135 lines
6.2 KiB
C++
135 lines
6.2 KiB
C++
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/*******************************************************************************
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* Copyright (c) 2021 Deeplearning4j Contributors
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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 AbdelRauf
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//
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#include <system/op_boilerplate.h>
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/ctcLoss.h>
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namespace sd {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(ctc_loss, 4, 1, false, 0, 1) {
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auto targetLabels = INPUT_VARIABLE(0);
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auto logitInput = INPUT_VARIABLE(1);
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auto targetLabelLengths = INPUT_VARIABLE(2);
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auto logitInputLengths = INPUT_VARIABLE(3);
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auto outputLosses = OUTPUT_VARIABLE(0);
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int blankIndex = INT_ARG(0);
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REQUIRE_TRUE(targetLabels->rankOf()==2, 0, "CtcLoss: target labels fails to meet rank requirement (batch_size, max_label_sequence_length): %i == 2 ", targetLabels->rankOf());
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REQUIRE_TRUE(logitInput->rankOf()==3, 0, "CtcLoss: logit Input fails to meet rank requirement (batch_size, frames, classes): %i == 3 ", logitInput->rankOf());
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REQUIRE_TRUE(targetLabelLengths->rankOf()==1, 0, "CtcLoss: target label length fails to meet rank requirement (batch_size): %i == 1 ", targetLabelLengths->rankOf());
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REQUIRE_TRUE(logitInputLengths->rankOf()==1, 0, "CtcLoss: logit Input lengths fails to meet rank requirement (batch_size): %i == 1 ", logitInputLengths->rankOf());
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int batchSize0 = targetLabels->sizeAt(0);
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int batchSize1 = logitInput->sizeAt(0);
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int batchSize2 = targetLabelLengths->sizeAt(0);
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int batchSize3 = logitInputLengths->sizeAt(0);
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int batchSize4 = outputLosses->sizeAt(0);
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bool check_batches = (batchSize0 == batchSize1) && (batchSize2 == batchSize3);
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check_batches = check_batches && (batchSize0 == batchSize4) && (batchSize0 == batchSize2);
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REQUIRE_TRUE(check_batches, 0, "CtcLoss: All batch sizes should be equal %i", batchSize0);
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REQUIRE_TRUE(outputLosses->isSameShape(targetLabelLengths), 0, "CtcLoss: wrong shape of output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString(targetLabelLengths).c_str(), ShapeUtils::shapeAsString(outputLosses).c_str());
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auto emptyGradients = NDArrayFactory::empty<float>();
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sd::ops::helpers::ctcLoss(block, *logitInput, *targetLabels, *logitInputLengths, *targetLabelLengths, *outputLosses, emptyGradients, blankIndex);
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_TYPES(ctc_loss) {
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getOpDescriptor()->setAllowedInputTypes({ALL_INDICES})
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->setAllowedInputTypes(1,{ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_SHAPE_FN(ctc_loss) {
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auto yShapeInfo = inputShape->at(1);
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auto zShapeInfo = inputShape->at(2);
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auto dtype = ArrayOptions::dataType(yShapeInfo);
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return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(zShapeInfo, dtype)));
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(ctc_loss_grad, 4, 1, false, 0, 1) {
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auto targetLabels = INPUT_VARIABLE(0);
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auto logitInput = INPUT_VARIABLE(1);
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auto targetLabelLengths = INPUT_VARIABLE(2);
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auto logitInputLengths = INPUT_VARIABLE(3);
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auto outputGradients = OUTPUT_VARIABLE(0);
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int blankIndex = INT_ARG(0);
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REQUIRE_TRUE(targetLabels->rankOf()==2, 0, "CtcLoss: target labels fails to meet rank requirement (batch_size, max_label_sequence_length): %i == 2 ", targetLabels->rankOf());
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REQUIRE_TRUE(logitInput->rankOf()==3, 0, "CtcLoss: logit Input fails to meet rank requirement (batch_size, frames, classes): %i == 3 ", logitInput->rankOf());
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REQUIRE_TRUE(targetLabelLengths->rankOf()==1, 0, "CtcLoss: target label length fails to meet rank requirement (batch_size): %i == 1 ", targetLabelLengths->rankOf());
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REQUIRE_TRUE(logitInputLengths->rankOf()==1, 0, "CtcLoss: logit Input lengths fails to meet rank requirement (batch_size): %i == 1 ", logitInputLengths->rankOf());
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int batchSize0 = targetLabels->sizeAt(0);
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int batchSize1 = logitInput->sizeAt(0);
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int batchSize2 = targetLabelLengths->sizeAt(0);
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int batchSize3 = logitInputLengths->sizeAt(0);
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int batchSize4 = outputGradients->sizeAt(0);
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bool check_batches = (batchSize0 == batchSize1) && (batchSize2 == batchSize3);
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check_batches = check_batches && (batchSize0 == batchSize4) && (batchSize0 == batchSize2);
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REQUIRE_TRUE(check_batches, 0, "CtcLoss Gradient: All batch sizes should be equal %i", batchSize0);
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REQUIRE_TRUE(outputGradients->isSameShape(logitInput), 0, "CtcLoss Gradient: wrong shape of output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString(logitInput).c_str(), ShapeUtils::shapeAsString(outputGradients).c_str());
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auto emptyLoss = NDArrayFactory::empty<float>();
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sd::ops::helpers::ctcLoss(block, *logitInput, *targetLabels, *logitInputLengths, *targetLabelLengths, emptyLoss, *outputGradients, blankIndex);
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return Status::OK();
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_TYPES(ctc_loss_grad) {
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getOpDescriptor()->setAllowedInputTypes({ALL_INDICES})
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->setAllowedInputTypes(1,{ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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//////////////////////////////////////////////////////////////////////////
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DECLARE_SHAPE_FN(ctc_loss_grad) {
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auto yShapeInfo = inputShape->at(1);
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auto dtype = ArrayOptions::dataType(yShapeInfo);
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return SHAPELIST(ConstantShapeHelper::getInstance().createShapeInfo(ShapeDescriptor(yShapeInfo, dtype)));
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}
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}
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}
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