86 lines
2.8 KiB
C++
86 lines
2.8 KiB
C++
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/delegates/gpu/gl/kernels/mean.h"
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#include <algorithm>
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#include <cstdint>
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#include <cstring>
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#include <string>
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#include <vector>
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#include "absl/memory/memory.h"
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#include "tensorflow/lite/delegates/gpu/common/status.h"
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#include "tensorflow/lite/delegates/gpu/common/types.h"
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namespace tflite {
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namespace gpu {
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namespace gl {
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namespace {
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class Mean : public NodeShader {
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public:
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absl::Status GenerateCode(const GenerationContext& ctx,
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GeneratedCode* generated_code) const final {
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const auto& attr = absl::any_cast<const MeanAttributes&>(ctx.op_attr);
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if (attr.dims != std::set<Axis>({Axis::HEIGHT, Axis::WIDTH})) {
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return absl::InvalidArgumentError(
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"Mean calculation is supported only for height and width.");
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}
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std::vector<Variable> parameters = {
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{"input_data_0_h", static_cast<int>(ctx.input_shapes[0][1])},
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{"input_data_0_w", static_cast<int>(ctx.input_shapes[0][2])}};
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std::string source = R"(
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// Shaders may be compiled with a precision hint mediump, which means that
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// GLSL compiler may drop the size of float data type from 32 to 16 bits.
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// If "sum" and "size" variables are 16bit floats, their values range
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// become not enough for providing a good results accuracy. That is why
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// their precision is forced to be 32bit by using highp qualifier.
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highp vec4 sum = vec4(0.0);
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highp float size = float($input_data_0_w$ * $input_data_0_h$);
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for (int w = 0; w < $input_data_0_w$; w++) {
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for (int h = 0; h < $input_data_0_h$; h++) {
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sum += $input_data_0[w, h, gid.z]$;
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}
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}
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value_0 = sum / size;
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)";
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*generated_code = {
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/*parameters=*/std::move(parameters),
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/*objects=*/{},
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/*shared_variables=*/{},
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/*workload=*/uint3(),
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/*workgroup=*/uint3(1, 1, 4),
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/*source_code=*/std::move(source),
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/*input=*/IOStructure::ONLY_DEFINITIONS,
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/*output=*/IOStructure::AUTO,
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};
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return absl::OkStatus();
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}
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};
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} // namespace
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std::unique_ptr<NodeShader> NewMeanNodeShader() {
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return absl::make_unique<Mean>();
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}
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} // namespace gl
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} // namespace gpu
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} // namespace tflite
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