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94 lines
2.9 KiB
94 lines
2.9 KiB
/* Copyright (c) 2018 PaddlePaddle 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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#include <gtest/gtest.h>
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#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
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#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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TEST(prelu_op, test_channel_wise) {
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std::unordered_set<std::string> parameters({"prelu_alpha"});
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framework::Scope scope;
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TRTConvertValidation validator(10, parameters, scope, 1000);
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validator.DeclInputVar("prelu_input", nvinfer1::DimsCHW(3, 2, 2));
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validator.DeclParamVar("prelu_alpha", nvinfer1::Dims3(3, 1, 1));
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validator.DeclOutputVar("prelu_out", nvinfer1::DimsCHW(3, 2, 2));
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// Prepare Op description
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framework::OpDesc desc;
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desc.SetType("prelu");
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desc.SetInput("X", {"prelu_input"});
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desc.SetInput("Alpha", {"prelu_alpha"});
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desc.SetOutput("Out", {"prelu_out"});
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desc.SetAttr("mode", std::string("channel"));
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validator.SetOp(*desc.Proto());
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validator.Execute(1);
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}
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TEST(prelu_op, test_element_wise) {
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std::unordered_set<std::string> parameters({"prelu_alpha"});
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framework::Scope scope;
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TRTConvertValidation validator(10, parameters, scope, 1000);
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validator.DeclInputVar("prelu_input", nvinfer1::DimsCHW(3, 2, 2));
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validator.DeclParamVar("prelu_alpha", nvinfer1::Dims4(10, 3, 2, 2));
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validator.DeclOutputVar("prelu_out", nvinfer1::DimsCHW(3, 2, 2));
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// Prepare Op description
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framework::OpDesc desc;
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desc.SetType("prelu");
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desc.SetInput("X", {"prelu_input"});
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desc.SetInput("Alpha", {"prelu_alpha"});
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desc.SetOutput("Out", {"prelu_out"});
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desc.SetAttr("mode", std::string("element"));
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validator.SetOp(*desc.Proto());
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validator.Execute(1);
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}
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TEST(prelu_op, test_scalar) {
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std::unordered_set<std::string> parameters({"prelu_alpha"});
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framework::Scope scope;
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TRTConvertValidation validator(10, parameters, scope, 1000);
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validator.DeclInputVar("prelu_input", nvinfer1::DimsCHW(3, 2, 2));
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validator.DeclParamVar("prelu_alpha", nvinfer1::Dims3(1, 1, 1));
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validator.DeclOutputVar("prelu_out", nvinfer1::DimsCHW(3, 2, 2));
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// Prepare Op description
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framework::OpDesc desc;
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desc.SetType("prelu");
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desc.SetInput("X", {"prelu_input"});
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desc.SetInput("Alpha", {"prelu_alpha"});
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desc.SetOutput("Out", {"prelu_out"});
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desc.SetAttr("mode", std::string("all"));
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validator.SetOp(*desc.Proto());
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validator.Execute(1);
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}
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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USE_OP(prelu);
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