1972 lines
209 KiB
C++
1972 lines
209 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://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, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author raver119@gmail.com
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// @author Yurii Shyrma (iuriish@yahoo.com), created 02.04.2019
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//
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#ifndef LIBND4J_CONVOLUTIONTESTS2_H
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#define LIBND4J_CONVOLUTIONTESTS2_H
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#include "testlayers.h"
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#include <NDArray.h>
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#include <Context.h>
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#include <Node.h>
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#include <graph/Variable.h>
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#include <graph/VariableSpace.h>
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include <ops/declarable/helpers/col2im.h>
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#include <PointersManager.h>
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#include <GradCheck.h>
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using namespace nd4j;
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using namespace nd4j::graph;
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class ConvolutionTests2 : public testing::Test {
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public:
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const int bS = 2; // batch size
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const int iD = 1; // input depth (number of picture channels, for example rgb=3)
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const int iH = 28; // picture height in pixels
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const int iW = 28; // picture width in pixels
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const int oD = 3; // output depth (= N for dense layer)
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const int kH = 5; // kernel height in pixels
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const int kW = 5; // kernel width in pixels
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const int sH = 1; // stride step in horizontal direction
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const int sW = 1; // stride step in vertical direction
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const int pH = 0; // padding height
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const int pW = 0; // padding width
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const int dH = 2; // dilation height
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const int dW = 2; // dilation width
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const int oH = (iH - kH - (kH-1)*(dH-1) + 2*pH)/sH + 1; // output height
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const int oW = (iW - kW - (kW-1)*(dW-1) + 2*pW)/sW + 1; // output width
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};
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//////////////////////////////////////////////////////////////////////
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TEST_F(ConvolutionTests2, im2col_1) {
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int bS=2, iH=4,iW=3, iC=4, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
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int oH = (iH - (kH + (kH-1)*(dH-1)) + 2*pH)/sH + 1; // VALID
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int oW = (iW - (kW + (kW-1)*(dW-1)) + 2*pW)/sW + 1; // VALID
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int paddingMode = 0; // 1-SAME, 0-VALID;
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NDArray image('c', {bS, iC, iH, iW}, nd4j::DataType::DOUBLE);
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NDArray expected('c', {bS, iC, kH, kW, oH, oW}, {1, 2, 4, 5, 2, 3, 5, 6, 4, 5, 7, 8, 5, 6, 8, 9, 7, 8, 10, 11, 8, 9, 11, 12, 13, 14, 16, 17, 14,
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15, 17, 18, 16, 17, 19, 20, 17, 18, 20, 21, 19, 20, 22, 23, 20, 21, 23, 24, 25, 26, 28, 29, 26, 27, 29, 30,
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28, 29, 31, 32, 29, 30, 32, 33, 31, 32, 34, 35, 32, 33, 35, 36, 37, 38, 40, 41, 38, 39, 41, 42, 40, 41, 43,
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44, 41, 42, 44, 45, 43, 44, 46, 47, 44, 45, 47, 48, 49, 50, 52, 53, 50, 51, 53, 54, 52, 53, 55, 56, 53, 54,
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56, 57, 55, 56, 58, 59, 56, 57, 59, 60, 61, 62, 64, 65, 62, 63, 65, 66, 64, 65, 67, 68, 65, 66, 68, 69, 67,
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68, 70, 71, 68, 69, 71, 72, 73, 74, 76, 77, 74, 75, 77, 78, 76, 77, 79, 80, 77, 78, 80, 81, 79, 80, 82, 83,
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80, 81, 83, 84, 85, 86, 88, 89, 86, 87, 89, 90, 88, 89, 91, 92, 89, 90, 92, 93, 91, 92, 94, 95, 92, 93, 95, 96});
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image.linspace(1, 1);
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nd4j::ops::im2col op;
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auto results = op.execute({&image}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode});
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auto column = results->at(0);
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ASSERT_EQ(Status::OK(), results->status());
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ASSERT_TRUE(expected.isSameShape(column));
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ASSERT_TRUE(expected.equalsTo(column));
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delete results;
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}
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template <typename T>
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class TypedConvolutionTests2 : public testing::Test {
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public:
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};
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typedef ::testing::Types<double, float> TestingTypes;
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TYPED_TEST_CASE(TypedConvolutionTests2, TestingTypes);
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//////////////////////////////////////////////////////////////////////
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TYPED_TEST(TypedConvolutionTests2, deconv2d_tf_test2) {
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int bS=2, iH=4,iW=4, iC=5,oC=10, kH=2,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
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int oH=4,oW=4;
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int paddingMode = 1; // 1-SAME, 0-VALID;
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int dataFormat = 1; // 1-NHWC, 0-NCHW
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auto input = NDArrayFactory::create<TypeParam>('c', {bS, oH, oW, oC});
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auto weights = NDArrayFactory::create<TypeParam>('c', {kH, kW, iC, oC});
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auto outShape = NDArrayFactory::create<TypeParam>('c', {4}, {static_cast<TypeParam>(bS), static_cast<TypeParam>(iH), static_cast<TypeParam>(iW), static_cast<TypeParam>(iC)});
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auto exp = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC}, {2.75, 7.75, 12.75, 17.75, 22.75, 30.5 , 40.5 , 50.5 , 60.5 , 70.5 , 30.5 , 40.5 , 50.5 , 60.5 , 70.5 , 30.5 , 40.5 , 50.5 , 60.5 , 70.5 ,
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55.5 , 65.5 , 75.5 , 85.5 , 95.5 ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,
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55.5 , 65.5 , 75.5 , 85.5 , 95.5 ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,
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55.5 , 65.5 , 75.5 , 85.5 , 95.5 ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,
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2.75, 7.75, 12.75, 17.75, 22.75, 30.5 , 40.5 , 50.5 , 60.5 , 70.5 , 30.5 , 40.5 , 50.5 , 60.5 , 70.5 , 30.5 , 40.5 , 50.5 , 60.5 , 70.5 ,
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55.5 , 65.5 , 75.5 , 85.5 , 95.5 ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,
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55.5 , 65.5 , 75.5 , 85.5 , 95.5 ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,
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55.5 , 65.5 , 75.5 , 85.5 , 95.5 ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. ,161. , 181. , 201. , 221. , 241. });
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input = 0.5;
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weights.linspace(0.1, 0.1);
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nd4j::ops::deconv2d_tf op;
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auto results = op.execute({&outShape, &weights, &input}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
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auto output = results->at(0);
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ASSERT_EQ(Status::OK(), results->status());
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ASSERT_TRUE(exp.isSameShape(output));
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ASSERT_TRUE(exp.equalsTo(output));
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delete results;
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}
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//////////////////////////////////////////////////////////////////////
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TYPED_TEST(TypedConvolutionTests2, Test_DeConv2D_TF_1) {
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auto input0 = NDArrayFactory::create<TypeParam>('c', {4}, {12.f, 5.f, 5.f, 32.f});
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auto input1 = NDArrayFactory::create<TypeParam>('c', {2, 2, 32, 16});
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auto input2 = NDArrayFactory::create<TypeParam>('c', {12, 4, 4, 16});
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auto exp = NDArrayFactory::create<TypeParam>('c', {12, 5, 5, 32});
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nd4j::ops::deconv2d_tf op;
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auto result = op.execute({&input0, &input1, &input2}, {}, {2, 2, 1, 1, 0, 0, 1, 1, 0, 1});
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ASSERT_EQ(Status::OK(), result->status());
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ASSERT_EQ(exp, *result->at(0));
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delete result;
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}
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//////////////////////////////////////////////////////////////////////
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TYPED_TEST(TypedConvolutionTests2, Test_DeConv2D_TF_2) {
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auto input0 = NDArrayFactory::create<TypeParam>('c', {4}, {3, 8, 8, 16});
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auto input1 = NDArrayFactory::create<TypeParam>('c', {7, 7, 16, 5}, {1.05293429f, -0.89349967f, 0.31027254f, 1.22991478f, -0.62926656f, 0.56918693f,
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-1.60992694f, 1.10167944f, -0.80843484f, 0.07521993f, -1.15994942f, 0.76016301f, -0.40056285f, -1.16872537f, -0.91384381f, -0.36700436f, 1.82389200f, -1.18200207f, 0.51612782f, -0.92479187f, -0.09307563f, -0.55122334f, 1.23532486f, -1.11124146f, -0.05812126f, 0.68159896f, 0.69125599f, -0.77127314f, -0.10874277f, 0.86469102f,
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-1.31614351f, 0.33354419f, -1.71750402f, 0.17197680f, -1.03965557f, 1.10570908f, -1.19115615f, 1.05115080f, 0.18277600f, 1.08820546f, -0.72191417f, -0.10999311f, 1.56521320f, -0.35433730f, -1.11799145f, 0.34499285f, 0.64998639f, -1.64371550f, 0.92592359f, -0.47659501f, 0.49101439f, -0.15613313f, 1.47486567f, 0.43576995f,
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2.19538260f, -0.83567709f, -1.21846950f, 0.80400819f, 1.14637423f, -1.01503456f, -0.61992753f, -0.47378838f, 0.86503726f, 0.27147385f, 0.37073180f, -0.19951358f, 0.79167330f, -0.33982825f, 0.18631981f, -1.54715073f, 0.39967480f, 0.95067030f, 1.12508667f, -0.86676019f, -1.10341156f, 2.33141375f, 1.10972047f, 0.71407092f,
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1.70640314f, 1.80666339f, 0.59465605f, -0.39653218f, -2.61163163f, -1.15013492f, -1.19908321f, 0.41783467f, -0.22730024f, 0.31425011f, -0.58562893f, -0.10131568f, -0.85047537f, -2.59974790f, 1.22072542f, -2.08812046f, -0.19363593f, -1.27664304f, -0.02703438f, 1.08477545f, -0.65506506f, 0.46040919f, -0.13715318f,
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-0.74945593f, -0.69006950f, -1.29617655f, -0.15865716f, 1.38956285f, 0.90216327f, -1.31185400f, -0.15067385f, -0.63093358f, -0.05895613f, 0.26545224f, 0.29332840f, 0.42852548f, 0.72409540f, 0.12879130f, 1.43038857f, 0.68647617f, 2.19654775f, 0.51878077f, -0.03769343f, 0.52877223f, -0.21733910f, 1.13710785f, -0.59003806f,
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1.54624867f, -0.64997369f, -1.03239334f, 0.19708300f, 0.68658423f, 0.71048903f, -1.55250466f, -1.38636279f, 0.32385820f, 0.81226677f, 0.19209047f, -0.23002781f, -0.63631231f, 1.02101684f, 0.65428704f, -0.17206922f, 1.09488952f, 1.03022420f, -0.95567745f, -0.07595373f, -1.48606372f, 2.57174873f, -1.75366247f, 1.12913883f,
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0.97053039f, -0.28552356f, 0.56511772f, -0.79568213f, 0.07561764f, -1.02085686f, 1.05770981f, -1.25715709f, 0.42046708f, -2.57390857f, 0.96947151f, 1.05215812f, 0.65624017f, -1.29019403f, 0.64157075f, -0.40509227f, -0.65354455f, 0.42348680f, -1.34107757f, 0.05931387f, -0.54337227f, 0.95460182f, 1.59319806f, -0.44433126f,
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-0.33717924f, 0.79566282f, 0.50112695f, -0.22244534f, 1.76904583f, -0.89817202f, 1.82985342f, 0.17671813f, 0.80720717f, 1.32469308f, 0.39417782f, -0.23720963f, 0.96796370f, -1.02348757f, -0.86615551f, -1.58120525f, -0.37634999f, 0.00905940f, 0.01880967f, 1.75771821f, -0.64372772f, 0.36687651f, 0.15854552f, -0.67599791f,
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0.53726906f, -1.20158446f, -1.78549063f, 0.96476388f, -0.66158366f, -0.41681561f, -0.97541636f, 2.35928202f, 0.32130197f, 1.06886065f, 1.38736427f, -0.73718959f, 0.11215294f, 2.12865782f, -0.37927702f, 0.55621815f, -1.10108411f, -0.02032263f, 0.29595461f, 1.58737493f, 1.24001300f, -0.66748160f, 0.80729002f, -0.10575818f,
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-1.03175950f, 1.80755460f, 0.10825710f, 2.20666361f, 1.33633149f, 1.39290452f, 0.45211342f, -0.07837920f, 2.08304930f, -0.28387162f, -0.70775616f, 0.43626297f, 0.53556961f, 0.06201901f, -0.59255266f, -0.11854446f, 2.10024118f, 0.37638292f, -0.56178707f, -0.25220188f, -1.23731256f, -1.30002999f, 0.34283713f, 0.30502397f,
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-1.09233856f, 1.12430644f, 0.52273953f, -0.68507338f, -0.69913578f, 0.88440478f, -0.76959240f, 1.07093310f, -0.34802195f, 0.35683727f, -0.76079178f, -1.92807376f, 0.84499562f, 1.39131641f, 0.44825050f, 0.34567752f, 0.44607711f, -1.00986362f, -0.50038189f, -0.09060892f, -2.55645394f, 0.56416476f, -0.83058155f, -0.65931624f,
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-0.73649710f, 0.59814465f, -0.86736494f, -0.32200798f, -1.28087902f, -0.76818323f, 0.86848933f, -0.98678392f, -1.30813944f, -0.20255326f, 0.26557815f, -0.31090519f, -1.46331608f, -0.62782109f, 0.59034890f, 1.63147473f, -0.17727259f, -0.37636510f, 1.27368402f, 0.19096918f, -0.29936951f, -1.99038267f, 0.54831523f, 0.48849005f, -2.55680346f, -0.63126534f, 1.21715927f, 1.22841084f, -0.67416084f, 0.02927168f, -0.36693662f, 0.63204330f, 0.13721083f, 0.28742912f, 0.19470036f, 0.74873924f, -1.47602463f, 0.86264688f, -0.23730527f, -0.99978864f, -1.17048764f, -0.34996086f, 1.43019187f, 0.26224539f, 0.60689932f, -0.75002515f, -0.79823422f, -1.37300086f, -0.19951135f, -0.12150808f, -0.75272322f, 0.23755015f, 0.31270382f, 1.66539109f, -1.04104745f, 0.79540199f, -0.54042423f, -0.54150617f, 0.43871084f, 0.24163951f, -0.24517761f, -0.66178995f, -1.13064528f, -0.84426326f, 0.56437236f, 0.09088907f, -0.82823074f, 0.81753862f, -1.74096012f, -1.80599844f, -0.60943592f, 1.36094582f, -1.47762752f, 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auto input2 = NDArrayFactory::create<TypeParam>('c', {3, 4, 4, 5}, {0.98114507,0.96400015,0.58669623,0.60073098,0.75425418,0.44258752,0.76373084,0.96593234,0.34067846,0.57962620,0.77517051,0.97472977,0.79237527,0.68690428,0.21719366,0.79959206,0.84814187,0.22496814,0.08646965,0.31110474,0.79813162,0.19661444,0.57760099,0.72138960,0.15244268,0.87687051,0.11130344,0.01087698,0.34817841,0.54992017,0.23443850,0.31725614,0.59755220,0.20364695,0.00531392,0.23403114,0.07442912,0.83707647,0.89291743,0.09044587,0.69041462,0.29904183,0.61904680,0.85306847,0.34467042,0.95839152,0.54517124,0.29640937,0.94855959,0.95970016,0.94045145,0.95510301,0.34666505,0.34717010,0.69245678,0.71669175,0.59043738,0.64924132,0.06033522,0.60185199,0.04690073,0.59241154,0.40229547,0.23002481,0.45161195,0.73743778,0.93209113,0.37294358,0.50177744,0.15072501,0.26146917,0.05252146,0.04758931,0.76448288,0.85149045,0.08840467,0.07692576,0.33180160,0.27241259,0.74834620,0.56453640,0.23057286,0.68429752,0.11961551,0.39045977,0.44356094,0.77018807,0.07984410,0.47926806,0.26165759,0.18606064,0.89972877,0.17962874,0.47273120,0.64641705,0.61890443,0.58730015,0.25937832,0.35231561,0.10243882,0.17459193,0.95906995,0.09227025,0.30003223,0.41601210,0.38269713,0.84799751,0.59295173,0.76277990,0.68910424,0.37672606,0.40675461,0.94346058,0.91438505,0.84728183,0.64367667,0.74899979,0.60570691,0.16417363,0.68852426,0.85486889,0.22585792,0.86953176,0.07465519,0.93096301,0.38008822,0.38752587,0.44004038,0.13170612,0.94541045,0.89349973,0.69245307,0.94978877,0.98776658,0.79445884,0.30607409,0.58264961,0.37980538,0.41810784,0.48903038,0.51615888,0.57682794,0.82481897,0.78341080,0.48446465,0.17447931,0.71125424,0.30263851,0.70675352,0.03215584,0.92381065,0.22343694,0.08851149,0.91402490,0.70074717,0.30912192,0.37723206,0.97579397,0.23554587,0.95939133,0.41565709,0.01741416,0.58362787,0.22106662,0.89065537,0.31900249,0.41280911,0.67947610,0.04545590,0.15352812,0.85412524,0.84933222,0.80000225,0.93147073,0.70094105,0.69269875,0.95282194,0.65913582,0.79186874,0.59855248,0.39707430,0.95126239,0.15618217,0.33446689,0.98123758,0.84770758,0.98081012,0.54427413,0.18728519,0.89792955,0.53360126,0.72812986,0.13307744,0.51217443,0.66708084,0.29416915,0.31298995,0.39155037,0.29288291,0.87063305,0.61759154,0.73723332,0.37167635,0.82122716,0.22937430,0.76570536,0.47911792,0.02826214,0.94277323,0.59945469,0.19042060,0.68173155,0.82771295,0.95649538,0.40833101,0.90838542,0.55245881,0.49011012,0.36773444,0.34513527,0.42050683,0.16113964,0.30969388,0.27174174,0.12117655,0.35270175,0.81967867,0.63723136,0.84309389,0.71822576,0.84883484,0.32306117,0.08176457,0.56175486,0.34892198,0.09306929,0.85437582,0.13925577,0.48629188,0.29923539});
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auto exp = NDArrayFactory::create<TypeParam>('c', {3, 8, 8, 16}, 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1.30276895,-3.17103505,-0.26310229,-1.00843918,-0.77664804,-2.05240250,0.04728425,1.15720487,4.01001406,7.24615860,2.55452180,-5.76347876,0.34683830,-6.05540276,-4.70677900,-0.93182588,-4.37759733,2.93209839,1.63947964,-2.43563962,1.35213876,0.00670356,-0.02742785,-2.16460943,1.39449501,0.23929763,2.37476778,-4.17733765,-0.81475425,-6.15027046,-5.74441719,3.53978682,0.66798484});
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|
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nd4j::ops::deconv2d_tf op;
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auto result = op.execute({&input0, &input1, &input2}, {}, {7,7, 2,2, 0,0, 1,1, 1,1});
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ASSERT_EQ(Status::OK(), result->status());
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auto z = result->at(0);
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ASSERT_TRUE(exp.isSameShape(z));
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ASSERT_TRUE(exp.equalsTo(z));
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delete result;
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}
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//////////////////////////////////////////////////////////////////////
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TEST_F(ConvolutionTests2, Test_Dilation2D_Again_1) {
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auto x = NDArrayFactory::create<double>('c', {4, 128, 128, 4});
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auto w = NDArrayFactory::create<double>('c', {4, 5, 4});
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auto exp = NDArrayFactory::create<double>('c', {4, 64, 43, 4});
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nd4j::ops::dilation2d op;
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auto result = op.execute({&x, &w}, {}, {1, 1,5,7,1, 1,2,3,1});
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ASSERT_EQ(Status::OK(), result->status());
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|
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auto z = result->at(0);
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|
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ASSERT_TRUE(exp.isSameShape(z));
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|
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delete result;
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}
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//////////////////////////////////////////////////////////////////////
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TEST_F(ConvolutionTests2, Test_Dilation2D_Again_2) {
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auto x = NDArrayFactory::create<double>('c', {4, 26, 19, 4});
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auto w = NDArrayFactory::create<double>('c', {11, 7, 4});
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|
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nd4j::ops::dilation2d op;
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auto result = op.execute({&x, &w}, {}, {0, 1,2,3,1, 1,3,2,1});
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ASSERT_EQ(Status::OK(), result->status());
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|
|
delete result;
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}
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TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_1) {
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TypeParam _expGradWpB[] = {1603.7102981f, 10645.6278024f, 5975.4227995f, 17697.0903052f, 12133.6353024f, 26535.0528052f, 1779.221097f, 11795.5686029f, 6721.9835994f, 19904.0811062f, 13775.2461029f, 30123.0936062f, 1954.7318976f, 12945.5094033f, 7468.5443993f, 22111.071907f, 15416.8569033f, 33711.134407f, 2130.2426974f, 14095.4502038f, 8215.1051992f, 24318.0627081f, 17058.4677038f, 37299.1752081f, 2305.7534972f, 15245.3910042f, 8961.6659991f, 26525.0535091f, 18700.0785042f, 40887.2160091f, 2481.2642970f, 16395.3318047f, 9708.2267991f, 28732.0443100f, 20341.6893047f, 44475.2568100f, 2656.7750968f, 17545.2726051f, 10454.7875990f, 30939.0351110f, 21983.3001051f, 48063.2976110f, 2832.2858966f, 18695.2134056f, 11201.3483989f, 33146.0259119f, 23624.9109056f, 51651.3384119f, 3007.7966964f, 19845.1542060f, 11947.9091988f, 35353.0167129f, 25266.5217060f, 55239.3792129f, 3183.3074962f, 20995.095006f, 12694.4699987f, 37560.007513f, 26908.132506f, 58827.4200139};
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Nd4jLong _expGradWpS[] {4, 10, 6, 1, 1, 6, 1, 1, 1, typeid(TypeParam) == typeid(float) ? 8192 : 16384, 1, 99};
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NDArray expGWP(_expGradWpB, _expGradWpS);
|
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expGWP.permutei({2,3,1,0});
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TypeParam _expGradWdB[] = {2074.21032f, 2082.76104f, 2091.31176f, 2099.86248f, 2108.4132f, 2159.71752f, 2168.26824f, 2176.81896f, 2185.36968f, 2193.9204f, 2245.22472f, 2253.77544f, 2262.32616f, 2270.87688f, 2279.4276f, 2330.73192f, 2339.28264f, 2347.83336f, 2356.38408f, 2364.9348f, 2416.23912f, 2424.78984f, 2433.34056f, 2441.89128f, 2450.442f, 3112.99344f, 3122.06328f, 3131.13312f, 3140.20296f, 3149.2728f, 3203.69184f, 3212.76168f, 3221.83152f, 3230.90136f, 3239.9712f, 3294.39024f, 3303.46008f, 3312.52992f, 3321.59976f, 3330.6696f, 3385.08864f, 3394.15848f, 3403.22832f, 3412.29816f, 3421.368f, 3475.78704f, 3484.85688f, 3493.92672f, 3502.99656f, 3512.0664f, 4255.60056f, 4265.18952f, 4274.77848f, 4284.36744f, 4293.9564f, 4351.49016f, 4361.07912f, 4370.66808f, 4380.25704f, 4389.846f, 4447.37976f, 4456.96872f, 4466.55768f, 4476.14664f, 4485.7356f, 4543.26936f, 4552.85832f, 4562.44728f, 4572.03624f, 4581.6252f, 4639.15896f, 4648.74792f, 4658.33688f, 4667.92584f, 4677.5148f, 2140.10988f, 2148.92016f, 2157.73044f, 2166.54072f, 2175.351f, 2228.21268f, 2237.02296f, 2245.83324f, 2254.64352f, 2263.4538f, 2316.31548f, 2325.12576f, 2333.93604f, 2342.74632f, 2351.5566f, 2404.41828f, 2413.22856f, 2422.03884f, 2430.84912f, 2439.6594f, 2492.52108f, 2501.33136f, 2510.14164f, 2518.95192f, 2527.7622f, 3204.849f, 3214.1784f, 3223.5078f, 3232.8372f, 3242.1666f, 3298.143f, 3307.4724f, 3316.8018f, 3326.1312f, 3335.4606f, 3391.437f, 3400.7664f, 3410.0958f, 3419.4252f, 3428.7546f, 3484.731f, 3494.0604f, 3503.3898f, 3512.7192f, 3522.0486f, 3578.025f, 3587.3544f, 3596.6838f, 3606.0132f, 3615.3426f, 4373.41212f, 4383.26064f, 4393.10916f, 4402.95768f, 4412.8062f, 4471.89732f, 4481.74584f, 4491.59436f, 4501.44288f, 4511.2914f, 4570.38252f, 4580.23104f, 4590.07956f, 4599.92808f, 4609.7766f, 4668.86772f, 4678.71624f, 4688.56476f, 4698.41328f, 4708.2618f, 4767.35292f, 4777.20144f, 4787.04996f, 4796.89848f, 4806.747};
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|
Nd4jLong _expGradWdS[] = {4, 2, 3, 5, 5, 75, 25, 5, 1, typeid(TypeParam) == typeid(float) ? 8192 : 16384, 1, 99};
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|
NDArray expGWD(_expGradWdB, _expGradWdS);
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|
expGWD.permutei({2,3,1,0});
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|
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|
TypeParam _expEB[] = {5.0103f, 10.17147f, 15.48408f, 20.9487f, 26.5659f, 26.6832f, 21.65628f, 16.47507f, 11.139f, 5.6475f, 10.79727f, 21.90255f, 33.31698f, 45.0417f, 57.07785f, 57.3267f, 46.49334f, 35.34513f, 23.88093f, 12.0996f, 17.37801f, 35.22744f, 53.55f, 72.3474f, 91.62135f, 92.016f, 74.57958f, 56.66148f, 38.25999f, 19.3734f, 24.76962f, 50.18034f, 76.23444f, 102.9342f, 130.2819f, 130.8366f, 105.9834f, 80.47542f, 54.31038f, 27.486f, 32.9892f, 66.79545f, 101.4216f, 136.8705f, 173.145f, 173.874f, 140.7732f, 106.83825f, 72.0663f, 36.4545f, 33.8298f, 68.49375f, 103.9947f, 140.3355f, 177.519f, 178.248f, 144.3066f, 109.51395f, 73.8672f, 37.3635f, 28.85658f, 58.39302f, 88.6116f, 119.5146f, 151.1043f, 151.716f, 122.76444f, 93.11934f, 62.77842f, 31.7394f, 23.00409f, 46.52748f, 70.57188f, 95.139f, 120.23055f, 120.7107f, 97.6311f, 74.02194f, 49.88151f, 25.2081f, 16.25523f, 32.86293f, 49.82424f, 67.1403f, 84.81225f, 85.1466f, 68.83818f, 52.17045f, 35.14227f, 17.7525f, 8.5929f, 17.36517f, 26.31738f, 35.4501f, 44.7639f, 44.9382f, 36.31728f, 27.51357f, 18.5265f, 9.3555f, 8.63807f, 17.45032f, 26.43736f, 35.5998f, 44.93825f, 45.1399f, 36.46882f, 27.6199f, 18.59253f, 9.3861f, 18.18615f, 36.72737f, 55.62488f, 74.8799f, 94.49365f, 94.9122f, 76.65698f, 58.03937f, 39.05815f, 19.7121f, 28.66254f, 57.86775f, 87.61746f, 117.9135f, 148.7577f, 149.4084f, 120.63768f, 91.31331f, 61.43346f, 30.9963f, 40.08554f, 80.90806f, 122.47f, 164.7738f, 207.8219f, 208.72f, 168.48412f, 127.49662f, 85.75506f, 43.257f, 52.47345f, 105.8849f, 160.2374f, 215.534f, 271.77775f, 272.9385f, 220.2695f, 166.6442f, 112.05955f, 56.5125f, 53.82975f, 108.6158f, 164.3612f, 221.069f, 278.74225f, 279.903f, 225.8777f, 170.8778f, 114.90025f, 57.942f, 45.14002f, 91.0585f, 137.75788f, 185.2406f, 233.5091f, 234.4682f, 189.16564f, 143.06998f, 96.17878f, 48.4896f, 35.43048f, 71.45487f, 108.075f, 145.2927f, 183.1098f, 183.852f, 148.29504f, 112.13319f, 75.36462f, 37.9875f, 24.68283f, 49.76831f, 75.25766f, 101.1521f, 127.45285f, 127.9629f, 103.1927f, 78.01253f, 52.42117f, 26.4174f, 12.87877f, 25.96222f, 39.25096f, 52.7456f, 66.44675f, 66.7094f, 53.78542f, 40.6531f, 27.31183f, 13.761f, 12.59184f, 25.38317f, 38.37464f, 51.5669f, 64.9606f, 65.2566f, 52.61336f, 39.76673f, 26.71606f, 13.4607f, 26.23903f, 52.88419f, 79.93678f, 107.3981f, 135.26945f, 135.8777f, 109.53262f, 82.77361f, 55.59937f, 28.0086f, 40.96107f, 82.54206f, 124.74492f, 167.5716f, 211.02405f, 211.9608f, 170.83578f, 129.07914f, 86.68893f, 43.6632f, 56.77746f, 114.39578f, 172.85756f, 232.1654f, 292.3219f, 293.6034f, 236.60084f, 178.74182f, 120.02374f, 60.444f, 73.7077f, 148.48435f, 224.3332f, 301.2575f, 379.2605f, 380.903f, 306.9058f, 231.82015f, 155.6428f, 78.3705f, 75.6397f, 152.36785f, 230.1877f, 309.1025f, 389.1155f, 390.758f, 314.8288f, 237.79165f, 159.6433f, 80.3805f, 62.89546f, 126.67598f, 191.34416f, 256.9026f, 323.3539f, 324.7004f, 261.56684f, 197.53262f, 132.59514f, 66.7518f, 48.97887f, 98.63226f, 148.96212f, 199.9704f, 251.65905f, 252.6933f, 203.53098f, 153.68244f, 103.14573f, 51.9189f, 33.87043f, 68.19769f, 102.98308f, 138.2279f, 173.93345f, 174.6392f, 140.64322f, 106.18261f, 71.25607f, 35.8623f, 17.55064f, 35.33327f, 53.34854f, 71.5971f, 90.0796f, 90.4406f, 72.82556f, 54.97463f, 36.88716f, 18.5625f, 13.0455f, 26.44707f, 40.20528f, 54.3207f, 68.7939f, 68.9112f, 55.84908f, 42.42747f, 28.6458f, 14.5035f, 27.89367f, 56.50575f, 85.83738f, 115.8897f, 146.66385f, 146.9127f, 118.98294f, 90.32793f, 60.94653f, 30.8376f, 44.56161f, 90.21024f, 136.9476f, 184.7754f, 233.69535f, 234.09f, 189.46998f, 143.75268f, 96.93639f, 49.0194f, 63.06642f, 127.59474f, 193.58724f, 261.0462f, 329.9739f, 330.5286f, 267.3786f, 202.75302f, 136.64958f, 69.066f, 83.4252f, 168.69345f, 255.8076f, 344.7705f, 435.585f, 436.314f, 352.7772f, 267.38025f, 180.1203f, 90.9945f, 84.2658f, 170.39175f, 258.3807f, 348.2355f, 439.959f, 440.688f, 356.3106f, 270.05595f, 181.9212f, 91.9035f, 71.25738f, 144.01542f, 218.2764f, 294.0426f, 371.3163f, 371.928f, 300.57564f, 227.70894f, 153.32562f, 77.4234f, 56.34369f, 113.82228f, 172.43748f, 232.191f, 293.08455f, 293.5647f, 237.1455f, 179.58114f, 120.86991f, 61.0101f, 39.50763f, 79.77813f, 120.81264f, 162.6123f, 205.17825f, 205.5126f, 165.95178f, 125.62125f, 84.51987f, 42.6465f, 20.7321f, 41.84877f, 63.35058f, 85.2381f, 107.5119f, 107.6862f, 86.92608f, 65.77797f, 44.2413f, 22.3155f, 22.71767f, 45.82912f, 69.33496f, 93.2358f, 117.53225f, 117.7339f, 94.98322f, 71.8351f, 48.28893f, 24.3441f, 47.44335f, 95.68097f, 144.71408f, 194.5439f, 245.17165f, 245.5902f, 198.07778f, 149.76377f, 100.64695f, 50.7261f, 74.19534f, 149.59215f, 226.19226f, 303.9975f, 383.0097f, 383.6604f, 309.35688f, 233.84091f, 157.11066f, 79.1643f, 102.99194f, 207.59926f, 313.8244f, 421.6698f, 531.1379f, 532.036f, 428.89372f, 324.12142f, 217.71666f, 109.677f, 133.85145f, 269.7389f, 407.6654f, 547.634f, 689.64775f, 690.8085f, 556.7615f, 420.6602f, 282.50155f, 142.2825f, 135.20775f, 272.4698f, 411.7892f, 553.169f, 696.61225f, 697.773f, 562.3697f, 424.8938f, 285.34225f, 143.712f, 112.43842f, 226.5337f, 342.28828f, 459.7046f, 578.7851f, 579.7442f, 467.14324f, 352.87078f, 236.92438f, 119.3016f, 87.55128f, 176.35527f, 266.4138f, 357.7287f, 450.3018f, 451.044f, 363.36624f, 274.42479f, 184.21782f, 92.7435f, 60.52803f, 121.89791f, 184.11086f, 247.1681f, 311.07085f, 311.5809f, 250.9655f, 189.50093f, 127.18597f, 64.0194f, 31.35037f, 63.12502f, 95.32456f, 127.9496f, 161.00075f, 161.2634f, 129.86782f, 98.0443f, 65.79223f, 33.111f, 33.43584f, 67.30517f, 101.60864f, 136.3469f, 171.5206f, 171.8166f, 138.32936f, 104.40473f, 70.04206f, 35.2407f, 69.09703f, 139.06819f, 209.91478f, 281.6381f, 354.23945f, 354.8477f, 285.64462f, 215.55961f, 144.59137f, 72.7386f, 107.00307f, 215.32806f, 324.97692f, 435.9516f, 548.25405f, 549.1908f, 442.02378f, 333.52314f, 223.68693f, 112.5132f, 147.17346f, 296.12378f, 446.85356f, 599.3654f, 753.6619f, 754.9434f, 607.54484f, 458.35382f, 307.36774f, 154.584f, 189.6277f, 381.49435f, 575.6032f, 771.9575f, 970.5605f, 972.203f, 782.2858f, 590.11015f, 395.6728f, 198.9705f, 191.5597f, 385.37785f, 581.4577f, 779.8025f, 980.4155f, 982.058f, 790.2088f, 596.08165f, 399.6733f, 200.9805f, 157.97146f, 317.76398f, 479.38016f, 642.8226f, 808.0939f, 809.4404f, 651.23084f, 491.18462f, 329.29914f, 165.5718f, 122.04087f, 245.45826f, 370.25412f, 496.4304f, 623.98905f, 625.0233f, 502.79898f, 379.18644f, 254.18373f, 127.7889f, 83.74843f, 168.42169f, 254.02108f, 340.5479f, 428.00345f, 428.7092f, 344.83522f, 260.02861f, 174.28807f, 87.6123f, 43.07464f, 86.61527f, 130.62254f, 175.0971f, 220.0396f, 220.4006f, 177.26156f, 133.65263f, 89.57316f, 45.0225f };
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|
Nd4jLong _expES[] = {4, 2, 3, 10, 10, 300, 100, 10, 1, typeid(TypeParam) == typeid(float) ? 8192 : 16384, 1, 99};
|
|
NDArray expE(_expEB, _expES);
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {2, 3, 10, 10});
|
|
auto weightsD = NDArrayFactory::create<TypeParam>('c', {2, 3, 5, 5});
|
|
auto weightsP = NDArrayFactory::create<TypeParam>('c', {10, 6, 1, 1});
|
|
|
|
auto epsilon = NDArrayFactory::create<TypeParam>('c', {2, 3, 10, 10});
|
|
auto epsilonNext = NDArrayFactory::create<TypeParam>('c', {2, 10, 6, 6});
|
|
|
|
input.linspace(1);
|
|
weightsD.linspace(1);
|
|
weightsP.linspace(1);
|
|
epsilonNext.linspace(1);
|
|
weightsD.permutei({2,3,1,0});
|
|
weightsP.permutei({2,3,1,0});
|
|
|
|
input.applyScalar(scalar::Divide, 100.0);
|
|
weightsD.applyScalar(scalar::Divide, 100.0);
|
|
weightsP.applyScalar(scalar::Divide, 100.0);
|
|
epsilonNext.applyScalar(scalar::Divide, 100.0);
|
|
|
|
nd4j::ops::sconv2d_bp op;
|
|
auto resultBP = op.execute({&input, &epsilonNext, &weightsD, &weightsP },{}, {5, 5, 1, 1, 0, 0, 1, 1, 0}, {});
|
|
|
|
ASSERT_EQ(3, resultBP->size());
|
|
|
|
auto _epsilon = resultBP->at(0);
|
|
auto _gradWD = resultBP->at(1);
|
|
auto _gradWP = resultBP->at(2);
|
|
|
|
//_gradWP->printBuffer("gradWP");
|
|
|
|
ASSERT_TRUE(_gradWP->isSameShape(&expGWP));
|
|
ASSERT_TRUE(_gradWP->isSameShape(&weightsP));
|
|
|
|
ASSERT_TRUE(_gradWP->equalsTo(&expGWP));
|
|
|
|
//_gradWD->printShapeInfo("gradWD shape");
|
|
|
|
ASSERT_TRUE(_gradWD->isSameShape(&expGWD));
|
|
ASSERT_TRUE(_gradWD->isSameShape(&weightsD));
|
|
// _gradWD->printIndexedBuffer();
|
|
ASSERT_TRUE(_gradWD->equalsTo(&expGWD));
|
|
|
|
ASSERT_TRUE(_epsilon->isSameShape(&input));
|
|
ASSERT_TRUE(_epsilon->isSameShape(&expE));
|
|
|
|
ASSERT_TRUE(_epsilon->equalsTo(&expE));
|
|
|
|
delete resultBP;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_2) {
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|
|
|
int bS=3, iH=16,iW=16, iC=3,mC=3, kH=1,kW=1, sH=1,sW=1, pH=0,pW=0, dH=2,dW=2;
|
|
int oH=16,oW=16;
|
|
int oC=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID;
|
|
int dataFormat = 0; // 1-NHWC, 0-NCHW
|
|
|
|
NDArray input('c', {bS, iC, iH, iW}, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE);
|
|
NDArray gradO('c', {bS, oC, oH, oW}, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE);
|
|
NDArray weightsDepth('c', {kH, kW, iC, mC}, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE);
|
|
NDArray weightsPoint('f', {1, 1, iC*mC, oC}, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE);
|
|
NDArray bias('c', {1,oC}, {0.5, 0.5}, typeid(TypeParam) == typeid(float) ? nd4j::DataType::FLOAT32 : nd4j::DataType::DOUBLE);
|
|
|
|
NDArray gradI(&input);
|
|
NDArray gradWD(&weightsDepth);
|
|
NDArray gradWP(&weightsPoint);
|
|
NDArray gradB(&bias);
|
|
|
|
input = 2.;
|
|
weightsDepth.linspace(0.1, 0.1);
|
|
weightsPoint.linspace(0.15, 0.1);
|
|
gradO.linspace(0.01, 0.01);
|
|
|
|
nd4j::ops::sconv2d_bp op;
|
|
Nd4jStatus status = op.execute({&input, &gradO, &weightsDepth, & weightsPoint, &bias},
|
|
{&gradI, &gradWD, &gradWP, &gradB},
|
|
{}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}, {});
|
|
|
|
ASSERT_EQ(Status::OK(), status);
|
|
|
|
NDArray expGradI = gradI;
|
|
NDArray expGradWD = gradWD;
|
|
NDArray expGradWP = gradWP;
|
|
NDArray expGradB = gradB;
|
|
|
|
for( int i=0; i<10; i++ ) {
|
|
Nd4jStatus status = op.execute({&input, &gradO, &weightsDepth, & weightsPoint, &bias},
|
|
{&gradI, &gradWD, &gradWP, &gradB},
|
|
{}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}, {});
|
|
ASSERT_EQ(Status::OK(), status);
|
|
|
|
ASSERT_TRUE(expGradI.equalsTo(gradI));
|
|
ASSERT_TRUE(expGradWD.equalsTo(gradWD));
|
|
ASSERT_TRUE(expGradWP.equalsTo(gradWP));
|
|
ASSERT_TRUE(expGradB.equalsTo(expGradB));
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_3) {
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {3, 3, 16, 16});
|
|
auto weightsD = NDArrayFactory::create<TypeParam>('c', {1, 3, 2, 2});
|
|
auto weightsP = NDArrayFactory::create<TypeParam>('c', {2, 3, 1, 1});
|
|
auto bias = NDArrayFactory::create<TypeParam>('c', {1, 2});
|
|
|
|
weightsD.permutei({2,3,1,0});
|
|
weightsP.permutei({2,3,1,0});
|
|
|
|
auto epsilonNext = NDArrayFactory::create<TypeParam>('c', {3, 2, 14, 14});
|
|
|
|
auto epsilon = NDArrayFactory::create<TypeParam>('c', {3, 3, 16, 16});
|
|
|
|
nd4j::ops::sconv2d_bp op;
|
|
auto result = op.execute({&input, &epsilonNext, &weightsD, &weightsP}, {}, {2, 2, 1, 1, 0, 0, 2, 2, 0});
|
|
|
|
auto eps = result->at(0);
|
|
auto gWD = result->at(1);
|
|
auto gWP = result->at(2);
|
|
|
|
|
|
ASSERT_TRUE(epsilon.isSameShape(eps));
|
|
|
|
delete result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_4) {
|
|
|
|
int bS=2, iH=4,iW=3, iC=2,mC=2, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=4,oW=3;
|
|
int oC=iC*mC;
|
|
int paddingMode = 1; // 1-SAME, 0-VALID;
|
|
int dataFormat = 1; // 1-NHWC, 0-NCHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC});
|
|
auto weightsDepth = NDArrayFactory::create<TypeParam>('c', {kH, kW, iC, mC});
|
|
auto bias = NDArrayFactory::create<TypeParam>('c', {oC}, {1,2,3,4});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, oH, oW, oC});
|
|
|
|
auto expGradI = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC},{0.07 , 0.19 , 0.348, 0.652, 0.588, 0.956, 0.387, 0.687, 1.326, 2.022, 1.878, 2.67 , 1.071, 1.515, 2.982, 3.966, 3.534, 4.614, 1.606, 1.982, 3.932, 4.748, 4.428, 5.308,
|
|
1.126, 1.63 , 3.228, 4.3 , 3.468, 4.604, 3.123, 3.999, 7.95 , 9.798, 8.502, 10.446, 3.807, 4.827, 9.606, 11.742,10.158, 12.39 , 4.198, 4.958, 9.884, 11.468,10.38 , 12.028});
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|
|
|
auto expGradW = NDArrayFactory::create<TypeParam>('c', {kH, kW, iC, mC},{19.08, 19.44,19.8 , 20.16,12.24, 12.48,12.72, 12.96,22.56, 23.04,23.52, 24. ,14.4 , 14.72,15.04, 15.36,14.76, 15.12,15.48, 15.84, 9.36, 9.6 , 9.84, 10.08});
|
|
|
|
input = 2.;
|
|
weightsDepth.linspace(0.1, 0.1);
|
|
gradO.linspace(0.01, 0.01);
|
|
|
|
nd4j::ops::sconv2d_bp op;
|
|
auto results = op.execute({&input, &gradO, &weightsDepth, &bias}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
|
|
auto* gradI = results->at(0);
|
|
auto* gradWD = results->at(1);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
|
|
ASSERT_TRUE(expGradI.isSameShape(gradI));
|
|
ASSERT_TRUE(expGradI.equalsTo(gradI));
|
|
|
|
ASSERT_TRUE(expGradW.isSameShape(gradWD));
|
|
ASSERT_TRUE(expGradW.equalsTo(gradWD));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, sconv2d_bp_5) {
|
|
|
|
int bS=1, iH=8,iW=8, iC=3,mC=3, kH=1,kW=1, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=8,oW=8;
|
|
int oC=2; // iC*mC if weightsPoint = nullptr
|
|
int paddingMode = 0; // 1-SAME, 0-VALID;
|
|
int dataFormat = 0; // 1-NHWC, 0-NCHW
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {bS, iC, iH, iW});
|
|
auto gradO = NDArrayFactory::create<double>('c', {bS, oC, oH, oW});
|
|
auto weightsDepth = NDArrayFactory::create<double>('c', {kH, kW, iC, mC});
|
|
auto weightsPoint = NDArrayFactory::create<double>('c', {1, 1, iC*mC, oC});
|
|
auto bias = NDArrayFactory::create<double>('c', {1,oC}, {1,2});
|
|
|
|
auto gradI = NDArrayFactory::create<double>('c', {bS, iC, iH, iW});
|
|
auto gradWD = NDArrayFactory::create<double>('f', {kH, kW, iC, mC});
|
|
auto gradWP = NDArrayFactory::create<double>('c', {1, 1, iC*mC, oC});
|
|
auto gradB = NDArrayFactory::create<double>('c', {1,oC}, {1,2});
|
|
|
|
input = 2.;
|
|
weightsDepth.linspace(0.1, 0.1);
|
|
weightsDepth.linspace(-0.5, 0.1);
|
|
gradO.linspace(0.01, 0.01);
|
|
|
|
nd4j::ops::sconv2d_bp op;
|
|
auto status = op.execute({&input, &gradO, &weightsDepth, &weightsPoint, &bias}, {&gradI, &gradWD, &gradWP, &gradB}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat}, {});
|
|
ASSERT_EQ(Status::OK(), status);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, im2col_bp_1) {
|
|
|
|
int bS=3, iH=12,iW=12, iC=6,oC=3, kH=2,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=12,oW=12;
|
|
|
|
// [bS, iC, kH, kW, oH, oW] is de-convoluted to [bS, iC, iH, iW]
|
|
NDArray input('c', {bS, iC, iH, iW}, nd4j::DataType::DOUBLE);
|
|
NDArray gradO('c', {bS, iC, kH, kW, oH, oW}, nd4j::DataType::DOUBLE);
|
|
NDArray gradI('c', {bS, iC, iH, iW}, nd4j::DataType::DOUBLE); // output
|
|
|
|
nd4j::ops::im2col_bp op;
|
|
Nd4jStatus status = op.execute({&input, &gradO}, {&gradI}, {}, {kH, kW, sH, sW, pH, pW, dH, dW, 1}, {});
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, deconv3d_test1) {
|
|
|
|
int bS=2, iD=4,iH=4,iW=4, iC=2,oC=3, kD=2,kH=2,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=3,oH=3,oW=3;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID;
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {bS, oD, oH, oW, oC});
|
|
auto weights = NDArrayFactory::create<double>('c', {kD, kH, kW, iC, oC});
|
|
auto exp = NDArrayFactory::create<double>('c', {bS, iD, iH, iW, iC}, {0.3 , 0.75, 1.5 , 2.4 , 1.5 , 2.4 , 1.2 , 1.65, 2.4 , 3.3 , 6.6 , 8.4 , 6.6 , 8.4 , 4.2 , 5.1 , 2.4 , 3.3 , 6.6 , 8.4 , 6.6 , 8.4 , 4.2 , 5.1 , 2.1 , 2.55, 5.1 , 6. , 5.1 , 6. , 3. , 3.45,
|
|
4.2 , 5.1 ,10.2 ,12. ,10.2 ,12. , 6. , 6.9 ,12. ,13.8 ,27.6 ,31.2 ,27.6 ,31.2 ,15.6 ,17.4 ,12. ,13.8 ,27.6 ,31.2 ,27.6 ,31.2 ,15.6 ,17.4 , 7.8 , 8.7 ,17.4 ,19.2 ,17.4 ,19.2 , 9.6 ,10.5 ,
|
|
4.2 , 5.1 ,10.2 ,12. ,10.2 ,12. , 6. , 6.9 ,12. ,13.8 ,27.6 ,31.2 ,27.6 ,31.2 ,15.6 ,17.4 ,12. ,13.8 ,27.6 ,31.2 ,27.6 ,31.2 ,15.6 ,17.4 , 7.8 , 8.7 ,17.4 ,19.2 ,17.4 ,19.2 , 9.6 ,10.5 ,
|
|
3.9 , 4.35, 8.7 , 9.6 , 8.7 , 9.6 , 4.8 , 5.25, 9.6 ,10.5 ,21. ,22.8 ,21. ,22.8 ,11.4 ,12.3 , 9.6 ,10.5 ,21. ,22.8 ,21. ,22.8 ,11.4 ,12.3 , 5.7 , 6.15,12.3 ,13.2 ,12.3 ,13.2 , 6.6 , 7.05,
|
|
0.3 , 0.75, 1.5 , 2.4 , 1.5 , 2.4 , 1.2 , 1.65, 2.4 , 3.3 , 6.6 , 8.4 , 6.6 , 8.4 , 4.2 , 5.1 , 2.4 , 3.3 , 6.6 , 8.4 , 6.6 , 8.4 , 4.2 , 5.1 , 2.1 , 2.55, 5.1 , 6. , 5.1 , 6. , 3. , 3.45,
|
|
4.2 , 5.1 ,10.2 ,12. ,10.2 ,12. , 6. , 6.9 ,12. ,13.8 ,27.6 ,31.2 ,27.6 ,31.2 ,15.6 ,17.4 ,12. ,13.8 ,27.6 ,31.2 ,27.6 ,31.2 ,15.6 ,17.4 , 7.8 , 8.7 ,17.4 ,19.2 ,17.4 ,19.2 , 9.6 ,10.5 ,
|
|
4.2 , 5.1 ,10.2 ,12. ,10.2 ,12. , 6. , 6.9 ,12. ,13.8 ,27.6 ,31.2 ,27.6 ,31.2 ,15.6 ,17.4 ,12. ,13.8 ,27.6 ,31.2 ,27.6 ,31.2 ,15.6 ,17.4 , 7.8 , 8.7 ,17.4 ,19.2 ,17.4 ,19.2 , 9.6 ,10.5 ,
|
|
3.9 , 4.35, 8.7 , 9.6 , 8.7 , 9.6 , 4.8 , 5.25, 9.6 ,10.5 ,21. ,22.8 ,21. ,22.8 ,11.4 ,12.3 , 9.6 ,10.5 ,21. ,22.8 ,21. ,22.8 ,11.4 ,12.3 , 5.7 , 6.15,12.3 ,13.2 ,12.3 ,13.2 , 6.6 , 7.05});
|
|
input = 0.5;
|
|
weights.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::deconv3d op;
|
|
auto results = op.execute({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {});
|
|
auto output = results->at(0);
|
|
|
|
// output->printBuffer();
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(exp.isSameShape(output));
|
|
ASSERT_TRUE(exp.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, deconv3d_test2) {
|
|
|
|
int bS=2, iD=4,iH=4,iW=4, iC=2,oC=3, kD=2,kH=2,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=4,oH=4,oW=4;
|
|
int paddingMode = 1; // 1-SAME, 0-VALID;
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {bS, oD, oH, oW, oC});
|
|
auto weights = NDArrayFactory::create<double>('c', {kD, kH, kW, iC, oC});
|
|
auto exp = NDArrayFactory::create<double>('c', {bS, iD, iH, iW, iC}, {0.3 , 0.75, 1.5 , 2.4 , 1.5 , 2.4 , 1.5 , 2.4 , 2.4 , 3.3 , 6.6 , 8.4 , 6.6 , 8.4 , 6.6 , 8.4 , 2.4 , 3.3 , 6.6 , 8.4 , 6.6 , 8.4 , 6.6 , 8.4 , 2.4 , 3.3 , 6.6 , 8.4 , 6.6 , 8.4 , 6.6 , 8.4 ,
|
|
4.2 , 5.1 ,10.2 , 12. ,10.2 , 12. ,10.2 , 12. ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,
|
|
4.2 , 5.1 ,10.2 , 12. ,10.2 , 12. ,10.2 , 12. ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,
|
|
4.2 , 5.1 ,10.2 , 12. ,10.2 , 12. ,10.2 , 12. ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,
|
|
0.3 , 0.75, 1.5 , 2.4 , 1.5 , 2.4 , 1.5 , 2.4 , 2.4 , 3.3 , 6.6 , 8.4 , 6.6 , 8.4 , 6.6 , 8.4 , 2.4 , 3.3 , 6.6 , 8.4 , 6.6 , 8.4 , 6.6 , 8.4 , 2.4 , 3.3 , 6.6 , 8.4 , 6.6 , 8.4 , 6.6 , 8.4 ,
|
|
4.2 , 5.1 ,10.2 , 12. ,10.2 , 12. ,10.2 , 12. ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,
|
|
4.2 , 5.1 ,10.2 , 12. ,10.2 , 12. ,10.2 , 12. ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,
|
|
4.2 , 5.1 ,10.2 , 12. ,10.2 , 12. ,10.2 , 12. ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 ,12. , 13.8 ,27.6 , 31.2 ,27.6 , 31.2 ,27.6 , 31.2 });
|
|
input = 0.5;
|
|
weights.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::deconv3d op;
|
|
auto results = op.execute({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(exp.isSameShape(output));
|
|
ASSERT_TRUE(exp.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, deconv3d_test3) {
|
|
|
|
int bS=2, iD=4,iH=4,iW=4, iC=2,oC=3, kD=2,kH=2,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=3,oH=3,oW=3;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID;
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {bS, oC, oD, oH, oW});
|
|
auto weights = NDArrayFactory::create<double>('c', {oC, iC, kD, kH, kW});
|
|
auto exp = NDArrayFactory::create<double>('c', {bS, iC, iD, iH, iW}, {2.55, 5.25, 5.25, 2.7, 5.4 , 11.1 , 11.1 , 5.7, 5.4 , 11.1 , 11.1 , 5.7, 2.85, 5.85, 5.85, 3. , 5.7 , 11.7 , 11.7 , 6. ,12. , 24.6 , 24.6 , 12.6,12. , 24.6 , 24.6 , 12.6, 6.3 , 12.9 , 12.9 , 6.6,
|
|
5.7 , 11.7 , 11.7 , 6. ,12. , 24.6 , 24.6 , 12.6,12. , 24.6 , 24.6 , 12.6, 6.3 , 12.9 , 12.9 , 6.6, 3.15, 6.45, 6.45, 3.3, 6.6 , 13.5 , 13.5 , 6.9, 6.6 , 13.5 , 13.5 , 6.9, 3.45, 7.05, 7.05, 3.6,
|
|
3.75, 7.65, 7.65, 3.9, 7.8 , 15.9 , 15.9 , 8.1, 7.8 , 15.9 , 15.9 , 8.1, 4.05, 8.25, 8.25, 4.2, 8.1 , 16.5 , 16.5 , 8.4,16.8 , 34.2 , 34.2 , 17.4,16.8 , 34.2 , 34.2 , 17.4, 8.7 , 17.7 , 17.7 , 9. ,
|
|
8.1 , 16.5 , 16.5 , 8.4,16.8 , 34.2 , 34.2 , 17.4,16.8 , 34.2 , 34.2 , 17.4, 8.7 , 17.7 , 17.7 , 9. , 4.35, 8.85, 8.85, 4.5, 9. , 18.3 , 18.3 , 9.3, 9. , 18.3 , 18.3 , 9.3, 4.65, 9.45, 9.45, 4.8,
|
|
2.55, 5.25, 5.25, 2.7, 5.4 , 11.1 , 11.1 , 5.7, 5.4 , 11.1 , 11.1 , 5.7, 2.85, 5.85, 5.85, 3. , 5.7 , 11.7 , 11.7 , 6. ,12. , 24.6 , 24.6 , 12.6,12. , 24.6 , 24.6 , 12.6, 6.3 , 12.9 , 12.9 , 6.6,
|
|
5.7 , 11.7 , 11.7 , 6. ,12. , 24.6 , 24.6 , 12.6,12. , 24.6 , 24.6 , 12.6, 6.3 , 12.9 , 12.9 , 6.6, 3.15, 6.45, 6.45, 3.3, 6.6 , 13.5 , 13.5 , 6.9, 6.6 , 13.5 , 13.5 , 6.9, 3.45, 7.05, 7.05, 3.6,
|
|
3.75, 7.65, 7.65, 3.9, 7.8 , 15.9 , 15.9 , 8.1, 7.8 , 15.9 , 15.9 , 8.1, 4.05, 8.25, 8.25, 4.2, 8.1 , 16.5 , 16.5 , 8.4,16.8 , 34.2 , 34.2 , 17.4,16.8 , 34.2 , 34.2 , 17.4, 8.7 , 17.7 , 17.7 , 9. ,
|
|
8.1 , 16.5 , 16.5 , 8.4,16.8 , 34.2 , 34.2 , 17.4,16.8 , 34.2 , 34.2 , 17.4, 8.7 , 17.7 , 17.7 , 9. , 4.35, 8.85, 8.85, 4.5, 9. , 18.3 , 18.3 , 9.3, 9. , 18.3 , 18.3 , 9.3, 4.65, 9.45, 9.45, 4.8});
|
|
input = 0.5;
|
|
weights.linspace(0.1, 0.1);
|
|
weights.permutei({2, 3, 4, 1, 0});
|
|
|
|
nd4j::ops::deconv3d op;
|
|
auto results = op.execute({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(exp.isSameShape(output));
|
|
ASSERT_TRUE(exp.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, deconv3d_test4) {
|
|
|
|
int bS=2, iD=2,iH=2,iW=2, iC=2,oC=3, kD=2,kH=2,kW=2, sD=1,sH=1,sW=1, pD=1,pH=1,pW=1, dD=1,dH=1,dW=1;
|
|
int oD=3,oH=3,oW=3;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID;
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {bS, oC, oD, oH, oW});
|
|
auto weights = NDArrayFactory::create<double>('c', {oC, iC, kD, kH, kW});
|
|
auto exp = NDArrayFactory::create<double>('c', {bS, iC, iD, iH, iW}, {24.6, 24.6,24.6, 24.6,24.6, 24.6,24.6, 24.6,34.2, 34.2,34.2, 34.2,34.2, 34.2,34.2, 34.2,24.6, 24.6,24.6, 24.6,
|
|
24.6, 24.6,24.6, 24.6,34.2, 34.2,34.2, 34.2,34.2, 34.2,34.2, 34.2});
|
|
input = 0.5;
|
|
weights.linspace(0.1, 0.1);
|
|
weights.permutei({2, 3, 4, 1, 0});
|
|
|
|
nd4j::ops::deconv3d op;
|
|
auto results = op.execute({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat}, {});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(exp.isSameShape(output));
|
|
ASSERT_TRUE(exp.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, deconv3d_bp_test1) {
|
|
|
|
int bS=1, iD=3,iH=3,iW=3, iC=1,oC=2, kD=2,kH=2,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=2,oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID;
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {bS, oD, oH, oW, oC});
|
|
auto weights = NDArrayFactory::create<double>('c', {kD, kH, kW, iC, oC});
|
|
auto bias = NDArrayFactory::create<double>('c', {iC});
|
|
auto gradO = NDArrayFactory::create<double>('c', {bS, iD, iH, iW, iC});
|
|
|
|
input = 0.5;
|
|
weights.linspace(0.1, 0.1);
|
|
gradO.linspace(0.5);
|
|
|
|
const OpArgsHolder argsHolderFF({&input, &weights, &bias}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat});
|
|
const OpArgsHolder argsHolderBP({&input, &weights, &bias, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat});
|
|
|
|
nd4j::ops::deconv3d opFF;
|
|
nd4j::ops::deconv3d_bp opBP;
|
|
|
|
const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP);
|
|
|
|
ASSERT_TRUE(isGradCorrect);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, deconv3d_bp_test2) {
|
|
|
|
int bS=1, iD=2,iH=2,iW=2, iC=1,oC=2, kD=2,kH=2,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=2,oH=2,oW=2;
|
|
int paddingMode = 1; // 1-SAME, 0-VALID;
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {bS, oD, oH, oW, oC});
|
|
auto weights = NDArrayFactory::create<double>('c', {kD, kH, kW, iC, oC});
|
|
auto gradO = NDArrayFactory::create<double>('c', {bS, iD, iH, iW, iC});
|
|
|
|
input = 0.5;
|
|
weights.linspace(0.1, 0.1);
|
|
gradO.linspace(0.5);
|
|
|
|
const OpArgsHolder argsHolderFF({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat});
|
|
const OpArgsHolder argsHolderBP({&input, &weights, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat});
|
|
|
|
nd4j::ops::deconv3d opFF;
|
|
nd4j::ops::deconv3d_bp opBP;
|
|
|
|
const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP);
|
|
|
|
ASSERT_TRUE(isGradCorrect);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, deconv3d_bp_test3) {
|
|
|
|
int bS=1, iD=3,iH=3,iW=3, iC=1,oC=2, kD=2,kH=2,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=2,oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID;
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {bS, oC, oD, oH, oW});
|
|
auto weights = NDArrayFactory::create<double>('c', {oC, iC, kD, kH, kW});
|
|
auto gradO = NDArrayFactory::create<double>('c', {bS, iC, iD, iH, iW});
|
|
|
|
input = 0.5;
|
|
weights.linspace(0.1, 0.1);
|
|
gradO.linspace(0.5);
|
|
weights.permutei({2, 3, 4, 1, 0});
|
|
|
|
const OpArgsHolder argsHolderFF({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat});
|
|
const OpArgsHolder argsHolderBP({&input, &weights, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat});
|
|
|
|
nd4j::ops::deconv3d opFF;
|
|
nd4j::ops::deconv3d_bp opBP;
|
|
|
|
const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP);
|
|
|
|
ASSERT_TRUE(isGradCorrect);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, deconv3d_bp_test4) {
|
|
|
|
int bS=1, iD=2,iH=2,iW=2, iC=1,oC=2, kD=2,kH=2,kW=2, sD=1,sH=1,sW=1, pD=1,pH=1,pW=1, dD=1,dH=1,dW=1;
|
|
int oD=3,oH=3,oW=3;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID;
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<double>('c', {bS, oC, oD, oH, oW});
|
|
auto weights = NDArrayFactory::create<double>('c', {oC, iC, kD, kH, kW});
|
|
auto gradO = NDArrayFactory::create<double>('c', {bS, iC, iD, iH, iW});
|
|
|
|
input = 0.5;
|
|
weights.linspace(0.1, 0.1);
|
|
gradO.linspace(0.5);
|
|
weights.permutei({2, 3, 4, 1, 0});
|
|
|
|
const OpArgsHolder argsHolderFF({&input, &weights}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat});
|
|
const OpArgsHolder argsHolderBP({&input, &weights, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, dataFormat});
|
|
|
|
nd4j::ops::deconv3d opFF;
|
|
nd4j::ops::deconv3d_bp opBP;
|
|
|
|
const bool isGradCorrect = GradCheck::checkGrad(opFF, opBP, argsHolderFF, argsHolderBP);
|
|
|
|
ASSERT_TRUE(isGradCorrect);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, maxpool2d_1) {
|
|
|
|
auto x = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
|
|
auto exp = NDArrayFactory::create<float>('c',{bS,iD,oH,oW});
|
|
// auto z('c',{bS,iD,oH,oW});
|
|
|
|
auto variableSpace = new VariableSpace();
|
|
variableSpace->putVariable(-1, x);
|
|
// variableSpace->putVariable(1, &z);
|
|
|
|
auto block = new Context(1, variableSpace, false);
|
|
block->fillInputs({-1});
|
|
std::vector<int>* argI = block->getIArguments();
|
|
*argI = {kH,kW, sH,sW, pH,pW, dH,dW, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
|
|
|
|
nd4j::ops::maxpool2d pooling;
|
|
Nd4jStatus status = pooling.execute(block);
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
|
|
// result->printShapeInfo();
|
|
ASSERT_TRUE(exp.isSameShape(result));
|
|
|
|
delete variableSpace;
|
|
delete block;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, maxpool2d_2) {
|
|
|
|
const int bS = 2;
|
|
const int iD = 1;
|
|
const int iH = 28;
|
|
const int iW = 28;
|
|
const int kH = 5;
|
|
const int kW = 5;
|
|
const int sH = 1;
|
|
const int sW = 1;
|
|
const int pH = 0;
|
|
const int pW = 0;
|
|
const int dH = 1;
|
|
const int dW = 1;
|
|
const int oH = (iH - kH - (kH-1)*(dH-1) + 2*pH)/sH + 1; // output height
|
|
const int oW = (iW - kW - (kW-1)*(dW-1) + 2*pW)/sW + 1; // output width
|
|
|
|
|
|
auto x = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
|
|
auto exp = NDArrayFactory::create<float>('c',{bS,iD,oH,oW});
|
|
// auto z('c',{bS,iD,oH,oW});
|
|
|
|
auto variableSpace = new VariableSpace();
|
|
variableSpace->putVariable(-1, x);
|
|
// variableSpace->putVariable(1, &z);
|
|
|
|
auto block = new Context(1, variableSpace, false);
|
|
block->fillInputs({-1});
|
|
std::vector<int>* argI = block->getIArguments();
|
|
*argI = {kH,kW, sH,sW, pH,pW, dH,dW, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
|
|
|
|
nd4j::ops::maxpool2d pooling;
|
|
Nd4jStatus status = pooling.execute(block);
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
|
|
// result->printShapeInfo();
|
|
ASSERT_TRUE(exp.isSameShape(result));
|
|
|
|
delete variableSpace;
|
|
delete block;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, maxpool2d_3) {
|
|
|
|
const int bS = 2;
|
|
const int iD = 1;
|
|
const int iH = 28;
|
|
const int iW = 28;
|
|
const int kH = 5;
|
|
const int kW = 5;
|
|
const int sH = 1;
|
|
const int sW = 1;
|
|
const int pH = 0;
|
|
const int pW = 0;
|
|
const int dH = 1;
|
|
const int dW = 1;
|
|
const int oH = (int) nd4j::math::nd4j_ceil<float, int>(iH * 1.f / sH);
|
|
const int oW = (int) nd4j::math::nd4j_ceil<float, int>(iW * 1.f / sW);
|
|
|
|
|
|
auto x = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
|
|
auto exp = NDArrayFactory::create<float>('c',{bS,iD,oH,oW});
|
|
// auto z('c',{bS,iD,oH,oW});
|
|
|
|
auto variableSpace = new VariableSpace();
|
|
variableSpace->putVariable(-1, x);
|
|
// variableSpace->putVariable(1, &z);
|
|
|
|
auto block = new Context(1, variableSpace, false);
|
|
block->fillInputs({-1});
|
|
std::vector<int>* argI = block->getIArguments();
|
|
*argI = {kH,kW, sH,sW, pH,pW, dH,dW, 1}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
|
|
|
|
nd4j::ops::maxpool2d pooling;
|
|
Nd4jStatus status = pooling.execute(block);
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
|
|
// result->printShapeInfo();
|
|
ASSERT_TRUE(exp.isSameShape(result));
|
|
|
|
delete variableSpace;
|
|
delete block;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, maxpool2d_4) {
|
|
|
|
const int bS = 2;
|
|
const int iD = 1;
|
|
const int iH = 24;
|
|
const int iW = 24;
|
|
const int kH = 3;
|
|
const int kW = 3;
|
|
const int sH = 1;
|
|
const int sW = 1;
|
|
const int pH = 0;
|
|
const int pW = 0;
|
|
const int dH = 1;
|
|
const int dW = 1;
|
|
const int oH = (iH - kH - (kH-1)*(dH-1) + 2*pH)/sH + 1; // output height
|
|
const int oW = (iW - kW - (kW-1)*(dW-1) + 2*pW)/sW + 1; // output width
|
|
|
|
|
|
auto x = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
|
|
auto exp = NDArrayFactory::create<float>('c',{bS,iD,oH,oW});
|
|
// auto z('c',{bS,iD,oH,oW});
|
|
|
|
auto variableSpace = new VariableSpace();
|
|
variableSpace->putVariable(-1, x);
|
|
// variableSpace->putVariable(1, &z);
|
|
|
|
auto block = new Context(1, variableSpace, false);
|
|
block->fillInputs({-1});
|
|
std::vector<int>* argI = block->getIArguments();
|
|
*argI = {kH,kW, sH,sW, pH,pW, dH,dW, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
|
|
|
|
nd4j::ops::maxpool2d pooling;
|
|
Nd4jStatus status = pooling.execute(block);
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
|
|
// result->printShapeInfo();
|
|
ASSERT_TRUE(exp.isSameShape(result));
|
|
|
|
delete variableSpace;
|
|
delete block;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, maxpool2d_5) {
|
|
|
|
const int bS = 2;
|
|
const int iD = 1;
|
|
const int iH = 24;
|
|
const int iW = 24;
|
|
const int kH = 3;
|
|
const int kW = 3;
|
|
const int sH = 1;
|
|
const int sW = 1;
|
|
const int pH = 0;
|
|
const int pW = 0;
|
|
const int dH = 1;
|
|
const int dW = 1;
|
|
const int oH = (int) nd4j::math::nd4j_ceil<float, int>(iH * 1.f / sH);
|
|
const int oW = (int) nd4j::math::nd4j_ceil<float, int>(iW * 1.f / sW);
|
|
|
|
|
|
auto x = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
|
|
auto exp = NDArrayFactory::create<float>('c',{bS,iD,oH,oW});
|
|
// auto z('c',{bS,iD,oH,oW});
|
|
|
|
auto variableSpace = new VariableSpace();
|
|
variableSpace->putVariable(-1, x);
|
|
// variableSpace->putVariable(1, &z);
|
|
|
|
auto block = new Context(1, variableSpace, false);
|
|
block->fillInputs({-1});
|
|
std::vector<int>* argI = block->getIArguments();
|
|
*argI = {kH,kW, sH,sW, pH,pW, dH,dW, 1}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
|
|
|
|
nd4j::ops::maxpool2d pooling;
|
|
Nd4jStatus status = pooling.execute(block);
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
|
|
// result->printShapeInfo();
|
|
ASSERT_TRUE(exp.isSameShape(result));
|
|
|
|
delete variableSpace;
|
|
delete block;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool2d_6) {
|
|
auto x = NDArrayFactory::create<TypeParam>('c', {2, 4, 4, 2});
|
|
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {11.f, 12.f, 15.f, 16.f, 27.f, 28.f, 31.f, 32.f, 43.f, 44.f, 47.f, 48.f, 59.f, 60.f, 63.f, 64.f});
|
|
|
|
x.linspace(1);
|
|
|
|
|
|
nd4j::ops::maxpool2d op;
|
|
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 1, 1, 1});
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, result->status());
|
|
|
|
auto z = result->at(0);
|
|
|
|
ASSERT_TRUE(exp.isSameShape(z));
|
|
ASSERT_TRUE(exp.equalsTo(z));
|
|
|
|
delete result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool2d_7) {
|
|
auto x = NDArrayFactory::create<TypeParam>('c', {2, 4, 4, 2});
|
|
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {11.f, 12.f, 15.f, 16.f, 27.f, 28.f, 31.f, 32.f, 43.f, 44.f, 47.f, 48.f, 59.f, 60.f, 63.f, 64.f});
|
|
|
|
x.linspace(1);
|
|
|
|
|
|
nd4j::ops::maxpool2d op;
|
|
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 0, 1, 1});
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, result->status());
|
|
|
|
auto z = result->at(0);
|
|
|
|
ASSERT_TRUE(exp.isSameShape(z));
|
|
ASSERT_TRUE(exp.equalsTo(z));
|
|
|
|
delete result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool2d_8) {
|
|
auto x = NDArrayFactory::create<TypeParam>('c', {2, 2, 5, 5});
|
|
auto exp = NDArrayFactory::create<TypeParam>('c', {2, 2, 2, 2}, {7.f, 9.f, 17.f, 19.f, 32.f, 34.f, 42.f, 44.f, 57.f, 59.f, 67.f, 69.f, 82.f, 84.f, 92.f, 94.f});
|
|
|
|
x.linspace(1);
|
|
|
|
|
|
nd4j::ops::maxpool2d op;
|
|
auto result = op.execute({&x}, {}, {2, 2, 2, 2, 0, 0, 1, 1, 0, 1, 0});
|
|
|
|
ASSERT_EQ(ND4J_STATUS_OK, result->status());
|
|
|
|
auto z = result->at(0);
|
|
|
|
ASSERT_TRUE(exp.isSameShape(z));
|
|
ASSERT_TRUE(exp.equalsTo(z));
|
|
|
|
delete result;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool2d_9) {
|
|
|
|
int bS = 3; // batch size (number of samples)
|
|
int iC = 3; // input channels
|
|
int iH = 28, iW = 28; // input height/width
|
|
int kH = 2, kW = 2; // kernel (filter) height/width
|
|
int sH = 1, sW = 1; // stride height/width
|
|
int pH = 0, pW = 0; // padding height/width
|
|
int dH = 1, dW = 1; // dilation height/width
|
|
|
|
int oH = 27, oW = 27; // output height/width
|
|
|
|
int isSameMode = 0; // 1-SAME, 0-VALID
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW});
|
|
|
|
nd4j::ops::maxpool2d op;
|
|
auto results = op.execute({&input}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, isSameMode, 1, 0});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(output->isSameShape({bS, iC, oH, oW}));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool2d_10) {
|
|
|
|
int bS=1, iH=4,iW=4, iC=3, kH=2,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=3,oW=3;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID;
|
|
int dataFormat = 0; // 1-NHWC, 0-NCHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW}, {0.27620894, 0.21801452, 0.062078513, 7.348895E-4, 0.24149609, 0.4948205, 0.93483436, 0.52035654, 0.30292067, 0.3289706, 0.7977864,
|
|
0.03180518, 0.1455722, 0.90352905, 0.9405744, 0.0048329555, 0.44062102, 0.111197524, 0.31742015, 0.1933705, 0.23825112, 0.35076278, 0.7135856, 0.28229436, 0.18310733,
|
|
0.9613717, 0.56823575, 0.78289545, 0.62195826, 0.5244586, 0.5040889, 0.025349546, 0.41400263, 0.28420195, 0.8536445, 0.3044107, 0.7997134, 0.45762005, 0.7653578,
|
|
0.07198584, 0.5304998, 0.7334402, 0.85019743, 0.031957153, 0.37088063, 0.85722464, 0.06376881, 0.39791203});
|
|
|
|
auto expOutput = NDArrayFactory::create<TypeParam>('c', {bS, iC, oH, oW}, {0.4948205, 0.93483436, 0.93483436, 0.4948205, 0.93483436, 0.93483436, 0.90352905, 0.9405744, 0.9405744, 0.44062102, 0.7135856,
|
|
0.7135856, 0.9613717, 0.9613717, 0.78289545, 0.9613717, 0.9613717, 0.78289545, 0.7997134, 0.8536445, 0.8536445, 0.7997134, 0.85019743, 0.85019743,
|
|
0.85722464, 0.85722464, 0.85019743});
|
|
|
|
nd4j::ops::maxpool2d op;
|
|
auto results = op.execute({&input}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode});
|
|
auto* output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
|
|
ASSERT_TRUE(expOutput.isSameShape(output));
|
|
ASSERT_TRUE(expOutput.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, maxpool2d_11) {
|
|
|
|
NDArray input('c', {1,1,4,5}, nd4j::DataType::FLOAT32);
|
|
NDArray z('c', {1,1,4,5}, nd4j::DataType::FLOAT32);
|
|
|
|
input.linspace(1.);
|
|
|
|
nd4j::ops::maxpool2d op;
|
|
auto results = op.execute({&input}, {}, {2,2, 1,1, 1,1, 2,2, 1,0,0});
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool3d_test1) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=2,oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, oD, oH, oW}, {10.5, 11.5, 13.5, 14.5, 22.5, 23.5, 25.5, 26.5, 46.5, 47.5, 49.5, 50.5, 58.5, 59.5, 61.5, 62.5,
|
|
82.5, 83.5, 85.5, 86.5, 94.5, 95.5, 97.5, 98.5,118.5,119.5,121.5,122.5,130.5,131.5,133.5,134.5,
|
|
154.5,155.5,157.5,158.5,166.5,167.5,169.5,170.5,190.5,191.5,193.5,194.5,202.5,203.5,205.5,206.5});
|
|
input.linspace(1.);
|
|
|
|
nd4j::ops::avgpool3dnew op;
|
|
auto results = op.execute({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool3d_test2) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=3,oH=4,oW=3;
|
|
int paddingMode = 1; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, oD, oH, oW, iC}, { 25. , 26. , 27. , 28. , 29. , 30. , 29.5, 30.5, 31.5, 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 34. , 35. , 36. , 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 43. , 44. , 45. , 43. , 44. , 45. , 46. , 47. , 48. , 47.5, 48.5, 49.5,
|
|
61. , 62. , 63. , 64. , 65. , 66. , 65.5, 66.5, 67.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 70. , 71. , 72. , 74.5, 75.5, 76.5, 77.5, 78.5, 79.5, 79. , 80. , 81. , 79. , 80. , 81. , 82. , 83. , 84. , 83.5, 84.5, 85.5,
|
|
79. , 80. , 81. , 82. , 83. , 84. , 83.5, 84.5, 85.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 88. , 89. , 90. , 92.5, 93.5, 94.5, 95.5, 96.5, 97.5, 97. , 98. , 99. , 97. , 98. , 99. ,100. ,101. ,102. ,101.5,102.5,103.5,
|
|
133. ,134. ,135. ,136. ,137. ,138. ,137.5,138.5,139.5,137.5,138.5,139.5,140.5,141.5,142.5,142. ,143. ,144. ,146.5,147.5,148.5,149.5,150.5,151.5,151. ,152. ,153. ,151. ,152. ,153. ,154. ,155. ,156. ,155.5,156.5,157.5,
|
|
169. ,170. ,171. ,172. ,173. ,174. ,173.5,174.5,175.5,173.5,174.5,175.5,176.5,177.5,178.5,178. ,179. ,180. ,182.5,183.5,184.5,185.5,186.5,187.5,187. ,188. ,189. ,187. ,188. ,189. ,190. ,191. ,192. ,191.5,192.5,193.5,
|
|
187. ,188. ,189. ,190. ,191. ,192. ,191.5,192.5,193.5,191.5,192.5,193.5,194.5,195.5,196.5,196. ,197. ,198. ,200.5,201.5,202.5,203.5,204.5,205.5,205. ,206. ,207. ,205. ,206. ,207. ,208. ,209. ,210. ,209.5,210.5,211.5});
|
|
input.linspace(1.);
|
|
|
|
nd4j::ops::avgpool3dnew op;
|
|
auto results = op.execute({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 0, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool3d_test3) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=2,oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, oD, oH, oW, iC}, { 29.5, 30.5, 31.5, 32.5, 33.5, 34.5, 38.5, 39.5, 40.5, 41.5, 42.5, 43.5, 65.5, 66.5, 67.5, 68.5, 69.5, 70.5,
|
|
74.5, 75.5, 76.5, 77.5, 78.5, 79.5,137.5,138.5,139.5,140.5,141.5,142.5,146.5,147.5,148.5,149.5,150.5,151.5,
|
|
173.5,174.5,175.5,176.5,177.5,178.5,182.5,183.5,184.5,185.5,186.5,187.5});
|
|
input.linspace(1.);
|
|
|
|
nd4j::ops::avgpool3dnew op;
|
|
auto results = op.execute({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool3d_test4) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=1,pH=1,pW=1, dD=1,dH=1,dW=1;
|
|
int oD=4,oH=4,oW=4;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, oD, oH, oW},{0.416667, 1.00, 1.333333, 0.75, 1.00, 2.25, 2.75, 1.50, 1.75, 3.75, 4.25, 2.25, 1.416667, 3.00, 3.333333, 1.75, 2.833333, 6.00, 6.666667, 3.50, 5.00, 10.50, 11.50, 6.00, 6.50,
|
|
13.50, 14.50, 7.50, 4.833333, 10.00, 10.666667, 5.50, 6.833333, 14.00, 14.666667, 7.50, 11.00, 22.50, 23.50, 12.00, 12.50, 25.50, 26.50, 13.50, 8.833333, 18.00, 18.666666, 9.50,
|
|
4.416667, 9.00, 9.333333, 4.75, 7.00, 14.25, 14.75, 7.50, 7.75, 15.75, 16.25, 8.25, 5.416667, 11.00, 11.333333, 5.75, 6.416667, 13.00, 13.333333, 6.75, 10.00, 20.25, 20.75,
|
|
10.50, 10.75, 21.75, 22.25, 11.25, 7.416667, 15.00, 15.333333, 7.75, 14.833333, 30.00, 30.666666, 15.50, 23.00, 46.50, 47.50, 24.00, 24.50, 49.50, 50.50, 25.50, 16.833334,
|
|
34.00, 34.666668, 17.50, 18.833334, 38.00, 38.666668, 19.50, 29.00, 58.50, 59.50, 30.00, 30.50, 61.50, 62.50, 31.50, 20.833334, 42.00, 42.666668, 21.50, 10.416667, 21.00,
|
|
21.333334, 10.75, 16.00, 32.25, 32.75, 16.50, 16.75, 33.75, 34.25, 17.25, 11.416667, 23.00, 23.333334, 11.75, 12.416667, 25.00, 25.333334, 12.75, 19.00, 38.25, 38.75, 19.50,
|
|
19.75, 39.75, 40.25, 20.25, 13.416667, 27.00, 27.333334, 13.75, 26.833334, 54.00, 54.666668, 27.50, 41.00, 82.50, 83.50, 42.00, 42.50, 85.50, 86.50, 43.50, 28.833334, 58.00,
|
|
58.666668, 29.50, 30.833334, 62.00, 62.666668, 31.50, 47.00, 94.50, 95.50, 48.00, 48.50, 97.50, 98.50, 49.50, 32.833332, 66.00, 66.666664, 33.50, 16.416666, 33.00, 33.333332,
|
|
16.75, 25.00, 50.25, 50.75, 25.50, 25.75, 51.75, 52.25, 26.25, 17.416666, 35.00, 35.333332, 17.75, 18.416666, 37.00, 37.333332, 18.75, 28.00, 56.25, 56.75, 28.50, 28.75,
|
|
57.75, 58.25, 29.25, 19.416666, 39.00, 39.333332, 19.75, 38.833332, 78.00, 78.666664, 39.50, 59.00, 118.50, 119.50, 60.00, 60.50, 121.50, 122.50, 61.50, 40.833332, 82.00,
|
|
82.666664, 41.50, 42.833332, 86.00, 86.666664, 43.50, 65.00, 130.50, 131.50, 66.00, 66.50, 133.50, 134.50, 67.50, 44.833332, 90.00, 90.666664, 45.50, 22.416666, 45.00,
|
|
45.333332, 22.75, 34.00, 68.25, 68.75, 34.50, 34.75, 69.75, 70.25, 35.25, 23.416666, 47.00, 47.333332, 23.75, 24.416666, 49.00, 49.333332, 24.75, 37.00, 74.25, 74.75,
|
|
37.50, 37.75, 75.75, 76.25, 38.25, 25.416666, 51.00, 51.333332, 25.75, 50.833332, 102.00, 102.666664, 51.50, 77.00, 154.50, 155.50, 78.00, 78.50, 157.50, 158.50, 79.50,
|
|
52.833332, 106.00, 106.666664, 53.50, 54.833332, 110.00, 110.666664, 55.50, 83.00, 166.50, 167.50, 84.00, 84.50, 169.50, 170.50, 85.50, 56.833332, 114.00, 114.666664,
|
|
57.50, 28.416666, 57.00, 57.333332, 28.75, 43.00, 86.25, 86.75, 43.50, 43.75, 87.75, 88.25, 44.25, 29.416666, 59.00, 59.333332, 29.75, 30.416666, 61.00, 61.333332, 30.75,
|
|
46.00, 92.25, 92.75, 46.50, 46.75, 93.75, 94.25, 47.25, 31.416666, 63.00, 63.333332, 31.75, 62.833332, 126.00, 126.666664, 63.50, 95.00, 190.50, 191.50, 96.00, 96.50,
|
|
193.50, 194.50, 97.50, 64.833336, 130.00, 130.666672, 65.50, 66.833336, 134.00, 134.666672, 67.50, 101.00, 202.50, 203.50, 102.00, 102.50, 205.50, 206.50, 103.50,
|
|
68.833336, 138.00, 138.666672, 69.50, 34.416668, 69.00, 69.333336, 34.75, 52.00, 104.25, 104.75, 52.50, 52.75, 105.75, 106.25, 53.25, 35.416668, 71.00, 71.333336, 35.75});
|
|
input.linspace(1.);
|
|
|
|
nd4j::ops::avgpool3dnew op;
|
|
auto results = op.execute({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool3d_test1) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=2,oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, oD, oH, oW}, {20., 21., 23., 24., 32., 33., 35., 36., 56., 57., 59., 60., 68., 69., 71., 72., 92., 93., 95., 96.,104.,105.,107.,108.,
|
|
128.,129.,131.,132.,140.,141.,143.,144.,164.,165.,167.,168.,176.,177.,179.,180.,200.,201.,203.,204.,212.,213.,215.,216.});
|
|
input.linspace(1.);
|
|
|
|
nd4j::ops::maxpool3dnew op;
|
|
auto results = op.execute({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool3d_test2) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=3,oH=4,oW=3;
|
|
int paddingMode = 1; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, oD, oH, oW, iC}, { 49., 50., 51., 52., 53., 54., 52., 53., 54., 58., 59., 60., 61., 62., 63., 61., 62., 63., 67., 68., 69., 70., 71., 72., 70., 71., 72., 67., 68., 69., 70., 71., 72., 70., 71., 72.,
|
|
85., 86., 87., 88., 89., 90., 88., 89., 90., 94., 95., 96., 97., 98., 99., 97., 98., 99.,103., 104., 105.,106., 107., 108.,106., 107., 108.,103., 104., 105.,106., 107., 108.,106., 107., 108.,
|
|
85., 86., 87., 88., 89., 90., 88., 89., 90., 94., 95., 96., 97., 98., 99., 97., 98., 99.,103., 104., 105.,106., 107., 108.,106., 107., 108.,103., 104., 105.,106., 107., 108.,106., 107., 108.,
|
|
157., 158., 159.,160., 161., 162.,160., 161., 162.,166., 167., 168.,169., 170., 171.,169., 170., 171.,175., 176., 177.,178., 179., 180.,178., 179., 180.,175., 176., 177.,178., 179., 180.,178., 179., 180.,
|
|
193., 194., 195.,196., 197., 198.,196., 197., 198.,202., 203., 204.,205., 206., 207.,205., 206., 207.,211., 212., 213.,214., 215., 216.,214., 215., 216.,211., 212., 213.,214., 215., 216.,214., 215., 216.,
|
|
193., 194., 195.,196., 197., 198.,196., 197., 198.,202., 203., 204.,205., 206., 207.,205., 206., 207.,211., 212., 213.,214., 215., 216.,214., 215., 216.,211., 212., 213.,214., 215., 216.,214., 215., 216.});
|
|
input.linspace(1.);
|
|
|
|
nd4j::ops::maxpool3dnew op;
|
|
auto results = op.execute({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool3d_test3) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=2,oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, oD, oH, oW, iC}, {58., 59., 60., 61., 62., 63., 67., 68., 69., 70., 71., 72., 94., 95., 96., 97., 98., 99.,103., 104., 105.,106., 107., 108.,
|
|
166., 167., 168.,169., 170., 171.,175., 176., 177.,178., 179., 180.,202., 203., 204.,205., 206., 207.,211., 212., 213.,214., 215., 216.});
|
|
input.linspace(1.);
|
|
|
|
nd4j::ops::maxpool3dnew op;
|
|
auto results = op.execute({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool3d_test4) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=1,pH=1,pW=1, dD=1,dH=1,dW=1;
|
|
int oD=4,oH=4,oW=4;
|
|
int paddingMode = 0; // -SAME, 0-VALID
|
|
int dataFormat = 0; // -NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, oD, oH, oW},{ 4., 5., 6., 6., 7., 8., 9., 9., 10., 11., 12., 12., 10., 11., 12., 12., 16., 17., 18., 18., 19., 20., 21., 21., 22., 23., 24., 24., 22., 23., 24., 24., 28., 29., 30., 30., 31., 32., 33., 33., 34., 35., 36., 36., 34., 35., 36., 36.,
|
|
28., 29., 30., 30., 31., 32., 33., 33., 34., 35., 36., 36., 34., 35., 36., 36., 40., 41., 42., 42., 43., 44., 45., 45., 46., 47., 48., 48., 46., 47., 48., 48., 52., 53., 54., 54., 55., 56., 57., 57., 58., 59., 60., 60., 58., 59., 60., 60.,
|
|
64., 65., 66., 66., 67., 68., 69., 69., 70., 71., 72., 72., 70., 71., 72., 72., 64., 65., 66., 66., 67., 68., 69., 69., 70., 71., 72., 72., 70., 71., 72., 72., 76., 77., 78., 78., 79., 80., 81., 81., 82., 83., 84., 84., 82., 83., 84., 84.,
|
|
88., 89., 90., 90., 91., 92., 93., 93., 94., 95., 96., 96., 94., 95., 96., 96.,100., 101., 102., 102.,103., 104., 105., 105.,106., 107., 108., 108.,106., 107., 108., 108.,100., 101., 102., 102.,103., 104., 105., 105.,106., 107., 108., 108.,106., 107., 108., 108.,
|
|
112., 113., 114., 114.,115., 116., 117., 117.,118., 119., 120., 120.,118., 119., 120., 120.,124., 125., 126., 126.,127., 128., 129., 129.,130., 131., 132., 132.,130., 131., 132., 132.,136., 137., 138., 138.,139., 140., 141., 141.,142., 143., 144., 144.,142., 143., 144., 144.,
|
|
136., 137., 138., 138.,139., 140., 141., 141.,142., 143., 144., 144.,142., 143., 144., 144.,148., 149., 150., 150.,151., 152., 153., 153.,154., 155., 156., 156.,154., 155., 156., 156.,160., 161., 162., 162.,163., 164., 165., 165.,166., 167., 168., 168.,166., 167., 168., 168.,
|
|
172., 173., 174., 174.,175., 176., 177., 177.,178., 179., 180., 180.,178., 179., 180., 180.,172., 173., 174., 174.,175., 176., 177., 177.,178., 179., 180., 180.,178., 179., 180., 180.,184., 185., 186., 186.,187., 188., 189., 189.,190., 191., 192., 192.,190., 191., 192., 192.,
|
|
196., 197., 198., 198.,199., 200., 201., 201.,202., 203., 204., 204.,202., 203., 204., 204.,208., 209., 210., 210.,211., 212., 213., 213.,214., 215., 216., 216.,214., 215., 216., 216.,208., 209., 210., 210.,211., 212., 213., 213.,214., 215., 216., 216.,214., 215., 216., 216.});
|
|
input.linspace(1.);
|
|
|
|
nd4j::ops::maxpool3dnew op;
|
|
auto results = op.execute({&input}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool3d_bp_test1) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=2,oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, iC, oD, oH, oW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW}, {0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.666667, 1.333333, 0.666667,0.666667, 1.333333, 0.666667,0.333333, 0.666667, 0.333333,
|
|
0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,
|
|
0.333333, 0.666667, 0.333333,0.666667, 1.333333, 0.666667,0.666667, 1.333333, 0.666667,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,
|
|
0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.666667, 1.333333, 0.666667,0.666667, 1.333333, 0.666667,0.333333, 0.666667, 0.333333,
|
|
0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,
|
|
0.333333, 0.666667, 0.333333,0.666667, 1.333333, 0.666667,0.666667, 1.333333, 0.666667,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,
|
|
0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.666667, 1.333333, 0.666667,0.666667, 1.333333, 0.666667,0.333333, 0.666667, 0.333333,
|
|
0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,
|
|
0.333333, 0.666667, 0.333333,0.666667, 1.333333, 0.666667,0.666667, 1.333333, 0.666667,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667,0.333333, 0.666667, 0.333333,0.333333, 0.666667, 0.333333,0.166667, 0.333333, 0.166667});
|
|
input.linspace(1.);
|
|
gradO = 2.;
|
|
|
|
nd4j::ops::avgpool3dnew_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool3d_bp_test2) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=1,pH=1,pW=1, dD=1,dH=1,dW=1;
|
|
int oD=4,oH=4,oW=4;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, iC, oD, oH, oW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW}, {1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,
|
|
1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,
|
|
1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,
|
|
1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,
|
|
1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,
|
|
1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,
|
|
1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,
|
|
1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,
|
|
1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333,1.333333, 1.333333, 1.333333,2. , 2. , 2. ,2. , 2. , 2. ,1.333333, 1.333333, 1.333333});
|
|
input.linspace(1.);
|
|
gradO = 2.;
|
|
|
|
nd4j::ops::avgpool3dnew_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
// output->printBuffer();
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool3d_bp_test3) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=3,oH=4,oW=3;
|
|
int paddingMode = 1; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, oD, oH, oW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC}, {0.41667, 0.41667, 0.41667,0.83333, 0.83333, 0.83333,1.25, 1.25, 1.25 ,0.58333, 0.58333, 0.58333,1.16667, 1.16667, 1.16667,1.75, 1.75, 1.75 ,0.58333, 0.58333, 0.58333,1.16667, 1.16667, 1.16667,1.75, 1.75, 1.75 ,
|
|
0.41667, 0.41667, 0.41667,0.83333, 0.83333, 0.83333,1.25, 1.25, 1.25 ,0.83333, 0.83333, 0.83333,1.66667, 1.66667, 1.66667,2.5 , 2.5 , 2.5 ,1.16667, 1.16667, 1.16667,2.33333, 2.33333, 2.33333,3.5 , 3.5 , 3.5 ,
|
|
1.16667, 1.16667, 1.16667,2.33333, 2.33333, 2.33333,3.5 , 3.5 , 3.5 ,0.83333, 0.83333, 0.83333,1.66667, 1.66667, 1.66667,2.5 , 2.5 , 2.5 ,1.25 , 1.25 , 1.25 ,2.5 , 2.5 , 2.5 ,3.75, 3.75, 3.75 ,
|
|
1.75 , 1.75 , 1.75 ,3.5 , 3.5 , 3.5 ,5.25, 5.25, 5.25 ,1.75 , 1.75 , 1.75 ,3.5 , 3.5 , 3.5 ,5.25, 5.25, 5.25 ,1.25 , 1.25 , 1.25 ,2.5 , 2.5 , 2.5 ,3.75, 3.75, 3.75 ,
|
|
0.41667, 0.41667, 0.41667,0.83333, 0.83333, 0.83333,1.25, 1.25, 1.25 ,0.58333, 0.58333, 0.58333,1.16667, 1.16667, 1.16667,1.75, 1.75, 1.75 ,0.58333, 0.58333, 0.58333,1.16667, 1.16667, 1.16667,1.75, 1.75, 1.75 ,
|
|
0.41667, 0.41667, 0.41667,0.83333, 0.83333, 0.83333,1.25, 1.25, 1.25 ,0.83333, 0.83333, 0.83333,1.66667, 1.66667, 1.66667,2.5 , 2.5 , 2.5 ,1.16667, 1.16667, 1.16667,2.33333, 2.33333, 2.33333,3.5 , 3.5 , 3.5 ,
|
|
1.16667, 1.16667, 1.16667,2.33333, 2.33333, 2.33333,3.5 , 3.5 , 3.5 ,0.83333, 0.83333, 0.83333,1.66667, 1.66667, 1.66667,2.5 , 2.5 , 2.5 ,1.25 , 1.25 , 1.25 ,2.5 , 2.5 , 2.5 ,3.75, 3.75, 3.75 ,
|
|
1.75 , 1.75 , 1.75 ,3.5 , 3.5 , 3.5 ,5.25, 5.25, 5.25 ,1.75 , 1.75 , 1.75 ,3.5 , 3.5 , 3.5 ,5.25, 5.25, 5.25 ,1.25 , 1.25 , 1.25 ,2.5 , 2.5 , 2.5 ,3.75, 3.75, 3.75 });
|
|
input.linspace(1.);
|
|
gradO = 2.;
|
|
|
|
nd4j::ops::avgpool3dnew_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 0, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool3d_bp_test4) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=3,oH=4,oW=3;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, oD, oH, oW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC}, {0.16667, 0.16667, 0.16667,0.33333, 0.33333, 0.33333,0.5 , 0.5 , 0.5 ,0.33333, 0.33333, 0.33333,0.66667, 0.66667, 0.66667,1. , 1. , 1. ,0.58333, 0.58333, 0.58333,1.16667, 1.16667, 1.16667,1.75, 1.75 , 1.75 ,
|
|
0.91667, 0.91667, 0.91667,1.83333, 1.83333, 1.83333,2.75, 2.75 , 2.75 ,0.33333, 0.33333, 0.33333,0.66667, 0.66667, 0.66667,1. , 1. , 1. ,0.66667, 0.66667, 0.66667,1.33333, 1.33333, 1.33333,2. , 2. , 2. ,
|
|
1.16667, 1.16667, 1.16667,2.33333, 2.33333, 2.33333,3.5 , 3.5 , 3.5 ,1.83333, 1.83333, 1.83333,3.66667, 3.66667, 3.66667,5.5 , 5.5 , 5.5 ,0.5 , 0.5 , 0.5 ,1. , 1. , 1. ,1.5 , 1.5 , 1.5 ,
|
|
1. , 1. , 1. ,2. , 2. , 2. ,3. , 3. , 3. ,1.75 , 1.75 , 1.75 ,3.5 , 3.5 , 3.5 ,5.25, 5.25 , 5.25 ,2.75 , 2.75 , 2.75 ,5.5 , 5.5 , 5.5 ,8.25, 8.25 , 8.25 ,
|
|
0.16667, 0.16667, 0.16667,0.33333, 0.33333, 0.33333,0.5 , 0.5 , 0.5 ,0.33333, 0.33333, 0.33333,0.66667, 0.66667, 0.66667,1. , 1. , 1. ,0.58333, 0.58333, 0.58333,1.16667, 1.16667, 1.16667,1.75, 1.75 , 1.75 ,
|
|
0.91667, 0.91667, 0.91667,1.83333, 1.83333, 1.83333,2.75, 2.75 , 2.75 ,0.33333, 0.33333, 0.33333,0.66667, 0.66667, 0.66667,1. , 1. , 1. ,0.66667, 0.66667, 0.66667,1.33333, 1.33333, 1.33333,2. , 2. , 2. ,
|
|
1.16667, 1.16667, 1.16667,2.33333, 2.33333, 2.33333,3.5 , 3.5 , 3.5 ,1.83333, 1.83333, 1.83333,3.66667, 3.66667, 3.66667,5.5 , 5.5 , 5.5 ,0.5 , 0.5 , 0.5 ,1. , 1. , 1. ,1.5 , 1.5 , 1.5 ,
|
|
1. , 1. , 1. ,2. , 2. , 2. ,3. , 3. , 3. ,1.75 , 1.75 , 1.75 ,3.5 , 3.5 , 3.5 ,5.25, 5.25 , 5.25 ,2.75 , 2.75 , 2.75 ,5.5 , 5.5 , 5.5 ,8.25, 8.25 , 8.25 });
|
|
input.linspace(1.);
|
|
gradO = 2.;
|
|
|
|
nd4j::ops::avgpool3dnew_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 0, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool3d_bp_test1) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=2,oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, iC, oD, oH, oW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW}, {0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0.1, 0.2,0. , 0.3, 0.4,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0.5, 0.6,0. , 0.7, 0.8,
|
|
0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0.9, 1. ,0. , 1.1, 1.2,0. , 0. , 0. ,0. , 0. , 0. ,0. , 1.3, 1.4,0. , 1.5, 1.6,
|
|
0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 1.7, 1.8,0. , 1.9, 2. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 2.1, 2.2,0. , 2.3, 2.4,
|
|
0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 2.5, 2.6,0. , 2.7, 2.8,0. , 0. , 0. ,0. , 0. , 0. ,0. , 2.9, 3. ,0. , 3.1, 3.2,
|
|
0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 3.3, 3.4,0. , 3.5, 3.6,0. , 0. , 0. ,0. , 0. , 0. ,0. , 3.7, 3.8,0. , 3.9, 4. ,
|
|
0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 4.1, 4.2,0. , 4.3, 4.4,0. , 0. , 0. ,0. , 0. , 0. ,0. , 4.5, 4.6,0. , 4.7, 4.8});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::maxpool3dnew_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool3d_bp_test2) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=1,pH=1,pW=1, dD=1,dH=1,dW=1;
|
|
int oD=4,oH=4,oW=4;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, iC, oD, oH, oW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, iD, iH, iW}, {0.000e+00, 0.000e+00, 0.000e+00,1.000e-01, 2.000e-01, 7.000e-01,5.000e-01, 6.000e-01, 1.500e+00,2.200e+00, 2.400e+00, 5.400e+00,0.000e+00, 0.000e+00, 0.000e+00,1.700e+00, 1.800e+00, 3.900e+00,2.100e+00, 2.200e+00, 4.700e+00,5.400e+00, 5.600e+00, 1.180e+01,
|
|
0.000e+00, 0.000e+00, 0.000e+00,8.200e+00, 8.400e+00, 1.740e+01,9.000e+00, 9.200e+00, 1.900e+01,2.040e+01, 2.080e+01, 4.280e+01,0.000e+00, 0.000e+00, 0.000e+00,6.500e+00, 6.600e+00, 1.350e+01,6.900e+00, 7.000e+00, 1.430e+01,1.500e+01, 1.520e+01, 3.100e+01,
|
|
0.000e+00, 0.000e+00, 0.000e+00,8.100e+00, 8.200e+00, 1.670e+01,8.500e+00, 8.600e+00, 1.750e+01,1.820e+01, 1.840e+01, 3.740e+01,0.000e+00, 0.000e+00, 0.000e+00,2.100e+01, 2.120e+01, 4.300e+01,2.180e+01, 2.200e+01, 4.460e+01,4.600e+01, 4.640e+01, 9.400e+01,
|
|
0.000e+00, 0.000e+00, 0.000e+00,1.290e+01, 1.300e+01, 2.630e+01,1.330e+01, 1.340e+01, 2.710e+01,2.780e+01, 2.800e+01, 5.660e+01,0.000e+00, 0.000e+00, 0.000e+00,1.450e+01, 1.460e+01, 2.950e+01,1.490e+01, 1.500e+01, 3.030e+01,3.100e+01, 3.120e+01, 6.300e+01,
|
|
0.000e+00, 0.000e+00, 0.000e+00,3.380e+01, 3.400e+01, 6.860e+01,3.460e+01, 3.480e+01, 7.020e+01,7.160e+01, 7.200e+01, 1.452e+02,0.000e+00, 0.000e+00, 0.000e+00,1.930e+01, 1.940e+01, 3.910e+01,1.970e+01, 1.980e+01, 3.990e+01,4.060e+01, 4.080e+01, 8.220e+01,
|
|
0.000e+00, 0.000e+00, 0.000e+00,2.090e+01, 2.100e+01, 4.230e+01,2.130e+01, 2.140e+01, 4.310e+01,4.380e+01, 4.400e+01, 8.860e+01,0.000e+00, 0.000e+00, 0.000e+00,4.660e+01, 4.680e+01, 9.420e+01,4.740e+01, 4.760e+01, 9.580e+01,9.720e+01, 9.760e+01, 1.964e+02,
|
|
0.000e+00, 0.000e+00, 0.000e+00,2.570e+01, 2.580e+01, 5.190e+01,2.610e+01, 2.620e+01, 5.270e+01,5.340e+01, 5.360e+01, 1.078e+02,0.000e+00, 0.000e+00, 0.000e+00,2.730e+01, 2.740e+01, 5.510e+01,2.770e+01, 2.780e+01, 5.590e+01,5.660e+01, 5.680e+01, 1.142e+02,
|
|
0.000e+00, 0.000e+00, 0.000e+00,5.940e+01, 5.960e+01, 1.198e+02,6.020e+01, 6.040e+01, 1.214e+02,1.228e+02, 1.232e+02, 2.476e+02,0.000e+00, 0.000e+00, 0.000e+00,3.210e+01, 3.220e+01, 6.470e+01,3.250e+01, 3.260e+01, 6.550e+01,6.620e+01, 6.640e+01, 1.334e+02,
|
|
0.000e+00, 0.000e+00, 0.000e+00,3.370e+01, 3.380e+01, 6.790e+01,3.410e+01, 3.420e+01, 6.870e+01,6.940e+01, 6.960e+01, 1.398e+02,0.000e+00, 0.000e+00, 0.000e+00,7.220e+01, 7.240e+01, 1.454e+02,7.300e+01, 7.320e+01, 1.470e+02,1.484e+02, 1.488e+02, 2.988e+02});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::maxpool3dnew_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool3d_bp_test3) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=3,oH=4,oW=3;
|
|
int paddingMode = 1; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, oD, oH, oW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC}, { 0., 0., 0., 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
|
|
0., 0., 0., 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.1, 0.2 , 0.3, 1.1, 1.3 , 1.5,
|
|
0., 0., 0., 1. , 1.1, 1.2, 2.9, 3.1 , 3.3, 0. , 0. , 0. , 4.7, 4.9 , 5.1, 11.2, 11.6 , 12. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
|
|
0., 0., 0., 11. , 11.2, 11.4, 23.8, 24.2 , 24.6, 0. , 0. , 0. , 12.8, 13. , 13.2, 27.4, 27.8 , 28.2, 0. , 0. , 0. , 31. , 31.4 , 31.8, 65.6, 66.39999, 67.2,
|
|
0., 0., 0., 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
|
|
0., 0., 0., 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 10.9, 11. , 11.1, 22.7, 22.9 , 23.1,
|
|
0., 0., 0., 11.8, 11.9, 12. , 24.5, 24.7 , 24.9, 0. , 0. , 0. , 26.3, 26.5 , 26.7, 54.4, 54.8 , 55.2, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
|
|
0., 0., 0., 32.6, 32.8, 33. , 67. , 67.4 , 67.8, 0. , 0. , 0. , 34.4, 34.6 , 34.8, 70.6, 71. , 71.4, 0. , 0. , 0. , 74.2, 74.6 , 75. ,152. , 152.8 ,153.6});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::maxpool3dnew_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool3d_bp_test4) {
|
|
|
|
int bS=2, iD=3,iH=4,iW=3, iC=3, kD=2,kH=3,kW=2, sD=1,sH=1,sW=1, pD=0,pH=0,pW=0, dD=1,dH=1,dW=1;
|
|
int oD=3,oH=4,oW=3;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, oD, oH, oW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iD, iH, iW, iC}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0.2, 0.3, 1.1, 1.3, 1.5, 0, 0, 0, 5.7, 6, 6.3,
|
|
14.1, 14.7, 15.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 11.2, 11.4, 23.8, 24.2,
|
|
24.6, 0, 0, 0, 43.8, 44.4, 45, 93, 94.2, 95.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
10.9, 11, 11.1, 22.7, 22.9, 23.1, 0, 0, 0, 38.1, 38.4, 38.7, 78.9, 79.5, 80.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32.6, 32.8, 33, 67, 67.4, 67.8, 0, 0, 0, 108.6, 109.2, 109.8, 222.6, 223.8, 225,});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::maxpool3dnew_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kD,kH,kW, sD,sH,sW, pD,pH,pW, dD,dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, maxpool2d_bp_1) {
|
|
|
|
auto input = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
|
|
auto epsilon = NDArrayFactory::create_<float>('c', {bS,iD,oH,oW});
|
|
auto exp = NDArrayFactory::create<float>('c', {bS,iD,iH,iW});
|
|
|
|
auto variableSpace = new VariableSpace();
|
|
variableSpace->putVariable(-1, input);
|
|
variableSpace->putVariable(-2, epsilon);
|
|
// variableSpace->putVariable(1, &z);
|
|
|
|
auto block = new Context(1, variableSpace, false);
|
|
block->fillInputs({-1});
|
|
block->fillInputs({-2});
|
|
std::vector<int>* argI = block->getIArguments();
|
|
*argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 0, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
|
|
|
|
nd4j::ops::maxpool2d_bp bp;
|
|
Nd4jStatus status = bp.execute(block);
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
|
|
ASSERT_TRUE(exp.isSameShape(result));
|
|
|
|
delete variableSpace;
|
|
delete block;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, maxpool2d_bp_2) {
|
|
|
|
int bS=2, iD=1, iH=4,iW=4, oD=3, kH=2,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH = (iH - kH - (kH-1)*(dH-1) + 2*pH)/sH + 1;
|
|
int oW = (iW - kW - (kW-1)*(dW-1) + 2*pW)/sW + 1;
|
|
|
|
// TypeParam epsilonBuff[] = {6., 7., 8., 10., 11., 12., 14., 15., 16., 22., 23., 24., 26., 27., 28., 30., 31., 32.};
|
|
// TypeParam expectedBuff[] = {0., 0., 0., 0.,0., 6., 7., 8.,0.,10.,11.,12.,0.,14.,15.,16.,0., 0., 0., 0.,0.,22.,23.,24.,0.,26.,27.,28.,0.,30.,31.,32.};
|
|
|
|
NDArray input('c', {bS,iD,iH,iW});
|
|
NDArray epsilon('c', {bS,iD,oH,oW}, {6., 7., 8., 10., 11., 12., 14., 15., 16., 22., 23., 24., 26., 27., 28., 30., 31., 32.});
|
|
NDArray expected('c', {bS,iD,iH,iW}, {0., 0., 0., 0.,0., 6., 7., 8.,0.,10.,11.,12.,0.,14.,15.,16.,0., 0., 0., 0.,0.,22.,23.,24.,0.,26.,27.,28.,0.,30.,31.,32.});
|
|
|
|
|
|
input.linspace(1.);
|
|
|
|
std::initializer_list<Nd4jLong> argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 0, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode;
|
|
|
|
nd4j::ops::maxpool2d_bp op;
|
|
auto results = op.execute({&input, &epsilon}, {}, argI);
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool2d_bp_3) {
|
|
|
|
int bS=2, iH=4,iW=3, iC=3, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, iC, oH, oW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW}, {0. , 0. , 0. ,0. , 0. , 0. ,0. , 0.1, 0.2,0. , 0.3, 0.4,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0.5, 0.6,0. , 0.7, 0.8,
|
|
0. , 0. , 0. ,0. , 0. , 0. ,0. , 0.9, 1. ,0. , 1.1, 1.2,0. , 0. , 0. ,0. , 0. , 0. ,0. , 1.3, 1.4,0. , 1.5, 1.6,
|
|
0. , 0. , 0. ,0. , 0. , 0. ,0. , 1.7, 1.8,0. , 1.9, 2. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 2.1, 2.2,0. , 2.3, 2.4});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::maxpool2d_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool2d_bp_4) {
|
|
|
|
int bS=2, iH=4,iW=3, iC=3, kH=3,kW=2, sH=1,sW=1, pH=1,pW=1, dH=1,dW=1;
|
|
int oH=4,oW=4;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, iC, oH, oW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW}, {0. , 0. , 0. , 0.1, 0.2, 0.7, 0.5, 0.6, 1.5, 2.2, 2.4, 5.4, 0. , 0. , 0. , 1.7, 1.8, 3.9, 2.1, 2.2, 4.7, 5.4, 5.6, 11.8,
|
|
0. , 0. , 0. , 3.3, 3.4, 7.1, 3.7, 3.8, 7.9, 8.6, 8.8, 18.2, 0. , 0. , 0. , 4.9, 5. , 10.3, 5.3, 5.4, 11.1,11.8, 12. , 24.6,
|
|
0. , 0. , 0. , 6.5, 6.6, 13.5, 6.9, 7. , 14.3,15. , 15.2, 31. , 0. , 0. , 0. , 8.1, 8.2, 16.7, 8.5, 8.6, 17.5,18.2, 18.4, 37.4});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::maxpool2d_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool2d_bp_5) {
|
|
|
|
int bS=2, iH=4,iW=3, iC=3, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=4,oW=3;
|
|
int paddingMode = 1; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, oH, oW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC}, {0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.1, 0.2, 0.3, 1.1, 1.3, 1.5, 0. , 0. , 0. , 1. , 1.1, 1.2, 2.9, 3.1, 3.3,
|
|
0. , 0. , 0. , 4.7, 4.9, 5.1,11.2,11.6,12. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 3.7, 3.8, 3.9, 8.3, 8.5, 8.7,
|
|
0. , 0. , 0. , 4.6, 4.7, 4.8,10.1,10.3,10.5, 0. , 0. , 0. ,11.9,12.1,12.3,25.6,26. ,26.4});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::maxpool2d_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, maxpool2d_bp_6) {
|
|
|
|
int bS=2, iH=4,iW=3, iC=3, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, oH, oW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC}, {0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0.1, 0.2, 0.3,0.4, 0.5, 0.6,
|
|
0. , 0. , 0. ,0.7, 0.8, 0.9,1. , 1.1, 1.2,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,0. , 0. , 0. ,
|
|
0. , 0. , 0. ,1.3, 1.4, 1.5,1.6, 1.7, 1.8,0. , 0. , 0. ,1.9, 2. , 2.1,2.2, 2.3, 2.4});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::maxpool2d_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, maxpool2d_bp_7) {
|
|
|
|
int bS=2, iH=56,iW=56, iC=3, kH=2,kW=2, sH=2,sW=2, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=28,oW=28;
|
|
int paddingMode = 1; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<float16>('c', {bS, iC, iH, iW});
|
|
auto gradO = NDArrayFactory::create<float16>('c', {bS, iC, oH, oW});
|
|
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::maxpool2d_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat});
|
|
// auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
// ASSERT_TRUE(expected.isSameShape(output));
|
|
// ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, avgpool2d_bp_1) {
|
|
|
|
auto input = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
|
|
auto epsilon = NDArrayFactory::create_<float>('c', {bS,iD,oH,oW});
|
|
auto exp = NDArrayFactory::create<float>('c', {bS,iD,iH,iW});
|
|
|
|
auto variableSpace = new VariableSpace();
|
|
variableSpace->putVariable(-1, input);
|
|
variableSpace->putVariable(-2, epsilon);
|
|
// variableSpace->putVariable(1, &z);
|
|
|
|
auto block = new Context(1, variableSpace, false);
|
|
block->fillInputs({-1});
|
|
block->fillInputs({-2});
|
|
std::vector<int>* argI = block->getIArguments();
|
|
*argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 1, 0}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode, 9 - extraParam0 (unnecessary for avg mode), 10 - data format
|
|
|
|
nd4j::ops::avgpool2d_bp bp;
|
|
Nd4jStatus status = bp.execute(block);
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
|
|
ASSERT_TRUE(exp.isSameShape(result));
|
|
|
|
delete variableSpace;
|
|
delete block;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool2d_bp_2) {
|
|
|
|
int bS=2, iD=1, iH=4,iW=4, oD=3, kH=2,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH = (iH - kH - (kH-1)*(dH-1) + 2*pH)/sH + 1;
|
|
int oW = (iW - kW - (kW-1)*(dW-1) + 2*pW)/sW + 1;
|
|
|
|
// TypeParam epsilonBuff[] = {3.5 , 4.5 , 5.5, 7.5 , 8.5 , 9.5, 11.5, 12.5, 13.5, 19.5, 20.5, 21.5, 23.5, 24.5, 25.5, 27.5, 28.5, 29.5};
|
|
// TypeParam expectedBuff[] = {0.875, 2., 2.5,1.375, 2.75 , 6., 7., 3.75, 4.75 ,10., 11., 5.75, 2.875, 6., 6.5, 3.375, 4.875, 10.,10.5, 5.375, 10.75, 22.,23., 11.75, 12.75, 26.,27., 13.75, 6.875, 14.,14.5, 7.375};
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS,iD,iH,iW});
|
|
auto epsilon = NDArrayFactory::create<TypeParam>('c', {bS,iD,oH,oW}, {3.5 , 4.5 , 5.5, 7.5 , 8.5 , 9.5, 11.5, 12.5, 13.5, 19.5, 20.5, 21.5, 23.5, 24.5, 25.5, 27.5, 28.5, 29.5});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS,iD,iH,iW}, {0.875, 2., 2.5,1.375, 2.75 , 6., 7., 3.75, 4.75 ,10., 11., 5.75, 2.875, 6., 6.5, 3.375, 4.875, 10.,10.5, 5.375, 10.75, 22.,23., 11.75, 12.75, 26.,27., 13.75, 6.875, 14.,14.5, 7.375});
|
|
|
|
input.linspace(1.);
|
|
|
|
std::initializer_list<Nd4jLong> argI = {kH,kW, sH,sW, pH,pW, dW,dH, 1, 1, 0};
|
|
|
|
nd4j::ops::avgpool2d_bp op;
|
|
auto results = op.execute({&input, &epsilon}, {}, argI);
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool2d_bp_3) {
|
|
|
|
int bS=2, iH=4,iW=3, iC=3, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, iC, oH, oW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW}, {0.016667,0.05 ,0.033333,0.066667,0.166667,0.1 ,0.066667,0.166667,0.1 ,0.05 ,0.116667,0.066667,
|
|
0.083333,0.183333,0.1 ,0.2 ,0.433333,0.233333,0.2 ,0.433333,0.233333,0.116667,0.25 ,0.133333,
|
|
0.15 ,0.316667,0.166667,0.333333,0.7 ,0.366667,0.333333,0.7 ,0.366667,0.183333,0.383333,0.2 ,
|
|
0.216667,0.45 ,0.233333,0.466667,0.966667,0.5 ,0.466667,0.966667,0.5 ,0.25 ,0.516667,0.266667,
|
|
0.283333,0.583333,0.3 ,0.6 ,1.233333,0.633333,0.6 ,1.233333,0.633333,0.316667,0.65 ,0.333333,
|
|
0.35 ,0.716667,0.366667,0.733333,1.5 ,0.766667,0.733333,1.5 ,0.766667,0.383333,0.783333,0.4 });
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::avgpool2d_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool2d_bp_4) {
|
|
|
|
int bS=2, iH=4,iW=3, iC=3, kH=3,kW=2, sH=1,sW=1, pH=1,pW=1, dH=1,dW=1;
|
|
int oH=4,oW=4;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 0; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, iC, oH, oW});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW}, {0.233333,0.3 ,0.366667,0.55 ,0.65 ,0.75 ,0.95 ,1.05 ,1.15 ,0.766667,0.833333,0.9 ,
|
|
1.3 ,1.366667,1.433333,2.15 ,2.25 ,2.35 ,2.55 ,2.65 ,2.75 ,1.833333,1.9 ,1.966667,
|
|
2.366667,2.433333,2.5 ,3.75 ,3.85 ,3.95 ,4.15 ,4.25 ,4.35 ,2.9 ,2.966667,3.033333,
|
|
3.433333,3.5 ,3.566667,5.35 ,5.45 ,5.55 ,5.75 ,5.85 ,5.95 ,3.966667,4.033333,4.1 ,
|
|
4.5 ,4.566667,4.633333,6.95 ,7.05 ,7.15 ,7.35 ,7.45 ,7.55 ,5.033333,5.1 ,5.166667,
|
|
5.566667,5.633333,5.7 ,8.549999,8.65 ,8.75 ,8.95 ,9.05 ,9.150001,6.1 ,6.166667,6.233334});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::avgpool2d_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool2d_bp_5) {
|
|
|
|
int bS=2, iH=4,iW=3, iC=3, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=4,oW=3;
|
|
int paddingMode = 1; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, oH, oW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC}, {0.19167, 0.23333, 0.275, 0.50833, 0.59167, 0.675, 1.2 , 1.325, 1.45 ,0.50833,0.56667, 0.625, 1.19167,1.30833, 1.425, 2.4 ,2.575, 2.75 ,
|
|
1.18333, 1.24167, 1.3 , 2.54167, 2.65833, 2.775, 4.425, 4.6 , 4.775,1.01667,1.05833, 1.1 , 2.15833,2.24167, 2.325, 3.675,3.8 , 3.925,
|
|
1.69167, 1.73333, 1.775, 3.50833, 3.59167, 3.675, 5.7 , 5.825, 5.95 ,2.60833,2.66667, 2.725, 5.39167,5.50833, 5.625, 8.7 ,8.875, 9.05 ,
|
|
3.28333, 3.34167, 3.4 , 6.74167, 6.85833, 6.975,10.725,10.9 ,11.075,2.51667,2.55833, 2.6 , 5.15833,5.24167, 5.325, 8.175,8.3 , 8.425});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::avgpool2d_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 0, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, avgpool2d_bp_6) {
|
|
|
|
int bS=2, iH=4,iW=3, iC=3, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=2,oW=2;
|
|
int paddingMode = 0; // 1-SAME, 0-VALID
|
|
int dataFormat = 1; // 1-NDHWC, 0-NCDHW
|
|
|
|
auto input = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC});
|
|
auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, oH, oW, iC});
|
|
auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iH, iW, iC}, {0.01667,0.03333,0.05,0.08333,0.11667,0.15,0.06667,0.08333,0.1,0.13333,0.16667,0.2 ,0.36667,0.43333,0.5 ,0.23333,0.26667,0.3,
|
|
0.13333,0.16667,0.2 ,0.36667,0.43333,0.5 ,0.23333,0.26667,0.3,0.11667,0.13333,0.15,0.28333,0.31667,0.35,0.16667,0.18333,0.2,
|
|
0.21667,0.23333,0.25,0.48333,0.51667,0.55,0.26667,0.28333,0.3,0.53333,0.56667,0.6 ,1.16667,1.23333,1.3 ,0.63333,0.66667,0.7,
|
|
0.53333,0.56667,0.6 ,1.16667,1.23333,1.3 ,0.63333,0.66667,0.7,0.31667,0.33333,0.35,0.68333,0.71667,0.75,0.36667,0.38333,0.4});
|
|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
|
|
|
|
nd4j::ops::avgpool2d_bp op;
|
|
auto results = op.execute({&input, &gradO}, {}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, 1, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TEST_F(ConvolutionTests2, pnormpool2d_bp_1) {
|
|
|
|
auto input = NDArrayFactory::create_<float>('c', {bS,iD,iH,iW});
|
|
auto epsilon = NDArrayFactory::create_<float>('c', {bS,iD,oH,oW});
|
|
auto exp = NDArrayFactory::create<float>('c', {bS,iD,iH,iW});
|
|
|
|
auto variableSpace = new VariableSpace();
|
|
variableSpace->putVariable(-1, input);
|
|
variableSpace->putVariable(-2, epsilon);
|
|
// variableSpace->putVariable(1, &z);
|
|
|
|
auto block = new Context(1, variableSpace, false);
|
|
block->fillInputs({-1});
|
|
block->fillInputs({-2});
|
|
auto argI = block->getIArguments();
|
|
*argI = {kH,kW, sH,sW, pH,pW, dW,dH, 0, 3}; // 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 - same mode; 9 - divisor
|
|
std::vector<double>* argT = block->getTArguments();
|
|
*argT = {0.000001};
|
|
|
|
nd4j::ops::pnormpool2d_bp bp;
|
|
Nd4jStatus status = bp.execute(block);
|
|
ASSERT_EQ(ND4J_STATUS_OK, status);
|
|
|
|
auto result = variableSpace->getVariable(block->getNodeId())->getNDArray();
|
|
ASSERT_TRUE(exp.isSameShape(result));
|
|
|
|
delete variableSpace;
|
|
delete block;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
TYPED_TEST(TypedConvolutionTests2, pnormpool2d_bp_2) {
|
|
|
|
int bS=2, iH=4,iW=3, iC=3, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
|
|
int oH=2,oW=2;
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int pnorm = 3;
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double eps = 0.;
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int paddingMode = 0; // 1-SAME, 0-VALID
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int dataFormat = 0; // 1-NDHWC, 0-NCDHW
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auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW});
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auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, iC, oH, oW});
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auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW}, {9.661570e-04,9.671602e-03,1.306569e-02,3.679184e-02,1.297220e-01,1.040181e-01,1.126750e-01,3.320884e-01,2.340406e-01,1.333333e-01,3.352886e-01,2.070211e-01,
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8.991618e-02,2.160601e-01,1.283173e-01,2.744226e-01,6.364498e-01,3.662123e-01,3.869788e-01,8.808994e-01,4.984556e-01,2.613189e-01,5.818475e-01,3.225517e-01,
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2.065654e-01,4.553546e-01,2.501175e-01,5.190718e-01,1.131343e+00,6.148388e-01,6.362602e-01,1.377521e+00,7.439550e-01,3.833026e-01,8.227519e-01,4.407146e-01,
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3.261206e-01,6.969233e-01,3.717564e-01,7.627507e-01,1.620991e+00,8.600952e-01,8.814538e-01,1.866888e+00,9.873542e-01,5.046682e-01,1.064004e+00,5.602558e-01,
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4.464697e-01,9.389536e-01,4.932274e-01,1.005908e+00,2.108550e+00,1.104095e+00,1.125322e+00,2.354009e+00,1.230180e+00,6.258913e-01,1.305581e+00,6.804127e-01,
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5.671396e-01,1.181128e+00,6.145977e-01,1.248783e+00,2.595083e+00,1.347494e+00,1.368600e+00,2.840157e+00,1.472778e+00,7.470673e-01,1.547362e+00,8.008900e-01});
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input.linspace(1.);
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gradO.linspace(0.1, 0.1);
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nd4j::ops::pnormpool2d_bp op;
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auto results = op.execute({&input, &gradO}, {eps}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, pnorm, dataFormat});
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auto output = results->at(0);
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ASSERT_EQ(Status::OK(), results->status());
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ASSERT_TRUE(expected.isSameShape(output));
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ASSERT_TRUE(expected.equalsTo(output));
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delete results;
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}
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//////////////////////////////////////////////////////////////////////
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TYPED_TEST(TypedConvolutionTests2, pnormpool2d_bp_3) {
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int bS=2, iH=4,iW=3, iC=3, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
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int oH=2,oW=2;
|
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int pnorm = 2;
|
|
double eps = 0.;
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|
|
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int paddingMode = 0; // 1-SAME, 0-VALID
|
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int dataFormat = 0; // 1-NDHWC, 0-NCDHW
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|
|
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auto input = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW});
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auto gradO = NDArrayFactory::create<TypeParam>('c', {bS, iC, oH, oW});
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auto expected = NDArrayFactory::create<TypeParam>('c', {bS, iC, iH, iW}, {0.007931,0.042891,0.040544,0.09369 ,0.276841,0.191675,0.163957,0.442946,0.287512,0.154919,0.373153,0.221172,
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|
0.15901 ,0.365232,0.207846,0.428282,0.959455,0.534076,0.508585,1.128771,0.623089,0.319794,0.698063,0.379547,
|
|
0.321068,0.692438,0.372316,0.757521,1.620323,0.864566,0.838684,1.787943,0.951023,0.483194,1.023434,0.541058,
|
|
0.483937,1.019414,0.536145,1.085348,2.276996,1.192917,1.166749,2.443606,1.278126,0.646499,1.349361,0.703463,
|
|
0.647021,1.346249,0.699745,1.412654,2.932174,1.520512,1.494153,3.098146,1.604985,0.809791,1.675544,0.866229,
|
|
0.810192,1.673009,0.863237,1.739711,3.58665 ,1.847753,1.82126 ,3.752188,1.931741,0.973081,2.001861,1.029173});
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|
input.linspace(1.);
|
|
gradO.linspace(0.1, 0.1);
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|
|
|
nd4j::ops::pnormpool2d_bp op;
|
|
auto results = op.execute({&input, &gradO}, {eps}, {kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, pnorm, dataFormat});
|
|
auto output = results->at(0);
|
|
|
|
ASSERT_EQ(Status::OK(), results->status());
|
|
ASSERT_TRUE(expected.isSameShape(output));
|
|
ASSERT_TRUE(expected.equalsTo(output));
|
|
|
|
delete results;
|
|
}
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#endif //LIBND4J_CONVOLUTIONTESTS2_H
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