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, 49 deletions

diff --git a/third_party/aom/av1/encoder/ml.h b/third_party/aom/av1/encoder/ml.h
deleted file mode 100644
index cb8ef2871..000000000
--- a/third_party/aom/av1/encoder/ml.h
+++ /dev/null
@@ -1,49 +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.
- */
-
-#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_