wehub-resource-sync
141 lines
4.7 KiB
C++
141 lines
4.7 KiB
C++
/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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 <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_adjust_contrast)
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#include <array/NDArrayFactory.h>
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#include <ops/declarable/headers/parity_ops.h>
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namespace sd {
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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()) 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",
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input->rankOf());
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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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#ifdef HAS_DOUBLE
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factor->p(0, static_cast<double>(T_ARG(0)));
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#elif defined(HAS_FLOAT32)
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factor->p(0, static_cast<float>(T_ARG(0)));
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#else
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#error "No floating-point type available for adjust_contrast operation"
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#endif
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}
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// fill up axes vector first
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std::vector<LongType> axes(input->rankOf() - 1);
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for (size_t i = 0; i < axes.size(); ++i) axes[i] = i;
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// mean as reduction for last dimension set
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auto* mean = input->reduceAlongDimension(reduce::Mean, &axes);
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auto* part1 = (*input) - (*mean);
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auto* part2 = (*part1) * (*factor);
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delete part1;
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auto* part3 = (*part2) + (*mean);
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delete part2;
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delete mean;
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// this is contrast calculation
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output->assign(part3);
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delete part3;
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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(ANY)->setAllowedOutputTypes({ALL_FLOATS})->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()) return Status::OK;
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REQUIRE_TRUE(input->rankOf() > 2, 0,
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"ADJUST_CONTRAST_V2: op expects rank of input array to be >= 3, but got %i instead", input->rankOf());
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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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auto size = input->sizeAt(-2) * input->sizeAt(-3);
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auto channels = input->sizeAt(-1);
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int sizeChannels = sd::math::sd_max<int>(1,size * channels);
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auto batch = input->lengthOf() / sizeChannels;
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std::vector<LongType> shape = {batch, size, channels};
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auto* input3D = input->reshape(input->ordering(), shape);
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auto* output3D = input->reshape(input->ordering(), shape);
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if (block.width() > 1) {
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factor = INPUT_VARIABLE(1);
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}
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else {
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factor = new NDArray(output->dataType(), block.launchContext());
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#ifdef HAS_DOUBLE
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factor->p(0, static_cast<double>(T_ARG(0)));
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#elif defined(HAS_FLOAT32)
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factor->p(0, static_cast<float>(T_ARG(0)));
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#else
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#error "No floating-point type available for adjust_contrast_v2 operation"
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#endif
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}
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std::vector<LongType> axes({1}); // dim 1 of pseudoresult
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// mean as reduction for last dimension set over size (dim 1) of result3D
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auto* mean = input3D->reduceAlongDimension(reduce::Mean, &axes);
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// result as (x - mean) * factor + mean
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auto* temp = input3D->ulike();
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std::vector<LongType> zeroTwo = {0, 2};
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input3D->applyBroadcast(broadcast::Subtract,&zeroTwo, mean, temp);
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temp->applyScalarArr(scalar::Multiply, factor, temp);
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temp->applyBroadcast(broadcast::Add, &zeroTwo, mean, output3D);
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output->assign(output3D);
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delete temp;
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delete mean;
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delete input3D;
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delete output3D;
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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({ALL_FLOATS})->setAllowedOutputTypes({ALL_FLOATS})->setSameMode(true);
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}
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} // namespace ops
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} // namespace sd
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#endif
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