From 617cf313ccb1fe005db3f7d88dec04a04bd97cc2 Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Tue, 28 Nov 2017 10:19:23 +0000
Subject: [PATCH] Update Readme.md - fixed typo
---
src/deconvolutional_layer.c | 226 +++++++++++++++++++++++++++++---------------------------
1 files changed, 116 insertions(+), 110 deletions(-)
diff --git a/src/deconvolutional_layer.c b/src/deconvolutional_layer.c
index d4a8426..fbef9d5 100644
--- a/src/deconvolutional_layer.c
+++ b/src/deconvolutional_layer.c
@@ -8,192 +8,198 @@
#include <stdio.h>
#include <time.h>
-int deconvolutional_out_height(deconvolutional_layer layer)
+int deconvolutional_out_height(deconvolutional_layer l)
{
- int h = layer.stride*(layer.h - 1) + layer.size;
+ int h = l.stride*(l.h - 1) + l.size;
return h;
}
-int deconvolutional_out_width(deconvolutional_layer layer)
+int deconvolutional_out_width(deconvolutional_layer l)
{
- int w = layer.stride*(layer.w - 1) + layer.size;
+ int w = l.stride*(l.w - 1) + l.size;
return w;
}
-int deconvolutional_out_size(deconvolutional_layer layer)
+int deconvolutional_out_size(deconvolutional_layer l)
{
- return deconvolutional_out_height(layer) * deconvolutional_out_width(layer);
+ return deconvolutional_out_height(l) * deconvolutional_out_width(l);
}
-image get_deconvolutional_image(deconvolutional_layer layer)
+image get_deconvolutional_image(deconvolutional_layer l)
{
int h,w,c;
- h = deconvolutional_out_height(layer);
- w = deconvolutional_out_width(layer);
- c = layer.n;
- return float_to_image(h,w,c,layer.output);
+ h = deconvolutional_out_height(l);
+ w = deconvolutional_out_width(l);
+ c = l.n;
+ return float_to_image(w,h,c,l.output);
}
-image get_deconvolutional_delta(deconvolutional_layer layer)
+image get_deconvolutional_delta(deconvolutional_layer l)
{
int h,w,c;
- h = deconvolutional_out_height(layer);
- w = deconvolutional_out_width(layer);
- c = layer.n;
- return float_to_image(h,w,c,layer.delta);
+ h = deconvolutional_out_height(l);
+ w = deconvolutional_out_width(l);
+ c = l.n;
+ return float_to_image(w,h,c,l.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));
+ deconvolutional_layer l = {0};
+ l.type = DECONVOLUTIONAL;
- layer->learning_rate = learning_rate;
- layer->momentum = momentum;
- layer->decay = decay;
+ l.h = h;
+ l.w = w;
+ l.c = c;
+ l.n = n;
+ l.batch = batch;
+ l.stride = stride;
+ l.size = size;
- layer->h = h;
- layer->w = w;
- layer->c = c;
- layer->n = n;
- layer->batch = batch;
- layer->stride = stride;
- layer->size = size;
+ l.weights = calloc(c*n*size*size, sizeof(float));
+ l.weight_updates = calloc(c*n*size*size, sizeof(float));
- layer->filters = calloc(c*n*size*size, sizeof(float));
- layer->filter_updates = calloc(c*n*size*size, sizeof(float));
-
- layer->biases = calloc(n, sizeof(float));
- layer->bias_updates = calloc(n, sizeof(float));
+ l.biases = calloc(n, sizeof(float));
+ l.bias_updates = calloc(n, sizeof(float));
float scale = 1./sqrt(size*size*c);
- for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*rand_normal();
+ for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal();
for(i = 0; i < n; ++i){
- layer->biases[i] = scale;
+ l.biases[i] = scale;
}
- int out_h = deconvolutional_out_height(*layer);
- int out_w = deconvolutional_out_width(*layer);
+ int out_h = deconvolutional_out_height(l);
+ int out_w = deconvolutional_out_width(l);
- layer->col_image = calloc(h*w*size*size*n, sizeof(float));
- layer->output = calloc(layer->batch*out_h * out_w * n, sizeof(float));
- layer->delta = calloc(layer->batch*out_h * out_w * n, sizeof(float));
+ l.out_h = out_h;
+ l.out_w = out_w;
+ l.out_c = n;
+ l.outputs = l.out_w * l.out_h * l.out_c;
+ l.inputs = l.w * l.h * l.c;
+
+ l.col_image = calloc(h*w*size*size*n, sizeof(float));
+ l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
+ l.delta = calloc(l.batch*out_h * out_w * n, sizeof(float));
+
+ l.forward = forward_deconvolutional_layer;
+ l.backward = backward_deconvolutional_layer;
+ l.update = update_deconvolutional_layer;
#ifdef GPU
- layer->filters_gpu = cuda_make_array(layer->filters, c*n*size*size);
- layer->filter_updates_gpu = cuda_make_array(layer->filter_updates, c*n*size*size);
+ l.weights_gpu = cuda_make_array(l.weights, c*n*size*size);
+ l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size);
- layer->biases_gpu = cuda_make_array(layer->biases, n);
- layer->bias_updates_gpu = cuda_make_array(layer->bias_updates, n);
+ l.biases_gpu = cuda_make_array(l.biases, n);
+ l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
- layer->col_image_gpu = cuda_make_array(layer->col_image, h*w*size*size*n);
- layer->delta_gpu = cuda_make_array(layer->delta, layer->batch*out_h*out_w*n);
- layer->output_gpu = cuda_make_array(layer->output, layer->batch*out_h*out_w*n);
+ l.col_image_gpu = cuda_make_array(l.col_image, h*w*size*size*n);
+ 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);
#endif
- layer->activation = activation;
+ l.activation = activation;
fprintf(stderr, "Deconvolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
- return layer;
+ return l;
}
-void resize_deconvolutional_layer(deconvolutional_layer *layer, int h, int w)
+void resize_deconvolutional_layer(deconvolutional_layer *l, int h, int w)
{
- layer->h = h;
- layer->w = w;
- int out_h = deconvolutional_out_height(*layer);
- int out_w = deconvolutional_out_width(*layer);
+ l->h = h;
+ l->w = w;
+ int out_h = deconvolutional_out_height(*l);
+ int out_w = deconvolutional_out_width(*l);
- layer->col_image = realloc(layer->col_image,
- out_h*out_w*layer->size*layer->size*layer->c*sizeof(float));
- layer->output = realloc(layer->output,
- layer->batch*out_h * out_w * layer->n*sizeof(float));
- layer->delta = realloc(layer->delta,
- layer->batch*out_h * out_w * layer->n*sizeof(float));
+ l->col_image = realloc(l->col_image,
+ out_h*out_w*l->size*l->size*l->c*sizeof(float));
+ l->output = realloc(l->output,
+ l->batch*out_h * out_w * l->n*sizeof(float));
+ l->delta = realloc(l->delta,
+ l->batch*out_h * out_w * l->n*sizeof(float));
#ifdef GPU
- cuda_free(layer->col_image_gpu);
- cuda_free(layer->delta_gpu);
- cuda_free(layer->output_gpu);
+ cuda_free(l->col_image_gpu);
+ cuda_free(l->delta_gpu);
+ cuda_free(l->output_gpu);
- layer->col_image_gpu = cuda_make_array(layer->col_image, out_h*out_w*layer->size*layer->size*layer->c);
- layer->delta_gpu = cuda_make_array(layer->delta, layer->batch*out_h*out_w*layer->n);
- layer->output_gpu = cuda_make_array(layer->output, layer->batch*out_h*out_w*layer->n);
+ l->col_image_gpu = cuda_make_array(l->col_image, out_h*out_w*l->size*l->size*l->c);
+ l->delta_gpu = cuda_make_array(l->delta, l->batch*out_h*out_w*l->n);
+ l->output_gpu = cuda_make_array(l->output, l->batch*out_h*out_w*l->n);
#endif
}
-void forward_deconvolutional_layer(const deconvolutional_layer layer, float *in)
+void forward_deconvolutional_layer(const deconvolutional_layer l, network_state state)
{
int i;
- int out_h = deconvolutional_out_height(layer);
- int out_w = deconvolutional_out_width(layer);
+ int out_h = deconvolutional_out_height(l);
+ int out_w = deconvolutional_out_width(l);
int size = out_h*out_w;
- int m = layer.size*layer.size*layer.n;
- int n = layer.h*layer.w;
- int k = layer.c;
+ int m = l.size*l.size*l.n;
+ int n = l.h*l.w;
+ int k = l.c;
- bias_output(layer.output, layer.biases, layer.batch, layer.n, size);
+ fill_cpu(l.outputs*l.batch, 0, l.output, 1);
- for(i = 0; i < layer.batch; ++i){
- float *a = layer.filters;
- float *b = in + i*layer.c*layer.h*layer.w;
- float *c = layer.col_image;
+ for(i = 0; i < l.batch; ++i){
+ float *a = l.weights;
+ float *b = state.input + i*l.c*l.h*l.w;
+ float *c = l.col_image;
gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
- col2im_cpu(c, layer.n, out_h, out_w, layer.size, layer.stride, 0, layer.output+i*layer.n*size);
+ col2im_cpu(c, l.n, out_h, out_w, l.size, l.stride, 0, l.output+i*l.n*size);
}
- activate_array(layer.output, layer.batch*layer.n*size, layer.activation);
+ add_bias(l.output, l.biases, l.batch, l.n, size);
+ activate_array(l.output, l.batch*l.n*size, l.activation);
}
-void backward_deconvolutional_layer(deconvolutional_layer layer, float *in, float *delta)
+void backward_deconvolutional_layer(deconvolutional_layer l, network_state state)
{
- float alpha = 1./layer.batch;
- int out_h = deconvolutional_out_height(layer);
- int out_w = deconvolutional_out_width(layer);
+ float alpha = 1./l.batch;
+ int out_h = deconvolutional_out_height(l);
+ int out_w = deconvolutional_out_width(l);
int size = out_h*out_w;
int i;
- 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);
+ gradient_array(l.output, size*l.n*l.batch, l.activation, l.delta);
+ backward_bias(l.bias_updates, l.delta, l.batch, l.n, size);
- if(delta) memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+ for(i = 0; i < l.batch; ++i){
+ int m = l.c;
+ int n = l.size*l.size*l.n;
+ int k = l.h*l.w;
- 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 = state.input + i*m*n;
+ float *b = l.col_image;
+ float *c = l.weight_updates;
- float *a = in + i*m*n;
- float *b = layer.col_image;
- float *c = layer.filter_updates;
-
- im2col_cpu(layer.delta + i*layer.n*size, layer.n, out_h, out_w,
- layer.size, layer.stride, 0, b);
+ im2col_cpu(l.delta + i*l.n*size, l.n, out_h, out_w,
+ l.size, l.stride, 0, b);
gemm(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
- if(delta){
- int m = layer.c;
- int n = layer.h*layer.w;
- int k = layer.size*layer.size*layer.n;
+ if(state.delta){
+ int m = l.c;
+ int n = l.h*l.w;
+ int k = l.size*l.size*l.n;
- float *a = layer.filters;
- float *b = layer.col_image;
- float *c = delta + i*n*m;
+ float *a = l.weights;
+ float *b = l.col_image;
+ 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 l, 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);
+ int size = l.size*l.size*l.c*l.n;
+ axpy_cpu(l.n, learning_rate, l.bias_updates, 1, l.biases, 1);
+ scal_cpu(l.n, momentum, l.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, l.weights, 1, l.weight_updates, 1);
+ axpy_cpu(size, learning_rate, l.weight_updates, 1, l.weights, 1);
+ scal_cpu(size, momentum, l.weight_updates, 1);
}
--
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