mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
866 lines
29 KiB
C
866 lines
29 KiB
C
#include <stdio.h>
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#include <string.h>
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#include <stdlib.h>
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#include "parser.h"
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#include "activations.h"
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#include "crop_layer.h"
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#include "cost_layer.h"
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#include "convolutional_layer.h"
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#include "deconvolutional_layer.h"
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#include "connected_layer.h"
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#include "maxpool_layer.h"
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#include "normalization_layer.h"
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#include "softmax_layer.h"
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#include "dropout_layer.h"
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#include "detection_layer.h"
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#include "freeweight_layer.h"
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#include "list.h"
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#include "option_list.h"
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#include "utils.h"
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typedef struct{
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char *type;
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list *options;
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}section;
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int is_convolutional(section *s);
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int is_deconvolutional(section *s);
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int is_connected(section *s);
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int is_maxpool(section *s);
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int is_dropout(section *s);
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int is_freeweight(section *s);
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int is_softmax(section *s);
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int is_crop(section *s);
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int is_cost(section *s);
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int is_detection(section *s);
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int is_normalization(section *s);
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list *read_cfg(char *filename);
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void free_section(section *s)
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{
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free(s->type);
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node *n = s->options->front;
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while(n){
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kvp *pair = (kvp *)n->val;
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free(pair->key);
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free(pair);
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node *next = n->next;
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free(n);
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n = next;
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}
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free(s->options);
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free(s);
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}
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void parse_data(char *data, float *a, int n)
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{
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int i;
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if(!data) return;
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char *curr = data;
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char *next = data;
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int done = 0;
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for(i = 0; i < n && !done; ++i){
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while(*++next !='\0' && *next != ',');
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if(*next == '\0') done = 1;
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*next = '\0';
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sscanf(curr, "%g", &a[i]);
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curr = next+1;
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}
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}
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deconvolutional_layer *parse_deconvolutional(list *options, network *net, int count)
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{
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int h,w,c;
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float learning_rate, momentum, decay;
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int n = option_find_int(options, "filters",1);
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int size = option_find_int(options, "size",1);
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int stride = option_find_int(options, "stride",1);
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char *activation_s = option_find_str(options, "activation", "logistic");
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ACTIVATION activation = get_activation(activation_s);
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if(count == 0){
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learning_rate = option_find_float(options, "learning_rate", .001);
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momentum = option_find_float(options, "momentum", .9);
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decay = option_find_float(options, "decay", .0001);
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h = option_find_int(options, "height",1);
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w = option_find_int(options, "width",1);
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c = option_find_int(options, "channels",1);
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net->batch = option_find_int(options, "batch",1);
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net->learning_rate = learning_rate;
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net->momentum = momentum;
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net->decay = decay;
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net->seen = option_find_int(options, "seen",0);
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}else{
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learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
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momentum = option_find_float_quiet(options, "momentum", net->momentum);
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decay = option_find_float_quiet(options, "decay", net->decay);
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image m = get_network_image_layer(*net, count-1);
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h = m.h;
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w = m.w;
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c = m.c;
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if(h == 0) error("Layer before deconvolutional layer must output image.");
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}
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deconvolutional_layer *layer = make_deconvolutional_layer(net->batch,h,w,c,n,size,stride,activation,learning_rate,momentum,decay);
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char *weights = option_find_str(options, "weights", 0);
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char *biases = option_find_str(options, "biases", 0);
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parse_data(weights, layer->filters, c*n*size*size);
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parse_data(biases, layer->biases, n);
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#ifdef GPU
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if(weights || biases) push_deconvolutional_layer(*layer);
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#endif
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option_unused(options);
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return layer;
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}
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convolutional_layer *parse_convolutional(list *options, network *net, int count)
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{
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int h,w,c;
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float learning_rate, momentum, decay;
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int n = option_find_int(options, "filters",1);
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int size = option_find_int(options, "size",1);
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int stride = option_find_int(options, "stride",1);
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int pad = option_find_int(options, "pad",0);
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char *activation_s = option_find_str(options, "activation", "logistic");
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ACTIVATION activation = get_activation(activation_s);
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if(count == 0){
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learning_rate = option_find_float(options, "learning_rate", .001);
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momentum = option_find_float(options, "momentum", .9);
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decay = option_find_float(options, "decay", .0001);
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h = option_find_int(options, "height",1);
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w = option_find_int(options, "width",1);
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c = option_find_int(options, "channels",1);
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net->batch = option_find_int(options, "batch",1);
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net->learning_rate = learning_rate;
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net->momentum = momentum;
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net->decay = decay;
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net->seen = option_find_int(options, "seen",0);
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}else{
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learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
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momentum = option_find_float_quiet(options, "momentum", net->momentum);
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decay = option_find_float_quiet(options, "decay", net->decay);
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image m = get_network_image_layer(*net, count-1);
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h = m.h;
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w = m.w;
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c = m.c;
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if(h == 0) error("Layer before convolutional layer must output image.");
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}
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convolutional_layer *layer = make_convolutional_layer(net->batch,h,w,c,n,size,stride,pad,activation,learning_rate,momentum,decay);
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char *weights = option_find_str(options, "weights", 0);
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char *biases = option_find_str(options, "biases", 0);
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parse_data(weights, layer->filters, c*n*size*size);
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parse_data(biases, layer->biases, n);
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#ifdef GPU
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if(weights || biases) push_convolutional_layer(*layer);
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#endif
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option_unused(options);
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return layer;
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}
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connected_layer *parse_connected(list *options, network *net, int count)
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{
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int input;
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float learning_rate, momentum, decay;
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int output = option_find_int(options, "output",1);
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char *activation_s = option_find_str(options, "activation", "logistic");
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ACTIVATION activation = get_activation(activation_s);
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if(count == 0){
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input = option_find_int(options, "input",1);
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net->batch = option_find_int(options, "batch",1);
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learning_rate = option_find_float(options, "learning_rate", .001);
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momentum = option_find_float(options, "momentum", .9);
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decay = option_find_float(options, "decay", .0001);
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net->learning_rate = learning_rate;
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net->momentum = momentum;
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net->decay = decay;
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}else{
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learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
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momentum = option_find_float_quiet(options, "momentum", net->momentum);
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decay = option_find_float_quiet(options, "decay", net->decay);
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input = get_network_output_size_layer(*net, count-1);
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}
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connected_layer *layer = make_connected_layer(net->batch, input, output, activation,learning_rate,momentum,decay);
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char *weights = option_find_str(options, "weights", 0);
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char *biases = option_find_str(options, "biases", 0);
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parse_data(biases, layer->biases, output);
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parse_data(weights, layer->weights, input*output);
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#ifdef GPU
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if(weights || biases) push_connected_layer(*layer);
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#endif
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option_unused(options);
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return layer;
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}
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softmax_layer *parse_softmax(list *options, network *net, int count)
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{
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int input;
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int groups = option_find_int(options, "groups",1);
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if(count == 0){
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input = option_find_int(options, "input",1);
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net->batch = option_find_int(options, "batch",1);
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net->seen = option_find_int(options, "seen",0);
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}else{
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input = get_network_output_size_layer(*net, count-1);
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}
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softmax_layer *layer = make_softmax_layer(net->batch, groups, input);
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option_unused(options);
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return layer;
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}
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detection_layer *parse_detection(list *options, network *net, int count)
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{
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int input;
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if(count == 0){
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input = option_find_int(options, "input",1);
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net->batch = option_find_int(options, "batch",1);
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net->seen = option_find_int(options, "seen",0);
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}else{
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input = get_network_output_size_layer(*net, count-1);
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}
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int coords = option_find_int(options, "coords", 1);
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int classes = option_find_int(options, "classes", 1);
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int rescore = option_find_int(options, "rescore", 1);
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detection_layer *layer = make_detection_layer(net->batch, input, classes, coords, rescore);
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option_unused(options);
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return layer;
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}
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cost_layer *parse_cost(list *options, network *net, int count)
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{
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int input;
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if(count == 0){
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input = option_find_int(options, "input",1);
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net->batch = option_find_int(options, "batch",1);
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net->seen = option_find_int(options, "seen",0);
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}else{
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input = get_network_output_size_layer(*net, count-1);
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}
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char *type_s = option_find_str(options, "type", "sse");
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COST_TYPE type = get_cost_type(type_s);
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cost_layer *layer = make_cost_layer(net->batch, input, type);
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option_unused(options);
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return layer;
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}
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crop_layer *parse_crop(list *options, network *net, int count)
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{
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float learning_rate, momentum, decay;
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int h,w,c;
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int crop_height = option_find_int(options, "crop_height",1);
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int crop_width = option_find_int(options, "crop_width",1);
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int flip = option_find_int(options, "flip",0);
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if(count == 0){
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h = option_find_int(options, "height",1);
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w = option_find_int(options, "width",1);
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c = option_find_int(options, "channels",1);
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net->batch = option_find_int(options, "batch",1);
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learning_rate = option_find_float(options, "learning_rate", .001);
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momentum = option_find_float(options, "momentum", .9);
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decay = option_find_float(options, "decay", .0001);
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net->learning_rate = learning_rate;
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net->momentum = momentum;
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net->decay = decay;
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net->seen = option_find_int(options, "seen",0);
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}else{
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image m = get_network_image_layer(*net, count-1);
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h = m.h;
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w = m.w;
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c = m.c;
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if(h == 0) error("Layer before crop layer must output image.");
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}
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crop_layer *layer = make_crop_layer(net->batch,h,w,c,crop_height,crop_width,flip);
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option_unused(options);
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return layer;
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}
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maxpool_layer *parse_maxpool(list *options, network *net, int count)
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{
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int h,w,c;
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int stride = option_find_int(options, "stride",1);
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int size = option_find_int(options, "size",stride);
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if(count == 0){
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h = option_find_int(options, "height",1);
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w = option_find_int(options, "width",1);
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c = option_find_int(options, "channels",1);
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net->batch = option_find_int(options, "batch",1);
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net->seen = option_find_int(options, "seen",0);
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}else{
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image m = get_network_image_layer(*net, count-1);
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h = m.h;
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w = m.w;
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c = m.c;
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if(h == 0) error("Layer before convolutional layer must output image.");
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}
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maxpool_layer *layer = make_maxpool_layer(net->batch,h,w,c,size,stride);
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option_unused(options);
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return layer;
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}
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/*
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freeweight_layer *parse_freeweight(list *options, network *net, int count)
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{
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int input;
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if(count == 0){
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net->batch = option_find_int(options, "batch",1);
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input = option_find_int(options, "input",1);
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}else{
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input = get_network_output_size_layer(*net, count-1);
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}
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freeweight_layer *layer = make_freeweight_layer(net->batch,input);
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option_unused(options);
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return layer;
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}
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*/
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dropout_layer *parse_dropout(list *options, network *net, int count)
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{
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int input;
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float probability = option_find_float(options, "probability", .5);
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if(count == 0){
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net->batch = option_find_int(options, "batch",1);
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input = option_find_int(options, "input",1);
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float learning_rate = option_find_float(options, "learning_rate", .001);
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float momentum = option_find_float(options, "momentum", .9);
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float decay = option_find_float(options, "decay", .0001);
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net->learning_rate = learning_rate;
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net->momentum = momentum;
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net->decay = decay;
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net->seen = option_find_int(options, "seen",0);
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}else{
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input = get_network_output_size_layer(*net, count-1);
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}
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dropout_layer *layer = make_dropout_layer(net->batch,input,probability);
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option_unused(options);
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return layer;
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}
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normalization_layer *parse_normalization(list *options, network *net, int count)
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{
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int h,w,c;
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int size = option_find_int(options, "size",1);
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float alpha = option_find_float(options, "alpha", 0.);
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float beta = option_find_float(options, "beta", 1.);
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float kappa = option_find_float(options, "kappa", 1.);
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if(count == 0){
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h = option_find_int(options, "height",1);
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w = option_find_int(options, "width",1);
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c = option_find_int(options, "channels",1);
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net->batch = option_find_int(options, "batch",1);
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net->seen = option_find_int(options, "seen",0);
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}else{
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image m = get_network_image_layer(*net, count-1);
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h = m.h;
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w = m.w;
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c = m.c;
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if(h == 0) error("Layer before convolutional layer must output image.");
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}
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normalization_layer *layer = make_normalization_layer(net->batch,h,w,c,size, alpha, beta, kappa);
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option_unused(options);
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return layer;
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}
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network parse_network_cfg(char *filename)
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{
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list *sections = read_cfg(filename);
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network net = make_network(sections->size, 0);
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node *n = sections->front;
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int count = 0;
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while(n){
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section *s = (section *)n->val;
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list *options = s->options;
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if(is_convolutional(s)){
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convolutional_layer *layer = parse_convolutional(options, &net, count);
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net.types[count] = CONVOLUTIONAL;
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net.layers[count] = layer;
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}else if(is_deconvolutional(s)){
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deconvolutional_layer *layer = parse_deconvolutional(options, &net, count);
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net.types[count] = DECONVOLUTIONAL;
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net.layers[count] = layer;
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}else if(is_connected(s)){
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connected_layer *layer = parse_connected(options, &net, count);
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net.types[count] = CONNECTED;
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net.layers[count] = layer;
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}else if(is_crop(s)){
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crop_layer *layer = parse_crop(options, &net, count);
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net.types[count] = CROP;
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net.layers[count] = layer;
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}else if(is_cost(s)){
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cost_layer *layer = parse_cost(options, &net, count);
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net.types[count] = COST;
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net.layers[count] = layer;
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}else if(is_detection(s)){
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detection_layer *layer = parse_detection(options, &net, count);
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net.types[count] = DETECTION;
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net.layers[count] = layer;
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}else if(is_softmax(s)){
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softmax_layer *layer = parse_softmax(options, &net, count);
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net.types[count] = SOFTMAX;
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net.layers[count] = layer;
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}else if(is_maxpool(s)){
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maxpool_layer *layer = parse_maxpool(options, &net, count);
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net.types[count] = MAXPOOL;
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net.layers[count] = layer;
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}else if(is_normalization(s)){
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normalization_layer *layer = parse_normalization(options, &net, count);
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net.types[count] = NORMALIZATION;
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net.layers[count] = layer;
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}else if(is_dropout(s)){
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dropout_layer *layer = parse_dropout(options, &net, count);
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net.types[count] = DROPOUT;
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net.layers[count] = layer;
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}else if(is_freeweight(s)){
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//freeweight_layer *layer = parse_freeweight(options, &net, count);
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//net.types[count] = FREEWEIGHT;
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//net.layers[count] = layer;
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fprintf(stderr, "Type not recognized: %s\n", s->type);
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}else{
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fprintf(stderr, "Type not recognized: %s\n", s->type);
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}
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free_section(s);
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++count;
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n = n->next;
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}
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free_list(sections);
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net.outputs = get_network_output_size(net);
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net.output = get_network_output(net);
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return net;
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}
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int is_crop(section *s)
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{
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return (strcmp(s->type, "[crop]")==0);
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}
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int is_cost(section *s)
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{
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return (strcmp(s->type, "[cost]")==0);
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}
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int is_detection(section *s)
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{
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return (strcmp(s->type, "[detection]")==0);
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}
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int is_deconvolutional(section *s)
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{
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return (strcmp(s->type, "[deconv]")==0
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|| strcmp(s->type, "[deconvolutional]")==0);
|
|
}
|
|
int is_convolutional(section *s)
|
|
{
|
|
return (strcmp(s->type, "[conv]")==0
|
|
|| strcmp(s->type, "[convolutional]")==0);
|
|
}
|
|
int is_connected(section *s)
|
|
{
|
|
return (strcmp(s->type, "[conn]")==0
|
|
|| strcmp(s->type, "[connected]")==0);
|
|
}
|
|
int is_maxpool(section *s)
|
|
{
|
|
return (strcmp(s->type, "[max]")==0
|
|
|| strcmp(s->type, "[maxpool]")==0);
|
|
}
|
|
int is_dropout(section *s)
|
|
{
|
|
return (strcmp(s->type, "[dropout]")==0);
|
|
}
|
|
int is_freeweight(section *s)
|
|
{
|
|
return (strcmp(s->type, "[freeweight]")==0);
|
|
}
|
|
|
|
int is_softmax(section *s)
|
|
{
|
|
return (strcmp(s->type, "[soft]")==0
|
|
|| strcmp(s->type, "[softmax]")==0);
|
|
}
|
|
int is_normalization(section *s)
|
|
{
|
|
return (strcmp(s->type, "[lrnorm]")==0
|
|
|| strcmp(s->type, "[localresponsenormalization]")==0);
|
|
}
|
|
|
|
int read_option(char *s, list *options)
|
|
{
|
|
size_t i;
|
|
size_t len = strlen(s);
|
|
char *val = 0;
|
|
for(i = 0; i < len; ++i){
|
|
if(s[i] == '='){
|
|
s[i] = '\0';
|
|
val = s+i+1;
|
|
break;
|
|
}
|
|
}
|
|
if(i == len-1) return 0;
|
|
char *key = s;
|
|
option_insert(options, key, val);
|
|
return 1;
|
|
}
|
|
|
|
list *read_cfg(char *filename)
|
|
{
|
|
FILE *file = fopen(filename, "r");
|
|
if(file == 0) file_error(filename);
|
|
char *line;
|
|
int nu = 0;
|
|
list *sections = make_list();
|
|
section *current = 0;
|
|
while((line=fgetl(file)) != 0){
|
|
++ nu;
|
|
strip(line);
|
|
switch(line[0]){
|
|
case '[':
|
|
current = malloc(sizeof(section));
|
|
list_insert(sections, current);
|
|
current->options = make_list();
|
|
current->type = line;
|
|
break;
|
|
case '\0':
|
|
case '#':
|
|
case ';':
|
|
free(line);
|
|
break;
|
|
default:
|
|
if(!read_option(line, current->options)){
|
|
fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
|
|
free(line);
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
fclose(file);
|
|
return sections;
|
|
}
|
|
|
|
void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int count)
|
|
{
|
|
#ifdef GPU
|
|
if(gpu_index >= 0) pull_convolutional_layer(*l);
|
|
#endif
|
|
int i;
|
|
fprintf(fp, "[convolutional]\n");
|
|
if(count == 0) {
|
|
fprintf(fp, "batch=%d\n"
|
|
"height=%d\n"
|
|
"width=%d\n"
|
|
"channels=%d\n"
|
|
"learning_rate=%g\n"
|
|
"momentum=%g\n"
|
|
"decay=%g\n"
|
|
"seen=%d\n",
|
|
l->batch,l->h, l->w, l->c, l->learning_rate, l->momentum, l->decay, net.seen);
|
|
} else {
|
|
if(l->learning_rate != net.learning_rate)
|
|
fprintf(fp, "learning_rate=%g\n", l->learning_rate);
|
|
if(l->momentum != net.momentum)
|
|
fprintf(fp, "momentum=%g\n", l->momentum);
|
|
if(l->decay != net.decay)
|
|
fprintf(fp, "decay=%g\n", l->decay);
|
|
}
|
|
fprintf(fp, "filters=%d\n"
|
|
"size=%d\n"
|
|
"stride=%d\n"
|
|
"pad=%d\n"
|
|
"activation=%s\n",
|
|
l->n, l->size, l->stride, l->pad,
|
|
get_activation_string(l->activation));
|
|
fprintf(fp, "biases=");
|
|
for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
|
|
fprintf(fp, "\n");
|
|
fprintf(fp, "weights=");
|
|
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_deconvolutional_cfg(FILE *fp, deconvolutional_layer *l, network net, int count)
|
|
{
|
|
#ifdef GPU
|
|
if(gpu_index >= 0) pull_deconvolutional_layer(*l);
|
|
#endif
|
|
int i;
|
|
fprintf(fp, "[deconvolutional]\n");
|
|
if(count == 0) {
|
|
fprintf(fp, "batch=%d\n"
|
|
"height=%d\n"
|
|
"width=%d\n"
|
|
"channels=%d\n"
|
|
"learning_rate=%g\n"
|
|
"momentum=%g\n"
|
|
"decay=%g\n"
|
|
"seen=%d\n",
|
|
l->batch,l->h, l->w, l->c, l->learning_rate, l->momentum, l->decay, net.seen);
|
|
} else {
|
|
if(l->learning_rate != net.learning_rate)
|
|
fprintf(fp, "learning_rate=%g\n", l->learning_rate);
|
|
if(l->momentum != net.momentum)
|
|
fprintf(fp, "momentum=%g\n", l->momentum);
|
|
if(l->decay != net.decay)
|
|
fprintf(fp, "decay=%g\n", l->decay);
|
|
}
|
|
fprintf(fp, "filters=%d\n"
|
|
"size=%d\n"
|
|
"stride=%d\n"
|
|
"activation=%s\n",
|
|
l->n, l->size, l->stride,
|
|
get_activation_string(l->activation));
|
|
fprintf(fp, "biases=");
|
|
for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
|
|
fprintf(fp, "\n");
|
|
fprintf(fp, "weights=");
|
|
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_freeweight_cfg(FILE *fp, freeweight_layer *l, network net, int count)
|
|
{
|
|
fprintf(fp, "[freeweight]\n");
|
|
if(count == 0){
|
|
fprintf(fp, "batch=%d\ninput=%d\n",l->batch, l->inputs);
|
|
}
|
|
fprintf(fp, "\n");
|
|
}
|
|
|
|
void print_dropout_cfg(FILE *fp, dropout_layer *l, network net, int count)
|
|
{
|
|
fprintf(fp, "[dropout]\n");
|
|
if(count == 0){
|
|
fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
|
|
}
|
|
fprintf(fp, "probability=%g\n\n", l->probability);
|
|
}
|
|
|
|
void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
|
|
{
|
|
#ifdef GPU
|
|
if(gpu_index >= 0) pull_connected_layer(*l);
|
|
#endif
|
|
int i;
|
|
fprintf(fp, "[connected]\n");
|
|
if(count == 0){
|
|
fprintf(fp, "batch=%d\n"
|
|
"input=%d\n"
|
|
"learning_rate=%g\n"
|
|
"momentum=%g\n"
|
|
"decay=%g\n"
|
|
"seen=%d\n",
|
|
l->batch, l->inputs, l->learning_rate, l->momentum, l->decay, net.seen);
|
|
} else {
|
|
if(l->learning_rate != net.learning_rate)
|
|
fprintf(fp, "learning_rate=%g\n", l->learning_rate);
|
|
if(l->momentum != net.momentum)
|
|
fprintf(fp, "momentum=%g\n", l->momentum);
|
|
if(l->decay != net.decay)
|
|
fprintf(fp, "decay=%g\n", l->decay);
|
|
}
|
|
fprintf(fp, "output=%d\n"
|
|
"activation=%s\n",
|
|
l->outputs,
|
|
get_activation_string(l->activation));
|
|
fprintf(fp, "biases=");
|
|
for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
|
|
fprintf(fp, "\n");
|
|
fprintf(fp, "weights=");
|
|
for(i = 0; i < l->outputs*l->inputs; ++i) fprintf(fp, "%g,", l->weights[i]);
|
|
fprintf(fp, "\n\n");
|
|
}
|
|
|
|
void print_crop_cfg(FILE *fp, crop_layer *l, network net, int count)
|
|
{
|
|
fprintf(fp, "[crop]\n");
|
|
if(count == 0) {
|
|
fprintf(fp, "batch=%d\n"
|
|
"height=%d\n"
|
|
"width=%d\n"
|
|
"channels=%d\n"
|
|
"learning_rate=%g\n"
|
|
"momentum=%g\n"
|
|
"decay=%g\n"
|
|
"seen=%d\n",
|
|
l->batch,l->h, l->w, l->c, net.learning_rate, net.momentum, net.decay, net.seen);
|
|
}
|
|
fprintf(fp, "crop_height=%d\ncrop_width=%d\nflip=%d\n\n", l->crop_height, l->crop_width, l->flip);
|
|
}
|
|
|
|
void print_maxpool_cfg(FILE *fp, maxpool_layer *l, network net, int count)
|
|
{
|
|
fprintf(fp, "[maxpool]\n");
|
|
if(count == 0) fprintf(fp, "batch=%d\n"
|
|
"height=%d\n"
|
|
"width=%d\n"
|
|
"channels=%d\n",
|
|
l->batch,l->h, l->w, l->c);
|
|
fprintf(fp, "size=%d\nstride=%d\n\n", l->size, l->stride);
|
|
}
|
|
|
|
void print_normalization_cfg(FILE *fp, normalization_layer *l, network net, int count)
|
|
{
|
|
fprintf(fp, "[localresponsenormalization]\n");
|
|
if(count == 0) fprintf(fp, "batch=%d\n"
|
|
"height=%d\n"
|
|
"width=%d\n"
|
|
"channels=%d\n",
|
|
l->batch,l->h, l->w, l->c);
|
|
fprintf(fp, "size=%d\n"
|
|
"alpha=%g\n"
|
|
"beta=%g\n"
|
|
"kappa=%g\n\n", l->size, l->alpha, l->beta, l->kappa);
|
|
}
|
|
|
|
void print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count)
|
|
{
|
|
fprintf(fp, "[softmax]\n");
|
|
if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
|
|
fprintf(fp, "\n");
|
|
}
|
|
|
|
void print_detection_cfg(FILE *fp, detection_layer *l, network net, int count)
|
|
{
|
|
fprintf(fp, "[detection]\n");
|
|
fprintf(fp, "classes=%d\ncoords=%d\nrescore=%d\n", l->classes, l->coords, l->rescore);
|
|
fprintf(fp, "\n");
|
|
}
|
|
|
|
void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
|
|
{
|
|
fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type));
|
|
if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
|
|
fprintf(fp, "\n");
|
|
}
|
|
|
|
void save_weights(network net, char *filename)
|
|
{
|
|
fprintf(stderr, "Saving weights to %s\n", filename);
|
|
FILE *fp = fopen(filename, "w");
|
|
if(!fp) file_error(filename);
|
|
|
|
fwrite(&net.learning_rate, sizeof(float), 1, fp);
|
|
fwrite(&net.momentum, sizeof(float), 1, fp);
|
|
fwrite(&net.decay, sizeof(float), 1, fp);
|
|
fwrite(&net.seen, sizeof(int), 1, fp);
|
|
|
|
int i;
|
|
for(i = 0; i < net.n; ++i){
|
|
if(net.types[i] == CONVOLUTIONAL){
|
|
convolutional_layer layer = *(convolutional_layer *) net.layers[i];
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
pull_convolutional_layer(layer);
|
|
}
|
|
#endif
|
|
int num = layer.n*layer.c*layer.size*layer.size;
|
|
fwrite(layer.biases, sizeof(float), layer.n, fp);
|
|
fwrite(layer.filters, sizeof(float), num, fp);
|
|
}
|
|
if(net.types[i] == DECONVOLUTIONAL){
|
|
deconvolutional_layer layer = *(deconvolutional_layer *) net.layers[i];
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
pull_deconvolutional_layer(layer);
|
|
}
|
|
#endif
|
|
int num = layer.n*layer.c*layer.size*layer.size;
|
|
fwrite(layer.biases, sizeof(float), layer.n, fp);
|
|
fwrite(layer.filters, sizeof(float), num, fp);
|
|
}
|
|
if(net.types[i] == CONNECTED){
|
|
connected_layer layer = *(connected_layer *) net.layers[i];
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
pull_connected_layer(layer);
|
|
}
|
|
#endif
|
|
fwrite(layer.biases, sizeof(float), layer.outputs, fp);
|
|
fwrite(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
|
|
}
|
|
}
|
|
fclose(fp);
|
|
}
|
|
|
|
void load_weights_upto(network *net, char *filename, int cutoff)
|
|
{
|
|
fprintf(stderr, "Loading weights from %s\n", filename);
|
|
FILE *fp = fopen(filename, "r");
|
|
if(!fp) file_error(filename);
|
|
|
|
fread(&net->learning_rate, sizeof(float), 1, fp);
|
|
fread(&net->momentum, sizeof(float), 1, fp);
|
|
fread(&net->decay, sizeof(float), 1, fp);
|
|
fread(&net->seen, sizeof(int), 1, fp);
|
|
set_learning_network(net, net->learning_rate, net->momentum, net->decay);
|
|
|
|
int i;
|
|
for(i = 0; i < net->n && i < cutoff; ++i){
|
|
if(net->types[i] == CONVOLUTIONAL){
|
|
convolutional_layer layer = *(convolutional_layer *) net->layers[i];
|
|
int num = layer.n*layer.c*layer.size*layer.size;
|
|
fread(layer.biases, sizeof(float), layer.n, fp);
|
|
fread(layer.filters, sizeof(float), num, fp);
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
push_convolutional_layer(layer);
|
|
}
|
|
#endif
|
|
}
|
|
if(net->types[i] == DECONVOLUTIONAL){
|
|
deconvolutional_layer layer = *(deconvolutional_layer *) net->layers[i];
|
|
int num = layer.n*layer.c*layer.size*layer.size;
|
|
fread(layer.biases, sizeof(float), layer.n, fp);
|
|
fread(layer.filters, sizeof(float), num, fp);
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
push_deconvolutional_layer(layer);
|
|
}
|
|
#endif
|
|
}
|
|
if(net->types[i] == CONNECTED){
|
|
connected_layer layer = *(connected_layer *) net->layers[i];
|
|
fread(layer.biases, sizeof(float), layer.outputs, fp);
|
|
fread(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
push_connected_layer(layer);
|
|
}
|
|
#endif
|
|
}
|
|
}
|
|
fclose(fp);
|
|
}
|
|
|
|
void load_weights(network *net, char *filename)
|
|
{
|
|
load_weights_upto(net, filename, net->n);
|
|
}
|
|
|
|
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], net, i);
|
|
else if(net.types[i] == DECONVOLUTIONAL)
|
|
print_deconvolutional_cfg(fp, (deconvolutional_layer *)net.layers[i], net, i);
|
|
else if(net.types[i] == CONNECTED)
|
|
print_connected_cfg(fp, (connected_layer *)net.layers[i], net, i);
|
|
else if(net.types[i] == CROP)
|
|
print_crop_cfg(fp, (crop_layer *)net.layers[i], net, i);
|
|
else if(net.types[i] == MAXPOOL)
|
|
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], net, i);
|
|
else if(net.types[i] == FREEWEIGHT)
|
|
print_freeweight_cfg(fp, (freeweight_layer *)net.layers[i], net, i);
|
|
else if(net.types[i] == DROPOUT)
|
|
print_dropout_cfg(fp, (dropout_layer *)net.layers[i], net, i);
|
|
else if(net.types[i] == NORMALIZATION)
|
|
print_normalization_cfg(fp, (normalization_layer *)net.layers[i], net, i);
|
|
else if(net.types[i] == SOFTMAX)
|
|
print_softmax_cfg(fp, (softmax_layer *)net.layers[i], net, i);
|
|
else if(net.types[i] == DETECTION)
|
|
print_detection_cfg(fp, (detection_layer *)net.layers[i], net, i);
|
|
else if(net.types[i] == COST)
|
|
print_cost_cfg(fp, (cost_layer *)net.layers[i], net, i);
|
|
}
|
|
fclose(fp);
|
|
}
|
|
|