raver119 763a225c6a [WIP] More of CUDA operations (#69)
* initial commit

Signed-off-by: raver119 <raver119@gmail.com>

* - gruCell_bp further

Signed-off-by: Yurii <yurii@skymind.io>

* - further work on gruCell_bp

Signed-off-by: Yurii <yurii@skymind.io>

* Inverse matrix cublas implementation. Partial working revision.

* Separation of segment ops helpers. Max separation.

* Separated segment_min ops.

* Separation of segment_mean/sum/prod/sqrtN ops heleprs.

* Fixed diagonal processing with LUP decomposition.

* Modified inversion approach using current state of LU decomposition.

* Implementation of matrix_inverse op with cuda kernels. Working revision.

* Implemented sequence_mask cuda helper. Eliminated waste printf with matrix_inverse implementation. Added proper tests.

* - further work on gruCell_bp (ff/cuda)

Signed-off-by: Yurii <yurii@skymind.io>

* comment one test for gruCell_bp

Signed-off-by: Yurii <yurii@skymind.io>

* - provide cuda static_rnn

Signed-off-by: Yurii <yurii@skymind.io>

* Refactored random_shuffle op to use new random generator.

* Refactored random_shuffle op helper.

* Fixed debug tests with random ops tests.

* Implement random_shuffle op cuda kernel helper and tests.

* - provide cuda scatter_update

Signed-off-by: Yurii <yurii@skymind.io>

* Implementation of random_shuffle for linear case with cuda kernels and tests.

* Implemented random_shuffle with cuda kernels. Final revision.

* - finally gruCell_bp is completed

Signed-off-by: Yurii <yurii@skymind.io>

* Dropout op cuda helper implementation.

* Implemented dropout_bp cuda helper.

* Implemented alpha_dropout_bp with cuda kernel helpers.

* Refactored helper.

* Implementation of suppresion helper with cuda kernels.

* - provide cpu code fot hsvToRgb, rgbToHsv, adjustHue

Signed-off-by: Yurii <yurii@skymind.io>

* Using sort by value method.

* Implementation of image.non_max_suppression op cuda-based helper.

* - correcting and testing adjust_hue, adjust_saturation cpu/cuda code

Signed-off-by: Yurii <yurii@skymind.io>

* Added cuda device prefixes to declarations.

* Implementation of hashcode op with cuda helper. Initital revision.

* rnn cu impl removed

Signed-off-by: raver119 <raver119@gmail.com>
2019-07-20 23:20:41 +10:00

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/*******************************************************************************
* 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 raver119@gmail.com
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_adjust_saturation)
#include <ops/declarable/headers/parity_ops.h>
#include <ops/declarable/helpers/adjust_saturation.h>
#include <NDArrayFactory.h>
namespace nd4j {
namespace ops {
CONFIGURABLE_OP_IMPL(adjust_saturation, 1, 1, true, 1, -2) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
const int rank = input->rankOf();
const int dimC = block.getIArguments()->size() > 0 ? (INT_ARG(0) >= 0 ? INT_ARG(0) : INT_ARG(0) + rank) : rank - 1;
const double factor = T_ARG(0);
REQUIRE_TRUE(rank >= 3, 0, "ADJUST_SATURATION: op expects rank of input array to be >= 3, but got %i instead", rank);
REQUIRE_TRUE(input->sizeAt(dimC) == 3, 0, "ADJUST_SATURATION: operation expects image with 3 channels (R, G, B), but got %i instead", input->sizeAt(dimC));
NDArray factorScalarArr = NDArrayFactory::create<double>(factor, block.launchContext());
helpers::adjustSaturation(block.launchContext(), input, &factorScalarArr, output, dimC);
return Status::OK();
}
DECLARE_TYPES(adjust_saturation) {
getOpDescriptor()->setAllowedInputTypes(nd4j::DataType::ANY)
->setSameMode(true);
}
}
}
#endif