From 7e9e289b164e9bbfb77f644b1cbfac48bc8c8408 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Tue, 11 Sep 2018 00:51:08 +0000
Subject: [PATCH] training w/ full yolo cfg
---
src/parser.c | 220 +++++++++++++++++++++++++++++-------------------------
1 files changed, 119 insertions(+), 101 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 7441ae2..c716ea9 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -49,7 +49,7 @@
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, "[yolo]") == 0) return YOLO;
if (strcmp(type, "[local]")==0) return LOCAL;
if (strcmp(type, "[conv]")==0
|| strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL;
@@ -64,7 +64,7 @@
if (strcmp(type, "[max]")==0
|| strcmp(type, "[maxpool]")==0) return MAXPOOL;
if (strcmp(type, "[reorg]")==0) return REORG;
- if (strcmp(type, "[reorg_old]") == 0) return REORG_OLD;
+ if (strcmp(type, "[reorg_old]") == 0) return REORG_OLD;
if (strcmp(type, "[avg]")==0
|| strcmp(type, "[avgpool]")==0) return AVGPOOL;
if (strcmp(type, "[dropout]")==0) return DROPOUT;
@@ -74,7 +74,7 @@
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, "[upsample]") == 0) return UPSAMPLE;
return BLANK;
}
@@ -241,63 +241,68 @@
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 = 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;
+ 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 = 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;
+ 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", 30);
- layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes);
- assert(l.outputs == params.inputs);
+ 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.max_boxes = option_find_int_quiet(options, "max", 90);
- l.jitter = option_find_float(options, "jitter", .2);
- l.focal_loss = option_find_int_quiet(options, "focal_loss", 0);
+ //l.max_boxes = option_find_int_quiet(options, "max", 90);
+ 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.random = option_find_int_quiet(options, "random", 0);
+ l.ignore_thresh = option_find_float(options, "ignore_thresh", .5);
+ l.truth_thresh = option_find_float(options, "truth_thresh", 1);
+ 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);
+ 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;
+ 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;
}
layer parse_region(list *options, size_params params)
@@ -305,16 +310,21 @@
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", 30);
+ 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);
- assert(l.outputs == params.inputs);
+ 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.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);
@@ -327,7 +337,7 @@
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.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);
@@ -428,26 +438,26 @@
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);
+ 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.");
+ 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;
+ 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 size = option_find_int(options, "size",stride);
- int padding = option_find_int_quiet(options, "padding", (size-1)/2);
+ int padding = option_find_int_quiet(options, "padding", size-1);
int batch,h,w,c;
h = params.h;
@@ -501,7 +511,7 @@
layer parse_shortcut(list *options, size_params params, network net)
{
- char *l = option_find(options, "from");
+ char *l = option_find(options, "from");
int index = atoi(l);
if(index < 0) index = params.index + index;
@@ -537,15 +547,15 @@
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;
+ 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, network net)
{
- char *l = option_find(options, "layers");
+ char *l = option_find(options, "layers");
int len = strlen(l);
if(!l) error("Route Layer must specify input layers");
int n = 1;
@@ -622,15 +632,15 @@
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->flip = option_find_int_quiet(options, "flip", 1);
- net->small_object = option_find_int_quiet(options, "small_object", 0);
+ net->small_object = option_find_int_quiet(options, "small_object", 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);
+ net->power = option_find_float_quiet(options, "power", 4);
if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
@@ -638,14 +648,14 @@
net->policy = get_policy(policy_s);
net->burn_in = option_find_int_quiet(options, "burn_in", 0);
#ifdef CUDNN_HALF
- net->burn_in = 0;
+ net->burn_in = 0;
#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){
- char *l = option_find(options, "steps");
- char *p = option_find(options, "scales");
+ char *l = option_find(options, "steps");
+ char *p = option_find(options, "scales");
if(!l || !p) error("STEPS policy must have steps and scales in cfg file");
int len = strlen(l);
@@ -686,7 +696,7 @@
network parse_network_cfg(char *filename)
{
- return parse_network_cfg_custom(filename, 0);
+ return parse_network_cfg_custom(filename, 0);
}
network parse_network_cfg_custom(char *filename, int batch)
@@ -707,11 +717,12 @@
params.w = net.w;
params.c = net.c;
params.inputs = net.inputs;
- if (batch > 0) net.batch = batch;
+ if (batch > 0) net.batch = batch;
params.batch = net.batch;
params.time_steps = net.time_steps;
params.net = net;
+ float bflops = 0;
size_t workspace_size = 0;
n = n->next;
int count = 0;
@@ -719,7 +730,7 @@
fprintf(stderr, "layer filters size input output\n");
while(n){
params.index = count;
- fprintf(stderr, "%5d ", count);
+ fprintf(stderr, "%4d ", count);
s = (section *)n->val;
options = s->options;
layer l = {0};
@@ -744,8 +755,8 @@
l = parse_cost(options, params);
}else if(lt == REGION){
l = parse_region(options, params);
- }else if (lt == YOLO) {
- l = parse_yolo(options, params);
+ }else if (lt == YOLO) {
+ l = parse_yolo(options, params);
}else if(lt == DETECTION){
l = parse_detection(options, params);
}else if(lt == SOFTMAX){
@@ -758,15 +769,15 @@
}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);
+ 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, net);
- }else if (lt == UPSAMPLE) {
- l = parse_upsample(options, params, net);
+ }else if (lt == UPSAMPLE) {
+ l = parse_upsample(options, params, net);
}else if(lt == SHORTCUT){
l = parse_shortcut(options, params, net);
}else if(lt == DROPOUT){
@@ -796,15 +807,17 @@
params.c = l.out_c;
params.inputs = l.outputs;
}
- }
+ if (l.bflops > 0) bflops += l.bflops;
+ }
free_list(sections);
net.outputs = get_network_output_size(net);
net.output = get_network_output(net);
+ printf("Total BFLOPS %5.3f \n", bflops);
if(workspace_size){
//printf("%ld\n", workspace_size);
#ifdef GPU
if(gpu_index >= 0){
- net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+ net.workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1);
}else {
net.workspace = calloc(1, workspace_size);
}
@@ -812,6 +825,11 @@
net.workspace = 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;
}
@@ -1142,16 +1160,16 @@
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");
- fread(net->seen, 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");
+ fread(net->seen, sizeof(int), 1, fp);
+ }
int transpose = (major > 1000) || (minor > 1000);
int i;
--
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