From a392bbd0c957a00e3782c96e7ced84a29ff9dd88 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 15 Mar 2016 05:33:02 +0000
Subject: [PATCH] Play along w/ alphago
---
src/network.c | 629 ++++++++++++++++++++++++++++++++++++++++++++++++++++++--
1 files changed, 604 insertions(+), 25 deletions(-)
diff --git a/src/network.c b/src/network.c
index e55535c..e6fb51e 100644
--- a/src/network.c
+++ b/src/network.c
@@ -1,48 +1,627 @@
+#include <stdio.h>
+#include <time.h>
#include "network.h"
#include "image.h"
+#include "data.h"
+#include "utils.h"
+#include "blas.h"
+#include "crop_layer.h"
#include "connected_layer.h"
+#include "rnn_layer.h"
+#include "crnn_layer.h"
+#include "local_layer.h"
#include "convolutional_layer.h"
+#include "activation_layer.h"
+#include "deconvolutional_layer.h"
+#include "detection_layer.h"
+#include "normalization_layer.h"
#include "maxpool_layer.h"
+#include "avgpool_layer.h"
+#include "cost_layer.h"
+#include "softmax_layer.h"
+#include "dropout_layer.h"
+#include "route_layer.h"
+#include "shortcut_layer.h"
-void run_network(image input, network net)
+int get_current_batch(network net)
+{
+ int batch_num = (*net.seen)/(net.batch*net.subdivisions);
+ return batch_num;
+}
+
+void reset_momentum(network net)
+{
+ if (net.momentum == 0) return;
+ net.learning_rate = 0;
+ net.momentum = 0;
+ net.decay = 0;
+ #ifdef GPU
+ if(gpu_index >= 0) update_network_gpu(net);
+ #endif
+}
+
+float get_current_rate(network net)
+{
+ int batch_num = get_current_batch(net);
+ int i;
+ float rate;
+ switch (net.policy) {
+ case CONSTANT:
+ return net.learning_rate;
+ case STEP:
+ return net.learning_rate * pow(net.scale, batch_num/net.step);
+ case STEPS:
+ rate = net.learning_rate;
+ for(i = 0; i < net.num_steps; ++i){
+ if(net.steps[i] > batch_num) return rate;
+ rate *= net.scales[i];
+ if(net.steps[i] > batch_num - 1) reset_momentum(net);
+ }
+ return rate;
+ case EXP:
+ return net.learning_rate * pow(net.gamma, batch_num);
+ case POLY:
+ return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+ case SIG:
+ return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step))));
+ default:
+ fprintf(stderr, "Policy is weird!\n");
+ return net.learning_rate;
+ }
+}
+
+char *get_layer_string(LAYER_TYPE a)
+{
+ switch(a){
+ case CONVOLUTIONAL:
+ return "convolutional";
+ case ACTIVE:
+ return "activation";
+ case LOCAL:
+ return "local";
+ case DECONVOLUTIONAL:
+ return "deconvolutional";
+ case CONNECTED:
+ return "connected";
+ case RNN:
+ return "rnn";
+ case CRNN:
+ return "crnn";
+ case MAXPOOL:
+ return "maxpool";
+ case AVGPOOL:
+ return "avgpool";
+ case SOFTMAX:
+ return "softmax";
+ case DETECTION:
+ return "detection";
+ case DROPOUT:
+ return "dropout";
+ case CROP:
+ return "crop";
+ case COST:
+ return "cost";
+ case ROUTE:
+ return "route";
+ case SHORTCUT:
+ return "shortcut";
+ case NORMALIZATION:
+ return "normalization";
+ default:
+ break;
+ }
+ return "none";
+}
+
+network make_network(int n)
+{
+ network net = {0};
+ net.n = n;
+ net.layers = calloc(net.n, sizeof(layer));
+ net.seen = calloc(1, sizeof(int));
+ #ifdef GPU
+ net.input_gpu = calloc(1, sizeof(float *));
+ net.truth_gpu = calloc(1, sizeof(float *));
+ #endif
+ return net;
+}
+
+void forward_network(network net, network_state state)
{
int i;
- double *input_d = 0;
for(i = 0; i < net.n; ++i){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- run_convolutional_layer(input, layer);
- input = layer.output;
- input_d = layer.output.data;
+ state.index = i;
+ layer l = net.layers[i];
+ if(l.delta){
+ scal_cpu(l.outputs * l.batch, 0, l.delta, 1);
}
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- run_connected_layer(input_d, layer);
- input_d = layer.output;
+ if(l.type == CONVOLUTIONAL){
+ forward_convolutional_layer(l, state);
+ } else if(l.type == DECONVOLUTIONAL){
+ forward_deconvolutional_layer(l, state);
+ } else if(l.type == ACTIVE){
+ forward_activation_layer(l, state);
+ } else if(l.type == LOCAL){
+ forward_local_layer(l, state);
+ } else if(l.type == NORMALIZATION){
+ forward_normalization_layer(l, state);
+ } else if(l.type == DETECTION){
+ forward_detection_layer(l, state);
+ } else if(l.type == CONNECTED){
+ forward_connected_layer(l, state);
+ } else if(l.type == RNN){
+ forward_rnn_layer(l, state);
+ } else if(l.type == CRNN){
+ forward_crnn_layer(l, state);
+ } else if(l.type == CROP){
+ forward_crop_layer(l, state);
+ } else if(l.type == COST){
+ forward_cost_layer(l, state);
+ } else if(l.type == SOFTMAX){
+ forward_softmax_layer(l, state);
+ } else if(l.type == MAXPOOL){
+ forward_maxpool_layer(l, state);
+ } else if(l.type == AVGPOOL){
+ forward_avgpool_layer(l, state);
+ } else if(l.type == DROPOUT){
+ forward_dropout_layer(l, state);
+ } else if(l.type == ROUTE){
+ forward_route_layer(l, net);
+ } else if(l.type == SHORTCUT){
+ forward_shortcut_layer(l, state);
}
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- run_maxpool_layer(input, layer);
- input = layer.output;
- input_d = layer.output.data;
+ state.input = l.output;
+ }
+}
+
+void update_network(network net)
+{
+ int i;
+ int update_batch = net.batch*net.subdivisions;
+ float rate = get_current_rate(net);
+ for(i = 0; i < net.n; ++i){
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ update_convolutional_layer(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == DECONVOLUTIONAL){
+ update_deconvolutional_layer(l, rate, net.momentum, net.decay);
+ } else if(l.type == CONNECTED){
+ update_connected_layer(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == RNN){
+ update_rnn_layer(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == CRNN){
+ update_crnn_layer(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == LOCAL){
+ update_local_layer(l, update_batch, rate, net.momentum, net.decay);
}
}
}
+float *get_network_output(network net)
+{
+ int i;
+ for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
+ return net.layers[i].output;
+}
+
+float get_network_cost(network net)
+{
+ int i;
+ float sum = 0;
+ int count = 0;
+ for(i = 0; i < net.n; ++i){
+ if(net.layers[i].type == COST){
+ sum += net.layers[i].cost[0];
+ ++count;
+ }
+ if(net.layers[i].type == DETECTION){
+ sum += net.layers[i].cost[0];
+ ++count;
+ }
+ }
+ return sum/count;
+}
+
+int get_predicted_class_network(network net)
+{
+ float *out = get_network_output(net);
+ int k = get_network_output_size(net);
+ return max_index(out, k);
+}
+
+void backward_network(network net, network_state state)
+{
+ int i;
+ float *original_input = state.input;
+ float *original_delta = state.delta;
+ for(i = net.n-1; i >= 0; --i){
+ state.index = i;
+ if(i == 0){
+ state.input = original_input;
+ state.delta = original_delta;
+ }else{
+ layer prev = net.layers[i-1];
+ state.input = prev.output;
+ state.delta = prev.delta;
+ }
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ backward_convolutional_layer(l, state);
+ } else if(l.type == DECONVOLUTIONAL){
+ backward_deconvolutional_layer(l, state);
+ } else if(l.type == ACTIVE){
+ backward_activation_layer(l, state);
+ } else if(l.type == NORMALIZATION){
+ backward_normalization_layer(l, state);
+ } else if(l.type == MAXPOOL){
+ if(i != 0) backward_maxpool_layer(l, state);
+ } else if(l.type == AVGPOOL){
+ backward_avgpool_layer(l, state);
+ } else if(l.type == DROPOUT){
+ backward_dropout_layer(l, state);
+ } else if(l.type == DETECTION){
+ backward_detection_layer(l, state);
+ } else if(l.type == SOFTMAX){
+ if(i != 0) backward_softmax_layer(l, state);
+ } else if(l.type == CONNECTED){
+ backward_connected_layer(l, state);
+ } else if(l.type == RNN){
+ backward_rnn_layer(l, state);
+ } else if(l.type == CRNN){
+ backward_crnn_layer(l, state);
+ } else if(l.type == LOCAL){
+ backward_local_layer(l, state);
+ } else if(l.type == COST){
+ backward_cost_layer(l, state);
+ } else if(l.type == ROUTE){
+ backward_route_layer(l, net);
+ } else if(l.type == SHORTCUT){
+ backward_shortcut_layer(l, state);
+ }
+ }
+}
+
+float train_network_datum(network net, float *x, float *y)
+{
+ *net.seen += net.batch;
+#ifdef GPU
+ if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
+#endif
+ network_state state;
+ state.index = 0;
+ state.net = net;
+ state.input = x;
+ state.delta = 0;
+ state.truth = y;
+ state.train = 1;
+ forward_network(net, state);
+ backward_network(net, state);
+ float error = get_network_cost(net);
+ if(((*net.seen)/net.batch)%net.subdivisions == 0) update_network(net);
+ return error;
+}
+
+float train_network_sgd(network net, data d, int n)
+{
+ int batch = net.batch;
+ float *X = calloc(batch*d.X.cols, sizeof(float));
+ float *y = calloc(batch*d.y.cols, sizeof(float));
+
+ int i;
+ float sum = 0;
+ for(i = 0; i < n; ++i){
+ get_random_batch(d, batch, X, y);
+ float err = train_network_datum(net, X, y);
+ sum += err;
+ }
+ free(X);
+ free(y);
+ return (float)sum/(n*batch);
+}
+
+float train_network(network net, data d)
+{
+ int batch = net.batch;
+ int n = d.X.rows / batch;
+ float *X = calloc(batch*d.X.cols, sizeof(float));
+ float *y = calloc(batch*d.y.cols, sizeof(float));
+
+ int i;
+ float sum = 0;
+ for(i = 0; i < n; ++i){
+ get_next_batch(d, batch, i*batch, X, y);
+ float err = train_network_datum(net, X, y);
+ sum += err;
+ }
+ free(X);
+ free(y);
+ return (float)sum/(n*batch);
+}
+
+float train_network_batch(network net, data d, int n)
+{
+ int i,j;
+ network_state state;
+ state.index = 0;
+ state.net = net;
+ state.train = 1;
+ state.delta = 0;
+ float sum = 0;
+ int batch = 2;
+ for(i = 0; i < n; ++i){
+ for(j = 0; j < batch; ++j){
+ int index = rand()%d.X.rows;
+ state.input = d.X.vals[index];
+ state.truth = d.y.vals[index];
+ forward_network(net, state);
+ backward_network(net, state);
+ sum += get_network_cost(net);
+ }
+ update_network(net);
+ }
+ return (float)sum/(n*batch);
+}
+
+void set_batch_network(network *net, int b)
+{
+ net->batch = b;
+ int i;
+ for(i = 0; i < net->n; ++i){
+ net->layers[i].batch = b;
+ }
+}
+
+int resize_network(network *net, int w, int h)
+{
+ int i;
+ //if(w == net->w && h == net->h) return 0;
+ net->w = w;
+ net->h = h;
+ int inputs = 0;
+ //fprintf(stderr, "Resizing to %d x %d...", w, h);
+ //fflush(stderr);
+ for (i = 0; i < net->n; ++i){
+ layer l = net->layers[i];
+ if(l.type == CONVOLUTIONAL){
+ resize_convolutional_layer(&l, w, h);
+ }else if(l.type == CROP){
+ resize_crop_layer(&l, w, h);
+ }else if(l.type == MAXPOOL){
+ resize_maxpool_layer(&l, w, h);
+ }else if(l.type == AVGPOOL){
+ resize_avgpool_layer(&l, w, h);
+ }else if(l.type == NORMALIZATION){
+ resize_normalization_layer(&l, w, h);
+ }else if(l.type == COST){
+ resize_cost_layer(&l, inputs);
+ }else{
+ error("Cannot resize this type of layer");
+ }
+ inputs = l.outputs;
+ net->layers[i] = l;
+ w = l.out_w;
+ h = l.out_h;
+ if(l.type == AVGPOOL) break;
+ }
+ //fprintf(stderr, " Done!\n");
+ return 0;
+}
+
+int get_network_output_size(network net)
+{
+ int i;
+ for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
+ return net.layers[i].outputs;
+}
+
+int get_network_input_size(network net)
+{
+ return net.layers[0].inputs;
+}
+
+detection_layer get_network_detection_layer(network net)
+{
+ int i;
+ for(i = 0; i < net.n; ++i){
+ if(net.layers[i].type == DETECTION){
+ return net.layers[i];
+ }
+ }
+ fprintf(stderr, "Detection layer not found!!\n");
+ detection_layer l = {0};
+ return l;
+}
+
+image get_network_image_layer(network net, int i)
+{
+ layer l = net.layers[i];
+ if (l.out_w && l.out_h && l.out_c){
+ return float_to_image(l.out_w, l.out_h, l.out_c, l.output);
+ }
+ image def = {0};
+ return def;
+}
+
image get_network_image(network net)
{
int i;
for(i = net.n-1; i >= 0; --i){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.output;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output;
- }
+ image m = get_network_image_layer(net, i);
+ if(m.h != 0) return m;
}
- return make_image(1,1,1);
+ image def = {0};
+ return def;
}
+void visualize_network(network net)
+{
+ image *prev = 0;
+ int i;
+ char buff[256];
+ for(i = 0; i < net.n; ++i){
+ sprintf(buff, "Layer %d", i);
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ prev = visualize_convolutional_layer(l, buff, prev);
+ }
+ }
+}
+
+void top_predictions(network net, int k, int *index)
+{
+ int size = get_network_output_size(net);
+ float *out = get_network_output(net);
+ top_k(out, size, k, index);
+}
+
+
+float *network_predict(network net, float *input)
+{
+#ifdef GPU
+ if(gpu_index >= 0) return network_predict_gpu(net, input);
+#endif
+
+ network_state state;
+ state.net = net;
+ state.index = 0;
+ state.input = input;
+ state.truth = 0;
+ state.train = 0;
+ state.delta = 0;
+ forward_network(net, state);
+ float *out = get_network_output(net);
+ return out;
+}
+
+matrix network_predict_data_multi(network net, data test, int n)
+{
+ int i,j,b,m;
+ int k = get_network_output_size(net);
+ matrix pred = make_matrix(test.X.rows, k);
+ float *X = calloc(net.batch*test.X.rows, sizeof(float));
+ for(i = 0; i < test.X.rows; i += net.batch){
+ for(b = 0; b < net.batch; ++b){
+ if(i+b == test.X.rows) break;
+ memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
+ }
+ for(m = 0; m < n; ++m){
+ float *out = network_predict(net, X);
+ for(b = 0; b < net.batch; ++b){
+ if(i+b == test.X.rows) break;
+ for(j = 0; j < k; ++j){
+ pred.vals[i+b][j] += out[j+b*k]/n;
+ }
+ }
+ }
+ }
+ free(X);
+ return pred;
+}
+
+matrix network_predict_data(network net, data test)
+{
+ int i,j,b;
+ int k = get_network_output_size(net);
+ matrix pred = make_matrix(test.X.rows, k);
+ float *X = calloc(net.batch*test.X.cols, sizeof(float));
+ for(i = 0; i < test.X.rows; i += net.batch){
+ for(b = 0; b < net.batch; ++b){
+ if(i+b == test.X.rows) break;
+ memcpy(X+b*test.X.cols, test.X.vals[i+b], test.X.cols*sizeof(float));
+ }
+ float *out = network_predict(net, X);
+ for(b = 0; b < net.batch; ++b){
+ if(i+b == test.X.rows) break;
+ for(j = 0; j < k; ++j){
+ pred.vals[i+b][j] = out[j+b*k];
+ }
+ }
+ }
+ free(X);
+ return pred;
+}
+
+void print_network(network net)
+{
+ int i,j;
+ for(i = 0; i < net.n; ++i){
+ layer l = net.layers[i];
+ float *output = l.output;
+ int n = l.outputs;
+ float mean = mean_array(output, n);
+ float vari = variance_array(output, n);
+ fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
+ if(n > 100) n = 100;
+ for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]);
+ if(n == 100)fprintf(stderr,".....\n");
+ fprintf(stderr, "\n");
+ }
+}
+
+void compare_networks(network n1, network n2, data test)
+{
+ matrix g1 = network_predict_data(n1, test);
+ matrix g2 = network_predict_data(n2, test);
+ int i;
+ int a,b,c,d;
+ a = b = c = d = 0;
+ for(i = 0; i < g1.rows; ++i){
+ int truth = max_index(test.y.vals[i], test.y.cols);
+ int p1 = max_index(g1.vals[i], g1.cols);
+ int p2 = max_index(g2.vals[i], g2.cols);
+ if(p1 == truth){
+ if(p2 == truth) ++d;
+ else ++c;
+ }else{
+ if(p2 == truth) ++b;
+ else ++a;
+ }
+ }
+ printf("%5d %5d\n%5d %5d\n", a, b, c, d);
+ float num = pow((abs(b - c) - 1.), 2.);
+ float den = b + c;
+ printf("%f\n", num/den);
+}
+
+float network_accuracy(network net, data d)
+{
+ matrix guess = network_predict_data(net, d);
+ float acc = matrix_topk_accuracy(d.y, guess,1);
+ free_matrix(guess);
+ return acc;
+}
+
+float *network_accuracies(network net, data d, int n)
+{
+ static float acc[2];
+ matrix guess = network_predict_data(net, d);
+ acc[0] = matrix_topk_accuracy(d.y, guess, 1);
+ acc[1] = matrix_topk_accuracy(d.y, guess, n);
+ free_matrix(guess);
+ return acc;
+}
+
+
+float network_accuracy_multi(network net, data d, int n)
+{
+ matrix guess = network_predict_data_multi(net, d, n);
+ float acc = matrix_topk_accuracy(d.y, guess,1);
+ free_matrix(guess);
+ return acc;
+}
+
+void free_network(network net)
+{
+ int i;
+ for(i = 0; i < net.n; ++i){
+ free_layer(net.layers[i]);
+ }
+ free(net.layers);
+ #ifdef GPU
+ if(*net.input_gpu) cuda_free(*net.input_gpu);
+ if(*net.truth_gpu) cuda_free(*net.truth_gpu);
+ if(net.input_gpu) free(net.input_gpu);
+ if(net.truth_gpu) free(net.truth_gpu);
+ #endif
+}
--
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