mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
1190 lines
40 KiB
C
1190 lines
40 KiB
C
#include <stdio.h>
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#include <string.h>
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#include <stdlib.h>
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#include <assert.h>
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#include "activation_layer.h"
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#include "activations.h"
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#include "avgpool_layer.h"
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#include "batchnorm_layer.h"
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#include "blas.h"
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#include "connected_layer.h"
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#include "deconvolutional_layer.h"
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#include "convolutional_layer.h"
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#include "cost_layer.h"
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#include "crnn_layer.h"
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#include "crop_layer.h"
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#include "detection_layer.h"
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#include "dropout_layer.h"
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#include "gru_layer.h"
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#include "list.h"
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#include "local_layer.h"
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#include "maxpool_layer.h"
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#include "normalization_layer.h"
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#include "option_list.h"
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#include "parser.h"
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#include "region_layer.h"
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#include "reorg_layer.h"
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#include "rnn_layer.h"
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#include "route_layer.h"
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#include "shortcut_layer.h"
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#include "softmax_layer.h"
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#include "lstm_layer.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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list *read_cfg(char *filename);
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LAYER_TYPE string_to_layer_type(char * type)
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{
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if (strcmp(type, "[shortcut]")==0) return SHORTCUT;
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if (strcmp(type, "[crop]")==0) return CROP;
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if (strcmp(type, "[cost]")==0) return COST;
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if (strcmp(type, "[detection]")==0) return DETECTION;
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if (strcmp(type, "[region]")==0) return REGION;
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if (strcmp(type, "[local]")==0) return LOCAL;
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if (strcmp(type, "[conv]")==0
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|| strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL;
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if (strcmp(type, "[deconv]")==0
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|| strcmp(type, "[deconvolutional]")==0) return DECONVOLUTIONAL;
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if (strcmp(type, "[activation]")==0) return ACTIVE;
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if (strcmp(type, "[net]")==0
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|| strcmp(type, "[network]")==0) return NETWORK;
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if (strcmp(type, "[crnn]")==0) return CRNN;
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if (strcmp(type, "[gru]")==0) return GRU;
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if (strcmp(type, "[lstm]") == 0) return LSTM;
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if (strcmp(type, "[rnn]")==0) return RNN;
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if (strcmp(type, "[conn]")==0
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|| strcmp(type, "[connected]")==0) return CONNECTED;
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if (strcmp(type, "[max]")==0
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|| strcmp(type, "[maxpool]")==0) return MAXPOOL;
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if (strcmp(type, "[reorg]")==0) return REORG;
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if (strcmp(type, "[avg]")==0
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|| strcmp(type, "[avgpool]")==0) return AVGPOOL;
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if (strcmp(type, "[dropout]")==0) return DROPOUT;
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if (strcmp(type, "[lrn]")==0
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|| strcmp(type, "[normalization]")==0) return NORMALIZATION;
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if (strcmp(type, "[batchnorm]")==0) return BATCHNORM;
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if (strcmp(type, "[soft]")==0
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|| strcmp(type, "[softmax]")==0) return SOFTMAX;
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if (strcmp(type, "[route]")==0) return ROUTE;
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return BLANK;
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}
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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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int index;
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int time_steps;
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network *net;
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} size_params;
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local_layer parse_local(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 local layer must output image.");
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local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation);
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return layer;
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}
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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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int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
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int pad = option_find_int_quiet(options, "pad",0);
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int padding = option_find_int_quiet(options, "padding",0);
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if(pad) padding = size/2;
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layer l = make_deconvolutional_layer(batch,h,w,c,n,size,stride,padding, activation, batch_normalize, params.net->adam);
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return l;
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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_quiet(options, "pad",0);
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int padding = option_find_int_quiet(options, "padding",0);
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int groups = option_find_int_quiet(options, "groups", 1);
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if(pad) padding = size/2;
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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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int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
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int binary = option_find_int_quiet(options, "binary", 0);
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int xnor = option_find_int_quiet(options, "xnor", 0);
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convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,groups,size,stride,padding,activation, batch_normalize, binary, xnor, params.net->adam);
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layer.flipped = option_find_int_quiet(options, "flipped", 0);
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layer.dot = option_find_float_quiet(options, "dot", 0);
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return layer;
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}
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layer parse_crnn(list *options, size_params params)
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{
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int output_filters = option_find_int(options, "output_filters",1);
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int hidden_filters = option_find_int(options, "hidden_filters",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_normalize = option_find_int_quiet(options, "batch_normalize", 0);
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layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, activation, batch_normalize);
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l.shortcut = option_find_int_quiet(options, "shortcut", 0);
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return l;
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}
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layer parse_rnn(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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int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
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layer l = make_rnn_layer(params.batch, params.inputs, output, params.time_steps, activation, batch_normalize, params.net->adam);
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l.shortcut = option_find_int_quiet(options, "shortcut", 0);
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return l;
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}
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layer parse_gru(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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int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
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layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam);
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l.tanh = option_find_int_quiet(options, "tanh", 0);
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return l;
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}
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layer parse_lstm(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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int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
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layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize, params.net->adam);
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return l;
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}
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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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int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
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layer l = make_connected_layer(params.batch, params.inputs, output, activation, batch_normalize, params.net->adam);
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return l;
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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_quiet(options, "groups",1);
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softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups);
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layer.temperature = option_find_float_quiet(options, "temperature", 1);
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char *tree_file = option_find_str(options, "tree", 0);
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if (tree_file) layer.softmax_tree = read_tree(tree_file);
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layer.w = params.w;
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layer.h = params.h;
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layer.c = params.c;
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layer.spatial = option_find_float_quiet(options, "spatial", 0);
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return layer;
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}
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layer parse_region(list *options, size_params params)
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{
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int coords = option_find_int(options, "coords", 4);
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int classes = option_find_int(options, "classes", 20);
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int num = option_find_int(options, "num", 1);
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layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords);
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assert(l.outputs == params.inputs);
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l.log = option_find_int_quiet(options, "log", 0);
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l.sqrt = option_find_int_quiet(options, "sqrt", 0);
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l.softmax = option_find_int(options, "softmax", 0);
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l.background = option_find_int_quiet(options, "background", 0);
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l.max_boxes = option_find_int_quiet(options, "max",30);
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l.jitter = option_find_float(options, "jitter", .2);
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l.rescore = option_find_int_quiet(options, "rescore",0);
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l.thresh = option_find_float(options, "thresh", .5);
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l.classfix = option_find_int_quiet(options, "classfix", 0);
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l.absolute = option_find_int_quiet(options, "absolute", 0);
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l.random = option_find_int_quiet(options, "random", 0);
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l.coord_scale = option_find_float(options, "coord_scale", 1);
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l.object_scale = option_find_float(options, "object_scale", 1);
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l.noobject_scale = option_find_float(options, "noobject_scale", 1);
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l.mask_scale = option_find_float(options, "mask_scale", 1);
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l.class_scale = option_find_float(options, "class_scale", 1);
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l.bias_match = option_find_int_quiet(options, "bias_match",0);
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char *tree_file = option_find_str(options, "tree", 0);
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if (tree_file) l.softmax_tree = read_tree(tree_file);
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char *map_file = option_find_str(options, "map", 0);
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if (map_file) l.map = read_map(map_file);
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char *a = option_find_str(options, "anchors", 0);
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if(a){
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int len = strlen(a);
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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 (a[i] == ',') ++n;
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}
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for(i = 0; i < n; ++i){
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float bias = atof(a);
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l.biases[i] = bias;
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a = strchr(a, ',')+1;
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}
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}
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return l;
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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 num = option_find_int(options, "num", 1);
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int side = option_find_int(options, "side", 7);
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detection_layer layer = make_detection_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.max_boxes = option_find_int_quiet(options, "max",30);
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layer.coord_scale = option_find_float(options, "coord_scale", 1);
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layer.forced = option_find_int(options, "forced", 0);
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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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layer.jitter = option_find_float(options, "jitter", .2);
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layer.random = option_find_int_quiet(options, "random", 0);
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layer.reorg = option_find_int_quiet(options, "reorg", 0);
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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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layer.ratio = option_find_float_quiet(options, "ratio",0);
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layer.noobject_scale = option_find_float_quiet(options, "noobj", 1);
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layer.thresh = option_find_float_quiet(options, "thresh",0);
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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.shift = option_find_float(options, "shift", 0);
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l.noadjust = noadjust;
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return l;
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}
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layer parse_reorg(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 reverse = option_find_int_quiet(options, "reverse",0);
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int flatten = option_find_int_quiet(options, "flatten",0);
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int extra = option_find_int_quiet(options, "extra",0);
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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 reorg layer must output image.");
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layer layer = make_reorg_layer(batch,w,h,c,stride,reverse, flatten, extra);
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return layer;
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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 padding = option_find_int_quiet(options, "padding", (size-1)/2);
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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,padding);
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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;
|
|
layer.out_c = params.c;
|
|
return layer;
|
|
}
|
|
|
|
layer parse_normalization(list *options, size_params params)
|
|
{
|
|
float alpha = option_find_float(options, "alpha", .0001);
|
|
float beta = option_find_float(options, "beta" , .75);
|
|
float kappa = option_find_float(options, "kappa", 1);
|
|
int size = option_find_int(options, "size", 5);
|
|
layer l = make_normalization_layer(params.batch, params.w, params.h, params.c, size, alpha, beta, kappa);
|
|
return l;
|
|
}
|
|
|
|
layer parse_batchnorm(list *options, size_params params)
|
|
{
|
|
layer l = make_batchnorm_layer(params.batch, params.w, params.h, params.c);
|
|
return l;
|
|
}
|
|
|
|
layer parse_shortcut(list *options, size_params params, network *net)
|
|
{
|
|
char *l = option_find(options, "from");
|
|
int index = atoi(l);
|
|
if(index < 0) index = params.index + index;
|
|
|
|
int batch = params.batch;
|
|
layer from = net->layers[index];
|
|
|
|
layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c);
|
|
|
|
char *activation_s = option_find_str(options, "activation", "linear");
|
|
ACTIVATION activation = get_activation(activation_s);
|
|
s.activation = activation;
|
|
return s;
|
|
}
|
|
|
|
|
|
layer parse_activation(list *options, size_params params)
|
|
{
|
|
char *activation_s = option_find_str(options, "activation", "linear");
|
|
ACTIVATION activation = get_activation(activation_s);
|
|
|
|
layer l = make_activation_layer(params.batch, params.inputs, activation);
|
|
|
|
l.out_h = params.h;
|
|
l.out_w = params.w;
|
|
l.out_c = params.c;
|
|
l.h = params.h;
|
|
l.w = params.w;
|
|
l.c = params.c;
|
|
|
|
return l;
|
|
}
|
|
|
|
route_layer parse_route(list *options, size_params params, network *net)
|
|
{
|
|
char *l = option_find(options, "layers");
|
|
int len = strlen(l);
|
|
if(!l) error("Route Layer must specify input layers");
|
|
int n = 1;
|
|
int i;
|
|
for(i = 0; i < len; ++i){
|
|
if (l[i] == ',') ++n;
|
|
}
|
|
|
|
int *layers = calloc(n, sizeof(int));
|
|
int *sizes = calloc(n, sizeof(int));
|
|
for(i = 0; i < n; ++i){
|
|
int index = atoi(l);
|
|
l = strchr(l, ',')+1;
|
|
if(index < 0) index = params.index + index;
|
|
layers[i] = index;
|
|
sizes[i] = net->layers[index].outputs;
|
|
}
|
|
int batch = params.batch;
|
|
|
|
route_layer layer = make_route_layer(batch, n, layers, sizes);
|
|
|
|
convolutional_layer first = net->layers[layers[0]];
|
|
layer.out_w = first.out_w;
|
|
layer.out_h = first.out_h;
|
|
layer.out_c = first.out_c;
|
|
for(i = 1; i < n; ++i){
|
|
int index = layers[i];
|
|
convolutional_layer next = net->layers[index];
|
|
if(next.out_w == first.out_w && next.out_h == first.out_h){
|
|
layer.out_c += next.out_c;
|
|
}else{
|
|
layer.out_h = layer.out_w = layer.out_c = 0;
|
|
}
|
|
}
|
|
|
|
return layer;
|
|
}
|
|
|
|
learning_rate_policy get_policy(char *s)
|
|
{
|
|
if (strcmp(s, "random")==0) return RANDOM;
|
|
if (strcmp(s, "poly")==0) return POLY;
|
|
if (strcmp(s, "constant")==0) return CONSTANT;
|
|
if (strcmp(s, "step")==0) return STEP;
|
|
if (strcmp(s, "exp")==0) return EXP;
|
|
if (strcmp(s, "sigmoid")==0) return SIG;
|
|
if (strcmp(s, "steps")==0) return STEPS;
|
|
fprintf(stderr, "Couldn't find policy %s, going with constant\n", s);
|
|
return CONSTANT;
|
|
}
|
|
|
|
void parse_net_options(list *options, network *net)
|
|
{
|
|
net->batch = option_find_int(options, "batch",1);
|
|
net->learning_rate = option_find_float(options, "learning_rate", .001);
|
|
net->momentum = option_find_float(options, "momentum", .9);
|
|
net->decay = option_find_float(options, "decay", .0001);
|
|
int subdivs = option_find_int(options, "subdivisions",1);
|
|
net->time_steps = option_find_int_quiet(options, "time_steps",1);
|
|
net->notruth = option_find_int_quiet(options, "notruth",0);
|
|
net->batch /= subdivs;
|
|
net->batch *= net->time_steps;
|
|
net->subdivisions = subdivs;
|
|
net->random = option_find_int_quiet(options, "random", 0);
|
|
|
|
net->adam = option_find_int_quiet(options, "adam", 0);
|
|
if(net->adam){
|
|
net->B1 = option_find_float(options, "B1", .9);
|
|
net->B2 = option_find_float(options, "B2", .999);
|
|
net->eps = option_find_float(options, "eps", .0000001);
|
|
}
|
|
|
|
net->h = option_find_int_quiet(options, "height",0);
|
|
net->w = option_find_int_quiet(options, "width",0);
|
|
net->c = option_find_int_quiet(options, "channels",0);
|
|
net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c);
|
|
net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2);
|
|
net->min_crop = option_find_int_quiet(options, "min_crop",net->w);
|
|
net->max_ratio = option_find_float_quiet(options, "max_ratio", (float) net->max_crop / net->w);
|
|
net->min_ratio = option_find_float_quiet(options, "min_ratio", (float) net->min_crop / net->w);
|
|
net->center = option_find_int_quiet(options, "center",0);
|
|
|
|
net->angle = option_find_float_quiet(options, "angle", 0);
|
|
net->aspect = option_find_float_quiet(options, "aspect", 1);
|
|
net->saturation = option_find_float_quiet(options, "saturation", 1);
|
|
net->exposure = option_find_float_quiet(options, "exposure", 1);
|
|
net->hue = option_find_float_quiet(options, "hue", 0);
|
|
|
|
if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
|
|
|
|
char *policy_s = option_find_str(options, "policy", "constant");
|
|
net->policy = get_policy(policy_s);
|
|
net->burn_in = option_find_int_quiet(options, "burn_in", 0);
|
|
net->power = option_find_float_quiet(options, "power", 4);
|
|
if(net->policy == STEP){
|
|
net->step = option_find_int(options, "step", 1);
|
|
net->scale = option_find_float(options, "scale", 1);
|
|
} else if (net->policy == STEPS){
|
|
char *l = option_find(options, "steps");
|
|
char *p = option_find(options, "scales");
|
|
if(!l || !p) error("STEPS policy must have steps and scales in cfg file");
|
|
|
|
int len = strlen(l);
|
|
int n = 1;
|
|
int i;
|
|
for(i = 0; i < len; ++i){
|
|
if (l[i] == ',') ++n;
|
|
}
|
|
int *steps = calloc(n, sizeof(int));
|
|
float *scales = calloc(n, sizeof(float));
|
|
for(i = 0; i < n; ++i){
|
|
int step = atoi(l);
|
|
float scale = atof(p);
|
|
l = strchr(l, ',')+1;
|
|
p = strchr(p, ',')+1;
|
|
steps[i] = step;
|
|
scales[i] = scale;
|
|
}
|
|
net->scales = scales;
|
|
net->steps = steps;
|
|
net->num_steps = n;
|
|
} else if (net->policy == EXP){
|
|
net->gamma = option_find_float(options, "gamma", 1);
|
|
} else if (net->policy == SIG){
|
|
net->gamma = option_find_float(options, "gamma", 1);
|
|
net->step = option_find_int(options, "step", 1);
|
|
} else if (net->policy == POLY || net->policy == RANDOM){
|
|
}
|
|
net->max_batches = option_find_int(options, "max_batches", 0);
|
|
}
|
|
|
|
int is_network(section *s)
|
|
{
|
|
return (strcmp(s->type, "[net]")==0
|
|
|| strcmp(s->type, "[network]")==0);
|
|
}
|
|
|
|
network *parse_network_cfg(char *filename)
|
|
{
|
|
list *sections = read_cfg(filename);
|
|
node *n = sections->front;
|
|
if(!n) error("Config file has no sections");
|
|
network *net = make_network(sections->size - 1);
|
|
net->gpu_index = gpu_index;
|
|
size_params params;
|
|
|
|
section *s = (section *)n->val;
|
|
list *options = s->options;
|
|
if(!is_network(s)) error("First section must be [net] or [network]");
|
|
parse_net_options(options, net);
|
|
|
|
params.h = net->h;
|
|
params.w = net->w;
|
|
params.c = net->c;
|
|
params.inputs = net->inputs;
|
|
params.batch = net->batch;
|
|
params.time_steps = net->time_steps;
|
|
params.net = net;
|
|
|
|
size_t workspace_size = 0;
|
|
n = n->next;
|
|
int count = 0;
|
|
free_section(s);
|
|
fprintf(stderr, "layer filters size input output\n");
|
|
while(n){
|
|
params.index = count;
|
|
fprintf(stderr, "%5d ", count);
|
|
s = (section *)n->val;
|
|
options = s->options;
|
|
layer l = {0};
|
|
LAYER_TYPE lt = string_to_layer_type(s->type);
|
|
if(lt == CONVOLUTIONAL){
|
|
l = parse_convolutional(options, params);
|
|
}else if(lt == DECONVOLUTIONAL){
|
|
l = parse_deconvolutional(options, params);
|
|
}else if(lt == LOCAL){
|
|
l = parse_local(options, params);
|
|
}else if(lt == ACTIVE){
|
|
l = parse_activation(options, params);
|
|
}else if(lt == RNN){
|
|
l = parse_rnn(options, params);
|
|
}else if(lt == GRU){
|
|
l = parse_gru(options, params);
|
|
}else if (lt == LSTM) {
|
|
l = parse_lstm(options, params);
|
|
}else if(lt == CRNN){
|
|
l = parse_crnn(options, params);
|
|
}else if(lt == CONNECTED){
|
|
l = parse_connected(options, params);
|
|
}else if(lt == CROP){
|
|
l = parse_crop(options, params);
|
|
}else if(lt == COST){
|
|
l = parse_cost(options, params);
|
|
}else if(lt == REGION){
|
|
l = parse_region(options, params);
|
|
}else if(lt == DETECTION){
|
|
l = parse_detection(options, params);
|
|
}else if(lt == SOFTMAX){
|
|
l = parse_softmax(options, params);
|
|
net->hierarchy = l.softmax_tree;
|
|
}else if(lt == NORMALIZATION){
|
|
l = parse_normalization(options, params);
|
|
}else if(lt == BATCHNORM){
|
|
l = parse_batchnorm(options, params);
|
|
}else if(lt == MAXPOOL){
|
|
l = parse_maxpool(options, params);
|
|
}else if(lt == REORG){
|
|
l = parse_reorg(options, params);
|
|
}else if(lt == AVGPOOL){
|
|
l = parse_avgpool(options, params);
|
|
}else if(lt == ROUTE){
|
|
l = parse_route(options, params, net);
|
|
}else if(lt == SHORTCUT){
|
|
l = parse_shortcut(options, params, net);
|
|
}else if(lt == DROPOUT){
|
|
l = parse_dropout(options, params);
|
|
l.output = net->layers[count-1].output;
|
|
l.delta = net->layers[count-1].delta;
|
|
#ifdef GPU
|
|
l.output_gpu = net->layers[count-1].output_gpu;
|
|
l.delta_gpu = net->layers[count-1].delta_gpu;
|
|
#endif
|
|
}else{
|
|
fprintf(stderr, "Type not recognized: %s\n", s->type);
|
|
}
|
|
l.truth = option_find_int_quiet(options, "truth", 0);
|
|
l.onlyforward = option_find_int_quiet(options, "onlyforward", 0);
|
|
l.stopbackward = option_find_int_quiet(options, "stopbackward", 0);
|
|
l.dontload = option_find_int_quiet(options, "dontload", 0);
|
|
l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
|
|
l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1);
|
|
l.smooth = option_find_float_quiet(options, "smooth", 0);
|
|
option_unused(options);
|
|
net->layers[count] = l;
|
|
if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
|
|
free_section(s);
|
|
n = n->next;
|
|
++count;
|
|
if(n){
|
|
params.h = l.out_h;
|
|
params.w = l.out_w;
|
|
params.c = l.out_c;
|
|
params.inputs = l.outputs;
|
|
}
|
|
}
|
|
free_list(sections);
|
|
layer out = get_network_output_layer(net);
|
|
net->outputs = out.outputs;
|
|
net->truths = out.outputs;
|
|
if(net->layers[net->n-1].truths) net->truths = net->layers[net->n-1].truths;
|
|
net->output = out.output;
|
|
net->input = calloc(net->inputs*net->batch, sizeof(float));
|
|
net->truth = calloc(net->truths*net->batch, sizeof(float));
|
|
#ifdef GPU
|
|
net->output_gpu = out.output_gpu;
|
|
net->input_gpu = cuda_make_array(net->input, net->inputs*net->batch);
|
|
net->truth_gpu = cuda_make_array(net->truth, net->truths*net->batch);
|
|
#endif
|
|
if(workspace_size){
|
|
//printf("%ld\n", workspace_size);
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
|
|
}else {
|
|
net->workspace = calloc(1, workspace_size);
|
|
}
|
|
#else
|
|
net->workspace = calloc(1, workspace_size);
|
|
#endif
|
|
}
|
|
return net;
|
|
}
|
|
|
|
list *read_cfg(char *filename)
|
|
{
|
|
FILE *file = fopen(filename, "r");
|
|
if(file == 0) file_error(filename);
|
|
char *line;
|
|
int nu = 0;
|
|
list *options = make_list();
|
|
section *current = 0;
|
|
while((line=fgetl(file)) != 0){
|
|
++ nu;
|
|
strip(line);
|
|
switch(line[0]){
|
|
case '[':
|
|
current = malloc(sizeof(section));
|
|
list_insert(options, 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 options;
|
|
}
|
|
|
|
void save_convolutional_weights_binary(layer l, FILE *fp)
|
|
{
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
pull_convolutional_layer(l);
|
|
}
|
|
#endif
|
|
binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
|
|
int size = l.c*l.size*l.size;
|
|
int i, j, k;
|
|
fwrite(l.biases, sizeof(float), l.n, fp);
|
|
if (l.batch_normalize){
|
|
fwrite(l.scales, sizeof(float), l.n, fp);
|
|
fwrite(l.rolling_mean, sizeof(float), l.n, fp);
|
|
fwrite(l.rolling_variance, sizeof(float), l.n, fp);
|
|
}
|
|
for(i = 0; i < l.n; ++i){
|
|
float mean = l.binary_weights[i*size];
|
|
if(mean < 0) mean = -mean;
|
|
fwrite(&mean, sizeof(float), 1, fp);
|
|
for(j = 0; j < size/8; ++j){
|
|
int index = i*size + j*8;
|
|
unsigned char c = 0;
|
|
for(k = 0; k < 8; ++k){
|
|
if (j*8 + k >= size) break;
|
|
if (l.binary_weights[index + k] > 0) c = (c | 1<<k);
|
|
}
|
|
fwrite(&c, sizeof(char), 1, fp);
|
|
}
|
|
}
|
|
}
|
|
|
|
void save_convolutional_weights(layer l, FILE *fp)
|
|
{
|
|
if(l.binary){
|
|
//save_convolutional_weights_binary(l, fp);
|
|
//return;
|
|
}
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
pull_convolutional_layer(l);
|
|
}
|
|
#endif
|
|
int num = l.nweights;
|
|
fwrite(l.biases, sizeof(float), l.n, fp);
|
|
if (l.batch_normalize){
|
|
fwrite(l.scales, sizeof(float), l.n, fp);
|
|
fwrite(l.rolling_mean, sizeof(float), l.n, fp);
|
|
fwrite(l.rolling_variance, sizeof(float), l.n, fp);
|
|
}
|
|
fwrite(l.weights, sizeof(float), num, fp);
|
|
}
|
|
|
|
void save_batchnorm_weights(layer l, FILE *fp)
|
|
{
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
pull_batchnorm_layer(l);
|
|
}
|
|
#endif
|
|
fwrite(l.scales, sizeof(float), l.c, fp);
|
|
fwrite(l.rolling_mean, sizeof(float), l.c, fp);
|
|
fwrite(l.rolling_variance, sizeof(float), l.c, fp);
|
|
}
|
|
|
|
void save_connected_weights(layer l, FILE *fp)
|
|
{
|
|
#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);
|
|
if (l.batch_normalize){
|
|
fwrite(l.scales, sizeof(float), l.outputs, fp);
|
|
fwrite(l.rolling_mean, sizeof(float), l.outputs, fp);
|
|
fwrite(l.rolling_variance, sizeof(float), l.outputs, fp);
|
|
}
|
|
}
|
|
|
|
void save_weights_upto(network *net, char *filename, int cutoff)
|
|
{
|
|
#ifdef GPU
|
|
if(net->gpu_index >= 0){
|
|
cuda_set_device(net->gpu_index);
|
|
}
|
|
#endif
|
|
fprintf(stderr, "Saving weights to %s\n", filename);
|
|
FILE *fp = fopen(filename, "wb");
|
|
if(!fp) file_error(filename);
|
|
|
|
int major = 0;
|
|
int minor = 2;
|
|
int revision = 0;
|
|
fwrite(&major, sizeof(int), 1, fp);
|
|
fwrite(&minor, sizeof(int), 1, fp);
|
|
fwrite(&revision, sizeof(int), 1, fp);
|
|
fwrite(net->seen, sizeof(size_t), 1, fp);
|
|
|
|
int i;
|
|
for(i = 0; i < net->n && i < cutoff; ++i){
|
|
layer l = net->layers[i];
|
|
if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){
|
|
save_convolutional_weights(l, fp);
|
|
} if(l.type == CONNECTED){
|
|
save_connected_weights(l, fp);
|
|
} if(l.type == BATCHNORM){
|
|
save_batchnorm_weights(l, fp);
|
|
} if(l.type == RNN){
|
|
save_connected_weights(*(l.input_layer), fp);
|
|
save_connected_weights(*(l.self_layer), fp);
|
|
save_connected_weights(*(l.output_layer), fp);
|
|
} if (l.type == LSTM) {
|
|
save_connected_weights(*(l.wi), fp);
|
|
save_connected_weights(*(l.wf), fp);
|
|
save_connected_weights(*(l.wo), fp);
|
|
save_connected_weights(*(l.wg), fp);
|
|
save_connected_weights(*(l.ui), fp);
|
|
save_connected_weights(*(l.uf), fp);
|
|
save_connected_weights(*(l.uo), fp);
|
|
save_connected_weights(*(l.ug), fp);
|
|
} if (l.type == GRU) {
|
|
if(1){
|
|
save_connected_weights(*(l.wz), fp);
|
|
save_connected_weights(*(l.wr), fp);
|
|
save_connected_weights(*(l.wh), fp);
|
|
save_connected_weights(*(l.uz), fp);
|
|
save_connected_weights(*(l.ur), fp);
|
|
save_connected_weights(*(l.uh), fp);
|
|
}else{
|
|
save_connected_weights(*(l.reset_layer), fp);
|
|
save_connected_weights(*(l.update_layer), fp);
|
|
save_connected_weights(*(l.state_layer), fp);
|
|
}
|
|
} if(l.type == CRNN){
|
|
save_convolutional_weights(*(l.input_layer), fp);
|
|
save_convolutional_weights(*(l.self_layer), fp);
|
|
save_convolutional_weights(*(l.output_layer), fp);
|
|
} if(l.type == LOCAL){
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
pull_local_layer(l);
|
|
}
|
|
#endif
|
|
int locations = l.out_w*l.out_h;
|
|
int size = l.size*l.size*l.c*l.n*locations;
|
|
fwrite(l.biases, sizeof(float), l.outputs, fp);
|
|
fwrite(l.weights, sizeof(float), size, fp);
|
|
}
|
|
}
|
|
fclose(fp);
|
|
}
|
|
void save_weights(network *net, char *filename)
|
|
{
|
|
save_weights_upto(net, filename, net->n);
|
|
}
|
|
|
|
void transpose_matrix(float *a, int rows, int cols)
|
|
{
|
|
float *transpose = calloc(rows*cols, sizeof(float));
|
|
int x, y;
|
|
for(x = 0; x < rows; ++x){
|
|
for(y = 0; y < cols; ++y){
|
|
transpose[y*rows + x] = a[x*cols + y];
|
|
}
|
|
}
|
|
memcpy(a, transpose, rows*cols*sizeof(float));
|
|
free(transpose);
|
|
}
|
|
|
|
void load_connected_weights(layer l, FILE *fp, int transpose)
|
|
{
|
|
fread(l.biases, sizeof(float), l.outputs, fp);
|
|
fread(l.weights, sizeof(float), l.outputs*l.inputs, fp);
|
|
if(transpose){
|
|
transpose_matrix(l.weights, l.inputs, l.outputs);
|
|
}
|
|
//printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs));
|
|
//printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs));
|
|
if (l.batch_normalize && (!l.dontloadscales)){
|
|
fread(l.scales, sizeof(float), l.outputs, fp);
|
|
fread(l.rolling_mean, sizeof(float), l.outputs, fp);
|
|
fread(l.rolling_variance, sizeof(float), l.outputs, fp);
|
|
//printf("Scales: %f mean %f variance\n", mean_array(l.scales, l.outputs), variance_array(l.scales, l.outputs));
|
|
//printf("rolling_mean: %f mean %f variance\n", mean_array(l.rolling_mean, l.outputs), variance_array(l.rolling_mean, l.outputs));
|
|
//printf("rolling_variance: %f mean %f variance\n", mean_array(l.rolling_variance, l.outputs), variance_array(l.rolling_variance, l.outputs));
|
|
}
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
push_connected_layer(l);
|
|
}
|
|
#endif
|
|
}
|
|
|
|
void load_batchnorm_weights(layer l, FILE *fp)
|
|
{
|
|
fread(l.scales, sizeof(float), l.c, fp);
|
|
fread(l.rolling_mean, sizeof(float), l.c, fp);
|
|
fread(l.rolling_variance, sizeof(float), l.c, fp);
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
push_batchnorm_layer(l);
|
|
}
|
|
#endif
|
|
}
|
|
|
|
void load_convolutional_weights_binary(layer l, FILE *fp)
|
|
{
|
|
fread(l.biases, sizeof(float), l.n, fp);
|
|
if (l.batch_normalize && (!l.dontloadscales)){
|
|
fread(l.scales, sizeof(float), l.n, fp);
|
|
fread(l.rolling_mean, sizeof(float), l.n, fp);
|
|
fread(l.rolling_variance, sizeof(float), l.n, fp);
|
|
}
|
|
int size = l.c*l.size*l.size;
|
|
int i, j, k;
|
|
for(i = 0; i < l.n; ++i){
|
|
float mean = 0;
|
|
fread(&mean, sizeof(float), 1, fp);
|
|
for(j = 0; j < size/8; ++j){
|
|
int index = i*size + j*8;
|
|
unsigned char c = 0;
|
|
fread(&c, sizeof(char), 1, fp);
|
|
for(k = 0; k < 8; ++k){
|
|
if (j*8 + k >= size) break;
|
|
l.weights[index + k] = (c & 1<<k) ? mean : -mean;
|
|
}
|
|
}
|
|
}
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
push_convolutional_layer(l);
|
|
}
|
|
#endif
|
|
}
|
|
|
|
void load_convolutional_weights(layer l, FILE *fp)
|
|
{
|
|
if(l.binary){
|
|
//load_convolutional_weights_binary(l, fp);
|
|
//return;
|
|
}
|
|
int num = l.nweights;
|
|
fread(l.biases, sizeof(float), l.n, fp);
|
|
if (l.batch_normalize && (!l.dontloadscales)){
|
|
fread(l.scales, sizeof(float), l.n, fp);
|
|
fread(l.rolling_mean, sizeof(float), l.n, fp);
|
|
fread(l.rolling_variance, sizeof(float), l.n, fp);
|
|
if(0){
|
|
int i;
|
|
for(i = 0; i < l.n; ++i){
|
|
printf("%g, ", l.rolling_mean[i]);
|
|
}
|
|
printf("\n");
|
|
for(i = 0; i < l.n; ++i){
|
|
printf("%g, ", l.rolling_variance[i]);
|
|
}
|
|
printf("\n");
|
|
}
|
|
if(0){
|
|
fill_cpu(l.n, 0, l.rolling_mean, 1);
|
|
fill_cpu(l.n, 0, l.rolling_variance, 1);
|
|
}
|
|
if(0){
|
|
int i;
|
|
for(i = 0; i < l.n; ++i){
|
|
printf("%g, ", l.rolling_mean[i]);
|
|
}
|
|
printf("\n");
|
|
for(i = 0; i < l.n; ++i){
|
|
printf("%g, ", l.rolling_variance[i]);
|
|
}
|
|
printf("\n");
|
|
}
|
|
}
|
|
fread(l.weights, sizeof(float), num, fp);
|
|
//if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1);
|
|
if (l.flipped) {
|
|
transpose_matrix(l.weights, l.c*l.size*l.size, l.n);
|
|
}
|
|
//if (l.binary) binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.weights);
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
push_convolutional_layer(l);
|
|
}
|
|
#endif
|
|
}
|
|
|
|
|
|
void load_weights_upto(network *net, char *filename, int start, int cutoff)
|
|
{
|
|
#ifdef GPU
|
|
if(net->gpu_index >= 0){
|
|
cuda_set_device(net->gpu_index);
|
|
}
|
|
#endif
|
|
fprintf(stderr, "Loading weights from %s...", filename);
|
|
fflush(stdout);
|
|
FILE *fp = fopen(filename, "rb");
|
|
if(!fp) file_error(filename);
|
|
|
|
int major;
|
|
int minor;
|
|
int revision;
|
|
fread(&major, sizeof(int), 1, fp);
|
|
fread(&minor, sizeof(int), 1, fp);
|
|
fread(&revision, sizeof(int), 1, fp);
|
|
if ((major*10 + minor) >= 2){
|
|
fread(net->seen, sizeof(size_t), 1, fp);
|
|
} else {
|
|
int iseen = 0;
|
|
fread(&iseen, sizeof(int), 1, fp);
|
|
*net->seen = iseen;
|
|
}
|
|
int transpose = (major > 1000) || (minor > 1000);
|
|
|
|
int i;
|
|
for(i = start; i < net->n && i < cutoff; ++i){
|
|
layer l = net->layers[i];
|
|
if (l.dontload) continue;
|
|
if(l.type == CONVOLUTIONAL || l.type == DECONVOLUTIONAL){
|
|
load_convolutional_weights(l, fp);
|
|
}
|
|
if(l.type == CONNECTED){
|
|
load_connected_weights(l, fp, transpose);
|
|
}
|
|
if(l.type == BATCHNORM){
|
|
load_batchnorm_weights(l, fp);
|
|
}
|
|
if(l.type == CRNN){
|
|
load_convolutional_weights(*(l.input_layer), fp);
|
|
load_convolutional_weights(*(l.self_layer), fp);
|
|
load_convolutional_weights(*(l.output_layer), fp);
|
|
}
|
|
if(l.type == RNN){
|
|
load_connected_weights(*(l.input_layer), fp, transpose);
|
|
load_connected_weights(*(l.self_layer), fp, transpose);
|
|
load_connected_weights(*(l.output_layer), fp, transpose);
|
|
}
|
|
if (l.type == LSTM) {
|
|
load_connected_weights(*(l.wi), fp, transpose);
|
|
load_connected_weights(*(l.wf), fp, transpose);
|
|
load_connected_weights(*(l.wo), fp, transpose);
|
|
load_connected_weights(*(l.wg), fp, transpose);
|
|
load_connected_weights(*(l.ui), fp, transpose);
|
|
load_connected_weights(*(l.uf), fp, transpose);
|
|
load_connected_weights(*(l.uo), fp, transpose);
|
|
load_connected_weights(*(l.ug), fp, transpose);
|
|
}
|
|
if (l.type == GRU) {
|
|
if(1){
|
|
load_connected_weights(*(l.wz), fp, transpose);
|
|
load_connected_weights(*(l.wr), fp, transpose);
|
|
load_connected_weights(*(l.wh), fp, transpose);
|
|
load_connected_weights(*(l.uz), fp, transpose);
|
|
load_connected_weights(*(l.ur), fp, transpose);
|
|
load_connected_weights(*(l.uh), fp, transpose);
|
|
}else{
|
|
load_connected_weights(*(l.reset_layer), fp, transpose);
|
|
load_connected_weights(*(l.update_layer), fp, transpose);
|
|
load_connected_weights(*(l.state_layer), fp, transpose);
|
|
}
|
|
}
|
|
if(l.type == LOCAL){
|
|
int locations = l.out_w*l.out_h;
|
|
int size = l.size*l.size*l.c*l.n*locations;
|
|
fread(l.biases, sizeof(float), l.outputs, fp);
|
|
fread(l.weights, sizeof(float), size, fp);
|
|
#ifdef GPU
|
|
if(gpu_index >= 0){
|
|
push_local_layer(l);
|
|
}
|
|
#endif
|
|
}
|
|
}
|
|
fprintf(stderr, "Done!\n");
|
|
fclose(fp);
|
|
}
|
|
|
|
void load_weights(network *net, char *filename)
|
|
{
|
|
load_weights_upto(net, filename, 0, net->n);
|
|
}
|
|
|