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d38aa4560c
* add poros to fastdeploy * update readme * update readme & add license for all files * update benchmark * update copyright for some files Co-authored-by: tianjinjin <tianjinjin@baidu.com>
70 lines
3.0 KiB
C++
70 lines
3.0 KiB
C++
// Copyright (c) 2022 Baidu, Inc. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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* @file conv2d_test.cpp
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* @author tianshaoqing@baidu.com
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* @date Wed Sep 27 11:24:21 CST 2021
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* @brief
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**/
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#include <string>
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#include <vector>
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#include <gflags/gflags.h>
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#include <gtest/gtest.h>
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#include "poros/util/test_util.h"
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#include "poros/converter/gpu/convolution.h"
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static void conv2d_test_helper(const std::string& graph_IR,
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baidu::mirana::poros::IConverter* converter,
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std::vector<int64_t> shape_inputs,
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std::vector<int64_t> shape_weights,
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std::vector<int64_t> shape_bias) {
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std::vector<at::Tensor> input_data;
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// auto in = at::randn({1, 3, 10, 10}, {at::kCUDA});
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// auto w = at::randn({8, 3, 5, 5}, {at::kCUDA});
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// auto b = at::randn({8}, {at::kCUDA});
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auto in = at::randn(shape_inputs, {at::kCUDA});
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auto w = at::randn(shape_weights, {at::kCUDA});
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auto b = at::randn(shape_bias, {at::kCUDA});
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input_data.push_back(in);
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input_data.push_back(w);
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input_data.push_back(b);
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baidu::mirana::poros::PorosOptions poros_option; // default device GPU
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// 运行原图与engine获取结果
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std::vector<at::Tensor> graph_output;
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std::vector<at::Tensor> poros_output;
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ASSERT_TRUE(baidu::mirana::poros::testutil::run_graph_and_poros(graph_IR, poros_option, converter, input_data, graph_output, poros_output));
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ASSERT_EQ(1, graph_output.size());
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ASSERT_EQ(1, poros_output.size());
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//ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 2e-6));
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ASSERT_TRUE(baidu::mirana::poros::testutil::almost_equal(graph_output[0], poros_output[0], 0.0001));
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}
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TEST(Converters, ATenConv2dVggishTestConvertsCorrectly) {
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// aten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensor
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const auto graph_IR = R"IR(
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graph(%0 : Tensor, %1 : Tensor, %2 : Tensor):
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%3 : int[] = prim::Constant[value=[1, 1]]()
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%4 : int[] = prim::Constant[value=[1, 1]]()
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%5 : int[] = prim::Constant[value=[1, 1]]()
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%6 : int = prim::Constant[value=1]()
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%7 : Tensor = aten::conv2d(%0, %1, %2, %3, %4, %5, %6)
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return (%7))IR";
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baidu::mirana::poros::ConvolutionConverter convolutionconverter;
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conv2d_test_helper(graph_IR, &convolutionconverter, {60, 256, 12, 8}, {512, 256, 3, 3}, {512});
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}
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