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/local_layer.c | 566 ++++++++++++++++++++++++++++----------------------------
1 files changed, 283 insertions(+), 283 deletions(-)
diff --git a/lib/detecter_tools/darknet/local_layer.c b/lib/detecter_tools/darknet/local_layer.c
index 76bb9bb..88c7b12 100644
--- a/lib/detecter_tools/darknet/local_layer.c
+++ b/lib/detecter_tools/darknet/local_layer.c
@@ -1,283 +1,283 @@
-#include "local_layer.h"
-#include "utils.h"
-#include "im2col.h"
-#include "col2im.h"
-#include "blas.h"
-#include "gemm.h"
-#include <stdio.h>
-#include <time.h>
-
-int local_out_height(local_layer l)
-{
- int h = l.h;
- if (!l.pad) h -= l.size;
- else h -= 1;
- return h/l.stride + 1;
-}
-
-int local_out_width(local_layer l)
-{
- int w = l.w;
- if (!l.pad) w -= l.size;
- else w -= 1;
- return w/l.stride + 1;
-}
-
-local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
-{
- int i;
- local_layer l = { (LAYER_TYPE)0 };
- l.type = LOCAL;
-
- l.h = h;
- l.w = w;
- l.c = c;
- l.n = n;
- l.batch = batch;
- l.stride = stride;
- l.size = size;
- l.pad = pad;
-
- int out_h = local_out_height(l);
- int out_w = local_out_width(l);
- int locations = out_h*out_w;
- l.out_h = out_h;
- l.out_w = out_w;
- l.out_c = n;
- l.outputs = l.out_h * l.out_w * l.out_c;
- l.inputs = l.w * l.h * l.c;
-
- l.weights = (float*)xcalloc(c * n * size * size * locations, sizeof(float));
- l.weight_updates = (float*)xcalloc(c * n * size * size * locations, sizeof(float));
-
- l.biases = (float*)xcalloc(l.outputs, sizeof(float));
- l.bias_updates = (float*)xcalloc(l.outputs, sizeof(float));
-
- // float scale = 1./sqrt(size*size*c);
- float scale = sqrt(2./(size*size*c));
- for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1,1);
-
- l.col_image = (float*)xcalloc(out_h * out_w * size * size * c, 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_local_layer;
- l.backward = backward_local_layer;
- l.update = update_local_layer;
-
-#ifdef GPU
- l.forward_gpu = forward_local_layer_gpu;
- l.backward_gpu = backward_local_layer_gpu;
- l.update_gpu = update_local_layer_gpu;
-
- l.weights_gpu = cuda_make_array(l.weights, c*n*size*size*locations);
- l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size*locations);
-
- l.biases_gpu = cuda_make_array(l.biases, l.outputs);
- l.bias_updates_gpu = cuda_make_array(l.bias_updates, l.outputs);
-
- l.col_image_gpu = cuda_make_array(l.col_image, out_h*out_w*size*size*c);
- 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, "Local 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 forward_local_layer(const local_layer l, network_state state)
-{
- int out_h = local_out_height(l);
- int out_w = local_out_width(l);
- int i, j;
- int locations = out_h * out_w;
-
- for(i = 0; i < l.batch; ++i){
- copy_cpu(l.outputs, l.biases, 1, l.output + i*l.outputs, 1);
- }
-
- for(i = 0; i < l.batch; ++i){
- float *input = state.input + i*l.w*l.h*l.c;
- im2col_cpu(input, l.c, l.h, l.w,
- l.size, l.stride, l.pad, l.col_image);
- float *output = l.output + i*l.outputs;
- for(j = 0; j < locations; ++j){
- float *a = l.weights + j*l.size*l.size*l.c*l.n;
- float *b = l.col_image + j;
- float *c = output + j;
-
- int m = l.n;
- int n = 1;
- int k = l.size*l.size*l.c;
-
- gemm(0,0,m,n,k,1,a,k,b,locations,1,c,locations);
- }
- }
- activate_array(l.output, l.outputs*l.batch, l.activation);
-}
-
-void backward_local_layer(local_layer l, network_state state)
-{
- int i, j;
- int locations = l.out_w*l.out_h;
-
- gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
-
- for(i = 0; i < l.batch; ++i){
- axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1);
- }
-
- for(i = 0; i < l.batch; ++i){
- float *input = state.input + i*l.w*l.h*l.c;
- im2col_cpu(input, l.c, l.h, l.w,
- l.size, l.stride, l.pad, l.col_image);
-
- for(j = 0; j < locations; ++j){
- float *a = l.delta + i*l.outputs + j;
- float *b = l.col_image + j;
- float *c = l.weight_updates + j*l.size*l.size*l.c*l.n;
- int m = l.n;
- int n = l.size*l.size*l.c;
- int k = 1;
-
- gemm(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
- }
-
- if(state.delta){
- for(j = 0; j < locations; ++j){
- float *a = l.weights + j*l.size*l.size*l.c*l.n;
- float *b = l.delta + i*l.outputs + j;
- float *c = l.col_image + j;
-
- int m = l.size*l.size*l.c;
- int n = 1;
- int k = l.n;
-
- gemm(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
- }
-
- col2im_cpu(l.col_image, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
- }
- }
-}
-
-void update_local_layer(local_layer l, int batch, float learning_rate, float momentum, float decay)
-{
- int locations = l.out_w*l.out_h;
- int size = l.size*l.size*l.c*l.n*locations;
- axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
- scal_cpu(l.outputs, momentum, l.bias_updates, 1);
-
- axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1);
- axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
- scal_cpu(size, momentum, l.weight_updates, 1);
-}
-
-#ifdef GPU
-
-void forward_local_layer_gpu(const local_layer l, network_state state)
-{
- int out_h = local_out_height(l);
- int out_w = local_out_width(l);
- int i, j;
- int locations = out_h * out_w;
-
- for(i = 0; i < l.batch; ++i){
- copy_ongpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1);
- }
-
- for(i = 0; i < l.batch; ++i){
- float *input = state.input + i*l.w*l.h*l.c;
- im2col_ongpu(input, l.c, l.h, l.w,
- l.size, l.stride, l.pad, l.col_image_gpu);
- float *output = l.output_gpu + i*l.outputs;
- for(j = 0; j < locations; ++j){
- float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n;
- float *b = l.col_image_gpu + j;
- float *c = output + j;
-
- int m = l.n;
- int n = 1;
- int k = l.size*l.size*l.c;
-
- gemm_ongpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations);
- }
- }
- activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
-}
-
-void backward_local_layer_gpu(local_layer l, network_state state)
-{
- int i, j;
- int locations = l.out_w*l.out_h;
-
- gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
- for(i = 0; i < l.batch; ++i){
- axpy_ongpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1);
- }
-
- for(i = 0; i < l.batch; ++i){
- float *input = state.input + i*l.w*l.h*l.c;
- im2col_ongpu(input, l.c, l.h, l.w,
- l.size, l.stride, l.pad, l.col_image_gpu);
-
- for(j = 0; j < locations; ++j){
- float *a = l.delta_gpu + i*l.outputs + j;
- float *b = l.col_image_gpu + j;
- float *c = l.weight_updates_gpu + j*l.size*l.size*l.c*l.n;
- int m = l.n;
- int n = l.size*l.size*l.c;
- int k = 1;
-
- gemm_ongpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
- }
-
- if(state.delta){
- for(j = 0; j < locations; ++j){
- float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n;
- float *b = l.delta_gpu + i*l.outputs + j;
- float *c = l.col_image_gpu + j;
-
- int m = l.size*l.size*l.c;
- int n = 1;
- int k = l.n;
-
- gemm_ongpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
- }
-
- col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
- }
- }
-}
-
-void update_local_layer_gpu(local_layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale)
-{
- int locations = l.out_w*l.out_h;
- int size = l.size*l.size*l.c*l.n*locations;
- axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
- scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1);
-
- axpy_ongpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
- axpy_ongpu(size, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
- scal_ongpu(size, momentum, l.weight_updates_gpu, 1);
-}
-
-void pull_local_layer(local_layer l)
-{
- int locations = l.out_w*l.out_h;
- int size = l.size*l.size*l.c*l.n*locations;
- cuda_pull_array(l.weights_gpu, l.weights, size);
- cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
-}
-
-void push_local_layer(local_layer l)
-{
- int locations = l.out_w*l.out_h;
- int size = l.size*l.size*l.c*l.n*locations;
- cuda_push_array(l.weights_gpu, l.weights, size);
- cuda_push_array(l.biases_gpu, l.biases, l.outputs);
-}
-#endif
+#include "local_layer.h"
+#include "utils.h"
+#include "im2col.h"
+#include "col2im.h"
+#include "blas.h"
+#include "gemm.h"
+#include <stdio.h>
+#include <time.h>
+
+int local_out_height(local_layer l)
+{
+ int h = l.h;
+ if (!l.pad) h -= l.size;
+ else h -= 1;
+ return h/l.stride + 1;
+}
+
+int local_out_width(local_layer l)
+{
+ int w = l.w;
+ if (!l.pad) w -= l.size;
+ else w -= 1;
+ return w/l.stride + 1;
+}
+
+local_layer make_local_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
+{
+ int i;
+ local_layer l = { (LAYER_TYPE)0 };
+ l.type = LOCAL;
+
+ l.h = h;
+ l.w = w;
+ l.c = c;
+ l.n = n;
+ l.batch = batch;
+ l.stride = stride;
+ l.size = size;
+ l.pad = pad;
+
+ int out_h = local_out_height(l);
+ int out_w = local_out_width(l);
+ int locations = out_h*out_w;
+ l.out_h = out_h;
+ l.out_w = out_w;
+ l.out_c = n;
+ l.outputs = l.out_h * l.out_w * l.out_c;
+ l.inputs = l.w * l.h * l.c;
+
+ l.weights = (float*)xcalloc(c * n * size * size * locations, sizeof(float));
+ l.weight_updates = (float*)xcalloc(c * n * size * size * locations, sizeof(float));
+
+ l.biases = (float*)xcalloc(l.outputs, sizeof(float));
+ l.bias_updates = (float*)xcalloc(l.outputs, sizeof(float));
+
+ // float scale = 1./sqrt(size*size*c);
+ float scale = sqrt(2./(size*size*c));
+ for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1,1);
+
+ l.col_image = (float*)xcalloc(out_h * out_w * size * size * c, 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_local_layer;
+ l.backward = backward_local_layer;
+ l.update = update_local_layer;
+
+#ifdef GPU
+ l.forward_gpu = forward_local_layer_gpu;
+ l.backward_gpu = backward_local_layer_gpu;
+ l.update_gpu = update_local_layer_gpu;
+
+ l.weights_gpu = cuda_make_array(l.weights, c*n*size*size*locations);
+ l.weight_updates_gpu = cuda_make_array(l.weight_updates, c*n*size*size*locations);
+
+ l.biases_gpu = cuda_make_array(l.biases, l.outputs);
+ l.bias_updates_gpu = cuda_make_array(l.bias_updates, l.outputs);
+
+ l.col_image_gpu = cuda_make_array(l.col_image, out_h*out_w*size*size*c);
+ 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, "Local 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 forward_local_layer(const local_layer l, network_state state)
+{
+ int out_h = local_out_height(l);
+ int out_w = local_out_width(l);
+ int i, j;
+ int locations = out_h * out_w;
+
+ for(i = 0; i < l.batch; ++i){
+ copy_cpu(l.outputs, l.biases, 1, l.output + i*l.outputs, 1);
+ }
+
+ for(i = 0; i < l.batch; ++i){
+ float *input = state.input + i*l.w*l.h*l.c;
+ im2col_cpu(input, l.c, l.h, l.w,
+ l.size, l.stride, l.pad, l.col_image);
+ float *output = l.output + i*l.outputs;
+ for(j = 0; j < locations; ++j){
+ float *a = l.weights + j*l.size*l.size*l.c*l.n;
+ float *b = l.col_image + j;
+ float *c = output + j;
+
+ int m = l.n;
+ int n = 1;
+ int k = l.size*l.size*l.c;
+
+ gemm(0,0,m,n,k,1,a,k,b,locations,1,c,locations);
+ }
+ }
+ activate_array(l.output, l.outputs*l.batch, l.activation);
+}
+
+void backward_local_layer(local_layer l, network_state state)
+{
+ int i, j;
+ int locations = l.out_w*l.out_h;
+
+ gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
+
+ for(i = 0; i < l.batch; ++i){
+ axpy_cpu(l.outputs, 1, l.delta + i*l.outputs, 1, l.bias_updates, 1);
+ }
+
+ for(i = 0; i < l.batch; ++i){
+ float *input = state.input + i*l.w*l.h*l.c;
+ im2col_cpu(input, l.c, l.h, l.w,
+ l.size, l.stride, l.pad, l.col_image);
+
+ for(j = 0; j < locations; ++j){
+ float *a = l.delta + i*l.outputs + j;
+ float *b = l.col_image + j;
+ float *c = l.weight_updates + j*l.size*l.size*l.c*l.n;
+ int m = l.n;
+ int n = l.size*l.size*l.c;
+ int k = 1;
+
+ gemm(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
+ }
+
+ if(state.delta){
+ for(j = 0; j < locations; ++j){
+ float *a = l.weights + j*l.size*l.size*l.c*l.n;
+ float *b = l.delta + i*l.outputs + j;
+ float *c = l.col_image + j;
+
+ int m = l.size*l.size*l.c;
+ int n = 1;
+ int k = l.n;
+
+ gemm(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
+ }
+
+ col2im_cpu(l.col_image, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
+ }
+ }
+}
+
+void update_local_layer(local_layer l, int batch, float learning_rate, float momentum, float decay)
+{
+ int locations = l.out_w*l.out_h;
+ int size = l.size*l.size*l.c*l.n*locations;
+ axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
+ scal_cpu(l.outputs, momentum, l.bias_updates, 1);
+
+ axpy_cpu(size, -decay*batch, l.weights, 1, l.weight_updates, 1);
+ axpy_cpu(size, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
+ scal_cpu(size, momentum, l.weight_updates, 1);
+}
+
+#ifdef GPU
+
+void forward_local_layer_gpu(const local_layer l, network_state state)
+{
+ int out_h = local_out_height(l);
+ int out_w = local_out_width(l);
+ int i, j;
+ int locations = out_h * out_w;
+
+ for(i = 0; i < l.batch; ++i){
+ copy_ongpu(l.outputs, l.biases_gpu, 1, l.output_gpu + i*l.outputs, 1);
+ }
+
+ for(i = 0; i < l.batch; ++i){
+ float *input = state.input + i*l.w*l.h*l.c;
+ im2col_ongpu(input, l.c, l.h, l.w,
+ l.size, l.stride, l.pad, l.col_image_gpu);
+ float *output = l.output_gpu + i*l.outputs;
+ for(j = 0; j < locations; ++j){
+ float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n;
+ float *b = l.col_image_gpu + j;
+ float *c = output + j;
+
+ int m = l.n;
+ int n = 1;
+ int k = l.size*l.size*l.c;
+
+ gemm_ongpu(0,0,m,n,k,1,a,k,b,locations,1,c,locations);
+ }
+ }
+ activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
+}
+
+void backward_local_layer_gpu(local_layer l, network_state state)
+{
+ int i, j;
+ int locations = l.out_w*l.out_h;
+
+ gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
+ for(i = 0; i < l.batch; ++i){
+ axpy_ongpu(l.outputs, 1, l.delta_gpu + i*l.outputs, 1, l.bias_updates_gpu, 1);
+ }
+
+ for(i = 0; i < l.batch; ++i){
+ float *input = state.input + i*l.w*l.h*l.c;
+ im2col_ongpu(input, l.c, l.h, l.w,
+ l.size, l.stride, l.pad, l.col_image_gpu);
+
+ for(j = 0; j < locations; ++j){
+ float *a = l.delta_gpu + i*l.outputs + j;
+ float *b = l.col_image_gpu + j;
+ float *c = l.weight_updates_gpu + j*l.size*l.size*l.c*l.n;
+ int m = l.n;
+ int n = l.size*l.size*l.c;
+ int k = 1;
+
+ gemm_ongpu(0,1,m,n,k,1,a,locations,b,locations,1,c,n);
+ }
+
+ if(state.delta){
+ for(j = 0; j < locations; ++j){
+ float *a = l.weights_gpu + j*l.size*l.size*l.c*l.n;
+ float *b = l.delta_gpu + i*l.outputs + j;
+ float *c = l.col_image_gpu + j;
+
+ int m = l.size*l.size*l.c;
+ int n = 1;
+ int k = l.n;
+
+ gemm_ongpu(1,0,m,n,k,1,a,m,b,locations,0,c,locations);
+ }
+
+ col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
+ }
+ }
+}
+
+void update_local_layer_gpu(local_layer l, int batch, float learning_rate, float momentum, float decay, float loss_scale)
+{
+ int locations = l.out_w*l.out_h;
+ int size = l.size*l.size*l.c*l.n*locations;
+ axpy_ongpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
+ scal_ongpu(l.outputs, momentum, l.bias_updates_gpu, 1);
+
+ axpy_ongpu(size, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
+ axpy_ongpu(size, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
+ scal_ongpu(size, momentum, l.weight_updates_gpu, 1);
+}
+
+void pull_local_layer(local_layer l)
+{
+ int locations = l.out_w*l.out_h;
+ int size = l.size*l.size*l.c*l.n*locations;
+ cuda_pull_array(l.weights_gpu, l.weights, size);
+ cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
+}
+
+void push_local_layer(local_layer l)
+{
+ int locations = l.out_w*l.out_h;
+ int size = l.size*l.size*l.c*l.n*locations;
+ cuda_push_array(l.weights_gpu, l.weights, size);
+ cuda_push_array(l.biases_gpu, l.biases, l.outputs);
+}
+#endif
--
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