/** * Copyright 2020 Huawei Technologies Co., Ltd * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ #include <vector> #include <memory> #include "common/common_test.h" #include "ops/conv2d.h" #include "ir/dtype/type.h" #include "abstract/dshape.h" #include "utils/tensor_construct_utils.h" namespace mindspore { namespace ops { class TestConv2d : public UT::Common { public: TestConv2d() {} void SetUp() {} void TearDown() {} }; TEST_F(TestConv2d, test_ops_conv2d) { auto conv_2d = std::make_shared<Conv2D>(); conv_2d->Init(64, {7, 7}); std::vector<int64_t> kernel_size = conv_2d->get_kernel_size(); for (auto item : kernel_size) { EXPECT_EQ(item, 7); } std::vector<int64_t> stride = conv_2d->get_stride(); for (auto item : stride) { EXPECT_EQ(item, 1); } std::vector<int64_t> dilation = conv_2d->get_dilation(); for (auto item : dilation) { EXPECT_EQ(item, 1); } EXPECT_EQ(conv_2d->get_pad_mode(), VALID); std::vector<int64_t> pad = conv_2d->get_pad(); for (auto item : pad) { EXPECT_EQ(item, 0); } EXPECT_EQ(conv_2d->get_mode(), 1); EXPECT_EQ(conv_2d->get_group(), 1); EXPECT_EQ(conv_2d->get_out_channel(), 64); EXPECT_EQ(conv_2d->get_format(), NCHW); auto tensor_x = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, std::vector<int64_t>{32, 3, 224, 224}); auto tensor_w = TensorConstructUtils::CreateOnesTensor(kNumberTypeFloat32, std::vector<int64_t>{64, 3, 7, 7}); MS_EXCEPTION_IF_NULL(tensor_x); MS_EXCEPTION_IF_NULL(tensor_w); auto conv_abstract = conv_2d->Infer({tensor_x->ToAbstract(), tensor_w->ToAbstract()}); MS_EXCEPTION_IF_NULL(conv_abstract); EXPECT_EQ(conv_abstract->isa<abstract::AbstractTensor>(), true); auto shape_ptr = conv_abstract->BuildShape(); MS_EXCEPTION_IF_NULL(shape_ptr); EXPECT_EQ(shape_ptr->isa<abstract::Shape>(), true); auto conv_shape = shape_ptr->cast<abstract::ShapePtr>(); MS_EXCEPTION_IF_NULL(conv_shape); auto shape_vec = conv_shape->shape(); auto type = conv_abstract->BuildType(); MS_EXCEPTION_IF_NULL(type); EXPECT_EQ(type->isa<TensorType>(), true); auto tensor_type = type->cast<TensorTypePtr>(); MS_EXCEPTION_IF_NULL(tensor_type); auto elem_type = tensor_type->element(); EXPECT_EQ(elem_type->type_id(), kNumberTypeFloat32); EXPECT_EQ(shape_vec.size(), 4); EXPECT_EQ(shape_vec[0], 32); EXPECT_EQ(shape_vec[1], 64); EXPECT_EQ(shape_vec[2], 218); EXPECT_EQ(shape_vec[3], 218); } } // namespace ops } // namespace mindspore