From ae43c2bc32fbb838bfebeeaf2c2b058ccab5c83c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@burninator.cs.washington.edu>
Date: Thu, 23 Jun 2016 05:31:14 +0000
Subject: [PATCH] hi
---
src/cost_layer.c | 114 +++++++++++++++++++++++++++++++++++++-------------------
1 files changed, 75 insertions(+), 39 deletions(-)
diff --git a/src/cost_layer.c b/src/cost_layer.c
index 34c8fb5..fdba777 100644
--- a/src/cost_layer.c
+++ b/src/cost_layer.c
@@ -10,8 +10,9 @@
COST_TYPE get_cost_type(char *s)
{
if (strcmp(s, "sse")==0) return SSE;
- if (strcmp(s, "detection")==0) return DETECTION;
- fprintf(stderr, "Couldn't find activation function %s, going with SSE\n", s);
+ 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;
}
@@ -20,68 +21,103 @@
switch(a){
case SSE:
return "sse";
- case DETECTION:
- return "detection";
+ case MASKED:
+ return "masked";
+ case SMOOTH:
+ return "smooth";
}
return "sse";
}
-cost_layer *make_cost_layer(int batch, int inputs, COST_TYPE type)
+cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale)
{
fprintf(stderr, "Cost Layer: %d inputs\n", inputs);
- cost_layer *layer = calloc(1, sizeof(cost_layer));
- layer->batch = batch;
- layer->inputs = inputs;
- layer->type = type;
- layer->delta = calloc(inputs*batch, sizeof(float));
- layer->output = calloc(1, sizeof(float));
+ cost_layer l = {0};
+ l.type = COST;
+
+ l.scale = scale;
+ l.batch = batch;
+ l.inputs = inputs;
+ l.outputs = inputs;
+ l.cost_type = cost_type;
+ l.delta = calloc(inputs*batch, sizeof(float));
+ l.output = calloc(inputs*batch, sizeof(float));
+ l.cost = calloc(1, sizeof(float));
#ifdef GPU
- layer->delta_gpu = cuda_make_array(layer->delta, inputs*batch);
+ l.delta_gpu = cuda_make_array(l.output, inputs*batch);
+ l.output_gpu = cuda_make_array(l.delta, inputs*batch);
#endif
- return layer;
+ return l;
}
-void forward_cost_layer(cost_layer layer, float *input, float *truth)
+void resize_cost_layer(cost_layer *l, int inputs)
{
- if (!truth) return;
- copy_cpu(layer.batch*layer.inputs, truth, 1, layer.delta, 1);
- axpy_cpu(layer.batch*layer.inputs, -1, input, 1, layer.delta, 1);
- if(layer.type == DETECTION){
+ 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
+}
+
+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 < layer.batch*layer.inputs; ++i){
- if((i%25) && !truth[(i/25)*25]) layer.delta[i] = 0;
+ for(i = 0; i < l.batch*l.inputs; ++i){
+ if(state.truth[i] == SECRET_NUM) state.input[i] = SECRET_NUM;
}
}
- *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
- //printf("cost: %f\n", *layer.output);
+ 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 layer, float *input, float *delta)
+void backward_cost_layer(const cost_layer l, network_state state)
{
- copy_cpu(layer.batch*layer.inputs, layer.delta, 1, delta, 1);
+ axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, state.delta, 1);
}
#ifdef GPU
-void forward_cost_layer_gpu(cost_layer layer, float * input, float * truth)
+void pull_cost_layer(cost_layer l)
{
- if (!truth) return;
-
- copy_ongpu(layer.batch*layer.inputs, truth, 1, layer.delta_gpu, 1);
- axpy_ongpu(layer.batch*layer.inputs, -1, input, 1, layer.delta_gpu, 1);
-
- if(layer.type==DETECTION){
- mask_ongpu(layer.inputs*layer.batch, layer.delta_gpu, truth, 25);
- }
-
- cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs);
- *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
- //printf("cost: %f\n", *layer.output);
+ cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
}
-void backward_cost_layer_gpu(const cost_layer layer, float * input, float * delta)
+void push_cost_layer(cost_layer l)
{
- copy_ongpu(layer.batch*layer.inputs, layer.delta_gpu, 1, delta, 1);
+ cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);
+}
+
+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);
+ }
+
+ 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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