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// Copyright (c) 2020-2022, 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 <type_traits>
using namespace TosaReference;
using namespace Eigen;
using namespace tosa;
template <DType InDtype, DType OutDtype, typename resize_t>
OpResize<InDtype, OutDtype, resize_t>::OpResize(SubgraphTraverser* sgt_,
TosaAttributeBase* attribute_,
uint64_t id_)
: GraphNode(sgt_, Op_RESIZE, id_)
{
setRequiredOperands(1, 1);
setRequiredRank(4, 4);
INIT_ATTRIBUTE(Resize);
}
template <DType InDtype, DType OutDtype, typename resize_t>
OpResize<InDtype, OutDtype, resize_t>::~OpResize()
{
if (attribute)
delete attribute;
}
template <DType InDtype, DType OutDtype, typename resize_t>
int OpResize<InDtype, OutDtype, resize_t>::checkTensorAttributes()
{
if (validateRequiredOperands())
return 1;
if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0]))
return 1;
if (this->attribute->scale().size() != 4)
{
printNodeValidationError("OpResize: illegal size for attribute scale");
return 1;
}
scale = this->attribute->scale();
offset = this->attribute->offset();
border = this->attribute->border();
mode = this->attribute->mode();
if (this->mode == ResizeMode_BILINEAR)
{
if (OutDtype != DType_INT32 && OutDtype != DType_INT48 && OutDtype != DType_FLOAT && OutDtype != DType_FP16)
{
printNodeValidationError("OpResize: invalid data type for BILINEAR");
return 1;
}
}
else
{
if (OutDtype != DType_INT8 && OutDtype != DType_INT16 && OutDtype != DType_FLOAT && OutDtype != DType_FP16)
{
printNodeValidationError("OpResize: invalid data type for NEAREST");
return 1;
}
}
in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
ASSERT_MEM(in && out);
return 0;
}
template <DType InDtype, DType OutDtype, typename resize_t>
int OpResize<InDtype, OutDtype, resize_t>::eval()
{
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[0];
int16_t scale_y_d = scale[1];
int16_t scale_x_n = scale[2];
int16_t scale_x_d = scale[3];
int16_t offset_y = offset[0];
int16_t offset_x = offset[1];
int16_t border_y = border[0];
int16_t border_x = border[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");
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;
float fy = static_cast<float>(y) / static_cast<float>(scale_y_n);
float fx = static_cast<float>(x) / static_cast<float>(scale_x_n);
int32_t iy = floor(fy);
int32_t ix = floor(fx);
resize_t dy;
resize_t dx;
if (std::is_floating_point<resize_t>::value)
{
dy = fy - iy;
dx = fx - ix;
}
else
{
dy = y - (iy * scale_y_n);
dx = 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
{
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)
{
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);
}
out->getTensor()(b, oy, ox, c) = acc;
}
return GraphNode::eval();
}
// template explicit instantiation
DEF_INSTANTIATE_THREE_TYPE(OpResize, INT8, INT32, int16_t);
DEF_INSTANTIATE_THREE_TYPE(OpResize, INT8, INT8, int16_t);
DEF_INSTANTIATE_THREE_TYPE(OpResize, INT16, INT48, int16_t);
DEF_INSTANTIATE_THREE_TYPE(OpResize, INT16, INT16, int16_t);
DEF_INSTANTIATE_THREE_TYPE(OpResize, FP16, FP16, float);
DEF_INSTANTIATE_THREE_TYPE(OpResize, FLOAT, FLOAT, float);
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