From 9361292c429c0ba3400c31c7fa5d5e3d3cb6ab47 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 19 Jul 2016 21:50:01 +0000
Subject: [PATCH] updates
---
src/parser.c | 80 +++++++++++++++++++++++++++++++++++++--
1 files changed, 75 insertions(+), 5 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 6c88fd5..b5c399f 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -19,6 +19,7 @@
#include "softmax_layer.h"
#include "dropout_layer.h"
#include "detection_layer.h"
+#include "region_layer.h"
#include "avgpool_layer.h"
#include "local_layer.h"
#include "route_layer.h"
@@ -51,6 +52,7 @@
int is_shortcut(section *s);
int is_cost(section *s);
int is_detection(section *s);
+int is_region(section *s);
int is_route(section *s);
list *read_cfg(char *filename);
@@ -245,6 +247,25 @@
return layer;
}
+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);
+ layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords);
+ assert(l.outputs == params.inputs);
+
+ l.softmax = option_find_int(options, "softmax", 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.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.class_scale = option_find_float(options, "class_scale", 1);
+ return l;
+}
detection_layer parse_detection(list *options, size_params params)
{
int coords = option_find_int(options, "coords", 1);
@@ -257,12 +278,14 @@
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);
return layer;
}
@@ -432,6 +455,7 @@
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;
@@ -465,6 +489,7 @@
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);
if(net->policy == STEP){
net->step = option_find_int(options, "step", 1);
net->scale = option_find_float(options, "scale", 1);
@@ -497,7 +522,7 @@
} 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){
+ } 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);
@@ -523,6 +548,7 @@
params.batch = net.batch;
params.time_steps = net.time_steps;
+ size_t workspace_size = 0;
n = n->next;
int count = 0;
free_section(s);
@@ -552,6 +578,8 @@
l = parse_crop(options, params);
}else if(is_cost(s)){
l = parse_cost(options, params);
+ }else if(is_region(s)){
+ l = parse_region(options, params);
}else if(is_detection(s)){
l = parse_detection(options, params);
}else if(is_softmax(s)){
@@ -583,6 +611,7 @@
l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
option_unused(options);
net.layers[count] = l;
+ if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
free_section(s);
n = n->next;
++count;
@@ -596,6 +625,14 @@
free_list(sections);
net.outputs = get_network_output_size(net);
net.output = get_network_output(net);
+ if(workspace_size){
+ //printf("%ld\n", workspace_size);
+#ifdef GPU
+ net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+#else
+ net.workspace = calloc(1, workspace_size);
+#endif
+ }
return net;
}
@@ -606,6 +643,7 @@
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, "[local]")==0) return LOCAL;
if (strcmp(type, "[deconv]")==0
|| strcmp(type, "[deconvolutional]")==0) return DECONVOLUTIONAL;
@@ -645,6 +683,10 @@
{
return (strcmp(s->type, "[cost]")==0);
}
+int is_region(section *s)
+{
+ return (strcmp(s->type, "[region]")==0);
+}
int is_detection(section *s)
{
return (strcmp(s->type, "[detection]")==0);
@@ -852,6 +894,18 @@
fwrite(l.filters, 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
@@ -889,6 +943,8 @@
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);
@@ -943,8 +999,8 @@
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));
+ //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);
@@ -960,6 +1016,18 @@
#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);
@@ -983,7 +1051,6 @@
}
}
}
- binarize_filters2(l.filters, l.n, l.c*l.size*l.size, l.cfilters, l.scales);
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(l);
@@ -1008,7 +1075,7 @@
if (l.flipped) {
transpose_matrix(l.filters, l.c*l.size*l.size, l.n);
}
- if (l.binary) binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.filters);
+ //if (l.binary) binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.filters);
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(l);
@@ -1053,6 +1120,9 @@
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);
--
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