#include "xnor_layer.h"
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#include "binary_convolution.h"
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#include "convolutional_layer.h"
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layer make_xnor_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize)
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{
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int i;
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layer l = {0};
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l.type = XNOR;
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l.h = h;
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l.w = w;
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l.c = c;
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l.n = n;
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l.batch = batch;
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l.stride = stride;
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l.size = size;
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l.pad = pad;
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l.batch_normalize = batch_normalize;
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l.filters = calloc(c*n*size*size, sizeof(float));
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l.biases = calloc(n, sizeof(float));
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int out_h = convolutional_out_height(l);
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int out_w = convolutional_out_width(l);
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l.out_h = out_h;
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l.out_w = out_w;
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l.out_c = n;
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l.outputs = l.out_h * l.out_w * l.out_c;
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l.inputs = l.w * l.h * l.c;
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l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
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if(batch_normalize){
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l.scales = calloc(n, sizeof(float));
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for(i = 0; i < n; ++i){
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l.scales[i] = 1;
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}
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l.mean = calloc(n, sizeof(float));
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l.variance = calloc(n, sizeof(float));
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l.rolling_mean = calloc(n, sizeof(float));
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l.rolling_variance = calloc(n, sizeof(float));
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}
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l.activation = activation;
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fprintf(stderr, "XNOR Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
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return l;
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}
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void forward_xnor_layer(const layer l, network_state state)
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{
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int b = l.n;
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int c = l.c;
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int ix = l.w;
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int iy = l.h;
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int wx = l.size;
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int wy = l.size;
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int s = l.stride;
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int pad = l.pad * (l.size/2);
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// MANDATORY: Make the binary layer
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ai2_bin_conv_layer al = ai2_make_bin_conv_layer(b, c, ix, iy, wx, wy, s, pad);
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// OPTIONAL: You need to set the real-valued input like:
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ai2_setFltInput_unpadded(&al, state.input);
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// The above function will automatically binarize the input for the layer (channel wise).
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// If commented: using the default 0-valued input.
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ai2_setFltWeights(&al, l.filters);
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// The above function will automatically binarize the input for the layer (channel wise).
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// If commented: using the default 0-valued weights.
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// MANDATORY: Call forward
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ai2_bin_forward(&al);
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// OPTIONAL: Inspect outputs
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float *output = ai2_getFltOutput(&al); // output is of size l.px * l.py where px and py are the padded outputs
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memcpy(l.output, output, l.outputs*sizeof(float));
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// MANDATORY: Free layer
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ai2_free_bin_conv_layer(&al);
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}
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