void convert_to_binary_row(mxnet::NDArray& array) {
CHECK(array.shape().ndim() >= 2); // second dimension is input depth from prev. layer, needed for next line
std::cout << "array shape: " << array.shape() << std::endl;
//if(array.shape()[1] < BITS_PER_BINARY_WORD) return;
CHECK(array.shape()[1] % BITS_PER_BINARY_WORD == 0); // depth from input has to be divisible by 32 (or 64)
nnvm::TShape binarized_shape(1);
size_t size = array.shape().Size();
binarized_shape[0] = size / BITS_PER_BINARY_WORD;
mxnet::NDArray temp(binarized_shape, mxnet::Context::CPU(), false, mxnet::op::xnor_cpu::corresponding_dtype());
mxnet::op::xnor_cpu::get_binary_row((float*) array.data().dptr_, (BINARY_WORD*) temp.data().dptr_, size);
array = temp;
}
void transpose(mxnet::NDArray& array) {
CHECK(array.shape().ndim() == 2);
nnvm::TShape tansposed_shape(2);
int rows = array.shape()[0];
int cols = array.shape()[1];
tansposed_shape[0] = cols;
tansposed_shape[1] = rows;
mxnet::NDArray temp(tansposed_shape, mxnet::Context::CPU(), false, array.dtype());
MSHADOW_REAL_TYPE_SWITCH(array.dtype(), DType, {
for (int row = 0; row < rows; row++) {
for (int col = 0; col < cols; col++) {
((DType*)temp.data().dptr_)[col * rows + row] = ((DType*)array.data().dptr_)[row * cols + col];
}
}
})