From eadf8170a676cedc6ab4f1928567a1ed3cbee050 Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期二, 06 七月 2021 10:15:06 +0800
Subject: [PATCH] up cuda
---
lib/detecter_tools/darknet/parser.c | 4492 +++++++++++++++++++++++++++++++----------------------------
1 files changed, 2,352 insertions(+), 2,140 deletions(-)
diff --git a/lib/detecter_tools/darknet/parser.c b/lib/detecter_tools/darknet/parser.c
index 0572bef..8f8f584 100644
--- a/lib/detecter_tools/darknet/parser.c
+++ b/lib/detecter_tools/darknet/parser.c
@@ -1,2140 +1,2352 @@
-#include <stdio.h>
-#include <string.h>
-#include <stdlib.h>
-#include <stdint.h>
-
-#include "activation_layer.h"
-#include "activations.h"
-#include "assert.h"
-#include "avgpool_layer.h"
-#include "batchnorm_layer.h"
-#include "blas.h"
-#include "connected_layer.h"
-#include "convolutional_layer.h"
-#include "cost_layer.h"
-#include "crnn_layer.h"
-#include "crop_layer.h"
-#include "detection_layer.h"
-#include "dropout_layer.h"
-#include "gru_layer.h"
-#include "list.h"
-#include "local_layer.h"
-#include "lstm_layer.h"
-#include "conv_lstm_layer.h"
-#include "maxpool_layer.h"
-#include "normalization_layer.h"
-#include "option_list.h"
-#include "parser.h"
-#include "region_layer.h"
-#include "reorg_layer.h"
-#include "reorg_old_layer.h"
-#include "rnn_layer.h"
-#include "route_layer.h"
-#include "shortcut_layer.h"
-#include "scale_channels_layer.h"
-#include "sam_layer.h"
-#include "softmax_layer.h"
-#include "utils.h"
-#include "upsample_layer.h"
-#include "version.h"
-#include "yolo_layer.h"
-#include "gaussian_yolo_layer.h"
-
-typedef struct{
- char *type;
- list *options;
-}section;
-
-list *read_cfg(char *filename);
-
-LAYER_TYPE string_to_layer_type(char * type)
-{
-
- if (strcmp(type, "[shortcut]")==0) return SHORTCUT;
- if (strcmp(type, "[scale_channels]") == 0) return SCALE_CHANNELS;
- if (strcmp(type, "[sam]") == 0) return SAM;
- if (strcmp(type, "[crop]")==0) return CROP;
- if (strcmp(type, "[cost]")==0) return COST;
- if (strcmp(type, "[detection]")==0) return DETECTION;
- if (strcmp(type, "[region]")==0) return REGION;
- if (strcmp(type, "[yolo]") == 0) return YOLO;
- if (strcmp(type, "[Gaussian_yolo]") == 0) return GAUSSIAN_YOLO;
- if (strcmp(type, "[local]")==0) return LOCAL;
- if (strcmp(type, "[conv]")==0
- || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL;
- if (strcmp(type, "[activation]")==0) return ACTIVE;
- if (strcmp(type, "[net]")==0
- || strcmp(type, "[network]")==0) return NETWORK;
- if (strcmp(type, "[crnn]")==0) return CRNN;
- if (strcmp(type, "[gru]")==0) return GRU;
- if (strcmp(type, "[lstm]")==0) return LSTM;
- if (strcmp(type, "[conv_lstm]") == 0) return CONV_LSTM;
- if (strcmp(type, "[rnn]")==0) return RNN;
- if (strcmp(type, "[conn]")==0
- || strcmp(type, "[connected]")==0) return CONNECTED;
- if (strcmp(type, "[max]")==0
- || strcmp(type, "[maxpool]")==0) return MAXPOOL;
- if (strcmp(type, "[local_avg]") == 0
- || strcmp(type, "[local_avgpool]") == 0) return LOCAL_AVGPOOL;
- if (strcmp(type, "[reorg3d]")==0) return REORG;
- if (strcmp(type, "[reorg]") == 0) return REORG_OLD;
- if (strcmp(type, "[avg]")==0
- || strcmp(type, "[avgpool]")==0) return AVGPOOL;
- if (strcmp(type, "[dropout]")==0) return DROPOUT;
- if (strcmp(type, "[lrn]")==0
- || strcmp(type, "[normalization]")==0) return NORMALIZATION;
- if (strcmp(type, "[batchnorm]")==0) return BATCHNORM;
- if (strcmp(type, "[soft]")==0
- || strcmp(type, "[softmax]")==0) return SOFTMAX;
- if (strcmp(type, "[route]")==0) return ROUTE;
- if (strcmp(type, "[upsample]") == 0) return UPSAMPLE;
- if (strcmp(type, "[empty]") == 0) return EMPTY;
- return BLANK;
-}
-
-void free_section(section *s)
-{
- free(s->type);
- node *n = s->options->front;
- while(n){
- kvp *pair = (kvp *)n->val;
- free(pair->key);
- free(pair);
- node *next = n->next;
- free(n);
- n = next;
- }
- free(s->options);
- free(s);
-}
-
-void parse_data(char *data, float *a, int n)
-{
- int i;
- if(!data) return;
- char *curr = data;
- char *next = data;
- int done = 0;
- for(i = 0; i < n && !done; ++i){
- while(*++next !='\0' && *next != ',');
- if(*next == '\0') done = 1;
- *next = '\0';
- sscanf(curr, "%g", &a[i]);
- curr = next+1;
- }
-}
-
-typedef struct size_params{
- int batch;
- int inputs;
- int h;
- int w;
- int c;
- int index;
- int time_steps;
- int train;
- network net;
-} size_params;
-
-local_layer parse_local(list *options, size_params params)
-{
- int n = option_find_int(options, "filters",1);
- int size = option_find_int(options, "size",1);
- int stride = option_find_int(options, "stride",1);
- int pad = option_find_int(options, "pad",0);
- char *activation_s = option_find_str(options, "activation", "logistic");
- ACTIVATION activation = get_activation(activation_s);
-
- 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 layer must output image.");
-
- local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation);
-
- return layer;
-}
-
-convolutional_layer parse_convolutional(list *options, size_params params)
-{
- int n = option_find_int(options, "filters",1);
- int groups = option_find_int_quiet(options, "groups", 1);
- int size = option_find_int(options, "size",1);
- int stride = -1;
- //int stride = option_find_int(options, "stride",1);
- int stride_x = option_find_int_quiet(options, "stride_x", -1);
- int stride_y = option_find_int_quiet(options, "stride_y", -1);
- if (stride_x < 1 || stride_y < 1) {
- stride = option_find_int(options, "stride", 1);
- if (stride_x < 1) stride_x = stride;
- if (stride_y < 1) stride_y = stride;
- }
- else {
- stride = option_find_int_quiet(options, "stride", 1);
- }
- int dilation = option_find_int_quiet(options, "dilation", 1);
- int antialiasing = option_find_int_quiet(options, "antialiasing", 0);
- if (size == 1) dilation = 1;
- int pad = option_find_int_quiet(options, "pad",0);
- int padding = option_find_int_quiet(options, "padding",0);
- if(pad) padding = size/2;
-
- char *activation_s = option_find_str(options, "activation", "logistic");
- ACTIVATION activation = get_activation(activation_s);
-
- int assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0);
-
- int share_index = option_find_int_quiet(options, "share_index", -1000000000);
- convolutional_layer *share_layer = NULL;
- if(share_index >= 0) share_layer = ¶ms.net.layers[share_index];
- else if(share_index != -1000000000) share_layer = ¶ms.net.layers[params.index + share_index];
-
- int batch,h,w,c;
- h = params.h;
- w = params.w;
- c = params.c;
- batch=params.batch;
- if(!(h && w && c)) error("Layer before convolutional layer must output image.");
- int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
- int cbn = option_find_int_quiet(options, "cbn", 0);
- if (cbn) batch_normalize = 2;
- int binary = option_find_int_quiet(options, "binary", 0);
- int xnor = option_find_int_quiet(options, "xnor", 0);
- int use_bin_output = option_find_int_quiet(options, "bin_output", 0);
- int sway = option_find_int_quiet(options, "sway", 0);
- int rotate = option_find_int_quiet(options, "rotate", 0);
- int stretch = option_find_int_quiet(options, "stretch", 0);
- int stretch_sway = option_find_int_quiet(options, "stretch_sway", 0);
- if ((sway + rotate + stretch + stretch_sway) > 1) {
- printf(" Error: should be used only 1 param: sway=1, rotate=1 or stretch=1 in the [convolutional] layer \n");
- exit(0);
- }
- int deform = sway || rotate || stretch || stretch_sway;
- if (deform && size == 1) {
- printf(" Error: params (sway=1, rotate=1 or stretch=1) should be used only with size >=3 in the [convolutional] layer \n");
- exit(0);
- }
-
- 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);
- layer.flipped = option_find_int_quiet(options, "flipped", 0);
- layer.dot = option_find_float_quiet(options, "dot", 0);
- layer.sway = sway;
- layer.rotate = rotate;
- layer.stretch = stretch;
- layer.stretch_sway = stretch_sway;
- layer.angle = option_find_float_quiet(options, "angle", 15);
- layer.grad_centr = option_find_int_quiet(options, "grad_centr", 0);
-
- if(params.net.adam){
- layer.B1 = params.net.B1;
- layer.B2 = params.net.B2;
- layer.eps = params.net.eps;
- }
-
- return layer;
-}
-
-layer parse_crnn(list *options, size_params params)
-{
- int size = option_find_int_quiet(options, "size", 3);
- int stride = option_find_int_quiet(options, "stride", 1);
- int dilation = option_find_int_quiet(options, "dilation", 1);
- int pad = option_find_int_quiet(options, "pad", 0);
- int padding = option_find_int_quiet(options, "padding", 0);
- if (pad) padding = size / 2;
-
- int output_filters = option_find_int(options, "output",1);
- int hidden_filters = option_find_int(options, "hidden",1);
- int groups = option_find_int_quiet(options, "groups", 1);
- char *activation_s = option_find_str(options, "activation", "logistic");
- ACTIVATION activation = get_activation(activation_s);
- int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
- int xnor = option_find_int_quiet(options, "xnor", 0);
-
- 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);
-
- l.shortcut = option_find_int_quiet(options, "shortcut", 0);
-
- return l;
-}
-
-layer parse_rnn(list *options, size_params params)
-{
- int output = option_find_int(options, "output",1);
- int hidden = option_find_int(options, "hidden",1);
- char *activation_s = option_find_str(options, "activation", "logistic");
- ACTIVATION activation = get_activation(activation_s);
- int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
- int logistic = option_find_int_quiet(options, "logistic", 0);
-
- layer l = make_rnn_layer(params.batch, params.inputs, hidden, output, params.time_steps, activation, batch_normalize, logistic);
-
- l.shortcut = option_find_int_quiet(options, "shortcut", 0);
-
- return l;
-}
-
-layer parse_gru(list *options, size_params params)
-{
- int output = option_find_int(options, "output",1);
- int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
-
- layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize);
-
- return l;
-}
-
-layer parse_lstm(list *options, size_params params)
-{
- int output = option_find_int(options, "output",1);
- int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
-
- layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize);
-
- return l;
-}
-
-layer parse_conv_lstm(list *options, size_params params)
-{
- // a ConvLSTM with a larger transitional kernel should be able to capture faster motions
- int size = option_find_int_quiet(options, "size", 3);
- int stride = option_find_int_quiet(options, "stride", 1);
- int dilation = option_find_int_quiet(options, "dilation", 1);
- int pad = option_find_int_quiet(options, "pad", 0);
- int padding = option_find_int_quiet(options, "padding", 0);
- if (pad) padding = size / 2;
-
- int output_filters = option_find_int(options, "output", 1);
- int groups = option_find_int_quiet(options, "groups", 1);
- char *activation_s = option_find_str(options, "activation", "LINEAR");
- ACTIVATION activation = get_activation(activation_s);
- int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
- int xnor = option_find_int_quiet(options, "xnor", 0);
- int peephole = option_find_int_quiet(options, "peephole", 0);
-
- 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);
-
- l.state_constrain = option_find_int_quiet(options, "state_constrain", params.time_steps * 32);
- l.shortcut = option_find_int_quiet(options, "shortcut", 0);
-
- return l;
-}
-
-connected_layer parse_connected(list *options, size_params params)
-{
- int output = option_find_int(options, "output",1);
- char *activation_s = option_find_str(options, "activation", "logistic");
- ACTIVATION activation = get_activation(activation_s);
- int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
-
- connected_layer layer = make_connected_layer(params.batch, 1, params.inputs, output, activation, batch_normalize);
-
- return layer;
-}
-
-softmax_layer parse_softmax(list *options, size_params params)
-{
- int groups = option_find_int_quiet(options, "groups", 1);
- softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups);
- layer.temperature = option_find_float_quiet(options, "temperature", 1);
- char *tree_file = option_find_str(options, "tree", 0);
- if (tree_file) layer.softmax_tree = read_tree(tree_file);
- layer.w = params.w;
- layer.h = params.h;
- layer.c = params.c;
- layer.spatial = option_find_float_quiet(options, "spatial", 0);
- layer.noloss = option_find_int_quiet(options, "noloss", 0);
- return layer;
-}
-
-int *parse_yolo_mask(char *a, int *num)
-{
- 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*)xcalloc(n, sizeof(int));
- for (i = 0; i < n; ++i) {
- int val = atoi(a);
- mask[i] = val;
- a = strchr(a, ',') + 1;
- }
- *num = n;
- }
- return mask;
-}
-
-float *get_classes_multipliers(char *cpc, const int classes)
-{
- float *classes_multipliers = NULL;
- if (cpc) {
- int classes_counters = classes;
- int *counters_per_class = parse_yolo_mask(cpc, &classes_counters);
- if (classes_counters != classes) {
- printf(" number of values in counters_per_class = %d doesn't match with classes = %d \n", classes_counters, classes);
- exit(0);
- }
- float max_counter = 0;
- int i;
- for (i = 0; i < classes_counters; ++i) if (max_counter < counters_per_class[i]) max_counter = counters_per_class[i];
- classes_multipliers = (float *)calloc(classes_counters, sizeof(float));
- for (i = 0; i < classes_counters; ++i) classes_multipliers[i] = max_counter / counters_per_class[i];
- free(counters_per_class);
- printf(" classes_multipliers: ");
- for (i = 0; i < classes_counters; ++i) printf("%.1f, ", classes_multipliers[i]);
- printf("\n");
- }
- return classes_multipliers;
-}
-
-layer parse_yolo(list *options, size_params params)
-{
- int classes = option_find_int(options, "classes", 20);
- int total = option_find_int(options, "num", 1);
- int num = total;
- char *a = option_find_str(options, "mask", 0);
- int *mask = parse_yolo_mask(a, &num);
- int max_boxes = option_find_int_quiet(options, "max", 90);
- layer l = make_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 [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.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75);
- l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1);
- 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;
- fprintf(stderr, "[yolo] params: iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale_x_y: %2.2f\n",
- iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y);
-
- 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 l.nms_kind = DEFAULT_NMS;
- printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms);
- }
-
- l.jitter = option_find_float(options, "jitter", .2);
- l.resize = option_find_float_quiet(options, "resize", 1.0);
- l.focal_loss = option_find_int_quiet(options, "focal_loss", 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 < total*2; ++i) {
- float bias = atof(a);
- 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;
-}
+#include <stdio.h>
+#include <string.h>
+#include <stdlib.h>
+#include <stdint.h>
+
+#include "activation_layer.h"
+#include "activations.h"
+#include "assert.h"
+#include "avgpool_layer.h"
+#include "batchnorm_layer.h"
+#include "blas.h"
+#include "connected_layer.h"
+#include "convolutional_layer.h"
+#include "cost_layer.h"
+#include "crnn_layer.h"
+#include "crop_layer.h"
+#include "detection_layer.h"
+#include "dropout_layer.h"
+#include "gru_layer.h"
+#include "list.h"
+#include "local_layer.h"
+#include "lstm_layer.h"
+#include "conv_lstm_layer.h"
+#include "maxpool_layer.h"
+#include "normalization_layer.h"
+#include "option_list.h"
+#include "parser.h"
+#include "region_layer.h"
+#include "reorg_layer.h"
+#include "reorg_old_layer.h"
+#include "rnn_layer.h"
+#include "route_layer.h"
+#include "shortcut_layer.h"
+#include "scale_channels_layer.h"
+#include "sam_layer.h"
+#include "softmax_layer.h"
+#include "utils.h"
+#include "upsample_layer.h"
+#include "version.h"
+#include "yolo_layer.h"
+#include "gaussian_yolo_layer.h"
+#include "representation_layer.h"
+
+void empty_func(dropout_layer l, network_state state) {
+ //l.output_gpu = state.input;
+}
+
+typedef struct{
+ char *type;
+ list *options;
+}section;
+
+list *read_cfg(char *filename);
+
+LAYER_TYPE string_to_layer_type(char * type)
+{
+
+ if (strcmp(type, "[shortcut]")==0) return SHORTCUT;
+ if (strcmp(type, "[scale_channels]") == 0) return SCALE_CHANNELS;
+ if (strcmp(type, "[sam]") == 0) return SAM;
+ if (strcmp(type, "[crop]")==0) return CROP;
+ if (strcmp(type, "[cost]")==0) return COST;
+ if (strcmp(type, "[detection]")==0) return DETECTION;
+ if (strcmp(type, "[region]")==0) return REGION;
+ if (strcmp(type, "[yolo]") == 0) return YOLO;
+ if (strcmp(type, "[Gaussian_yolo]") == 0) return GAUSSIAN_YOLO;
+ if (strcmp(type, "[local]")==0) return LOCAL;
+ if (strcmp(type, "[conv]")==0
+ || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL;
+ if (strcmp(type, "[activation]")==0) return ACTIVE;
+ if (strcmp(type, "[net]")==0
+ || strcmp(type, "[network]")==0) return NETWORK;
+ if (strcmp(type, "[crnn]")==0) return CRNN;
+ if (strcmp(type, "[gru]")==0) return GRU;
+ if (strcmp(type, "[lstm]")==0) return LSTM;
+ if (strcmp(type, "[conv_lstm]") == 0) return CONV_LSTM;
+ if (strcmp(type, "[history]") == 0) return HISTORY;
+ if (strcmp(type, "[rnn]")==0) return RNN;
+ if (strcmp(type, "[conn]")==0
+ || strcmp(type, "[connected]")==0) return CONNECTED;
+ if (strcmp(type, "[max]")==0
+ || strcmp(type, "[maxpool]")==0) return MAXPOOL;
+ if (strcmp(type, "[local_avg]") == 0
+ || strcmp(type, "[local_avgpool]") == 0) return LOCAL_AVGPOOL;
+ if (strcmp(type, "[reorg3d]")==0) return REORG;
+ if (strcmp(type, "[reorg]") == 0) return REORG_OLD;
+ if (strcmp(type, "[avg]")==0
+ || strcmp(type, "[avgpool]")==0) return AVGPOOL;
+ if (strcmp(type, "[dropout]")==0) return DROPOUT;
+ if (strcmp(type, "[lrn]")==0
+ || strcmp(type, "[normalization]")==0) return NORMALIZATION;
+ if (strcmp(type, "[batchnorm]")==0) return BATCHNORM;
+ if (strcmp(type, "[soft]")==0
+ || strcmp(type, "[softmax]")==0) return SOFTMAX;
+ if (strcmp(type, "[contrastive]") == 0) return CONTRASTIVE;
+ if (strcmp(type, "[route]")==0) return ROUTE;
+ if (strcmp(type, "[upsample]") == 0) return UPSAMPLE;
+ if (strcmp(type, "[empty]") == 0
+ || strcmp(type, "[silence]") == 0) return EMPTY;
+ if (strcmp(type, "[implicit]") == 0) return IMPLICIT;
+ return BLANK;
+}
+
+void free_section(section *s)
+{
+ free(s->type);
+ node *n = s->options->front;
+ while(n){
+ kvp *pair = (kvp *)n->val;
+ free(pair->key);
+ free(pair);
+ node *next = n->next;
+ free(n);
+ n = next;
+ }
+ free(s->options);
+ free(s);
+}
+
+void parse_data(char *data, float *a, int n)
+{
+ int i;
+ if(!data) return;
+ char *curr = data;
+ char *next = data;
+ int done = 0;
+ for(i = 0; i < n && !done; ++i){
+ while(*++next !='\0' && *next != ',');
+ if(*next == '\0') done = 1;
+ *next = '\0';
+ sscanf(curr, "%g", &a[i]);
+ curr = next+1;
+ }
+}
+
+typedef struct size_params{
+ int batch;
+ int inputs;
+ int h;
+ int w;
+ int c;
+ int index;
+ int time_steps;
+ int train;
+ network net;
+} size_params;
+
+local_layer parse_local(list *options, size_params params)
+{
+ int n = option_find_int(options, "filters",1);
+ int size = option_find_int(options, "size",1);
+ int stride = option_find_int(options, "stride",1);
+ int pad = option_find_int(options, "pad",0);
+ char *activation_s = option_find_str(options, "activation", "logistic");
+ ACTIVATION activation = get_activation(activation_s);
+
+ 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 layer must output image.");
+
+ local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation);
+
+ return layer;
+}
+
+convolutional_layer parse_convolutional(list *options, size_params params)
+{
+ int n = option_find_int(options, "filters",1);
+ int groups = option_find_int_quiet(options, "groups", 1);
+ int size = option_find_int(options, "size",1);
+ int stride = -1;
+ //int stride = option_find_int(options, "stride",1);
+ int stride_x = option_find_int_quiet(options, "stride_x", -1);
+ int stride_y = option_find_int_quiet(options, "stride_y", -1);
+ if (stride_x < 1 || stride_y < 1) {
+ stride = option_find_int(options, "stride", 1);
+ if (stride_x < 1) stride_x = stride;
+ if (stride_y < 1) stride_y = stride;
+ }
+ else {
+ stride = option_find_int_quiet(options, "stride", 1);
+ }
+ int dilation = option_find_int_quiet(options, "dilation", 1);
+ int antialiasing = option_find_int_quiet(options, "antialiasing", 0);
+ if (size == 1) dilation = 1;
+ int pad = option_find_int_quiet(options, "pad",0);
+ int padding = option_find_int_quiet(options, "padding",0);
+ if(pad) padding = size/2;
+
+ char *activation_s = option_find_str(options, "activation", "logistic");
+ ACTIVATION activation = get_activation(activation_s);
+
+ int assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0);
+
+ int share_index = option_find_int_quiet(options, "share_index", -1000000000);
+ convolutional_layer *share_layer = NULL;
+ if(share_index >= 0) share_layer = ¶ms.net.layers[share_index];
+ else if(share_index != -1000000000) share_layer = ¶ms.net.layers[params.index + share_index];
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before convolutional layer must output image.");
+ int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+ int cbn = option_find_int_quiet(options, "cbn", 0);
+ if (cbn) batch_normalize = 2;
+ int binary = option_find_int_quiet(options, "binary", 0);
+ int xnor = option_find_int_quiet(options, "xnor", 0);
+ int use_bin_output = option_find_int_quiet(options, "bin_output", 0);
+ int sway = option_find_int_quiet(options, "sway", 0);
+ int rotate = option_find_int_quiet(options, "rotate", 0);
+ int stretch = option_find_int_quiet(options, "stretch", 0);
+ int stretch_sway = option_find_int_quiet(options, "stretch_sway", 0);
+ if ((sway + rotate + stretch + stretch_sway) > 1) {
+ printf(" Error: should be used only 1 param: sway=1, rotate=1 or stretch=1 in the [convolutional] layer \n");
+ exit(0);
+ }
+ int deform = sway || rotate || stretch || stretch_sway;
+ if (deform && size == 1) {
+ printf(" Error: params (sway=1, rotate=1 or stretch=1) should be used only with size >=3 in the [convolutional] layer \n");
+ exit(0);
+ }
+
+ 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);
+ layer.flipped = option_find_int_quiet(options, "flipped", 0);
+ layer.dot = option_find_float_quiet(options, "dot", 0);
+ layer.sway = sway;
+ layer.rotate = rotate;
+ layer.stretch = stretch;
+ layer.stretch_sway = stretch_sway;
+ layer.angle = option_find_float_quiet(options, "angle", 15);
+ layer.grad_centr = option_find_int_quiet(options, "grad_centr", 0);
+ layer.reverse = option_find_float_quiet(options, "reverse", 0);
+ layer.coordconv = option_find_int_quiet(options, "coordconv", 0);
+
+ layer.stream = option_find_int_quiet(options, "stream", -1);
+ layer.wait_stream_id = option_find_int_quiet(options, "wait_stream", -1);
+
+ if(params.net.adam){
+ layer.B1 = params.net.B1;
+ layer.B2 = params.net.B2;
+ layer.eps = params.net.eps;
+ }
+
+ return layer;
+}
+
+layer parse_crnn(list *options, size_params params)
+{
+ int size = option_find_int_quiet(options, "size", 3);
+ int stride = option_find_int_quiet(options, "stride", 1);
+ int dilation = option_find_int_quiet(options, "dilation", 1);
+ int pad = option_find_int_quiet(options, "pad", 0);
+ int padding = option_find_int_quiet(options, "padding", 0);
+ if (pad) padding = size / 2;
+
+ int output_filters = option_find_int(options, "output",1);
+ int hidden_filters = option_find_int(options, "hidden",1);
+ int groups = option_find_int_quiet(options, "groups", 1);
+ char *activation_s = option_find_str(options, "activation", "logistic");
+ ACTIVATION activation = get_activation(activation_s);
+ int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+ int xnor = option_find_int_quiet(options, "xnor", 0);
+
+ 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);
+
+ l.shortcut = option_find_int_quiet(options, "shortcut", 0);
+
+ return l;
+}
+
+layer parse_rnn(list *options, size_params params)
+{
+ int output = option_find_int(options, "output",1);
+ int hidden = option_find_int(options, "hidden",1);
+ char *activation_s = option_find_str(options, "activation", "logistic");
+ ACTIVATION activation = get_activation(activation_s);
+ int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+ int logistic = option_find_int_quiet(options, "logistic", 0);
+
+ layer l = make_rnn_layer(params.batch, params.inputs, hidden, output, params.time_steps, activation, batch_normalize, logistic);
+
+ l.shortcut = option_find_int_quiet(options, "shortcut", 0);
+
+ return l;
+}
+
+layer parse_gru(list *options, size_params params)
+{
+ int output = option_find_int(options, "output",1);
+ int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+
+ layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize);
+
+ return l;
+}
+
+layer parse_lstm(list *options, size_params params)
+{
+ int output = option_find_int(options, "output",1);
+ int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+
+ layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize);
+
+ return l;
+}
+
+layer parse_conv_lstm(list *options, size_params params)
+{
+ // a ConvLSTM with a larger transitional kernel should be able to capture faster motions
+ int size = option_find_int_quiet(options, "size", 3);
+ int stride = option_find_int_quiet(options, "stride", 1);
+ int dilation = option_find_int_quiet(options, "dilation", 1);
+ int pad = option_find_int_quiet(options, "pad", 0);
+ int padding = option_find_int_quiet(options, "padding", 0);
+ if (pad) padding = size / 2;
+
+ int output_filters = option_find_int(options, "output", 1);
+ int groups = option_find_int_quiet(options, "groups", 1);
+ char *activation_s = option_find_str(options, "activation", "linear");
+ ACTIVATION activation = get_activation(activation_s);
+ int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+ int xnor = option_find_int_quiet(options, "xnor", 0);
+ int peephole = option_find_int_quiet(options, "peephole", 0);
+ int bottleneck = option_find_int_quiet(options, "bottleneck", 0);
+
+ 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, bottleneck, params.train);
+
+ l.state_constrain = option_find_int_quiet(options, "state_constrain", params.time_steps * 32);
+ l.shortcut = option_find_int_quiet(options, "shortcut", 0);
+
+ char *lstm_activation_s = option_find_str(options, "lstm_activation", "tanh");
+ l.lstm_activation = get_activation(lstm_activation_s);
+ l.time_normalizer = option_find_float_quiet(options, "time_normalizer", 1.0);
+
+ return l;
+}
+
+layer parse_history(list *options, size_params params)
+{
+ int history_size = option_find_int(options, "history_size", 4);
+ layer l = make_history_layer(params.batch, params.h, params.w, params.c, history_size, params.time_steps, params.train);
+ return l;
+}
+
+connected_layer parse_connected(list *options, size_params params)
+{
+ int output = option_find_int(options, "output",1);
+ char *activation_s = option_find_str(options, "activation", "logistic");
+ ACTIVATION activation = get_activation(activation_s);
+ int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
+
+ connected_layer layer = make_connected_layer(params.batch, 1, params.inputs, output, activation, batch_normalize);
+
+ return layer;
+}
+
+softmax_layer parse_softmax(list *options, size_params params)
+{
+ int groups = option_find_int_quiet(options, "groups", 1);
+ softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups);
+ layer.temperature = option_find_float_quiet(options, "temperature", 1);
+ char *tree_file = option_find_str(options, "tree", 0);
+ if (tree_file) layer.softmax_tree = read_tree(tree_file);
+ layer.w = params.w;
+ layer.h = params.h;
+ layer.c = params.c;
+ layer.spatial = option_find_float_quiet(options, "spatial", 0);
+ layer.noloss = option_find_int_quiet(options, "noloss", 0);
+ return layer;
+}
+
+contrastive_layer parse_contrastive(list *options, size_params params)
+{
+ int classes = option_find_int(options, "classes", 1000);
+ layer *yolo_layer = NULL;
+ int yolo_layer_id = option_find_int_quiet(options, "yolo_layer", 0);
+ if (yolo_layer_id < 0) yolo_layer_id = params.index + yolo_layer_id;
+ if(yolo_layer_id != 0) yolo_layer = params.net.layers + yolo_layer_id;
+ if (yolo_layer->type != YOLO) {
+ printf(" Error: [contrastive] layer should point to the [yolo] layer instead of %d layer! \n", yolo_layer_id);
+ getchar();
+ exit(0);
+ }
+
+ contrastive_layer layer = make_contrastive_layer(params.batch, params.w, params.h, params.c, classes, params.inputs, yolo_layer);
+ layer.temperature = option_find_float_quiet(options, "temperature", 1);
+ layer.steps = params.time_steps;
+ layer.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1);
+ layer.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10
+ layer.contrastive_neg_max = option_find_int_quiet(options, "contrastive_neg_max", 3);
+ return layer;
+}
+
+int *parse_yolo_mask(char *a, int *num)
+{
+ int *mask = 0;
+ if (a) {
+ int len = strlen(a);
+ int n = 1;
+ int i;
+ for (i = 0; i < len; ++i) {
+ if (a[i] == '#') break;
+ if (a[i] == ',') ++n;
+ }
+ mask = (int*)xcalloc(n, sizeof(int));
+ for (i = 0; i < n; ++i) {
+ int val = atoi(a);
+ mask[i] = val;
+ a = strchr(a, ',') + 1;
+ }
+ *num = n;
+ }
+ return mask;
+}
+
+float *get_classes_multipliers(char *cpc, const int classes, const float max_delta)
+{
+ float *classes_multipliers = NULL;
+ if (cpc) {
+ int classes_counters = classes;
+ int *counters_per_class = parse_yolo_mask(cpc, &classes_counters);
+ if (classes_counters != classes) {
+ printf(" number of values in counters_per_class = %d doesn't match with classes = %d \n", classes_counters, classes);
+ exit(0);
+ }
+ float max_counter = 0;
+ int i;
+ for (i = 0; i < classes_counters; ++i) {
+ if (counters_per_class[i] < 1) counters_per_class[i] = 1;
+ if (max_counter < counters_per_class[i]) max_counter = counters_per_class[i];
+ }
+ classes_multipliers = (float *)calloc(classes_counters, sizeof(float));
+ for (i = 0; i < classes_counters; ++i) {
+ classes_multipliers[i] = max_counter / counters_per_class[i];
+ if(classes_multipliers[i] > max_delta) classes_multipliers[i] = max_delta;
+ }
+ free(counters_per_class);
+ printf(" classes_multipliers: ");
+ for (i = 0; i < classes_counters; ++i) printf("%.1f, ", classes_multipliers[i]);
+ printf("\n");
+ }
+ return classes_multipliers;
+}
+
+layer parse_yolo(list *options, size_params params)
+{
+ int classes = option_find_int(options, "classes", 20);
+ int total = option_find_int(options, "num", 1);
+ int num = total;
+ char *a = option_find_str(options, "mask", 0);
+ int *mask = parse_yolo_mask(a, &num);
+ int max_boxes = option_find_int_quiet(options, "max", 200);
+ layer l = make_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 [yolo]-layer \n");
+ exit(EXIT_FAILURE);
+ }
+ //assert(l.outputs == params.inputs);
+
+ l.show_details = option_find_int_quiet(options, "show_details", 1);
+ l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10
+ char *cpc = option_find_str(options, "counters_per_class", 0);
+ l.classes_multipliers = get_classes_multipliers(cpc, classes, l.max_delta);
+
+ 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.new_coords = option_find_int_quiet(options, "new_coords", 0);
+ l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75);
+ l.obj_normalizer = option_find_float_quiet(options, "obj_normalizer", 1);
+ l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1);
+ l.delta_normalizer = option_find_float_quiet(options, "delta_normalizer", 1);
+ 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;
+ fprintf(stderr, "[yolo] params: iou loss: %s (%d), iou_norm: %2.2f, obj_norm: %2.2f, cls_norm: %2.2f, delta_norm: %2.2f, scale_x_y: %2.2f\n",
+ iou_loss, l.iou_loss, l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, l.delta_normalizer, l.scale_x_y);
+
+ 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 l.nms_kind = DEFAULT_NMS;
+ printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms);
+ }
+
+ l.jitter = option_find_float(options, "jitter", .2);
+ l.resize = option_find_float_quiet(options, "resize", 1.0);
+ l.focal_loss = option_find_int_quiet(options, "focal_loss", 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);
+
+ l.track_history_size = option_find_int_quiet(options, "track_history_size", 5);
+ l.sim_thresh = option_find_int_quiet(options, "sim_thresh", 0.8);
+ l.dets_for_track = option_find_int_quiet(options, "dets_for_track", 1);
+ l.dets_for_show = option_find_int_quiet(options, "dets_for_show", 1);
+ l.track_ciou_norm = option_find_float_quiet(options, "track_ciou_norm", 0.01);
+ int embedding_layer_id = option_find_int_quiet(options, "embedding_layer", 999999);
+ if (embedding_layer_id < 0) embedding_layer_id = params.index + embedding_layer_id;
+ if (embedding_layer_id != 999999) {
+ printf(" embedding_layer_id = %d, ", embedding_layer_id);
+ layer le = params.net.layers[embedding_layer_id];
+ l.embedding_layer_id = embedding_layer_id;
+ l.embedding_output = (float*)xcalloc(le.batch * le.outputs, sizeof(float));
+ l.embedding_size = le.n / l.n;
+ printf(" embedding_size = %d \n", l.embedding_size);
+ if (le.n % l.n != 0) {
+ printf(" Warning: filters=%d number in embedding_layer=%d isn't divisable by number of anchors %d \n", le.n, embedding_layer_id, l.n);
+ getchar();
+ }
+ }
+
+ 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] == '#') break;
+ if (a[i] == ',') ++n;
+ }
+ for (i = 0; i < n && i < total*2; ++i) {
+ float bias = atof(a);
+ 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] == '#') break;
+ 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", 200);
+ 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);
+ l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10
+ char *cpc = option_find_str(options, "counters_per_class", 0);
+ l.classes_multipliers = get_classes_multipliers(cpc, classes, l.max_delta);
+
+ 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.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0);
+ l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75);
+ l.obj_normalizer = option_find_float_quiet(options, "obj_normalizer", 1.0);
+ l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1);
+ l.delta_normalizer = option_find_float_quiet(options, "delta_normalizer", 1);
+ 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, obj_norm: %2.2f, cls_norm: %2.2f, delta_norm: %2.2f, scale: %2.2f, point: %d\n",
+ iou_loss, l.iou_loss, l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, l.delta_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", 200);
+
+ 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",200);
+ 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);
+ layer.maxpool_zero_nonmax = option_find_int_quiet(options, "maxpool_zero_nonmax", 0);
+ 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_implicit(list *options, size_params params, network net)
+{
+ float mean_init = option_find_float(options, "mean", 0.0);
+ float std_init = option_find_float(options, "std", 0.2);
+ int filters = option_find_int(options, "filters", 128);
+ int atoms = option_find_int_quiet(options, "atoms", 1);
+
+ layer s = make_implicit_layer(params.batch, params.index, mean_init, std_init, filters, atoms);
+
+ 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;
+
+ layer.stream = option_find_int_quiet(options, "stream", -1);
+ layer.wait_stream_id = option_find_int_quiet(options, "wait_stream", -1);
+
+ 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; // mini_batch
+ const int mini_batch = net->batch;
+ net->batch *= net->time_steps; // mini_batch * time_steps
+ net->subdivisions = subdivs; // number of mini_batches
+
+ net->weights_reject_freq = option_find_int_quiet(options, "weights_reject_freq", 0);
+ net->equidistant_point = option_find_int_quiet(options, "equidistant_point", 0);
+ net->badlabels_rejection_percentage = option_find_float_quiet(options, "badlabels_rejection_percentage", 0);
+ net->num_sigmas_reject_badlabels = option_find_float_quiet(options, "num_sigmas_reject_badlabels", 0);
+ net->ema_alpha = option_find_float_quiet(options, "ema_alpha", 0);
+ *net->badlabels_reject_threshold = 0;
+ *net->delta_rolling_max = 0;
+ *net->delta_rolling_avg = 0;
+ *net->delta_rolling_std = 0;
+ *net->seen = 0;
+ *net->cur_iteration = 0;
+ *net->cuda_graph_ready = 0;
+ net->use_cuda_graph = option_find_int_quiet(options, "use_cuda_graph", 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->mosaic_bound = option_find_int_quiet(options, "mosaic_bound", 0);
+ net->contrastive = option_find_int_quiet(options, "contrastive", 0);
+ net->contrastive_jit_flip = option_find_int_quiet(options, "contrastive_jit_flip", 0);
+ net->contrastive_color = option_find_int_quiet(options, "contrastive_color", 0);
+ net->unsupervised = option_find_int_quiet(options, "unsupervised", 0);
+ if (net->contrastive && mini_batch < 2) {
+ printf(" Error: mini_batch size (batch/subdivisions) should be higher than 1 for Contrastive loss \n");
+ exit(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->max_chart_loss = option_find_float_quiet(options, "max_chart_loss", 20.0);
+
+ 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] == '#') break;
+ 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 == HISTORY) {
+ l = parse_history(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 == CONTRASTIVE) {
+ l = parse_contrastive(options, params);
+ 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 == IMPLICIT) {
+ l = parse_implicit(options, params, net);
+ }else if(lt == DROPOUT){
+ l = parse_dropout(options, params);
+ l.output = net.layers[count-1].output;
+ l.delta = net.layers[count-1].delta;
+#ifdef GPU
+ l.output_gpu = net.layers[count-1].output_gpu;
+ l.delta_gpu = net.layers[count-1].delta_gpu;
+ l.keep_delta_gpu = 1;
+#endif
+ }
+ else if (lt == EMPTY) {
+ layer empty_layer = {(LAYER_TYPE)0};
+ l = empty_layer;
+ l.type = EMPTY;
+ l.w = l.out_w = params.w;
+ l.h = l.out_h = params.h;
+ l.c = l.out_c = params.c;
+ l.batch = params.batch;
+ l.inputs = l.outputs = params.inputs;
+ l.output = net.layers[count - 1].output;
+ l.delta = net.layers[count - 1].delta;
+ l.forward = empty_func;
+ l.backward = empty_func;
+#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;
+ l.forward_gpu = empty_func;
+ l.backward_gpu = empty_func;
+#endif
+ fprintf(stderr, "empty \n");
+ }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
+ l.optimized_memory = net.optimized_memory;
+ if (net.optimized_memory >= 2 && params.train && l.type != DROPOUT)
+ {
+ 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)
+ *net.max_input16_size = 0;
+ *net.max_output16_size = 0;
+ 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;
+ //if(l.weight_updates) 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_implicit_weights(layer l, FILE *fp)
+{
+#ifdef GPU
+ if (gpu_index >= 0) {
+ pull_implicit_layer(l);
+ //printf("\n pull_implicit_layer \n");
+ }
+#endif
+ int i;
+ //if(l.weight_updates) 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_convolutional_weights_ema(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_ema, sizeof(float), l.n, fp);
+ if (l.batch_normalize) {
+ fwrite(l.scales_ema, 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_ema, 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, int save_ema)
+{
+#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) {
+ if (save_ema) {
+ save_convolutional_weights_ema(l, fp);
+ }
+ else {
+ save_convolutional_weights(l, fp);
+ }
+ } if (l.type == SHORTCUT && l.nweights > 0) {
+ save_shortcut_weights(l, fp);
+ } if (l.type == IMPLICIT) {
+ save_implicit_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);
+ if (!l.bottleneck) {
+ 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, 0);
+}
+
+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_implicit_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_implicit_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 == IMPLICIT) {
+ load_implicit_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);
+ if (!l.bottleneck) {
+ 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;
+}
--
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