From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/cost_layer.c | 296 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 148 insertions(+), 148 deletions(-)
diff --git a/lib/detecter_tools/darknet/cost_layer.c b/lib/detecter_tools/darknet/cost_layer.c
index dcddb88..ed1cc13 100644
--- a/lib/detecter_tools/darknet/cost_layer.c
+++ b/lib/detecter_tools/darknet/cost_layer.c
@@ -1,148 +1,148 @@
-#include "cost_layer.h"
-#include "utils.h"
-#include "dark_cuda.h"
-#include "blas.h"
-#include <math.h>
-#include <string.h>
-#include <stdlib.h>
-#include <stdio.h>
-
-COST_TYPE get_cost_type(char *s)
-{
- if (strcmp(s, "sse")==0) return SSE;
- if (strcmp(s, "masked")==0) return MASKED;
- if (strcmp(s, "smooth")==0) return SMOOTH;
- fprintf(stderr, "Couldn't find cost type %s, going with SSE\n", s);
- return SSE;
-}
-
-char *get_cost_string(COST_TYPE a)
-{
- switch(a){
- case SSE:
- return "sse";
- case MASKED:
- return "masked";
- case SMOOTH:
- return "smooth";
- default:
- return "sse";
- }
-}
-
-cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale)
-{
- fprintf(stderr, "cost %4d\n", inputs);
- cost_layer l = { (LAYER_TYPE)0 };
- l.type = COST;
-
- l.scale = scale;
- l.batch = batch;
- l.inputs = inputs;
- l.outputs = inputs;
- l.cost_type = cost_type;
- l.delta = (float*)xcalloc(inputs * batch, sizeof(float));
- l.output = (float*)xcalloc(inputs * batch, sizeof(float));
- l.cost = (float*)xcalloc(1, sizeof(float));
-
- l.forward = forward_cost_layer;
- l.backward = backward_cost_layer;
- #ifdef GPU
- l.forward_gpu = forward_cost_layer_gpu;
- l.backward_gpu = backward_cost_layer_gpu;
-
- l.delta_gpu = cuda_make_array(l.delta, inputs*batch);
- l.output_gpu = cuda_make_array(l.output, inputs*batch);
- #endif
- return l;
-}
-
-void resize_cost_layer(cost_layer *l, int inputs)
-{
- l->inputs = inputs;
- l->outputs = inputs;
- l->delta = (float*)xrealloc(l->delta, inputs * l->batch * sizeof(float));
- l->output = (float*)xrealloc(l->output, inputs * l->batch * sizeof(float));
-#ifdef GPU
- cuda_free(l->delta_gpu);
- cuda_free(l->output_gpu);
- l->delta_gpu = cuda_make_array(l->delta, inputs*l->batch);
- l->output_gpu = cuda_make_array(l->output, inputs*l->batch);
-#endif
-}
-
-void forward_cost_layer(cost_layer l, network_state state)
-{
- if (!state.truth) return;
- if(l.cost_type == MASKED){
- int i;
- for(i = 0; i < l.batch*l.inputs; ++i){
- if(state.truth[i] == SECRET_NUM) state.input[i] = SECRET_NUM;
- }
- }
- if(l.cost_type == SMOOTH){
- smooth_l1_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output);
- } else {
- l2_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output);
- }
- l.cost[0] = sum_array(l.output, l.batch*l.inputs);
-}
-
-void backward_cost_layer(const cost_layer l, network_state state)
-{
- axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, state.delta, 1);
-}
-
-#ifdef GPU
-
-void pull_cost_layer(cost_layer l)
-{
- cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
-}
-
-void push_cost_layer(cost_layer l)
-{
- cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);
-}
-
-int float_abs_compare (const void * a, const void * b)
-{
- float fa = *(const float*) a;
- if(fa < 0) fa = -fa;
- float fb = *(const float*) b;
- if(fb < 0) fb = -fb;
- return (fa > fb) - (fa < fb);
-}
-
-void forward_cost_layer_gpu(cost_layer l, network_state state)
-{
- if (!state.truth) return;
- if (l.cost_type == MASKED) {
- mask_ongpu(l.batch*l.inputs, state.input, SECRET_NUM, state.truth);
- }
-
- if(l.cost_type == SMOOTH){
- smooth_l1_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu);
- } else {
- l2_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu);
- }
-
- if(l.ratio){
- cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
- qsort(l.delta, l.batch*l.inputs, sizeof(float), float_abs_compare);
- int n = (1-l.ratio) * l.batch*l.inputs;
- float thresh = l.delta[n];
- thresh = 0;
- printf("%f\n", thresh);
- supp_ongpu(l.batch*l.inputs, thresh, l.delta_gpu, 1);
- }
-
- cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs);
- l.cost[0] = sum_array(l.output, l.batch*l.inputs);
-}
-
-void backward_cost_layer_gpu(const cost_layer l, network_state state)
-{
- axpy_ongpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, state.delta, 1);
-}
-#endif
+#include "cost_layer.h"
+#include "utils.h"
+#include "dark_cuda.h"
+#include "blas.h"
+#include <math.h>
+#include <string.h>
+#include <stdlib.h>
+#include <stdio.h>
+
+COST_TYPE get_cost_type(char *s)
+{
+ if (strcmp(s, "sse")==0) return SSE;
+ if (strcmp(s, "masked")==0) return MASKED;
+ if (strcmp(s, "smooth")==0) return SMOOTH;
+ fprintf(stderr, "Couldn't find cost type %s, going with SSE\n", s);
+ return SSE;
+}
+
+char *get_cost_string(COST_TYPE a)
+{
+ switch(a){
+ case SSE:
+ return "sse";
+ case MASKED:
+ return "masked";
+ case SMOOTH:
+ return "smooth";
+ default:
+ return "sse";
+ }
+}
+
+cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale)
+{
+ fprintf(stderr, "cost %4d\n", inputs);
+ cost_layer l = { (LAYER_TYPE)0 };
+ l.type = COST;
+
+ l.scale = scale;
+ l.batch = batch;
+ l.inputs = inputs;
+ l.outputs = inputs;
+ l.cost_type = cost_type;
+ l.delta = (float*)xcalloc(inputs * batch, sizeof(float));
+ l.output = (float*)xcalloc(inputs * batch, sizeof(float));
+ l.cost = (float*)xcalloc(1, sizeof(float));
+
+ l.forward = forward_cost_layer;
+ l.backward = backward_cost_layer;
+ #ifdef GPU
+ l.forward_gpu = forward_cost_layer_gpu;
+ l.backward_gpu = backward_cost_layer_gpu;
+
+ l.delta_gpu = cuda_make_array(l.delta, inputs*batch);
+ l.output_gpu = cuda_make_array(l.output, inputs*batch);
+ #endif
+ return l;
+}
+
+void resize_cost_layer(cost_layer *l, int inputs)
+{
+ l->inputs = inputs;
+ l->outputs = inputs;
+ l->delta = (float*)xrealloc(l->delta, inputs * l->batch * sizeof(float));
+ l->output = (float*)xrealloc(l->output, inputs * l->batch * sizeof(float));
+#ifdef GPU
+ cuda_free(l->delta_gpu);
+ cuda_free(l->output_gpu);
+ l->delta_gpu = cuda_make_array(l->delta, inputs*l->batch);
+ l->output_gpu = cuda_make_array(l->output, inputs*l->batch);
+#endif
+}
+
+void forward_cost_layer(cost_layer l, network_state state)
+{
+ if (!state.truth) return;
+ if(l.cost_type == MASKED){
+ int i;
+ for(i = 0; i < l.batch*l.inputs; ++i){
+ if(state.truth[i] == SECRET_NUM) state.input[i] = SECRET_NUM;
+ }
+ }
+ if(l.cost_type == SMOOTH){
+ smooth_l1_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output);
+ } else {
+ l2_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output);
+ }
+ l.cost[0] = sum_array(l.output, l.batch*l.inputs);
+}
+
+void backward_cost_layer(const cost_layer l, network_state state)
+{
+ axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, state.delta, 1);
+}
+
+#ifdef GPU
+
+void pull_cost_layer(cost_layer l)
+{
+ cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
+}
+
+void push_cost_layer(cost_layer l)
+{
+ cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);
+}
+
+int float_abs_compare (const void * a, const void * b)
+{
+ float fa = *(const float*) a;
+ if(fa < 0) fa = -fa;
+ float fb = *(const float*) b;
+ if(fb < 0) fb = -fb;
+ return (fa > fb) - (fa < fb);
+}
+
+void forward_cost_layer_gpu(cost_layer l, network_state state)
+{
+ if (!state.truth) return;
+ if (l.cost_type == MASKED) {
+ mask_ongpu(l.batch*l.inputs, state.input, SECRET_NUM, state.truth);
+ }
+
+ if(l.cost_type == SMOOTH){
+ smooth_l1_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu);
+ } else {
+ l2_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu);
+ }
+
+ if(l.ratio){
+ cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
+ qsort(l.delta, l.batch*l.inputs, sizeof(float), float_abs_compare);
+ int n = (1-l.ratio) * l.batch*l.inputs;
+ float thresh = l.delta[n];
+ thresh = 0;
+ printf("%f\n", thresh);
+ supp_ongpu(l.batch*l.inputs, thresh, l.delta_gpu, 1);
+ }
+
+ cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs);
+ l.cost[0] = sum_array(l.output, l.batch*l.inputs);
+}
+
+void backward_cost_layer_gpu(const cost_layer l, network_state state)
+{
+ axpy_ongpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, state.delta, 1);
+}
+#endif
--
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