| | |
| | | #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 |