From 13209df7bb53de19aa3f82e870db11eb5b7587f1 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 13 May 2016 18:59:43 +0000
Subject: [PATCH] art, cudnn
---
src/parser.c | 1116 ++++++++++++++++++++++++++++++++++++++++++++++++++++++----
1 files changed, 1,030 insertions(+), 86 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index dc1db2b..d5288aa 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -4,10 +4,25 @@
#include "parser.h"
#include "activations.h"
+#include "crop_layer.h"
+#include "cost_layer.h"
#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"
+#include "dropout_layer.h"
+#include "detection_layer.h"
+#include "avgpool_layer.h"
+#include "local_layer.h"
+#include "route_layer.h"
+#include "shortcut_layer.h"
#include "list.h"
#include "option_list.h"
#include "utils.h"
@@ -17,104 +32,666 @@
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_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_route(section *s);
list *read_cfg(char *filename);
+void free_section(section *s)
+{
+ free(s->type);
+ node *n = s->options->front;
+ while(n){
+ kvp *pair = (kvp *)n->val;
+ free(pair->key);
+ free(pair);
+ node *next = n->next;
+ free(n);
+ n = next;
+ }
+ free(s->options);
+ free(s);
+}
+
+void parse_data(char *data, float *a, int n)
+{
+ int i;
+ if(!data) return;
+ char *curr = data;
+ char *next = data;
+ int done = 0;
+ for(i = 0; i < n && !done; ++i){
+ while(*++next !='\0' && *next != ',');
+ if(*next == '\0') done = 1;
+ *next = '\0';
+ sscanf(curr, "%g", &a[i]);
+ curr = next+1;
+ }
+}
+
+typedef struct size_params{
+ int batch;
+ int inputs;
+ int h;
+ int w;
+ int c;
+ int index;
+ 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);
+
+ 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
+ if(weights || biases) push_deconvolutional_layer(layer);
+ #endif
+ return layer;
+}
+
+local_layer parse_local(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);
+ int pad = option_find_int(options, "pad",0);
+ 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 local layer must output image.");
+
+ local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation);
+
+ return layer;
+}
+
+convolutional_layer parse_convolutional(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);
+ int pad = option_find_int(options, "pad",0);
+ 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,pad,activation, batch_normalize, binary, xnor);
+ layer.flipped = option_find_int_quiet(options, "flipped", 0);
+ layer.dot = option_find_float_quiet(options, "dot", 0);
+
+ 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
+ if(weights || biases) push_convolutional_layer(layer);
+ #endif
+ 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);
+ int hidden = option_find_int(options, "hidden",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);
+ 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);
+
+ 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, params.inputs*output);
+ #ifdef GPU
+ if(weights || biases) push_connected_layer(layer);
+ #endif
+ return layer;
+}
+
+softmax_layer parse_softmax(list *options, size_params params)
+{
+ 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);
+ return layer;
+}
+
+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.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);
+ 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);
+ float scale = option_find_float_quiet(options, "scale",1);
+ cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale);
+ return layer;
+}
+
+crop_layer parse_crop(list *options, size_params params)
+{
+ 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);
+ 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;
+}
+
+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 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);
+ return layer;
+}
+
+avgpool_layer parse_avgpool(list *options, size_params params)
+{
+ 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, size_params params)
+{
+ float probability = option_find_float(options, "probability", .5);
+ 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;
+}
+
+layer parse_normalization(list *options, size_params params)
+{
+ 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;
+ }
+
+ 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->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);
+
+ 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);
+ 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);
+}
network parse_network_cfg(char *filename)
{
list *sections = read_cfg(filename);
- network net = make_network(sections->size);
-
node *n = sections->front;
+ if(!n) error("Config file has no sections");
+ network net = make_network(sections->size - 1);
+ 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;
+ params.batch = net.batch;
+ params.time_steps = net.time_steps;
+
+ size_t workspace_size = 0;
+ n = n->next;
int count = 0;
+ free_section(s);
while(n){
- section *s = (section *)n->val;
- list *options = s->options;
+ params.index = count;
+ fprintf(stderr, "%d: ", count);
+ s = (section *)n->val;
+ options = s->options;
+ layer l = {0};
if(is_convolutional(s)){
- int h,w,c;
- 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", "sigmoid");
- ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",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.");
- }
- convolutional_layer *layer = make_convolutional_layer(h,w,c,n,size,stride, activation);
- net.types[count] = CONVOLUTIONAL;
- net.layers[count] = layer;
- option_unused(options);
- }
- else if(is_connected(s)){
- int input;
- int output = option_find_int(options, "output",1);
- char *activation_s = option_find_str(options, "activation", "sigmoid");
- ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- input = option_find_int(options, "input",1);
- }else{
- input = get_network_output_size_layer(net, count-1);
- }
- connected_layer *layer = make_connected_layer(input, output, activation);
- net.types[count] = CONNECTED;
- net.layers[count] = layer;
- option_unused(options);
+ l = parse_convolutional(options, params);
+ }else if(is_local(s)){
+ l = parse_local(options, params);
+ }else if(is_activation(s)){
+ l = parse_activation(options, params);
+ }else if(is_deconvolutional(s)){
+ 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)){
+ l = parse_connected(options, params);
+ }else if(is_crop(s)){
+ l = parse_crop(options, params);
+ }else if(is_cost(s)){
+ l = parse_cost(options, params);
+ }else if(is_detection(s)){
+ l = parse_detection(options, params);
}else if(is_softmax(s)){
- int input;
- if(count == 0){
- input = option_find_int(options, "input",1);
- }else{
- input = get_network_output_size_layer(net, count-1);
- }
- softmax_layer *layer = make_softmax_layer(input);
- net.types[count] = SOFTMAX;
- net.layers[count] = layer;
- option_unused(options);
+ 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)){
- int h,w,c;
- int stride = option_find_int(options, "stride",1);
- //char *activation_s = option_find_str(options, "activation", "sigmoid");
- if(count == 0){
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",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(h,w,c,stride);
- net.types[count] = MAXPOOL;
- net.layers[count] = layer;
- option_unused(options);
+ l = parse_maxpool(options, params);
+ }else if(is_avgpool(s)){
+ l = parse_avgpool(options, params);
+ }else if(is_route(s)){
+ l = parse_route(options, params, net);
+ }else if(is_shortcut(s)){
+ l = parse_shortcut(options, params, net);
+ }else if(is_dropout(s)){
+ 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);
}
- ++count;
+ 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);
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){
+#ifdef GPU
+ net.workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+#endif
+ }
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);
+}
+int is_crop(section *s)
+{
+ return (strcmp(s->type, "[crop]")==0);
+}
+int is_cost(section *s)
+{
+ return (strcmp(s->type, "[cost]")==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
@@ -125,29 +702,35 @@
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 read_option(char *s, list *options)
+int is_route(section *s)
{
- int i;
- int 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;
+ return (strcmp(s->type, "[route]")==0);
}
list *read_cfg(char *filename)
@@ -175,7 +758,7 @@
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;
@@ -185,3 +768,364 @@
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
+ if(gpu_index >= 0){
+ pull_convolutional_layer(l);
+ }
+#endif
+ binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters);
+ 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);
+ }
+ for(i = 0; i < l.n; ++i){
+ float mean = l.binary_filters[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_filters[index + k] > 0) c = (c | 1<<k);
+ }
+ fwrite(&c, sizeof(char), 1, fp);
+ }
+ }
+}
+
+void save_convolutional_weights(layer l, FILE *fp)
+{
+ if(l.binary){
+ //save_convolutional_weights_binary(l, fp);
+ //return;
+ }
+#ifdef GPU
+ if(gpu_index >= 0){
+ pull_convolutional_layer(l);
+ }
+#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.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
+ 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 save_weights_upto(network net, char *filename, int cutoff)
+{
+ fprintf(stderr, "Saving weights to %s\n", filename);
+ FILE *fp = fopen(filename, "w");
+ 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 < 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.filters, 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.filters[index + k] = (c & 1<<k) ? mean : -mean;
+ }
+ }
+ }
+ 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);
+ }
+#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);
+ }
+ fread(l.filters, sizeof(float), num, fp);
+ 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);
+#ifdef GPU
+ if(gpu_index >= 0){
+ push_convolutional_layer(l);
+ }
+#endif
+}
+
+
+void load_weights_upto(network *net, char *filename, int cutoff)
+{
+ 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);
+ 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 == 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);
+ }
+ 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.filters, 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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