From d0b9326a352ed2fbc3ae66fdef40b4533a2f211d Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 11 Aug 2015 06:22:27 +0000
Subject: [PATCH] Hacks to get nightmare to not break gridsizing
---
src/cost_layer.c | 118 +++++++++++++++++++++++++----------------------------------
1 files changed, 50 insertions(+), 68 deletions(-)
diff --git a/src/cost_layer.c b/src/cost_layer.c
index 6951956..d1ae6e5 100644
--- a/src/cost_layer.c
+++ b/src/cost_layer.c
@@ -1,6 +1,7 @@
#include "cost_layer.h"
#include "utils.h"
-#include "mini_blas.h"
+#include "cuda.h"
+#include "blas.h"
#include <math.h>
#include <string.h>
#include <stdlib.h>
@@ -9,7 +10,7 @@
COST_TYPE get_cost_type(char *s)
{
if (strcmp(s, "sse")==0) return SSE;
- if (strcmp(s, "detection")==0) return DETECTION;
+ if (strcmp(s, "masked")==0) return MASKED;
fprintf(stderr, "Couldn't find activation function %s, going with SSE\n", s);
return SSE;
}
@@ -19,98 +20,79 @@
switch(a){
case SSE:
return "sse";
- case DETECTION:
- return "detection";
+ case MASKED:
+ return "masked";
}
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)
{
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.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);
- if(layer.type == DETECTION){
+ if (!state.truth) return;
+ if(l.cost_type == MASKED){
int i;
- for(i = 0; i < layer.batch*layer.inputs; ++i){
- if((i%5) && !truth[(i/5)*5]) 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);
+ 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);
+ axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
}
#ifdef GPU
-cl_kernel get_mask_kernel()
+void pull_cost_layer(cost_layer l)
{
- static int init = 0;
- static cl_kernel kernel;
- if(!init){
- kernel = get_kernel("src/axpy.cl", "mask", 0);
- init = 1;
+ 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);
+}
+
+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);
}
- return kernel;
+
+ 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 mask_ongpu(int n, cl_mem x, cl_mem mask, int mod)
+void backward_cost_layer_gpu(const cost_layer l, network_state state)
{
- cl_kernel kernel = get_mask_kernel();
- cl_command_queue queue = cl.queue;
-
- cl_uint i = 0;
- cl.error = clSetKernelArg(kernel, i++, sizeof(n), (void*) &n);
- cl.error = clSetKernelArg(kernel, i++, sizeof(x), (void*) &x);
- cl.error = clSetKernelArg(kernel, i++, sizeof(mask), (void*) &mask);
- cl.error = clSetKernelArg(kernel, i++, sizeof(mod), (void*) &mod);
- check_error(cl);
-
- const size_t global_size[] = {n};
-
- cl.error = clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
- check_error(cl);
-
-}
-
-void forward_cost_layer_gpu(cost_layer layer, cl_mem input, cl_mem truth)
-{
- 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);
-
- if(layer.type==DETECTION){
- mask_ongpu(layer.inputs*layer.batch, layer.delta_cl, truth, 5);
- }
-
- 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);
- //printf("cost: %f\n", *layer.output);
-}
-
-void backward_cost_layer_gpu(const cost_layer layer, cl_mem input, cl_mem delta)
-{
- copy_ongpu(layer.batch*layer.inputs, layer.delta_cl, 1, delta, 1);
+ axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1);
}
#endif
--
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