/******************************************************************************* * Copyright (c) 2015-2018 Skymind, Inc. * * 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. * * 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 ******************************************************************************/ // // @author George A. Shulinok // #include #if NOT_EXCLUDED(OP_adjust_contrast) #include #include namespace nd4j { namespace ops { CONFIGURABLE_OP_IMPL(adjust_contrast, 1, 1, true, 1, 0) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); const double factor = T_ARG(0); REQUIRE_TRUE(input->rankOf() > 2, 0, "ADJUST_CONTRAST: op expects rank of input array to be >= 3, but got %i instead", input->rankOf()); 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)); // compute mean before reduce_mean meanOp; auto axes = NDArrayFactory::create('c', {input->rankOf() - 1}, block.launchContext()); for (int i = 0; i < input->rankOf() -1; i++) axes.p(i, i); auto meanRes = meanOp.execute({input, &axes}, {}, {}); REQUIRE_TRUE(meanRes->status() == Status::OK(), 0, "ADJUST_CONTRAST: op should be successful, but error code %i occured.", meanRes->status()); auto mean = meanRes->at(0); NDArray factorT(output->dataType(), block.launchContext()); // = NDArrayFactory::create(factor, block.launchContext()); factorT.p(0, factor); // this is contrast calculation *output = (*input - *mean) * factorT + *mean; delete meanRes; return Status::OK(); } DECLARE_TYPES(adjust_contrast) { getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}) ->setSameMode(true); } CONFIGURABLE_OP_IMPL(adjust_contrast_v2, 1, 1, true, 1, 0) { auto input = INPUT_VARIABLE(0); auto output = OUTPUT_VARIABLE(0); const double factor = T_ARG(0); REQUIRE_TRUE(input->rankOf() > 2, 0, "ADJUST_CONTRAST: op expects rank of input array to be >= 3, but got %i instead", input->rankOf()); 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)); // compute mean before reduce_mean meanOp; auto axes = NDArrayFactory::create('c', {input->rankOf() - 1}, block.launchContext()); for (int i = 0; i < input->rankOf() -1; i++) axes.p(i, i); auto meanRes = meanOp.execute({input, &axes}, {}, {}); REQUIRE_TRUE(meanRes->status() == Status::OK(), 0, "ADJUST_CONTRAST: op should be successful, but error code %i occured.", meanRes->status()); auto mean = meanRes->at(0); // NDArray factorT(output->dataType(), block.launchContext()); // = NDArrayFactory::create(factor, block.launchContext()); // factorT.p(0, factor); // this is contrast calculation std::unique_ptr temp(input->dup()); input->applyTrueBroadcast(BroadcastOpsTuple::Subtract(), mean, temp.get()); temp->applyScalar(scalar::Multiply, factor); temp->applyTrueBroadcast(BroadcastOpsTuple::Add(), mean, output); // *output = (*input - *mean) * factorT + *mean; delete meanRes; return Status::OK(); } DECLARE_TYPES(adjust_contrast_v2) { getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY) ->setAllowedOutputTypes({ALL_FLOATS}) ->setSameMode(true); } } } #endif