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/scale_channels_layer.c |  300 ++++++++++++++++++++++++++++++------------------------------
 1 files changed, 150 insertions(+), 150 deletions(-)

diff --git a/lib/detecter_tools/darknet/scale_channels_layer.c b/lib/detecter_tools/darknet/scale_channels_layer.c
index 8055c13..c4f6410 100644
--- a/lib/detecter_tools/darknet/scale_channels_layer.c
+++ b/lib/detecter_tools/darknet/scale_channels_layer.c
@@ -1,150 +1,150 @@
-#include "scale_channels_layer.h"
-#include "utils.h"
-#include "dark_cuda.h"
-#include "blas.h"
-#include <stdio.h>
-#include <assert.h>
-
-layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int scale_wh)
-{
-    fprintf(stderr,"scale Layer: %d\n", index);
-    layer l = { (LAYER_TYPE)0 };
-    l.type = SCALE_CHANNELS;
-    l.batch = batch;
-    l.scale_wh = scale_wh;
-    l.w = w;
-    l.h = h;
-    l.c = c;
-    if (!l.scale_wh) assert(w == 1 && h == 1);
-    else assert(c == 1);
-
-    l.out_w = w2;
-    l.out_h = h2;
-    l.out_c = c2;
-    if (!l.scale_wh) assert(l.out_c == l.c);
-    else assert(l.out_w == l.w && l.out_h == l.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_scale_channels_layer;
-    l.backward = backward_scale_channels_layer;
-#ifdef GPU
-    l.forward_gpu = forward_scale_channels_layer_gpu;
-    l.backward_gpu = backward_scale_channels_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_scale_channels_layer(layer *l, network *net)
-{
-    layer first = net->layers[l->index];
-    l->out_w = first.out_w;
-    l->out_h = first.out_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_scale_channels_layer(const layer l, network_state state)
-{
-    int size = l.batch * l.out_c * l.out_w * l.out_h;
-    int channel_size = l.out_w * l.out_h;
-    int batch_size = l.out_c * l.out_w * l.out_h;
-    float *from_output = state.net.layers[l.index].output;
-
-    if (l.scale_wh) {
-        int i;
-        #pragma omp parallel for
-        for (i = 0; i < size; ++i) {
-            int input_index = i % channel_size + (i / batch_size)*channel_size;
-
-            l.output[i] = state.input[input_index] * from_output[i];
-        }
-    }
-    else {
-        int i;
-        #pragma omp parallel for
-        for (i = 0; i < size; ++i) {
-            l.output[i] = state.input[i / channel_size] * from_output[i];
-        }
-    }
-
-    activate_array(l.output, l.outputs*l.batch, l.activation);
-}
-
-void backward_scale_channels_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 = l.out_w * l.out_h;
-    int batch_size = l.out_c * l.out_w * l.out_h;
-    float *from_output = state.net.layers[l.index].output;
-    float *from_delta = state.net.layers[l.index].delta;
-
-    if (l.scale_wh) {
-        int i;
-        #pragma omp parallel for
-        for (i = 0; i < size; ++i) {
-            int input_index = i % channel_size + (i / batch_size)*channel_size;
-
-            state.delta[input_index] += l.delta[i] * from_output[i];// / l.out_c; // l.delta * from  (should be divided by l.out_c?)
-
-            from_delta[i] += state.input[input_index] * l.delta[i]; // input * l.delta
-        }
-    }
-    else {
-        int i;
-        #pragma omp parallel for
-        for (i = 0; i < size; ++i) {
-            state.delta[i / channel_size] += l.delta[i] * from_output[i];// / channel_size; // l.delta * from  (should be divided by channel_size?)
-
-            from_delta[i] += state.input[i / channel_size] * l.delta[i]; // input * l.delta
-        }
-    }
-}
-
-#ifdef GPU
-void forward_scale_channels_layer_gpu(const layer l, network_state state)
-{
-    int size = l.batch * l.out_c * l.out_w * l.out_h;
-    int channel_size = l.out_w * l.out_h;
-    int batch_size = l.out_c * l.out_w * l.out_h;
-
-    scale_channels_gpu(state.net.layers[l.index].output_gpu, size, channel_size, batch_size, l.scale_wh, state.input, l.output_gpu);
-
-    activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
-}
-
-void backward_scale_channels_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 = l.out_w * l.out_h;
-    int batch_size = l.out_c * l.out_w * l.out_h;
-    float *from_output = state.net.layers[l.index].output_gpu;
-    float *from_delta = state.net.layers[l.index].delta_gpu;
-
-    backward_scale_channels_gpu(l.delta_gpu, size, channel_size, batch_size, l.scale_wh, state.input, from_delta, from_output, state.delta);
-}
-#endif
+#include "scale_channels_layer.h"
+#include "utils.h"
+#include "dark_cuda.h"
+#include "blas.h"
+#include <stdio.h>
+#include <assert.h>
+
+layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int scale_wh)
+{
+    fprintf(stderr,"scale Layer: %d\n", index);
+    layer l = { (LAYER_TYPE)0 };
+    l.type = SCALE_CHANNELS;
+    l.batch = batch;
+    l.scale_wh = scale_wh;
+    l.w = w;
+    l.h = h;
+    l.c = c;
+    if (!l.scale_wh) assert(w == 1 && h == 1);
+    else assert(c == 1);
+
+    l.out_w = w2;
+    l.out_h = h2;
+    l.out_c = c2;
+    if (!l.scale_wh) assert(l.out_c == l.c);
+    else assert(l.out_w == l.w && l.out_h == l.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_scale_channels_layer;
+    l.backward = backward_scale_channels_layer;
+#ifdef GPU
+    l.forward_gpu = forward_scale_channels_layer_gpu;
+    l.backward_gpu = backward_scale_channels_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_scale_channels_layer(layer *l, network *net)
+{
+    layer first = net->layers[l->index];
+    l->out_w = first.out_w;
+    l->out_h = first.out_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_scale_channels_layer(const layer l, network_state state)
+{
+    int size = l.batch * l.out_c * l.out_w * l.out_h;
+    int channel_size = l.out_w * l.out_h;
+    int batch_size = l.out_c * l.out_w * l.out_h;
+    float *from_output = state.net.layers[l.index].output;
+
+    if (l.scale_wh) {
+        int i;
+        #pragma omp parallel for
+        for (i = 0; i < size; ++i) {
+            int input_index = i % channel_size + (i / batch_size)*channel_size;
+
+            l.output[i] = state.input[input_index] * from_output[i];
+        }
+    }
+    else {
+        int i;
+        #pragma omp parallel for
+        for (i = 0; i < size; ++i) {
+            l.output[i] = state.input[i / channel_size] * from_output[i];
+        }
+    }
+
+    activate_array(l.output, l.outputs*l.batch, l.activation);
+}
+
+void backward_scale_channels_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 = l.out_w * l.out_h;
+    int batch_size = l.out_c * l.out_w * l.out_h;
+    float *from_output = state.net.layers[l.index].output;
+    float *from_delta = state.net.layers[l.index].delta;
+
+    if (l.scale_wh) {
+        int i;
+        #pragma omp parallel for
+        for (i = 0; i < size; ++i) {
+            int input_index = i % channel_size + (i / batch_size)*channel_size;
+
+            state.delta[input_index] += l.delta[i] * from_output[i];// / l.out_c; // l.delta * from  (should be divided by l.out_c?)
+
+            from_delta[i] += state.input[input_index] * l.delta[i]; // input * l.delta
+        }
+    }
+    else {
+        int i;
+        #pragma omp parallel for
+        for (i = 0; i < size; ++i) {
+            state.delta[i / channel_size] += l.delta[i] * from_output[i];// / channel_size; // l.delta * from  (should be divided by channel_size?)
+
+            from_delta[i] += state.input[i / channel_size] * l.delta[i]; // input * l.delta
+        }
+    }
+}
+
+#ifdef GPU
+void forward_scale_channels_layer_gpu(const layer l, network_state state)
+{
+    int size = l.batch * l.out_c * l.out_w * l.out_h;
+    int channel_size = l.out_w * l.out_h;
+    int batch_size = l.out_c * l.out_w * l.out_h;
+
+    scale_channels_gpu(state.net.layers[l.index].output_gpu, size, channel_size, batch_size, l.scale_wh, state.input, l.output_gpu);
+
+    activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
+}
+
+void backward_scale_channels_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 = l.out_w * l.out_h;
+    int batch_size = l.out_c * l.out_w * l.out_h;
+    float *from_output = state.net.layers[l.index].output_gpu;
+    float *from_delta = state.net.layers[l.index].delta_gpu;
+
+    backward_scale_channels_gpu(l.delta_gpu, size, channel_size, batch_size, l.scale_wh, state.input, from_delta, from_output, state.delta);
+}
+#endif

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