#include <stdio.h>
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#include <string.h>
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#include <stdlib.h>
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#include <stdint.h>
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#include "activation_layer.h"
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#include "activations.h"
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#include "assert.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 "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 "lstm_layer.h"
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#include "conv_lstm_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 "reorg_old_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 "scale_channels_layer.h"
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#include "sam_layer.h"
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#include "softmax_layer.h"
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#include "utils.h"
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#include "upsample_layer.h"
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#include "version.h"
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#include "yolo_layer.h"
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#include "gaussian_yolo_layer.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, "[scale_channels]") == 0) return SCALE_CHANNELS;
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if (strcmp(type, "[sam]") == 0) return SAM;
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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, "[yolo]") == 0) return YOLO;
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if (strcmp(type, "[Gaussian_yolo]") == 0) return GAUSSIAN_YOLO;
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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, "[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, "[conv_lstm]") == 0) return CONV_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, "[local_avg]") == 0
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|| strcmp(type, "[local_avgpool]") == 0) return LOCAL_AVGPOOL;
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if (strcmp(type, "[reorg3d]")==0) return REORG;
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if (strcmp(type, "[reorg]") == 0) return REORG_OLD;
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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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if (strcmp(type, "[upsample]") == 0) return UPSAMPLE;
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if (strcmp(type, "[empty]") == 0) return EMPTY;
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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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int train;
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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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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 groups = option_find_int_quiet(options, "groups", 1);
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int size = option_find_int(options, "size",1);
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int stride = -1;
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//int stride = option_find_int(options, "stride",1);
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int stride_x = option_find_int_quiet(options, "stride_x", -1);
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int stride_y = option_find_int_quiet(options, "stride_y", -1);
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if (stride_x < 1 || stride_y < 1) {
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stride = option_find_int(options, "stride", 1);
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if (stride_x < 1) stride_x = stride;
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if (stride_y < 1) stride_y = stride;
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}
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else {
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stride = option_find_int_quiet(options, "stride", 1);
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}
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int dilation = option_find_int_quiet(options, "dilation", 1);
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int antialiasing = option_find_int_quiet(options, "antialiasing", 0);
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if (size == 1) dilation = 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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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 assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0);
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int share_index = option_find_int_quiet(options, "share_index", -1000000000);
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convolutional_layer *share_layer = NULL;
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if(share_index >= 0) share_layer = ¶ms.net.layers[share_index];
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else if(share_index != -1000000000) share_layer = ¶ms.net.layers[params.index + share_index];
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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 cbn = option_find_int_quiet(options, "cbn", 0);
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if (cbn) batch_normalize = 2;
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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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int use_bin_output = option_find_int_quiet(options, "bin_output", 0);
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int sway = option_find_int_quiet(options, "sway", 0);
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int rotate = option_find_int_quiet(options, "rotate", 0);
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int stretch = option_find_int_quiet(options, "stretch", 0);
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int stretch_sway = option_find_int_quiet(options, "stretch_sway", 0);
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if ((sway + rotate + stretch + stretch_sway) > 1) {
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printf(" Error: should be used only 1 param: sway=1, rotate=1 or stretch=1 in the [convolutional] layer \n");
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exit(0);
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}
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int deform = sway || rotate || stretch || stretch_sway;
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if (deform && size == 1) {
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printf(" Error: params (sway=1, rotate=1 or stretch=1) should be used only with size >=3 in the [convolutional] layer \n");
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exit(0);
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}
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convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, deform, params.train);
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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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layer.sway = sway;
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layer.rotate = rotate;
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layer.stretch = stretch;
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layer.stretch_sway = stretch_sway;
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layer.angle = option_find_float_quiet(options, "angle", 15);
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layer.grad_centr = option_find_int_quiet(options, "grad_centr", 0);
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if(params.net.adam){
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layer.B1 = params.net.B1;
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layer.B2 = params.net.B2;
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layer.eps = params.net.eps;
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}
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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 size = option_find_int_quiet(options, "size", 3);
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int stride = option_find_int_quiet(options, "stride", 1);
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int dilation = option_find_int_quiet(options, "dilation", 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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if (pad) padding = size / 2;
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int output_filters = option_find_int(options, "output",1);
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int hidden_filters = option_find_int(options, "hidden",1);
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int groups = option_find_int_quiet(options, "groups", 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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int xnor = option_find_int_quiet(options, "xnor", 0);
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layer l = make_crnn_layer(params.batch, params.h, params.w, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, xnor, params.train);
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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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int hidden = option_find_int(options, "hidden",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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int logistic = option_find_int_quiet(options, "logistic", 0);
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layer l = make_rnn_layer(params.batch, params.inputs, hidden, output, params.time_steps, activation, batch_normalize, logistic);
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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);
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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);
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return l;
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}
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layer parse_conv_lstm(list *options, size_params params)
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{
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// a ConvLSTM with a larger transitional kernel should be able to capture faster motions
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int size = option_find_int_quiet(options, "size", 3);
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int stride = option_find_int_quiet(options, "stride", 1);
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int dilation = option_find_int_quiet(options, "dilation", 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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if (pad) padding = size / 2;
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int output_filters = option_find_int(options, "output", 1);
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int groups = option_find_int_quiet(options, "groups", 1);
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char *activation_s = option_find_str(options, "activation", "LINEAR");
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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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int xnor = option_find_int_quiet(options, "xnor", 0);
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int peephole = option_find_int_quiet(options, "peephole", 0);
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layer l = make_conv_lstm_layer(params.batch, params.h, params.w, params.c, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, peephole, xnor, params.train);
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l.state_constrain = option_find_int_quiet(options, "state_constrain", params.time_steps * 32);
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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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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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int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
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connected_layer layer = make_connected_layer(params.batch, 1, params.inputs, output, activation, batch_normalize);
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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_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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layer.noloss = option_find_int_quiet(options, "noloss", 0);
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return layer;
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}
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int *parse_yolo_mask(char *a, int *num)
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{
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int *mask = 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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mask = (int*)xcalloc(n, sizeof(int));
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for (i = 0; i < n; ++i) {
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int val = atoi(a);
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mask[i] = val;
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a = strchr(a, ',') + 1;
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}
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*num = n;
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}
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return mask;
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}
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float *get_classes_multipliers(char *cpc, const int classes)
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{
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float *classes_multipliers = NULL;
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if (cpc) {
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int classes_counters = classes;
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int *counters_per_class = parse_yolo_mask(cpc, &classes_counters);
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if (classes_counters != classes) {
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printf(" number of values in counters_per_class = %d doesn't match with classes = %d \n", classes_counters, classes);
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exit(0);
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}
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float max_counter = 0;
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int i;
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for (i = 0; i < classes_counters; ++i) if (max_counter < counters_per_class[i]) max_counter = counters_per_class[i];
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classes_multipliers = (float *)calloc(classes_counters, sizeof(float));
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for (i = 0; i < classes_counters; ++i) classes_multipliers[i] = max_counter / counters_per_class[i];
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free(counters_per_class);
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printf(" classes_multipliers: ");
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for (i = 0; i < classes_counters; ++i) printf("%.1f, ", classes_multipliers[i]);
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printf("\n");
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}
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return classes_multipliers;
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}
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layer parse_yolo(list *options, size_params params)
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{
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int classes = option_find_int(options, "classes", 20);
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int total = option_find_int(options, "num", 1);
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int num = total;
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char *a = option_find_str(options, "mask", 0);
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int *mask = parse_yolo_mask(a, &num);
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int max_boxes = option_find_int_quiet(options, "max", 90);
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layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes);
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if (l.outputs != params.inputs) {
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printf("Error: l.outputs == params.inputs \n");
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printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer \n");
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exit(EXIT_FAILURE);
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}
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//assert(l.outputs == params.inputs);
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char *cpc = option_find_str(options, "counters_per_class", 0);
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l.classes_multipliers = get_classes_multipliers(cpc, classes);
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l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f);
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l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1);
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l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0);
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l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10
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l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75);
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l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1);
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char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou");
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if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE;
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else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU;
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else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU;
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else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU;
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else l.iou_loss = IOU;
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fprintf(stderr, "[yolo] params: iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale_x_y: %2.2f\n",
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iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y);
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char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou");
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if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU;
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else if (strcmp(iou_thresh_kind_str, "giou") == 0) l.iou_thresh_kind = GIOU;
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else if (strcmp(iou_thresh_kind_str, "diou") == 0) l.iou_thresh_kind = DIOU;
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else if (strcmp(iou_thresh_kind_str, "ciou") == 0) l.iou_thresh_kind = CIOU;
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else {
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fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str);
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l.iou_thresh_kind = IOU;
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}
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l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6);
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char *nms_kind = option_find_str_quiet(options, "nms_kind", "default");
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if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS;
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else {
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if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS;
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else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS;
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else l.nms_kind = DEFAULT_NMS;
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printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms);
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}
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l.jitter = option_find_float(options, "jitter", .2);
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l.resize = option_find_float_quiet(options, "resize", 1.0);
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l.focal_loss = option_find_int_quiet(options, "focal_loss", 0);
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l.ignore_thresh = option_find_float(options, "ignore_thresh", .5);
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l.truth_thresh = option_find_float(options, "truth_thresh", 1);
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l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo]
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l.random = option_find_float_quiet(options, "random", 0);
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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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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 < total*2; ++i) {
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float bias = atof(a);
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l.biases[i] = bias;
|
a = strchr(a, ',') + 1;
|
}
|
}
|
return l;
|
}
|
|
|
int *parse_gaussian_yolo_mask(char *a, int *num) // Gaussian_YOLOv3
|
{
|
int *mask = 0;
|
if (a) {
|
int len = strlen(a);
|
int n = 1;
|
int i;
|
for (i = 0; i < len; ++i) {
|
if (a[i] == ',') ++n;
|
}
|
mask = (int *)calloc(n, sizeof(int));
|
for (i = 0; i < n; ++i) {
|
int val = atoi(a);
|
mask[i] = val;
|
a = strchr(a, ',') + 1;
|
}
|
*num = n;
|
}
|
return mask;
|
}
|
|
|
layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3
|
{
|
int classes = option_find_int(options, "classes", 20);
|
int max_boxes = option_find_int_quiet(options, "max", 90);
|
int total = option_find_int(options, "num", 1);
|
int num = total;
|
|
char *a = option_find_str(options, "mask", 0);
|
int *mask = parse_gaussian_yolo_mask(a, &num);
|
layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes);
|
if (l.outputs != params.inputs) {
|
printf("Error: l.outputs == params.inputs \n");
|
printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [Gaussian_yolo]-layer \n");
|
exit(EXIT_FAILURE);
|
}
|
//assert(l.outputs == params.inputs);
|
|
char *cpc = option_find_str(options, "counters_per_class", 0);
|
l.classes_multipliers = get_classes_multipliers(cpc, classes);
|
|
l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f);
|
l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1);
|
l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0);
|
l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10
|
l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0);
|
l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75);
|
l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1.0);
|
char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou");
|
|
if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE;
|
else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU;
|
else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU;
|
else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU;
|
else l.iou_loss = IOU;
|
|
char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou");
|
if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU;
|
else if (strcmp(iou_thresh_kind_str, "giou") == 0) l.iou_thresh_kind = GIOU;
|
else if (strcmp(iou_thresh_kind_str, "diou") == 0) l.iou_thresh_kind = DIOU;
|
else if (strcmp(iou_thresh_kind_str, "ciou") == 0) l.iou_thresh_kind = CIOU;
|
else {
|
fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str);
|
l.iou_thresh_kind = IOU;
|
}
|
|
l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6);
|
char *nms_kind = option_find_str_quiet(options, "nms_kind", "default");
|
if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS;
|
else {
|
if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS;
|
else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS;
|
else if (strcmp(nms_kind, "cornersnms") == 0) l.nms_kind = CORNERS_NMS;
|
else l.nms_kind = DEFAULT_NMS;
|
printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms);
|
}
|
|
char *yolo_point = option_find_str_quiet(options, "yolo_point", "center");
|
if (strcmp(yolo_point, "left_top") == 0) l.yolo_point = YOLO_LEFT_TOP;
|
else if (strcmp(yolo_point, "right_bottom") == 0) l.yolo_point = YOLO_RIGHT_BOTTOM;
|
else l.yolo_point = YOLO_CENTER;
|
|
fprintf(stderr, "[Gaussian_yolo] iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale: %2.2f, point: %d\n",
|
iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y, l.yolo_point);
|
|
l.jitter = option_find_float(options, "jitter", .2);
|
l.resize = option_find_float_quiet(options, "resize", 1.0);
|
|
l.ignore_thresh = option_find_float(options, "ignore_thresh", .5);
|
l.truth_thresh = option_find_float(options, "truth_thresh", 1);
|
l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo]
|
l.random = option_find_float_quiet(options, "random", 0);
|
|
char *map_file = option_find_str(options, "map", 0);
|
if (map_file) l.map = read_map(map_file);
|
|
a = option_find_str(options, "anchors", 0);
|
if (a) {
|
int len = strlen(a);
|
int n = 1;
|
int i;
|
for (i = 0; i < len; ++i) {
|
if (a[i] == ',') ++n;
|
}
|
for (i = 0; i < n; ++i) {
|
float bias = atof(a);
|
l.biases[i] = bias;
|
a = strchr(a, ',') + 1;
|
}
|
}
|
return l;
|
}
|
|
layer parse_region(list *options, size_params params)
|
{
|
int coords = option_find_int(options, "coords", 4);
|
int classes = option_find_int(options, "classes", 20);
|
int num = option_find_int(options, "num", 1);
|
int max_boxes = option_find_int_quiet(options, "max", 90);
|
|
layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords, max_boxes);
|
if (l.outputs != params.inputs) {
|
printf("Error: l.outputs == params.inputs \n");
|
printf("filters= in the [convolutional]-layer doesn't correspond to classes= or num= in [region]-layer \n");
|
exit(EXIT_FAILURE);
|
}
|
//assert(l.outputs == params.inputs);
|
|
l.log = option_find_int_quiet(options, "log", 0);
|
l.sqrt = option_find_int_quiet(options, "sqrt", 0);
|
|
l.softmax = option_find_int(options, "softmax", 0);
|
l.focal_loss = option_find_int_quiet(options, "focal_loss", 0);
|
//l.max_boxes = option_find_int_quiet(options, "max",30);
|
l.jitter = option_find_float(options, "jitter", .2);
|
l.resize = option_find_float_quiet(options, "resize", 1.0);
|
l.rescore = option_find_int_quiet(options, "rescore",0);
|
|
l.thresh = option_find_float(options, "thresh", .5);
|
l.classfix = option_find_int_quiet(options, "classfix", 0);
|
l.absolute = option_find_int_quiet(options, "absolute", 0);
|
l.random = option_find_float_quiet(options, "random", 0);
|
|
l.coord_scale = option_find_float(options, "coord_scale", 1);
|
l.object_scale = option_find_float(options, "object_scale", 1);
|
l.noobject_scale = option_find_float(options, "noobject_scale", 1);
|
l.mask_scale = option_find_float(options, "mask_scale", 1);
|
l.class_scale = option_find_float(options, "class_scale", 1);
|
l.bias_match = option_find_int_quiet(options, "bias_match",0);
|
|
char *tree_file = option_find_str(options, "tree", 0);
|
if (tree_file) l.softmax_tree = read_tree(tree_file);
|
char *map_file = option_find_str(options, "map", 0);
|
if (map_file) l.map = read_map(map_file);
|
|
char *a = option_find_str(options, "anchors", 0);
|
if(a){
|
int len = strlen(a);
|
int n = 1;
|
int i;
|
for(i = 0; i < len; ++i){
|
if (a[i] == ',') ++n;
|
}
|
for(i = 0; i < n && i < num*2; ++i){
|
float bias = atof(a);
|
l.biases[i] = bias;
|
a = strchr(a, ',')+1;
|
}
|
}
|
return l;
|
}
|
detection_layer parse_detection(list *options, size_params params)
|
{
|
int coords = option_find_int(options, "coords", 1);
|
int classes = option_find_int(options, "classes", 1);
|
int rescore = option_find_int(options, "rescore", 0);
|
int num = option_find_int(options, "num", 1);
|
int side = option_find_int(options, "side", 7);
|
detection_layer layer = make_detection_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
|
|
layer.softmax = option_find_int(options, "softmax", 0);
|
layer.sqrt = option_find_int(options, "sqrt", 0);
|
|
layer.max_boxes = option_find_int_quiet(options, "max",30);
|
layer.coord_scale = option_find_float(options, "coord_scale", 1);
|
layer.forced = option_find_int(options, "forced", 0);
|
layer.object_scale = option_find_float(options, "object_scale", 1);
|
layer.noobject_scale = option_find_float(options, "noobject_scale", 1);
|
layer.class_scale = option_find_float(options, "class_scale", 1);
|
layer.jitter = option_find_float(options, "jitter", .2);
|
layer.resize = option_find_float_quiet(options, "resize", 1.0);
|
layer.random = option_find_float_quiet(options, "random", 0);
|
layer.reorg = option_find_int_quiet(options, "reorg", 0);
|
return layer;
|
}
|
|
cost_layer parse_cost(list *options, size_params params)
|
{
|
char *type_s = option_find_str(options, "type", "sse");
|
COST_TYPE type = get_cost_type(type_s);
|
float scale = option_find_float_quiet(options, "scale",1);
|
cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale);
|
layer.ratio = option_find_float_quiet(options, "ratio",0);
|
return layer;
|
}
|
|
crop_layer parse_crop(list *options, size_params params)
|
{
|
int crop_height = option_find_int(options, "crop_height",1);
|
int crop_width = option_find_int(options, "crop_width",1);
|
int flip = option_find_int(options, "flip",0);
|
float angle = option_find_float(options, "angle",0);
|
float saturation = option_find_float(options, "saturation",1);
|
float exposure = option_find_float(options, "exposure",1);
|
|
int batch,h,w,c;
|
h = params.h;
|
w = params.w;
|
c = params.c;
|
batch=params.batch;
|
if(!(h && w && c)) error("Layer before crop layer must output image.");
|
|
int noadjust = option_find_int_quiet(options, "noadjust",0);
|
|
crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure);
|
l.shift = option_find_float(options, "shift", 0);
|
l.noadjust = noadjust;
|
return l;
|
}
|
|
layer parse_reorg(list *options, size_params params)
|
{
|
int stride = option_find_int(options, "stride",1);
|
int reverse = option_find_int_quiet(options, "reverse",0);
|
|
int batch,h,w,c;
|
h = params.h;
|
w = params.w;
|
c = params.c;
|
batch=params.batch;
|
if(!(h && w && c)) error("Layer before reorg layer must output image.");
|
|
layer layer = make_reorg_layer(batch,w,h,c,stride,reverse);
|
return layer;
|
}
|
|
layer parse_reorg_old(list *options, size_params params)
|
{
|
printf("\n reorg_old \n");
|
int stride = option_find_int(options, "stride", 1);
|
int reverse = option_find_int_quiet(options, "reverse", 0);
|
|
int batch, h, w, c;
|
h = params.h;
|
w = params.w;
|
c = params.c;
|
batch = params.batch;
|
if (!(h && w && c)) error("Layer before reorg layer must output image.");
|
|
layer layer = make_reorg_old_layer(batch, w, h, c, stride, reverse);
|
return layer;
|
}
|
|
maxpool_layer parse_local_avgpool(list *options, size_params params)
|
{
|
int stride = option_find_int(options, "stride", 1);
|
int stride_x = option_find_int_quiet(options, "stride_x", stride);
|
int stride_y = option_find_int_quiet(options, "stride_y", stride);
|
int size = option_find_int(options, "size", stride);
|
int padding = option_find_int_quiet(options, "padding", size - 1);
|
int maxpool_depth = 0;
|
int out_channels = 1;
|
int antialiasing = 0;
|
const int avgpool = 1;
|
|
int batch, h, w, c;
|
h = params.h;
|
w = params.w;
|
c = params.c;
|
batch = params.batch;
|
if (!(h && w && c)) error("Layer before [local_avgpool] layer must output image.");
|
|
maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train);
|
return layer;
|
}
|
|
maxpool_layer parse_maxpool(list *options, size_params params)
|
{
|
int stride = option_find_int(options, "stride",1);
|
int stride_x = option_find_int_quiet(options, "stride_x", stride);
|
int stride_y = option_find_int_quiet(options, "stride_y", stride);
|
int size = option_find_int(options, "size",stride);
|
int padding = option_find_int_quiet(options, "padding", size-1);
|
int maxpool_depth = option_find_int_quiet(options, "maxpool_depth", 0);
|
int out_channels = option_find_int_quiet(options, "out_channels", 1);
|
int antialiasing = option_find_int_quiet(options, "antialiasing", 0);
|
const int avgpool = 0;
|
|
int batch,h,w,c;
|
h = params.h;
|
w = params.w;
|
c = params.c;
|
batch=params.batch;
|
if(!(h && w && c)) error("Layer before [maxpool] layer must output image.");
|
|
maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train);
|
return layer;
|
}
|
|
avgpool_layer parse_avgpool(list *options, size_params params)
|
{
|
int batch,w,h,c;
|
w = params.w;
|
h = params.h;
|
c = params.c;
|
batch=params.batch;
|
if(!(h && w && c)) error("Layer before avgpool layer must output image.");
|
|
avgpool_layer layer = make_avgpool_layer(batch,w,h,c);
|
return layer;
|
}
|
|
dropout_layer parse_dropout(list *options, size_params params)
|
{
|
float probability = option_find_float(options, "probability", .2);
|
int dropblock = option_find_int_quiet(options, "dropblock", 0);
|
float dropblock_size_rel = option_find_float_quiet(options, "dropblock_size_rel", 0);
|
int dropblock_size_abs = option_find_float_quiet(options, "dropblock_size_abs", 0);
|
if (dropblock_size_abs > params.w || dropblock_size_abs > params.h) {
|
printf(" [dropout] - dropblock_size_abs = %d that is bigger than layer size %d x %d \n", dropblock_size_abs, params.w, params.h);
|
dropblock_size_abs = min_val_cmp(params.w, params.h);
|
}
|
if (dropblock && !dropblock_size_rel && !dropblock_size_abs) {
|
printf(" [dropout] - None of the parameters (dropblock_size_rel or dropblock_size_abs) are set, will be used: dropblock_size_abs = 7 \n");
|
dropblock_size_abs = 7;
|
}
|
if (dropblock_size_rel && dropblock_size_abs) {
|
printf(" [dropout] - Both parameters are set, only the parameter will be used: dropblock_size_abs = %d \n", dropblock_size_abs);
|
dropblock_size_rel = 0;
|
}
|
dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability, dropblock, dropblock_size_rel, dropblock_size_abs, params.w, params.h, params.c);
|
layer.out_w = params.w;
|
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, params.train);
|
return l;
|
}
|
|
layer parse_shortcut(list *options, size_params params, network net)
|
{
|
char *activation_s = option_find_str(options, "activation", "linear");
|
ACTIVATION activation = get_activation(activation_s);
|
|
char *weights_type_str = option_find_str_quiet(options, "weights_type", "none");
|
WEIGHTS_TYPE_T weights_type = NO_WEIGHTS;
|
if(strcmp(weights_type_str, "per_feature") == 0 || strcmp(weights_type_str, "per_layer") == 0) weights_type = PER_FEATURE;
|
else if (strcmp(weights_type_str, "per_channel") == 0) weights_type = PER_CHANNEL;
|
else if (strcmp(weights_type_str, "none") != 0) {
|
printf("Error: Incorrect weights_type = %s \n Use one of: none, per_feature, per_channel \n", weights_type_str);
|
getchar();
|
exit(0);
|
}
|
|
char *weights_normalization_str = option_find_str_quiet(options, "weights_normalization", "none");
|
WEIGHTS_NORMALIZATION_T weights_normalization = NO_NORMALIZATION;
|
if (strcmp(weights_normalization_str, "relu") == 0 || strcmp(weights_normalization_str, "avg_relu") == 0) weights_normalization = RELU_NORMALIZATION;
|
else if (strcmp(weights_normalization_str, "softmax") == 0) weights_normalization = SOFTMAX_NORMALIZATION;
|
else if (strcmp(weights_type_str, "none") != 0) {
|
printf("Error: Incorrect weights_normalization = %s \n Use one of: none, relu, softmax \n", weights_normalization_str);
|
getchar();
|
exit(0);
|
}
|
|
char *l = option_find(options, "from");
|
int len = strlen(l);
|
if (!l) error("Route Layer must specify input layers: from = ...");
|
int n = 1;
|
int i;
|
for (i = 0; i < len; ++i) {
|
if (l[i] == ',') ++n;
|
}
|
|
int* layers = (int*)calloc(n, sizeof(int));
|
int* sizes = (int*)calloc(n, sizeof(int));
|
float **layers_output = (float **)calloc(n, sizeof(float *));
|
float **layers_delta = (float **)calloc(n, sizeof(float *));
|
float **layers_output_gpu = (float **)calloc(n, sizeof(float *));
|
float **layers_delta_gpu = (float **)calloc(n, sizeof(float *));
|
|
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] = params.net.layers[index].outputs;
|
layers_output[i] = params.net.layers[index].output;
|
layers_delta[i] = params.net.layers[index].delta;
|
}
|
|
#ifdef GPU
|
for (i = 0; i < n; ++i) {
|
layers_output_gpu[i] = params.net.layers[layers[i]].output_gpu;
|
layers_delta_gpu[i] = params.net.layers[layers[i]].delta_gpu;
|
}
|
#endif// GPU
|
|
layer s = make_shortcut_layer(params.batch, n, layers, sizes, params.w, params.h, params.c, layers_output, layers_delta,
|
layers_output_gpu, layers_delta_gpu, weights_type, weights_normalization, activation, params.train);
|
|
free(layers_output_gpu);
|
free(layers_delta_gpu);
|
|
for (i = 0; i < n; ++i) {
|
int index = layers[i];
|
assert(params.w == net.layers[index].out_w && params.h == net.layers[index].out_h);
|
|
if (params.w != net.layers[index].out_w || params.h != net.layers[index].out_h || params.c != net.layers[index].out_c)
|
fprintf(stderr, " (%4d x%4d x%4d) + (%4d x%4d x%4d) \n",
|
params.w, params.h, params.c, net.layers[index].out_w, net.layers[index].out_h, params.net.layers[index].out_c);
|
}
|
|
return s;
|
}
|
|
|
layer parse_scale_channels(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 scale_wh = option_find_int_quiet(options, "scale_wh", 0);
|
|
int batch = params.batch;
|
layer from = net.layers[index];
|
|
layer s = make_scale_channels_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, scale_wh);
|
|
char *activation_s = option_find_str_quiet(options, "activation", "linear");
|
ACTIVATION activation = get_activation(activation_s);
|
s.activation = activation;
|
if (activation == SWISH || activation == MISH) {
|
printf(" [scale_channels] layer doesn't support SWISH or MISH activations \n");
|
}
|
return s;
|
}
|
|
layer parse_sam(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_sam_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c);
|
|
char *activation_s = option_find_str_quiet(options, "activation", "linear");
|
ACTIVATION activation = get_activation(activation_s);
|
s.activation = activation;
|
if (activation == SWISH || activation == MISH) {
|
printf(" [sam] layer doesn't support SWISH or MISH activations \n");
|
}
|
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;
|
}
|
|
layer parse_upsample(list *options, size_params params, network net)
|
{
|
|
int stride = option_find_int(options, "stride", 2);
|
layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride);
|
l.scale = option_find_float_quiet(options, "scale", 1);
|
return l;
|
}
|
|
route_layer parse_route(list *options, size_params params)
|
{
|
char *l = option_find(options, "layers");
|
if(!l) error("Route Layer must specify input layers");
|
int len = strlen(l);
|
int n = 1;
|
int i;
|
for(i = 0; i < len; ++i){
|
if (l[i] == ',') ++n;
|
}
|
|
int* layers = (int*)xcalloc(n, sizeof(int));
|
int* sizes = (int*)xcalloc(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] = params.net.layers[index].outputs;
|
}
|
int batch = params.batch;
|
|
int groups = option_find_int_quiet(options, "groups", 1);
|
int group_id = option_find_int_quiet(options, "group_id", 0);
|
|
route_layer layer = make_route_layer(batch, n, layers, sizes, groups, group_id);
|
|
convolutional_layer first = params.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 = params.net.layers[index];
|
if(next.out_w == first.out_w && next.out_h == first.out_h){
|
layer.out_c += next.out_c;
|
}else{
|
fprintf(stderr, " The width and height of the input layers are different. \n");
|
layer.out_h = layer.out_w = layer.out_c = 0;
|
}
|
}
|
layer.out_c = layer.out_c / layer.groups;
|
|
layer.w = first.w;
|
layer.h = first.h;
|
layer.c = layer.out_c;
|
|
if (n > 3) fprintf(stderr, " \t ");
|
else if (n > 1) fprintf(stderr, " \t ");
|
else fprintf(stderr, " \t\t ");
|
|
fprintf(stderr, " ");
|
if (layer.groups > 1) fprintf(stderr, "%d/%d", layer.group_id, layer.groups);
|
else fprintf(stderr, " ");
|
fprintf(stderr, " -> %4d x%4d x%4d \n", layer.out_w, layer.out_h, layer.out_c);
|
|
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;
|
if (strcmp(s, "sgdr")==0) return SGDR;
|
fprintf(stderr, "Couldn't find policy %s, going with constant\n", s);
|
return CONSTANT;
|
}
|
|
void parse_net_options(list *options, network *net)
|
{
|
net->max_batches = option_find_int(options, "max_batches", 0);
|
net->batch = option_find_int(options, "batch",1);
|
net->learning_rate = option_find_float(options, "learning_rate", .001);
|
net->learning_rate_min = option_find_float_quiet(options, "learning_rate_min", .00001);
|
net->batches_per_cycle = option_find_int_quiet(options, "sgdr_cycle", net->max_batches);
|
net->batches_cycle_mult = option_find_int_quiet(options, "sgdr_mult", 2);
|
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->track = option_find_int_quiet(options, "track", 0);
|
net->augment_speed = option_find_int_quiet(options, "augment_speed", 2);
|
net->init_sequential_subdivisions = net->sequential_subdivisions = option_find_int_quiet(options, "sequential_subdivisions", subdivs);
|
if (net->sequential_subdivisions > subdivs) net->init_sequential_subdivisions = net->sequential_subdivisions = subdivs;
|
net->try_fix_nan = option_find_int_quiet(options, "try_fix_nan", 0);
|
net->batch /= subdivs;
|
net->batch *= net->time_steps;
|
net->subdivisions = subdivs;
|
|
*net->seen = 0;
|
*net->cur_iteration = 0;
|
net->loss_scale = option_find_float_quiet(options, "loss_scale", 1);
|
net->dynamic_minibatch = option_find_int_quiet(options, "dynamic_minibatch", 0);
|
net->optimized_memory = option_find_int_quiet(options, "optimized_memory", 0);
|
net->workspace_size_limit = (size_t)1024*1024 * option_find_float_quiet(options, "workspace_size_limit_MB", 1024); // 1024 MB by default
|
|
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", .000001);
|
}
|
|
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->flip = option_find_int_quiet(options, "flip", 1);
|
net->blur = option_find_int_quiet(options, "blur", 0);
|
net->gaussian_noise = option_find_int_quiet(options, "gaussian_noise", 0);
|
net->mixup = option_find_int_quiet(options, "mixup", 0);
|
int cutmix = option_find_int_quiet(options, "cutmix", 0);
|
int mosaic = option_find_int_quiet(options, "mosaic", 0);
|
if (mosaic && cutmix) net->mixup = 4;
|
else if (cutmix) net->mixup = 2;
|
else if (mosaic) net->mixup = 3;
|
net->letter_box = option_find_int_quiet(options, "letter_box", 0);
|
net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f);
|
net->resize_step = option_find_float_quiet(options, "resize_step", 32);
|
net->attention = option_find_int_quiet(options, "attention", 0);
|
net->adversarial_lr = option_find_float_quiet(options, "adversarial_lr", 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);
|
net->power = option_find_float_quiet(options, "power", 4);
|
|
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);
|
#ifdef GPU
|
if (net->gpu_index >= 0) {
|
char device_name[1024];
|
int compute_capability = get_gpu_compute_capability(net->gpu_index, device_name);
|
#ifdef CUDNN_HALF
|
if (compute_capability >= 700) net->cudnn_half = 1;
|
else net->cudnn_half = 0;
|
#endif// CUDNN_HALF
|
fprintf(stderr, " %d : compute_capability = %d, cudnn_half = %d, GPU: %s \n", net->gpu_index, compute_capability, net->cudnn_half, device_name);
|
}
|
else fprintf(stderr, " GPU isn't used \n");
|
#endif// GPU
|
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 || net->policy == SGDR){
|
char *l = option_find(options, "steps");
|
char *p = option_find(options, "scales");
|
char *s = option_find(options, "seq_scales");
|
if(net->policy == STEPS && (!l || !p)) error("STEPS policy must have steps and scales in cfg file");
|
|
if (l) {
|
int len = strlen(l);
|
int n = 1;
|
int i;
|
for (i = 0; i < len; ++i) {
|
if (l[i] == ',') ++n;
|
}
|
int* steps = (int*)xcalloc(n, sizeof(int));
|
float* scales = (float*)xcalloc(n, sizeof(float));
|
float* seq_scales = (float*)xcalloc(n, sizeof(float));
|
for (i = 0; i < n; ++i) {
|
float scale = 1.0;
|
if (p) {
|
scale = atof(p);
|
p = strchr(p, ',') + 1;
|
}
|
float sequence_scale = 1.0;
|
if (s) {
|
sequence_scale = atof(s);
|
s = strchr(s, ',') + 1;
|
}
|
int step = atoi(l);
|
l = strchr(l, ',') + 1;
|
steps[i] = step;
|
scales[i] = scale;
|
seq_scales[i] = sequence_scale;
|
}
|
net->scales = scales;
|
net->steps = steps;
|
net->seq_scales = seq_scales;
|
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->power = option_find_float(options, "power", 1);
|
}
|
|
}
|
|
int is_network(section *s)
|
{
|
return (strcmp(s->type, "[net]")==0
|
|| strcmp(s->type, "[network]")==0);
|
}
|
|
void set_train_only_bn(network net)
|
{
|
int train_only_bn = 0;
|
int i;
|
for (i = net.n - 1; i >= 0; --i) {
|
if (net.layers[i].train_only_bn) train_only_bn = net.layers[i].train_only_bn; // set l.train_only_bn for all previous layers
|
if (train_only_bn) {
|
net.layers[i].train_only_bn = train_only_bn;
|
|
if (net.layers[i].type == CONV_LSTM) {
|
net.layers[i].wf->train_only_bn = train_only_bn;
|
net.layers[i].wi->train_only_bn = train_only_bn;
|
net.layers[i].wg->train_only_bn = train_only_bn;
|
net.layers[i].wo->train_only_bn = train_only_bn;
|
net.layers[i].uf->train_only_bn = train_only_bn;
|
net.layers[i].ui->train_only_bn = train_only_bn;
|
net.layers[i].ug->train_only_bn = train_only_bn;
|
net.layers[i].uo->train_only_bn = train_only_bn;
|
if (net.layers[i].peephole) {
|
net.layers[i].vf->train_only_bn = train_only_bn;
|
net.layers[i].vi->train_only_bn = train_only_bn;
|
net.layers[i].vo->train_only_bn = train_only_bn;
|
}
|
}
|
else if (net.layers[i].type == CRNN) {
|
net.layers[i].input_layer->train_only_bn = train_only_bn;
|
net.layers[i].self_layer->train_only_bn = train_only_bn;
|
net.layers[i].output_layer->train_only_bn = train_only_bn;
|
}
|
}
|
}
|
}
|
|
network parse_network_cfg(char *filename)
|
{
|
return parse_network_cfg_custom(filename, 0, 0);
|
}
|
|
network parse_network_cfg_custom(char *filename, int batch, int time_steps)
|
{
|
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;
|
|
if (batch > 0) params.train = 0; // allocates memory for Detection only
|
else params.train = 1; // allocates memory for Detection & Training
|
|
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);
|
|
#ifdef GPU
|
printf("net.optimized_memory = %d \n", net.optimized_memory);
|
if (net.optimized_memory >= 2 && params.train) {
|
pre_allocate_pinned_memory((size_t)1024 * 1024 * 1024 * 8); // pre-allocate 8 GB CPU-RAM for pinned memory
|
}
|
#endif // GPU
|
|
params.h = net.h;
|
params.w = net.w;
|
params.c = net.c;
|
params.inputs = net.inputs;
|
if (batch > 0) net.batch = batch;
|
if (time_steps > 0) net.time_steps = time_steps;
|
if (net.batch < 1) net.batch = 1;
|
if (net.time_steps < 1) net.time_steps = 1;
|
if (net.batch < net.time_steps) net.batch = net.time_steps;
|
params.batch = net.batch;
|
params.time_steps = net.time_steps;
|
params.net = net;
|
printf("mini_batch = %d, batch = %d, time_steps = %d, train = %d \n", net.batch, net.batch * net.subdivisions, net.time_steps, params.train);
|
|
int avg_outputs = 0;
|
int avg_counter = 0;
|
float bflops = 0;
|
size_t workspace_size = 0;
|
size_t max_inputs = 0;
|
size_t max_outputs = 0;
|
int receptive_w = 1, receptive_h = 1;
|
int receptive_w_scale = 1, receptive_h_scale = 1;
|
const int show_receptive_field = option_find_float_quiet(options, "show_receptive_field", 0);
|
|
n = n->next;
|
int count = 0;
|
free_section(s);
|
fprintf(stderr, " layer filters size/strd(dil) input output\n");
|
while(n){
|
params.index = count;
|
fprintf(stderr, "%4d ", count);
|
s = (section *)n->val;
|
options = s->options;
|
layer l = { (LAYER_TYPE)0 };
|
LAYER_TYPE lt = string_to_layer_type(s->type);
|
if(lt == CONVOLUTIONAL){
|
l = parse_convolutional(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 == CONV_LSTM) {
|
l = parse_conv_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);
|
l.keep_delta_gpu = 1;
|
}else if(lt == REGION){
|
l = parse_region(options, params);
|
l.keep_delta_gpu = 1;
|
}else if (lt == YOLO) {
|
l = parse_yolo(options, params);
|
l.keep_delta_gpu = 1;
|
}else if (lt == GAUSSIAN_YOLO) {
|
l = parse_gaussian_yolo(options, params);
|
l.keep_delta_gpu = 1;
|
}else if(lt == DETECTION){
|
l = parse_detection(options, params);
|
}else if(lt == SOFTMAX){
|
l = parse_softmax(options, params);
|
net.hierarchy = l.softmax_tree;
|
l.keep_delta_gpu = 1;
|
}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 == LOCAL_AVGPOOL) {
|
l = parse_local_avgpool(options, params);
|
}else if(lt == REORG){
|
l = parse_reorg(options, params); }
|
else if (lt == REORG_OLD) {
|
l = parse_reorg_old(options, params);
|
}else if(lt == AVGPOOL){
|
l = parse_avgpool(options, params);
|
}else if(lt == ROUTE){
|
l = parse_route(options, params);
|
int k;
|
for (k = 0; k < l.n; ++k) {
|
net.layers[l.input_layers[k]].use_bin_output = 0;
|
net.layers[l.input_layers[k]].keep_delta_gpu = 1;
|
}
|
}else if (lt == UPSAMPLE) {
|
l = parse_upsample(options, params, net);
|
}else if(lt == SHORTCUT){
|
l = parse_shortcut(options, params, net);
|
net.layers[count - 1].use_bin_output = 0;
|
net.layers[l.index].use_bin_output = 0;
|
net.layers[l.index].keep_delta_gpu = 1;
|
}else if (lt == SCALE_CHANNELS) {
|
l = parse_scale_channels(options, params, net);
|
net.layers[count - 1].use_bin_output = 0;
|
net.layers[l.index].use_bin_output = 0;
|
net.layers[l.index].keep_delta_gpu = 1;
|
}
|
else if (lt == SAM) {
|
l = parse_sam(options, params, net);
|
net.layers[count - 1].use_bin_output = 0;
|
net.layers[l.index].use_bin_output = 0;
|
net.layers[l.index].keep_delta_gpu = 1;
|
}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;
|
l.keep_delta_gpu = 1;
|
#endif
|
}
|
else if (lt == EMPTY) {
|
layer empty_layer = {(LAYER_TYPE)0};
|
empty_layer.out_w = params.w;
|
empty_layer.out_h = params.h;
|
empty_layer.out_c = params.c;
|
l = empty_layer;
|
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);
|
}
|
|
// calculate receptive field
|
if(show_receptive_field)
|
{
|
int dilation = max_val_cmp(1, l.dilation);
|
int stride = max_val_cmp(1, l.stride);
|
int size = max_val_cmp(1, l.size);
|
|
if (l.type == UPSAMPLE || (l.type == REORG))
|
{
|
|
l.receptive_w = receptive_w;
|
l.receptive_h = receptive_h;
|
l.receptive_w_scale = receptive_w_scale = receptive_w_scale / stride;
|
l.receptive_h_scale = receptive_h_scale = receptive_h_scale / stride;
|
|
}
|
else {
|
if (l.type == ROUTE) {
|
receptive_w = receptive_h = receptive_w_scale = receptive_h_scale = 0;
|
int k;
|
for (k = 0; k < l.n; ++k) {
|
layer route_l = net.layers[l.input_layers[k]];
|
receptive_w = max_val_cmp(receptive_w, route_l.receptive_w);
|
receptive_h = max_val_cmp(receptive_h, route_l.receptive_h);
|
receptive_w_scale = max_val_cmp(receptive_w_scale, route_l.receptive_w_scale);
|
receptive_h_scale = max_val_cmp(receptive_h_scale, route_l.receptive_h_scale);
|
}
|
}
|
else
|
{
|
int increase_receptive = size + (dilation - 1) * 2 - 1;// stride;
|
increase_receptive = max_val_cmp(0, increase_receptive);
|
|
receptive_w += increase_receptive * receptive_w_scale;
|
receptive_h += increase_receptive * receptive_h_scale;
|
receptive_w_scale *= stride;
|
receptive_h_scale *= stride;
|
}
|
|
l.receptive_w = receptive_w;
|
l.receptive_h = receptive_h;
|
l.receptive_w_scale = receptive_w_scale;
|
l.receptive_h_scale = receptive_h_scale;
|
}
|
//printf(" size = %d, dilation = %d, stride = %d, receptive_w = %d, receptive_w_scale = %d - ", size, dilation, stride, receptive_w, receptive_w_scale);
|
|
int cur_receptive_w = receptive_w;
|
int cur_receptive_h = receptive_h;
|
|
fprintf(stderr, "%4d - receptive field: %d x %d \n", count, cur_receptive_w, cur_receptive_h);
|
}
|
|
#ifdef GPU
|
// futher GPU-memory optimization: net.optimized_memory == 2
|
if (net.optimized_memory >= 2 && params.train && l.type != DROPOUT)
|
{
|
l.optimized_memory = net.optimized_memory;
|
if (l.output_gpu) {
|
cuda_free(l.output_gpu);
|
//l.output_gpu = cuda_make_array_pinned(l.output, l.batch*l.outputs); // l.steps
|
l.output_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps
|
}
|
if (l.activation_input_gpu) {
|
cuda_free(l.activation_input_gpu);
|
l.activation_input_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps
|
}
|
|
if (l.x_gpu) {
|
cuda_free(l.x_gpu);
|
l.x_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps
|
}
|
|
// maximum optimization
|
if (net.optimized_memory >= 3 && l.type != DROPOUT) {
|
if (l.delta_gpu) {
|
cuda_free(l.delta_gpu);
|
//l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps
|
//printf("\n\n PINNED DELTA GPU = %d \n", l.batch*l.outputs);
|
}
|
}
|
|
if (l.type == CONVOLUTIONAL) {
|
set_specified_workspace_limit(&l, net.workspace_size_limit); // workspace size limit 1 GB
|
}
|
}
|
#endif // GPU
|
|
l.clip = option_find_float_quiet(options, "clip", 0);
|
l.dynamic_minibatch = net.dynamic_minibatch;
|
l.onlyforward = option_find_int_quiet(options, "onlyforward", 0);
|
l.dont_update = option_find_int_quiet(options, "dont_update", 0);
|
l.burnin_update = option_find_int_quiet(options, "burnin_update", 0);
|
l.stopbackward = option_find_int_quiet(options, "stopbackward", 0);
|
l.train_only_bn = option_find_int_quiet(options, "train_only_bn", 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);
|
option_unused(options);
|
net.layers[count] = l;
|
if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
|
if (l.inputs > max_inputs) max_inputs = l.inputs;
|
if (l.outputs > max_outputs) max_outputs = l.outputs;
|
free_section(s);
|
n = n->next;
|
++count;
|
if(n){
|
if (l.antialiasing) {
|
params.h = l.input_layer->out_h;
|
params.w = l.input_layer->out_w;
|
params.c = l.input_layer->out_c;
|
params.inputs = l.input_layer->outputs;
|
}
|
else {
|
params.h = l.out_h;
|
params.w = l.out_w;
|
params.c = l.out_c;
|
params.inputs = l.outputs;
|
}
|
}
|
if (l.bflops > 0) bflops += l.bflops;
|
|
if (l.w > 1 && l.h > 1) {
|
avg_outputs += l.outputs;
|
avg_counter++;
|
}
|
}
|
free_list(sections);
|
|
#ifdef GPU
|
if (net.optimized_memory && params.train)
|
{
|
int k;
|
for (k = 0; k < net.n; ++k) {
|
layer l = net.layers[k];
|
// delta GPU-memory optimization: net.optimized_memory == 1
|
if (!l.keep_delta_gpu) {
|
const size_t delta_size = l.outputs*l.batch; // l.steps
|
if (net.max_delta_gpu_size < delta_size) {
|
net.max_delta_gpu_size = delta_size;
|
if (net.global_delta_gpu) cuda_free(net.global_delta_gpu);
|
if (net.state_delta_gpu) cuda_free(net.state_delta_gpu);
|
assert(net.max_delta_gpu_size > 0);
|
net.global_delta_gpu = (float *)cuda_make_array(NULL, net.max_delta_gpu_size);
|
net.state_delta_gpu = (float *)cuda_make_array(NULL, net.max_delta_gpu_size);
|
}
|
if (l.delta_gpu) {
|
if (net.optimized_memory >= 3) {}
|
else cuda_free(l.delta_gpu);
|
}
|
l.delta_gpu = net.global_delta_gpu;
|
}
|
|
// maximum optimization
|
if (net.optimized_memory >= 3 && l.type != DROPOUT) {
|
if (l.delta_gpu && l.keep_delta_gpu) {
|
//cuda_free(l.delta_gpu); // already called above
|
l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps
|
//printf("\n\n PINNED DELTA GPU = %d \n", l.batch*l.outputs);
|
}
|
}
|
|
net.layers[k] = l;
|
}
|
}
|
#endif
|
|
set_train_only_bn(net); // set l.train_only_bn for all required layers
|
|
net.outputs = get_network_output_size(net);
|
net.output = get_network_output(net);
|
avg_outputs = avg_outputs / avg_counter;
|
fprintf(stderr, "Total BFLOPS %5.3f \n", bflops);
|
fprintf(stderr, "avg_outputs = %d \n", avg_outputs);
|
#ifdef GPU
|
get_cuda_stream();
|
get_cuda_memcpy_stream();
|
if (gpu_index >= 0)
|
{
|
int size = get_network_input_size(net) * net.batch;
|
net.input_state_gpu = cuda_make_array(0, size);
|
if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;
|
else {
|
cudaGetLastError(); // reset CUDA-error
|
net.input_pinned_cpu = (float*)xcalloc(size, sizeof(float));
|
}
|
|
// pre-allocate memory for inference on Tensor Cores (fp16)
|
if (net.cudnn_half) {
|
*net.max_input16_size = max_inputs;
|
CHECK_CUDA(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half)
|
*net.max_output16_size = max_outputs;
|
CHECK_CUDA(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half)
|
}
|
if (workspace_size) {
|
fprintf(stderr, " Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size/1000000);
|
net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1);
|
}
|
else {
|
net.workspace = (float*)xcalloc(1, workspace_size);
|
}
|
}
|
#else
|
if (workspace_size) {
|
net.workspace = (float*)xcalloc(1, workspace_size);
|
}
|
#endif
|
|
LAYER_TYPE lt = net.layers[net.n - 1].type;
|
if ((net.w % 32 != 0 || net.h % 32 != 0) && (lt == YOLO || lt == REGION || lt == DETECTION)) {
|
printf("\n Warning: width=%d and height=%d in cfg-file must be divisible by 32 for default networks Yolo v1/v2/v3!!! \n\n",
|
net.w, net.h);
|
}
|
return net;
|
}
|
|
|
|
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 = (section*)xmalloc(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_convolutional_weights_binary(layer l, FILE *fp)
|
{
|
#ifdef GPU
|
if(gpu_index >= 0){
|
pull_convolutional_layer(l);
|
}
|
#endif
|
int size = (l.c/l.groups)*l.size*l.size;
|
binarize_weights(l.weights, l.n, size, l.binary_weights);
|
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_shortcut_weights(layer l, FILE *fp)
|
{
|
#ifdef GPU
|
if (gpu_index >= 0) {
|
pull_shortcut_layer(l);
|
printf("\n pull_shortcut_layer \n");
|
}
|
#endif
|
int i;
|
for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weight_updates[i]);
|
printf(" l.nweights = %d - update \n", l.nweights);
|
for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]);
|
printf(" l.nweights = %d \n\n", l.nweights);
|
|
int num = l.nweights;
|
fwrite(l.weights, sizeof(float), num, 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);
|
//if(l.adam){
|
// fwrite(l.m, sizeof(float), num, fp);
|
// fwrite(l.v, 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.biases, sizeof(float), l.c, fp);
|
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 = MAJOR_VERSION;
|
int minor = MINOR_VERSION;
|
int revision = PATCH_VERSION;
|
fwrite(&major, sizeof(int), 1, fp);
|
fwrite(&minor, sizeof(int), 1, fp);
|
fwrite(&revision, sizeof(int), 1, fp);
|
(*net.seen) = get_current_iteration(net) * net.batch * net.subdivisions; // remove this line, when you will save to weights-file both: seen & cur_iteration
|
fwrite(net.seen, sizeof(uint64_t), 1, fp);
|
|
int i;
|
for(i = 0; i < net.n && i < cutoff; ++i){
|
layer l = net.layers[i];
|
if (l.type == CONVOLUTIONAL && l.share_layer == NULL) {
|
save_convolutional_weights(l, fp);
|
} if (l.type == SHORTCUT && l.nweights > 0) {
|
save_shortcut_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 == GRU){
|
save_connected_weights(*(l.input_z_layer), fp);
|
save_connected_weights(*(l.input_r_layer), fp);
|
save_connected_weights(*(l.input_h_layer), fp);
|
save_connected_weights(*(l.state_z_layer), fp);
|
save_connected_weights(*(l.state_r_layer), fp);
|
save_connected_weights(*(l.state_h_layer), fp);
|
} if(l.type == LSTM){
|
save_connected_weights(*(l.wf), fp);
|
save_connected_weights(*(l.wi), fp);
|
save_connected_weights(*(l.wg), fp);
|
save_connected_weights(*(l.wo), fp);
|
save_connected_weights(*(l.uf), fp);
|
save_connected_weights(*(l.ui), fp);
|
save_connected_weights(*(l.ug), fp);
|
save_connected_weights(*(l.uo), fp);
|
} if (l.type == CONV_LSTM) {
|
if (l.peephole) {
|
save_convolutional_weights(*(l.vf), fp);
|
save_convolutional_weights(*(l.vi), fp);
|
save_convolutional_weights(*(l.vo), fp);
|
}
|
save_convolutional_weights(*(l.wf), fp);
|
save_convolutional_weights(*(l.wi), fp);
|
save_convolutional_weights(*(l.wg), fp);
|
save_convolutional_weights(*(l.wo), fp);
|
save_convolutional_weights(*(l.uf), fp);
|
save_convolutional_weights(*(l.ui), fp);
|
save_convolutional_weights(*(l.ug), fp);
|
save_convolutional_weights(*(l.uo), 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 = (float*)xcalloc(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.biases, sizeof(float), l.c, 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.groups)*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;
|
int read_bytes;
|
read_bytes = fread(l.biases, sizeof(float), l.n, fp);
|
if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.biases - l.index = %d \n", l.index);
|
//fread(l.weights, sizeof(float), num, fp); // as in connected layer
|
if (l.batch_normalize && (!l.dontloadscales)){
|
read_bytes = fread(l.scales, sizeof(float), l.n, fp);
|
if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.scales - l.index = %d \n", l.index);
|
read_bytes = fread(l.rolling_mean, sizeof(float), l.n, fp);
|
if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_mean - l.index = %d \n", l.index);
|
read_bytes = fread(l.rolling_variance, sizeof(float), l.n, fp);
|
if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_variance - l.index = %d \n", l.index);
|
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);
|
}
|
}
|
read_bytes = fread(l.weights, sizeof(float), num, fp);
|
if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index);
|
//if(l.adam){
|
// fread(l.m, sizeof(float), num, fp);
|
// fread(l.v, 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.groups)*l.size*l.size, l.n);
|
}
|
//if (l.binary) binarize_weights(l.weights, l.n, (l.c/l.groups)*l.size*l.size, l.weights);
|
#ifdef GPU
|
if(gpu_index >= 0){
|
push_convolutional_layer(l);
|
}
|
#endif
|
}
|
|
void load_shortcut_weights(layer l, FILE *fp)
|
{
|
int num = l.nweights;
|
int read_bytes;
|
read_bytes = fread(l.weights, sizeof(float), num, fp);
|
if (read_bytes > 0 && read_bytes < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index);
|
//for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]);
|
//printf(" read_bytes = %d \n\n", read_bytes);
|
#ifdef GPU
|
if (gpu_index >= 0) {
|
push_shortcut_layer(l);
|
}
|
#endif
|
}
|
|
void load_weights_upto(network *net, char *filename, 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) {
|
printf("\n seen 64");
|
uint64_t iseen = 0;
|
fread(&iseen, sizeof(uint64_t), 1, fp);
|
*net->seen = iseen;
|
}
|
else {
|
printf("\n seen 32");
|
uint32_t iseen = 0;
|
fread(&iseen, sizeof(uint32_t), 1, fp);
|
*net->seen = iseen;
|
}
|
*net->cur_iteration = get_current_batch(*net);
|
printf(", trained: %.0f K-images (%.0f Kilo-batches_64) \n", (float)(*net->seen / 1000), (float)(*net->seen / 64000));
|
int transpose = (major > 1000) || (minor > 1000);
|
|
int i;
|
for(i = 0; i < net->n && i < cutoff; ++i){
|
layer l = net->layers[i];
|
if (l.dontload) continue;
|
if(l.type == CONVOLUTIONAL && l.share_layer == NULL){
|
load_convolutional_weights(l, fp);
|
}
|
if (l.type == SHORTCUT && l.nweights > 0) {
|
load_shortcut_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 == GRU){
|
load_connected_weights(*(l.input_z_layer), fp, transpose);
|
load_connected_weights(*(l.input_r_layer), fp, transpose);
|
load_connected_weights(*(l.input_h_layer), fp, transpose);
|
load_connected_weights(*(l.state_z_layer), fp, transpose);
|
load_connected_weights(*(l.state_r_layer), fp, transpose);
|
load_connected_weights(*(l.state_h_layer), fp, transpose);
|
}
|
if(l.type == LSTM){
|
load_connected_weights(*(l.wf), fp, transpose);
|
load_connected_weights(*(l.wi), fp, transpose);
|
load_connected_weights(*(l.wg), fp, transpose);
|
load_connected_weights(*(l.wo), fp, transpose);
|
load_connected_weights(*(l.uf), fp, transpose);
|
load_connected_weights(*(l.ui), fp, transpose);
|
load_connected_weights(*(l.ug), fp, transpose);
|
load_connected_weights(*(l.uo), fp, transpose);
|
}
|
if (l.type == CONV_LSTM) {
|
if (l.peephole) {
|
load_convolutional_weights(*(l.vf), fp);
|
load_convolutional_weights(*(l.vi), fp);
|
load_convolutional_weights(*(l.vo), fp);
|
}
|
load_convolutional_weights(*(l.wf), fp);
|
load_convolutional_weights(*(l.wi), fp);
|
load_convolutional_weights(*(l.wg), fp);
|
load_convolutional_weights(*(l.wo), fp);
|
load_convolutional_weights(*(l.uf), fp);
|
load_convolutional_weights(*(l.ui), fp);
|
load_convolutional_weights(*(l.ug), fp);
|
load_convolutional_weights(*(l.uo), fp);
|
}
|
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
|
}
|
if (feof(fp)) break;
|
}
|
fprintf(stderr, "Done! Loaded %d layers from weights-file \n", i);
|
fclose(fp);
|
}
|
|
void load_weights(network *net, char *filename)
|
{
|
load_weights_upto(net, filename, net->n);
|
}
|
|
// load network & force - set batch size
|
network *load_network_custom(char *cfg, char *weights, int clear, int batch)
|
{
|
printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear);
|
network* net = (network*)xcalloc(1, sizeof(network));
|
*net = parse_network_cfg_custom(cfg, batch, 1);
|
if (weights && weights[0] != 0) {
|
printf(" Try to load weights: %s \n", weights);
|
load_weights(net, weights);
|
}
|
fuse_conv_batchnorm(*net);
|
if (clear) {
|
(*net->seen) = 0;
|
(*net->cur_iteration) = 0;
|
}
|
return net;
|
}
|
|
// load network & get batch size from cfg-file
|
network *load_network(char *cfg, char *weights, int clear)
|
{
|
printf(" Try to load cfg: %s, clear = %d \n", cfg, clear);
|
network* net = (network*)xcalloc(1, sizeof(network));
|
*net = parse_network_cfg(cfg);
|
if (weights && weights[0] != 0) {
|
printf(" Try to load weights: %s \n", weights);
|
load_weights(net, weights);
|
}
|
if (clear) {
|
(*net->seen) = 0;
|
(*net->cur_iteration) = 0;
|
}
|
return net;
|
}
|
