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lots of stuff

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This commit is contained in:
Joseph Redmon
2016-01-28 12:30:38 -08:00
parent 1578ec70d7
commit 913d355ec1
35 changed files with 913 additions and 86 deletions
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71
src/convolutional_layer.c
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@ -41,7 +41,65 @@ image get_convolutional_delta(convolutional_layer l)
return float_to_image(w,h,c,l.delta);
}
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize)
void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
{
int i,b,f;
for(f = 0; f < n; ++f){
float sum = 0;
for(b = 0; b < batch; ++b){
for(i = 0; i < size; ++i){
int index = i + size*(f + n*b);
sum += delta[index] * x_norm[index];
}
}
scale_updates[f] += sum;
}
}
void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
{
int i,j,k;
for(i = 0; i < filters; ++i){
mean_delta[i] = 0;
for (j = 0; j < batch; ++j) {
for (k = 0; k < spatial; ++k) {
int index = j*filters*spatial + i*spatial + k;
mean_delta[i] += delta[index];
}
}
mean_delta[i] *= (-1./sqrt(variance[i] + .00001f));
}
}
void variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
{
int i,j,k;
for(i = 0; i < filters; ++i){
variance_delta[i] = 0;
for(j = 0; j < batch; ++j){
for(k = 0; k < spatial; ++k){
int index = j*filters*spatial + i*spatial + k;
variance_delta[i] += delta[index]*(x[index] - mean[i]);
}
}
variance_delta[i] *= -.5 * pow(variance[i] + .00001f, (float)(-3./2.));
}
}
void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
{
int f, j, k;
for(j = 0; j < batch; ++j){
for(f = 0; f < filters; ++f){
for(k = 0; k < spatial; ++k){
int index = j*filters*spatial + f*spatial + k;
delta[index] = delta[index] * 1./(sqrt(variance[f]) + .00001f) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch);
}
}
}
}
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int binary)
{
int i;
convolutional_layer l = {0};
@ -51,6 +109,7 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.w = w;
l.c = c;
l.n = n;
l.binary = binary;
l.batch = batch;
l.stride = stride;
l.size = size;
@ -78,6 +137,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float));
if(binary){
l.binary_filters = calloc(c*n*size*size, sizeof(float));
}
if(batch_normalize){
l.scales = calloc(n, sizeof(float));
l.scale_updates = calloc(n, sizeof(float));
@ -106,6 +169,10 @@ convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int
l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
if(binary){
l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size);
}
if(batch_normalize){
l.mean_gpu = cuda_make_array(l.mean, n);
l.variance_gpu = cuda_make_array(l.variance, n);
@ -141,7 +208,7 @@ void denormalize_convolutional_layer(convolutional_layer l)
void test_convolutional_layer()
{
convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1);
convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0);
l.batch_normalize = 1;
float data[] = {1,1,1,1,1,
1,1,1,1,1,