reference_model/src/ops/image.cc - tosa/reference_model - Gitiles


 // Copyright (c) 2020-2023, ARM Limited.
 //
 //    Licensed under the Apache License, Version 2.0 (the "License");
 //    you may not use this file except in compliance with the License.
 //    You may obtain a copy of the License at
 //
 //         http://www.apache.org/licenses/LICENSE-2.0
 //
 //    Unless required by applicable law or agreed to in writing, software
 //    distributed under the License is distributed on an "AS IS" BASIS,
 //    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 //    See the License for the specific language governing permissions and
 //    limitations under the License.

 #include "image.h"
 #include "arith_util.h"
 #include "half.hpp"

 #include <type_traits>

 using namespace TosaReference;
 using namespace Eigen;
 using namespace tosa;

 template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype, typename resize_t>
 OpResize<InDtype, OutDtype, resize_t>::OpResize(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_)
     : GraphNode(sgt_, Op_RESIZE, id_)
 {
     setRequiredOperands(4, 1);
     setRequiredRank(4, 4);

     INIT_ATTRIBUTE(Resize);
 }

 template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype, typename resize_t>
 OpResize<InDtype, OutDtype, resize_t>::~OpResize()
 {
     if (attribute)
         delete attribute;
 }

 template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype, typename resize_t>
 int OpResize<InDtype, OutDtype, resize_t>::checkTensorAttributes()
 {
     if (validateRequiredOperands())
         return 1;

     if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0]))
         return 1;

     mode = this->attribute->mode();

     if (this->mode == ResizeMode_BILINEAR)
     {
         if (OutDtype != TOSA_REF_TYPE_INT32 && OutDtype != TOSA_REF_TYPE_INT48 && OutDtype != TOSA_REF_TYPE_FP32 &&
             OutDtype != TOSA_REF_TYPE_FP16 && OutDtype != TOSA_REF_TYPE_BF16 && OutDtype != TOSA_REF_TYPE_FP64)
         {
             printNodeValidationError("OpResize: invalid data type for BILINEAR");
             return 1;
         }
     }
     else
     {
         if (OutDtype != TOSA_REF_TYPE_INT8 && OutDtype != TOSA_REF_TYPE_INT16 && OutDtype != TOSA_REF_TYPE_FP32 &&
             OutDtype != TOSA_REF_TYPE_FP16 && OutDtype != TOSA_REF_TYPE_BF16 && OutDtype != TOSA_REF_TYPE_FP64)
         {
             printNodeValidationError("OpResize: invalid data type for NEAREST");
             return 1;
         }
     }

     in     = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
     scale  = dynamic_cast<TosaReference::TensorTemplate<TInShape>*>(inputs[1]);
     offset = dynamic_cast<TosaReference::TensorTemplate<TInShape>*>(inputs[2]);
     border = dynamic_cast<TosaReference::TensorTemplate<TInShape>*>(inputs[3]);
     out    = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);

     ASSERT_MEM(in && out);

     return 0;
 }

 template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype, typename resize_t>
 int OpResize<InDtype, OutDtype, resize_t>::eval()
 {
     // validate scale/offset/border number of elements
     TInShape scale_val  = this->scale->getTensor();
     TInShape offset_val = this->offset->getTensor();
     TInShape border_val = this->border->getTensor();
     ERROR_IF(scale_val.size() != 4, "OpResize: illegal size for input scale");
     ERROR_IF(offset_val.size() != 2, "OpResize: illegal size for input offset");
     ERROR_IF(border_val.size() != 2, "OpResize: illegal size for input border");

     int in_batch    = in->getShape()[0];
     int in_height   = in->getShape()[1];
     int in_width    = in->getShape()[2];
     int in_channels = in->getShape()[3];

     int out_batch    = out->getShape()[0];
     int out_height   = out->getShape()[1];
     int out_width    = out->getShape()[2];
     int out_channels = out->getShape()[3];

     int16_t scale_y_n = scale_val(0);
     int16_t scale_y_d = scale_val(1);
     int16_t scale_x_n = scale_val(2);
     int16_t scale_x_d = scale_val(3);

     int16_t offset_y = offset_val(0);
     int16_t offset_x = offset_val(1);

     int16_t border_y = border_val(0);
     int16_t border_x = border_val(1);

     ERROR_IF(std::max<int>({ in_height, in_width, out_height, out_width }) >= 16384,
              "OpResize: exceeds maximum dimension");
     ERROR_IF(in_batch != out_batch, "OpResize: output tensor batch mismatch");
     ERROR_IF(in_channels != out_channels, "OpResize: output tensor channel mismatch");
     ERROR_IF(scale_y_n <= 0 || scale_y_d <= 0 || scale_x_n <= 0 || scale_x_d <= 0,
              "OpResize: attribute scale must not be negative");
     // If data type is int8_t then ensure that an int32_t accumulator can be used.
     ERROR_IF(scale_y_n > (1 << 11) || scale_x_n > (1 << 11), "OpResize: invalid attribute scale");
     // Set a consistent lower limit of 1/16 downscale to simplify implementations
     ERROR_IF((scale_y_d >= 16 * scale_y_n) || (scale_x_d >= 16 * scale_x_n), "OpResize: invalid attribute scale");
     ERROR_IF((offset_y < -scale_y_n) || (offset_y >= 16 * scale_y_n),
              "OpResize: invalid attribute offset height dimension");
     ERROR_IF((offset_x < -scale_x_n) || (offset_x >= 16 * scale_x_n),
              "OpResize: invalid attribute offset width dimension");
     ERROR_IF((border_y < -16 * scale_y_n || border_y >= scale_y_n),
              "OpResize: invalid attribute border height dimension");
     ERROR_IF((border_x < -16 * scale_x_n || border_x >= scale_x_n),
              "OpResize: invalid attribute border width dimension");

     // Check Tosa Level
     auto tosa_level = g_func_config.tosa_level;
     LEVEL_CHECK(scale_y_n / scale_y_d <= tosa_level.MAX_SCALE,
                 "scale_y_n / scale_y_d should be smaller than or equal to MAX_SCALE");
     LEVEL_CHECK(scale_x_n / scale_x_d <= tosa_level.MAX_SCALE,
                 "scale_x_n / scale_x_d should be smaller than or equal to MAX_SCALE");

     int32_t res_height = 0;
     int32_t res_width  = 0;

     if (idiv_check((in_height - 1) * scale_y_n - offset_y + border_y, scale_y_d, res_height))
         return 1;

     if (idiv_check((in_width - 1) * scale_x_n - offset_x + border_x, scale_x_d, res_width))
         return 1;

     ERROR_IF(out_height != res_height + 1,
              "OpResize: mismatch between output height dimension provided and expected shape");
     ERROR_IF(out_width != res_width + 1,
              "OpResize: mismatch between output width dimension provided and expected shape");

     for (int b = 0; b < out_batch; b++)
         for (int c = 0; c < out_channels; c++)
             for (int oy = 0; oy < out_height; oy++)
                 for (int ox = 0; ox < out_width; ox++)
                 {
                     int32_t y = oy * scale_y_d + offset_y;
                     int32_t x = ox * scale_x_d + offset_x;

                     int16_t iy = idiv_floor(y, scale_y_n);
                     int16_t ix = idiv_floor(x, scale_x_n);

                     resize_t dy;
                     resize_t dx;
                     if (std::is_same<resize_t, double>::value)
                     {
                         const double fy_double = static_cast<double>(y) / static_cast<double>(scale_y_n);
                         const double fx_double = static_cast<double>(x) / static_cast<double>(scale_x_n);

                         dy = (resize_t)(fy_double - iy);
                         dx = (resize_t)(fx_double - ix);
                     }
                     else
                     {
                         const float fy = static_cast<float>(y) / static_cast<float>(scale_y_n);
                         const float fx = static_cast<float>(x) / static_cast<float>(scale_x_n);

                         if (std::is_floating_point<resize_t>::value || (typeid(resize_t) == typeid(Eigen::bfloat16)) ||
                             (typeid(resize_t) == typeid(half_float::half)))
                         {
                             dy = (resize_t)(fy - iy);
                             dx = (resize_t)(fx - ix);
                         }
                         else
                         {
                             dy = (resize_t)(y - (iy * scale_y_n));
                             dx = (resize_t)(x - (ix * scale_x_n));
                         }
                     }

                     int32_t iy0 = MAX(iy, 0);
                     int32_t iy1 = MIN(iy + 1, in_height - 1);
                     int32_t ix0 = MAX(ix, 0);
                     int32_t ix1 = MIN(ix + 1, in_width - 1);

                     OutEigenType acc;
                     if (mode == ResizeMode_BILINEAR)
                     {
                         InEigenType v00 = in->getTensor()(b, iy0, ix0, c);
                         InEigenType v01 = in->getTensor()(b, iy0, ix1, c);
                         InEigenType v10 = in->getTensor()(b, iy1, ix0, c);
                         InEigenType v11 = in->getTensor()(b, iy1, ix1, c);

                         if (std::is_floating_point<resize_t>::value)
                         {
                             acc = (OutEigenType)v00 * (1.0 - dy) * (1.0 - dx);
                             acc += (OutEigenType)v01 * (1.0 - dy) * dx;
                             acc += (OutEigenType)v10 * dy * (1.0 - dx);
                             acc += (OutEigenType)v11 * dy * dx;
                         }
                         else if ((typeid(resize_t) == typeid(Eigen::bfloat16)) ||
                                  (typeid(resize_t) == typeid(half_float::half)))
                         {
                             resize_t f16_acc;
                             f16_acc = (resize_t)v00 * (resize_t)(1.0 - dy) * (resize_t)(1.0 - dx);
                             f16_acc += (resize_t)v01 * (resize_t)(1.0 - dy) * (resize_t)dx;
                             f16_acc += (resize_t)v10 * (resize_t)dy * (resize_t)(1.0 - dx);
                             f16_acc += (resize_t)v11 * (resize_t)dy * (resize_t)dx;
                             acc = (float)f16_acc;
                         }
                         else
                         {
                             acc = (OutEigenType)v00 * (scale_y_n - dy) * (scale_x_n - dx);
                             acc += (OutEigenType)v01 * (scale_y_n - dy) * dx;
                             acc += (OutEigenType)v10 * dy * (scale_x_n - dx);
                             acc += (OutEigenType)v11 * dy * dx;
                         }
                     }
                     else
                     {
                         ASSERT_MSG(mode == ResizeMode_NEAREST, "OpResize: invalid mode");
                         if (std::is_floating_point<resize_t>::value || (typeid(resize_t) == typeid(Eigen::bfloat16)) ||
                             (typeid(resize_t) == typeid(half_float::half)))
                         {
                             iy = (dy >= 0.5) ? iy1 : iy0;
                             ix = (dx >= 0.5) ? ix1 : ix0;
                         }
                         else
                         {
                             iy = (2 * dy >= scale_y_n) ? iy1 : iy0;
                             ix = (2 * dx >= scale_x_n) ? ix1 : ix0;
                         }
                         acc = in->getTensor()(b, iy, ix, c);
                     }
                     if ((typeid(resize_t) == typeid(Eigen::bfloat16)))
                     {
                         ASSERT_MSG(checkValidBFloat(acc),
                                    "Resize accumulator float value is not a valid bfloat16 value.");
                     }
                     out->getTensor()(b, oy, ox, c) = acc;
                 }

     return GraphNode::eval();
 }

 // template explicit instantiation
 DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, INT8, INT32, int16_t);
 DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, INT8, INT8, int16_t);
 DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, INT16, INT48, int16_t);
 DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, INT16, INT16, int16_t);
 DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, FP16, FP16, half_float::half);
 DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, BF16, BF16, Eigen::bfloat16);
 DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, FP32, FP32, float);
 DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, FP64, FP64, double);

	// Copyright (c) 2020-2023, ARM Limited.
	//
	// Licensed under the Apache License, Version 2.0 (the "License");
	// you may not use this file except in compliance with the License.
	// You may obtain a copy of the License at
	//
	// http://www.apache.org/licenses/LICENSE-2.0
	//
	// Unless required by applicable law or agreed to in writing, software
	// distributed under the License is distributed on an "AS IS" BASIS,
	// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	// See the License for the specific language governing permissions and
	// limitations under the License.

	#include "image.h"
	#include "arith_util.h"
	#include "half.hpp"

	#include <type_traits>

	using namespace TosaReference;
	using namespace Eigen;
	using namespace tosa;

	template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype, typename resize_t>
	OpResize<InDtype, OutDtype, resize_t>::OpResize(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, uint64_t id_)
	: GraphNode(sgt_, Op_RESIZE, id_)
	{
	setRequiredOperands(4, 1);
	setRequiredRank(4, 4);

	INIT_ATTRIBUTE(Resize);
	}

	template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype, typename resize_t>
	OpResize<InDtype, OutDtype, resize_t>::~OpResize()
	{
	if (attribute)
	delete attribute;
	}

	template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype, typename resize_t>
	int OpResize<InDtype, OutDtype, resize_t>::checkTensorAttributes()
	{
	if (validateRequiredOperands())
	return 1;

	if (validateRequiredRank(inputs[0]) \|\| validateRequiredRank(outputs[0]))
	return 1;

	mode = this->attribute->mode();

	if (this->mode == ResizeMode_BILINEAR)
	{
	if (OutDtype != TOSA_REF_TYPE_INT32 && OutDtype != TOSA_REF_TYPE_INT48 && OutDtype != TOSA_REF_TYPE_FP32 &&
	OutDtype != TOSA_REF_TYPE_FP16 && OutDtype != TOSA_REF_TYPE_BF16 && OutDtype != TOSA_REF_TYPE_FP64)
	{
	printNodeValidationError("OpResize: invalid data type for BILINEAR");
	return 1;
	}
	}
	else
	{
	if (OutDtype != TOSA_REF_TYPE_INT8 && OutDtype != TOSA_REF_TYPE_INT16 && OutDtype != TOSA_REF_TYPE_FP32 &&
	OutDtype != TOSA_REF_TYPE_FP16 && OutDtype != TOSA_REF_TYPE_BF16 && OutDtype != TOSA_REF_TYPE_FP64)
	{
	printNodeValidationError("OpResize: invalid data type for NEAREST");
	return 1;
	}
	}

	in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
	scale = dynamic_cast<TosaReference::TensorTemplate<TInShape>*>(inputs[1]);
	offset = dynamic_cast<TosaReference::TensorTemplate<TInShape>*>(inputs[2]);
	border = dynamic_cast<TosaReference::TensorTemplate<TInShape>*>(inputs[3]);
	out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);

	ASSERT_MEM(in && out);

	return 0;
	}

	template <TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype, typename resize_t>
	int OpResize<InDtype, OutDtype, resize_t>::eval()
	{
	// validate scale/offset/border number of elements
	TInShape scale_val = this->scale->getTensor();
	TInShape offset_val = this->offset->getTensor();
	TInShape border_val = this->border->getTensor();
	ERROR_IF(scale_val.size() != 4, "OpResize: illegal size for input scale");
	ERROR_IF(offset_val.size() != 2, "OpResize: illegal size for input offset");
	ERROR_IF(border_val.size() != 2, "OpResize: illegal size for input border");

	int in_batch = in->getShape()[0];
	int in_height = in->getShape()[1];
	int in_width = in->getShape()[2];
	int in_channels = in->getShape()[3];

	int out_batch = out->getShape()[0];
	int out_height = out->getShape()[1];
	int out_width = out->getShape()[2];
	int out_channels = out->getShape()[3];

	int16_t scale_y_n = scale_val(0);
	int16_t scale_y_d = scale_val(1);
	int16_t scale_x_n = scale_val(2);
	int16_t scale_x_d = scale_val(3);

	int16_t offset_y = offset_val(0);
	int16_t offset_x = offset_val(1);

	int16_t border_y = border_val(0);
	int16_t border_x = border_val(1);

	ERROR_IF(std::max<int>({ in_height, in_width, out_height, out_width }) >= 16384,
	"OpResize: exceeds maximum dimension");
	ERROR_IF(in_batch != out_batch, "OpResize: output tensor batch mismatch");
	ERROR_IF(in_channels != out_channels, "OpResize: output tensor channel mismatch");
	ERROR_IF(scale_y_n <= 0 \|\| scale_y_d <= 0 \|\| scale_x_n <= 0 \|\| scale_x_d <= 0,
	"OpResize: attribute scale must not be negative");
	// If data type is int8_t then ensure that an int32_t accumulator can be used.
	ERROR_IF(scale_y_n > (1 << 11) \|\| scale_x_n > (1 << 11), "OpResize: invalid attribute scale");
	// Set a consistent lower limit of 1/16 downscale to simplify implementations
	ERROR_IF((scale_y_d >= 16 * scale_y_n) \|\| (scale_x_d >= 16 * scale_x_n), "OpResize: invalid attribute scale");
	ERROR_IF((offset_y < -scale_y_n) \|\| (offset_y >= 16 * scale_y_n),
	"OpResize: invalid attribute offset height dimension");
	ERROR_IF((offset_x < -scale_x_n) \|\| (offset_x >= 16 * scale_x_n),
	"OpResize: invalid attribute offset width dimension");
	ERROR_IF((border_y < -16 * scale_y_n \|\| border_y >= scale_y_n),
	"OpResize: invalid attribute border height dimension");
	ERROR_IF((border_x < -16 * scale_x_n \|\| border_x >= scale_x_n),
	"OpResize: invalid attribute border width dimension");

	// Check Tosa Level
	auto tosa_level = g_func_config.tosa_level;
	LEVEL_CHECK(scale_y_n / scale_y_d <= tosa_level.MAX_SCALE,
	"scale_y_n / scale_y_d should be smaller than or equal to MAX_SCALE");
	LEVEL_CHECK(scale_x_n / scale_x_d <= tosa_level.MAX_SCALE,
	"scale_x_n / scale_x_d should be smaller than or equal to MAX_SCALE");

	int32_t res_height = 0;
	int32_t res_width = 0;

	if (idiv_check((in_height - 1) * scale_y_n - offset_y + border_y, scale_y_d, res_height))
	return 1;

	if (idiv_check((in_width - 1) * scale_x_n - offset_x + border_x, scale_x_d, res_width))
	return 1;

	ERROR_IF(out_height != res_height + 1,
	"OpResize: mismatch between output height dimension provided and expected shape");
	ERROR_IF(out_width != res_width + 1,
	"OpResize: mismatch between output width dimension provided and expected shape");

	for (int b = 0; b < out_batch; b++)
	for (int c = 0; c < out_channels; c++)
	for (int oy = 0; oy < out_height; oy++)
	for (int ox = 0; ox < out_width; ox++)
	{
	int32_t y = oy * scale_y_d + offset_y;
	int32_t x = ox * scale_x_d + offset_x;

	int16_t iy = idiv_floor(y, scale_y_n);
	int16_t ix = idiv_floor(x, scale_x_n);

	resize_t dy;
	resize_t dx;
	if (std::is_same<resize_t, double>::value)
	{
	const double fy_double = static_cast<double>(y) / static_cast<double>(scale_y_n);
	const double fx_double = static_cast<double>(x) / static_cast<double>(scale_x_n);

	dy = (resize_t)(fy_double - iy);
	dx = (resize_t)(fx_double - ix);
	}
	else
	{
	const float fy = static_cast<float>(y) / static_cast<float>(scale_y_n);
	const float fx = static_cast<float>(x) / static_cast<float>(scale_x_n);

	if (std::is_floating_point<resize_t>::value \|\| (typeid(resize_t) == typeid(Eigen::bfloat16)) \|\|
	(typeid(resize_t) == typeid(half_float::half)))
	{
	dy = (resize_t)(fy - iy);
	dx = (resize_t)(fx - ix);
	}
	else
	{
	dy = (resize_t)(y - (iy * scale_y_n));
	dx = (resize_t)(x - (ix * scale_x_n));
	}
	}

	int32_t iy0 = MAX(iy, 0);
	int32_t iy1 = MIN(iy + 1, in_height - 1);
	int32_t ix0 = MAX(ix, 0);
	int32_t ix1 = MIN(ix + 1, in_width - 1);

	OutEigenType acc;
	if (mode == ResizeMode_BILINEAR)
	{
	InEigenType v00 = in->getTensor()(b, iy0, ix0, c);
	InEigenType v01 = in->getTensor()(b, iy0, ix1, c);
	InEigenType v10 = in->getTensor()(b, iy1, ix0, c);
	InEigenType v11 = in->getTensor()(b, iy1, ix1, c);

	if (std::is_floating_point<resize_t>::value)
	{
	acc = (OutEigenType)v00 * (1.0 - dy) * (1.0 - dx);
	acc += (OutEigenType)v01 * (1.0 - dy) * dx;
	acc += (OutEigenType)v10 * dy * (1.0 - dx);
	acc += (OutEigenType)v11 * dy * dx;
	}
	else if ((typeid(resize_t) == typeid(Eigen::bfloat16)) \|\|
	(typeid(resize_t) == typeid(half_float::half)))
	{
	resize_t f16_acc;
	f16_acc = (resize_t)v00 * (resize_t)(1.0 - dy) * (resize_t)(1.0 - dx);
	f16_acc += (resize_t)v01 * (resize_t)(1.0 - dy) * (resize_t)dx;
	f16_acc += (resize_t)v10 * (resize_t)dy * (resize_t)(1.0 - dx);
	f16_acc += (resize_t)v11 * (resize_t)dy * (resize_t)dx;
	acc = (float)f16_acc;
	}
	else
	{
	acc = (OutEigenType)v00 * (scale_y_n - dy) * (scale_x_n - dx);
	acc += (OutEigenType)v01 * (scale_y_n - dy) * dx;
	acc += (OutEigenType)v10 * dy * (scale_x_n - dx);
	acc += (OutEigenType)v11 * dy * dx;
	}
	}
	else
	{
	ASSERT_MSG(mode == ResizeMode_NEAREST, "OpResize: invalid mode");
	if (std::is_floating_point<resize_t>::value \|\| (typeid(resize_t) == typeid(Eigen::bfloat16)) \|\|
	(typeid(resize_t) == typeid(half_float::half)))
	{
	iy = (dy >= 0.5) ? iy1 : iy0;
	ix = (dx >= 0.5) ? ix1 : ix0;
	}
	else
	{
	iy = (2 * dy >= scale_y_n) ? iy1 : iy0;
	ix = (2 * dx >= scale_x_n) ? ix1 : ix0;
	}
	acc = in->getTensor()(b, iy, ix, c);
	}
	if ((typeid(resize_t) == typeid(Eigen::bfloat16)))
	{
	ASSERT_MSG(checkValidBFloat(acc),
	"Resize accumulator float value is not a valid bfloat16 value.");
	}
	out->getTensor()(b, oy, ox, c) = acc;
	}

	return GraphNode::eval();
	}

	// template explicit instantiation
	DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, INT8, INT32, int16_t);
	DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, INT8, INT8, int16_t);
	DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, INT16, INT48, int16_t);
	DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, INT16, INT16, int16_t);
	DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, FP16, FP16, half_float::half);
	DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, BF16, BF16, Eigen::bfloat16);
	DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, FP32, FP32, float);
	DEF_INSTANTIATE_THREE_TYPE_RESIZE(OpResize, FP64, FP64, double);