From afe245329bdd14f36ca773fd7d497fc628b0535a Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 10 Jun 2015 18:14:04 +0000
Subject: [PATCH] Better load messaging

---
 src/cost_layer.c |   97 ++++++++++++++++++++++++++++++++++++------------
 1 files changed, 73 insertions(+), 24 deletions(-)

diff --git a/src/cost_layer.c b/src/cost_layer.c
index dd0ff90..24f6ffa 100644
--- a/src/cost_layer.c
+++ b/src/cost_layer.c
@@ -1,49 +1,98 @@
 #include "cost_layer.h"
-#include "mini_blas.h"
+#include "utils.h"
+#include "cuda.h"
+#include "blas.h"
 #include <math.h>
+#include <string.h>
 #include <stdlib.h>
 #include <stdio.h>
 
-cost_layer *make_cost_layer(int batch, int inputs)
+COST_TYPE get_cost_type(char *s)
+{
+    if (strcmp(s, "sse")==0) return SSE;
+    if (strcmp(s, "masked")==0) return MASKED;
+    fprintf(stderr, "Couldn't find activation function %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";
+    }
+    return "sse";
+}
+
+cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type)
 {
     fprintf(stderr, "Cost Layer: %d inputs\n", inputs);
-    cost_layer *layer = calloc(1, sizeof(cost_layer));
-    layer->batch = batch;
-    layer->inputs = inputs;
-    layer->delta = calloc(inputs*batch, sizeof(float));
-    layer->output = calloc(1, sizeof(float));
+    cost_layer l = {0};
+    l.type = COST;
+
+    l.batch = batch;
+    l.inputs = inputs;
+    l.outputs = inputs;
+    l.cost_type = cost_type;
+    l.delta = calloc(inputs*batch, sizeof(float));
+    l.output = calloc(1, sizeof(float));
     #ifdef GPU
-    layer->delta_cl = cl_make_array(layer->delta, inputs*batch);
+    l.delta_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 forward_cost_layer(cost_layer l, network_state state)
 {
-    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);
-    *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
+    if (!state.truth) return;
+    if(l.cost_type == MASKED){
+        int i;
+        for(i = 0; i < l.batch*l.inputs; ++i){
+            if(state.truth[i] == 0) state.input[i] = 0;
+        }
+    }
+    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);
+    *(l.output) = dot_cpu(l.batch*l.inputs, l.delta, 1, l.delta, 1);
+    //printf("cost: %f\n", *l.output);
 }
 
-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);
+    copy_cpu(l.batch*l.inputs, l.delta, 1, state.delta, 1);
 }
 
 #ifdef GPU
-void forward_cost_layer_gpu(cost_layer layer, cl_mem input, cl_mem truth)
+
+void pull_cost_layer(cost_layer l)
 {
-    if (!truth) return;
-    copy_ongpu(layer.batch*layer.inputs, truth, 1, layer.delta_cl, 1);
-    axpy_ongpu(layer.batch*layer.inputs, -1, input, 1, layer.delta_cl, 1);
-    cl_read_array(layer.delta_cl, layer.delta, layer.batch*layer.inputs);
-    *(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
+    cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
 }
 
-void backward_cost_layer_gpu(const cost_layer layer, cl_mem input, cl_mem delta)
+void push_cost_layer(cost_layer l)
 {
-    copy_ongpu(layer.batch*layer.inputs, layer.delta_cl, 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, state.truth);
+    }
+    
+    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);
+
+    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);
+}
+
+void backward_cost_layer_gpu(const cost_layer l, network_state state)
+{
+    copy_ongpu(l.batch*l.inputs, l.delta_gpu, 1, state.delta, 1);
 }
 #endif
 

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