From 76276fdbeade20f30f9474e32a289dba5c09d920 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Wed, 23 Aug 2017 18:54:24 +0000
Subject: [PATCH] You can specify filename for output video by using -out_filename res.avi
---
src/network.c | 784 ++++++++++++++++++++++++++++++++-----------------------
1 files changed, 461 insertions(+), 323 deletions(-)
diff --git a/src/network.c b/src/network.c
index f5fea60..79940e6 100644
--- a/src/network.c
+++ b/src/network.c
@@ -1,203 +1,197 @@
#include <stdio.h>
+#include <time.h>
+#include <assert.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 "gru_layer.h"
+#include "rnn_layer.h"
+#include "crnn_layer.h"
+#include "local_layer.h"
#include "convolutional_layer.h"
-//#include "old_conv.h"
+#include "activation_layer.h"
+#include "detection_layer.h"
+#include "region_layer.h"
+#include "normalization_layer.h"
+#include "batchnorm_layer.h"
#include "maxpool_layer.h"
+#include "reorg_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"
+
+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(net.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 && net.scales[i] > 1) reset_momentum(net);
+ }
+ return rate;
+ case EXP:
+ return net.learning_rate * pow(net.gamma, batch_num);
+ case POLY:
+ if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
+ return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+ case RANDOM:
+ return net.learning_rate * pow(rand_uniform(0,1), 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 GRU:
+ return "gru";
+ case CRNN:
+ return "crnn";
+ case MAXPOOL:
+ return "maxpool";
+ case REORG:
+ return "reorg";
+ case AVGPOOL:
+ return "avgpool";
+ case SOFTMAX:
+ return "softmax";
+ case DETECTION:
+ return "detection";
+ case REGION:
+ return "region";
+ case DROPOUT:
+ return "dropout";
+ case CROP:
+ return "crop";
+ case COST:
+ return "cost";
+ case ROUTE:
+ return "route";
+ case SHORTCUT:
+ return "shortcut";
+ case NORMALIZATION:
+ return "normalization";
+ case BATCHNORM:
+ return "batchnorm";
+ default:
+ break;
+ }
+ return "none";
+}
network make_network(int n)
{
- network net;
+ network net = {0};
net.n = n;
- net.layers = calloc(net.n, sizeof(void *));
- net.types = calloc(net.n, sizeof(LAYER_TYPE));
- net.outputs = 0;
- net.output = 0;
+ 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 print_convolutional_cfg(FILE *fp, convolutional_layer *l)
+void forward_network(network net, network_state state)
{
- int i;
- fprintf(fp, "[convolutional]\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n"
- "filters=%d\n"
- "size=%d\n"
- "stride=%d\n"
- "activation=%s\n",
- l->h, l->w, l->c,
- l->n, l->size, l->stride,
- get_activation_string(l->activation));
- fprintf(fp, "data=");
- for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
- for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
- fprintf(fp, "\n\n");
-}
-void print_connected_cfg(FILE *fp, connected_layer *l)
-{
- int i;
- fprintf(fp, "[connected]\n"
- "input=%d\n"
- "output=%d\n"
- "activation=%s\n",
- l->inputs, l->outputs,
- get_activation_string(l->activation));
- fprintf(fp, "data=");
- for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
- for(i = 0; i < l->inputs*l->outputs; ++i) fprintf(fp, "%g,", l->weights[i]);
- fprintf(fp, "\n\n");
-}
-
-void print_maxpool_cfg(FILE *fp, maxpool_layer *l)
-{
- fprintf(fp, "[maxpool]\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n"
- "stride=%d\n\n",
- l->h, l->w, l->c,
- l->stride);
-}
-
-void print_softmax_cfg(FILE *fp, softmax_layer *l)
-{
- fprintf(fp, "[softmax]\n"
- "input=%d\n\n",
- l->inputs);
-}
-
-void save_network(network net, char *filename)
-{
- FILE *fp = fopen(filename, "w");
- if(!fp) file_error(filename);
- int i;
- for(i = 0; i < net.n; ++i)
- {
- if(net.types[i] == CONVOLUTIONAL)
- print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i]);
- else if(net.types[i] == CONNECTED)
- print_connected_cfg(fp, (connected_layer *)net.layers[i]);
- else if(net.types[i] == MAXPOOL)
- print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i]);
- else if(net.types[i] == SOFTMAX)
- print_softmax_cfg(fp, (softmax_layer *)net.layers[i]);
- }
- fclose(fp);
-}
-
-void forward_network(network net, float *input)
-{
+ state.workspace = net.workspace;
int i;
for(i = 0; i < net.n; ++i){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- forward_convolutional_layer(layer, input);
- input = layer.output;
+ 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];
- forward_connected_layer(layer, input);
- input = layer.output;
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- forward_softmax_layer(layer, input);
- input = layer.output;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- forward_maxpool_layer(layer, input);
- input = layer.output;
- }
+ l.forward(l, state);
+ state.input = l.output;
}
}
-void update_network(network net, float step, float momentum, float decay)
+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){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- update_convolutional_layer(layer, step, momentum, decay);
- }
- else if(net.types[i] == MAXPOOL){
- //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- }
- else if(net.types[i] == SOFTMAX){
- //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer(layer, step, momentum, decay);
+ layer l = net.layers[i];
+ if(l.update){
+ l.update(l, update_batch, rate, net.momentum, net.decay);
}
}
}
-float *get_network_output_layer(network net, int 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;
- } else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- return layer.output;
- } else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- return layer.output;
- }
- return 0;
-}
float *get_network_output(network net)
{
- return get_network_output_layer(net, net.n-1);
+#ifdef GPU
+ if (gpu_index >= 0) return get_network_output_gpu(net);
+#endif
+ 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_delta_layer(network net, int i)
+float get_network_cost(network net)
{
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.delta;
- } else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.delta;
- } else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- return layer.delta;
- } else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- return layer.delta;
- }
- return 0;
-}
-
-float *get_network_delta(network net)
-{
- return get_network_delta_layer(net, net.n-1);
-}
-
-float calculate_error_network(network net, float *truth)
-{
+ int i;
float sum = 0;
- float *delta = get_network_delta(net);
- float *out = get_network_output(net);
- int i, k = get_network_output_size(net);
- for(i = 0; i < k; ++i){
- printf("%f, ", out[i]);
- delta[i] = truth[i] - out[i];
- sum += delta[i]*delta[i];
+ int count = 0;
+ for(i = 0; i < net.n; ++i){
+ if(net.layers[i].cost){
+ sum += net.layers[i].cost[0];
+ ++count;
+ }
}
- printf("\n");
- return sum;
+ return sum/count;
}
int get_predicted_class_network(network net)
@@ -207,175 +201,224 @@
return max_index(out, k);
}
-float backward_network(network net, float *input, float *truth)
+void backward_network(network net, network_state state)
{
- float error = calculate_error_network(net, truth);
int i;
- float *prev_input;
- float *prev_delta;
+ float *original_input = state.input;
+ float *original_delta = state.delta;
+ state.workspace = net.workspace;
for(i = net.n-1; i >= 0; --i){
+ state.index = i;
if(i == 0){
- prev_input = input;
- prev_delta = 0;
+ state.input = original_input;
+ state.delta = original_delta;
}else{
- prev_input = get_network_output_layer(net, i-1);
- prev_delta = get_network_delta_layer(net, i-1);
+ layer prev = net.layers[i-1];
+ state.input = prev.output;
+ state.delta = prev.delta;
}
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- learn_convolutional_layer(layer);
- //learn_convolutional_layer(layer);
- if(i != 0) backward_convolutional_layer(layer, prev_delta);
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- if(i != 0) backward_maxpool_layer(layer, prev_input, prev_delta);
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- if(i != 0) backward_softmax_layer(layer, prev_input, prev_delta);
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- learn_connected_layer(layer, prev_input);
- if(i != 0) backward_connected_layer(layer, prev_input, prev_delta);
- }
+ layer l = net.layers[i];
+ l.backward(l, state);
}
+}
+
+float train_network_datum(network net, float *x, float *y)
+{
+#ifdef GPU
+ if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
+#endif
+ network_state state;
+ *net.seen += net.batch;
+ 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_datum(network net, float *x, float *y, float step, float momentum, float decay)
+float train_network_sgd(network net, data d, int n)
{
- forward_network(net, x);
- //int class = get_predicted_class_network(net);
- float error = backward_network(net, x, y);
- update_network(net, step, momentum, decay);
- //return (y[class]?1:0);
- return error;
-}
+ int batch = net.batch;
+ float *X = calloc(batch*d.X.cols, sizeof(float));
+ float *y = calloc(batch*d.y.cols, sizeof(float));
-float train_network_sgd(network net, data d, int n, float step, float momentum,float decay)
-{
int i;
- float error = 0;
- int correct = 0;
+ float sum = 0;
for(i = 0; i < n; ++i){
- int index = rand()%d.X.rows;
- error += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
- float *y = d.y.vals[index];
- int class = get_predicted_class_network(net);
- correct += (y[class]?1:0);
- //printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
- //if((i+1)%10 == 0){
- // printf("%d: %f\n", (i+1), (float)correct/(i+1));
- //}
+ get_random_batch(d, batch, X, y);
+ float err = train_network_datum(net, X, y);
+ sum += err;
}
- printf("Accuracy: %f\n",(float) correct/n);
- return error/n;
+ free(X);
+ free(y);
+ return (float)sum/(n*batch);
}
-float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
+
+float train_network(network net, data d)
{
+ assert(d.X.rows % net.batch == 0);
+ 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;
- int correct = 0;
+ float sum = 0;
for(i = 0; i < n; ++i){
- int index = rand()%d.X.rows;
- float *x = d.X.vals[index];
- float *y = d.y.vals[index];
- forward_network(net, x);
- int class = get_predicted_class_network(net);
- backward_network(net, x, y);
- correct += (y[class]?1:0);
+ get_next_batch(d, batch, i*batch, X, y);
+ float err = train_network_datum(net, X, y);
+ sum += err;
}
- update_network(net, step, momentum, decay);
- return (float)correct/n;
-
+ free(X);
+ free(y);
+ return (float)sum/(n*batch);
}
-void train_network(network net, data d, float step, float momentum, float decay)
+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;
- int correct = 0;
- for(i = 0; i < d.X.rows; ++i){
- correct += train_network_datum(net, d.X.vals[i], d.y.vals[i], step, momentum, decay);
- if(i%100 == 0){
- visualize_network(net);
- cvWaitKey(10);
+ for(i = 0; i < net->n; ++i){
+ net->layers[i].batch = b;
+#ifdef CUDNN
+ if(net->layers[i].type == CONVOLUTIONAL){
+ cudnn_convolutional_setup(net->layers + i);
}
+#endif
}
- visualize_network(net);
- cvWaitKey(100);
- printf("Accuracy: %f\n", (float)correct/d.X.rows);
}
-int get_network_output_size_layer(network net, int i)
+int resize_network(network *net, int w, int h)
{
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- image output = get_convolutional_image(layer);
- return output.h*output.w*output.c;
+#ifdef GPU
+ cuda_set_device(net->gpu_index);
+ if(gpu_index >= 0){
+ cuda_free(net->workspace);
}
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- image output = get_maxpool_image(layer);
- return output.h*output.w*output.c;
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- return layer.outputs;
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- return layer.inputs;
- }
- return 0;
-}
-
-int reset_network_size(network net, int h, int w, int c)
-{
+#endif
int i;
- for (i = 0; i < net.n; ++i){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer *layer = (convolutional_layer *)net.layers[i];
- layer->h = h;
- layer->w = w;
- layer->c = c;
- image output = get_convolutional_image(*layer);
- h = output.h;
- w = output.w;
- c = output.c;
+ //if(w == net->w && h == net->h) return 0;
+ net->w = w;
+ net->h = h;
+ int inputs = 0;
+ size_t workspace_size = 0;
+ //fprintf(stderr, "Resizing to %d x %d...\n", 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 == REGION){
+ resize_region_layer(&l, w, h);
+ }else if(l.type == ROUTE){
+ resize_route_layer(&l, net);
+ }else if(l.type == REORG){
+ resize_reorg_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");
}
- else if(net.types[i] == MAXPOOL){
- maxpool_layer *layer = (maxpool_layer *)net.layers[i];
- layer->h = h;
- layer->w = w;
- layer->c = c;
- image output = get_maxpool_image(*layer);
- h = output.h;
- w = output.w;
- c = output.c;
- }
+ if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
+ inputs = l.outputs;
+ net->layers[i] = l;
+ w = l.out_w;
+ h = l.out_h;
+ if(l.type == AVGPOOL) break;
}
+#ifdef GPU
+ if(gpu_index >= 0){
+ if(net->input_gpu) {
+ cuda_free(*net->input_gpu);
+ *net->input_gpu = 0;
+ cuda_free(*net->truth_gpu);
+ *net->truth_gpu = 0;
+ }
+ net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+ }else {
+ free(net->workspace);
+ net->workspace = calloc(1, workspace_size);
+ }
+#else
+ free(net->workspace);
+ net->workspace = calloc(1, workspace_size);
+#endif
+ //fprintf(stderr, " Done!\n");
return 0;
}
int get_network_output_size(network net)
{
- int i = net.n-1;
- return get_network_output_size_layer(net, i);
+ 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)
{
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return get_convolutional_image(layer);
+ 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);
}
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return get_maxpool_image(layer);
- }
- return make_empty_image(0,0,0);
+ image def = {0};
+ return def;
}
image get_network_image(network net)
@@ -385,40 +428,95 @@
image m = get_network_image_layer(net, i);
if(m.h != 0) return m;
}
- return make_empty_image(0,0,0);
+ 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);
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- visualize_convolutional_layer(layer, buff);
+ 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)
{
- forward_network(net, 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(network net, data test)
+matrix network_predict_data_multi(network net, data test, int n)
{
- int i,j;
+ int i,j,b,m;
int k = get_network_output_size(net);
matrix pred = make_matrix(test.X.rows, k);
- for(i = 0; i < test.X.rows; ++i){
- float *out = network_predict(net, test.X.vals[i]);
- for(j = 0; j < k; ++j){
- pred.vals[i][j] = out[j];
+ 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;
}
@@ -426,30 +524,9 @@
{
int i,j;
for(i = 0; i < net.n; ++i){
- float *output = 0;
- int n = 0;
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- output = layer.output;
- image m = get_convolutional_image(layer);
- n = m.h*m.w*m.c;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- output = layer.output;
- image m = get_maxpool_image(layer);
- n = m.h*m.w*m.c;
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- output = layer.output;
- n = layer.outputs;
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- output = layer.output;
- n = layer.inputs;
- }
+ 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);
@@ -460,11 +537,72 @@
}
}
+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_accuracy(d.y, guess);
+ 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 (gpu_index >= 0) cuda_free(net.workspace);
+ else free(net.workspace);
+ 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);
+#else
+ free(net.workspace);
+#endif
+}
--
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