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/route_layer.c | 314 ++++++++++++++++++++++++++-------------------------
1 files changed, 161 insertions(+), 153 deletions(-)
diff --git a/lib/detecter_tools/darknet/route_layer.c b/lib/detecter_tools/darknet/route_layer.c
index f576632..9a3410c 100644
--- a/lib/detecter_tools/darknet/route_layer.c
+++ b/lib/detecter_tools/darknet/route_layer.c
@@ -1,153 +1,161 @@
-#include "route_layer.h"
-#include "utils.h"
-#include "dark_cuda.h"
-#include "blas.h"
-#include <stdio.h>
-
-route_layer make_route_layer(int batch, int n, int *input_layers, int *input_sizes, int groups, int group_id)
-{
- fprintf(stderr,"route ");
- route_layer l = { (LAYER_TYPE)0 };
- l.type = ROUTE;
- l.batch = batch;
- l.n = n;
- l.input_layers = input_layers;
- l.input_sizes = input_sizes;
- l.groups = groups;
- l.group_id = group_id;
- int i;
- int outputs = 0;
- for(i = 0; i < n; ++i){
- fprintf(stderr," %d", input_layers[i]);
- outputs += input_sizes[i];
- }
- outputs = outputs / groups;
- l.outputs = outputs;
- l.inputs = outputs;
- //fprintf(stderr, " inputs = %d \t outputs = %d, groups = %d, group_id = %d \n", l.inputs, l.outputs, l.groups, l.group_id);
- l.delta = (float*)xcalloc(outputs * batch, sizeof(float));
- l.output = (float*)xcalloc(outputs * batch, sizeof(float));
-
- l.forward = forward_route_layer;
- l.backward = backward_route_layer;
- #ifdef GPU
- l.forward_gpu = forward_route_layer_gpu;
- l.backward_gpu = backward_route_layer_gpu;
-
- l.delta_gpu = cuda_make_array(l.delta, outputs*batch);
- l.output_gpu = cuda_make_array(l.output, outputs*batch);
- #endif
- return l;
-}
-
-void resize_route_layer(route_layer *l, network *net)
-{
- int i;
- layer first = net->layers[l->input_layers[0]];
- l->out_w = first.out_w;
- l->out_h = first.out_h;
- l->out_c = first.out_c;
- l->outputs = first.outputs;
- l->input_sizes[0] = first.outputs;
- for(i = 1; i < l->n; ++i){
- int index = l->input_layers[i];
- layer next = net->layers[index];
- l->outputs += next.outputs;
- l->input_sizes[i] = next.outputs;
- if(next.out_w == first.out_w && next.out_h == first.out_h){
- l->out_c += next.out_c;
- }else{
- printf("Error: Different size of input layers: %d x %d, %d x %d\n", next.out_w, next.out_h, first.out_w, first.out_h);
- l->out_h = l->out_w = l->out_c = 0;
- exit(EXIT_FAILURE);
- }
- }
- l->out_c = l->out_c / l->groups;
- l->outputs = l->outputs / l->groups;
- l->inputs = l->outputs;
- l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float));
- l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float));
-
-#ifdef GPU
- cuda_free(l->output_gpu);
- cuda_free(l->delta_gpu);
- l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch);
- l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch);
-#endif
-
-}
-
-void forward_route_layer(const route_layer l, network_state state)
-{
- int i, j;
- int offset = 0;
- for(i = 0; i < l.n; ++i){
- int index = l.input_layers[i];
- float *input = state.net.layers[index].output;
- int input_size = l.input_sizes[i];
- int part_input_size = input_size / l.groups;
- for(j = 0; j < l.batch; ++j){
- //copy_cpu(input_size, input + j*input_size, 1, l.output + offset + j*l.outputs, 1);
- copy_cpu(part_input_size, input + j*input_size + part_input_size*l.group_id, 1, l.output + offset + j*l.outputs, 1);
- }
- //offset += input_size;
- offset += part_input_size;
- }
-}
-
-void backward_route_layer(const route_layer l, network_state state)
-{
- int i, j;
- int offset = 0;
- for(i = 0; i < l.n; ++i){
- int index = l.input_layers[i];
- float *delta = state.net.layers[index].delta;
- int input_size = l.input_sizes[i];
- int part_input_size = input_size / l.groups;
- for(j = 0; j < l.batch; ++j){
- //axpy_cpu(input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size, 1);
- axpy_cpu(part_input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size + part_input_size*l.group_id, 1);
- }
- //offset += input_size;
- offset += part_input_size;
- }
-}
-
-#ifdef GPU
-void forward_route_layer_gpu(const route_layer l, network_state state)
-{
- int i, j;
- int offset = 0;
- for(i = 0; i < l.n; ++i){
- int index = l.input_layers[i];
- float *input = state.net.layers[index].output_gpu;
- int input_size = l.input_sizes[i];
- int part_input_size = input_size / l.groups;
- for(j = 0; j < l.batch; ++j){
- //copy_ongpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1);
- //simple_copy_ongpu(input_size, input + j*input_size, l.output_gpu + offset + j*l.outputs);
- simple_copy_ongpu(part_input_size, input + j*input_size + part_input_size*l.group_id, l.output_gpu + offset + j*l.outputs);
- }
- //offset += input_size;
- offset += part_input_size;
- }
-}
-
-void backward_route_layer_gpu(const route_layer l, network_state state)
-{
- int i, j;
- int offset = 0;
- for(i = 0; i < l.n; ++i){
- int index = l.input_layers[i];
- float *delta = state.net.layers[index].delta_gpu;
- int input_size = l.input_sizes[i];
- int part_input_size = input_size / l.groups;
- for(j = 0; j < l.batch; ++j){
- //axpy_ongpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1);
- axpy_ongpu(part_input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size + part_input_size*l.group_id, 1);
- }
- //offset += input_size;
- offset += part_input_size;
- }
-}
-#endif
+#include "route_layer.h"
+#include "utils.h"
+#include "dark_cuda.h"
+#include "blas.h"
+#include <stdio.h>
+
+route_layer make_route_layer(int batch, int n, int *input_layers, int *input_sizes, int groups, int group_id)
+{
+ fprintf(stderr,"route ");
+ route_layer l = { (LAYER_TYPE)0 };
+ l.type = ROUTE;
+ l.batch = batch;
+ l.n = n;
+ l.input_layers = input_layers;
+ l.input_sizes = input_sizes;
+ l.groups = groups;
+ l.group_id = group_id;
+ int i;
+ int outputs = 0;
+ for(i = 0; i < n; ++i){
+ fprintf(stderr," %d", input_layers[i]);
+ outputs += input_sizes[i];
+ }
+ outputs = outputs / groups;
+ l.outputs = outputs;
+ l.inputs = outputs;
+ //fprintf(stderr, " inputs = %d \t outputs = %d, groups = %d, group_id = %d \n", l.inputs, l.outputs, l.groups, l.group_id);
+ l.delta = (float*)xcalloc(outputs * batch, sizeof(float));
+ l.output = (float*)xcalloc(outputs * batch, sizeof(float));
+
+ l.forward = forward_route_layer;
+ l.backward = backward_route_layer;
+ #ifdef GPU
+ l.forward_gpu = forward_route_layer_gpu;
+ l.backward_gpu = backward_route_layer_gpu;
+
+ l.delta_gpu = cuda_make_array(l.delta, outputs*batch);
+ l.output_gpu = cuda_make_array(l.output, outputs*batch);
+ #endif
+ return l;
+}
+
+void resize_route_layer(route_layer *l, network *net)
+{
+ int i;
+ layer first = net->layers[l->input_layers[0]];
+ l->out_w = first.out_w;
+ l->out_h = first.out_h;
+ l->out_c = first.out_c;
+ l->outputs = first.outputs;
+ l->input_sizes[0] = first.outputs;
+ for(i = 1; i < l->n; ++i){
+ int index = l->input_layers[i];
+ layer next = net->layers[index];
+ l->outputs += next.outputs;
+ l->input_sizes[i] = next.outputs;
+ if(next.out_w == first.out_w && next.out_h == first.out_h){
+ l->out_c += next.out_c;
+ }else{
+ printf("Error: Different size of input layers: %d x %d, %d x %d\n", next.out_w, next.out_h, first.out_w, first.out_h);
+ l->out_h = l->out_w = l->out_c = 0;
+ exit(EXIT_FAILURE);
+ }
+ }
+ l->out_c = l->out_c / l->groups;
+ l->outputs = l->outputs / l->groups;
+ l->inputs = l->outputs;
+ l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float));
+ l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float));
+
+#ifdef GPU
+ cuda_free(l->output_gpu);
+ cuda_free(l->delta_gpu);
+ l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch);
+ l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch);
+#endif
+
+}
+
+void forward_route_layer(const route_layer l, network_state state)
+{
+ int i, j;
+ int offset = 0;
+ for(i = 0; i < l.n; ++i){
+ int index = l.input_layers[i];
+ float *input = state.net.layers[index].output;
+ int input_size = l.input_sizes[i];
+ int part_input_size = input_size / l.groups;
+ for(j = 0; j < l.batch; ++j){
+ //copy_cpu(input_size, input + j*input_size, 1, l.output + offset + j*l.outputs, 1);
+ copy_cpu(part_input_size, input + j*input_size + part_input_size*l.group_id, 1, l.output + offset + j*l.outputs, 1);
+ }
+ //offset += input_size;
+ offset += part_input_size;
+ }
+}
+
+void backward_route_layer(const route_layer l, network_state state)
+{
+ int i, j;
+ int offset = 0;
+ for(i = 0; i < l.n; ++i){
+ int index = l.input_layers[i];
+ float *delta = state.net.layers[index].delta;
+ int input_size = l.input_sizes[i];
+ int part_input_size = input_size / l.groups;
+ for(j = 0; j < l.batch; ++j){
+ //axpy_cpu(input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size, 1);
+ axpy_cpu(part_input_size, 1, l.delta + offset + j*l.outputs, 1, delta + j*input_size + part_input_size*l.group_id, 1);
+ }
+ //offset += input_size;
+ offset += part_input_size;
+ }
+}
+
+#ifdef GPU
+void forward_route_layer_gpu(const route_layer l, network_state state)
+{
+ if (l.stream >= 0) {
+ switch_stream(l.stream);
+ }
+
+ if (l.wait_stream_id >= 0) {
+ wait_stream(l.wait_stream_id);
+ }
+
+ int i, j;
+ int offset = 0;
+ for(i = 0; i < l.n; ++i){
+ int index = l.input_layers[i];
+ float *input = state.net.layers[index].output_gpu;
+ int input_size = l.input_sizes[i];
+ int part_input_size = input_size / l.groups;
+ for(j = 0; j < l.batch; ++j){
+ //copy_ongpu(input_size, input + j*input_size, 1, l.output_gpu + offset + j*l.outputs, 1);
+ //simple_copy_ongpu(input_size, input + j*input_size, l.output_gpu + offset + j*l.outputs);
+ simple_copy_ongpu(part_input_size, input + j*input_size + part_input_size*l.group_id, l.output_gpu + offset + j*l.outputs);
+ }
+ //offset += input_size;
+ offset += part_input_size;
+ }
+}
+
+void backward_route_layer_gpu(const route_layer l, network_state state)
+{
+ int i, j;
+ int offset = 0;
+ for(i = 0; i < l.n; ++i){
+ int index = l.input_layers[i];
+ float *delta = state.net.layers[index].delta_gpu;
+ int input_size = l.input_sizes[i];
+ int part_input_size = input_size / l.groups;
+ for(j = 0; j < l.batch; ++j){
+ //axpy_ongpu(input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size, 1);
+ axpy_ongpu(part_input_size, 1, l.delta_gpu + offset + j*l.outputs, 1, delta + j*input_size + part_input_size*l.group_id, 1);
+ }
+ //offset += input_size;
+ offset += part_input_size;
+ }
+}
+#endif
--
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