/* * ****************************************************************************** * * * * * * This program and the accompanying materials are made available under the * * terms of the Apache License, Version 2.0 which is available at * * https://www.apache.org/licenses/LICENSE-2.0. * * * * See the NOTICE file distributed with this work for additional * * information regarding copyright ownership. * * Unless required by applicable law or agreed to in writing, software * * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT * * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the * * License for the specific language governing permissions and limitations * * under the License. * * * * SPDX-License-Identifier: Apache-2.0 * ***************************************************************************** */ // Created by Abdelrauf (rauf@konduit.ai) 2020 #include #include #include #include #include "armcomputeUtils.h" namespace sd { namespace ops { namespace platforms { ////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(conv2d, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW) auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC] auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW) sd::LongType sH = INT_ARG(2); // strides height sd::LongType sW = INT_ARG(3); // strides width sd::LongType pH = INT_ARG(4); // paddings height sd::LongType pW = INT_ARG(5); // paddings width sd::LongType dH = INT_ARG(6); // dilations height sd::LongType dW = INT_ARG(7); // dilations width int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC] sd::LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(weights->sizeAt(0)); // filter(kernel) height sd::LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(weights->sizeAt(1)); // filter(kernel) width // Calculate individual paddings sd::LongType padLeft, padTop, padRight, padBottom; sd::LongType bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width; sd::LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH); ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode); int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2 : pW; // dH == 1 for causal mode in conv1d padLeft = pW; padTop = pH; padRight = (oW - 1) * sW - iW + kW - pWSame; padBottom = (oH - 1) * sH - iH + kH - pH; std::vector expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC); REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CONV2D ARMCOMPUTE OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str()); if (bias) REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CONV2D ARMCOMPUTE OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, " "%i instead !", oC, bias->rankOf(), bias->lengthOf()); auto dataLayout = isNCHW ? arm_compute::DataLayout::NCHW : arm_compute::DataLayout::NHWC; // check weight input datalayout match bool dataLayoutMatch = (isNCHW && wFormat == 1) || (!isNCHW && wFormat == 2); arm_compute::PermutationVector permuteVector; if (!dataLayoutMatch) { // lets premute if (wFormat == 0) { if (isNCHW) { // reshape permuteVector = arm_compute::PermutationVector(2U, 3U, 1U, 0U); } else { // reshape permuteVector = arm_compute::PermutationVector(1U, 2U, 3U, 0U); } } else if (wFormat == 1) { permuteVector = arm_compute::PermutationVector(2U, 0U, 1U, 3U); } else { permuteVector = arm_compute::PermutationVector(1U, 2U, 0U, 3U); } } else { // set 0 permuteVector.set_num_dimensions(0); } Arm_WeightsInfo wInfo(false, kW, kH, 1); arm_compute::Size2D dilation(dW, dH); arm_compute::PadStrideInfo pad(sW, sH, padLeft, padRight, padTop, padBottom, arm_compute::DimensionRoundingType::FLOOR); ArmFunctionWeighted conv; conv.configure(input, weights, bias, output, dataLayout, permuteVector, pad, wInfo, dilation); conv.run(); // run function return sd::Status::OK; } PLATFORM_CHECK(conv2d, ENGINE_CPU) { auto input = INPUT_VARIABLE(0); auto weights = INPUT_VARIABLE(1); auto output = OUTPUT_VARIABLE(0); // Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. Requirements req("ARMCOMPUTE CONV2d OP"); req.expectEq(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT0), DataType::FLOAT32) && req.expectEq(makeInfoVariable(weights->dataType(), TYPE_MSG_INPUT1), DataType::FLOAT32) && req.expectEq(makeInfoVariable(output->dataType(), TYPE_MSG_OUTPUT), DataType::FLOAT32) && req.expectLessEq(makeInfoVariable(input->rankOf(), RANK_MSG_INPUT0), arm_compute::MAX_DIMS) && req.expectEq(makeInfoVariable(input->ordering(), ORDERING_MSG_INPUT0), 'c') && req.expectEq(makeInfoVariable(input->stridesOf()[input->rankOf() - 1], "input0#lastStride"), 1) && req.expectLessEq(makeInfoVariable(weights->rankOf(), RANK_MSG_INPUT1), arm_compute::MAX_DIMS) && req.expectEq(makeInfoVariable(weights->ordering(), ORDERING_MSG_INPUT1), 'c') && req.expectEq(makeInfoVariable(weights->stridesOf()[weights->rankOf() - 1], "input1#lastStride"), 1) && req.expectLessEq(makeInfoVariable(output->rankOf(), RANK_MSG_OUTPUT), arm_compute::MAX_DIMS) && req.expectEq(makeInfoVariable(output->ordering(), ORDERING_MSG_OUTPUT), 'c') && req.expectEq(makeInfoVariable(output->stridesOf()[output->rankOf() - 1], "output#lastStride"), 1); req.logTheSuccess(); return req; } } // namespace platforms } // namespace ops } // namespace sd