From 0dab894a5be9f7d10d85e89dea91d02c71bae84d Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sun, 16 Sep 2018 03:24:45 +0000
Subject: [PATCH] Moving files from MTGCardDetector repo
---
src/network.c | 402 ++++++++++++++++++++++++++++++--------------------------
1 files changed, 215 insertions(+), 187 deletions(-)
diff --git a/src/network.c b/src/network.c
index 499bf0f..2ad5141 100644
--- a/src/network.c
+++ b/src/network.c
@@ -33,19 +33,19 @@
network *load_network_custom(char *cfg, char *weights, int clear, int batch)
{
- printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear);
- network *net = calloc(1, sizeof(network));
- *net = parse_network_cfg_custom(cfg, batch);
- if (weights && weights[0] != 0) {
- load_weights(net, weights);
- }
- if (clear) (*net->seen) = 0;
- return net;
+ printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear);
+ network *net = calloc(1, sizeof(network));
+ *net = parse_network_cfg_custom(cfg, batch);
+ if (weights && weights[0] != 0) {
+ load_weights(net, weights);
+ }
+ if (clear) (*net->seen) = 0;
+ return net;
}
network *load_network(char *cfg, char *weights, int clear)
{
- return load_network_custom(cfg, weights, clear, 0);
+ return load_network_custom(cfg, weights, clear, 0);
}
int get_current_batch(network net)
@@ -67,23 +67,23 @@
void reset_network_state(network *net, int b)
{
- int i;
- for (i = 0; i < net->n; ++i) {
+ int i;
+ for (i = 0; i < net->n; ++i) {
#ifdef GPU
- layer l = net->layers[i];
- if (l.state_gpu) {
- fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1);
- }
- if (l.h_gpu) {
- fill_ongpu(l.outputs, 0, l.h_gpu + l.outputs*b, 1);
- }
+ layer l = net->layers[i];
+ if (l.state_gpu) {
+ fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1);
+ }
+ if (l.h_gpu) {
+ fill_ongpu(l.outputs, 0, l.h_gpu + l.outputs*b, 1);
+ }
#endif
- }
+ }
}
void reset_rnn(network *net)
{
- reset_network_state(net, 0);
+ reset_network_state(net, 0);
}
float get_current_rate(network net)
@@ -91,7 +91,7 @@
int batch_num = get_current_batch(net);
int i;
float rate;
- if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
+ if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
switch (net.policy) {
case CONSTANT:
return net.learning_rate;
@@ -108,7 +108,7 @@
case EXP:
return net.learning_rate * pow(net.gamma, batch_num);
case POLY:
- return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+ return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
//if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
//return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
case RANDOM:
@@ -182,10 +182,10 @@
net.input_gpu = calloc(1, sizeof(float *));
net.truth_gpu = calloc(1, sizeof(float *));
- net.input16_gpu = calloc(1, sizeof(float *));
- net.output16_gpu = calloc(1, sizeof(float *));
- net.max_input16_size = calloc(1, sizeof(size_t));
- net.max_output16_size = calloc(1, sizeof(size_t));
+ net.input16_gpu = calloc(1, sizeof(float *));
+ net.output16_gpu = calloc(1, sizeof(float *));
+ net.max_input16_size = calloc(1, sizeof(size_t));
+ net.max_output16_size = calloc(1, sizeof(size_t));
#endif
return net;
}
@@ -222,7 +222,7 @@
{
#ifdef GPU
if (gpu_index >= 0) return get_network_output_gpu(net);
-#endif
+#endif
int i;
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
return net.layers[i].output;
@@ -362,20 +362,20 @@
net->layers[i].batch = b;
#ifdef CUDNN
if(net->layers[i].type == CONVOLUTIONAL){
- cudnn_convolutional_setup(net->layers + i, cudnn_fastest);
- /*
- layer *l = net->layers + i;
+ cudnn_convolutional_setup(net->layers + i, cudnn_fastest);
+ /*
+ layer *l = net->layers + i;
cudnn_convolutional_setup(l, cudnn_fastest);
- // check for excessive memory consumption
- size_t free_byte;
- size_t total_byte;
- check_error(cudaMemGetInfo(&free_byte, &total_byte));
- if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) {
- printf(" used slow CUDNN algo without Workspace! \n");
- cudnn_convolutional_setup(l, cudnn_smallest);
- l->workspace_size = get_workspace_size(*l);
- }
- */
+ // check for excessive memory consumption
+ size_t free_byte;
+ size_t total_byte;
+ check_error(cudaMemGetInfo(&free_byte, &total_byte));
+ if (l->workspace_size > free_byte || l->workspace_size >= total_byte / 2) {
+ printf(" used slow CUDNN algo without Workspace! \n");
+ cudnn_convolutional_setup(l, cudnn_smallest);
+ l->workspace_size = get_workspace_size(*l);
+ }
+ */
}
#endif
}
@@ -387,12 +387,12 @@
cuda_set_device(net->gpu_index);
if(gpu_index >= 0){
cuda_free(net->workspace);
- if (net->input_gpu) {
- cuda_free(*net->input_gpu);
- *net->input_gpu = 0;
- cuda_free(*net->truth_gpu);
- *net->truth_gpu = 0;
- }
+ if (net->input_gpu) {
+ cuda_free(*net->input_gpu);
+ *net->input_gpu = 0;
+ cuda_free(*net->truth_gpu);
+ *net->truth_gpu = 0;
+ }
}
#endif
int i;
@@ -405,7 +405,7 @@
//fflush(stderr);
for (i = 0; i < net->n; ++i){
layer l = net->layers[i];
- //printf(" %d: layer = %d,", i, l.type);
+ //printf(" %d: layer = %d,", i, l.type);
if(l.type == CONVOLUTIONAL){
resize_convolutional_layer(&l, w, h);
}else if(l.type == CROP){
@@ -414,14 +414,14 @@
resize_maxpool_layer(&l, w, h);
}else if(l.type == REGION){
resize_region_layer(&l, w, h);
- }else if (l.type == YOLO) {
- resize_yolo_layer(&l, w, h);
+ }else if (l.type == YOLO) {
+ resize_yolo_layer(&l, w, h);
}else if(l.type == ROUTE){
resize_route_layer(&l, net);
- }else if (l.type == SHORTCUT) {
- resize_shortcut_layer(&l, w, h);
- }else if (l.type == UPSAMPLE) {
- resize_upsample_layer(&l, w, h);
+ }else if (l.type == SHORTCUT) {
+ resize_shortcut_layer(&l, w, h);
+ }else if (l.type == UPSAMPLE) {
+ resize_upsample_layer(&l, w, h);
}else if(l.type == REORG){
resize_reorg_layer(&l, w, h);
}else if(l.type == AVGPOOL){
@@ -431,7 +431,7 @@
}else if(l.type == COST){
resize_cost_layer(&l, inputs);
}else{
- fprintf(stderr, "Resizing type %d \n", (int)l.type);
+ fprintf(stderr, "Resizing type %d \n", (int)l.type);
error("Cannot resize this type of layer");
}
if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
@@ -443,9 +443,9 @@
}
#ifdef GPU
if(gpu_index >= 0){
- printf(" try to allocate workspace = %zu * sizeof(float), ", workspace_size / sizeof(float) + 1);
+ printf(" try to allocate workspace = %zu * sizeof(float), ", workspace_size / sizeof(float) + 1);
net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1);
- printf(" CUDA allocate done! \n");
+ printf(" CUDA allocate done! \n");
}else {
free(net->workspace);
net->workspace = calloc(1, workspace_size);
@@ -493,6 +493,11 @@
return def;
}
+layer* get_network_layer(network* net, int i)
+{
+ return net->layers + i;
+}
+
image get_network_image(network net)
{
int i;
@@ -515,7 +520,7 @@
if(l.type == CONVOLUTIONAL){
prev = visualize_convolutional_layer(l, buff, prev);
}
- }
+ }
}
void top_predictions(network net, int k, int *index)
@@ -546,112 +551,112 @@
int num_detections(network *net, float thresh)
{
- int i;
- int s = 0;
- for (i = 0; i < net->n; ++i) {
- layer l = net->layers[i];
- if (l.type == YOLO) {
- s += yolo_num_detections(l, thresh);
- }
- if (l.type == DETECTION || l.type == REGION) {
- s += l.w*l.h*l.n;
- }
- }
- return s;
+ int i;
+ int s = 0;
+ for (i = 0; i < net->n; ++i) {
+ layer l = net->layers[i];
+ if (l.type == YOLO) {
+ s += yolo_num_detections(l, thresh);
+ }
+ if (l.type == DETECTION || l.type == REGION) {
+ s += l.w*l.h*l.n;
+ }
+ }
+ return s;
}
detection *make_network_boxes(network *net, float thresh, int *num)
{
- layer l = net->layers[net->n - 1];
- int i;
- int nboxes = num_detections(net, thresh);
- if (num) *num = nboxes;
- detection *dets = calloc(nboxes, sizeof(detection));
- for (i = 0; i < nboxes; ++i) {
- dets[i].prob = calloc(l.classes, sizeof(float));
- if (l.coords > 4) {
- dets[i].mask = calloc(l.coords - 4, sizeof(float));
- }
- }
- return dets;
+ layer l = net->layers[net->n - 1];
+ int i;
+ int nboxes = num_detections(net, thresh);
+ if (num) *num = nboxes;
+ detection *dets = calloc(nboxes, sizeof(detection));
+ for (i = 0; i < nboxes; ++i) {
+ dets[i].prob = calloc(l.classes, sizeof(float));
+ if (l.coords > 4) {
+ dets[i].mask = calloc(l.coords - 4, sizeof(float));
+ }
+ }
+ return dets;
}
void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, float thresh, int *map, float hier, int relative, detection *dets, int letter)
{
- box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
- float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
- int i, j;
- for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float));
- get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map);
- for (j = 0; j < l.w*l.h*l.n; ++j) {
- dets[j].classes = l.classes;
- dets[j].bbox = boxes[j];
- dets[j].objectness = 1;
- for (i = 0; i < l.classes; ++i) {
- dets[j].prob[i] = probs[j][i];
- }
- }
+ box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
+ float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
+ int i, j;
+ for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float));
+ get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map);
+ for (j = 0; j < l.w*l.h*l.n; ++j) {
+ dets[j].classes = l.classes;
+ dets[j].bbox = boxes[j];
+ dets[j].objectness = 1;
+ for (i = 0; i < l.classes; ++i) {
+ dets[j].prob[i] = probs[j][i];
+ }
+ }
- free(boxes);
- free_ptrs((void **)probs, l.w*l.h*l.n);
+ free(boxes);
+ free_ptrs((void **)probs, l.w*l.h*l.n);
- //correct_region_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative);
- correct_yolo_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative, letter);
+ //correct_region_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative);
+ correct_yolo_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative, letter);
}
void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter)
{
- int prev_classes = -1;
- int j;
- for (j = 0; j < net->n; ++j) {
- layer l = net->layers[j];
- if (l.type == YOLO) {
- int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter);
- dets += count;
- if (prev_classes < 0) prev_classes = l.classes;
- else if (prev_classes != l.classes) {
- printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n",
- prev_classes, l.classes);
- }
- }
- if (l.type == REGION) {
- custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter);
- //get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets);
- dets += l.w*l.h*l.n;
- }
- if (l.type == DETECTION) {
- get_detection_detections(l, w, h, thresh, dets);
- dets += l.w*l.h*l.n;
- }
- }
+ int prev_classes = -1;
+ int j;
+ for (j = 0; j < net->n; ++j) {
+ layer l = net->layers[j];
+ if (l.type == YOLO) {
+ int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter);
+ dets += count;
+ if (prev_classes < 0) prev_classes = l.classes;
+ else if (prev_classes != l.classes) {
+ printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n",
+ prev_classes, l.classes);
+ }
+ }
+ if (l.type == REGION) {
+ custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter);
+ //get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets);
+ dets += l.w*l.h*l.n;
+ }
+ if (l.type == DETECTION) {
+ get_detection_detections(l, w, h, thresh, dets);
+ dets += l.w*l.h*l.n;
+ }
+ }
}
detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter)
{
- detection *dets = make_network_boxes(net, thresh, num);
- fill_network_boxes(net, w, h, thresh, hier, map, relative, dets, letter);
- return dets;
+ detection *dets = make_network_boxes(net, thresh, num);
+ fill_network_boxes(net, w, h, thresh, hier, map, relative, dets, letter);
+ return dets;
}
void free_detections(detection *dets, int n)
{
- int i;
- for (i = 0; i < n; ++i) {
- free(dets[i].prob);
- if (dets[i].mask) free(dets[i].mask);
- }
- free(dets);
+ int i;
+ for (i = 0; i < n; ++i) {
+ free(dets[i].prob);
+ if (dets[i].mask) free(dets[i].mask);
+ }
+ free(dets);
}
float *network_predict_image(network *net, image im)
{
- //image imr = letterbox_image(im, net->w, net->h);
- image imr = resize_image(im, net->w, net->h);
- set_batch_network(net, 1);
- float *p = network_predict(*net, imr.data);
- free_image(imr);
- return p;
+ //image imr = letterbox_image(im, net->w, net->h);
+ image imr = resize_image(im, net->w, net->h);
+ set_batch_network(net, 1);
+ float *p = network_predict(*net, imr.data);
+ free_image(imr);
+ return p;
}
int network_width(network *net) { return net->w; }
@@ -679,7 +684,7 @@
}
}
free(X);
- return pred;
+ return pred;
}
matrix network_predict_data(network net, data test)
@@ -702,7 +707,7 @@
}
}
free(X);
- return pred;
+ return pred;
}
void print_network(network net)
@@ -744,7 +749,7 @@
printf("%5d %5d\n%5d %5d\n", a, b, c, d);
float num = pow((abs(b - c) - 1.), 2.);
float den = b + c;
- printf("%f\n", num/den);
+ printf("%f\n", num/den);
}
float network_accuracy(network net, data d)
@@ -775,70 +780,93 @@
void free_network(network net)
{
- int i;
- for (i = 0; i < net.n; ++i) {
- free_layer(net.layers[i]);
- }
- free(net.layers);
+ int i;
+ for (i = 0; i < net.n; ++i) {
+ free_layer(net.layers[i]);
+ }
+ free(net.layers);
- free(net.scales);
- free(net.steps);
- free(net.seen);
+ free(net.scales);
+ free(net.steps);
+ free(net.seen);
#ifdef GPU
- if (gpu_index >= 0) cuda_free(net.workspace);
- else free(net.workspace);
- if (*net.input_gpu) cuda_free(*net.input_gpu);
- if (*net.truth_gpu) cuda_free(*net.truth_gpu);
- if (net.input_gpu) free(net.input_gpu);
- if (net.truth_gpu) free(net.truth_gpu);
+ if (gpu_index >= 0) cuda_free(net.workspace);
+ else free(net.workspace);
+ if (*net.input_gpu) cuda_free(*net.input_gpu);
+ if (*net.truth_gpu) cuda_free(*net.truth_gpu);
+ if (net.input_gpu) free(net.input_gpu);
+ if (net.truth_gpu) free(net.truth_gpu);
- if (*net.input16_gpu) cuda_free(*net.input16_gpu);
- if (*net.output16_gpu) cuda_free(*net.output16_gpu);
- if (net.input16_gpu) free(net.input16_gpu);
- if (net.output16_gpu) free(net.output16_gpu);
- if (net.max_input16_size) free(net.max_input16_size);
- if (net.max_output16_size) free(net.max_output16_size);
+ if (*net.input16_gpu) cuda_free(*net.input16_gpu);
+ if (*net.output16_gpu) cuda_free(*net.output16_gpu);
+ if (net.input16_gpu) free(net.input16_gpu);
+ if (net.output16_gpu) free(net.output16_gpu);
+ if (net.max_input16_size) free(net.max_input16_size);
+ if (net.max_output16_size) free(net.max_output16_size);
#else
- free(net.workspace);
+ free(net.workspace);
#endif
}
void fuse_conv_batchnorm(network net)
{
- int j;
- for (j = 0; j < net.n; ++j) {
- layer *l = &net.layers[j];
+ int j;
+ for (j = 0; j < net.n; ++j) {
+ layer *l = &net.layers[j];
- if (l->type == CONVOLUTIONAL) {
- //printf(" Merges Convolutional-%d and batch_norm \n", j);
+ if (l->type == CONVOLUTIONAL) {
+ //printf(" Merges Convolutional-%d and batch_norm \n", j);
- if (l->batch_normalize) {
- int f;
- for (f = 0; f < l->n; ++f)
- {
- l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f]) + .000001f);
+ if (l->batch_normalize) {
+ int f;
+ for (f = 0; f < l->n; ++f)
+ {
+ l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f]) + .000001f);
- const size_t filter_size = l->size*l->size*l->c;
- int i;
- for (i = 0; i < filter_size; ++i) {
- int w_index = f*filter_size + i;
+ const size_t filter_size = l->size*l->size*l->c;
+ int i;
+ for (i = 0; i < filter_size; ++i) {
+ int w_index = f*filter_size + i;
- l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f]) + .000001f);
- }
- }
+ l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f]) + .000001f);
+ }
+ }
- l->batch_normalize = 0;
+ l->batch_normalize = 0;
#ifdef GPU
- if (gpu_index >= 0) {
- push_convolutional_layer(*l);
- }
+ if (gpu_index >= 0) {
+ push_convolutional_layer(*l);
+ }
#endif
- }
- }
- else {
- //printf(" Fusion skip layer type: %d \n", l->type);
- }
- }
+ }
+ }
+ else {
+ //printf(" Fusion skip layer type: %d \n", l->type);
+ }
+ }
}
+
+
+
+void calculate_binary_weights(network net)
+{
+ int j;
+ for (j = 0; j < net.n; ++j) {
+ layer *l = &net.layers[j];
+
+ if (l->type == CONVOLUTIONAL) {
+ //printf(" Merges Convolutional-%d and batch_norm \n", j);
+
+ if (l->xnor) {
+ //printf("\n %d \n", j);
+ l->lda_align = 256; // 256bit for AVX2
+
+ binary_align_weights(l);
+ }
+ }
+ }
+ //printf("\n calculate_binary_weights Done! \n");
+
+}
\ No newline at end of file
--
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