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_kernels.cu | 212 ++++++++++++++++++++++++++--------------------------
1 files changed, 106 insertions(+), 106 deletions(-)
diff --git a/lib/detecter_tools/darknet/deconvolutional_kernels.cu b/lib/detecter_tools/darknet/deconvolutional_kernels.cu
index cc71cfc..6af65eb 100644
--- a/lib/detecter_tools/darknet/deconvolutional_kernels.cu
+++ b/lib/detecter_tools/darknet/deconvolutional_kernels.cu
@@ -1,106 +1,106 @@
-#include <cuda_runtime.h>
-#include <curand.h>
-#include <cublas_v2.h>
-
-#include "convolutional_layer.h"
-#include "deconvolutional_layer.h"
-#include "gemm.h"
-#include "blas.h"
-#include "im2col.h"
-#include "col2im.h"
-#include "utils.h"
-#include "dark_cuda.h"
-
-extern "C" void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state)
-{
- int i;
- int out_h = deconvolutional_out_height(layer);
- int out_w = deconvolutional_out_width(layer);
- int size = out_h*out_w;
-
- int m = layer.size*layer.size*layer.n;
- int n = layer.h*layer.w;
- int k = layer.c;
-
- fill_ongpu(layer.outputs*layer.batch, 0, layer.output_gpu, 1);
-
- for(i = 0; i < layer.batch; ++i){
- float *a = layer.weights_gpu;
- 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);
-
- col2im_ongpu(c, layer.n, out_h, out_w, layer.size, layer.stride, 0, layer.output_gpu+i*layer.n*size);
- }
- add_bias_gpu(layer.output_gpu, layer.biases_gpu, layer.batch, layer.n, size);
- activate_array(layer.output_gpu, layer.batch*layer.n*size, layer.activation);
-}
-
-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);
- int out_w = deconvolutional_out_width(layer);
- int size = out_h*out_w;
- int i;
-
- 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(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 = state.input + i*m*n;
- float *b = layer.col_image_gpu;
- float *c = layer.weight_updates_gpu;
-
- im2col_ongpu(layer.delta_gpu + i*layer.n*size, layer.n, out_h, out_w,
- layer.size, layer.stride, 0, b);
- gemm_ongpu(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
-
- if(state.delta){
- int m = layer.c;
- int n = layer.h*layer.w;
- int k = layer.size*layer.size*layer.n;
-
- float *a = layer.weights_gpu;
- float *b = layer.col_image_gpu;
- float *c = state.delta + i*n*m;
-
- gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
- }
- }
-}
-
-extern "C" void pull_deconvolutional_layer(deconvolutional_layer layer)
-{
- cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
- cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
- cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
- cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
-}
-
-extern "C" void push_deconvolutional_layer(deconvolutional_layer layer)
-{
- cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
- cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
- cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
- cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
-}
-
-extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer, int skip, float learning_rate, float momentum, float decay)
-{
- int size = layer.size*layer.size*layer.c*layer.n;
-
- 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, -decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
- axpy_ongpu(size, learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
- scal_ongpu(size, momentum, layer.weight_updates_gpu, 1);
-}
+#include <cuda_runtime.h>
+#include <curand.h>
+#include <cublas_v2.h>
+
+#include "convolutional_layer.h"
+#include "deconvolutional_layer.h"
+#include "gemm.h"
+#include "blas.h"
+#include "im2col.h"
+#include "col2im.h"
+#include "utils.h"
+#include "dark_cuda.h"
+
+extern "C" void forward_deconvolutional_layer_gpu(deconvolutional_layer layer, network_state state)
+{
+ int i;
+ int out_h = deconvolutional_out_height(layer);
+ int out_w = deconvolutional_out_width(layer);
+ int size = out_h*out_w;
+
+ int m = layer.size*layer.size*layer.n;
+ int n = layer.h*layer.w;
+ int k = layer.c;
+
+ fill_ongpu(layer.outputs*layer.batch, 0, layer.output_gpu, 1);
+
+ for(i = 0; i < layer.batch; ++i){
+ float *a = layer.weights_gpu;
+ 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);
+
+ col2im_ongpu(c, layer.n, out_h, out_w, layer.size, layer.stride, 0, layer.output_gpu+i*layer.n*size);
+ }
+ add_bias_gpu(layer.output_gpu, layer.biases_gpu, layer.batch, layer.n, size);
+ activate_array(layer.output_gpu, layer.batch*layer.n*size, layer.activation);
+}
+
+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);
+ int out_w = deconvolutional_out_width(layer);
+ int size = out_h*out_w;
+ int i;
+
+ 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(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 = state.input + i*m*n;
+ float *b = layer.col_image_gpu;
+ float *c = layer.weight_updates_gpu;
+
+ im2col_ongpu(layer.delta_gpu + i*layer.n*size, layer.n, out_h, out_w,
+ layer.size, layer.stride, 0, b);
+ gemm_ongpu(0,1,m,n,k,alpha,a,k,b,k,1,c,n);
+
+ if(state.delta){
+ int m = layer.c;
+ int n = layer.h*layer.w;
+ int k = layer.size*layer.size*layer.n;
+
+ float *a = layer.weights_gpu;
+ float *b = layer.col_image_gpu;
+ float *c = state.delta + i*n*m;
+
+ gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
+ }
+ }
+}
+
+extern "C" void pull_deconvolutional_layer(deconvolutional_layer layer)
+{
+ cuda_pull_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
+ cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
+ cuda_pull_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
+ cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
+}
+
+extern "C" void push_deconvolutional_layer(deconvolutional_layer layer)
+{
+ cuda_push_array(layer.weights_gpu, layer.weights, layer.c*layer.n*layer.size*layer.size);
+ cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
+ cuda_push_array(layer.weight_updates_gpu, layer.weight_updates, layer.c*layer.n*layer.size*layer.size);
+ cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
+}
+
+extern "C" void update_deconvolutional_layer_gpu(deconvolutional_layer layer, int skip, float learning_rate, float momentum, float decay)
+{
+ int size = layer.size*layer.size*layer.c*layer.n;
+
+ 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, -decay, layer.weights_gpu, 1, layer.weight_updates_gpu, 1);
+ axpy_ongpu(size, learning_rate, layer.weight_updates_gpu, 1, layer.weights_gpu, 1);
+ scal_ongpu(size, momentum, layer.weight_updates_gpu, 1);
+}
--
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