mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

Fixed batch stuff in conv layer

Browse Source
This commit is contained in:
Joseph Redmon 2014-07-17 10:14:59 -07:00
parent 1b94df24fd
commit 076009ebe3
6 changed files with 83 additions and 44 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
src/cnn.c
View File

@ -48,11 +48,10 @@ void test_convolve_matrix()
image edge = make_image((dog.h-size)/stride+1, (dog.w-size)/stride+1, n);
int i;
clock_t start = clock(), end;
for(i = 0; i < 1000; ++i){
im2col_cpu(dog.data, 1, dog.c, dog.h, dog.w, size, stride, 0, matrix);
im2col_cpu(dog.data, dog.c, dog.h, dog.w, size, stride, 0, matrix);
gemm(0,0,n,mw,mh,1,filters,mh,matrix,mw,1,edge.data,mw);
}
end = clock();
@ -317,8 +316,8 @@ void test_nist()
clock_t start = clock(), end;
float loss = train_network_sgd(net, train, iters, lr, momentum, decay);
end = clock();
//float test_acc = network_accuracy(net, test);
float test_acc = 0;
float test_acc = network_accuracy(net, test);
//float test_acc = 0;
printf("%d: Loss: %f, Test Acc: %f, Time: %lf seconds, LR: %f, Momentum: %f, Decay: %f\n", count, loss, test_acc,(float)(end-start)/CLOCKS_PER_SEC, lr, momentum, decay);
//printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*1000, loss, lr, momentum, decay);
@ -434,7 +433,7 @@ void test_im2row()
float *matrix = calloc(msize, sizeof(float));
int i;
for(i = 0; i < 1000; ++i){
im2col_cpu(test.data, 1, c, h, w, size, stride, 0, matrix);
im2col_cpu(test.data, c, h, w, size, stride, 0, matrix);
//image render = float_to_image(mh, mw, mc, matrix);
}
}

32
src/col2im.c
View File

@ -10,10 +10,10 @@ inline void col2im_set_pixel(float *im, int height, int width, int channels,
}
//This one might be too, can't remember.
void col2im_cpu(float* data_col,
const int batch, const int channels, const int height, const int width,
const int channels, const int height, const int width,
const int ksize, const int stride, int pad, float* data_im)
{
int c,h,w,b;
int c,h,w;
int height_col = (height - ksize) / stride + 1;
int width_col = (width - ksize) / stride + 1;
if (pad){
@ -22,25 +22,19 @@ void col2im_cpu(float* data_col,
pad = ksize/2;
}
int channels_col = channels * ksize * ksize;
int im_size = height*width*channels;
int col_size = height_col*width_col*channels_col;
for (b = 0; b < batch; ++b) {
for (c = 0; c < channels_col; ++c) {
int w_offset = c % ksize;
int h_offset = (c / ksize) % ksize;
int c_im = c / ksize / ksize;
for (h = 0; h < height_col; ++h) {
for (w = 0; w < width_col; ++w) {
int im_row = h_offset + h * stride;
int im_col = w_offset + w * stride;
double val = data_col[(c * height_col + h) * width_col + w];
col2im_set_pixel(data_im, height, width, channels,
im_row, im_col, c_im, pad, val);
}
for (c = 0; c < channels_col; ++c) {
int w_offset = c % ksize;
int h_offset = (c / ksize) % ksize;
int c_im = c / ksize / ksize;
for (h = 0; h < height_col; ++h) {
for (w = 0; w < width_col; ++w) {
int im_row = h_offset + h * stride;
int im_col = w_offset + w * stride;
double val = data_col[(c * height_col + h) * width_col + w];
col2im_set_pixel(data_im, height, width, channels,
im_row, im_col, c_im, pad, val);
}
}
data_im += im_size;
data_col+= col_size;
}
}

47
src/convolutional_layer.c
View File

@ -79,7 +79,7 @@ convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, in
layer->bias_updates_cl = cl_make_array(layer->bias_updates, n);
layer->bias_momentum_cl = cl_make_array(layer->bias_momentum, n);
layer->col_image_cl = cl_make_array(layer->col_image, layer->batch*out_h*out_w*size*size*c);
layer->col_image_cl = cl_make_array(layer->col_image, layer.batch*out_h*out_w*size*size*c);
layer->delta_cl = cl_make_array(layer->delta, layer->batch*out_h*out_w*n);
layer->output_cl = cl_make_array(layer->output, layer->batch*out_h*out_w*n);
#endif
@ -124,24 +124,32 @@ void forward_convolutional_layer(const convolutional_layer layer, float *in)
{
int out_h = convolutional_out_height(layer);
int out_w = convolutional_out_width(layer);
int i;
bias_output(layer);
int m = layer.n;
int k = layer.size*layer.size*layer.c;
int n = out_h*out_w*layer.batch;
int n = out_h*out_w;
float *a = layer.filters;
float *b = layer.col_image;
float *c = layer.output;
im2col_cpu(in, layer.batch, layer.c, layer.h, layer.w,
layer.size, layer.stride, layer.pad, b);
bias_output(layer);
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
for(i = 0; i < layer.batch; ++i){
im2col_cpu(in, 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.h*layer.w*layer.c;
b += k*n;
}
/*
int i;
for(i = 0; i < m*n; ++i) printf("%f, ", layer.output[i]);
printf("\n");
*/
activate_array(layer.output, m*n, layer.activation, 0.);
activate_array(layer.output, m*n*layer.batch, layer.activation, 0.);
}
#ifdef GPU
@ -178,35 +186,42 @@ void learn_bias_convolutional_layer(convolutional_layer layer)
void backward_convolutional_layer(convolutional_layer layer, float *delta)
{
int i;
int m = layer.n;
int n = layer.size*layer.size*layer.c;
int k = convolutional_out_height(layer)*
convolutional_out_width(layer)*
layer.batch;
gradient_array(layer.output, m*k, layer.activation, layer.delta);
convolutional_out_width(layer);
gradient_array(layer.output, m*k*layer.batch, layer.activation, layer.delta);
learn_bias_convolutional_layer(layer);
float *a = layer.delta;
float *b = layer.col_image;
float *c = layer.filter_updates;
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
for(i = 0; i < layer.batch; ++i){
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
a += m*k;
b += k*n;
}
if(delta){
m = layer.size*layer.size*layer.c;
k = layer.n;
n = convolutional_out_height(layer)*
convolutional_out_width(layer)*
layer.batch;
convolutional_out_width(layer);
a = layer.filters;
b = layer.delta;
c = layer.col_image;
gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
col2im_cpu(c, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta);
for(i = 0; i < layer.batch; ++i){
gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
col2im_cpu(c, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta);
c += k*n;
delta += layer.h*layer.w*layer.c;
}
}
}

33
src/im2col.c
View File

@ -14,7 +14,7 @@ inline float im2col_get_pixel(float *im, int height, int width, int channels,
//From Berkeley Vision's Caffe!
//https://github.com/BVLC/caffe/blob/master/LICENSE
void im2col_cpu(float* data_im,
void im2col_cpu_batch(float* data_im,
const int batch, const int channels, const int height, const int width,
const int ksize, const int stride, int pad, float* data_col)
{
@ -49,6 +49,37 @@ void im2col_cpu(float* data_im,
}
}
//From Berkeley Vision's Caffe!
//https://github.com/BVLC/caffe/blob/master/LICENSE
void im2col_cpu(float* data_im,
const int channels, const int height, const int width,
const int ksize, const int stride, int pad, float* data_col)
{
int c,h,w;
int height_col = (height - ksize) / stride + 1;
int width_col = (width - ksize) / stride + 1;
if (pad){
height_col = 1 + (height-1) / stride;
width_col = 1 + (width-1) / stride;
pad = ksize/2;
}
int channels_col = channels * ksize * ksize;
for (c = 0; c < channels_col; ++c) {
int w_offset = c % ksize;
int h_offset = (c / ksize) % ksize;
int c_im = c / ksize / ksize;
for (h = 0; h < height_col; ++h) {
for (w = 0; w < width_col; ++w) {
int im_row = h_offset + h * stride;
int im_col = w_offset + w * stride;
int col_index = (c * height_col + h) * width_col + w;
data_col[col_index] = im2col_get_pixel(data_im, height, width, channels,
im_row, im_col, c_im, pad);
}
}
}
}
#ifdef GPU

4
src/mini_blas.h
View File

@ -26,11 +26,11 @@ void gemm_ongpu(int TA, int TB, int M, int N, int K, float ALPHA,
#endif
void im2col_cpu(float* data_im,
const int batch, const int channels, const int height, const int width,
const int channels, const int height, const int width,
const int ksize, const int stride, int pad, float* data_col);
void col2im_cpu(float* data_col,
const int batch, const int channels, const int height, const int width,
const int channels, const int height, const int width,
const int ksize, const int stride, int pad, float* data_im);
void test_blas();

2
src/network.c
View File

@ -274,7 +274,7 @@ float calculate_error_network(network net, float *truth)
//printf("%5.2f %5.2f, ", out[i], truth[i]);
//if(i == get_network_output_size(net)) printf("\n");
delta[i] = truth[i] - out[i];
//printf("%f, ", delta[i]);
//printf("%.10f, ", out[i]);
sum += delta[i]*delta[i];
}
//printf("\n");