95 lines
3.1 KiB
C++
95 lines
3.1 KiB
C++
/* Copyright 2019 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/pad.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/operations.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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#include "tensorflow/lite/delegates/gpu/gl/variable.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 Pad : public NodeShader {
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public:
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Status GenerateCode(const GenerationContext& ctx,
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GeneratedCode* generated_code) const final {
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auto input = ctx.graph->FindInputs(ctx.node->id)[0];
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auto attr = absl::any_cast<PadAttributes>(ctx.node->operation.attributes);
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if (attr.type != PaddingContentType::ZEROS) {
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return UnimplementedError(
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"Padding with content type ~= ZEROS is not supported.");
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}
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if (attr.appended.h < 0 || attr.appended.w < 0 || attr.appended.c < 0 ||
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attr.prepended.h < 0 || attr.prepended.w < 0 || attr.prepended.c < 0) {
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return UnimplementedError("Negative padding is not supported.");
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}
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std::vector<Variable> parameters = {
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{"input_data_0_h", input->tensor.shape.h},
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{"input_data_0_w", input->tensor.shape.w},
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{"prepended",
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int4(attr.prepended.w, attr.prepended.h, attr.prepended.c, 0)},
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{"src_channels", input->tensor.shape.c},
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};
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std::string source = R"(
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int src_x = gid.x - $prepended.x$;
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int src_y = gid.y - $prepended.y$;
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if (src_x >= 0 && src_x < $input_data_0_w$ && src_y >= 0 && src_y < $input_data_0_h$) {
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int start_channel = gid.z * 4;
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for (int i = 0; i < 4; ++i) {
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int channel = start_channel + i;
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int src_z = channel - $prepended.z$;
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if (src_z >= 0 && src_z < $src_channels$) {
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value_0[i] = $input_data_0[src_x, src_y, src_z / 4]$[src_z % 4];
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}
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}
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}
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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(),
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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 OkStatus();
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}
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};
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} // namespace
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std::unique_ptr<NodeShader> NewPadNodeShader() {
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return absl::make_unique<Pad>();
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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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