Merge pull request #850 from trav90/add-av1-support

Add initial support for AV1 video.

1 files changed, 73 insertions, 0 deletions

diff --git a/third_party/aom/av1/encoder/ml.c b/third_party/aom/av1/encoder/ml.c
new file mode 100644
index 000000000..d21def43a
--- /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 <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;
+}