ac8f6785d3
This kernel currently only supports float type and has filter with the same data format as TensorFlow Conv3D op. The conversion change will be added in a follow-up cl. PiperOrigin-RevId: 351726918 Change-Id: Id2cc805cb89a1da1fda656a2260516eda8bd9119
257 lines
10 KiB
C++
257 lines
10 KiB
C++
/* Copyright 2021 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 <cstdint>
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#include <initializer_list>
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#include <vector>
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAre;
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using ::testing::ElementsAreArray;
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class Conv3dOpModel : public SingleOpModel {
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public:
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Conv3dOpModel(const TensorData& input, const TensorData& filter,
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const TensorData& bias, const TensorData& output,
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Padding padding = Padding_VALID, int32_t stride_depth = 1,
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int32_t stride_width = 1, int32_t stride_height = 1,
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ActivationFunctionType activation = ActivationFunctionType_NONE,
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int32_t dilation_depth = 1, int32_t dilation_width = 1,
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int32_t dilation_height = 1) {
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input_ = AddInput(input);
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filter_ = AddInput(filter);
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bias_ = AddInput(bias);
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output_ = AddOutput(output);
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SetBuiltinOp(
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BuiltinOperator_CONV_3D, BuiltinOptions_Conv3DOptions,
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CreateConv3DOptions(builder_, padding, stride_depth, stride_width,
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stride_height, activation, dilation_depth,
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dilation_width, dilation_height)
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.Union());
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BuildInterpreter({GetShape(input_), GetShape(filter_), GetShape(bias_)});
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}
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Conv3dOpModel(const TensorData& input, const TensorData& filter,
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const TensorData& output, Padding padding = Padding_VALID,
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int32_t stride_depth = 1, int32_t stride_width = 1,
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int32_t stride_height = 1,
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ActivationFunctionType activation = ActivationFunctionType_NONE,
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int32_t dilation_depth = 1, int32_t dilation_width = 1,
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int32_t dilation_height = 1) {
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input_ = AddInput(input);
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filter_ = AddInput(filter);
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output_ = AddOutput(output);
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SetBuiltinOp(
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BuiltinOperator_CONV_3D, BuiltinOptions_Conv3DOptions,
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CreateConv3DOptions(builder_, padding, stride_depth, stride_width,
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stride_height, activation, dilation_depth,
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dilation_width, dilation_height)
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.Union());
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BuildInterpreter({GetShape(input_), GetShape(filter_)});
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}
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void SetFilter(std::initializer_list<float> f) { PopulateTensor(filter_, f); }
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void SetBias(std::initializer_list<float> f) { PopulateTensor(bias_, f); }
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void SetInput(std::vector<float> data) { PopulateTensor(input_, data); }
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std::vector<float> GetOutput() { return ExtractVector<float>(output_); }
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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private:
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int input_;
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int filter_;
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int bias_;
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int output_;
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};
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template <typename T>
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std::vector<T> CreateRangeVector(int N) {
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std::vector<T> result;
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for (int i = 0; i < N; ++i) result.push_back(i);
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return result;
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}
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TEST(Conv3dOpModel, InvalidInputDimsTest) {
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EXPECT_DEATH_IF_SUPPORTED(Conv3dOpModel m({TensorType_FLOAT32, {2, 2, 4, 1}},
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{TensorType_FLOAT32, {3, 2, 2, 1}},
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{TensorType_FLOAT32, {}}),
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"input->dims->size != 5");
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}
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TEST(Conv3dOpModel, InvalidFilterDimsTest) {
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EXPECT_DEATH_IF_SUPPORTED(
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Conv3dOpModel m({TensorType_FLOAT32, {1, 2, 2, 4, 1}},
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{TensorType_FLOAT32, {3, 2, 2, 1}},
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{TensorType_FLOAT32, {}}),
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"filter->dims->size != 5");
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}
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TEST(Conv3dOpModel, MismatchChannelSizeTest) {
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EXPECT_DEATH_IF_SUPPORTED(
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Conv3dOpModel m({TensorType_FLOAT32, {1, 2, 2, 4, 1}},
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{TensorType_FLOAT32, {1, 3, 2, 2, 2}},
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{TensorType_FLOAT32, {}}),
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"input->dims->data.4. != filter->dims->data.3.");
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}
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TEST(Conv3dOpModel, MismatchBiasSizeTest) {
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EXPECT_DEATH_IF_SUPPORTED(
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Conv3dOpModel m({TensorType_FLOAT32, {1, 2, 2, 4, 2}},
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{TensorType_FLOAT32, {1, 3, 2, 2, 1}},
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{TensorType_FLOAT32, {2}}, {TensorType_FLOAT32, {}}),
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"NumElements.bias. != SizeOfDimension.filter, 4.");
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}
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TEST(Conv3dOpModel, SimpleFloat32Test) {
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Conv3dOpModel m({TensorType_FLOAT32, {1, 2, 2, 4, 2}},
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{TensorType_FLOAT32, {2, 2, 2, 2, 2}},
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{TensorType_FLOAT32, {}});
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m.SetInput(CreateRangeVector<float>(32));
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m.SetFilter({-1, -1, -1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1,
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1, -1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, -1, 1, -1});
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAre(1, 1, 1, 3, 2));
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({30, 6, 26, 10, 22, 14}));
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}
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TEST(Conv3dOpModel, PaddingValidTest) {
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Conv3dOpModel m({TensorType_FLOAT32, {1, 3, 4, 5, 2}},
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{TensorType_FLOAT32, {2, 2, 2, 2, 2}},
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{TensorType_FLOAT32, {}});
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m.SetInput(CreateRangeVector<float>(120));
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m.SetFilter({-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1,
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1, 1, -1, 1, -1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1, 1});
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAre(1, 2, 3, 4, 2));
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray({-214, 266, -234, 270, -254, 274, -274, 278, -314, 286,
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-334, 290, -354, 294, -374, 298, -414, 306, -434, 310,
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-454, 314, -474, 318, -614, 346, -634, 350, -654, 354,
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-674, 358, -714, 366, -734, 370, -754, 374, -774, 378,
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-814, 386, -834, 390, -854, 394, -874, 398}));
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}
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TEST(Conv3dOpModel, PaddingSameTest) {
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Conv3dOpModel m({TensorType_FLOAT32, {1, 3, 4, 5, 2}},
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{TensorType_FLOAT32, {2, 2, 2, 2, 2}},
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{TensorType_FLOAT32, {}}, Padding_SAME);
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m.SetInput(CreateRangeVector<float>(120));
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m.SetFilter({1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, 1, 1, -1, -1, 1,
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-1, 1, -1, 1, -1, -1, -1, 1, 1, 1, 1, 1, -1, 1, -1, 1});
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAre(1, 3, 4, 5, 2));
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EXPECT_THAT(
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m.GetOutput(),
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ElementsAreArray(
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{-172, 290, -176, 298, -180, 306, -184, 314, 36, 198, -192,
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330, -196, 338, -200, 346, -204, 354, 56, 218, -212, 370,
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-216, 378, -220, 386, -224, 394, 76, 238, -226, 82, -230,
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82, -234, 82, -238, 82, -80, 80, -252, 450, -256, 458,
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-260, 466, -264, 474, 116, 278, -272, 490, -276, 498, -280,
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506, -284, 514, 136, 298, -292, 530, -296, 538, -300, 546,
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-304, 554, 156, 318, -306, 82, -310, 82, -314, 82, -318,
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82, -80, 80, 158, -158, 162, -162, 166, -166, 170, -170,
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176, -176, 178, -178, 182, -182, 186, -186, 190, -190, 196,
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-196, 198, -198, 202, -202, 206, -206, 210, -210, 216, -216,
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220, -220, 224, -224, 228, -228, 232, -232, 237, -237}));
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}
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TEST(Conv3dOpModel, StrideTest) {
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Conv3dOpModel m({TensorType_FLOAT32, {2, 2, 3, 4, 2}},
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{TensorType_FLOAT32, {2, 2, 2, 2, 2}},
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{TensorType_FLOAT32, {}}, Padding_VALID, /*stride_depth=*/2,
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/*stride_width=*/2, /*stride_height=*/2);
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m.SetInput(CreateRangeVector<float>(96));
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m.SetFilter({1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1,
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1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1});
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAre(2, 1, 1, 2, 2));
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({52, 8, 68, 8, 244, 8, 260, 8}));
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}
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TEST(Conv3dOpModel, StrideAndPaddingSameTest) {
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Conv3dOpModel m({TensorType_FLOAT32, {2, 2, 3, 4, 2}},
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{TensorType_FLOAT32, {2, 2, 2, 2, 2}},
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{TensorType_FLOAT32, {}}, Padding_SAME, /*stride_depth=*/2,
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/*stride_width=*/2, /*stride_height=*/2);
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m.SetInput(CreateRangeVector<float>(96));
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m.SetFilter({-1, 1, -1, 1, 1, 1, 1, 1, -1, 1, -1, -1, -1, 1, 1, 1,
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1, 1, -1, -1, -1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, 1});
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAre(2, 1, 2, 2, 2));
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray({-70, -28, -86, -12, -82, -16, -90, -8, -262,
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164, -278, 180, -178, 80, -186, 88}));
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}
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TEST(Conv3dOpModel, DilationTest) {
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Conv3dOpModel m({TensorType_FLOAT32, {2, 2, 3, 4, 2}},
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{TensorType_FLOAT32, {2, 2, 2, 2, 2}},
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{TensorType_FLOAT32, {}}, Padding_VALID, /*stride_depth=*/1,
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/*stride_width=*/1, /*stride_height=*/1,
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/*activation=*/ActivationFunctionType_NONE,
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/*dilation_depth=*/1, /*dilation_width=*/1,
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/*dilation_height=*/2);
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m.SetInput(CreateRangeVector<float>(96));
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m.SetFilter({1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1,
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1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1});
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAre(2, 1, 1, 3, 2));
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray({52, 8, 60, 8, 68, 8, 244, 8, 252, 8, 260, 8}));
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}
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TEST(Conv3dOpModel, BiasTest) {
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Conv3dOpModel m({TensorType_FLOAT32, {2, 2, 3, 4, 2}},
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{TensorType_FLOAT32, {2, 2, 2, 2, 2}},
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{TensorType_FLOAT32, {2}}, {TensorType_FLOAT32, {}},
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Padding_VALID, /*stride_depth=*/2,
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/*stride_width=*/2, /*stride_height=*/2);
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m.SetInput(CreateRangeVector<float>(96));
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m.SetFilter({1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1,
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1, -1, 1, 1, -1, 1, 1, -1, 1, -1, -1, -1, -1, 1, 1, 1});
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m.SetBias({1, 2});
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m.Invoke();
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EXPECT_THAT(m.GetOutputShape(), ElementsAre(2, 1, 1, 2, 2));
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EXPECT_THAT(m.GetOutput(),
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ElementsAreArray({53, 10, 69, 10, 245, 10, 261, 10}));
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}
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} // namespace
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} // namespace tflite
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