From be90b8e8cb6bbf3951a5e185aa43ccfdd4a03f4d Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Thu, 08 Feb 2018 22:50:35 +0000
Subject: [PATCH] Optimal params for optical flow tracking. Some small box fixes.
---
src/parser.c | 1357 ++++++++++++++++++++++++++++++++++++++++------------------
1 files changed, 921 insertions(+), 436 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 768f48b..67b4bfb 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -2,38 +2,76 @@
#include <string.h>
#include <stdlib.h>
-#include "parser.h"
+#include "activation_layer.h"
#include "activations.h"
-#include "crop_layer.h"
-#include "cost_layer.h"
-#include "convolutional_layer.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 "maxpool_layer.h"
#include "normalization_layer.h"
-#include "softmax_layer.h"
-#include "dropout_layer.h"
-#include "freeweight_layer.h"
-#include "list.h"
#include "option_list.h"
+#include "parser.h"
+#include "region_layer.h"
+#include "reorg_layer.h"
+#include "rnn_layer.h"
+#include "route_layer.h"
+#include "shortcut_layer.h"
+#include "softmax_layer.h"
#include "utils.h"
-#include "opencl.h"
+#include <stdint.h>
typedef struct{
char *type;
list *options;
}section;
-int is_convolutional(section *s);
-int is_connected(section *s);
-int is_maxpool(section *s);
-int is_dropout(section *s);
-int is_freeweight(section *s);
-int is_softmax(section *s);
-int is_crop(section *s);
-int is_cost(section *s);
-int is_normalization(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);
@@ -66,343 +104,612 @@
}
}
-convolutional_layer *parse_convolutional(list *options, network *net, int count)
+typedef struct size_params{
+ int batch;
+ int inputs;
+ int h;
+ int w;
+ int c;
+ int index;
+ int time_steps;
+ network net;
+} size_params;
+
+local_layer parse_local(list *options, size_params params)
{
- int h,w,c;
- float learning_rate, momentum, decay;
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", "sigmoid");
+ char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- learning_rate = option_find_float(options, "learning_rate", .001);
- momentum = option_find_float(options, "momentum", .9);
- decay = option_find_float(options, "decay", .0001);
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",1);
- net->batch = option_find_int(options, "batch",1);
- net->learning_rate = learning_rate;
- net->momentum = momentum;
- net->decay = decay;
- }else{
- learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
- momentum = option_find_float_quiet(options, "momentum", net->momentum);
- decay = option_find_float_quiet(options, "decay", net->decay);
- image m = get_network_image_layer(*net, count-1);
- h = m.h;
- w = m.w;
- c = m.c;
- if(h == 0) error("Layer before convolutional layer must output image.");
- }
- convolutional_layer *layer = make_convolutional_layer(net->batch,h,w,c,n,size,stride,pad,activation,learning_rate,momentum,decay);
- char *weights = option_find_str(options, "weights", 0);
- char *biases = option_find_str(options, "biases", 0);
- parse_data(weights, layer->filters, c*n*size*size);
- parse_data(biases, layer->biases, n);
- #ifdef GPU
- push_convolutional_layer(*layer);
- #endif
- option_unused(options);
+
+ 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;
}
-connected_layer *parse_connected(list *options, network *net, int count)
+convolutional_layer parse_convolutional(list *options, size_params params)
{
- int input;
- float learning_rate, momentum, decay;
+ 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_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 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);
+
+ convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,padding,activation, batch_normalize, binary, xnor, params.net.adam);
+ 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 output_filters = option_find_int(options, "output_filters",1);
+ int hidden_filters = option_find_int(options, "hidden_filters",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);
+
+ layer l = make_crnn_layer(params.batch, params.w, params.h, params.c, hidden_filters, output_filters, params.time_steps, activation, batch_normalize);
+
+ 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);
- char *activation_s = option_find_str(options, "activation", "sigmoid");
+ int hidden = option_find_int(options, "hidden",1);
+ char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- input = option_find_int(options, "input",1);
- net->batch = option_find_int(options, "batch",1);
- learning_rate = option_find_float(options, "learning_rate", .001);
- momentum = option_find_float(options, "momentum", .9);
- decay = option_find_float(options, "decay", .0001);
- net->learning_rate = learning_rate;
- net->momentum = momentum;
- net->decay = decay;
- }else{
- learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
- momentum = option_find_float_quiet(options, "momentum", net->momentum);
- decay = option_find_float_quiet(options, "decay", net->decay);
- input = get_network_output_size_layer(*net, count-1);
- }
- connected_layer *layer = make_connected_layer(net->batch, input, output, activation,learning_rate,momentum,decay);
- char *weights = option_find_str(options, "weights", 0);
- char *biases = option_find_str(options, "biases", 0);
- parse_data(biases, layer->biases, output);
- parse_data(weights, layer->weights, input*output);
- #ifdef GPU
- push_connected_layer(*layer);
- #endif
- option_unused(options);
+ 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;
+}
+
+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, params.inputs, output, activation, batch_normalize);
+
return layer;
}
-softmax_layer *parse_softmax(list *options, network *net, int count)
+softmax_layer parse_softmax(list *options, size_params params)
{
- int input;
- if(count == 0){
- input = option_find_int(options, "input",1);
- net->batch = option_find_int(options, "batch",1);
- }else{
- input = get_network_output_size_layer(*net, count-1);
- }
- softmax_layer *layer = make_softmax_layer(net->batch, input);
- option_unused(options);
+ 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);
return layer;
}
-cost_layer *parse_cost(list *options, network *net, int count)
+layer parse_region(list *options, size_params params)
{
- int input;
- if(count == 0){
- input = option_find_int(options, "input",1);
- net->batch = option_find_int(options, "batch",1);
- }else{
- input = get_network_output_size_layer(*net, count-1);
+ 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.log = option_find_int_quiet(options, "log", 0);
+ l.sqrt = option_find_int_quiet(options, "sqrt", 0);
+
+ l.small_object = option_find_int(options, "small_object", 0);
+ 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.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.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){
+ 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);
- cost_layer *layer = make_cost_layer(net->batch, input, type);
- option_unused(options);
+ 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, network *net, int count)
+crop_layer parse_crop(list *options, size_params params)
{
- float learning_rate, momentum, decay;
- int h,w,c;
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);
- if(count == 0){
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",1);
- net->batch = option_find_int(options, "batch",1);
- learning_rate = option_find_float(options, "learning_rate", .001);
- momentum = option_find_float(options, "momentum", .9);
- decay = option_find_float(options, "decay", .0001);
- net->learning_rate = learning_rate;
- net->momentum = momentum;
- net->decay = decay;
- }else{
- image m = get_network_image_layer(*net, count-1);
- h = m.h;
- w = m.w;
- c = m.c;
- if(h == 0) error("Layer before crop layer must output image.");
- }
- crop_layer *layer = make_crop_layer(net->batch,h,w,c,crop_height,crop_width,flip);
- option_unused(options);
+ 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;
}
-maxpool_layer *parse_maxpool(list *options, network *net, int count)
+maxpool_layer parse_maxpool(list *options, size_params params)
{
- int h,w,c;
int stride = option_find_int(options, "stride",1);
int size = option_find_int(options, "size",stride);
- if(count == 0){
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",1);
- net->batch = option_find_int(options, "batch",1);
- }else{
- image m = get_network_image_layer(*net, count-1);
- h = m.h;
- w = m.w;
- c = m.c;
- if(h == 0) error("Layer before convolutional layer must output image.");
- }
- maxpool_layer *layer = make_maxpool_layer(net->batch,h,w,c,size,stride);
- option_unused(options);
+ int padding = option_find_int_quiet(options, "padding", (size-1)/2);
+
+ 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,padding);
return layer;
}
-freeweight_layer *parse_freeweight(list *options, network *net, int count)
+avgpool_layer parse_avgpool(list *options, size_params params)
{
- int input;
- if(count == 0){
- net->batch = option_find_int(options, "batch",1);
- input = option_find_int(options, "input",1);
- }else{
- input = get_network_output_size_layer(*net, count-1);
- }
- freeweight_layer *layer = make_freeweight_layer(net->batch,input);
- option_unused(options);
+ 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, network *net, int count)
+dropout_layer parse_dropout(list *options, size_params params)
{
- int input;
float probability = option_find_float(options, "probability", .5);
- if(count == 0){
- net->batch = option_find_int(options, "batch",1);
- input = option_find_int(options, "input",1);
- float learning_rate = option_find_float(options, "learning_rate", .001);
- float momentum = option_find_float(options, "momentum", .9);
- float decay = option_find_float(options, "decay", .0001);
- net->learning_rate = learning_rate;
- net->momentum = momentum;
- net->decay = decay;
- }else{
- input = get_network_output_size_layer(*net, count-1);
- }
- dropout_layer *layer = make_dropout_layer(net->batch,input,probability);
- option_unused(options);
+ 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;
}
-normalization_layer *parse_normalization(list *options, network *net, int count)
+layer parse_normalization(list *options, size_params params)
{
- int h,w,c;
- int size = option_find_int(options, "size",1);
- float alpha = option_find_float(options, "alpha", 0.);
- float beta = option_find_float(options, "beta", 1.);
- float kappa = option_find_float(options, "kappa", 1.);
- if(count == 0){
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",1);
- net->batch = option_find_int(options, "batch",1);
- }else{
- image m = get_network_image_layer(*net, count-1);
- h = m.h;
- w = m.w;
- c = m.c;
- if(h == 0) error("Layer before convolutional layer must output image.");
+ 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 *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_shortcut_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c);
+
+ char *activation_s = option_find_str(options, "activation", "linear");
+ ACTIVATION activation = get_activation(activation_s);
+ s.activation = activation;
+ 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;
+}
+
+route_layer parse_route(list *options, size_params params, network net)
+{
+ 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;
}
- normalization_layer *layer = make_normalization_layer(net->batch,h,w,c,size, alpha, beta, kappa);
- option_unused(options);
+
+ int *layers = calloc(n, sizeof(int));
+ int *sizes = 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] = net.layers[index].outputs;
+ }
+ int batch = params.batch;
+
+ route_layer layer = make_route_layer(batch, n, layers, sizes);
+
+ convolutional_layer first = 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 = 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;
+ }
+ }
+
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;
+ 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->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->batch /= subdivs;
+ net->batch *= net->time_steps;
+ net->subdivisions = subdivs;
+
+ 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->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);
+ 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");
+ if(!l || !p) error("STEPS policy must have steps and scales in cfg file");
+
+ int len = strlen(l);
+ int n = 1;
+ int i;
+ for(i = 0; i < len; ++i){
+ if (l[i] == ',') ++n;
+ }
+ int *steps = calloc(n, sizeof(int));
+ float *scales = calloc(n, sizeof(float));
+ for(i = 0; i < n; ++i){
+ int step = atoi(l);
+ float scale = atof(p);
+ l = strchr(l, ',')+1;
+ p = strchr(p, ',')+1;
+ steps[i] = step;
+ scales[i] = scale;
+ }
+ net->scales = scales;
+ net->steps = steps;
+ 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)
{
- list *sections = read_cfg(filename);
- network net = make_network(sections->size, 0);
+ return parse_network_cfg_custom(filename, 0);
+}
+network parse_network_cfg_custom(char *filename, int batch)
+{
+ 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;
+ list *options = s->options;
+ if(!is_network(s)) error("First section must be [net] or [network]");
+ parse_net_options(options, &net);
+
+ params.h = net.h;
+ params.w = net.w;
+ params.c = net.c;
+ params.inputs = net.inputs;
+ if (batch > 0) net.batch = batch;
+ params.batch = net.batch;
+ params.time_steps = net.time_steps;
+ params.net = net;
+
+ size_t workspace_size = 0;
+ n = n->next;
int count = 0;
+ free_section(s);
+ fprintf(stderr, "layer filters size input output\n");
while(n){
- section *s = (section *)n->val;
- list *options = s->options;
- if(is_convolutional(s)){
- convolutional_layer *layer = parse_convolutional(options, &net, count);
- net.types[count] = CONVOLUTIONAL;
- net.layers[count] = layer;
- }else if(is_connected(s)){
- connected_layer *layer = parse_connected(options, &net, count);
- net.types[count] = CONNECTED;
- net.layers[count] = layer;
- }else if(is_crop(s)){
- crop_layer *layer = parse_crop(options, &net, count);
- net.types[count] = CROP;
- net.layers[count] = layer;
- }else if(is_cost(s)){
- cost_layer *layer = parse_cost(options, &net, count);
- net.types[count] = COST;
- net.layers[count] = layer;
- }else if(is_softmax(s)){
- softmax_layer *layer = parse_softmax(options, &net, count);
- net.types[count] = SOFTMAX;
- net.layers[count] = layer;
- }else if(is_maxpool(s)){
- maxpool_layer *layer = parse_maxpool(options, &net, count);
- net.types[count] = MAXPOOL;
- net.layers[count] = layer;
- }else if(is_normalization(s)){
- normalization_layer *layer = parse_normalization(options, &net, count);
- net.types[count] = NORMALIZATION;
- net.layers[count] = layer;
- }else if(is_dropout(s)){
- dropout_layer *layer = parse_dropout(options, &net, count);
- net.types[count] = DROPOUT;
- net.layers[count] = layer;
- }else if(is_freeweight(s)){
- freeweight_layer *layer = parse_freeweight(options, &net, count);
- net.types[count] = FREEWEIGHT;
- net.layers[count] = layer;
+ params.index = count;
+ fprintf(stderr, "%5d ", count);
+ s = (section *)n->val;
+ options = s->options;
+ layer l = {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 == 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);
+ }else if(lt == REGION){
+ l = parse_region(options, params);
+ }else if(lt == DETECTION){
+ l = parse_detection(options, params);
+ }else if(lt == SOFTMAX){
+ l = parse_softmax(options, params);
+ net.hierarchy = l.softmax_tree;
+ }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 == AVGPOOL){
+ l = parse_avgpool(options, params);
+ }else if(lt == ROUTE){
+ l = parse_route(options, params, net);
+ }else if(lt == SHORTCUT){
+ l = parse_shortcut(options, params, net);
+ }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;
+#endif
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
+ 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);
+ option_unused(options);
+ net.layers[count] = l;
+ if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
free_section(s);
- ++count;
n = n->next;
+ ++count;
+ if(n){
+ params.h = l.out_h;
+ params.w = l.out_w;
+ params.c = l.out_c;
+ params.inputs = l.outputs;
+ }
}
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
+ if(gpu_index >= 0){
+ net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+ }else {
+ net.workspace = calloc(1, workspace_size);
+ }
+#else
+ net.workspace = calloc(1, workspace_size);
+#endif
+ }
return net;
}
-int is_crop(section *s)
-{
- return (strcmp(s->type, "[crop]")==0);
-}
-int is_cost(section *s)
-{
- return (strcmp(s->type, "[cost]")==0);
-}
-int is_convolutional(section *s)
-{
- return (strcmp(s->type, "[conv]")==0
- || strcmp(s->type, "[convolutional]")==0);
-}
-int is_connected(section *s)
-{
- return (strcmp(s->type, "[conn]")==0
- || strcmp(s->type, "[connected]")==0);
-}
-int is_maxpool(section *s)
-{
- return (strcmp(s->type, "[max]")==0
- || strcmp(s->type, "[maxpool]")==0);
-}
-int is_dropout(section *s)
-{
- return (strcmp(s->type, "[dropout]")==0);
-}
-int is_freeweight(section *s)
-{
- return (strcmp(s->type, "[freeweight]")==0);
-}
-int is_softmax(section *s)
-{
- return (strcmp(s->type, "[soft]")==0
- || strcmp(s->type, "[softmax]")==0);
-}
-int is_normalization(section *s)
-{
- return (strcmp(s->type, "[lrnorm]")==0
- || strcmp(s->type, "[localresponsenormalization]")==0);
-}
-
-int read_option(char *s, list *options)
-{
- size_t i;
- size_t len = strlen(s);
- char *val = 0;
- for(i = 0; i < len; ++i){
- if(s[i] == '='){
- s[i] = '\0';
- val = s+i+1;
- break;
- }
- }
- if(i == len-1) return 0;
- char *key = s;
- option_insert(options, key, val);
- return 1;
-}
list *read_cfg(char *filename)
{
@@ -429,7 +736,7 @@
break;
default:
if(!read_option(line, current->options)){
- printf("Config file error line %d, could parse: %s\n", nu, line);
+ fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
free(line);
}
break;
@@ -439,179 +746,357 @@
return sections;
}
-void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int count)
+void save_convolutional_weights_binary(layer l, FILE *fp)
{
- #ifdef GPU
- if(gpu_index >= 0) pull_convolutional_layer(*l);
- #endif
- int i;
- fprintf(fp, "[convolutional]\n");
- if(count == 0) {
- fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n"
- "learning_rate=%g\n"
- "momentum=%g\n"
- "decay=%g\n",
- l->batch,l->h, l->w, l->c, l->learning_rate, l->momentum, l->decay);
- } else {
- if(l->learning_rate != net.learning_rate)
- fprintf(fp, "learning_rate=%g\n", l->learning_rate);
- if(l->momentum != net.momentum)
- fprintf(fp, "momentum=%g\n", l->momentum);
- if(l->decay != net.decay)
- fprintf(fp, "decay=%g\n", l->decay);
+#ifdef GPU
+ if(gpu_index >= 0){
+ pull_convolutional_layer(l);
}
- fprintf(fp, "filters=%d\n"
- "size=%d\n"
- "stride=%d\n"
- "pad=%d\n"
- "activation=%s\n",
- l->n, l->size, l->stride, l->pad,
- get_activation_string(l->activation));
- fprintf(fp, "biases=");
- for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
- fprintf(fp, "\n");
- fprintf(fp, "weights=");
- for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
- fprintf(fp, "\n\n");
-}
-
-void print_freeweight_cfg(FILE *fp, freeweight_layer *l, network net, int count)
-{
- fprintf(fp, "[freeweight]\n");
- if(count == 0){
- fprintf(fp, "batch=%d\ninput=%d\n",l->batch, l->inputs);
+#endif
+ 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);
+ 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);
}
- fprintf(fp, "\n");
-}
-
-void print_dropout_cfg(FILE *fp, dropout_layer *l, network net, int count)
-{
- fprintf(fp, "[dropout]\n");
- if(count == 0){
- fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
+ 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);
+ }
}
- fprintf(fp, "probability=%g\n\n", l->probability);
}
-void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
+void save_convolutional_weights(layer l, FILE *fp)
{
- #ifdef GPU
- if(gpu_index >= 0) pull_connected_layer(*l);
- #endif
- int i;
- fprintf(fp, "[connected]\n");
- if(count == 0){
- fprintf(fp, "batch=%d\n"
- "input=%d\n"
- "learning_rate=%g\n"
- "momentum=%g\n"
- "decay=%g\n",
- l->batch, l->inputs, l->learning_rate, l->momentum, l->decay);
- } else {
- if(l->learning_rate != net.learning_rate)
- fprintf(fp, "learning_rate=%g\n", l->learning_rate);
- if(l->momentum != net.momentum)
- fprintf(fp, "momentum=%g\n", l->momentum);
- if(l->decay != net.decay)
- fprintf(fp, "decay=%g\n", l->decay);
+ if(l.binary){
+ //save_convolutional_weights_binary(l, fp);
+ //return;
}
- fprintf(fp, "output=%d\n"
- "activation=%s\n",
- l->outputs,
- get_activation_string(l->activation));
- fprintf(fp, "biases=");
- for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
- fprintf(fp, "\n");
- fprintf(fp, "weights=");
- for(i = 0; i < l->outputs*l->inputs; ++i) fprintf(fp, "%g,", l->weights[i]);
- fprintf(fp, "\n\n");
-}
-
-void print_crop_cfg(FILE *fp, crop_layer *l, network net, int count)
-{
- fprintf(fp, "[crop]\n");
- if(count == 0) {
- fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n"
- "learning_rate=%g\n"
- "momentum=%g\n"
- "decay=%g\n",
- l->batch,l->h, l->w, l->c, net.learning_rate, net.momentum, net.decay);
+#ifdef GPU
+ if(gpu_index >= 0){
+ pull_convolutional_layer(l);
}
- fprintf(fp, "crop_height=%d\ncrop_width=%d\nflip=%d\n\n", l->crop_height, l->crop_width, l->flip);
+#endif
+ int num = l.n*l.c*l.size*l.size;
+ 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 print_maxpool_cfg(FILE *fp, maxpool_layer *l, network net, int count)
+void save_batchnorm_weights(layer l, FILE *fp)
{
- fprintf(fp, "[maxpool]\n");
- if(count == 0) fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n",
- l->batch,l->h, l->w, l->c);
- fprintf(fp, "size=%d\nstride=%d\n\n", l->size, l->stride);
+#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 print_normalization_cfg(FILE *fp, normalization_layer *l, network net, int count)
+void save_connected_weights(layer l, FILE *fp)
{
- fprintf(fp, "[localresponsenormalization]\n");
- if(count == 0) fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n",
- l->batch,l->h, l->w, l->c);
- fprintf(fp, "size=%d\n"
- "alpha=%g\n"
- "beta=%g\n"
- "kappa=%g\n\n", l->size, l->alpha, l->beta, l->kappa);
+#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 print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count)
+void save_weights_upto(network net, char *filename, int cutoff)
{
- fprintf(fp, "[softmax]\n");
- if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
- fprintf(fp, "\n");
-}
-
-void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
-{
- fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type));
- if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
- fprintf(fp, "\n");
-}
-
-
-void save_network(network net, char *filename)
-{
- FILE *fp = fopen(filename, "w");
+#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 = 0;
+ int minor = 1;
+ int revision = 0;
+ fwrite(&major, sizeof(int), 1, fp);
+ fwrite(&minor, sizeof(int), 1, fp);
+ fwrite(&revision, sizeof(int), 1, fp);
+ fwrite(net.seen, sizeof(int), 1, fp);
+
int i;
- for(i = 0; i < net.n; ++i)
- {
- if(net.types[i] == CONVOLUTIONAL)
- print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], net, i);
- else if(net.types[i] == CONNECTED)
- print_connected_cfg(fp, (connected_layer *)net.layers[i], net, i);
- else if(net.types[i] == CROP)
- print_crop_cfg(fp, (crop_layer *)net.layers[i], net, i);
- else if(net.types[i] == MAXPOOL)
- print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], net, i);
- else if(net.types[i] == FREEWEIGHT)
- print_freeweight_cfg(fp, (freeweight_layer *)net.layers[i], net, i);
- else if(net.types[i] == DROPOUT)
- print_dropout_cfg(fp, (dropout_layer *)net.layers[i], net, i);
- else if(net.types[i] == NORMALIZATION)
- print_normalization_cfg(fp, (normalization_layer *)net.layers[i], net, i);
- else if(net.types[i] == SOFTMAX)
- print_softmax_cfg(fp, (softmax_layer *)net.layers[i], net, i);
- else if(net.types[i] == COST)
- print_cost_cfg(fp, (cost_layer *)net.layers[i], net, i);
+ for(i = 0; i < net.n && i < cutoff; ++i){
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ 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 == 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 = 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.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.n*l.c*l.size*l.size;
+ 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);
+ 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);
+ }
+ }
+ fread(l.weights, sizeof(float), num, fp);
+ 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.size*l.size, l.n);
+ }
+ //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);
+ }
+#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");
+ fread(net->seen, sizeof(int), 1, fp);
+ }
+ 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){
+ 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 == 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
+ }
+ }
+ fprintf(stderr, "Done!\n");
+ fclose(fp);
+}
+
+void load_weights(network *net, char *filename)
+{
+ load_weights_upto(net, filename, net->n);
+}
--
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