From c7b10ceadb1a78e7480d281444a31ae2a7dc1b05 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 06 May 2016 23:25:16 +0000
Subject: [PATCH] so much need to commit
---
src/parser.c | 91 +++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 90 insertions(+), 1 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index c109a14..6c88fd5 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -9,9 +9,11 @@
#include "convolutional_layer.h"
#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"
#include "crnn_layer.h"
#include "maxpool_layer.h"
#include "softmax_layer.h"
@@ -37,12 +39,14 @@
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_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);
@@ -157,8 +161,9 @@
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);
- convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation, batch_normalize, binary);
+ convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation, batch_normalize, binary, xnor);
layer.flipped = option_find_int_quiet(options, "flipped", 0);
layer.dot = option_find_float_quiet(options, "dot", 0);
@@ -203,6 +208,16 @@
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;
+}
+
connected_layer parse_connected(list *options, size_params params)
{
int output = option_find_int(options, "output",1);
@@ -333,6 +348,12 @@
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 *l = option_find(options, "from");
@@ -438,6 +459,7 @@
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);
if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
@@ -520,6 +542,8 @@
l = parse_deconvolutional(options, params);
}else if(is_rnn(s)){
l = parse_rnn(options, params);
+ }else if(is_gru(s)){
+ l = parse_gru(options, params);
}else if(is_crnn(s)){
l = parse_crnn(options, params);
}else if(is_connected(s)){
@@ -534,6 +558,8 @@
l = parse_softmax(options, params);
}else if(is_normalization(s)){
l = parse_normalization(options, params);
+ }else if(is_batchnorm(s)){
+ l = parse_batchnorm(options, params);
}else if(is_maxpool(s)){
l = parse_maxpool(options, params);
}else if(is_avgpool(s)){
@@ -573,6 +599,40 @@
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, "[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, "[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);
@@ -616,6 +676,10 @@
{
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);
@@ -646,6 +710,11 @@
|| 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
@@ -824,6 +893,13 @@
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 == CRNN){
save_convolutional_weights(*(l.input_layer), fp);
save_convolutional_weights(*(l.self_layer), fp);
@@ -867,10 +943,15 @@
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){
@@ -982,6 +1063,14 @@
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 == LOCAL){
int locations = l.out_w*l.out_h;
int size = l.size*l.size*l.c*l.n*locations;
--
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