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 <assert.h>
-#include <math.h>
-
-#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;
-}