#include "megdnn/handle.h" #include "megdnn/opr_param_defs.h" #include "megdnn/oprs.h" #include "src/common/utils.h" namespace megdnn { void ResizeBase::check_layout_fwd(const TensorLayout& src, const TensorLayout& dst) { auto errmsg = [&]() { return megdnn_layout_msg(src) + ", " + ", " + megdnn_layout_msg(dst); }; MEGDNN_MARK_USED_VAR(errmsg); megdnn_assert( dst.dtype == src.dtype && dst.shape[0] == src.shape[0], "%s", errmsg().c_str()); if (param().format == Param::Format::NCHW) { megdnn_assert(dst.shape[1] == src.shape[1], "%s", errmsg().c_str()); auto imode = param().imode; using IMode = param::Resize::InterpolationMode; megdnn_assert( imode == IMode::INTER_LINEAR || imode == IMode::NEAREST || imode == IMode::INTER_CUBIC); } else if (param().format == Param::Format::NHWC) { megdnn_assert(dst.shape[3] == src.shape[3], "%s", errmsg().c_str()); } else if (param().format == Param::Format::NCHW4) { megdnn_assert(src.ndim == 5); megdnn_assert(src.dtype.enumv() == DTypeEnum::QuantizedS8); megdnn_assert(src.shape[4] == 4); megdnn_assert(dst.shape[4] == 4); } else if (param().format == Param::Format::NCHW44) { megdnn_assert(src.ndim == 5); megdnn_assert(src.shape[4] == 4); megdnn_assert(dst.shape[4] == 4); megdnn_assert( param().imode == param::Resize::InterpolationMode::INTER_LINEAR || param().imode == param::Resize::InterpolationMode::INTER_NEAREST); } else if (param().format == Param::Format::NCHW88) { megdnn_assert(src.ndim == 5); megdnn_assert(src.shape[4] == 8); megdnn_assert(dst.shape[4] == 8); megdnn_assert( param().imode == param::Resize::InterpolationMode::INTER_LINEAR || param().imode == param::Resize::InterpolationMode::INTER_NEAREST); } else { megdnn_assert( param().format == Param::Format::NHWCD4, "invalid resize tensor format"); megdnn_assert( param().imode == param::Resize::InterpolationMode::INTER_LINEAR || param().imode == param::Resize::InterpolationMode::INTER_NEAREST); megdnn_assert(dst.shape[2] == src.shape[2], "%s", errmsg().c_str()); } } void Resize::check_exec( const TensorLayout& src, const TensorLayout& dst, size_t workspace_in_bytes) { check_layout_fwd(src, dst); auto required_workspace_in_bytes = get_workspace_in_bytes(src, dst); megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes); } void ResizeBackward::check_exec( const TensorLayout& diff, const TensorLayout& grad, size_t workspace_in_bytes) { check_layout_fwd(grad, diff); auto required_workspace_in_bytes = get_workspace_in_bytes(diff, grad); megdnn_assert(workspace_in_bytes >= required_workspace_in_bytes); megdnn_assert( param().format == Param::Format::NCHW && grad.dtype == dtype::Float32(), "Backward resize only supports Float32 and NCHW."); } std::pair ResizeBase::get_cubic_coord(float scale, int idx) { float alpha = (idx + 0.5f) / scale - 0.5f; int origin_idx = static_cast(floor(alpha)); alpha -= origin_idx; return {alpha, origin_idx}; } std::tuple ResizeBase::get_nearest_linear_coord( InterpolationMode imode, float scale, int size, int idx) { if (size == 1) { return std::make_tuple(1.0f, 0, 0.0f, 0); } float alpha = (idx + 0.5f) / scale - 0.5f; int origin_idx = static_cast(floor(alpha)); alpha -= origin_idx; if (imode == InterpolationMode::INTER_NEAREST) { origin_idx = get_nearest_src(scale, size, idx); alpha = 0; } if (origin_idx < 0) { origin_idx = 0; alpha = 0; } else if (origin_idx + 1 >= size) { origin_idx = size - 2; alpha = 1; } return std::make_tuple(1 - alpha, origin_idx, alpha, origin_idx + 1); } int ResizeBase::get_nearest_src(float scale, int size, int idx) { return std::min(static_cast(idx / scale), size - 1); } } // namespace megdnn // vim: syntax=cpp.doxygen