Array<T> wrap(const Array<T> &in,
const dim_t ox, const dim_t oy,
const dim_t wx, const dim_t wy,
const dim_t sx, const dim_t sy,
const dim_t px, const dim_t py,
const bool is_column)
{
af::dim4 idims = in.dims();
af::dim4 odims(ox, oy, idims[2], idims[3]);
Array<T> out = createValueArray<T>(odims, scalar<T>(0));
const T *inPtr = in.get();
T *outPtr = out.get();
af::dim4 istrides = in.strides();
af::dim4 ostrides = out.strides();
if (is_column) {
wrap_dim<T, true >(outPtr, inPtr, odims, idims, ostrides, istrides, wx, wy, sx, sy, px, py);
} else {
wrap_dim<T, false>(outPtr, inPtr, odims, idims, ostrides, istrides, wx, wy, sx, sy, px, py);
}
return out;
}
Array<T>* resize(const Array<T> &in, const dim_type odim0, const dim_type odim1,
const af_interp_type method)
{
af::dim4 idims = in.dims();
af::dim4 odims(odim0, odim1, idims[2], idims[3]);
// Create output placeholder
Array<T> *outArray = createValueArray(odims, (T)0);
// Get pointers to raw data
const T *inPtr = in.get();
T *outPtr = outArray->get();
af::dim4 ostrides = outArray->strides();
af::dim4 istrides = in.strides();
switch(method) {
case AF_INTERP_NEAREST:
resize_<T, AF_INTERP_NEAREST>(outPtr, inPtr, odims, idims, ostrides, istrides);
break;
case AF_INTERP_BILINEAR:
resize_<T, AF_INTERP_BILINEAR>(outPtr, inPtr, odims, idims, ostrides, istrides);
break;
default:
break;
}
return outArray;
}
void pool_layer_t::gparam(const tensor_t& output, scalar_t* gradient)
{
NANOCV_UNUSED1(gradient);
NANOCV_UNUSED1_RELEASE(output);
assert(odims() == output.dims());
assert(orows() == output.rows());
assert(ocols() == output.cols());
}
const tensor_t& pool_layer_t::ginput(const tensor_t& output)
{
assert(odims() == output.dims());
assert(orows() == output.rows());
assert(ocols() == output.cols());
m_odata = output;
for (size_t o = 0; o < odims(); o ++)
{
pooling::ginput(
m_idata.matrix(o),
m_wdata.matrix(o),
m_sdata.matrix(o),
m_cdata.matrix(o),
m_odata.matrix(o));
}
return m_idata;
}
Array<T> wrap(const Array<T> &in,
const dim_t ox, const dim_t oy,
const dim_t wx, const dim_t wy,
const dim_t sx, const dim_t sy,
const dim_t px, const dim_t py,
const bool is_column)
{
af::dim4 idims = in.dims();
af::dim4 odims(ox, oy, idims[2], idims[3]);
Array<T> out = createValueArray<T>(odims, scalar<T>(0));
kernel::wrap<T>(out, in, wx, wy, sx, sy, px, py, is_column);
return out;
}
Array<T> wrap(const Array<T> &in, const dim_t ox, const dim_t oy,
const dim_t wx, const dim_t wy, const dim_t sx, const dim_t sy,
const dim_t px, const dim_t py, const bool is_column) {
af::dim4 idims = in.dims();
af::dim4 odims(ox, oy, idims[2], idims[3]);
Array<T> out = createValueArray<T>(odims, scalar<T>(0));
out.eval();
in.eval();
if (is_column) {
getQueue().enqueue(kernel::wrap_dim<T, 1>, out, in, wx, wy, sx, sy, px,
py);
} else {
getQueue().enqueue(kernel::wrap_dim<T, 0>, out, in, wx, wy, sx, sy, px,
py);
}
return out;
}
const tensor_t& pool_layer_t::output(const tensor_t& input)
{
assert(idims() == input.dims());
assert(irows() <= input.rows());
assert(icols() <= input.cols());
m_idata = input;
for (size_t o = 0; o < odims(); o ++)
{
pooling::output(
m_idata.matrix(o), m_alpha,
m_wdata.matrix(o),
m_sdata.matrix(o),
m_cdata.matrix(o),
m_odata.matrix(o));
}
return m_odata;
}
Array<T> resize(const Array<T> &in, const dim_t odim0, const dim_t odim1,
const af_interp_type method)
{
af::dim4 idims = in.dims();
af::dim4 odims(odim0, odim1, idims[2], idims[3]);
// Create output placeholder
Array<T> out = createValueArray(odims, (T)0);
out.eval();
in.eval();
switch(method) {
case AF_INTERP_NEAREST:
getQueue().enqueue(kernel::resize<T, AF_INTERP_NEAREST>, out, in); break;
case AF_INTERP_BILINEAR:
getQueue().enqueue(kernel::resize<T, AF_INTERP_BILINEAR>, out, in); break;
case AF_INTERP_LOWER:
getQueue().enqueue(kernel::resize<T, AF_INTERP_LOWER>, out, in); break;
default: break;
}
return out;
}