From dcb000b553d051429a49c8729dc5b1af632e8532 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 12 Mar 2015 05:20:15 +0000
Subject: [PATCH] refactoring and added DARK ZONE
---
src/dropout_layer_kernels.cu | 18
src/softmax_layer_kernels.cu | 14
src/convolutional_layer.h | 19
src/maxpool_layer_kernels.cu | 8
src/crop_layer.c | 6
src/crop_layer.h | 5
src/deconvolutional_layer.h | 19
src/convolutional_kernels.cu | 33
src/dropout_layer.c | 21
src/maxpool_layer.c | 10
src/cost_layer.h | 9
src/network.c | 197 ++-----
src/deconvolutional_kernels.cu | 26
src/maxpool_layer.h | 9
src/normalization_layer.c | 15
src/cost_layer.c | 36
src/normalization_layer.h | 5
src/softmax_layer.h | 11
src/network.h | 12
src/params.h | 12
src/dropout_layer.h | 11
src/network_kernels.cu | 133 ++---
src/connected_layer.c | 80 --
src/connected_layer.h | 20
src/data.c | 86 ++
src/detection_layer.h | 10
src/softmax_layer.c | 10
src/deconvolutional_layer.c | 32
src/data.h | 4
src/detection_layer.c | 95 ++-
/dev/null | 14
src/detection.c | 37
src/convolutional_layer.c | 34
src/parser.c | 474 +++++------------
src/captcha.c | 2
src/crop_layer_kernels.cu | 6
36 files changed, 640 insertions(+), 893 deletions(-)
diff --git a/src/captcha.c b/src/captcha.c
index 17b3f06..40a4082 100644
--- a/src/captcha.c
+++ b/src/captcha.c
@@ -16,7 +16,7 @@
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
int imgs = 1024;
int i = net.seen/imgs;
- list *plist = get_paths("/data/captcha/train.list");
+ list *plist = get_paths("/data/captcha/train.base");
char **paths = (char **)list_to_array(plist);
printf("%d\n", plist->size);
clock_t time;
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 642570c..9df0e8f 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -9,15 +9,11 @@
#include <stdlib.h>
#include <string.h>
-connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, float learning_rate, float momentum, float decay)
+connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation)
{
int i;
connected_layer *layer = calloc(1, sizeof(connected_layer));
- layer->learning_rate = learning_rate;
- layer->momentum = momentum;
- layer->decay = decay;
-
layer->inputs = inputs;
layer->outputs = outputs;
layer->batch=batch;
@@ -59,41 +55,17 @@
return layer;
}
-void secret_update_connected_layer(connected_layer *layer)
+void update_connected_layer(connected_layer layer, float learning_rate, float momentum, float decay)
{
- int n = layer->outputs*layer->inputs;
- float dot = dot_cpu(n, layer->weight_updates, 1, layer->weight_prev, 1);
- float mag = sqrt(dot_cpu(n, layer->weight_updates, 1, layer->weight_updates, 1))
- * sqrt(dot_cpu(n, layer->weight_prev, 1, layer->weight_prev, 1));
- float cos = dot/mag;
- if(cos > .3) layer->learning_rate *= 1.1;
- else if (cos < -.3) layer-> learning_rate /= 1.1;
+ axpy_cpu(layer.outputs, learning_rate, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.outputs, momentum, layer.bias_updates, 1);
- scal_cpu(n, layer->momentum, layer->weight_prev, 1);
- axpy_cpu(n, 1, layer->weight_updates, 1, layer->weight_prev, 1);
- scal_cpu(n, 0, layer->weight_updates, 1);
-
- scal_cpu(layer->outputs, layer->momentum, layer->bias_prev, 1);
- axpy_cpu(layer->outputs, 1, layer->bias_updates, 1, layer->bias_prev, 1);
- scal_cpu(layer->outputs, 0, layer->bias_updates, 1);
-
- axpy_cpu(layer->outputs, layer->learning_rate, layer->bias_prev, 1, layer->biases, 1);
-
- axpy_cpu(layer->inputs*layer->outputs, -layer->decay, layer->weights, 1, layer->weight_prev, 1);
- axpy_cpu(layer->inputs*layer->outputs, layer->learning_rate, layer->weight_prev, 1, layer->weights, 1);
+ axpy_cpu(layer.inputs*layer.outputs, -decay, layer.weights, 1, layer.weight_updates, 1);
+ axpy_cpu(layer.inputs*layer.outputs, learning_rate, layer.weight_updates, 1, layer.weights, 1);
+ scal_cpu(layer.inputs*layer.outputs, momentum, layer.weight_updates, 1);
}
-void update_connected_layer(connected_layer layer)
-{
- axpy_cpu(layer.outputs, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
- scal_cpu(layer.outputs, layer.momentum, layer.bias_updates, 1);
-
- axpy_cpu(layer.inputs*layer.outputs, -layer.decay, layer.weights, 1, layer.weight_updates, 1);
- axpy_cpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates, 1, layer.weights, 1);
- scal_cpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates, 1);
-}
-
-void forward_connected_layer(connected_layer layer, float *input)
+void forward_connected_layer(connected_layer layer, network_state state)
{
int i;
for(i = 0; i < layer.batch; ++i){
@@ -102,14 +74,14 @@
int m = layer.batch;
int k = layer.inputs;
int n = layer.outputs;
- float *a = input;
+ float *a = state.input;
float *b = layer.weights;
float *c = layer.output;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
activate_array(layer.output, layer.outputs*layer.batch, layer.activation);
}
-void backward_connected_layer(connected_layer layer, float *input, float *delta)
+void backward_connected_layer(connected_layer layer, network_state state)
{
int i;
float alpha = 1./layer.batch;
@@ -120,7 +92,7 @@
int m = layer.inputs;
int k = layer.batch;
int n = layer.outputs;
- float *a = input;
+ float *a = state.input;
float *b = layer.delta;
float *c = layer.weight_updates;
gemm(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
@@ -131,7 +103,7 @@
a = layer.delta;
b = layer.weights;
- c = delta;
+ c = state.delta;
if(c) gemm(0,1,m,n,k,1,a,k,b,k,0,c,n);
}
@@ -154,23 +126,17 @@
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.outputs);
}
-void update_connected_layer_gpu(connected_layer layer)
+void update_connected_layer_gpu(connected_layer layer, float learning_rate, float momentum, float decay)
{
-/*
- cuda_pull_array(layer.weights_gpu, layer.weights, layer.inputs*layer.outputs);
- cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.inputs*layer.outputs);
- printf("Weights: %f updates: %f\n", mag_array(layer.weights, layer.inputs*layer.outputs), layer.learning_rate*mag_array(layer.weight_updates, layer.inputs*layer.outputs));
-*/
+ axpy_ongpu(layer.outputs, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
+ scal_ongpu(layer.outputs, momentum, layer.bias_updates_gpu, 1);
- axpy_ongpu(layer.outputs, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
- scal_ongpu(layer.outputs, layer.momentum, layer.bias_updates_gpu, 1);
-
- axpy_ongpu(layer.inputs*layer.outputs, -layer.decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
- axpy_ongpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
- scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_gpu, 1);
+ axpy_ongpu(layer.inputs*layer.outputs, -decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
+ axpy_ongpu(layer.inputs*layer.outputs, learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
+ scal_ongpu(layer.inputs*layer.outputs, momentum, layer.weight_updates_gpu, 1);
}
-void forward_connected_layer_gpu(connected_layer layer, float * input)
+void forward_connected_layer_gpu(connected_layer layer, network_state state)
{
int i;
for(i = 0; i < layer.batch; ++i){
@@ -179,14 +145,14 @@
int m = layer.batch;
int k = layer.inputs;
int n = layer.outputs;
- float * a = input;
+ float * a = state.input;
float * b = layer.weights_gpu;
float * c = layer.output_gpu;
gemm_ongpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
activate_array_ongpu(layer.output_gpu, layer.outputs*layer.batch, layer.activation);
}
-void backward_connected_layer_gpu(connected_layer layer, float * input, float * delta)
+void backward_connected_layer_gpu(connected_layer layer, network_state state)
{
float alpha = 1./layer.batch;
int i;
@@ -197,7 +163,7 @@
int m = layer.inputs;
int k = layer.batch;
int n = layer.outputs;
- float * a = input;
+ float * a = state.input;
float * b = layer.delta_gpu;
float * c = layer.weight_updates_gpu;
gemm_ongpu(1,0,m,n,k,alpha,a,m,b,n,1,c,n);
@@ -208,7 +174,7 @@
a = layer.delta_gpu;
b = layer.weights_gpu;
- c = delta;
+ c = state.delta;
if(c) gemm_ongpu(0,1,m,n,k,1,a,k,b,k,0,c,n);
}
diff --git a/src/connected_layer.h b/src/connected_layer.h
index 921f06f..2642599 100644
--- a/src/connected_layer.h
+++ b/src/connected_layer.h
@@ -2,12 +2,9 @@
#define CONNECTED_LAYER_H
#include "activations.h"
+#include "params.h"
typedef struct{
- float learning_rate;
- float momentum;
- float decay;
-
int batch;
int inputs;
int outputs;
@@ -37,17 +34,16 @@
} connected_layer;
-void secret_update_connected_layer(connected_layer *layer);
-connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, float learning_rate, float momentum, float decay);
+connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation);
-void forward_connected_layer(connected_layer layer, float *input);
-void backward_connected_layer(connected_layer layer, float *input, float *delta);
-void update_connected_layer(connected_layer layer);
+void forward_connected_layer(connected_layer layer, network_state state);
+void backward_connected_layer(connected_layer layer, network_state state);
+void update_connected_layer(connected_layer layer, float learning_rate, float momentum, float decay);
#ifdef GPU
-void forward_connected_layer_gpu(connected_layer layer, float * input);
-void backward_connected_layer_gpu(connected_layer layer, float * input, float * delta);
-void update_connected_layer_gpu(connected_layer layer);
+void forward_connected_layer_gpu(connected_layer layer, network_state state);
+void backward_connected_layer_gpu(connected_layer layer, network_state state);
+void update_connected_layer_gpu(connected_layer layer, float learning_rate, float momentum, float decay);
void push_connected_layer(connected_layer layer);
void pull_connected_layer(connected_layer layer);
#endif
diff --git a/src/convolutional_kernels.cu b/src/convolutional_kernels.cu
index bcf307f..77304aa 100644
--- a/src/convolutional_kernels.cu
+++ b/src/convolutional_kernels.cu
@@ -54,7 +54,7 @@
check_error(cudaPeekAtLastError());
}
-extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, float *in)
+extern "C" void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
{
int i;
int m = layer.n;
@@ -65,7 +65,7 @@
bias_output_gpu(layer.output_gpu, layer.biases_gpu, layer.batch, layer.n, n);
for(i = 0; i < layer.batch; ++i){
- im2col_ongpu(in + i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
+ im2col_ongpu(state.input + i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
float * a = layer.filters_gpu;
float * b = layer.col_image_gpu;
float * c = layer.output_gpu;
@@ -74,7 +74,7 @@
activate_array_ongpu(layer.output_gpu, m*n*layer.batch, layer.activation);
}
-extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, float *in, float *delta_gpu)
+extern "C" void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state)
{
float alpha = 1./layer.batch;
int i;
@@ -86,17 +86,17 @@
gradient_array_ongpu(layer.output_gpu, m*k*layer.batch, layer.activation, layer.delta_gpu);
backward_bias_gpu(layer.bias_updates_gpu, layer.delta_gpu, layer.batch, layer.n, k);
- if(delta_gpu) scal_ongpu(layer.batch*layer.h*layer.w*layer.c, 0, delta_gpu, 1);
+ if(state.delta) scal_ongpu(layer.batch*layer.h*layer.w*layer.c, 0, state.delta, 1);
for(i = 0; i < layer.batch; ++i){
float * a = layer.delta_gpu;
float * b = layer.col_image_gpu;
float * c = layer.filter_updates_gpu;
- im2col_ongpu(in + i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
+ im2col_ongpu(state.input + i*layer.c*layer.h*layer.w, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_gpu);
gemm_ongpu(0,1,m,n,k,alpha,a + i*m*k,k,b,k,1,c,n);
- if(delta_gpu){
+ if(state.delta){
float * a = layer.filters_gpu;
float * b = layer.delta_gpu;
@@ -104,7 +104,7 @@
gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k);
- col2im_ongpu(layer.col_image_gpu, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta_gpu + i*layer.c*layer.h*layer.w);
+ col2im_ongpu(layer.col_image_gpu, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, state.delta + i*layer.c*layer.h*layer.w);
}
}
}
@@ -125,22 +125,15 @@
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
}
-extern "C" void update_convolutional_layer_gpu(convolutional_layer layer)
+extern "C" void update_convolutional_layer_gpu(convolutional_layer layer, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
-/*
- cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, size);
- cuda_pull_array(layer.filters_gpu, layer.filters, size);
- printf("Filter: %f updates: %f\n", mag_array(layer.filters, size), layer.learning_rate*mag_array(layer.filter_updates, size));
- */
+ axpy_ongpu(layer.n, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
+ scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1);
- axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
- scal_ongpu(layer.n,layer.momentum, layer.bias_updates_gpu, 1);
-
- axpy_ongpu(size, -layer.decay, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
- axpy_ongpu(size, layer.learning_rate, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
- scal_ongpu(size, layer.momentum, layer.filter_updates_gpu, 1);
- //pull_convolutional_layer(layer);
+ axpy_ongpu(size, -decay, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
+ axpy_ongpu(size, learning_rate, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
+ scal_ongpu(size, momentum, layer.filter_updates_gpu, 1);
}
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 7782e3d..ad0d1c1 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -41,15 +41,11 @@
return float_to_image(h,w,c,layer.delta);
}
-convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, float learning_rate, float momentum, float decay)
+convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
{
int i;
convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
- layer->learning_rate = learning_rate;
- layer->momentum = momentum;
- layer->decay = decay;
-
layer->h = h;
layer->w = w;
layer->c = c;
@@ -143,7 +139,7 @@
}
-void forward_convolutional_layer(const convolutional_layer layer, float *in)
+void forward_convolutional_layer(const convolutional_layer layer, network_state state)
{
int out_h = convolutional_out_height(layer);
int out_w = convolutional_out_width(layer);
@@ -160,16 +156,16 @@
float *c = layer.output;
for(i = 0; i < layer.batch; ++i){
- im2col_cpu(in, layer.c, layer.h, layer.w,
+ im2col_cpu(state.input, layer.c, layer.h, layer.w,
layer.size, layer.stride, layer.pad, b);
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
c += n*m;
- in += layer.c*layer.h*layer.w;
+ state.input += layer.c*layer.h*layer.w;
}
activate_array(layer.output, m*n*layer.batch, layer.activation);
}
-void backward_convolutional_layer(convolutional_layer layer, float *in, float *delta)
+void backward_convolutional_layer(convolutional_layer layer, network_state state)
{
float alpha = 1./layer.batch;
int i;
@@ -181,40 +177,40 @@
gradient_array(layer.output, m*k*layer.batch, layer.activation, layer.delta);
backward_bias(layer.bias_updates, layer.delta, layer.batch, layer.n, k);
- if(delta) memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+ if(state.delta) memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
for(i = 0; i < layer.batch; ++i){
float *a = layer.delta + i*m*k;
float *b = layer.col_image;
float *c = layer.filter_updates;
- float *im = in+i*layer.c*layer.h*layer.w;
+ float *im = state.input+i*layer.c*layer.h*layer.w;
im2col_cpu(im, layer.c, layer.h, layer.w,
layer.size, layer.stride, layer.pad, b);
gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
- if(delta){
+ if(state.delta){
a = layer.filters;
b = layer.delta + i*m*k;
c = layer.col_image;
gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
- col2im_cpu(layer.col_image, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta+i*layer.c*layer.h*layer.w);
+ col2im_cpu(layer.col_image, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, state.delta+i*layer.c*layer.h*layer.w);
}
}
}
-void update_convolutional_layer(convolutional_layer layer)
+void update_convolutional_layer(convolutional_layer layer, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
- axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
- scal_cpu(layer.n, layer.momentum, layer.bias_updates, 1);
+ axpy_cpu(layer.n, learning_rate, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.n, momentum, layer.bias_updates, 1);
- axpy_cpu(size, -layer.decay, layer.filters, 1, layer.filter_updates, 1);
- axpy_cpu(size, layer.learning_rate, layer.filter_updates, 1, layer.filters, 1);
- scal_cpu(size, layer.momentum, layer.filter_updates, 1);
+ axpy_cpu(size, -decay, layer.filters, 1, layer.filter_updates, 1);
+ axpy_cpu(size, learning_rate, layer.filter_updates, 1, layer.filters, 1);
+ scal_cpu(size, momentum, layer.filter_updates, 1);
}
diff --git a/src/convolutional_layer.h b/src/convolutional_layer.h
index 72f3f72..eaf1562 100644
--- a/src/convolutional_layer.h
+++ b/src/convolutional_layer.h
@@ -2,14 +2,11 @@
#define CONVOLUTIONAL_LAYER_H
#include "cuda.h"
+#include "params.h"
#include "image.h"
#include "activations.h"
typedef struct {
- float learning_rate;
- float momentum;
- float decay;
-
int batch;
int h,w,c;
int n;
@@ -42,9 +39,9 @@
} convolutional_layer;
#ifdef GPU
-void forward_convolutional_layer_gpu(convolutional_layer layer, float * in);
-void backward_convolutional_layer_gpu(convolutional_layer layer, float * in, float * delta_gpu);
-void update_convolutional_layer_gpu(convolutional_layer layer);
+void forward_convolutional_layer_gpu(convolutional_layer layer, network_state state);
+void backward_convolutional_layer_gpu(convolutional_layer layer, network_state state);
+void update_convolutional_layer_gpu(convolutional_layer layer, float learning_rate, float momentum, float decay);
void push_convolutional_layer(convolutional_layer layer);
void pull_convolutional_layer(convolutional_layer layer);
@@ -53,13 +50,13 @@
void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size);
#endif
-convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, float learning_rate, float momentum, float decay);
+convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation);
void resize_convolutional_layer(convolutional_layer *layer, int h, int w);
-void forward_convolutional_layer(const convolutional_layer layer, float *in);
-void update_convolutional_layer(convolutional_layer layer);
+void forward_convolutional_layer(const convolutional_layer layer, network_state state);
+void update_convolutional_layer(convolutional_layer layer, float learning_rate, float momentum, float decay);
image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_filters);
-void backward_convolutional_layer(convolutional_layer layer, float *in, float *delta);
+void backward_convolutional_layer(convolutional_layer layer, network_state state);
void bias_output(float *output, float *biases, int batch, int n, int size);
void backward_bias(float *bias_updates, float *delta, int batch, int n, int size);
diff --git a/src/cost_layer.c b/src/cost_layer.c
index 8158275..d2c616f 100644
--- a/src/cost_layer.c
+++ b/src/cost_layer.c
@@ -47,48 +47,36 @@
cuda_push_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs);
}
-void forward_cost_layer(cost_layer layer, float *input, float *truth)
+void forward_cost_layer(cost_layer layer, 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 (!state.truth) return;
+ copy_cpu(layer.batch*layer.inputs, state.truth, 1, layer.delta, 1);
+ axpy_cpu(layer.batch*layer.inputs, -1, state.input, 1, layer.delta, 1);
*(layer.output) = dot_cpu(layer.batch*layer.inputs, layer.delta, 1, layer.delta, 1);
//printf("cost: %f\n", *layer.output);
}
-void backward_cost_layer(const cost_layer layer, float *input, float *delta)
+void backward_cost_layer(const cost_layer layer, network_state state)
{
- copy_cpu(layer.batch*layer.inputs, layer.delta, 1, delta, 1);
+ copy_cpu(layer.batch*layer.inputs, layer.delta, 1, state.delta, 1);
}
#ifdef GPU
-void forward_cost_layer_gpu(cost_layer layer, float * input, float * truth)
+void forward_cost_layer_gpu(cost_layer layer, network_state state)
{
- if (!truth) return;
+ if (!state.truth) return;
- /*
- float *in = calloc(layer.inputs*layer.batch, sizeof(float));
- float *t = calloc(layer.inputs*layer.batch, sizeof(float));
- cuda_pull_array(input, in, layer.batch*layer.inputs);
- cuda_pull_array(truth, t, layer.batch*layer.inputs);
- forward_cost_layer(layer, in, t);
- cuda_push_array(layer.delta_gpu, layer.delta, layer.batch*layer.inputs);
- free(in);
- free(t);
- */
-
- 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);
+ copy_ongpu(layer.batch*layer.inputs, state.truth, 1, layer.delta_gpu, 1);
+ axpy_ongpu(layer.batch*layer.inputs, -1, state.input, 1, layer.delta_gpu, 1);
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);
}
-void backward_cost_layer_gpu(const cost_layer layer, float * input, float * delta)
+void backward_cost_layer_gpu(const cost_layer layer, network_state state)
{
- copy_ongpu(layer.batch*layer.inputs, layer.delta_gpu, 1, delta, 1);
+ copy_ongpu(layer.batch*layer.inputs, layer.delta_gpu, 1, state.delta, 1);
}
#endif
diff --git a/src/cost_layer.h b/src/cost_layer.h
index 0855405..d441698 100644
--- a/src/cost_layer.h
+++ b/src/cost_layer.h
@@ -1,5 +1,6 @@
#ifndef COST_LAYER_H
#define COST_LAYER_H
+#include "params.h"
typedef enum{
SSE
@@ -21,12 +22,12 @@
COST_TYPE get_cost_type(char *s);
char *get_cost_string(COST_TYPE a);
cost_layer *make_cost_layer(int batch, int inputs, COST_TYPE type);
-void forward_cost_layer(const cost_layer layer, float *input, float *truth);
-void backward_cost_layer(const cost_layer layer, float *input, float *delta);
+void forward_cost_layer(const cost_layer layer, network_state state);
+void backward_cost_layer(const cost_layer layer, network_state state);
#ifdef GPU
-void forward_cost_layer_gpu(cost_layer layer, float * input, float * truth);
-void backward_cost_layer_gpu(const cost_layer layer, float * input, float * delta);
+void forward_cost_layer_gpu(cost_layer layer, network_state state);
+void backward_cost_layer_gpu(const cost_layer layer, network_state state);
#endif
#endif
diff --git a/src/crop_layer.c b/src/crop_layer.c
index 3f0011d..cf1383e 100644
--- a/src/crop_layer.c
+++ b/src/crop_layer.c
@@ -28,7 +28,7 @@
return layer;
}
-void forward_crop_layer(const crop_layer layer, int train, float *input)
+void forward_crop_layer(const crop_layer layer, network_state state)
{
int i,j,c,b,row,col;
int index;
@@ -36,7 +36,7 @@
int flip = (layer.flip && rand()%2);
int dh = rand()%(layer.h - layer.crop_height + 1);
int dw = rand()%(layer.w - layer.crop_width + 1);
- if(!train){
+ if(!state.train){
flip = 0;
dh = (layer.h - layer.crop_height)/2;
dw = (layer.w - layer.crop_width)/2;
@@ -52,7 +52,7 @@
}
row = i + dh;
index = col+layer.w*(row+layer.h*(c + layer.c*b));
- layer.output[count++] = input[index];
+ layer.output[count++] = state.input[index];
}
}
}
diff --git a/src/crop_layer.h b/src/crop_layer.h
index 0d2f03b..05a511b 100644
--- a/src/crop_layer.h
+++ b/src/crop_layer.h
@@ -2,6 +2,7 @@
#define CROP_LAYER_H
#include "image.h"
+#include "params.h"
typedef struct {
int batch;
@@ -17,10 +18,10 @@
image get_crop_image(crop_layer layer);
crop_layer *make_crop_layer(int batch, int h, int w, int c, int crop_height, int crop_width, int flip);
-void forward_crop_layer(const crop_layer layer, int train, float *input);
+void forward_crop_layer(const crop_layer layer, network_state state);
#ifdef GPU
-void forward_crop_layer_gpu(crop_layer layer, int train, float *input);
+void forward_crop_layer_gpu(crop_layer layer, network_state state);
#endif
#endif
diff --git a/src/crop_layer_kernels.cu b/src/crop_layer_kernels.cu
index 628c700..8c97f35 100644
--- a/src/crop_layer_kernels.cu
+++ b/src/crop_layer_kernels.cu
@@ -24,12 +24,12 @@
output[count] = input[index];
}
-extern "C" void forward_crop_layer_gpu(crop_layer layer, int train, float *input)
+extern "C" void forward_crop_layer_gpu(crop_layer layer, network_state state)
{
int flip = (layer.flip && rand()%2);
int dh = rand()%(layer.h - layer.crop_height + 1);
int dw = rand()%(layer.w - layer.crop_width + 1);
- if(!train){
+ if(!state.train){
flip = 0;
dh = (layer.h - layer.crop_height)/2;
dw = (layer.w - layer.crop_width)/2;
@@ -39,7 +39,7 @@
dim3 dimBlock(BLOCK, 1, 1);
dim3 dimGrid((size-1)/BLOCK + 1, 1, 1);
- forward_crop_layer_kernel<<<cuda_gridsize(size), BLOCK>>>(input, size, layer.c, layer.h, layer.w,
+ forward_crop_layer_kernel<<<cuda_gridsize(size), BLOCK>>>(state.input, size, layer.c, layer.h, layer.w,
layer.crop_height, layer.crop_width, dh, dw, flip, layer.output_gpu);
check_error(cudaPeekAtLastError());
}
diff --git a/src/data.c b/src/data.c
index a429476..342edfa 100644
--- a/src/data.c
+++ b/src/data.c
@@ -18,6 +18,7 @@
int nw;
int jitter;
int classes;
+ int background;
data *d;
};
@@ -62,17 +63,62 @@
return X;
}
-void fill_truth_detection(char *path, float *truth, int classes, int height, int width, int num_height, int num_width, int dy, int dx, int jitter, int flip)
+typedef struct box{
+ int id;
+ float x,y,w,h;
+} box;
+
+box *read_boxes(char *filename, int *n)
+{
+ box *boxes = calloc(1, sizeof(box));
+ FILE *file = fopen(filename, "r");
+ if(!file) file_error(filename);
+ float x, y, h, w;
+ int id;
+ int count = 0;
+ while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){
+ boxes = realloc(boxes, (count+1)*sizeof(box));
+ boxes[count].id = id;
+ boxes[count].x = x;
+ boxes[count].y = y;
+ boxes[count].h = h;
+ boxes[count].w = w;
+ ++count;
+ }
+ fclose(file);
+ *n = count;
+ return boxes;
+}
+
+void randomize_boxes(box *b, int n)
+{
+ int i;
+ for(i = 0; i < n; ++i){
+ box swap = b[i];
+ int index = rand()%n;
+ b[i] = b[index];
+ b[index] = swap;
+ }
+}
+
+void fill_truth_detection(char *path, float *truth, int classes, int height, int width, int num_height, int num_width, int dy, int dx, int jitter, int flip, int background)
{
int box_height = height/num_height;
int box_width = width/num_width;
char *labelpath = find_replace(path, "VOC2012/JPEGImages", "labels");
labelpath = find_replace(labelpath, ".jpg", ".txt");
- FILE *file = fopen(labelpath, "r");
- if(!file) file_error(labelpath);
+ int count = 0;
+ box *boxes = read_boxes(labelpath, &count);
+ randomize_boxes(boxes, count);
float x, y, h, w;
int id;
- while(fscanf(file, "%d %f %f %f %f", &id, &x, &y, &w, &h) == 5){
+ int i, j;
+ for(i = 0; i < count; ++i){
+ x = boxes[i].x;
+ y = boxes[i].y;
+ w = boxes[i].w;
+ h = boxes[i].h;
+ id = boxes[i].id;
if(flip) x = 1-x;
x *= width + jitter;
y *= height + jitter;
@@ -88,23 +134,24 @@
float dw = (x - i*box_width)/box_width;
float dh = (y - j*box_height)/box_height;
- //printf("%d %d %d %f %f\n", id, i, j, dh, dw);
- int index = (i+j*num_width)*(4+classes);
- if(truth[index+classes]) continue;
+
+ int index = (i+j*num_width)*(4+classes+background);
+ if(truth[index+classes+background]) continue;
truth[index+id] = 1;
- index += classes;
+ index += classes+background;
truth[index++] = dh;
truth[index++] = dw;
truth[index++] = h*(height+jitter)/height;
truth[index++] = w*(width+jitter)/width;
}
- int i, j;
- for(i = 0; i < num_height*num_width*(4+classes); i += 4+classes){
- int background = 1;
- for(j = i; j < i+classes; ++j) if (truth[j]) background = 0;
- truth[i+classes-1] = background;
+ free(boxes);
+ if(background){
+ for(i = 0; i < num_height*num_width*(4+classes+background); i += 4+classes+background){
+ int object = 0;
+ for(j = i; j < i+classes; ++j) if (truth[j]) object = 1;
+ truth[i+classes] = !object;
+ }
}
- fclose(file);
}
#define NUMCHARS 37
@@ -218,20 +265,20 @@
}
}
-data load_data_detection_jitter_random(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter)
+data load_data_detection_jitter_random(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, int background)
{
char **random_paths = get_random_paths(paths, n, m);
int i;
data d;
d.shallow = 0;
d.X = load_image_paths(random_paths, n, h, w);
- int k = nh*nw*(4+classes);
+ int k = nh*nw*(4+classes+background);
d.y = make_matrix(n, k);
for(i = 0; i < n; ++i){
int dx = rand()%jitter;
int dy = rand()%jitter;
int flip = rand()%2;
- fill_truth_detection(random_paths[i], d.y.vals[i], classes, h-jitter, w-jitter, nh, nw, dy, dx, jitter, flip);
+ fill_truth_detection(random_paths[i], d.y.vals[i], classes, h-jitter, w-jitter, nh, nw, dy, dx, jitter, flip, background);
image a = float_to_image(h, w, 3, d.X.vals[i]);
if(flip) flip_image(a);
jitter_image(a,h-jitter,w-jitter,dy,dx);
@@ -245,14 +292,14 @@
{
printf("Loading data: %d\n", rand());
struct load_args a = *(struct load_args*)ptr;
- *a.d = load_data_detection_jitter_random(a.n, a.paths, a.m, a.classes, a.h, a.w, a.nh, a.nw, a.jitter);
+ *a.d = load_data_detection_jitter_random(a.n, a.paths, a.m, a.classes, a.h, a.w, a.nh, a.nw, a.jitter, a.background);
translate_data_rows(*a.d, -128);
scale_data_rows(*a.d, 1./128);
free(ptr);
return 0;
}
-pthread_t load_data_detection_thread(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, data *d)
+pthread_t load_data_detection_thread(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, int background, data *d)
{
pthread_t thread;
struct load_args *args = calloc(1, sizeof(struct load_args));
@@ -265,6 +312,7 @@
args->nw = nw;
args->classes = classes;
args->jitter = jitter;
+ args->background = background;
args->d = d;
if(pthread_create(&thread, 0, load_detection_thread, args)) {
error("Thread creation failed");
diff --git a/src/data.h b/src/data.h
index 0cae7f5..ec18627 100644
--- a/src/data.h
+++ b/src/data.h
@@ -20,8 +20,8 @@
data load_data(char **paths, int n, int m, char **labels, int k, int h, int w);
pthread_t load_data_thread(char **paths, int n, int m, char **labels, int k, int h, int w, data *d);
-pthread_t load_data_detection_thread(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, data *d);
-data load_data_detection_jitter_random(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter);
+pthread_t load_data_detection_thread(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, int background, data *d);
+data load_data_detection_jitter_random(int n, char **paths, int m, int classes, int h, int w, int nh, int nw, int jitter, int background);
data load_data_image_pathfile(char *filename, char **labels, int k, int h, int w);
data load_cifar10_data(char *filename);
diff --git a/src/deconvolutional_kernels.cu b/src/deconvolutional_kernels.cu
index 1d05a80..aeab2c3 100644
--- a/src/deconvolutional_kernels.cu
+++ b/src/deconvolutional_kernels.cu
@@ -9,7 +9,7 @@
#include "cuda.h"
}
-extern "C" void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, float *in)
+extern "C" void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state)
{
int i;
int out_h = deconvolutional_out_height(layer);
@@ -24,7 +24,7 @@
for(i = 0; i < layer.batch; ++i){
float *a = layer.filters_gpu;
- float *b = in + i*layer.c*layer.h*layer.w;
+ float *b = state.input + i*layer.c*layer.h*layer.w;
float *c = layer.col_image_gpu;
gemm_ongpu(1,0,m,n,k,1,a,m,b,n,0,c,n);
@@ -34,7 +34,7 @@
activate_array(layer.output_gpu, layer.batch*layer.n*size, layer.activation);
}
-extern "C" void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, float *in, float *delta_gpu)
+extern "C" void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state)
{
float alpha = 1./layer.batch;
int out_h = deconvolutional_out_height(layer);
@@ -45,14 +45,14 @@
gradient_array(layer.output_gpu, size*layer.n*layer.batch, layer.activation, layer.delta_gpu);
backward_bias(layer.bias_updates_gpu, layer.delta, layer.batch, layer.n, size);
- if(delta_gpu) memset(delta_gpu, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+ if(state.delta) memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
for(i = 0; i < layer.batch; ++i){
int m = layer.c;
int n = layer.size*layer.size*layer.n;
int k = layer.h*layer.w;
- float *a = in + i*m*n;
+ float *a = state.input + i*m*n;
float *b = layer.col_image_gpu;
float *c = layer.filter_updates_gpu;
@@ -60,14 +60,14 @@
layer.size, layer.stride, 0, b);
gemm_ongpu(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
- if(delta_gpu){
+ if(state.delta){
int m = layer.c;
int n = layer.h*layer.w;
int k = layer.size*layer.size*layer.n;
float *a = layer.filters_gpu;
float *b = layer.col_image_gpu;
- float *c = delta_gpu + i*n*m;
+ float *c = state.delta + i*n*m;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
@@ -90,15 +90,15 @@
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
}
-extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer)
+extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
- axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
- scal_ongpu(layer.n,layer.momentum, layer.bias_updates_gpu, 1);
+ axpy_ongpu(layer.n, learning_rate, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
+ scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1);
- axpy_ongpu(size, -layer.decay, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
- axpy_ongpu(size, layer.learning_rate, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
- scal_ongpu(size, layer.momentum, layer.filter_updates_gpu, 1);
+ axpy_ongpu(size, -decay, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
+ axpy_ongpu(size, learning_rate, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
+ scal_ongpu(size, momentum, layer.filter_updates_gpu, 1);
}
diff --git a/src/deconvolutional_layer.c b/src/deconvolutional_layer.c
index d4a8426..83147b5 100644
--- a/src/deconvolutional_layer.c
+++ b/src/deconvolutional_layer.c
@@ -43,15 +43,11 @@
return float_to_image(h,w,c,layer.delta);
}
-deconvolutional_layer *make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation, float learning_rate, float momentum, float decay)
+deconvolutional_layer *make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
{
int i;
deconvolutional_layer *layer = calloc(1, sizeof(deconvolutional_layer));
- layer->learning_rate = learning_rate;
- layer->momentum = momentum;
- layer->decay = decay;
-
layer->h = h;
layer->w = w;
layer->c = c;
@@ -120,7 +116,7 @@
#endif
}
-void forward_deconvolutional_layer(const deconvolutional_layer layer, float *in)
+void forward_deconvolutional_layer(const deconvolutional_layer layer, network_state state)
{
int i;
int out_h = deconvolutional_out_height(layer);
@@ -135,7 +131,7 @@
for(i = 0; i < layer.batch; ++i){
float *a = layer.filters;
- float *b = in + i*layer.c*layer.h*layer.w;
+ float *b = state.input + i*layer.c*layer.h*layer.w;
float *c = layer.col_image;
gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
@@ -145,7 +141,7 @@
activate_array(layer.output, layer.batch*layer.n*size, layer.activation);
}
-void backward_deconvolutional_layer(deconvolutional_layer layer, float *in, float *delta)
+void backward_deconvolutional_layer(deconvolutional_layer layer, network_state state)
{
float alpha = 1./layer.batch;
int out_h = deconvolutional_out_height(layer);
@@ -156,14 +152,14 @@
gradient_array(layer.output, size*layer.n*layer.batch, layer.activation, layer.delta);
backward_bias(layer.bias_updates, layer.delta, layer.batch, layer.n, size);
- if(delta) memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+ if(state.delta) memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
for(i = 0; i < layer.batch; ++i){
int m = layer.c;
int n = layer.size*layer.size*layer.n;
int k = layer.h*layer.w;
- float *a = in + i*m*n;
+ float *a = state.input + i*m*n;
float *b = layer.col_image;
float *c = layer.filter_updates;
@@ -171,29 +167,29 @@
layer.size, layer.stride, 0, b);
gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
- if(delta){
+ if(state.delta){
int m = layer.c;
int n = layer.h*layer.w;
int k = layer.size*layer.size*layer.n;
float *a = layer.filters;
float *b = layer.col_image;
- float *c = delta + i*n*m;
+ float *c = state.delta + i*n*m;
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
}
}
-void update_deconvolutional_layer(deconvolutional_layer layer)
+void update_deconvolutional_layer(deconvolutional_layer layer, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
- axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
- scal_cpu(layer.n, layer.momentum, layer.bias_updates, 1);
+ axpy_cpu(layer.n, learning_rate, layer.bias_updates, 1, layer.biases, 1);
+ scal_cpu(layer.n, momentum, layer.bias_updates, 1);
- axpy_cpu(size, -layer.decay, layer.filters, 1, layer.filter_updates, 1);
- axpy_cpu(size, layer.learning_rate, layer.filter_updates, 1, layer.filters, 1);
- scal_cpu(size, layer.momentum, layer.filter_updates, 1);
+ axpy_cpu(size, -decay, layer.filters, 1, layer.filter_updates, 1);
+ axpy_cpu(size, learning_rate, layer.filter_updates, 1, layer.filters, 1);
+ scal_cpu(size, momentum, layer.filter_updates, 1);
}
diff --git a/src/deconvolutional_layer.h b/src/deconvolutional_layer.h
index 1da43dc..0ece76f 100644
--- a/src/deconvolutional_layer.h
+++ b/src/deconvolutional_layer.h
@@ -2,14 +2,11 @@
#define DECONVOLUTIONAL_LAYER_H
#include "cuda.h"
+#include "params.h"
#include "image.h"
#include "activations.h"
typedef struct {
- float learning_rate;
- float momentum;
- float decay;
-
int batch;
int h,w,c;
int n;
@@ -41,18 +38,18 @@
} deconvolutional_layer;
#ifdef GPU
-void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, float * in);
-void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, float * in, float * delta_gpu);
-void update_deconvolutional_layer_gpu(deconvolutional_layer layer);
+void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state);
+void backward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state);
+void update_deconvolutional_layer_gpu(deconvolutional_layer layer, float learning_rate, float momentum, float decay);
void push_deconvolutional_layer(deconvolutional_layer layer);
void pull_deconvolutional_layer(deconvolutional_layer layer);
#endif
-deconvolutional_layer *make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation, float learning_rate, float momentum, float decay);
+deconvolutional_layer *make_deconvolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation);
void resize_deconvolutional_layer(deconvolutional_layer *layer, int h, int w);
-void forward_deconvolutional_layer(const deconvolutional_layer layer, float *in);
-void update_deconvolutional_layer(deconvolutional_layer layer);
-void backward_deconvolutional_layer(deconvolutional_layer layer, float *in, float *delta);
+void forward_deconvolutional_layer(const deconvolutional_layer layer, network_state state);
+void update_deconvolutional_layer(deconvolutional_layer layer, float learning_rate, float momentum, float decay);
+void backward_deconvolutional_layer(deconvolutional_layer layer, network_state state);
image get_deconvolutional_image(deconvolutional_layer layer);
image get_deconvolutional_delta(deconvolutional_layer layer);
diff --git a/src/detection.c b/src/detection.c
index fa8b38c..f861347 100644
--- a/src/detection.c
+++ b/src/detection.c
@@ -61,15 +61,16 @@
data train, buffer;
int im_dim = 512;
int jitter = 64;
- int classes = 21;
- pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, &buffer);
+ int classes = 20;
+ int background = 1;
+ pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
clock_t time;
while(1){
i += 1;
time=clock();
pthread_join(load_thread, 0);
train = buffer;
- load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, &buffer);
+ load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
/*
image im = float_to_image(im_dim - jitter, im_dim-jitter, 3, train.X.vals[0]);
@@ -103,10 +104,12 @@
srand(time(0));
list *plist = get_paths("/home/pjreddie/data/voc/val.txt");
+ //list *plist = get_paths("/home/pjreddie/data/voc/train.txt");
char **paths = (char **)list_to_array(plist);
- int num_output = 1225;
int im_size = 448;
- int classes = 21;
+ int classes = 20;
+ int background = 0;
+ int num_output = 7*7*(4+classes+background);
int m = plist->size;
int i = 0;
@@ -130,26 +133,18 @@
matrix pred = network_predict_data(net, val);
int j, k, class;
for(j = 0; j < pred.rows; ++j){
- for(k = 0; k < pred.cols; k += classes+4){
-
- /*
- int z;
- for(z = 0; z < 25; ++z) printf("%f, ", pred.vals[j][k+z]);
- printf("\n");
- */
-
- //if (pred.vals[j][k] > .001){
- for(class = 0; class < classes-1; ++class){
- int index = (k)/(classes+4);
+ for(k = 0; k < pred.cols; k += classes+4+background){
+ for(class = 0; class < classes; ++class){
+ int index = (k)/(classes+4+background);
int r = index/7;
int c = index%7;
- float y = (r + pred.vals[j][k+0+classes])/7.;
- float x = (c + pred.vals[j][k+1+classes])/7.;
- float h = pred.vals[j][k+2+classes];
- float w = pred.vals[j][k+3+classes];
+ int ci = k+classes+background;
+ float y = (r + pred.vals[j][ci + 0])/7.;
+ float x = (c + pred.vals[j][ci + 1])/7.;
+ float h = pred.vals[j][ci + 2];
+ float w = pred.vals[j][ci + 3];
printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, pred.vals[j][k+class], y, x, h, w);
}
- //}
}
}
diff --git a/src/detection_layer.c b/src/detection_layer.c
index 68d151a..5ca7fa2 100644
--- a/src/detection_layer.c
+++ b/src/detection_layer.c
@@ -39,28 +39,52 @@
return layer;
}
-void forward_detection_layer(const detection_layer layer, float *in, float *truth)
+
+void forward_detection_layer(const detection_layer layer, network_state state)
{
int in_i = 0;
int out_i = 0;
int locations = get_detection_layer_locations(layer);
int i,j;
for(i = 0; i < layer.batch*locations; ++i){
- int mask = (!truth || !truth[out_i + layer.classes - 1]);
+ int mask = (!state.truth || state.truth[out_i + layer.classes + 2]);
float scale = 1;
- if(layer.rescore) scale = in[in_i++];
+ if(layer.rescore) scale = state.input[in_i++];
for(j = 0; j < layer.classes; ++j){
- layer.output[out_i++] = scale*in[in_i++];
+ layer.output[out_i++] = scale*state.input[in_i++];
}
- softmax_array(layer.output + out_i - layer.classes, layer.classes, layer.output + out_i - layer.classes);
- activate_array(in+in_i, layer.coords, LOGISTIC);
+ if(!layer.rescore){
+ softmax_array(layer.output + out_i - layer.classes, layer.classes, layer.output + out_i - layer.classes);
+ activate_array(state.input+in_i, layer.coords, LOGISTIC);
+ }
for(j = 0; j < layer.coords; ++j){
- layer.output[out_i++] = mask*in[in_i++];
+ layer.output[out_i++] = mask*state.input[in_i++];
}
}
}
-void backward_detection_layer(const detection_layer layer, float *in, float *delta)
+void dark_zone(detection_layer layer, int index, network_state state)
+{
+ int size = layer.classes+layer.rescore+layer.coords;
+ int location = (index%(7*7*size)) / size ;
+ int r = location / 7;
+ int c = location % 7;
+ int class = index%size;
+ if(layer.rescore) --class;
+ int dr, dc;
+ for(dr = -1; dr <= 1; ++dr){
+ for(dc = -1; dc <= 1; ++dc){
+ if(!(dr || dc)) continue;
+ if((r + dr) > 6 || (r + dr) < 0) continue;
+ if((c + dc) > 6 || (c + dc) < 0) continue;
+ int di = (dr*7 + dc) * size;
+ if(state.truth[index+di]) continue;
+ layer.delta[index + di] = 0;
+ }
+ }
+}
+
+void backward_detection_layer(const detection_layer layer, network_state state)
{
int locations = get_detection_layer_locations(layer);
int i,j;
@@ -69,49 +93,68 @@
for(i = 0; i < layer.batch*locations; ++i){
float scale = 1;
float latent_delta = 0;
- if(layer.rescore) scale = in[in_i++];
+ if(layer.rescore) scale = state.input[in_i++];
+ if(!layer.rescore){
+ for(j = 0; j < layer.classes-1; ++j){
+ if(state.truth[out_i + j]) dark_zone(layer, out_i+j, state);
+ }
+ }
for(j = 0; j < layer.classes; ++j){
- latent_delta += in[in_i]*layer.delta[out_i];
- delta[in_i++] = scale*layer.delta[out_i++];
+ latent_delta += state.input[in_i]*layer.delta[out_i];
+ state.delta[in_i++] = scale*layer.delta[out_i++];
}
-
- gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
+
+ if (!layer.rescore) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
for(j = 0; j < layer.coords; ++j){
- delta[in_i++] = layer.delta[out_i++];
+ state.delta[in_i++] = layer.delta[out_i++];
}
- if(layer.rescore) delta[in_i-layer.coords-layer.classes-layer.rescore] = latent_delta;
+ if(layer.rescore) state.delta[in_i-layer.coords-layer.classes-layer.rescore] = latent_delta;
}
}
#ifdef GPU
-void forward_detection_layer_gpu(const detection_layer layer, float *in, float *truth)
+void forward_detection_layer_gpu(const detection_layer layer, network_state state)
{
int outputs = get_detection_layer_output_size(layer);
float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
float *truth_cpu = 0;
- if(truth){
+ if(state.truth){
truth_cpu = calloc(layer.batch*outputs, sizeof(float));
- cuda_pull_array(truth, truth_cpu, layer.batch*outputs);
+ cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
}
- cuda_pull_array(in, in_cpu, layer.batch*layer.inputs);
- forward_detection_layer(layer, in_cpu, truth_cpu);
+ cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
+ network_state cpu_state;
+ cpu_state.train = state.train;
+ cpu_state.truth = truth_cpu;
+ cpu_state.input = in_cpu;
+ forward_detection_layer(layer, cpu_state);
cuda_push_array(layer.output_gpu, layer.output, layer.batch*outputs);
- free(in_cpu);
- if(truth_cpu) free(truth_cpu);
+ free(cpu_state.input);
+ if(cpu_state.truth) free(cpu_state.truth);
}
-void backward_detection_layer_gpu(detection_layer layer, float *in, float *delta)
+void backward_detection_layer_gpu(detection_layer layer, network_state state)
{
int outputs = get_detection_layer_output_size(layer);
float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
float *delta_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
+ float *truth_cpu = 0;
+ if(state.truth){
+ truth_cpu = calloc(layer.batch*outputs, sizeof(float));
+ cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
+ }
+ network_state cpu_state;
+ cpu_state.train = state.train;
+ cpu_state.input = in_cpu;
+ cpu_state.truth = truth_cpu;
+ cpu_state.delta = delta_cpu;
- cuda_pull_array(in, in_cpu, layer.batch*layer.inputs);
+ cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
- backward_detection_layer(layer, in_cpu, delta_cpu);
- cuda_push_array(delta, delta_cpu, layer.batch*layer.inputs);
+ backward_detection_layer(layer, cpu_state);
+ cuda_push_array(state.delta, delta_cpu, layer.batch*layer.inputs);
free(in_cpu);
free(delta_cpu);
diff --git a/src/detection_layer.h b/src/detection_layer.h
index e7e9e20..69a83a7 100644
--- a/src/detection_layer.h
+++ b/src/detection_layer.h
@@ -1,6 +1,8 @@
#ifndef DETECTION_LAYER_H
#define DETECTION_LAYER_H
+#include "params.h"
+
typedef struct {
int batch;
int inputs;
@@ -16,13 +18,13 @@
} detection_layer;
detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore);
-void forward_detection_layer(const detection_layer layer, float *in, float *truth);
-void backward_detection_layer(const detection_layer layer, float *in, float *delta);
+void forward_detection_layer(const detection_layer layer, network_state state);
+void backward_detection_layer(const detection_layer layer, network_state state);
int get_detection_layer_output_size(detection_layer layer);
#ifdef GPU
-void forward_detection_layer_gpu(const detection_layer layer, float *in, float *truth);
-void backward_detection_layer_gpu(detection_layer layer, float *in, float *delta);
+void forward_detection_layer_gpu(const detection_layer layer, network_state state);
+void backward_detection_layer_gpu(detection_layer layer, network_state state);
#endif
#endif
diff --git a/src/dropout_layer.c b/src/dropout_layer.c
index 32a3408..7fbf8ff 100644
--- a/src/dropout_layer.c
+++ b/src/dropout_layer.c
@@ -1,4 +1,5 @@
#include "dropout_layer.h"
+#include "params.h"
#include "utils.h"
#include "cuda.h"
#include <stdlib.h>
@@ -11,11 +12,9 @@
layer->probability = probability;
layer->inputs = inputs;
layer->batch = batch;
- layer->output = calloc(inputs*batch, sizeof(float));
layer->rand = calloc(inputs*batch, sizeof(float));
layer->scale = 1./(1.-probability);
#ifdef GPU
- layer->output_gpu = cuda_make_array(layer->output, inputs*batch);
layer->rand_gpu = cuda_make_array(layer->rand, inputs*batch);
#endif
return layer;
@@ -23,36 +22,34 @@
void resize_dropout_layer(dropout_layer *layer, int inputs)
{
- layer->output = realloc(layer->output, layer->inputs*layer->batch*sizeof(float));
layer->rand = realloc(layer->rand, layer->inputs*layer->batch*sizeof(float));
#ifdef GPU
- cuda_free(layer->output_gpu);
cuda_free(layer->rand_gpu);
- layer->output_gpu = cuda_make_array(layer->output, inputs*layer->batch);
layer->rand_gpu = cuda_make_array(layer->rand, inputs*layer->batch);
#endif
}
-void forward_dropout_layer(dropout_layer layer, float *input)
+void forward_dropout_layer(dropout_layer layer, network_state state)
{
int i;
+ if (!state.train) return;
for(i = 0; i < layer.batch * layer.inputs; ++i){
float r = rand_uniform();
layer.rand[i] = r;
- if(r < layer.probability) layer.output[i] = 0;
- else layer.output[i] = input[i]*layer.scale;
+ if(r < layer.probability) state.input[i] = 0;
+ else state.input[i] *= layer.scale;
}
}
-void backward_dropout_layer(dropout_layer layer, float *delta)
+void backward_dropout_layer(dropout_layer layer, network_state state)
{
int i;
- if(!delta) return;
+ if(!state.delta) return;
for(i = 0; i < layer.batch * layer.inputs; ++i){
float r = layer.rand[i];
- if(r < layer.probability) delta[i] = 0;
- else delta[i] *= layer.scale;
+ if(r < layer.probability) state.delta[i] = 0;
+ else state.delta[i] *= layer.scale;
}
}
diff --git a/src/dropout_layer.h b/src/dropout_layer.h
index 051ce47..d12d4a1 100644
--- a/src/dropout_layer.h
+++ b/src/dropout_layer.h
@@ -1,5 +1,6 @@
#ifndef DROPOUT_LAYER_H
#define DROPOUT_LAYER_H
+#include "params.h"
typedef struct{
int batch;
@@ -7,22 +8,20 @@
float probability;
float scale;
float *rand;
- float *output;
#ifdef GPU
float * rand_gpu;
- float * output_gpu;
#endif
} dropout_layer;
dropout_layer *make_dropout_layer(int batch, int inputs, float probability);
-void forward_dropout_layer(dropout_layer layer, float *input);
-void backward_dropout_layer(dropout_layer layer, float *delta);
+void forward_dropout_layer(dropout_layer layer, network_state state);
+void backward_dropout_layer(dropout_layer layer, network_state state);
void resize_dropout_layer(dropout_layer *layer, int inputs);
#ifdef GPU
-void forward_dropout_layer_gpu(dropout_layer layer, float * input);
-void backward_dropout_layer_gpu(dropout_layer layer, float * delta);
+void forward_dropout_layer_gpu(dropout_layer layer, network_state state);
+void backward_dropout_layer_gpu(dropout_layer layer, network_state state);
#endif
#endif
diff --git a/src/dropout_layer_kernels.cu b/src/dropout_layer_kernels.cu
index 371f0dc..94f61ab 100644
--- a/src/dropout_layer_kernels.cu
+++ b/src/dropout_layer_kernels.cu
@@ -2,32 +2,32 @@
#include "dropout_layer.h"
#include "cuda.h"
#include "utils.h"
+#include "params.h"
}
-__global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand, float prob, float scale, float *output)
+__global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand, float prob, float scale)
{
int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
- if(id < size) output[id] = (rand[id] < prob) ? 0 : input[id]*scale;
+ if(id < size) input[id] = (rand[id] < prob) ? 0 : input[id]*scale;
}
-extern "C" void forward_dropout_layer_gpu(dropout_layer layer, float * input)
+extern "C" void forward_dropout_layer_gpu(dropout_layer layer, network_state state)
{
+ if (!state.train) return;
int j;
int size = layer.inputs*layer.batch;
for(j = 0; j < size; ++j) layer.rand[j] = rand_uniform();
cuda_push_array(layer.rand_gpu, layer.rand, layer.inputs*layer.batch);
- yoloswag420blazeit360noscope<<<cuda_gridsize(size), BLOCK>>>(input, size, layer.rand_gpu, layer.probability,
- layer.scale, layer.output_gpu);
+ yoloswag420blazeit360noscope<<<cuda_gridsize(size), BLOCK>>>(state.input, size, layer.rand_gpu, layer.probability, layer.scale);
check_error(cudaPeekAtLastError());
}
-extern "C" void backward_dropout_layer_gpu(dropout_layer layer, float *delta)
+extern "C" void backward_dropout_layer_gpu(dropout_layer layer, network_state state)
{
- if(!delta) return;
+ if(!state.delta) return;
int size = layer.inputs*layer.batch;
- yoloswag420blazeit360noscope<<<cuda_gridsize(size), BLOCK>>>(delta, size, layer.rand_gpu, layer.probability,
- layer.scale, delta);
+ yoloswag420blazeit360noscope<<<cuda_gridsize(size), BLOCK>>>(state.delta, size, layer.rand_gpu, layer.probability, layer.scale);
check_error(cudaPeekAtLastError());
}
diff --git a/src/freeweight_layer.c b/src/freeweight_layer.c
deleted file mode 100644
index b4c02db..0000000
--- a/src/freeweight_layer.c
+++ /dev/null
@@ -1,25 +0,0 @@
-#include "freeweight_layer.h"
-#include "stdlib.h"
-#include "stdio.h"
-
-freeweight_layer *make_freeweight_layer(int batch, int inputs)
-{
- fprintf(stderr, "Freeweight Layer: %d inputs\n", inputs);
- freeweight_layer *layer = calloc(1, sizeof(freeweight_layer));
- layer->inputs = inputs;
- layer->batch = batch;
- return layer;
-}
-
-void forward_freeweight_layer(freeweight_layer layer, float *input)
-{
- int i;
- for(i = 0; i < layer.batch * layer.inputs; ++i){
- input[i] *= 2.*((float)rand()/RAND_MAX);
- }
-}
-
-void backward_freeweight_layer(freeweight_layer layer, float *input, float *delta)
-{
- // Don't do shit LULZ
-}
diff --git a/src/freeweight_layer.h b/src/freeweight_layer.h
deleted file mode 100644
index bfca2c1..0000000
--- a/src/freeweight_layer.h
+++ /dev/null
@@ -1,14 +0,0 @@
-#ifndef FREEWEIGHT_LAYER_H
-#define FREEWEIGHT_LAYER_H
-
-typedef struct{
- int batch;
- int inputs;
-} freeweight_layer;
-
-freeweight_layer *make_freeweight_layer(int batch, int inputs);
-
-void forward_freeweight_layer(freeweight_layer layer, float *input);
-void backward_freeweight_layer(freeweight_layer layer, float *input, float *delta);
-
-#endif
diff --git a/src/maxpool_layer.c b/src/maxpool_layer.c
index ef7176d..790cb28 100644
--- a/src/maxpool_layer.c
+++ b/src/maxpool_layer.c
@@ -58,7 +58,7 @@
#endif
}
-void forward_maxpool_layer(const maxpool_layer layer, float *input)
+void forward_maxpool_layer(const maxpool_layer layer, network_state state)
{
int b,i,j,k,l,m;
int w_offset = (-layer.size-1)/2 + 1;
@@ -82,7 +82,7 @@
int index = cur_w + layer.w*(cur_h + layer.h*(k + b*layer.c));
int valid = (cur_h >= 0 && cur_h < layer.h &&
cur_w >= 0 && cur_w < layer.w);
- float val = (valid != 0) ? input[index] : -FLT_MAX;
+ float val = (valid != 0) ? state.input[index] : -FLT_MAX;
max_i = (val > max) ? index : max_i;
max = (val > max) ? val : max;
}
@@ -95,16 +95,16 @@
}
}
-void backward_maxpool_layer(const maxpool_layer layer, float *delta)
+void backward_maxpool_layer(const maxpool_layer layer, network_state state)
{
int i;
int h = (layer.h-1)/layer.stride + 1;
int w = (layer.w-1)/layer.stride + 1;
int c = layer.c;
- memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+ memset(state.delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
for(i = 0; i < h*w*c*layer.batch; ++i){
int index = layer.indexes[i];
- delta[index] += layer.delta[i];
+ state.delta[index] += layer.delta[i];
}
}
diff --git a/src/maxpool_layer.h b/src/maxpool_layer.h
index 89fb245..cbd6a76 100644
--- a/src/maxpool_layer.h
+++ b/src/maxpool_layer.h
@@ -2,6 +2,7 @@
#define MAXPOOL_LAYER_H
#include "image.h"
+#include "params.h"
#include "cuda.h"
typedef struct {
@@ -22,12 +23,12 @@
image get_maxpool_image(maxpool_layer layer);
maxpool_layer *make_maxpool_layer(int batch, int h, int w, int c, int size, int stride);
void resize_maxpool_layer(maxpool_layer *layer, int h, int w);
-void forward_maxpool_layer(const maxpool_layer layer, float *input);
-void backward_maxpool_layer(const maxpool_layer layer, float *delta);
+void forward_maxpool_layer(const maxpool_layer layer, network_state state);
+void backward_maxpool_layer(const maxpool_layer layer, network_state state);
#ifdef GPU
-void forward_maxpool_layer_gpu(maxpool_layer layer, float * input);
-void backward_maxpool_layer_gpu(maxpool_layer layer, float * delta);
+void forward_maxpool_layer_gpu(maxpool_layer layer, network_state state);
+void backward_maxpool_layer_gpu(maxpool_layer layer, network_state state);
#endif
#endif
diff --git a/src/maxpool_layer_kernels.cu b/src/maxpool_layer_kernels.cu
index a5c8209..6c633a9 100644
--- a/src/maxpool_layer_kernels.cu
+++ b/src/maxpool_layer_kernels.cu
@@ -80,7 +80,7 @@
prev_delta[index] = d;
}
-extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, float *input)
+extern "C" void forward_maxpool_layer_gpu(maxpool_layer layer, network_state state)
{
int h = (layer.h-1)/layer.stride + 1;
int w = (layer.w-1)/layer.stride + 1;
@@ -88,15 +88,15 @@
size_t n = h*w*c*layer.batch;
- forward_maxpool_layer_kernel<<<cuda_gridsize(n), BLOCK>>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, input, layer.output_gpu, layer.indexes_gpu);
+ forward_maxpool_layer_kernel<<<cuda_gridsize(n), BLOCK>>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, state.input, layer.output_gpu, layer.indexes_gpu);
check_error(cudaPeekAtLastError());
}
-extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, float * delta)
+extern "C" void backward_maxpool_layer_gpu(maxpool_layer layer, network_state state)
{
size_t n = layer.h*layer.w*layer.c*layer.batch;
- backward_maxpool_layer_kernel<<<cuda_gridsize(n), BLOCK>>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.delta_gpu, delta, layer.indexes_gpu);
+ backward_maxpool_layer_kernel<<<cuda_gridsize(n), BLOCK>>>(n, layer.h, layer.w, layer.c, layer.stride, layer.size, layer.delta_gpu, state.delta, layer.indexes_gpu);
check_error(cudaPeekAtLastError());
}
diff --git a/src/network.c b/src/network.c
index b60f059..89c5621 100644
--- a/src/network.c
+++ b/src/network.c
@@ -4,6 +4,7 @@
#include "image.h"
#include "data.h"
#include "utils.h"
+#include "params.h"
#include "crop_layer.h"
#include "connected_layer.h"
@@ -13,7 +14,6 @@
#include "maxpool_layer.h"
#include "cost_layer.h"
#include "normalization_layer.h"
-#include "freeweight_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
@@ -36,8 +36,6 @@
return "normalization";
case DROPOUT:
return "dropout";
- case FREEWEIGHT:
- return "freeweight";
case CROP:
return "crop";
case COST:
@@ -48,16 +46,18 @@
return "none";
}
-network make_network(int n, int batch)
+network make_network(int n)
{
network net;
net.n = n;
- net.batch = batch;
net.layers = calloc(net.n, sizeof(void *));
net.types = calloc(net.n, sizeof(LAYER_TYPE));
net.outputs = 0;
net.output = 0;
net.seen = 0;
+ net.batch = 0;
+ net.inputs = 0;
+ net.h = net.w = net.c = 0;
#ifdef GPU
net.input_gpu = calloc(1, sizeof(float *));
net.truth_gpu = calloc(1, sizeof(float *));
@@ -65,68 +65,41 @@
return net;
}
-void forward_network(network net, float *input, float *truth, int train)
+void forward_network(network net, network_state state)
{
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- forward_convolutional_layer(layer, input);
- input = layer.output;
+ forward_convolutional_layer(*(convolutional_layer *)net.layers[i], state);
}
else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- forward_deconvolutional_layer(layer, input);
- input = layer.output;
+ forward_deconvolutional_layer(*(deconvolutional_layer *)net.layers[i], state);
}
else if(net.types[i] == DETECTION){
- detection_layer layer = *(detection_layer *)net.layers[i];
- forward_detection_layer(layer, input, truth);
- input = layer.output;
+ forward_detection_layer(*(detection_layer *)net.layers[i], state);
}
else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- forward_connected_layer(layer, input);
- input = layer.output;
+ forward_connected_layer(*(connected_layer *)net.layers[i], state);
}
else if(net.types[i] == CROP){
- crop_layer layer = *(crop_layer *)net.layers[i];
- forward_crop_layer(layer, train, input);
- input = layer.output;
+ forward_crop_layer(*(crop_layer *)net.layers[i], state);
}
else if(net.types[i] == COST){
- cost_layer layer = *(cost_layer *)net.layers[i];
- forward_cost_layer(layer, input, truth);
+ forward_cost_layer(*(cost_layer *)net.layers[i], state);
}
else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- forward_softmax_layer(layer, input);
- input = layer.output;
+ forward_softmax_layer(*(softmax_layer *)net.layers[i], state);
}
else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- forward_maxpool_layer(layer, input);
- input = layer.output;
+ forward_maxpool_layer(*(maxpool_layer *)net.layers[i], state);
}
else if(net.types[i] == NORMALIZATION){
- normalization_layer layer = *(normalization_layer *)net.layers[i];
- forward_normalization_layer(layer, input);
- input = layer.output;
+ forward_normalization_layer(*(normalization_layer *)net.layers[i], state);
}
else if(net.types[i] == DROPOUT){
- if(!train) continue;
- dropout_layer layer = *(dropout_layer *)net.layers[i];
- forward_dropout_layer(layer, input);
- input = layer.output;
+ forward_dropout_layer(*(dropout_layer *)net.layers[i], state);
}
- else if(net.types[i] == FREEWEIGHT){
- if(!train) continue;
- //freeweight_layer layer = *(freeweight_layer *)net.layers[i];
- //forward_freeweight_layer(layer, input);
- }
- //char buff[256];
- //sprintf(buff, "layer %d", i);
- //cuda_compare(get_network_output_gpu_layer(net, i), input, get_network_output_size_layer(net, i)*net.batch, buff);
+ state.input = get_network_output_layer(net, i);
}
}
@@ -136,15 +109,15 @@
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- update_convolutional_layer(layer);
+ update_convolutional_layer(layer, net.learning_rate, net.momentum, net.decay);
}
else if(net.types[i] == DECONVOLUTIONAL){
deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- update_deconvolutional_layer(layer);
+ update_deconvolutional_layer(layer, net.learning_rate, net.momentum, net.decay);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer(layer);
+ update_connected_layer(layer, net.learning_rate, net.momentum, net.decay);
}
}
}
@@ -152,37 +125,27 @@
float *get_network_output_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.output;
+ return ((convolutional_layer *)net.layers[i]) -> output;
} else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- return layer.output;
+ return ((deconvolutional_layer *)net.layers[i]) -> output;
} else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output;
+ return ((maxpool_layer *)net.layers[i]) -> output;
} else if(net.types[i] == DETECTION){
- detection_layer layer = *(detection_layer *)net.layers[i];
- return layer.output;
+ return ((detection_layer *)net.layers[i]) -> output;
} else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- return layer.output;
+ return ((softmax_layer *)net.layers[i]) -> output;
} else if(net.types[i] == DROPOUT){
- dropout_layer layer = *(dropout_layer *)net.layers[i];
- return layer.output;
- } else if(net.types[i] == FREEWEIGHT){
return get_network_output_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- return layer.output;
+ return ((connected_layer *)net.layers[i]) -> output;
} else if(net.types[i] == CROP){
- crop_layer layer = *(crop_layer *)net.layers[i];
- return layer.output;
+ return ((crop_layer *)net.layers[i]) -> output;
} else if(net.types[i] == NORMALIZATION){
- normalization_layer layer = *(normalization_layer *)net.layers[i];
- return layer.output;
+ return ((normalization_layer *)net.layers[i]) -> output;
}
return 0;
}
+
float *get_network_output(network net)
{
int i;
@@ -210,8 +173,6 @@
} else if(net.types[i] == DROPOUT){
if(i == 0) return 0;
return get_network_delta_layer(net, i-1);
- } else if(net.types[i] == FREEWEIGHT){
- return get_network_delta_layer(net, i-1);
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta;
@@ -257,54 +218,53 @@
return max_index(out, k);
}
-void backward_network(network net, float *input, float *truth)
+void backward_network(network net, network_state state)
{
int i;
- float *prev_input;
- float *prev_delta;
+ float *original_input = state.input;
for(i = net.n-1; i >= 0; --i){
if(i == 0){
- prev_input = input;
- prev_delta = 0;
+ state.input = original_input;
+ state.delta = 0;
}else{
- prev_input = get_network_output_layer(net, i-1);
- prev_delta = get_network_delta_layer(net, i-1);
+ state.input = get_network_output_layer(net, i-1);
+ state.delta = get_network_delta_layer(net, i-1);
}
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- backward_convolutional_layer(layer, prev_input, prev_delta);
+ backward_convolutional_layer(layer, state);
} else if(net.types[i] == DECONVOLUTIONAL){
deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- backward_deconvolutional_layer(layer, prev_input, prev_delta);
+ backward_deconvolutional_layer(layer, state);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- if(i != 0) backward_maxpool_layer(layer, prev_delta);
+ if(i != 0) backward_maxpool_layer(layer, state);
}
else if(net.types[i] == DROPOUT){
dropout_layer layer = *(dropout_layer *)net.layers[i];
- backward_dropout_layer(layer, prev_delta);
+ backward_dropout_layer(layer, state);
}
else if(net.types[i] == DETECTION){
detection_layer layer = *(detection_layer *)net.layers[i];
- backward_detection_layer(layer, prev_input, prev_delta);
+ backward_detection_layer(layer, state);
}
else if(net.types[i] == NORMALIZATION){
normalization_layer layer = *(normalization_layer *)net.layers[i];
- if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);
+ if(i != 0) backward_normalization_layer(layer, state);
}
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
- if(i != 0) backward_softmax_layer(layer, prev_delta);
+ if(i != 0) backward_softmax_layer(layer, state);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- backward_connected_layer(layer, prev_input, prev_delta);
+ backward_connected_layer(layer, state);
}
else if(net.types[i] == COST){
cost_layer layer = *(cost_layer *)net.layers[i];
- backward_cost_layer(layer, prev_input, prev_delta);
+ backward_cost_layer(layer, state);
}
}
}
@@ -314,8 +274,12 @@
#ifdef GPU
if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
#endif
- forward_network(net, x, y, 1);
- backward_network(net, x, y);
+ network_state state;
+ state.input = x;
+ state.truth = y;
+ state.train = 1;
+ forward_network(net, state);
+ backward_network(net, state);
float error = get_network_cost(net);
update_network(net);
return error;
@@ -361,15 +325,17 @@
float train_network_batch(network net, data d, int n)
{
int i,j;
+ network_state state;
+ state.train = 1;
float sum = 0;
int batch = 2;
for(i = 0; i < n; ++i){
for(j = 0; j < batch; ++j){
int index = rand()%d.X.rows;
- float *x = d.X.vals[index];
- float *y = d.y.vals[index];
- forward_network(net, x, y, 1);
- backward_network(net, x, y);
+ state.input = d.X.vals[index];
+ state.truth = d.y.vals[index];
+ forward_network(net, state);
+ backward_network(net, state);
sum += get_network_cost(net);
}
update_network(net);
@@ -377,28 +343,6 @@
return (float)sum/(n*batch);
}
-void set_learning_network(network *net, float rate, float momentum, float decay)
-{
- int i;
- net->learning_rate=rate;
- net->momentum = momentum;
- net->decay = decay;
- for(i = 0; i < net->n; ++i){
- if(net->types[i] == CONVOLUTIONAL){
- convolutional_layer *layer = (convolutional_layer *)net->layers[i];
- layer->learning_rate=rate;
- layer->momentum = momentum;
- layer->decay = decay;
- }
- else if(net->types[i] == CONNECTED){
- connected_layer *layer = (connected_layer *)net->layers[i];
- layer->learning_rate=rate;
- layer->momentum = momentum;
- layer->decay = decay;
- }
- }
-}
-
void set_batch_network(network *net, int b)
{
net->batch = b;
@@ -425,10 +369,6 @@
detection_layer *layer = (detection_layer *) net->layers[i];
layer->batch = b;
}
- else if(net->types[i] == FREEWEIGHT){
- freeweight_layer *layer = (freeweight_layer *) net->layers[i];
- layer->batch = b;
- }
else if(net->types[i] == SOFTMAX){
softmax_layer *layer = (softmax_layer *)net->layers[i];
layer->batch = b;
@@ -472,15 +412,11 @@
crop_layer layer = *(crop_layer *) net.layers[i];
return layer.c*layer.h*layer.w;
}
- else if(net.types[i] == FREEWEIGHT){
- freeweight_layer layer = *(freeweight_layer *) net.layers[i];
- return layer.inputs;
- }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
}
- printf("Can't find input size\n");
+ fprintf(stderr, "Can't find input size\n");
return 0;
}
@@ -505,7 +441,7 @@
image output = get_maxpool_image(layer);
return output.h*output.w*output.c;
}
- else if(net.types[i] == CROP){
+ else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *) net.layers[i];
return layer.c*layer.crop_height*layer.crop_width;
}
@@ -517,15 +453,11 @@
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
}
- else if(net.types[i] == FREEWEIGHT){
- freeweight_layer layer = *(freeweight_layer *) net.layers[i];
- return layer.inputs;
- }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.inputs;
}
- printf("Can't find output size\n");
+ fprintf(stderr, "Can't find output size\n");
return 0;
}
@@ -650,11 +582,16 @@
float *network_predict(network net, float *input)
{
- #ifdef GPU
+#ifdef GPU
if(gpu_index >= 0) return network_predict_gpu(net, input);
- #endif
+#endif
- forward_network(net, input, 0, 0);
+ network_state state;
+ state.input = input;
+ state.truth = 0;
+ state.train = 0;
+ state.delta = 0;
+ forward_network(net, state);
float *out = get_network_output(net);
return out;
}
diff --git a/src/network.h b/src/network.h
index d2fb346..9099b24 100644
--- a/src/network.h
+++ b/src/network.h
@@ -3,6 +3,7 @@
#define NETWORK_H
#include "image.h"
+#include "params.h"
#include "data.h"
typedef enum {
@@ -14,7 +15,6 @@
DETECTION,
NORMALIZATION,
DROPOUT,
- FREEWEIGHT,
CROP,
COST
} LAYER_TYPE;
@@ -31,6 +31,9 @@
int outputs;
float *output;
+ int inputs;
+ int h, w, c;
+
#ifdef GPU
float **input_gpu;
float **truth_gpu;
@@ -47,9 +50,9 @@
void compare_networks(network n1, network n2, data d);
char *get_layer_string(LAYER_TYPE a);
-network make_network(int n, int batch);
-void forward_network(network net, float *input, float *truth, int train);
-void backward_network(network net, float *input, float *truth);
+network make_network(int n);
+void forward_network(network net, network_state state);
+void backward_network(network net, network_state state);
void update_network(network net);
float train_network(network net, data d);
@@ -75,7 +78,6 @@
void visualize_network(network net);
int resize_network(network net, int h, int w, int c);
void set_batch_network(network *net, int b);
-void set_learning_network(network *net, float rate, float momentum, float decay);
int get_network_input_size(network net);
float get_network_cost(network net);
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 928c7f9..acc31d7 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -6,6 +6,7 @@
#include "image.h"
#include "data.h"
#include "utils.h"
+#include "params.h"
#include "crop_layer.h"
#include "connected_layer.h"
@@ -15,7 +16,6 @@
#include "maxpool_layer.h"
#include "cost_layer.h"
#include "normalization_layer.h"
-#include "freeweight_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
}
@@ -24,108 +24,78 @@
extern "C" float * get_network_delta_gpu_layer(network net, int i);
float *get_network_output_gpu(network net);
-void forward_network_gpu(network net, float * input, float * truth, int train)
+void forward_network_gpu(network net, network_state state)
{
int i;
for(i = 0; i < net.n; ++i){
- //clock_t time = clock();
if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- forward_convolutional_layer_gpu(layer, input);
- input = layer.output_gpu;
+ forward_convolutional_layer_gpu(*(convolutional_layer *)net.layers[i], state);
}
else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- forward_deconvolutional_layer_gpu(layer, input);
- input = layer.output_gpu;
+ forward_deconvolutional_layer_gpu(*(deconvolutional_layer *)net.layers[i], state);
}
else if(net.types[i] == COST){
- cost_layer layer = *(cost_layer *)net.layers[i];
- forward_cost_layer_gpu(layer, input, truth);
+ forward_cost_layer_gpu(*(cost_layer *)net.layers[i], state);
}
else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- forward_connected_layer_gpu(layer, input);
- input = layer.output_gpu;
+ forward_connected_layer_gpu(*(connected_layer *)net.layers[i], state);
}
else if(net.types[i] == DETECTION){
- detection_layer layer = *(detection_layer *)net.layers[i];
- forward_detection_layer_gpu(layer, input, truth);
- input = layer.output_gpu;
+ forward_detection_layer_gpu(*(detection_layer *)net.layers[i], state);
}
else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- forward_maxpool_layer_gpu(layer, input);
- input = layer.output_gpu;
+ forward_maxpool_layer_gpu(*(maxpool_layer *)net.layers[i], state);
}
else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- forward_softmax_layer_gpu(layer, input);
- input = layer.output_gpu;
+ forward_softmax_layer_gpu(*(softmax_layer *)net.layers[i], state);
}
else if(net.types[i] == DROPOUT){
- if(!train) continue;
- dropout_layer layer = *(dropout_layer *)net.layers[i];
- forward_dropout_layer_gpu(layer, input);
- input = layer.output_gpu;
+ forward_dropout_layer_gpu(*(dropout_layer *)net.layers[i], state);
}
else if(net.types[i] == CROP){
- crop_layer layer = *(crop_layer *)net.layers[i];
- forward_crop_layer_gpu(layer, train, input);
- input = layer.output_gpu;
+ forward_crop_layer_gpu(*(crop_layer *)net.layers[i], state);
}
- //cudaDeviceSynchronize();
- //printf("Forward %d %s %f\n", i, get_layer_string(net.types[i]), sec(clock() - time));
+ state.input = get_network_output_gpu_layer(net, i);
}
}
-void backward_network_gpu(network net, float * input, float *truth)
+void backward_network_gpu(network net, network_state state)
{
int i;
- float * prev_input;
- float * prev_delta;
+ float * original_input = state.input;
for(i = net.n-1; i >= 0; --i){
//clock_t time = clock();
if(i == 0){
- prev_input = input;
- prev_delta = 0;
+ state.input = original_input;
+ state.delta = 0;
}else{
- prev_input = get_network_output_gpu_layer(net, i-1);
- prev_delta = get_network_delta_gpu_layer(net, i-1);
+ state.input = get_network_output_gpu_layer(net, i-1);
+ state.delta = get_network_delta_gpu_layer(net, i-1);
}
if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- backward_convolutional_layer_gpu(layer, prev_input, prev_delta);
+ backward_convolutional_layer_gpu(*(convolutional_layer *)net.layers[i], state);
}
else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- backward_deconvolutional_layer_gpu(layer, prev_input, prev_delta);
+ backward_deconvolutional_layer_gpu(*(deconvolutional_layer *)net.layers[i], state);
}
else if(net.types[i] == COST){
- cost_layer layer = *(cost_layer *)net.layers[i];
- backward_cost_layer_gpu(layer, prev_input, prev_delta);
+ backward_cost_layer_gpu(*(cost_layer *)net.layers[i], state);
}
else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- backward_connected_layer_gpu(layer, prev_input, prev_delta);
+ backward_connected_layer_gpu(*(connected_layer *)net.layers[i], state);
}
else if(net.types[i] == DETECTION){
- detection_layer layer = *(detection_layer *)net.layers[i];
- backward_detection_layer_gpu(layer, prev_input, prev_delta);
+ backward_detection_layer_gpu(*(detection_layer *)net.layers[i], state);
}
else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- backward_maxpool_layer_gpu(layer, prev_delta);
+ backward_maxpool_layer_gpu(*(maxpool_layer *)net.layers[i], state);
}
else if(net.types[i] == DROPOUT){
- dropout_layer layer = *(dropout_layer *)net.layers[i];
- backward_dropout_layer_gpu(layer, prev_delta);
+ backward_dropout_layer_gpu(*(dropout_layer *)net.layers[i], state);
}
else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- backward_softmax_layer_gpu(layer, prev_delta);
+ backward_softmax_layer_gpu(*(softmax_layer *)net.layers[i], state);
}
- //printf("Backward %d %s %f\n", i, get_layer_string(net.types[i]), sec(clock() - time));
}
}
@@ -135,15 +105,15 @@
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- update_convolutional_layer_gpu(layer);
+ update_convolutional_layer_gpu(layer, net.learning_rate, net.momentum, net.decay);
}
else if(net.types[i] == DECONVOLUTIONAL){
deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- update_deconvolutional_layer_gpu(layer);
+ update_deconvolutional_layer_gpu(layer, net.learning_rate, net.momentum, net.decay);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer_gpu(layer);
+ update_connected_layer_gpu(layer, net.learning_rate, net.momentum, net.decay);
}
}
}
@@ -151,35 +121,28 @@
float * get_network_output_gpu_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.output_gpu;
+ return ((convolutional_layer *)net.layers[i]) -> output_gpu;
}
else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- return layer.output_gpu;
+ return ((deconvolutional_layer *)net.layers[i]) -> output_gpu;
}
else if(net.types[i] == DETECTION){
- detection_layer layer = *(detection_layer *)net.layers[i];
- return layer.output_gpu;
+ return ((detection_layer *)net.layers[i]) -> output_gpu;
}
else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- return layer.output_gpu;
+ return ((connected_layer *)net.layers[i]) -> output_gpu;
}
else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output_gpu;
+ return ((maxpool_layer *)net.layers[i]) -> output_gpu;
}
else if(net.types[i] == CROP){
- crop_layer layer = *(crop_layer *)net.layers[i];
- return layer.output_gpu;
+ return ((crop_layer *)net.layers[i]) -> output_gpu;
}
else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- return layer.output_gpu;
- } else if(net.types[i] == DROPOUT){
- dropout_layer layer = *(dropout_layer *)net.layers[i];
- return layer.output_gpu;
+ return ((softmax_layer *)net.layers[i]) -> output_gpu;
+ }
+ else if(net.types[i] == DROPOUT){
+ return get_network_output_gpu_layer(net, i-1);
}
return 0;
}
@@ -219,6 +182,7 @@
float train_network_datum_gpu(network net, float *x, float *y)
{
//clock_t time = clock();
+ network_state state;
int x_size = get_network_input_size(net)*net.batch;
int y_size = get_network_output_size(net)*net.batch;
if(!*net.input_gpu){
@@ -228,12 +192,15 @@
cuda_push_array(*net.input_gpu, x, x_size);
cuda_push_array(*net.truth_gpu, y, y_size);
}
+ state.input = *net.input_gpu;
+ state.truth = *net.truth_gpu;
+ state.train = 1;
//printf("trans %f\n", sec(clock() - time));
//time = clock();
- forward_network_gpu(net, *net.input_gpu, *net.truth_gpu, 1);
+ forward_network_gpu(net, state);
//printf("forw %f\n", sec(clock() - time));
//time = clock();
- backward_network_gpu(net, *net.input_gpu, *net.truth_gpu);
+ backward_network_gpu(net, state);
//printf("back %f\n", sec(clock() - time));
//time = clock();
update_network_gpu(net);
@@ -291,10 +258,14 @@
{
int size = get_network_input_size(net) * net.batch;
- float * input_gpu = cuda_make_array(input, size);
- forward_network_gpu(net, input_gpu, 0, 0);
+ network_state state;
+ state.input = cuda_make_array(input, size);
+ state.truth = 0;
+ state.train = 0;
+ state.delta = 0;
+ forward_network_gpu(net, state);
float *out = get_network_output_gpu(net);
- cuda_free(input_gpu);
+ cuda_free(state.input);
return out;
}
diff --git a/src/normalization_layer.c b/src/normalization_layer.c
index d82451b..3ab318b 100644
--- a/src/normalization_layer.c
+++ b/src/normalization_layer.c
@@ -59,28 +59,29 @@
}
}
-void forward_normalization_layer(const normalization_layer layer, float *in)
+void forward_normalization_layer(const normalization_layer layer, network_state state)
{
int i,j,k;
memset(layer.sums, 0, layer.h*layer.w*sizeof(float));
int imsize = layer.h*layer.w;
for(j = 0; j < layer.size/2; ++j){
- if(j < layer.c) add_square_array(in+j*imsize, layer.sums, imsize);
+ if(j < layer.c) add_square_array(state.input+j*imsize, layer.sums, imsize);
}
for(k = 0; k < layer.c; ++k){
int next = k+layer.size/2;
int prev = k-layer.size/2-1;
- if(next < layer.c) add_square_array(in+next*imsize, layer.sums, imsize);
- if(prev > 0) sub_square_array(in+prev*imsize, layer.sums, imsize);
+ if(next < layer.c) add_square_array(state.input+next*imsize, layer.sums, imsize);
+ if(prev > 0) sub_square_array(state.input+prev*imsize, layer.sums, imsize);
for(i = 0; i < imsize; ++i){
- layer.output[k*imsize + i] = in[k*imsize+i] / pow(layer.kappa + layer.alpha * layer.sums[i], layer.beta);
+ layer.output[k*imsize + i] = state.input[k*imsize+i] / pow(layer.kappa + layer.alpha * layer.sums[i], layer.beta);
}
}
}
-void backward_normalization_layer(const normalization_layer layer, float *in, float *delta)
+void backward_normalization_layer(const normalization_layer layer, network_state state)
{
- //TODO!
+ // TODO!
+ // OR NOT TODO!!
}
void visualize_normalization_layer(normalization_layer layer, char *window)
diff --git a/src/normalization_layer.h b/src/normalization_layer.h
index 914fe7d..11f2827 100644
--- a/src/normalization_layer.h
+++ b/src/normalization_layer.h
@@ -2,6 +2,7 @@
#define NORMALIZATION_LAYER_H
#include "image.h"
+#include "params.h"
typedef struct {
int batch;
@@ -18,8 +19,8 @@
image get_normalization_image(normalization_layer layer);
normalization_layer *make_normalization_layer(int batch, int h, int w, int c, int size, float alpha, float beta, float kappa);
void resize_normalization_layer(normalization_layer *layer, int h, int w);
-void forward_normalization_layer(const normalization_layer layer, float *in);
-void backward_normalization_layer(const normalization_layer layer, float *in, float *delta);
+void forward_normalization_layer(const normalization_layer layer, network_state state);
+void backward_normalization_layer(const normalization_layer layer, network_state state);
void visualize_normalization_layer(normalization_layer layer, char *window);
#endif
diff --git a/src/params.h b/src/params.h
new file mode 100644
index 0000000..7343a07
--- /dev/null
+++ b/src/params.h
@@ -0,0 +1,12 @@
+#ifndef PARAMS_H
+#define PARAMS_H
+
+typedef struct {
+ float *truth;
+ float *input;
+ float *delta;
+ int train;
+} network_state;
+
+#endif
+
diff --git a/src/parser.c b/src/parser.c
index 7b1057e..d7c4a31 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -14,7 +14,6 @@
#include "softmax_layer.h"
#include "dropout_layer.h"
#include "detection_layer.h"
-#include "freeweight_layer.h"
#include "list.h"
#include "option_list.h"
#include "utils.h"
@@ -24,12 +23,12 @@
list *options;
}section;
+int is_network(section *s);
int is_convolutional(section *s);
int is_deconvolutional(section *s);
int is_connected(section *s);
int is_maxpool(section *s);
int is_dropout(section *s);
-int is_freeweight(section *s);
int is_softmax(section *s);
int is_crop(section *s);
int is_cost(section *s);
@@ -69,38 +68,31 @@
}
}
-deconvolutional_layer *parse_deconvolutional(list *options, network *net, int count)
+typedef struct size_params{
+ int batch;
+ int inputs;
+ int h;
+ int w;
+ int c;
+} size_params;
+
+deconvolutional_layer *parse_deconvolutional(list *options, size_params params)
{
- int h,w,c;
- float learning_rate, momentum, decay;
int n = option_find_int(options, "filters",1);
int size = option_find_int(options, "size",1);
int stride = option_find_int(options, "stride",1);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- learning_rate = option_find_float(options, "learning_rate", .001);
- momentum = option_find_float(options, "momentum", .9);
- decay = option_find_float(options, "decay", .0001);
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",1);
- net->batch = option_find_int(options, "batch",1);
- net->learning_rate = learning_rate;
- net->momentum = momentum;
- net->decay = decay;
- net->seen = option_find_int(options, "seen",0);
- }else{
- learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
- momentum = option_find_float_quiet(options, "momentum", net->momentum);
- decay = option_find_float_quiet(options, "decay", net->decay);
- image m = get_network_image_layer(*net, count-1);
- h = m.h;
- w = m.w;
- c = m.c;
- if(h == 0) error("Layer before deconvolutional layer must output image.");
- }
- deconvolutional_layer *layer = make_deconvolutional_layer(net->batch,h,w,c,n,size,stride,activation,learning_rate,momentum,decay);
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before deconvolutional layer must output image.");
+
+ deconvolutional_layer *layer = make_deconvolutional_layer(batch,h,w,c,n,size,stride,activation);
+
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(weights, layer->filters, c*n*size*size);
@@ -112,39 +104,24 @@
return layer;
}
-convolutional_layer *parse_convolutional(list *options, network *net, int count)
+convolutional_layer *parse_convolutional(list *options, size_params params)
{
- int h,w,c;
- float learning_rate, momentum, decay;
int n = option_find_int(options, "filters",1);
int size = option_find_int(options, "size",1);
int stride = option_find_int(options, "stride",1);
int pad = option_find_int(options, "pad",0);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- learning_rate = option_find_float(options, "learning_rate", .001);
- momentum = option_find_float(options, "momentum", .9);
- decay = option_find_float(options, "decay", .0001);
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",1);
- net->batch = option_find_int(options, "batch",1);
- net->learning_rate = learning_rate;
- net->momentum = momentum;
- net->decay = decay;
- net->seen = option_find_int(options, "seen",0);
- }else{
- learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
- momentum = option_find_float_quiet(options, "momentum", net->momentum);
- decay = option_find_float_quiet(options, "decay", net->decay);
- image m = get_network_image_layer(*net, count-1);
- h = m.h;
- w = m.w;
- c = m.c;
- if(h == 0) error("Layer before convolutional layer must output image.");
- }
- convolutional_layer *layer = make_convolutional_layer(net->batch,h,w,c,n,size,stride,pad,activation,learning_rate,momentum,decay);
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before convolutional layer must output image.");
+
+ convolutional_layer *layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation);
+
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(weights, layer->filters, c*n*size*size);
@@ -156,33 +133,18 @@
return layer;
}
-connected_layer *parse_connected(list *options, network *net, int count)
+connected_layer *parse_connected(list *options, size_params params)
{
- int input;
- float learning_rate, momentum, decay;
int output = option_find_int(options, "output",1);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- input = option_find_int(options, "input",1);
- net->batch = option_find_int(options, "batch",1);
- learning_rate = option_find_float(options, "learning_rate", .001);
- momentum = option_find_float(options, "momentum", .9);
- decay = option_find_float(options, "decay", .0001);
- net->learning_rate = learning_rate;
- net->momentum = momentum;
- net->decay = decay;
- }else{
- learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
- momentum = option_find_float_quiet(options, "momentum", net->momentum);
- decay = option_find_float_quiet(options, "decay", net->decay);
- input = get_network_output_size_layer(*net, count-1);
- }
- connected_layer *layer = make_connected_layer(net->batch, input, output, activation,learning_rate,momentum,decay);
+
+ connected_layer *layer = make_connected_layer(params.batch, params.inputs, output, activation);
+
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(biases, layer->biases, output);
- parse_data(weights, layer->weights, input*output);
+ parse_data(weights, layer->weights, params.inputs*output);
#ifdef GPU
if(weights || biases) push_connected_layer(*layer);
#endif
@@ -190,235 +152,188 @@
return layer;
}
-softmax_layer *parse_softmax(list *options, network *net, int count)
+softmax_layer *parse_softmax(list *options, size_params params)
{
- int input;
int groups = option_find_int(options, "groups",1);
- if(count == 0){
- input = option_find_int(options, "input",1);
- net->batch = option_find_int(options, "batch",1);
- net->seen = option_find_int(options, "seen",0);
- }else{
- input = get_network_output_size_layer(*net, count-1);
- }
- softmax_layer *layer = make_softmax_layer(net->batch, groups, input);
+ softmax_layer *layer = make_softmax_layer(params.batch, params.inputs, groups);
option_unused(options);
return layer;
}
-detection_layer *parse_detection(list *options, network *net, int count)
+detection_layer *parse_detection(list *options, size_params params)
{
- int input;
- if(count == 0){
- input = option_find_int(options, "input",1);
- net->batch = option_find_int(options, "batch",1);
- net->seen = option_find_int(options, "seen",0);
- }else{
- input = get_network_output_size_layer(*net, count-1);
- }
int coords = option_find_int(options, "coords", 1);
int classes = option_find_int(options, "classes", 1);
int rescore = option_find_int(options, "rescore", 1);
- detection_layer *layer = make_detection_layer(net->batch, input, classes, coords, rescore);
+ detection_layer *layer = make_detection_layer(params.batch, params.inputs, classes, coords, rescore);
option_unused(options);
return layer;
}
-cost_layer *parse_cost(list *options, network *net, int count)
+cost_layer *parse_cost(list *options, size_params params)
{
- int input;
- if(count == 0){
- input = option_find_int(options, "input",1);
- net->batch = option_find_int(options, "batch",1);
- net->seen = option_find_int(options, "seen",0);
- }else{
- input = get_network_output_size_layer(*net, count-1);
- }
char *type_s = option_find_str(options, "type", "sse");
COST_TYPE type = get_cost_type(type_s);
- cost_layer *layer = make_cost_layer(net->batch, input, type);
+ cost_layer *layer = make_cost_layer(params.batch, params.inputs, type);
option_unused(options);
return layer;
}
-crop_layer *parse_crop(list *options, network *net, int count)
+crop_layer *parse_crop(list *options, size_params params)
{
- float learning_rate, momentum, decay;
- int h,w,c;
int crop_height = option_find_int(options, "crop_height",1);
int crop_width = option_find_int(options, "crop_width",1);
int flip = option_find_int(options, "flip",0);
- if(count == 0){
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",1);
- net->batch = option_find_int(options, "batch",1);
- learning_rate = option_find_float(options, "learning_rate", .001);
- momentum = option_find_float(options, "momentum", .9);
- decay = option_find_float(options, "decay", .0001);
- net->learning_rate = learning_rate;
- net->momentum = momentum;
- net->decay = decay;
- net->seen = option_find_int(options, "seen",0);
- }else{
- image m = get_network_image_layer(*net, count-1);
- h = m.h;
- w = m.w;
- c = m.c;
- if(h == 0) error("Layer before crop layer must output image.");
- }
- crop_layer *layer = make_crop_layer(net->batch,h,w,c,crop_height,crop_width,flip);
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before crop layer must output image.");
+
+ crop_layer *layer = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip);
option_unused(options);
return layer;
}
-maxpool_layer *parse_maxpool(list *options, network *net, int count)
+maxpool_layer *parse_maxpool(list *options, size_params params)
{
- int h,w,c;
int stride = option_find_int(options, "stride",1);
int size = option_find_int(options, "size",stride);
- if(count == 0){
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",1);
- net->batch = option_find_int(options, "batch",1);
- net->seen = option_find_int(options, "seen",0);
- }else{
- image m = get_network_image_layer(*net, count-1);
- h = m.h;
- w = m.w;
- c = m.c;
- if(h == 0) error("Layer before convolutional layer must output image.");
- }
- maxpool_layer *layer = make_maxpool_layer(net->batch,h,w,c,size,stride);
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before maxpool layer must output image.");
+
+ maxpool_layer *layer = make_maxpool_layer(batch,h,w,c,size,stride);
option_unused(options);
return layer;
}
-/*
-freeweight_layer *parse_freeweight(list *options, network *net, int count)
+dropout_layer *parse_dropout(list *options, size_params params)
{
- int input;
- if(count == 0){
- net->batch = option_find_int(options, "batch",1);
- input = option_find_int(options, "input",1);
- }else{
- input = get_network_output_size_layer(*net, count-1);
- }
- freeweight_layer *layer = make_freeweight_layer(net->batch,input);
- option_unused(options);
- return layer;
-}
-*/
-
-dropout_layer *parse_dropout(list *options, network *net, int count)
-{
- int input;
float probability = option_find_float(options, "probability", .5);
- if(count == 0){
- net->batch = option_find_int(options, "batch",1);
- input = option_find_int(options, "input",1);
- float learning_rate = option_find_float(options, "learning_rate", .001);
- float momentum = option_find_float(options, "momentum", .9);
- float decay = option_find_float(options, "decay", .0001);
- net->learning_rate = learning_rate;
- net->momentum = momentum;
- net->decay = decay;
- net->seen = option_find_int(options, "seen",0);
- }else{
- input = get_network_output_size_layer(*net, count-1);
- }
- dropout_layer *layer = make_dropout_layer(net->batch,input,probability);
+ dropout_layer *layer = make_dropout_layer(params.batch, params.inputs, probability);
option_unused(options);
return layer;
}
-normalization_layer *parse_normalization(list *options, network *net, int count)
+normalization_layer *parse_normalization(list *options, size_params params)
{
- int h,w,c;
int size = option_find_int(options, "size",1);
float alpha = option_find_float(options, "alpha", 0.);
float beta = option_find_float(options, "beta", 1.);
float kappa = option_find_float(options, "kappa", 1.);
- if(count == 0){
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",1);
- net->batch = option_find_int(options, "batch",1);
- net->seen = option_find_int(options, "seen",0);
- }else{
- image m = get_network_image_layer(*net, count-1);
- h = m.h;
- w = m.w;
- c = m.c;
- if(h == 0) error("Layer before convolutional layer must output image.");
- }
- normalization_layer *layer = make_normalization_layer(net->batch,h,w,c,size, alpha, beta, kappa);
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before normalization layer must output image.");
+
+ normalization_layer *layer = make_normalization_layer(batch,h,w,c,size, alpha, beta, kappa);
option_unused(options);
return layer;
}
+void parse_net_options(list *options, network *net)
+{
+ net->batch = option_find_int(options, "batch",1);
+ net->learning_rate = option_find_float(options, "learning_rate", .001);
+ net->momentum = option_find_float(options, "momentum", .9);
+ net->decay = option_find_float(options, "decay", .0001);
+ net->seen = option_find_int(options, "seen",0);
+
+ net->h = option_find_int_quiet(options, "height",0);
+ net->w = option_find_int_quiet(options, "width",0);
+ net->c = option_find_int_quiet(options, "channels",0);
+ net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c);
+ if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
+}
+
network parse_network_cfg(char *filename)
{
list *sections = read_cfg(filename);
- network net = make_network(sections->size, 0);
-
node *n = sections->front;
+ if(!n) error("Config file has no sections");
+ network net = make_network(sections->size - 1);
+ size_params params;
+
+ section *s = (section *)n->val;
+ list *options = s->options;
+ if(!is_network(s)) error("First section must be [net] or [network]");
+ parse_net_options(options, &net);
+
+ params.h = net.h;
+ params.w = net.w;
+ params.c = net.c;
+ params.inputs = net.inputs;
+ params.batch = net.batch;
+
+ n = n->next;
int count = 0;
while(n){
- section *s = (section *)n->val;
- list *options = s->options;
+ fprintf(stderr, "%d: ", count);
+ s = (section *)n->val;
+ options = s->options;
if(is_convolutional(s)){
- convolutional_layer *layer = parse_convolutional(options, &net, count);
+ convolutional_layer *layer = parse_convolutional(options, params);
net.types[count] = CONVOLUTIONAL;
net.layers[count] = layer;
}else if(is_deconvolutional(s)){
- deconvolutional_layer *layer = parse_deconvolutional(options, &net, count);
+ deconvolutional_layer *layer = parse_deconvolutional(options, params);
net.types[count] = DECONVOLUTIONAL;
net.layers[count] = layer;
}else if(is_connected(s)){
- connected_layer *layer = parse_connected(options, &net, count);
+ connected_layer *layer = parse_connected(options, params);
net.types[count] = CONNECTED;
net.layers[count] = layer;
}else if(is_crop(s)){
- crop_layer *layer = parse_crop(options, &net, count);
+ crop_layer *layer = parse_crop(options, params);
net.types[count] = CROP;
net.layers[count] = layer;
}else if(is_cost(s)){
- cost_layer *layer = parse_cost(options, &net, count);
+ cost_layer *layer = parse_cost(options, params);
net.types[count] = COST;
net.layers[count] = layer;
}else if(is_detection(s)){
- detection_layer *layer = parse_detection(options, &net, count);
+ detection_layer *layer = parse_detection(options, params);
net.types[count] = DETECTION;
net.layers[count] = layer;
}else if(is_softmax(s)){
- softmax_layer *layer = parse_softmax(options, &net, count);
+ softmax_layer *layer = parse_softmax(options, params);
net.types[count] = SOFTMAX;
net.layers[count] = layer;
}else if(is_maxpool(s)){
- maxpool_layer *layer = parse_maxpool(options, &net, count);
+ maxpool_layer *layer = parse_maxpool(options, params);
net.types[count] = MAXPOOL;
net.layers[count] = layer;
}else if(is_normalization(s)){
- normalization_layer *layer = parse_normalization(options, &net, count);
+ normalization_layer *layer = parse_normalization(options, params);
net.types[count] = NORMALIZATION;
net.layers[count] = layer;
}else if(is_dropout(s)){
- dropout_layer *layer = parse_dropout(options, &net, count);
+ dropout_layer *layer = parse_dropout(options, params);
net.types[count] = DROPOUT;
net.layers[count] = layer;
- }else if(is_freeweight(s)){
- //freeweight_layer *layer = parse_freeweight(options, &net, count);
- //net.types[count] = FREEWEIGHT;
- //net.layers[count] = layer;
- fprintf(stderr, "Type not recognized: %s\n", s->type);
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
free_section(s);
- ++count;
n = n->next;
+ if(n){
+ image im = get_network_image_layer(net, count);
+ params.h = im.h;
+ params.w = im.w;
+ params.c = im.c;
+ params.inputs = get_network_output_size_layer(net, count);
+ }
+ ++count;
}
free_list(sections);
net.outputs = get_network_output_size(net);
@@ -448,6 +363,11 @@
return (strcmp(s->type, "[conv]")==0
|| strcmp(s->type, "[convolutional]")==0);
}
+int is_network(section *s)
+{
+ return (strcmp(s->type, "[net]")==0
+ || strcmp(s->type, "[network]")==0);
+}
int is_connected(section *s)
{
return (strcmp(s->type, "[conn]")==0
@@ -462,10 +382,6 @@
{
return (strcmp(s->type, "[dropout]")==0);
}
-int is_freeweight(section *s)
-{
- return (strcmp(s->type, "[freeweight]")==0);
-}
int is_softmax(section *s)
{
@@ -533,29 +449,11 @@
void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int count)
{
- #ifdef GPU
+#ifdef GPU
if(gpu_index >= 0) pull_convolutional_layer(*l);
- #endif
+#endif
int i;
fprintf(fp, "[convolutional]\n");
- if(count == 0) {
- fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n"
- "learning_rate=%g\n"
- "momentum=%g\n"
- "decay=%g\n"
- "seen=%d\n",
- l->batch,l->h, l->w, l->c, l->learning_rate, l->momentum, l->decay, net.seen);
- } else {
- if(l->learning_rate != net.learning_rate)
- fprintf(fp, "learning_rate=%g\n", l->learning_rate);
- if(l->momentum != net.momentum)
- fprintf(fp, "momentum=%g\n", l->momentum);
- if(l->decay != net.decay)
- fprintf(fp, "decay=%g\n", l->decay);
- }
fprintf(fp, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
@@ -573,29 +471,11 @@
void print_deconvolutional_cfg(FILE *fp, deconvolutional_layer *l, network net, int count)
{
- #ifdef GPU
+#ifdef GPU
if(gpu_index >= 0) pull_deconvolutional_layer(*l);
- #endif
+#endif
int i;
fprintf(fp, "[deconvolutional]\n");
- if(count == 0) {
- fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n"
- "learning_rate=%g\n"
- "momentum=%g\n"
- "decay=%g\n"
- "seen=%d\n",
- l->batch,l->h, l->w, l->c, l->learning_rate, l->momentum, l->decay, net.seen);
- } else {
- if(l->learning_rate != net.learning_rate)
- fprintf(fp, "learning_rate=%g\n", l->learning_rate);
- if(l->momentum != net.momentum)
- fprintf(fp, "momentum=%g\n", l->momentum);
- if(l->decay != net.decay)
- fprintf(fp, "decay=%g\n", l->decay);
- }
fprintf(fp, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
@@ -610,47 +490,19 @@
fprintf(fp, "\n\n");
}
-void print_freeweight_cfg(FILE *fp, freeweight_layer *l, network net, int count)
-{
- fprintf(fp, "[freeweight]\n");
- if(count == 0){
- fprintf(fp, "batch=%d\ninput=%d\n",l->batch, l->inputs);
- }
- fprintf(fp, "\n");
-}
-
void print_dropout_cfg(FILE *fp, dropout_layer *l, network net, int count)
{
fprintf(fp, "[dropout]\n");
- if(count == 0){
- fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
- }
fprintf(fp, "probability=%g\n\n", l->probability);
}
void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
{
- #ifdef GPU
+#ifdef GPU
if(gpu_index >= 0) pull_connected_layer(*l);
- #endif
+#endif
int i;
fprintf(fp, "[connected]\n");
- if(count == 0){
- fprintf(fp, "batch=%d\n"
- "input=%d\n"
- "learning_rate=%g\n"
- "momentum=%g\n"
- "decay=%g\n"
- "seen=%d\n",
- l->batch, l->inputs, l->learning_rate, l->momentum, l->decay, net.seen);
- } else {
- if(l->learning_rate != net.learning_rate)
- fprintf(fp, "learning_rate=%g\n", l->learning_rate);
- if(l->momentum != net.momentum)
- fprintf(fp, "momentum=%g\n", l->momentum);
- if(l->decay != net.decay)
- fprintf(fp, "decay=%g\n", l->decay);
- }
fprintf(fp, "output=%d\n"
"activation=%s\n",
l->outputs,
@@ -666,39 +518,18 @@
void print_crop_cfg(FILE *fp, crop_layer *l, network net, int count)
{
fprintf(fp, "[crop]\n");
- if(count == 0) {
- fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n"
- "learning_rate=%g\n"
- "momentum=%g\n"
- "decay=%g\n"
- "seen=%d\n",
- l->batch,l->h, l->w, l->c, net.learning_rate, net.momentum, net.decay, net.seen);
- }
fprintf(fp, "crop_height=%d\ncrop_width=%d\nflip=%d\n\n", l->crop_height, l->crop_width, l->flip);
}
void print_maxpool_cfg(FILE *fp, maxpool_layer *l, network net, int count)
{
fprintf(fp, "[maxpool]\n");
- if(count == 0) fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n",
- l->batch,l->h, l->w, l->c);
fprintf(fp, "size=%d\nstride=%d\n\n", l->size, l->stride);
}
void print_normalization_cfg(FILE *fp, normalization_layer *l, network net, int count)
{
fprintf(fp, "[localresponsenormalization]\n");
- if(count == 0) fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n",
- l->batch,l->h, l->w, l->c);
fprintf(fp, "size=%d\n"
"alpha=%g\n"
"beta=%g\n"
@@ -708,7 +539,6 @@
void print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count)
{
fprintf(fp, "[softmax]\n");
- if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "\n");
}
@@ -722,7 +552,6 @@
void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
{
fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type));
- if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "\n");
}
@@ -741,33 +570,33 @@
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *) net.layers[i];
- #ifdef GPU
+#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(layer);
}
- #endif
+#endif
int num = layer.n*layer.c*layer.size*layer.size;
fwrite(layer.biases, sizeof(float), layer.n, fp);
fwrite(layer.filters, sizeof(float), num, fp);
}
if(net.types[i] == DECONVOLUTIONAL){
deconvolutional_layer layer = *(deconvolutional_layer *) net.layers[i];
- #ifdef GPU
+#ifdef GPU
if(gpu_index >= 0){
pull_deconvolutional_layer(layer);
}
- #endif
+#endif
int num = layer.n*layer.c*layer.size*layer.size;
fwrite(layer.biases, sizeof(float), layer.n, fp);
fwrite(layer.filters, sizeof(float), num, fp);
}
if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *) net.layers[i];
- #ifdef GPU
+#ifdef GPU
if(gpu_index >= 0){
pull_connected_layer(layer);
}
- #endif
+#endif
fwrite(layer.biases, sizeof(float), layer.outputs, fp);
fwrite(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
}
@@ -785,8 +614,7 @@
fread(&net->momentum, sizeof(float), 1, fp);
fread(&net->decay, sizeof(float), 1, fp);
fread(&net->seen, sizeof(int), 1, fp);
- set_learning_network(net, net->learning_rate, net->momentum, net->decay);
-
+
int i;
for(i = 0; i < net->n && i < cutoff; ++i){
if(net->types[i] == CONVOLUTIONAL){
@@ -794,32 +622,32 @@
int num = layer.n*layer.c*layer.size*layer.size;
fread(layer.biases, sizeof(float), layer.n, fp);
fread(layer.filters, sizeof(float), num, fp);
- #ifdef GPU
+#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(layer);
}
- #endif
+#endif
}
if(net->types[i] == DECONVOLUTIONAL){
deconvolutional_layer layer = *(deconvolutional_layer *) net->layers[i];
int num = layer.n*layer.c*layer.size*layer.size;
fread(layer.biases, sizeof(float), layer.n, fp);
fread(layer.filters, sizeof(float), num, fp);
- #ifdef GPU
+#ifdef GPU
if(gpu_index >= 0){
push_deconvolutional_layer(layer);
}
- #endif
+#endif
}
if(net->types[i] == CONNECTED){
connected_layer layer = *(connected_layer *) net->layers[i];
fread(layer.biases, sizeof(float), layer.outputs, fp);
fread(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
- #ifdef GPU
+#ifdef GPU
if(gpu_index >= 0){
push_connected_layer(layer);
}
- #endif
+#endif
}
}
fclose(fp);
@@ -847,8 +675,6 @@
print_crop_cfg(fp, (crop_layer *)net.layers[i], net, i);
else if(net.types[i] == MAXPOOL)
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], net, i);
- else if(net.types[i] == FREEWEIGHT)
- print_freeweight_cfg(fp, (freeweight_layer *)net.layers[i], net, i);
else if(net.types[i] == DROPOUT)
print_dropout_cfg(fp, (dropout_layer *)net.layers[i], net, i);
else if(net.types[i] == NORMALIZATION)
diff --git a/src/softmax_layer.c b/src/softmax_layer.c
index a200ae5..e344d16 100644
--- a/src/softmax_layer.c
+++ b/src/softmax_layer.c
@@ -7,7 +7,7 @@
#include <stdio.h>
#include <assert.h>
-softmax_layer *make_softmax_layer(int batch, int groups, int inputs)
+softmax_layer *make_softmax_layer(int batch, int inputs, int groups)
{
assert(inputs%groups == 0);
fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
@@ -42,21 +42,21 @@
}
}
-void forward_softmax_layer(const softmax_layer layer, float *input)
+void forward_softmax_layer(const softmax_layer layer, network_state state)
{
int b;
int inputs = layer.inputs / layer.groups;
int batch = layer.batch * layer.groups;
for(b = 0; b < batch; ++b){
- softmax_array(input+b*inputs, inputs, layer.output+b*inputs);
+ softmax_array(state.input+b*inputs, inputs, layer.output+b*inputs);
}
}
-void backward_softmax_layer(const softmax_layer layer, float *delta)
+void backward_softmax_layer(const softmax_layer layer, network_state state)
{
int i;
for(i = 0; i < layer.inputs*layer.batch; ++i){
- delta[i] = layer.delta[i];
+ state.delta[i] = layer.delta[i];
}
}
diff --git a/src/softmax_layer.h b/src/softmax_layer.h
index 3632c74..ecdec1e 100644
--- a/src/softmax_layer.h
+++ b/src/softmax_layer.h
@@ -1,5 +1,6 @@
#ifndef SOFTMAX_LAYER_H
#define SOFTMAX_LAYER_H
+#include "params.h"
typedef struct {
int inputs;
@@ -14,14 +15,14 @@
} softmax_layer;
void softmax_array(float *input, int n, float *output);
-softmax_layer *make_softmax_layer(int batch, int groups, int inputs);
-void forward_softmax_layer(const softmax_layer layer, float *input);
-void backward_softmax_layer(const softmax_layer layer, float *delta);
+softmax_layer *make_softmax_layer(int batch, int inputs, int groups);
+void forward_softmax_layer(const softmax_layer layer, network_state state);
+void backward_softmax_layer(const softmax_layer layer, network_state state);
#ifdef GPU
void pull_softmax_layer_output(const softmax_layer layer);
-void forward_softmax_layer_gpu(const softmax_layer layer, float *input);
-void backward_softmax_layer_gpu(const softmax_layer layer, float *delta);
+void forward_softmax_layer_gpu(const softmax_layer layer, network_state state);
+void backward_softmax_layer_gpu(const softmax_layer layer, network_state state);
#endif
#endif
diff --git a/src/softmax_layer_kernels.cu b/src/softmax_layer_kernels.cu
index c0e8bc3..0529f75 100644
--- a/src/softmax_layer_kernels.cu
+++ b/src/softmax_layer_kernels.cu
@@ -32,23 +32,17 @@
cuda_pull_array(layer.output_gpu, layer.output, layer.inputs*layer.batch);
}
-extern "C" void forward_softmax_layer_gpu(const softmax_layer layer, float *input)
+extern "C" void forward_softmax_layer_gpu(const softmax_layer layer, network_state state)
{
int inputs = layer.inputs / layer.groups;
int batch = layer.batch * layer.groups;
- forward_softmax_layer_kernel<<<cuda_gridsize(batch), BLOCK>>>(inputs, batch, input, layer.output_gpu);
+ forward_softmax_layer_kernel<<<cuda_gridsize(batch), BLOCK>>>(inputs, batch, state.input, layer.output_gpu);
check_error(cudaPeekAtLastError());
-
- /*
- cl_read_array(layer.output_cl, layer.output, layer.inputs*layer.batch);
- int z;
- for(z = 0; z < layer.inputs*layer.batch; ++z) printf("%f,",layer.output[z]);
- */
}
-extern "C" void backward_softmax_layer_gpu(const softmax_layer layer, float *delta)
+extern "C" void backward_softmax_layer_gpu(const softmax_layer layer, network_state state)
{
- copy_ongpu(layer.batch*layer.inputs, layer.delta_gpu, 1, delta, 1);
+ copy_ongpu(layer.batch*layer.inputs, layer.delta_gpu, 1, state.delta, 1);
}
/* This is if you want softmax w/o log-loss classification. You probably don't.
--
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