From 5b6be00d4b1ffd671c20c4c72d2239c924eaa3d4 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Thu, 23 Aug 2018 12:28:34 +0000
Subject: [PATCH] Added yolov3-tiny_xnor.cfg
---
src/cost_layer.c | 53 ++++++++++++++++++++++++++++++++++++++++-------------
1 files changed, 40 insertions(+), 13 deletions(-)
diff --git a/src/cost_layer.c b/src/cost_layer.c
index 39ae809..39d2398 100644
--- a/src/cost_layer.c
+++ b/src/cost_layer.c
@@ -31,7 +31,7 @@
cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale)
{
- fprintf(stderr, "Cost Layer: %d inputs\n", inputs);
+ fprintf(stderr, "cost %4d\n", inputs);
cost_layer l = {0};
l.type = COST;
@@ -41,9 +41,17 @@
l.outputs = inputs;
l.cost_type = cost_type;
l.delta = calloc(inputs*batch, sizeof(float));
- l.output = calloc(1, sizeof(float));
+ l.output = calloc(inputs*batch, sizeof(float));
+ l.cost = calloc(1, sizeof(float));
+
+ l.forward = forward_cost_layer;
+ l.backward = backward_cost_layer;
#ifdef GPU
- l.delta_gpu = cuda_make_array(l.delta, inputs*batch);
+ l.forward_gpu = forward_cost_layer_gpu;
+ l.backward_gpu = backward_cost_layer_gpu;
+
+ l.delta_gpu = cuda_make_array(l.output, inputs*batch);
+ l.output_gpu = cuda_make_array(l.delta, inputs*batch);
#endif
return l;
}
@@ -53,9 +61,12 @@
l->inputs = inputs;
l->outputs = inputs;
l->delta = realloc(l->delta, inputs*l->batch*sizeof(float));
+ l->output = realloc(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
}
@@ -69,13 +80,11 @@
}
}
if(l.cost_type == SMOOTH){
- smooth_l1_cpu(l.batch*l.inputs, state.input, state.truth, l.delta);
+ smooth_l1_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output);
} else {
- copy_cpu(l.batch*l.inputs, state.truth, 1, l.delta, 1);
- axpy_cpu(l.batch*l.inputs, -1, state.input, 1, l.delta, 1);
+ l2_cpu(l.batch*l.inputs, state.input, state.truth, l.delta, l.output);
}
- *(l.output) = dot_cpu(l.batch*l.inputs, l.delta, 1, l.delta, 1);
- //printf("cost: %f\n", *l.output);
+ l.cost[0] = sum_array(l.output, l.batch*l.inputs);
}
void backward_cost_layer(const cost_layer l, network_state state)
@@ -95,6 +104,15 @@
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;
@@ -103,14 +121,23 @@
}
if(l.cost_type == SMOOTH){
- smooth_l1_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu);
+ smooth_l1_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu);
} else {
- copy_ongpu(l.batch*l.inputs, state.truth, 1, l.delta_gpu, 1);
- axpy_ongpu(l.batch*l.inputs, -1, state.input, 1, l.delta_gpu, 1);
+ l2_gpu(l.batch*l.inputs, state.input, state.truth, l.delta_gpu, l.output_gpu);
}
- cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
- *(l.output) = dot_cpu(l.batch*l.inputs, l.delta, 1, l.delta, 1);
+ 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)
--
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