#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 "normalization_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 "softmax_layer.h"
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#include "dropout_layer.h"
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#include "detection_layer.h"
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#include "region_layer.h"
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#include "avgpool_layer.h"
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#include "route_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_network(section *s);
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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_avgpool(section *s);
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int is_dropout(section *s);
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int is_softmax(section *s);
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int is_normalization(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_region(section *s);
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int is_route(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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typedef struct size_params{
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int batch;
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int inputs;
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int h;
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int w;
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int c;
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} size_params;
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deconvolutional_layer parse_deconvolutional(list *options, size_params params)
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{
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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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int batch,h,w,c;
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h = params.h;
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w = params.w;
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c = params.c;
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batch=params.batch;
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if(!(h && w && c)) error("Layer before deconvolutional layer must output image.");
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deconvolutional_layer layer = make_deconvolutional_layer(batch,h,w,c,n,size,stride,activation);
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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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return layer;
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}
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convolutional_layer parse_convolutional(list *options, size_params params)
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{
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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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int batch,h,w,c;
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h = params.h;
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w = params.w;
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c = params.c;
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batch=params.batch;
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if(!(h && w && c)) error("Layer before convolutional layer must output image.");
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convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation);
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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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return layer;
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}
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connected_layer parse_connected(list *options, size_params params)
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{
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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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connected_layer layer = make_connected_layer(params.batch, params.inputs, output, activation);
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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, params.inputs*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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return layer;
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}
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softmax_layer parse_softmax(list *options, size_params params)
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{
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int groups = option_find_int(options, "groups",1);
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softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups);
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return layer;
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}
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detection_layer parse_detection(list *options, size_params params)
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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", 0);
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int joint = option_find_int(options, "joint", 0);
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int objectness = option_find_int(options, "objectness", 0);
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int background = option_find_int(options, "background", 0);
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detection_layer layer = make_detection_layer(params.batch, params.inputs, classes, coords, joint, rescore, background, objectness);
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return layer;
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}
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region_layer parse_region(list *options, size_params params)
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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", 0);
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int num = option_find_int(options, "num", 1);
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int side = option_find_int(options, "side", 7);
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region_layer layer = make_region_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
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layer.softmax = option_find_int(options, "softmax", 0);
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layer.sqrt = option_find_int(options, "sqrt", 0);
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layer.coord_scale = option_find_float(options, "coord_scale", 1);
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layer.object_scale = option_find_float(options, "object_scale", 1);
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layer.noobject_scale = option_find_float(options, "noobject_scale", 1);
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layer.class_scale = option_find_float(options, "class_scale", 1);
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return layer;
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}
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cost_layer parse_cost(list *options, size_params params)
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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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float scale = option_find_float_quiet(options, "scale",1);
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cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale);
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return layer;
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}
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crop_layer parse_crop(list *options, size_params params)
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{
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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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float angle = option_find_float(options, "angle",0);
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float saturation = option_find_float(options, "saturation",1);
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float exposure = option_find_float(options, "exposure",1);
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int batch,h,w,c;
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h = params.h;
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w = params.w;
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c = params.c;
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batch=params.batch;
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if(!(h && w && c)) error("Layer before crop layer must output image.");
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int noadjust = option_find_int_quiet(options, "noadjust",0);
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crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure);
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l.noadjust = noadjust;
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return l;
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}
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maxpool_layer parse_maxpool(list *options, size_params params)
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{
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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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int batch,h,w,c;
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h = params.h;
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w = params.w;
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c = params.c;
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batch=params.batch;
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if(!(h && w && c)) error("Layer before maxpool layer must output image.");
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maxpool_layer layer = make_maxpool_layer(batch,h,w,c,size,stride);
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return layer;
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}
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avgpool_layer parse_avgpool(list *options, size_params params)
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{
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int batch,w,h,c;
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w = params.w;
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h = params.h;
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c = params.c;
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batch=params.batch;
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if(!(h && w && c)) error("Layer before avgpool layer must output image.");
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avgpool_layer layer = make_avgpool_layer(batch,w,h,c);
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return layer;
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}
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dropout_layer parse_dropout(list *options, size_params params)
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{
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float probability = option_find_float(options, "probability", .5);
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dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability);
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layer.out_w = params.w;
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layer.out_h = params.h;
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layer.out_c = params.c;
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return layer;
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}
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layer parse_normalization(list *options, size_params params)
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{
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float alpha = option_find_float(options, "alpha", .0001);
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float beta = option_find_float(options, "beta" , .75);
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float kappa = option_find_float(options, "kappa", 1);
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int size = option_find_int(options, "size", 5);
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layer l = make_normalization_layer(params.batch, params.w, params.h, params.c, size, alpha, beta, kappa);
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return l;
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}
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route_layer parse_route(list *options, size_params params, network net)
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{
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char *l = option_find(options, "layers");
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int len = strlen(l);
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if(!l) error("Route Layer must specify input layers");
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int n = 1;
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int i;
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for(i = 0; i < len; ++i){
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if (l[i] == ',') ++n;
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}
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int *layers = calloc(n, sizeof(int));
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int *sizes = calloc(n, sizeof(int));
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for(i = 0; i < n; ++i){
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int index = atoi(l);
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l = strchr(l, ',')+1;
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layers[i] = index;
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sizes[i] = net.layers[index].outputs;
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}
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int batch = params.batch;
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route_layer layer = make_route_layer(batch, n, layers, sizes);
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convolutional_layer first = net.layers[layers[0]];
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layer.out_w = first.out_w;
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layer.out_h = first.out_h;
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layer.out_c = first.out_c;
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for(i = 1; i < n; ++i){
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int index = layers[i];
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convolutional_layer next = net.layers[index];
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if(next.out_w == first.out_w && next.out_h == first.out_h){
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layer.out_c += next.out_c;
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}else{
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layer.out_h = layer.out_w = layer.out_c = 0;
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}
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}
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return layer;
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}
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learning_rate_policy get_policy(char *s)
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{
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if (strcmp(s, "poly")==0) return POLY;
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if (strcmp(s, "constant")==0) return CONSTANT;
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if (strcmp(s, "step")==0) return STEP;
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if (strcmp(s, "exp")==0) return EXP;
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if (strcmp(s, "sigmoid")==0) return SIG;
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if (strcmp(s, "steps")==0) return STEPS;
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fprintf(stderr, "Couldn't find policy %s, going with constant\n", s);
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return CONSTANT;
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}
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void parse_net_options(list *options, network *net)
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{
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net->batch = option_find_int(options, "batch",1);
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net->learning_rate = option_find_float(options, "learning_rate", .001);
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net->momentum = option_find_float(options, "momentum", .9);
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net->decay = option_find_float(options, "decay", .0001);
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int subdivs = option_find_int(options, "subdivisions",1);
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net->batch /= subdivs;
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net->subdivisions = subdivs;
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net->h = option_find_int_quiet(options, "height",0);
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net->w = option_find_int_quiet(options, "width",0);
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net->c = option_find_int_quiet(options, "channels",0);
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net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c);
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if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
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char *policy_s = option_find_str(options, "policy", "constant");
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net->policy = get_policy(policy_s);
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if(net->policy == STEP){
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net->step = option_find_int(options, "step", 1);
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net->scale = option_find_float(options, "scale", 1);
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} else if (net->policy == STEPS){
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char *l = option_find(options, "steps");
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char *p = option_find(options, "scales");
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if(!l || !p) error("STEPS policy must have steps and scales in cfg file");
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int len = strlen(l);
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int n = 1;
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int i;
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for(i = 0; i < len; ++i){
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if (l[i] == ',') ++n;
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}
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int *steps = calloc(n, sizeof(int));
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float *scales = calloc(n, sizeof(float));
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for(i = 0; i < n; ++i){
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int step = atoi(l);
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float scale = atof(p);
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l = strchr(l, ',')+1;
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p = strchr(p, ',')+1;
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steps[i] = step;
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scales[i] = scale;
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}
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net->scales = scales;
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net->steps = steps;
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net->num_steps = n;
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} else if (net->policy == EXP){
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net->gamma = option_find_float(options, "gamma", 1);
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} else if (net->policy == SIG){
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net->gamma = option_find_float(options, "gamma", 1);
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net->step = option_find_int(options, "step", 1);
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} else if (net->policy == POLY){
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net->power = option_find_float(options, "power", 1);
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}
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net->max_batches = option_find_int(options, "max_batches", 0);
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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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node *n = sections->front;
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if(!n) error("Config file has no sections");
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network net = make_network(sections->size - 1);
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size_params params;
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section *s = (section *)n->val;
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list *options = s->options;
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if(!is_network(s)) error("First section must be [net] or [network]");
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parse_net_options(options, &net);
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params.h = net.h;
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params.w = net.w;
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params.c = net.c;
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params.inputs = net.inputs;
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params.batch = net.batch;
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n = n->next;
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int count = 0;
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free_section(s);
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while(n){
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fprintf(stderr, "%d: ", count);
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s = (section *)n->val;
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options = s->options;
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layer l = {0};
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if(is_convolutional(s)){
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l = parse_convolutional(options, params);
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}else if(is_deconvolutional(s)){
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l = parse_deconvolutional(options, params);
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}else if(is_connected(s)){
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l = parse_connected(options, params);
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}else if(is_crop(s)){
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l = parse_crop(options, params);
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}else if(is_cost(s)){
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l = parse_cost(options, params);
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}else if(is_detection(s)){
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l = parse_detection(options, params);
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}else if(is_region(s)){
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l = parse_region(options, params);
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}else if(is_softmax(s)){
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l = parse_softmax(options, params);
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}else if(is_normalization(s)){
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l = parse_normalization(options, params);
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}else if(is_maxpool(s)){
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l = parse_maxpool(options, params);
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}else if(is_avgpool(s)){
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l = parse_avgpool(options, params);
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}else if(is_route(s)){
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l = parse_route(options, params, net);
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}else if(is_dropout(s)){
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l = parse_dropout(options, params);
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l.output = net.layers[count-1].output;
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l.delta = net.layers[count-1].delta;
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#ifdef GPU
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l.output_gpu = net.layers[count-1].output_gpu;
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l.delta_gpu = net.layers[count-1].delta_gpu;
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#endif
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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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l.dontload = option_find_int_quiet(options, "dontload", 0);
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option_unused(options);
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net.layers[count] = l;
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free_section(s);
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n = n->next;
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if(n){
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params.h = l.out_h;
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params.w = l.out_w;
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params.c = l.out_c;
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params.inputs = l.outputs;
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}
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++count;
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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_region(section *s)
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{
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return (strcmp(s->type, "[region]")==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);
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}
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int is_convolutional(section *s)
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{
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return (strcmp(s->type, "[conv]")==0
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|| strcmp(s->type, "[convolutional]")==0);
|
}
|
int is_network(section *s)
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{
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return (strcmp(s->type, "[net]")==0
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|| strcmp(s->type, "[network]")==0);
|
}
|
int is_connected(section *s)
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{
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return (strcmp(s->type, "[conn]")==0
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|| strcmp(s->type, "[connected]")==0);
|
}
|
int is_maxpool(section *s)
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{
|
return (strcmp(s->type, "[max]")==0
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|| strcmp(s->type, "[maxpool]")==0);
|
}
|
int is_avgpool(section *s)
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{
|
return (strcmp(s->type, "[avg]")==0
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|| strcmp(s->type, "[avgpool]")==0);
|
}
|
int is_dropout(section *s)
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{
|
return (strcmp(s->type, "[dropout]")==0);
|
}
|
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int is_normalization(section *s)
|
{
|
return (strcmp(s->type, "[lrn]")==0
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|| strcmp(s->type, "[normalization]")==0);
|
}
|
|
int is_softmax(section *s)
|
{
|
return (strcmp(s->type, "[soft]")==0
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|| strcmp(s->type, "[softmax]")==0);
|
}
|
int is_route(section *s)
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{
|
return (strcmp(s->type, "[route]")==0);
|
}
|
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int read_option(char *s, list *options)
|
{
|
size_t i;
|
size_t len = strlen(s);
|
char *val = 0;
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for(i = 0; i < len; ++i){
|
if(s[i] == '='){
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s[i] = '\0';
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val = s+i+1;
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break;
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}
|
}
|
if(i == len-1) return 0;
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char *key = s;
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option_insert(options, key, val);
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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 save_weights_double(network net, char *filename)
|
{
|
fprintf(stderr, "Saving doubled 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,j,k;
|
for(i = 0; i < net.n; ++i){
|
layer l = net.layers[i];
|
if(l.type == CONVOLUTIONAL){
|
#ifdef GPU
|
if(gpu_index >= 0){
|
pull_convolutional_layer(l);
|
}
|
#endif
|
float zero = 0;
|
fwrite(l.biases, sizeof(float), l.n, fp);
|
fwrite(l.biases, sizeof(float), l.n, fp);
|
|
for (j = 0; j < l.n; ++j){
|
int index = j*l.c*l.size*l.size;
|
fwrite(l.filters+index, sizeof(float), l.c*l.size*l.size, fp);
|
for (k = 0; k < l.c*l.size*l.size; ++k) fwrite(&zero, sizeof(float), 1, fp);
|
}
|
for (j = 0; j < l.n; ++j){
|
int index = j*l.c*l.size*l.size;
|
for (k = 0; k < l.c*l.size*l.size; ++k) fwrite(&zero, sizeof(float), 1, fp);
|
fwrite(l.filters+index, sizeof(float), l.c*l.size*l.size, fp);
|
}
|
}
|
}
|
fclose(fp);
|
}
|
|
void save_weights_upto(network net, char *filename, int cutoff)
|
{
|
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 < cutoff; ++i){
|
layer l = net.layers[i];
|
if(l.type == CONVOLUTIONAL){
|
#ifdef GPU
|
if(gpu_index >= 0){
|
pull_convolutional_layer(l);
|
}
|
#endif
|
int num = l.n*l.c*l.size*l.size;
|
fwrite(l.biases, sizeof(float), l.n, fp);
|
fwrite(l.filters, sizeof(float), num, fp);
|
}
|
if(l.type == DECONVOLUTIONAL){
|
#ifdef GPU
|
if(gpu_index >= 0){
|
pull_deconvolutional_layer(l);
|
}
|
#endif
|
int num = l.n*l.c*l.size*l.size;
|
fwrite(l.biases, sizeof(float), l.n, fp);
|
fwrite(l.filters, sizeof(float), num, fp);
|
}
|
if(l.type == CONNECTED){
|
#ifdef GPU
|
if(gpu_index >= 0){
|
pull_connected_layer(l);
|
}
|
#endif
|
fwrite(l.biases, sizeof(float), l.outputs, fp);
|
fwrite(l.weights, sizeof(float), l.outputs*l.inputs, fp);
|
}
|
}
|
fclose(fp);
|
}
|
void save_weights(network net, char *filename)
|
{
|
save_weights_upto(net, filename, net.n);
|
}
|
|
void load_weights_upto(network *net, char *filename, int cutoff)
|
{
|
fprintf(stderr, "Loading weights from %s...", filename);
|
fflush(stdout);
|
FILE *fp = fopen(filename, "r");
|
if(!fp) file_error(filename);
|
|
float garbage;
|
fread(&garbage, sizeof(float), 1, fp);
|
fread(&garbage, sizeof(float), 1, fp);
|
fread(&garbage, sizeof(float), 1, fp);
|
fread(net->seen, sizeof(int), 1, fp);
|
|
int i;
|
for(i = 0; i < net->n && i < cutoff; ++i){
|
layer l = net->layers[i];
|
if (l.dontload) continue;
|
if(l.type == CONVOLUTIONAL){
|
int num = l.n*l.c*l.size*l.size;
|
fread(l.biases, sizeof(float), l.n, fp);
|
fread(l.filters, sizeof(float), num, fp);
|
#ifdef GPU
|
if(gpu_index >= 0){
|
push_convolutional_layer(l);
|
}
|
#endif
|
}
|
if(l.type == DECONVOLUTIONAL){
|
int num = l.n*l.c*l.size*l.size;
|
fread(l.biases, sizeof(float), l.n, fp);
|
fread(l.filters, sizeof(float), num, fp);
|
#ifdef GPU
|
if(gpu_index >= 0){
|
push_deconvolutional_layer(l);
|
}
|
#endif
|
}
|
if(l.type == CONNECTED){
|
fread(l.biases, sizeof(float), l.outputs, fp);
|
fread(l.weights, sizeof(float), l.outputs*l.inputs, fp);
|
#ifdef GPU
|
if(gpu_index >= 0){
|
push_connected_layer(l);
|
}
|
#endif
|
}
|
}
|
fprintf(stderr, "Done!\n");
|
fclose(fp);
|
}
|
|
void load_weights(network *net, char *filename)
|
{
|
load_weights_upto(net, filename, net->n);
|
}
|
