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temp fix, don't use it

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This commit is contained in:
AlexeyAB
2018-08-30 16:32:39 +03:00
parent 18d5e4f39c
commit 9753b72aeb
7 changed files with 384 additions and 39 deletions
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36
src/convolutional_layer.c
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@ -609,9 +609,9 @@ void binary_align_weights(convolutional_layer *l)
binarize_weights(l->weights, m, k, l->binary_weights);
size_t align_weights_size = new_lda * m;
size_t align_bit_weights_size = align_weights_size / 8;// +1;
l->align_bit_weights_size = align_weights_size / 8;// +1;
float *align_weights = calloc(align_weights_size, sizeof(float));
l->align_bit_weights = calloc(align_bit_weights_size, sizeof(char));
l->align_bit_weights = calloc(l->align_bit_weights_size, sizeof(char));
size_t i, j;
// align A without transpose
@ -625,29 +625,28 @@ void binary_align_weights(convolutional_layer *l)
l->mean_arr = calloc(l->n, sizeof(float));
get_mean_array(align_weights, align_weights_size, l->n, l->mean_arr);
#ifdef GPU
//l->align_bit_weights_gpu = cuda_make_array(l->align_bit_weights, l->align_bit_weights_size * sizeof(char)/sizeof(float));
cudaError_t status = cudaMalloc((void **)&l->align_bit_weights_gpu, l->align_bit_weights_size);
check_error(status);
status = cudaMemcpy(l->align_bit_weights_gpu, l->align_bit_weights, l->align_bit_weights_size, cudaMemcpyHostToDevice);
check_error(status);
l->mean_arr_gpu = cuda_make_array(l->mean_arr, l->n);
#endif // GPU
free(align_weights);
}
// further optimizations: im2col_bin() for XNOR, and then transpose_aling_bin()
// binary transpose
size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input, size_t ldb_align, int bit_align)
{
size_t new_ldb = k + (ldb_align - k%ldb_align); // (k / 8 + 1) * 8;
size_t t_intput_size = new_ldb * n;
size_t t_bit_input_size = t_intput_size / 8;// +1;
//float *t_input = calloc(t_intput_size, sizeof(float));
//char *
*t_bit_input = calloc(t_bit_input_size, sizeof(char));
//printf("\n bit_input_size = %d, n = %d, k = %d, ldb = %d \n", bit_input_size, n, k, n);
//printf("\n t_bit_input_size = %d, k = %d, n = %d, new_ldb = %d \n", t_bit_input_size, k, n, new_ldb);
//printf("\n align_weights_size = %d, k = %d, m = %d, lda = %d \n", align_weights_size, k, m, k);
//printf("\n align_bit_weights_size = %d, k = %d, m = %d, new_lda = %d \n", align_bit_weights_size, k, m, new_ldb);
int src_size = k * bit_align;
//printf("\n src_size = %d \n", src_size);
//float_to_bit(b, t_input, src_size);
// b - [bit_align, k] - [l.bit_align, l.size*l.size*l.c] = src_size
// t_input - [bit_align, k] - [n', k]
@ -656,8 +655,6 @@ size_t binary_transpose_align_input(int k, int n, float *b, char **t_bit_input,
//transpose_bin(t_input, *t_bit_input, k, n, bit_align, new_ldb, 8);
transpose_bin(b, *t_bit_input, k, n, bit_align, new_ldb, 8);
//free(t_input);
return t_intput_size;
}
@ -671,7 +668,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
fill_cpu(l.outputs*l.batch, 0, l.output, 1);
if(l.xnor){
if (!l.align_bit_weights) {
if (!l.align_bit_weights || state.train) {
binarize_weights(l.weights, l.n, l.c*l.size*l.size, l.binary_weights);
//printf("\n binarize_weights l.align_bit_weights = %p \n", l.align_bit_weights);
}
@ -709,7 +706,7 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
//gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
//gemm_nn_custom(m, n, k, 1, a, k, b, n, c, n);
if (l.xnor && (l.stride == 1 && l.pad == 1)) {
if (l.xnor && l.align_bit_weights && !state.train && (l.stride == 1 && l.pad == 1)) {
memset(b, 0, l.bit_align*l.size*l.size*l.c * sizeof(float));
//im2col_cpu_custom_align(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align);
im2col_cpu_custom_bin(state.input, l.c, l.h, l.w, l.size, l.stride, l.pad, b, l.bit_align);
@ -812,7 +809,6 @@ void forward_convolutional_layer(convolutional_layer l, network_state state)
//float_to_bit(t_input, t_bit_input, new_ldb * n); // for im2col_cpu_custom_transpose() only
// 5x times faster than gemm()-float32
// further optimizations: accelerate maxpool-layer with OpenMP/AVX
gemm_nn_custom_bin_mean_transposed(m, n, k, 1, l.align_bit_weights, new_ldb, t_bit_input, new_ldb, c, n, l.mean_arr);
//gemm_nn_custom_bin_mean_transposed(m, n, k, 1, bit_weights, k, t_bit_input, new_ldb, c, n, mean_arr);