#include "scale_channels_layer.h"
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#include "utils.h"
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#include "dark_cuda.h"
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#include "blas.h"
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#include <stdio.h>
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#include <assert.h>
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layer make_scale_channels_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2, int scale_wh)
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{
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fprintf(stderr,"scale Layer: %d\n", index);
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layer l = { (LAYER_TYPE)0 };
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l.type = SCALE_CHANNELS;
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l.batch = batch;
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l.scale_wh = scale_wh;
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l.w = w;
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l.h = h;
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l.c = c;
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if (!l.scale_wh) assert(w == 1 && h == 1);
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else assert(c == 1);
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l.out_w = w2;
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l.out_h = h2;
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l.out_c = c2;
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if (!l.scale_wh) assert(l.out_c == l.c);
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else assert(l.out_w == l.w && l.out_h == l.h);
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l.outputs = l.out_w*l.out_h*l.out_c;
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l.inputs = l.outputs;
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l.index = index;
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l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float));
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l.output = (float*)xcalloc(l.outputs * batch, sizeof(float));
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l.forward = forward_scale_channels_layer;
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l.backward = backward_scale_channels_layer;
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#ifdef GPU
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l.forward_gpu = forward_scale_channels_layer_gpu;
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l.backward_gpu = backward_scale_channels_layer_gpu;
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l.delta_gpu = cuda_make_array(l.delta, l.outputs*batch);
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l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
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#endif
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return l;
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}
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void resize_scale_channels_layer(layer *l, network *net)
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{
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layer first = net->layers[l->index];
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l->out_w = first.out_w;
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l->out_h = first.out_h;
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l->outputs = l->out_w*l->out_h*l->out_c;
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l->inputs = l->outputs;
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l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float));
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l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float));
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#ifdef GPU
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cuda_free(l->output_gpu);
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cuda_free(l->delta_gpu);
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l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch);
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l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch);
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#endif
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}
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void forward_scale_channels_layer(const layer l, network_state state)
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{
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int size = l.batch * l.out_c * l.out_w * l.out_h;
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int channel_size = l.out_w * l.out_h;
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int batch_size = l.out_c * l.out_w * l.out_h;
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float *from_output = state.net.layers[l.index].output;
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if (l.scale_wh) {
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int i;
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#pragma omp parallel for
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for (i = 0; i < size; ++i) {
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int input_index = i % channel_size + (i / batch_size)*channel_size;
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l.output[i] = state.input[input_index] * from_output[i];
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}
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}
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else {
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int i;
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#pragma omp parallel for
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for (i = 0; i < size; ++i) {
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l.output[i] = state.input[i / channel_size] * from_output[i];
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}
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}
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activate_array(l.output, l.outputs*l.batch, l.activation);
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}
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void backward_scale_channels_layer(const layer l, network_state state)
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{
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gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
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//axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1);
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//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);
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int size = l.batch * l.out_c * l.out_w * l.out_h;
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int channel_size = l.out_w * l.out_h;
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int batch_size = l.out_c * l.out_w * l.out_h;
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float *from_output = state.net.layers[l.index].output;
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float *from_delta = state.net.layers[l.index].delta;
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if (l.scale_wh) {
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int i;
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#pragma omp parallel for
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for (i = 0; i < size; ++i) {
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int input_index = i % channel_size + (i / batch_size)*channel_size;
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state.delta[input_index] += l.delta[i] * from_output[i];// / l.out_c; // l.delta * from (should be divided by l.out_c?)
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from_delta[i] += state.input[input_index] * l.delta[i]; // input * l.delta
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}
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}
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else {
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int i;
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#pragma omp parallel for
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for (i = 0; i < size; ++i) {
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state.delta[i / channel_size] += l.delta[i] * from_output[i];// / channel_size; // l.delta * from (should be divided by channel_size?)
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from_delta[i] += state.input[i / channel_size] * l.delta[i]; // input * l.delta
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}
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}
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}
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#ifdef GPU
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void forward_scale_channels_layer_gpu(const layer l, network_state state)
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{
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int size = l.batch * l.out_c * l.out_w * l.out_h;
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int channel_size = l.out_w * l.out_h;
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int batch_size = l.out_c * l.out_w * l.out_h;
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scale_channels_gpu(state.net.layers[l.index].output_gpu, size, channel_size, batch_size, l.scale_wh, state.input, l.output_gpu);
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activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
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}
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void backward_scale_channels_layer_gpu(const layer l, network_state state)
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{
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gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
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int size = l.batch * l.out_c * l.out_w * l.out_h;
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int channel_size = l.out_w * l.out_h;
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int batch_size = l.out_c * l.out_w * l.out_h;
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float *from_output = state.net.layers[l.index].output_gpu;
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float *from_delta = state.net.layers[l.index].delta_gpu;
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backward_scale_channels_gpu(l.delta_gpu, size, channel_size, batch_size, l.scale_wh, state.input, from_delta, from_output, state.delta);
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}
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#endif
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