From 481b57a96a9ef29b112caec1bb3e17ffb043ceae Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sun, 25 Sep 2016 06:12:54 +0000
Subject: [PATCH] So I have this new programming paradigm.......
---
src/parser.c | 345 ++++++++++++++-------------------------------------------
1 files changed, 84 insertions(+), 261 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 626f510..a27d245 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -12,7 +12,6 @@
#include "activation_layer.h"
#include "normalization_layer.h"
#include "batchnorm_layer.h"
-#include "deconvolutional_layer.h"
#include "connected_layer.h"
#include "rnn_layer.h"
#include "gru_layer.h"
@@ -36,30 +35,42 @@
list *options;
}section;
-int is_network(section *s);
-int is_convolutional(section *s);
-int is_activation(section *s);
-int is_local(section *s);
-int is_deconvolutional(section *s);
-int is_connected(section *s);
-int is_rnn(section *s);
-int is_gru(section *s);
-int is_crnn(section *s);
-int is_maxpool(section *s);
-int is_reorg(section *s);
-int is_avgpool(section *s);
-int is_dropout(section *s);
-int is_softmax(section *s);
-int is_normalization(section *s);
-int is_batchnorm(section *s);
-int is_crop(section *s);
-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);
+LAYER_TYPE string_to_layer_type(char * type)
+{
+
+ if (strcmp(type, "[shortcut]")==0) return SHORTCUT;
+ 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, "[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, "[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, "[reorg]")==0) return REORG;
+ 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;
+ return BLANK;
+}
+
void free_section(section *s)
{
free(s->type);
@@ -102,26 +113,6 @@
int time_steps;
} size_params;
-deconvolutional_layer parse_deconvolutional(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);
- 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 deconvolutional layer must output image.");
-
- deconvolutional_layer layer = make_deconvolutional_layer(batch,h,w,c,n,size,stride,activation);
-
- return layer;
-}
-
local_layer parse_local(list *options, size_params params)
{
int n = option_find_int(options, "filters",1);
@@ -497,6 +488,7 @@
net->min_crop = option_find_int_quiet(options, "min_crop",net->w);
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);
@@ -544,12 +536,19 @@
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)
{
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;
section *s = (section *)n->val;
@@ -574,47 +573,46 @@
s = (section *)n->val;
options = s->options;
layer l = {0};
- if(is_convolutional(s)){
+ LAYER_TYPE lt = string_to_layer_type(s->type);
+ if(lt == CONVOLUTIONAL){
l = parse_convolutional(options, params);
- }else if(is_local(s)){
+ }else if(lt == LOCAL){
l = parse_local(options, params);
- }else if(is_activation(s)){
+ }else if(lt == ACTIVE){
l = parse_activation(options, params);
- }else if(is_deconvolutional(s)){
- l = parse_deconvolutional(options, params);
- }else if(is_rnn(s)){
+ }else if(lt == RNN){
l = parse_rnn(options, params);
- }else if(is_gru(s)){
+ }else if(lt == GRU){
l = parse_gru(options, params);
- }else if(is_crnn(s)){
+ }else if(lt == CRNN){
l = parse_crnn(options, params);
- }else if(is_connected(s)){
+ }else if(lt == CONNECTED){
l = parse_connected(options, params);
- }else if(is_crop(s)){
+ }else if(lt == CROP){
l = parse_crop(options, params);
- }else if(is_cost(s)){
+ }else if(lt == COST){
l = parse_cost(options, params);
- }else if(is_region(s)){
+ }else if(lt == REGION){
l = parse_region(options, params);
- }else if(is_detection(s)){
+ }else if(lt == DETECTION){
l = parse_detection(options, params);
- }else if(is_softmax(s)){
+ }else if(lt == SOFTMAX){
l = parse_softmax(options, params);
- }else if(is_normalization(s)){
+ }else if(lt == NORMALIZATION){
l = parse_normalization(options, params);
- }else if(is_batchnorm(s)){
+ }else if(lt == BATCHNORM){
l = parse_batchnorm(options, params);
- }else if(is_maxpool(s)){
+ }else if(lt == MAXPOOL){
l = parse_maxpool(options, params);
- }else if(is_reorg(s)){
+ }else if(lt == REORG){
l = parse_reorg(options, params);
- }else if(is_avgpool(s)){
+ }else if(lt == AVGPOOL){
l = parse_avgpool(options, params);
- }else if(is_route(s)){
+ }else if(lt == ROUTE){
l = parse_route(options, params, net);
- }else if(is_shortcut(s)){
+ }else if(lt == SHORTCUT){
l = parse_shortcut(options, params, net);
- }else if(is_dropout(s)){
+ }else if(lt == DROPOUT){
l = parse_dropout(options, params);
l.output = net.layers[count-1].output;
l.delta = net.layers[count-1].delta;
@@ -658,142 +656,6 @@
return net;
}
-LAYER_TYPE string_to_layer_type(char * type)
-{
-
- if (strcmp(type, "[shortcut]")==0) return SHORTCUT;
- 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;
- 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, "[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, "[reorg]")==0) return REORG;
- 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;
- return BLANK;
-}
-
-int is_shortcut(section *s)
-{
- return (strcmp(s->type, "[shortcut]")==0);
-}
-int is_crop(section *s)
-{
- return (strcmp(s->type, "[crop]")==0);
-}
-int is_cost(section *s)
-{
- 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);
-}
-int is_local(section *s)
-{
- return (strcmp(s->type, "[local]")==0);
-}
-int is_deconvolutional(section *s)
-{
- return (strcmp(s->type, "[deconv]")==0
- || strcmp(s->type, "[deconvolutional]")==0);
-}
-int is_convolutional(section *s)
-{
- return (strcmp(s->type, "[conv]")==0
- || strcmp(s->type, "[convolutional]")==0);
-}
-int is_activation(section *s)
-{
- return (strcmp(s->type, "[activation]")==0);
-}
-int is_network(section *s)
-{
- return (strcmp(s->type, "[net]")==0
- || strcmp(s->type, "[network]")==0);
-}
-int is_crnn(section *s)
-{
- return (strcmp(s->type, "[crnn]")==0);
-}
-int is_gru(section *s)
-{
- return (strcmp(s->type, "[gru]")==0);
-}
-int is_rnn(section *s)
-{
- return (strcmp(s->type, "[rnn]")==0);
-}
-int is_connected(section *s)
-{
- return (strcmp(s->type, "[conn]")==0
- || strcmp(s->type, "[connected]")==0);
-}
-int is_reorg(section *s)
-{
- return (strcmp(s->type, "[reorg]")==0);
-}
-int is_maxpool(section *s)
-{
- return (strcmp(s->type, "[max]")==0
- || strcmp(s->type, "[maxpool]")==0);
-}
-int is_avgpool(section *s)
-{
- return (strcmp(s->type, "[avg]")==0
- || strcmp(s->type, "[avgpool]")==0);
-}
-int is_dropout(section *s)
-{
- return (strcmp(s->type, "[dropout]")==0);
-}
-
-int is_normalization(section *s)
-{
- return (strcmp(s->type, "[lrn]")==0
- || strcmp(s->type, "[normalization]")==0);
-}
-
-int is_batchnorm(section *s)
-{
- return (strcmp(s->type, "[batchnorm]")==0);
-}
-
-int is_softmax(section *s)
-{
- return (strcmp(s->type, "[soft]")==0
- || strcmp(s->type, "[softmax]")==0);
-}
-int is_route(section *s)
-{
- return (strcmp(s->type, "[route]")==0);
-}
-
list *read_cfg(char *filename)
{
FILE *file = fopen(filename, "r");
@@ -829,45 +691,6 @@
return sections;
}
-void save_weights_double(network net, char *filename)
-{
- fprintf(stderr, "Saving doubled weights to %s\n", filename);
- FILE *fp = fopen(filename, "w");
- if(!fp) file_error(filename);
-
- fwrite(&net.learning_rate, sizeof(float), 1, fp);
- fwrite(&net.momentum, sizeof(float), 1, fp);
- fwrite(&net.decay, sizeof(float), 1, fp);
- fwrite(net.seen, sizeof(int), 1, fp);
-
- int i,j,k;
- for(i = 0; i < net.n; ++i){
- layer l = net.layers[i];
- if(l.type == CONVOLUTIONAL){
-#ifdef GPU
- if(gpu_index >= 0){
- pull_convolutional_layer(l);
- }
-#endif
- float zero = 0;
- fwrite(l.biases, sizeof(float), l.n, fp);
- fwrite(l.biases, sizeof(float), l.n, fp);
-
- for (j = 0; j < l.n; ++j){
- int index = j*l.c*l.size*l.size;
- fwrite(l.filters+index, sizeof(float), l.c*l.size*l.size, fp);
- for (k = 0; k < l.c*l.size*l.size; ++k) fwrite(&zero, sizeof(float), 1, fp);
- }
- for (j = 0; j < l.n; ++j){
- int index = j*l.c*l.size*l.size;
- for (k = 0; k < l.c*l.size*l.size; ++k) fwrite(&zero, sizeof(float), 1, fp);
- fwrite(l.filters+index, sizeof(float), l.c*l.size*l.size, fp);
- }
- }
- }
- fclose(fp);
-}
-
void save_convolutional_weights_binary(layer l, FILE *fp)
{
#ifdef GPU
@@ -875,7 +698,7 @@
pull_convolutional_layer(l);
}
#endif
- binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters);
+ binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
int size = l.c*l.size*l.size;
int i, j, k;
fwrite(l.biases, sizeof(float), l.n, fp);
@@ -885,7 +708,7 @@
fwrite(l.rolling_variance, sizeof(float), l.n, fp);
}
for(i = 0; i < l.n; ++i){
- float mean = l.binary_filters[i*size];
+ 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){
@@ -893,7 +716,7 @@
unsigned char c = 0;
for(k = 0; k < 8; ++k){
if (j*8 + k >= size) break;
- if (l.binary_filters[index + k] > 0) c = (c | 1<<k);
+ if (l.binary_weights[index + k] > 0) c = (c | 1<<k);
}
fwrite(&c, sizeof(char), 1, fp);
}
@@ -918,7 +741,7 @@
fwrite(l.rolling_mean, sizeof(float), l.n, fp);
fwrite(l.rolling_variance, sizeof(float), l.n, fp);
}
- fwrite(l.filters, sizeof(float), num, fp);
+ fwrite(l.weights, sizeof(float), num, fp);
}
void save_batchnorm_weights(layer l, FILE *fp)
@@ -951,6 +774,11 @@
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, "w");
if(!fp) file_error(filename);
@@ -996,7 +824,7 @@
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.filters, sizeof(float), size, fp);
+ fwrite(l.weights, sizeof(float), size, fp);
}
}
fclose(fp);
@@ -1074,7 +902,7 @@
fread(&c, sizeof(char), 1, fp);
for(k = 0; k < 8; ++k){
if (j*8 + k >= size) break;
- l.filters[index + k] = (c & 1<<k) ? mean : -mean;
+ l.weights[index + k] = (c & 1<<k) ? mean : -mean;
}
}
}
@@ -1098,12 +926,12 @@
fread(l.rolling_mean, sizeof(float), l.n, fp);
fread(l.rolling_variance, sizeof(float), l.n, fp);
}
- fread(l.filters, sizeof(float), num, fp);
- //if(l.c == 3) scal_cpu(num, 1./256, l.filters, 1);
+ fread(l.weights, sizeof(float), num, fp);
+ //if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1);
if (l.flipped) {
- transpose_matrix(l.filters, l.c*l.size*l.size, l.n);
+ transpose_matrix(l.weights, 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_weights(l.weights, l.n, l.c*l.size*l.size, l.weights);
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(l);
@@ -1114,6 +942,11 @@
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");
@@ -1135,16 +968,6 @@
if(l.type == CONVOLUTIONAL){
load_convolutional_weights(l, fp);
}
- if(l.type == DECONVOLUTIONAL){
- int num = l.n*l.c*l.size*l.size;
- fread(l.biases, sizeof(float), l.n, fp);
- fread(l.filters, sizeof(float), num, fp);
-#ifdef GPU
- if(gpu_index >= 0){
- push_deconvolutional_layer(l);
- }
-#endif
- }
if(l.type == CONNECTED){
load_connected_weights(l, fp, transpose);
}
@@ -1173,7 +996,7 @@
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.filters, sizeof(float), size, fp);
+ fread(l.weights, sizeof(float), size, fp);
#ifdef GPU
if(gpu_index >= 0){
push_local_layer(l);
--
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