444 lines
20 KiB
C++
444 lines
20 KiB
C++
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/*******************************************************************************
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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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// Created by raver119 on 08.10.2017.
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_fullconv3d)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/convolutions.h>
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namespace nd4j {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(fullconv3d, 5, 1, false, 0, 13) {
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// auto input = INPUT_VARIABLE(0);
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// auto weights = INPUT_VARIABLE(1);
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// auto bias = INPUT_VARIABLE(2);
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// auto columns = INPUT_VARIABLE(3);
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// auto ones = INPUT_VARIABLE(4);
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// REQUIRE_TRUE(weights->rankOf() == 5, 0, "Weights should be 5D, got %i instead", weights->rankOf());
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// REQUIRE_TRUE(input->rankOf() == 5, 0, "Input should be 5D, got %i instead", input->rankOf());
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// // strides
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// int dT = INT_ARG(0);
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// int dW = INT_ARG(1);
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// int dH = INT_ARG(2);
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// // padding
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// int pT = INT_ARG(3);
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// int pW = INT_ARG(4);
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// int pH = INT_ARG(5);
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// // dilation
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// int dilationT = INT_ARG(6);
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// int dilationW = INT_ARG(7);
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// int dilationH = INT_ARG(8);
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// // output padding
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// int aT = INT_ARG(9);
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// int aW = INT_ARG(10);
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// int aH = INT_ARG(11);
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// // bias
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// bool biasUsed = INT_ARG(12) != 0;
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// REQUIRE_TRUE(dT > 0 && dW > 0 && dH > 0, 11,
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// "stride should be greater than zero, but got dT: %d dH: %d dW: %d", dT, dH, dW);
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// REQUIRE_TRUE(dilationT > 0 && dilationW > 0 && dilationH > 0, 15,
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// "dilation should be greater than zero, but got dilationT: %d, dilationH: %d, dilationW: %d",
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// dilationT, dilationH, dilationW);
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// REQUIRE_TRUE((aT < dT || aT < dilationT)
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// && (aW < dW || aW < dilationW)
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// && (aH < dH || aH < dilationH), 15,
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// "output padding must be smaller than either stride or dilation,"
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// " but got aT: %d aH: %d aW: %d dT: %d dH: %d dW: %d "
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// "dilationT: %d dilationH: %d dilationW: %d",
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// aT, aH, aW, dT, dH, dW, dilationT, dilationH, dilationW);
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// auto output = this->getZ(block);
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// const int nInputPlane = weights->shapeOf()[0];
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// const int nOutputPlane = weights->shapeOf()[1];
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// const int kT = weights->shapeOf()[2];
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// const int kH = weights->shapeOf()[3];
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// const int kW = weights->shapeOf()[4];
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// const Nd4jLong inputWidth = input->shapeOf()[4];
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// const Nd4jLong inputHeight = input->shapeOf()[3];
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// const Nd4jLong inputDepth = input->shapeOf()[2];
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// const Nd4jLong outputDepth = (inputDepth - 1) * dT - 2*pT + (dilationT * (kT - 1) + 1) + aT;
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// const Nd4jLong outputHeight = (inputHeight - 1) * dH - 2*pH + (dilationH * (kH - 1) + 1) + aH;
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// const Nd4jLong outputWidth = (inputWidth - 1) * dW - 2*pW + (dilationW * (kW - 1) + 1) + aW;
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// const Nd4jLong batchSize = input->shapeOf()[0];
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// REQUIRE_TRUE(output->isSameShape({ (int) batchSize, (int)nOutputPlane, (int)outputDepth, (int)outputHeight, (int)outputWidth}), 0, "Output should have shape of [%i, %i, %i, %i, %i], but got [%i, %i, %i, %i, %i] instead", (int) batchSize, (int)nOutputPlane, (int)outputDepth, (int)outputHeight, (int)outputWidth, output->sizeAt(0), output->sizeAt(1), output->sizeAt(2), output->sizeAt(3), output->sizeAt(4));
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// std::unique_ptr<ResultSet> inputs(input->allExamples());
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// std::unique_ptr<ResultSet> outputs(output->allExamples());
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// for (int e = 0; e < batchSize; e++) {
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// auto tadIn = inputs->at(e);
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// auto tadOut = outputs->at(e);
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// const int m = weights->shapeOf()[1] * weights->shapeOf()[2] * weights->shapeOf()[3] * weights->shapeOf()[4];
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// const int n = columns->shapeOf()[1];
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// const int k = weights->shapeOf()[0];
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// // FIXME: mmul helper should be used here
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// /*
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// nd4j::blas::GEMM<T>::op('c', 'n', 't', m, n, k,
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// 1.0,
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// tadIn->getBuffer(), n,
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// weights->getBuffer(), m,
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// 0.0,
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// columns->getBuffer(), n);
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// */
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// // ConvolutionUtils<T>::_col2vol(columns->getBuffer(),
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// // nOutputPlane, outputDepth, outputHeight, outputWidth,
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// // inputDepth, inputHeight, inputWidth,
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// // kT, kH, kW,
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// // pT, pH, pW,
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// // dT, dH, dW,
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// // dilationT, dilationH, dilationW,
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// // tadOut->getBuffer());
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// ConvolutionUtils::col2vol(*columns, *tadOut, dT, dH, dW, pT, pH, pW, dilationT, dilationH, dilationW);
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// const int m_ = nOutputPlane;
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// const int n_ = outputDepth * outputHeight * outputWidth;
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// const int k_ = 1;
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// if (biasUsed) {
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// // FIXME: mmul helper should be used here
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// /*
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// nd4j::blas::GEMM<T>::op('c', 't', 'n', n_, m_, k_,
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// 1.0,
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// ones->getBuffer(), k_,
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// bias->getBuffer(), k_,
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// 1.0,
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// tadOut->getBuffer(), n_);
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// */
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// }
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// }
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// STORE_RESULT(*output);
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return Status::OK();
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}
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DECLARE_TYPES(fullconv3d) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(fullconv3d) {
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// auto input = inputShape->at(0);
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// auto weights = inputShape->at(1);
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// // strides
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// int dT = INT_ARG(0);
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// int dW = INT_ARG(1);
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// int dH = INT_ARG(2);
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// // padding
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// int pT = INT_ARG(3);
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// int pW = INT_ARG(4);
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// int pH = INT_ARG(5);
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// // dilation
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// int dilationT = INT_ARG(6);
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// int dilationW = INT_ARG(7);
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// int dilationH = INT_ARG(8);
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// // output padding
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// int aT = INT_ARG(9);
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// int aW = INT_ARG(10);
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// int aH = INT_ARG(11);
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// // bias
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// bool biasUsed = INT_ARG(12) != 0;
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// Nd4jLong *shapeOf;
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// Nd4jLong *newShape;
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// ALLOCATE(shapeOf, block.getWorkspace(), 5, Nd4jLong);
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// ALLOCATE(newShape, block.getWorkspace(), shape::shapeInfoLength(5), Nd4jLong);
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// const int nInputPlane = weights[1];
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// const int nOutputPlane = weights[2];
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// const int kT = weights[3];
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// const int kH = weights[4];
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// const int kW = weights[5];
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// const int batchSize = input[1];
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// const Nd4jLong inputWidth = input[5];
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// const Nd4jLong inputHeight = input[4];
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// const Nd4jLong inputDepth = input[3];
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// const Nd4jLong outputDepth = (inputDepth - 1) * dT - 2*pT + (dilationT * (kT - 1) + 1) + aT;
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// const Nd4jLong outputHeight = (inputHeight - 1) * dH - 2*pH + (dilationH * (kH - 1) + 1) + aH;
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// const Nd4jLong outputWidth = (inputWidth - 1) * dW - 2*pW + (dilationW * (kW - 1) + 1) + aW;
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// nd4j::ArrayUtils::toLongPtr({(Nd4jLong) batchSize, (Nd4jLong)nOutputPlane, (Nd4jLong)outputDepth, (Nd4jLong)outputHeight, (Nd4jLong)outputWidth}, shapeOf);
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// shape::shapeBuffer(5, shapeOf, newShape);
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// RELEASE(shapeOf, block.getWorkspace());
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// return SHAPELIST(newShape);
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return SHAPELIST();
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}
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DECLARE_TYPES(fullconv3d_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(fullconv3d_bp, 5, 1, false, 0, 13) {
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// auto input = INPUT_VARIABLE(0);
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// auto gradNext = INPUT_VARIABLE(1);
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// auto weights = INPUT_VARIABLE(2);
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// auto finput = INPUT_VARIABLE(3);
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// // not used
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// auto fgradInput = INPUT_VARIABLE(4);
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// REQUIRE_TRUE(weights->rankOf() == 5, 0, "Weights should be 5D, got %i instead", weights->rankOf());
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// REQUIRE_TRUE(input->rankOf() == 5, 0, "Input should be 5D, got %i instead", input->rankOf());
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// auto output = OUTPUT_VARIABLE(0);
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// int dT = INT_ARG(0);
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// int dW = INT_ARG(1);
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// int dH = INT_ARG(2);
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// int pT = INT_ARG(3);
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// int pW = INT_ARG(4);
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// int pH = INT_ARG(5);
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// int dilationT = INT_ARG(6);
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// int dilationW = INT_ARG(7);
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// int dilationH = INT_ARG(8);
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// int aT = INT_ARG(9);
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// int aW = INT_ARG(10);
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// int aH = INT_ARG(11);
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// bool biasUsed = INT_ARG(12) != 0;
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// const int nInputPlane = (int)weights->shapeOf()[0];
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// const int nOutputPlane = (int)weights->shapeOf()[1];
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// const int kT = (int)weights->shapeOf()[2];
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// const int kH = (int)weights->shapeOf()[3];
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// const int kW = (int)weights->shapeOf()[4];
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// const Nd4jLong inputWidth = input->shapeOf()[4];
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// const Nd4jLong inputHeight = input->shapeOf()[3];
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// const Nd4jLong inputDepth = input->shapeOf()[2];
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// const Nd4jLong outputDepth = (inputDepth - 1) * dT - 2*pT + (dilationT * (kT - 1) + 1) + aT;
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// const Nd4jLong outputHeight = (inputHeight - 1) * dH - 2*pH + (dilationH * (kH - 1) + 1) + aH;
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// const Nd4jLong outputWidth = (inputWidth - 1) * dW - 2*pW + (dilationW * (kW - 1) + 1) + aW;
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// const Nd4jLong batchSize = input->shapeOf()[0];
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// REQUIRE_TRUE(output->isSameShape({(int) batchSize, (int) nInputPlane, (int) inputDepth, (int) inputHeight, (int) inputWidth}) ,0, "Output should have shape of [%i, %i, %i, %i, %i], but got [%i, %i, %i, %i, %i] instead", (int) batchSize, (int) nInputPlane, (int) inputDepth, (int) inputHeight, (int) inputWidth, output->sizeAt(0), output->sizeAt(1), output->sizeAt(2), output->sizeAt(3), output->sizeAt(4));
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// output->assign(0.0);
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// // FIXME: non-inplace reshape!!!!
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// NDArray *gradColumns;
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// //auto gradColumns = finput->reshape('c', {nOutputPlane*kW*kH*kT, inputDepth*inputHeight*inputWidth });
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// std::unique_ptr<ResultSet> tadsNext(gradNext->allExamples());
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// std::unique_ptr<ResultSet> tadsOutput(output->allExamples());
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// for (int e = 0; e < tadsNext->size(); e++) {
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// auto tadNext = tadsNext->at(e);
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// auto tadOutput = tadsOutput->at(e);
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// // ConvolutionUtils<T>::_vol2col(
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// // tadNext->getBuffer(),
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// // nOutputPlane, outputDepth, outputHeight, outputWidth,
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// // kT, kH, kW,
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// // pT, pH, pW,
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// // dT, dH, dW,
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// // dilationT, dilationH, dilationW,
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// // gradColumns->getBuffer());
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// ConvolutionUtils::vol2col(*tadNext, *gradColumns, dT, dH, dW, pT, pH, pW, dilationT, dilationH, dilationW);
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// const auto m = weights->shapeOf()[0];
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// const auto n = gradColumns->shapeOf()[1];
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// const auto k = weights->shapeOf()[1] * weights->shapeOf()[2] * weights->shapeOf()[3] * weights->shapeOf()[4];
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// // FIXME: mmul helper should be used here
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// /*
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// nd4j::blas::GEMM<T>::op('f', 'n', 'n',
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// n, m, k,
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// 1.0f,
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// gradColumns->getBuffer(), n,
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// weights->getBuffer(), k,
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// 0,
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// tadOutput->getBuffer(), n
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// );
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// */
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// }
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// STORE_RESULT(*output);
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// delete gradColumns;
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return ND4J_STATUS_OK;
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}
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DECLARE_SHAPE_FN(fullconv3d_bp) {
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// output shape equals to input shape, all out of sudden
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// Nd4jLong* newShape;
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// COPY_SHAPE(inputShape->at(0), newShape);
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// return SHAPELIST(newShape);
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return SHAPELIST();
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}
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DECLARE_TYPES(fullconv3d_grad) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(fullconv3d_grad, 4, 2, false, 1, 13) {
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// auto input = INPUT_VARIABLE(0);
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// auto epsilon = INPUT_VARIABLE(1);
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// auto columns = INPUT_VARIABLE(2);
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// auto ones = INPUT_VARIABLE(3);
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// REQUIRE_TRUE(input->rankOf() == epsilon->rankOf(), 0, "Rank of input (%i) & epsilon (%i) should be equal", input->rankOf(), epsilon->rankOf());
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// REQUIRE_TRUE(input->sizeAt(0) == epsilon->sizeAt(0), 1, "Batch size should be equal for input and epsilon");
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// auto gradWeight = OUTPUT_VARIABLE(0);
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// auto gradBias = OUTPUT_VARIABLE(1);
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// REQUIRE_TRUE(gradBias->sizeAt(0) == gradWeight->sizeAt(1), 0, "Bias shape mismatch");
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// int dT = INT_ARG(0);
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// int dW = INT_ARG(1);
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// int dH = INT_ARG(2);
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// int pT = INT_ARG(3);
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// int pW = INT_ARG(4);
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// int pH = INT_ARG(5);
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// int dilationT = INT_ARG(6);
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// int dilationW = INT_ARG(7);
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// int dilationH = INT_ARG(8);
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// int aT = INT_ARG(9);
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// int aW = INT_ARG(10);
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// int aH = INT_ARG(11);
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// bool biasUsed = INT_ARG(12) != 0;
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// double scale = block.getTArguments()->at(0);
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// int nInputPlane = (int)gradWeight->shapeOf()[0];
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// int nOutputPlane = (int)gradWeight->shapeOf()[1];
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// int kT = (int)gradWeight->shapeOf()[2];
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// int kH = (int)gradWeight->shapeOf()[3];
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// int kW = (int)gradWeight->shapeOf()[4];
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// const Nd4jLong inputWidth = input->shapeOf()[4];
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// const Nd4jLong inputHeight = input->shapeOf()[3];
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// const Nd4jLong inputDepth = input->shapeOf()[2];
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// const Nd4jLong outputDepth = (inputDepth - 1) * dT - 2*pT + (dilationT * (kT - 1) + 1) + aT;
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// const Nd4jLong outputHeight = (inputHeight - 1) * dH - 2*pH + (dilationH * (kH - 1) + 1) + aH;
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// const Nd4jLong outputWidth = (inputWidth - 1) * dW - 2*pW + (dilationW * (kW - 1) + 1) + aW;
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// REQUIRE_TRUE(gradWeight->isContiguous(), 0, "gradWight should be continuous");
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// REQUIRE_TRUE(gradBias->isContiguous(), 0, "gradBias should be continuous");
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// REQUIRE_TRUE(ones->rankOf() == 3, 0, "Ones should have rank 3, got %i instead", ones->rankOf());
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// REQUIRE_TRUE(ones->isSameShape({outputDepth, outputHeight, outputWidth}), 0, "");
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// ones->assign(1.0);
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// std::unique_ptr<ResultSet> tadsInput(input->allExamples());
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// std::unique_ptr<ResultSet> tadsEpsilon(epsilon->allExamples());
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// for (int e = 0; e < tadsInput->size(); e++) {
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// auto tadInput = tadsInput->at(e);
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// auto tadEpsilon = tadsEpsilon->at(e);
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// // ConvolutionUtils<T>::_vol2col(
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// // tadEpsilon->getBuffer(), nOutputPlane,
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// // outputDepth, outputHeight, outputWidth,
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// // kT, kH, kW,
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// // pT, pH, pW,
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// // dT, dH, dW,
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// // dilationT, dilationH, dilationW,
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// // columns->getBuffer()
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// // );
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// ConvolutionUtils::vol2col(*tadEpsilon, *columns, dT, dH, dW, pT, pH, pW, dilationT, dilationH, dilationW);
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// const Nd4jLong n = columns->shapeOf()[0]; // nOutputPlane * kt * kh * kw
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// const Nd4jLong m = tadInput->shapeOf()[0]; // nInputPlane
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// const Nd4jLong k = columns->shapeOf()[1];
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// // FIXME: mmul helper should be used here
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// /**
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// nd4j::blas::GEMM<T>::op('f', 't', 'n',
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// n, m, k,
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// scale,
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// columns->getBuffer(), k,
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// tadInput->getBuffer(), k,
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// 1,
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// gradWeight->getBuffer(), n);
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// */
|
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// const Nd4jLong m_ = nOutputPlane;
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// const Nd4jLong k_ = outputDepth * outputHeight * outputWidth;
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// if (gradBias) {
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// // FIXME: mmul helper should be used here
|
||
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|
// /*
|
||
|
|
// nd4j::blas::GEMV<T>::op('t',
|
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|
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// k_, m_,
|
||
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// scale,
|
||
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// tadEpsilon->getBuffer(), k_,
|
||
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// ones->getBuffer(), 1, (T)1.0f,
|
||
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// gradBias->getBuffer(), 1);
|
||
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// */
|
||
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// }
|
||
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|
// }
|
||
|
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||
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||
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// STORE_2_RESULTS(*gradWeight, *gradBias);
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||
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|
||
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|
return Status::OK();
|
||
|
|
}
|
||
|
|
DECLARE_SHAPE_FN(fullconv3d_grad) {
|
||
|
|
// auto list = SHAPELIST();
|
||
|
|
|
||
|
|
// _grad ops MUST have output arrays provided
|
||
|
|
|
||
|
|
// return list;
|
||
|
|
return SHAPELIST();
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
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|
||
|
|
#endif
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