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darknet/src/parser.c

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#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, "[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 = &params.net.layers[share_index];
else if(share_index != -1000000000) share_layer = &params.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 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);
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, params.train);
layer.flipped = option_find_int_quiet(options, "flipped", 0);
layer.dot = option_find_float_quiet(options, "dot", 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*)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_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);
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.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);
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.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_int_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);
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.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;
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.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_int_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.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_int_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.random = option_find_int_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_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);
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, 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", .5);
dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability);
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);
return l;
}
layer parse_shortcut(list *options, size_params params, network net)
{
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);
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];
if (from.antialiasing) from = *from.input_layer;
layer s = make_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, assisted_excitation, activation, params.train);
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");
int len = strlen(l);
if(!l) error("Route Layer must specify input layers");
int n = 1;
int i;
for(i = 0; i < len; ++i){
if (l[i] == ',') ++n;
}
int* layers = (int*)calloc(n, sizeof(int));
int* sizes = (int*)calloc(n, sizeof(int));
for(i = 0; i < n; ++i){
int index = atoi(l);
l = strchr(l, ',')+1;
if(index < 0) index = params.index + index;
layers[i] = index;
sizes[i] = 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{
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->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", 1000);
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->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->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->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 CUDNN_HALF
if (net->gpu_index >= 0) {
int compute_capability = get_gpu_compute_capability(net->gpu_index);
if (get_gpu_compute_capability(net->gpu_index) >= 700) net->cudnn_half = 1;
else net->cudnn_half = 0;
fprintf(stderr, " compute_capability = %d, cudnn_half = %d \n", compute_capability, net->cudnn_half);
}
else fprintf(stderr, " GPU isn't used \n");
#endif
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*)calloc(n, sizeof(int));
float* scales = (float*)calloc(n, sizeof(float));
float* seq_scales = (float*)calloc(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);
}
net->max_batches = option_find_int(options, "max_batches", 0);
}
int is_network(section *s)
{
return (strcmp(s->type, "[net]")==0
|| strcmp(s->type, "[network]")==0);
}
network parse_network_cfg(char *filename)
{
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("batch = %d, time_steps = %d, train = %d \n", net.batch, net.time_steps, params.train);
float bflops = 0;
size_t workspace_size = 0;
size_t max_inputs = 0;
size_t max_outputs = 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 == 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;
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);
}
#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.onlyforward = option_find_int_quiet(options, "onlyforward", 0);
l.stopbackward = option_find_int_quiet(options, "stopbackward", 0);
l.dontload = option_find_int_quiet(options, "dontload", 0);
l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1);
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;
}
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
net.outputs = get_network_output_size(net);
net.output = get_network_output(net);
fprintf(stderr, "Total BFLOPS %5.3f \n", bflops);
#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*)calloc(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*)calloc(1, workspace_size);
}
}
#else
if (workspace_size) {
net.workspace = (float*)calloc(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*)malloc(sizeof(section));
list_insert(sections, current);
current->options = make_list();
current->type = line;
break;
case '\0':
case '#':
case ';':
free(line);
break;
default:
if(!read_option(line, current->options)){
fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
free(line);
}
break;
}
}
fclose(file);
return sections;
}
void save_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_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.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);
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 == 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*)calloc(rows * cols, sizeof(float));
int x, y;
for(x = 0; x < rows; ++x){
for(y = 0; y < cols; ++y){
transpose[y*rows + x] = a[x*cols + y];
}
}
memcpy(a, transpose, rows*cols*sizeof(float));
free(transpose);
}
void load_connected_weights(layer l, FILE *fp, int transpose)
{
fread(l.biases, sizeof(float), l.outputs, fp);
fread(l.weights, sizeof(float), l.outputs*l.inputs, fp);
if(transpose){
transpose_matrix(l.weights, l.inputs, l.outputs);
}
//printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs));
//printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs));
if (l.batch_normalize && (!l.dontloadscales)){
fread(l.scales, sizeof(float), l.outputs, fp);
fread(l.rolling_mean, sizeof(float), l.outputs, fp);
fread(l.rolling_variance, sizeof(float), l.outputs, fp);
//printf("Scales: %f mean %f variance\n", mean_array(l.scales, l.outputs), variance_array(l.scales, l.outputs));
//printf("rolling_mean: %f mean %f variance\n", mean_array(l.rolling_mean, l.outputs), variance_array(l.rolling_mean, l.outputs));
//printf("rolling_variance: %f mean %f variance\n", mean_array(l.rolling_variance, l.outputs), variance_array(l.rolling_variance, l.outputs));
}
#ifdef GPU
if(gpu_index >= 0){
push_connected_layer(l);
}
#endif
}
void load_batchnorm_weights(layer l, FILE *fp)
{
fread(l.scales, sizeof(float), l.c, fp);
fread(l.rolling_mean, sizeof(float), l.c, fp);
fread(l.rolling_variance, sizeof(float), l.c, fp);
#ifdef GPU
if(gpu_index >= 0){
push_batchnorm_layer(l);
}
#endif
}
void load_convolutional_weights_binary(layer l, FILE *fp)
{
fread(l.biases, sizeof(float), l.n, fp);
if (l.batch_normalize && (!l.dontloadscales)){
fread(l.scales, sizeof(float), l.n, fp);
fread(l.rolling_mean, sizeof(float), l.n, fp);
fread(l.rolling_variance, sizeof(float), l.n, fp);
}
int size = (l.c / l.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_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 \n");
uint64_t iseen = 0;
fread(&iseen, sizeof(uint64_t), 1, fp);
*net->seen = iseen;
}
else {
printf("\n seen 32 \n");
uint32_t iseen = 0;
fread(&iseen, sizeof(uint32_t), 1, fp);
*net->seen = iseen;
}
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 == 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*)calloc(1, sizeof(network));
*net = parse_network_cfg_custom(cfg, batch, 0);
if (weights && weights[0] != 0) {
load_weights(net, weights);
}
if (clear) (*net->seen) = 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, weights: %s, clear = %d \n", cfg, weights, clear);
network* net = (network*)calloc(1, sizeof(network));
*net = parse_network_cfg(cfg);
if (weights && weights[0] != 0) {
load_weights(net, weights);
}
if (clear) (*net->seen) = 0;
return net;
}