From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/deconvolutional_layer.c | 406 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 203 insertions(+), 203 deletions(-)
diff --git a/lib/detecter_tools/darknet/deconvolutional_layer.c b/lib/detecter_tools/darknet/deconvolutional_layer.c
index 512b94f..4f4e4cc 100644
--- a/lib/detecter_tools/darknet/deconvolutional_layer.c
+++ b/lib/detecter_tools/darknet/deconvolutional_layer.c
@@ -1,203 +1,203 @@
-#include "deconvolutional_layer.h"
-#include "convolutional_layer.h"
-#include "utils.h"
-#include "im2col.h"
-#include "col2im.h"
-#include "blas.h"
-#include "gemm.h"
-#include <stdio.h>
-#include <time.h>
-
-int deconvolutional_out_height(deconvolutional_layer l)
-{
- int h = l.stride*(l.h - 1) + l.size;
- return h;
-}
-
-int deconvolutional_out_width(deconvolutional_layer l)
-{
- int w = l.stride*(l.w - 1) + l.size;
- return w;
-}
-
-int deconvolutional_out_size(deconvolutional_layer l)
-{
- return deconvolutional_out_height(l) * deconvolutional_out_width(l);
-}
-
-image get_deconvolutional_image(deconvolutional_layer l)
-{
- int h,w,c;
- 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 l)
-{
- int h,w,c;
- 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)
-{
- int i;
- deconvolutional_layer l = { (LAYER_TYPE)0 };
- l.type = DECONVOLUTIONAL;
-
- l.h = h;
- l.w = w;
- l.c = c;
- l.n = n;
- l.batch = batch;
- l.stride = stride;
- l.size = size;
-
- l.weights = (float*)xcalloc(c * n * size * size, sizeof(float));
- l.weight_updates = (float*)xcalloc(c * n * size * size, sizeof(float));
-
- l.biases = (float*)xcalloc(n, sizeof(float));
- l.bias_updates = (float*)xcalloc(n, sizeof(float));
- float scale = 1./sqrt(size*size*c);
- for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal();
- for(i = 0; i < n; ++i){
- l.biases[i] = scale;
- }
- int out_h = deconvolutional_out_height(l);
- int out_w = deconvolutional_out_width(l);
-
- 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 = (float*)xcalloc(h * w * size * size * n, sizeof(float));
- l.output = (float*)xcalloc(l.batch * out_h * out_w * n, sizeof(float));
- l.delta = (float*)xcalloc(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
- 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);
-
- l.biases_gpu = cuda_make_array(l.biases, n);
- l.bias_updates_gpu = cuda_make_array(l.bias_updates, 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
-
- 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 l;
-}
-
-void resize_deconvolutional_layer(deconvolutional_layer *l, int h, int w)
-{
- l->h = h;
- l->w = w;
- int out_h = deconvolutional_out_height(*l);
- int out_w = deconvolutional_out_width(*l);
-
- l->col_image = (float*)xrealloc(l->col_image,
- out_h*out_w*l->size*l->size*l->c*sizeof(float));
- l->output = (float*)xrealloc(l->output,
- l->batch*out_h * out_w * l->n*sizeof(float));
- l->delta = (float*)xrealloc(l->delta,
- l->batch*out_h * out_w * l->n*sizeof(float));
- #ifdef GPU
- cuda_free(l->col_image_gpu);
- cuda_free(l->delta_gpu);
- cuda_free(l->output_gpu);
-
- 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 l, network_state state)
-{
- int i;
- int out_h = deconvolutional_out_height(l);
- int out_w = deconvolutional_out_width(l);
- int size = out_h*out_w;
-
- int m = l.size*l.size*l.n;
- int n = l.h*l.w;
- int k = l.c;
-
- fill_cpu(l.outputs*l.batch, 0, l.output, 1);
-
- 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, l.n, out_h, out_w, l.size, l.stride, 0, l.output+i*l.n*size);
- }
- 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 l, network_state state)
-{
- 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(l.output, size*l.n*l.batch, l.activation, l.delta);
- backward_bias(l.bias_updates, l.delta, l.batch, l.n, size);
-
- 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;
-
- float *a = state.input + i*m*n;
- float *b = l.col_image;
- float *c = l.weight_updates;
-
- 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(state.delta){
- int m = l.c;
- int n = l.h*l.w;
- int k = l.size*l.size*l.n;
-
- 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 l, int skip, float learning_rate, float momentum, float decay)
-{
- 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, -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);
-}
+#include "deconvolutional_layer.h"
+#include "convolutional_layer.h"
+#include "utils.h"
+#include "im2col.h"
+#include "col2im.h"
+#include "blas.h"
+#include "gemm.h"
+#include <stdio.h>
+#include <time.h>
+
+int deconvolutional_out_height(deconvolutional_layer l)
+{
+ int h = l.stride*(l.h - 1) + l.size;
+ return h;
+}
+
+int deconvolutional_out_width(deconvolutional_layer l)
+{
+ int w = l.stride*(l.w - 1) + l.size;
+ return w;
+}
+
+int deconvolutional_out_size(deconvolutional_layer l)
+{
+ return deconvolutional_out_height(l) * deconvolutional_out_width(l);
+}
+
+image get_deconvolutional_image(deconvolutional_layer l)
+{
+ int h,w,c;
+ 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 l)
+{
+ int h,w,c;
+ 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)
+{
+ int i;
+ deconvolutional_layer l = { (LAYER_TYPE)0 };
+ l.type = DECONVOLUTIONAL;
+
+ l.h = h;
+ l.w = w;
+ l.c = c;
+ l.n = n;
+ l.batch = batch;
+ l.stride = stride;
+ l.size = size;
+
+ l.weights = (float*)xcalloc(c * n * size * size, sizeof(float));
+ l.weight_updates = (float*)xcalloc(c * n * size * size, sizeof(float));
+
+ l.biases = (float*)xcalloc(n, sizeof(float));
+ l.bias_updates = (float*)xcalloc(n, sizeof(float));
+ float scale = 1./sqrt(size*size*c);
+ for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_normal();
+ for(i = 0; i < n; ++i){
+ l.biases[i] = scale;
+ }
+ int out_h = deconvolutional_out_height(l);
+ int out_w = deconvolutional_out_width(l);
+
+ 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 = (float*)xcalloc(h * w * size * size * n, sizeof(float));
+ l.output = (float*)xcalloc(l.batch * out_h * out_w * n, sizeof(float));
+ l.delta = (float*)xcalloc(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
+ 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);
+
+ l.biases_gpu = cuda_make_array(l.biases, n);
+ l.bias_updates_gpu = cuda_make_array(l.bias_updates, 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
+
+ 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 l;
+}
+
+void resize_deconvolutional_layer(deconvolutional_layer *l, int h, int w)
+{
+ l->h = h;
+ l->w = w;
+ int out_h = deconvolutional_out_height(*l);
+ int out_w = deconvolutional_out_width(*l);
+
+ l->col_image = (float*)xrealloc(l->col_image,
+ out_h*out_w*l->size*l->size*l->c*sizeof(float));
+ l->output = (float*)xrealloc(l->output,
+ l->batch*out_h * out_w * l->n*sizeof(float));
+ l->delta = (float*)xrealloc(l->delta,
+ l->batch*out_h * out_w * l->n*sizeof(float));
+ #ifdef GPU
+ cuda_free(l->col_image_gpu);
+ cuda_free(l->delta_gpu);
+ cuda_free(l->output_gpu);
+
+ 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 l, network_state state)
+{
+ int i;
+ int out_h = deconvolutional_out_height(l);
+ int out_w = deconvolutional_out_width(l);
+ int size = out_h*out_w;
+
+ int m = l.size*l.size*l.n;
+ int n = l.h*l.w;
+ int k = l.c;
+
+ fill_cpu(l.outputs*l.batch, 0, l.output, 1);
+
+ 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, l.n, out_h, out_w, l.size, l.stride, 0, l.output+i*l.n*size);
+ }
+ 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 l, network_state state)
+{
+ 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(l.output, size*l.n*l.batch, l.activation, l.delta);
+ backward_bias(l.bias_updates, l.delta, l.batch, l.n, size);
+
+ 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;
+
+ float *a = state.input + i*m*n;
+ float *b = l.col_image;
+ float *c = l.weight_updates;
+
+ 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(state.delta){
+ int m = l.c;
+ int n = l.h*l.w;
+ int k = l.size*l.size*l.n;
+
+ 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 l, int skip, float learning_rate, float momentum, float decay)
+{
+ 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, -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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