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 -- Gitblit v1.8.0