resize.cpp

#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 ||
             param().format == Param::Format::NHWC) &&
                    (grad.dtype == dtype::Float32() DNN_INC_FLOAT16(
                                           || grad.dtype == dtype::Float16())),
            "Backward resize only supports NCHW and NHWC, the dtype only supports "
            "Float32 and Float16.");
}

std::pair<float, int> ResizeBase::get_cubic_coord(float scale, int idx) {
    float alpha = (idx + 0.5f) / scale - 0.5f;
    int origin_idx = static_cast<int>(floor(alpha));
    alpha -= origin_idx;
    return {alpha, origin_idx};
}

std::tuple<float, int, float, int> 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<int>(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<int>(idx / scale), size - 1);
}

void Resize3DBase::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(
            param().format == Param::Format::NCDHW, "Resize3D only support NCDHW");
    megdnn_assert(
            src.ndim == 5 && dst.ndim == 5, "shape dim mismatch: %s", errmsg().c_str());
    megdnn_assert(src.dtype == dst.dtype, "dtype mismatch: %s", errmsg().c_str());
    megdnn_assert(
            src.shape[0] == dst.shape[0], "batch size mismatch: %s", errmsg().c_str());
    megdnn_assert(
            src.shape[1] == dst.shape[1], "channel size mismatch: %s",
            errmsg().c_str());

    megdnn_assert_contiguous(src);
    megdnn_assert_contiguous(dst);

    auto imode = param().imode;
    using IMode = param::Resize3D::InterpolationMode;
    megdnn_assert(imode == IMode::INTER_LINEAR, "Resize3D only support TriLinear mode");
}

void Resize3D::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);
}

}  // namespace megdnn

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