128 lines
4.2 KiB
C++
128 lines
4.2 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 George A. Shulinok <sgazeos@gmail.com>
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_adjust_contrast)
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#include <ops/declarable/headers/parity_ops.h>
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#include <NDArrayFactory.h>
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namespace nd4j {
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namespace ops {
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////////////////////////////////////////////////////////////////////
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CONFIGURABLE_OP_IMPL(adjust_contrast, 1, 1, true, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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// just skip op if input is empty
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if (input->isEmpty())
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return Status::OK();
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REQUIRE_TRUE(block.numT() > 0 || block.width() > 1, 0, "ADJUST_CONTRAST: Scale factor required");
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REQUIRE_TRUE(input->rankOf() > 2, 0, "ADJUST_CONTRAST: op expects rank of input array to be >= 3, but got %i instead", input->rankOf());
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REQUIRE_TRUE(input->sizeAt(-1) == 3, 0, "ADJUST_CONTRAST: operation expects image with 3 channels (R, G, B), but got %i instead", input->sizeAt(-1));
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NDArray* factor = nullptr;
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if(block.width() > 1)
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factor = INPUT_VARIABLE(1);
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else {
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factor = new NDArray(output->dataType(), block.launchContext());
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factor->p(0, T_ARG(0));
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}
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// fill up axes vector first
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std::vector<int> axes(input->rankOf() - 1);
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for (auto i = 0; i < axes.size(); ++i)
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axes[i] = i;
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// mean as reduction for last dimension set
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auto mean = input->reduceAlongDims(reduce::Mean, axes);
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// this is contrast calculation
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output->assign((*input - mean) * (*factor) + mean);
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if(block.width() == 1)
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delete factor;
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return Status::OK();
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}
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DECLARE_TYPES(adjust_contrast) {
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getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS})
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->setSameMode(true);
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}
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////////////////////////////////////////////////////////////////////
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CONFIGURABLE_OP_IMPL(adjust_contrast_v2, 1, 1, true, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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// just skip op if input is empty
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if (input->isEmpty())
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return Status::OK();
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REQUIRE_TRUE(input->rankOf() > 2, 0, "ADJUST_CONTRAST_V2: op expects rank of input array to be >= 3, but got %i instead", input->rankOf());
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REQUIRE_TRUE(input->sizeAt(-1) == 3, 0, "ADJUST_CONTRAST_V2: operation expects image with 3 channels (R, G, B), but got %i instead", input->sizeAt(-1));
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REQUIRE_TRUE(block.numT() > 0 || block.width() > 1, 0, "ADJUST_CONTRAST_V2: Scale factor required");
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NDArray* factor = nullptr;
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if(block.width() > 1)
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factor = INPUT_VARIABLE(1);
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else {
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factor = new NDArray(output->dataType(), block.launchContext());
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factor->p(0, T_ARG(0));
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}
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// compute mean before
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std::vector<int> axes(input->rankOf() - 1);
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for (auto i = 0; i < axes.size(); ++i)
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axes[i] = i;
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// mean as reduction for last dimension set
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auto mean = input->reduceAlongDims(reduce::Mean, axes);
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// result as (x - mean) * factor + mean
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auto temp = input->ulike();
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input->applyTrueBroadcast(BroadcastOpsTuple::Subtract(), &mean, &temp);
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temp.applyScalarArr(scalar::Multiply, factor);
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temp.applyTrueBroadcast(BroadcastOpsTuple::Add(), &mean, output);
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if(block.width() == 1)
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delete factor;
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return Status::OK();
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}
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DECLARE_TYPES(adjust_contrast_v2) {
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getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS})
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->setSameMode(true);
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}
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}
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}
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#endif |