/src/aom/av1/encoder/ml.c
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1 | | /* |
2 | | * Copyright (c) 2016, Alliance for Open Media. All rights reserved |
3 | | * |
4 | | * This source code is subject to the terms of the BSD 2 Clause License and |
5 | | * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License |
6 | | * was not distributed with this source code in the LICENSE file, you can |
7 | | * obtain it at www.aomedia.org/license/software. If the Alliance for Open |
8 | | * Media Patent License 1.0 was not distributed with this source code in the |
9 | | * PATENTS file, you can obtain it at www.aomedia.org/license/patent. |
10 | | */ |
11 | | |
12 | | #include <assert.h> |
13 | | #include <math.h> |
14 | | |
15 | | #include "aom_dsp/aom_dsp_common.h" |
16 | | #include "av1/encoder/ml.h" |
17 | | |
18 | 0 | void av1_nn_output_prec_reduce(float *const output, int num_output) { |
19 | 0 | const int prec_bits = 9; |
20 | 0 | const int prec = 1 << prec_bits; |
21 | 0 | const float inv_prec = (float)(1.0 / prec); |
22 | 0 | for (int i = 0; i < num_output; i++) { |
23 | 0 | output[i] = ((int)(output[i] * prec + 0.5)) * inv_prec; |
24 | 0 | } |
25 | 0 | } |
26 | | |
27 | | // Calculate prediction based on the given input features and neural net config. |
28 | | // Assume there are no more than NN_MAX_NODES_PER_LAYER nodes in each hidden |
29 | | // layer. |
30 | | void av1_nn_predict_c(const float *input_nodes, |
31 | | const NN_CONFIG *const nn_config, int reduce_prec, |
32 | 0 | float *const output) { |
33 | 0 | int num_input_nodes = nn_config->num_inputs; |
34 | 0 | int buf_index = 0; |
35 | 0 | float buf[2][NN_MAX_NODES_PER_LAYER]; |
36 | | |
37 | | // Propagate hidden layers. |
38 | 0 | const int num_layers = nn_config->num_hidden_layers; |
39 | 0 | assert(num_layers <= NN_MAX_HIDDEN_LAYERS); |
40 | 0 | for (int layer = 0; layer < num_layers; ++layer) { |
41 | 0 | const float *layer_weights = nn_config->weights[layer]; |
42 | 0 | const float *layer_bias = nn_config->bias[layer]; |
43 | 0 | float *output_nodes = buf[buf_index]; |
44 | 0 | const int num_output_nodes = nn_config->num_hidden_nodes[layer]; |
45 | 0 | assert(num_output_nodes < NN_MAX_NODES_PER_LAYER); |
46 | 0 | for (int node = 0; node < num_output_nodes; ++node) { |
47 | 0 | float val = layer_bias[node]; |
48 | 0 | for (int i = 0; i < num_input_nodes; ++i) |
49 | 0 | val += layer_weights[node * num_input_nodes + i] * input_nodes[i]; |
50 | | // ReLU as activation function. |
51 | 0 | val = val > 0.0f ? val : 0.0f; // Could use AOMMAX(). |
52 | 0 | output_nodes[node] = val; |
53 | 0 | } |
54 | 0 | num_input_nodes = num_output_nodes; |
55 | 0 | input_nodes = output_nodes; |
56 | 0 | buf_index = 1 - buf_index; |
57 | 0 | } |
58 | | |
59 | | // Final output layer. |
60 | 0 | const float *layer_weights = nn_config->weights[num_layers]; |
61 | 0 | const float *layer_bias = nn_config->bias[num_layers]; |
62 | 0 | for (int node = 0; node < nn_config->num_outputs; ++node) { |
63 | 0 | float val = layer_bias[node]; |
64 | 0 | for (int i = 0; i < num_input_nodes; ++i) |
65 | 0 | val += layer_weights[node * num_input_nodes + i] * input_nodes[i]; |
66 | 0 | output[node] = val; |
67 | 0 | } |
68 | 0 | if (reduce_prec) av1_nn_output_prec_reduce(output, nn_config->num_outputs); |
69 | 0 | } |
70 | | |
71 | | #if CONFIG_NN_V2 |
72 | | // Applies the ReLu activation to one fc layer |
73 | | // output[i] = Max(input[i],0.0f) |
74 | | static float *nn_relu(const float *input, FC_LAYER *layer) { |
75 | | for (int i = 0; i < layer->num_outputs; ++i) { |
76 | | layer->output[i] = AOMMAX(input[i], 0.0f); |
77 | | } |
78 | | |
79 | | return layer->output; |
80 | | } |
81 | | |
82 | | // Applies the Sigmoid activation to one fc layer |
83 | | // output[i] = 1/(1+exp(input[i])) |
84 | | static float *nn_sigmoid(const float *input, FC_LAYER *layer) { |
85 | | for (int i = 0; i < layer->num_outputs; ++i) { |
86 | | const float tmp = AOMMIN(AOMMAX(input[i], -10.0f), 10.0f); |
87 | | layer->output[i] = 1.0f / (1.0f + expf(-tmp)); |
88 | | } |
89 | | |
90 | | return layer->output; |
91 | | } |
92 | | |
93 | | // Forward prediction in one fc layer, used in function av1_nn_predict_V2 |
94 | | static float *nn_fc_forward(const float *input, FC_LAYER *layer) { |
95 | | const float *weights = layer->weights; |
96 | | const float *bias = layer->bias; |
97 | | assert(layer->num_outputs < NN_MAX_NODES_PER_LAYER); |
98 | | // fc |
99 | | for (int node = 0; node < layer->num_outputs; ++node) { |
100 | | float val = bias[node]; |
101 | | for (int i = 0; i < layer->num_inputs; ++i) val += weights[i] * input[i]; |
102 | | layer->output[node] = val; |
103 | | weights += layer->num_inputs; |
104 | | } |
105 | | |
106 | | // activation |
107 | | switch (layer->activation) { |
108 | | case NONE: return layer->output; |
109 | | case RELU: return nn_relu(layer->output, layer); |
110 | | case SIGMOID: return nn_sigmoid(layer->output, layer); |
111 | | case SOFTSIGN: |
112 | | assert(0 && "Softsign has not been supported in NN."); // TO DO |
113 | | return NULL; |
114 | | default: |
115 | | assert(0 && "Unknown activation"); // Unknown activation |
116 | | return NULL; |
117 | | } |
118 | | } |
119 | | |
120 | | void av1_nn_predict_v2(const float *feature, NN_CONFIG_V2 *nn_config, |
121 | | int reduce_prec, float *output) { |
122 | | const float *input_nodes = feature; |
123 | | |
124 | | // Propagate the layers. |
125 | | const int num_layers = nn_config->num_hidden_layers; |
126 | | assert(num_layers <= NN_MAX_HIDDEN_LAYERS); |
127 | | for (int i = 0; i < num_layers; ++i) { |
128 | | input_nodes = nn_fc_forward(input_nodes, nn_config->layer + i); |
129 | | assert(nn_config->layer[i + 1].num_inputs == |
130 | | nn_config->layer[i].num_outputs); |
131 | | } |
132 | | |
133 | | // Final layer |
134 | | input_nodes = nn_fc_forward(input_nodes, nn_config->layer + num_layers); |
135 | | assert(nn_config->layer[num_layers].num_outputs == nn_config->num_logits); |
136 | | // Copy the final layer output |
137 | | memcpy(output, input_nodes, sizeof(*input_nodes) * nn_config->num_logits); |
138 | | if (reduce_prec) av1_nn_output_prec_reduce(output, nn_config->num_logits); |
139 | | } |
140 | | #endif // CONFIG_NN_V2 |
141 | | |
142 | 0 | void av1_nn_softmax(const float *input, float *output, int n) { |
143 | | // Softmax function is invariant to adding the same constant |
144 | | // to all input values, so we subtract the maximum input to avoid |
145 | | // possible overflow. |
146 | 0 | float max_input = input[0]; |
147 | 0 | for (int i = 1; i < n; i++) max_input = AOMMAX(max_input, input[i]); |
148 | 0 | float sum_out = 0.0f; |
149 | 0 | for (int i = 0; i < n; i++) { |
150 | | // Clamp to range [-10.0, 0.0] to prevent FE_UNDERFLOW errors. |
151 | 0 | const float normalized_input = AOMMAX(input[i] - max_input, -10.0f); |
152 | 0 | output[i] = expf(normalized_input); |
153 | 0 | sum_out += output[i]; |
154 | 0 | } |
155 | 0 | for (int i = 0; i < n; i++) output[i] /= sum_out; |
156 | 0 | } |
157 | | |
158 | 0 | static AOM_INLINE float approx_exp(float y) { |
159 | 0 | #define A ((1 << 23) / 0.69314718056f) // (1 << 23) / ln(2) |
160 | 0 | #define B \ |
161 | 0 | 127 // Offset for the exponent according to IEEE floating point standard. |
162 | 0 | #define C 60801 // Magic number controls the accuracy of approximation |
163 | 0 | union { |
164 | 0 | float as_float; |
165 | 0 | int32_t as_int32; |
166 | 0 | } container; |
167 | 0 | container.as_int32 = ((int32_t)(y * A)) + ((B << 23) - C); |
168 | 0 | return container.as_float; |
169 | 0 | #undef A |
170 | 0 | #undef B |
171 | 0 | #undef C |
172 | 0 | } |
173 | | |
174 | 0 | void av1_nn_fast_softmax_16_c(const float *input, float *output) { |
175 | 0 | const int kNumClasses = 16; |
176 | 0 | float max_input = input[0]; |
177 | 0 | for (int i = 1; i < kNumClasses; i++) max_input = AOMMAX(max_input, input[i]); |
178 | 0 | float sum_out = 0.0f; |
179 | 0 | for (int i = 0; i < kNumClasses; i++) { |
180 | | // Clamp to range [-10.0, 0.0] to prevent FE_UNDERFLOW errors. |
181 | 0 | const float normalized_input = AOMMAX(input[i] - max_input, -10.0f); |
182 | 0 | output[i] = approx_exp(normalized_input); |
183 | 0 | sum_out += output[i]; |
184 | 0 | } |
185 | 0 | for (int i = 0; i < kNumClasses; i++) output[i] /= sum_out; |
186 | 0 | } |