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XNOR-net on CPU AVX2

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This commit is contained in:
AlexeyAB
2018-08-07 23:20:25 +03:00
parent e6c97a53a7
commit 0a326e7afe
10 changed files with 710 additions and 77 deletions
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34
src/network.c
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@ -222,7 +222,7 @@ float *get_network_output(network net)
{
#ifdef GPU
if (gpu_index >= 0) return get_network_output_gpu(net);
#endif
#endif
int i;
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
return net.layers[i].output;
@ -366,7 +366,7 @@ void set_batch_network(network *net, int b)
/*
layer *l = net->layers + i;
cudnn_convolutional_setup(l, cudnn_fastest);
// check for excessive memory consumption
// check for excessive memory consumption
size_t free_byte;
size_t total_byte;
check_error(cudaMemGetInfo(&free_byte, &total_byte));
@ -520,7 +520,7 @@ void visualize_network(network net)
if(l.type == CONVOLUTIONAL){
prev = visualize_convolutional_layer(l, buff, prev);
}
}
}
}
void top_predictions(network net, int k, int *index)
@ -684,7 +684,7 @@ matrix network_predict_data_multi(network net, data test, int n)
}
}
free(X);
return pred;
return pred;
}
matrix network_predict_data(network net, data test)
@ -707,7 +707,7 @@ matrix network_predict_data(network net, data test)
}
}
free(X);
return pred;
return pred;
}
void print_network(network net)
@ -749,7 +749,7 @@ void compare_networks(network n1, network n2, data test)
printf("%5d %5d\n%5d %5d\n", a, b, c, d);
float num = pow((abs(b - c) - 1.), 2.);
float den = b + c;
printf("%f\n", num/den);
printf("%f\n", num/den);
}
float network_accuracy(network net, data d)
@ -847,3 +847,25 @@ void fuse_conv_batchnorm(network net)
}
}
}
void calculate_binary_weights(network net)
{
int j;
for (j = 0; j < net.n; ++j) {
layer *l = &net.layers[j];
if (l->type == CONVOLUTIONAL) {
//printf(" Merges Convolutional-%d and batch_norm \n", j);
if (l->xnor) {
//printf("\n %d \n", j);
size_t ldb_align = 256; // 256bit for AVX2
binary_transpose_align_weights(l, ldb_align);
}
}
}
//printf("\n calculate_binary_weights Done! \n");
}