forked from mindspore-Ecosystem/mindspore
85 lines
2.9 KiB
C++
85 lines
2.9 KiB
C++
/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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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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#include <vector>
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#include <memory>
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#include "common/common_test.h"
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#include "ops/conv2d.h"
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#include "ir/dtype/type.h"
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#include "abstract/dshape.h"
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#include "utils/tensor_construct_utils.h"
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namespace mindspore {
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namespace ops {
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class TestConv2d : public UT::Common {
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public:
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TestConv2d() {}
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void SetUp() {}
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void TearDown() {}
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};
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TEST_F(TestConv2d, test_ops_conv2d) {
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auto conv_2d = std::make_shared<Conv2D>();
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conv_2d->Init(64, {7, 7});
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std::vector<int64_t> kernel_size = conv_2d->get_kernel_size();
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for (auto item : kernel_size) {
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EXPECT_EQ(item, 7);
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}
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std::vector<int64_t> stride = conv_2d->get_stride();
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for (auto item : stride) {
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EXPECT_EQ(item, 1);
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}
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std::vector<int64_t> dilation = conv_2d->get_dilation();
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for (auto item : dilation) {
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EXPECT_EQ(item, 1);
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}
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EXPECT_EQ(conv_2d->get_pad_mode(), VALID);
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std::vector<int64_t> pad = conv_2d->get_pad();
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for (auto item : pad) {
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EXPECT_EQ(item, 0);
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}
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EXPECT_EQ(conv_2d->get_mode(), 1);
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EXPECT_EQ(conv_2d->get_group(), 1);
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EXPECT_EQ(conv_2d->get_out_channel(), 64);
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EXPECT_EQ(conv_2d->get_format(), NCHW);
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auto tensor_x = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, std::vector<int64_t>{32, 3, 224, 224});
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auto tensor_w = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, std::vector<int64_t>{64, 3, 7, 7});
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MS_EXCEPTION_IF_NULL(tensor_x);
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MS_EXCEPTION_IF_NULL(tensor_w);
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auto conv_abstract = conv_2d->Infer({tensor_x->ToAbstract(), tensor_w->ToAbstract()});
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MS_EXCEPTION_IF_NULL(conv_abstract);
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EXPECT_EQ(conv_abstract->isa<abstract::AbstractTensor>(), true);
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auto shape_ptr = conv_abstract->BuildShape();
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MS_EXCEPTION_IF_NULL(shape_ptr);
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EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true);
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auto conv_shape = shape_ptr->cast<abstract::ShapePtr>();
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MS_EXCEPTION_IF_NULL(conv_shape);
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auto shape_vec = conv_shape->shape();
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auto type = conv_abstract->BuildType();
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MS_EXCEPTION_IF_NULL(type);
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EXPECT_EQ(type->isa<TensorType>(), true);
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auto tensor_type = type->cast<TensorTypePtr>();
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MS_EXCEPTION_IF_NULL(tensor_type);
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auto elem_type = tensor_type->element();
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EXPECT_EQ(elem_type->type_id(), kNumberTypeFloat32);
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EXPECT_EQ(shape_vec.size(), 4);
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EXPECT_EQ(shape_vec[0], 32);
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EXPECT_EQ(shape_vec[1], 64);
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EXPECT_EQ(shape_vec[2], 218);
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EXPECT_EQ(shape_vec[3], 218);
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}
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} // namespace ops
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} // namespace mindspore
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