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/sam_layer.c | 238 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 119 insertions(+), 119 deletions(-)
diff --git a/lib/detecter_tools/darknet/sam_layer.c b/lib/detecter_tools/darknet/sam_layer.c
index a503741..ddb7046 100644
--- a/lib/detecter_tools/darknet/sam_layer.c
+++ b/lib/detecter_tools/darknet/sam_layer.c
@@ -1,119 +1,119 @@
-#include "sam_layer.h"
-#include "utils.h"
-#include "dark_cuda.h"
-#include "blas.h"
-#include <stdio.h>
-#include <assert.h>
-
-layer make_sam_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2)
-{
- fprintf(stderr,"scale Layer: %d\n", index);
- layer l = { (LAYER_TYPE)0 };
- l.type = SAM;
- l.batch = batch;
- l.w = w;
- l.h = h;
- l.c = c;
-
- l.out_w = w2;
- l.out_h = h2;
- l.out_c = c2;
- assert(l.out_c == l.c);
- assert(l.w == l.out_w && l.h == l.out_h);
-
- l.outputs = l.out_w*l.out_h*l.out_c;
- l.inputs = l.outputs;
- l.index = index;
-
- l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float));
- l.output = (float*)xcalloc(l.outputs * batch, sizeof(float));
-
- l.forward = forward_sam_layer;
- l.backward = backward_sam_layer;
-#ifdef GPU
- l.forward_gpu = forward_sam_layer_gpu;
- l.backward_gpu = backward_sam_layer_gpu;
-
- l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch);
- l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
-#endif
- return l;
-}
-
-void resize_sam_layer(layer *l, int w, int h)
-{
- l->out_w = w;
- l->out_h = h;
- l->outputs = l->out_w*l->out_h*l->out_c;
- 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_sam_layer(const layer l, network_state state)
-{
- int size = l.batch * l.out_c * l.out_w * l.out_h;
- //int channel_size = 1;
- float *from_output = state.net.layers[l.index].output;
-
- int i;
- #pragma omp parallel for
- for (i = 0; i < size; ++i) {
- l.output[i] = state.input[i] * from_output[i];
- }
-
- activate_array(l.output, l.outputs*l.batch, l.activation);
-}
-
-void backward_sam_layer(const layer l, network_state state)
-{
- gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
- //axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1);
- //scale_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta);
-
- int size = l.batch * l.out_c * l.out_w * l.out_h;
- //int channel_size = 1;
- float *from_output = state.net.layers[l.index].output;
- float *from_delta = state.net.layers[l.index].delta;
-
- int i;
- #pragma omp parallel for
- for (i = 0; i < size; ++i) {
- state.delta[i] += l.delta[i] * from_output[i]; // l.delta * from (should be divided by channel_size?)
-
- from_delta[i] = state.input[i] * l.delta[i]; // input * l.delta
- }
-}
-
-#ifdef GPU
-void forward_sam_layer_gpu(const layer l, network_state state)
-{
- int size = l.batch * l.out_c * l.out_w * l.out_h;
- int channel_size = 1;
-
- sam_gpu(state.net.layers[l.index].output_gpu, size, channel_size, state.input, l.output_gpu);
-
- activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
-}
-
-void backward_sam_layer_gpu(const layer l, network_state state)
-{
- gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
-
- int size = l.batch * l.out_c * l.out_w * l.out_h;
- int channel_size = 1;
- float *from_output = state.net.layers[l.index].output_gpu;
- float *from_delta = state.net.layers[l.index].delta_gpu;
-
-
- backward_sam_gpu(l.delta_gpu, size, channel_size, state.input, from_delta, from_output, state.delta);
-}
-#endif
+#include "sam_layer.h"
+#include "utils.h"
+#include "dark_cuda.h"
+#include "blas.h"
+#include <stdio.h>
+#include <assert.h>
+
+layer make_sam_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2)
+{
+ fprintf(stderr,"scale Layer: %d\n", index);
+ layer l = { (LAYER_TYPE)0 };
+ l.type = SAM;
+ l.batch = batch;
+ l.w = w;
+ l.h = h;
+ l.c = c;
+
+ l.out_w = w2;
+ l.out_h = h2;
+ l.out_c = c2;
+ assert(l.out_c == l.c);
+ assert(l.w == l.out_w && l.h == l.out_h);
+
+ l.outputs = l.out_w*l.out_h*l.out_c;
+ l.inputs = l.outputs;
+ l.index = index;
+
+ l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float));
+ l.output = (float*)xcalloc(l.outputs * batch, sizeof(float));
+
+ l.forward = forward_sam_layer;
+ l.backward = backward_sam_layer;
+#ifdef GPU
+ l.forward_gpu = forward_sam_layer_gpu;
+ l.backward_gpu = backward_sam_layer_gpu;
+
+ l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch);
+ l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
+#endif
+ return l;
+}
+
+void resize_sam_layer(layer *l, int w, int h)
+{
+ l->out_w = w;
+ l->out_h = h;
+ l->outputs = l->out_w*l->out_h*l->out_c;
+ 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_sam_layer(const layer l, network_state state)
+{
+ int size = l.batch * l.out_c * l.out_w * l.out_h;
+ //int channel_size = 1;
+ float *from_output = state.net.layers[l.index].output;
+
+ int i;
+ #pragma omp parallel for
+ for (i = 0; i < size; ++i) {
+ l.output[i] = state.input[i] * from_output[i];
+ }
+
+ activate_array(l.output, l.outputs*l.batch, l.activation);
+}
+
+void backward_sam_layer(const layer l, network_state state)
+{
+ gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
+ //axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1);
+ //scale_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta);
+
+ int size = l.batch * l.out_c * l.out_w * l.out_h;
+ //int channel_size = 1;
+ float *from_output = state.net.layers[l.index].output;
+ float *from_delta = state.net.layers[l.index].delta;
+
+ int i;
+ #pragma omp parallel for
+ for (i = 0; i < size; ++i) {
+ state.delta[i] += l.delta[i] * from_output[i]; // l.delta * from (should be divided by channel_size?)
+
+ from_delta[i] = state.input[i] * l.delta[i]; // input * l.delta
+ }
+}
+
+#ifdef GPU
+void forward_sam_layer_gpu(const layer l, network_state state)
+{
+ int size = l.batch * l.out_c * l.out_w * l.out_h;
+ int channel_size = 1;
+
+ sam_gpu(state.net.layers[l.index].output_gpu, size, channel_size, state.input, l.output_gpu);
+
+ activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
+}
+
+void backward_sam_layer_gpu(const layer l, network_state state)
+{
+ gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
+
+ int size = l.batch * l.out_c * l.out_w * l.out_h;
+ int channel_size = 1;
+ float *from_output = state.net.layers[l.index].output_gpu;
+ float *from_delta = state.net.layers[l.index].delta_gpu;
+
+
+ backward_sam_gpu(l.delta_gpu, size, channel_size, state.input, from_delta, from_output, state.delta);
+}
+#endif
--
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