| /* |
| * Copyright (c) 2021, Alliance for Open Media. All rights reserved |
| * |
| * This source code is subject to the terms of the BSD 3-Clause Clear License |
| * and the Alliance for Open Media Patent License 1.0. If the BSD 3-Clause Clear |
| * License was not distributed with this source code in the LICENSE file, you |
| * can obtain it at aomedia.org/license/software-license/bsd-3-c-c/. 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 |
| * aomedia.org/license/patent-license/. |
| */ |
| |
| #include <assert.h> |
| #include <math.h> |
| |
| #include "aom_dsp/aom_dsp_common.h" |
| #include "av1/encoder/ml.h" |
| |
| void av1_nn_output_prec_reduce(float *const output, int num_output) { |
| const int prec_bits = 9; |
| const int prec = 1 << prec_bits; |
| const float inv_prec = (float)(1.0 / prec); |
| for (int i = 0; i < num_output; i++) { |
| output[i] = ((int)(output[i] * prec + 0.5)) * inv_prec; |
| } |
| } |
| |
| // 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_c(const float *input_nodes, |
| const NN_CONFIG *const nn_config, int reduce_prec, |
| float *const output) { |
| int num_input_nodes = nn_config->num_inputs; |
| int buf_index = 0; |
| float buf[2][NN_MAX_NODES_PER_LAYER]; |
| |
| // 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 *layer_weights = nn_config->weights[layer]; |
| const float *layer_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 = layer_bias[node]; |
| for (int i = 0; i < num_input_nodes; ++i) |
| val += layer_weights[node * num_input_nodes + i] * input_nodes[i]; |
| // ReLU as activation function. |
| val = val > 0.0f ? val : 0.0f; // Could use AOMMAX(). |
| output_nodes[node] = val; |
| } |
| num_input_nodes = num_output_nodes; |
| input_nodes = output_nodes; |
| buf_index = 1 - buf_index; |
| } |
| |
| // Final output layer. |
| const float *layer_weights = nn_config->weights[num_layers]; |
| const float *layer_bias = nn_config->bias[num_layers]; |
| for (int node = 0; node < nn_config->num_outputs; ++node) { |
| float val = layer_bias[node]; |
| for (int i = 0; i < num_input_nodes; ++i) |
| val += layer_weights[node * num_input_nodes + i] * input_nodes[i]; |
| output[node] = val; |
| } |
| if (reduce_prec) av1_nn_output_prec_reduce(output, nn_config->num_outputs); |
| } |
| |
| #if CONFIG_NN_V2 |
| // Applies the ReLu activation to one fc layer |
| // output[i] = Max(input[i],0.0f) |
| static float *nn_relu(const float *input, FC_LAYER *layer) { |
| for (int i = 0; i < layer->num_outputs; ++i) { |
| layer->output[i] = AOMMAX(input[i], 0.0f); |
| } |
| |
| return layer->output; |
| } |
| |
| // Applies the Sigmoid activation to one fc layer |
| // output[i] = 1/(1+exp(input[i])) |
| static float *nn_sigmoid(const float *input, FC_LAYER *layer) { |
| for (int i = 0; i < layer->num_outputs; ++i) { |
| const float tmp = AOMMIN(AOMMAX(input[i], -10.0f), 10.0f); |
| layer->output[i] = 1.0f / (1.0f + expf(-tmp)); |
| } |
| |
| return layer->output; |
| } |
| |
| // Forward prediction in one fc layer, used in function av1_nn_predict_V2 |
| static float *nn_fc_forward(const float *input, FC_LAYER *layer) { |
| const float *weights = layer->weights; |
| const float *bias = layer->bias; |
| assert(layer->num_outputs < NN_MAX_NODES_PER_LAYER); |
| // fc |
| for (int node = 0; node < layer->num_outputs; ++node) { |
| float val = bias[node]; |
| for (int i = 0; i < layer->num_inputs; ++i) val += weights[i] * input[i]; |
| layer->output[node] = val; |
| weights += layer->num_inputs; |
| } |
| |
| // activation |
| switch (layer->activation) { |
| case NONE: return layer->output; |
| case RELU: return nn_relu(layer->output, layer); |
| case SIGMOID: return nn_sigmoid(layer->output, layer); |
| case SOFTSIGN: |
| assert(0 && "Softsign has not been supported in NN."); // TO DO |
| return NULL; |
| default: |
| assert(0 && "Unknown activation"); // Unknown activation |
| return NULL; |
| } |
| } |
| |
| void av1_nn_predict_v2(const float *feature, NN_CONFIG_V2 *nn_config, |
| int reduce_prec, float *output) { |
| const float *input_nodes = feature; |
| |
| // Propagate the layers. |
| const int num_layers = nn_config->num_hidden_layers; |
| assert(num_layers <= NN_MAX_HIDDEN_LAYERS); |
| for (int i = 0; i < num_layers; ++i) { |
| input_nodes = nn_fc_forward(input_nodes, nn_config->layer + i); |
| assert(nn_config->layer[i + 1].num_inputs == |
| nn_config->layer[i].num_outputs); |
| } |
| |
| // Final layer |
| input_nodes = nn_fc_forward(input_nodes, nn_config->layer + num_layers); |
| assert(nn_config->layer[num_layers].num_outputs == nn_config->num_logits); |
| // Copy the final layer output |
| memcpy(output, input_nodes, sizeof(*input_nodes) * nn_config->num_logits); |
| if (reduce_prec) av1_nn_output_prec_reduce(output, nn_config->num_logits); |
| } |
| #endif // CONFIG_NN_V2 |
| |
| 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++) { |
| // Clamp to range [-10.0, 0.0] to prevent FE_UNDERFLOW errors. |
| const float normalized_input = AOMMAX(input[i] - max_inp, -10.0f); |
| output[i] = (float)exp(normalized_input); |
| sum_out += output[i]; |
| } |
| for (int i = 0; i < n; i++) output[i] /= sum_out; |
| } |