103 lines
6.0 KiB
C++
103 lines
6.0 KiB
C++
/*
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* Copyright (c) 2017 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_TEST_SQUEEZENET_CONVOLUTION_LAYER_DATASET
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#define ARM_COMPUTE_TEST_SQUEEZENET_CONVOLUTION_LAYER_DATASET
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#include "tests/datasets/ConvolutionLayerDataset.h"
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#include "utils/TypePrinter.h"
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#include "arm_compute/core/TensorShape.h"
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#include "arm_compute/core/Types.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace datasets
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{
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class SqueezeNetWinogradLayerDataset final : public ConvolutionLayerDataset
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{
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public:
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SqueezeNetWinogradLayerDataset()
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{
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// fire2/expand3x3, fire3/expand3x3
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add_config(TensorShape(55U, 55U, 16U), TensorShape(3U, 3U, 16U, 64U), TensorShape(64U), TensorShape(55U, 55U, 64U), PadStrideInfo(1, 1, 1, 1));
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// fire4/expand3x3, fire5/expand3x3
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add_config(TensorShape(27U, 27U, 32U), TensorShape(3U, 3U, 32U, 128U), TensorShape(128U), TensorShape(27U, 27U, 128U), PadStrideInfo(1, 1, 1, 1));
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// fire6/expand3x3, fire7/expand3x3
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add_config(TensorShape(13U, 13U, 48U), TensorShape(3U, 3U, 48U, 192U), TensorShape(192U), TensorShape(13U, 13U, 192U), PadStrideInfo(1, 1, 1, 1));
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// fire8/expand3x3, fire9/expand3x3
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add_config(TensorShape(13U, 13U, 64U), TensorShape(3U, 3U, 64U, 256U), TensorShape(256U), TensorShape(13U, 13U, 256U), PadStrideInfo(1, 1, 1, 1));
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}
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};
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class SqueezeNetConvolutionLayerDataset final : public ConvolutionLayerDataset
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{
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public:
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SqueezeNetConvolutionLayerDataset()
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{
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// conv1
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add_config(TensorShape(224U, 224U, 3U), TensorShape(3U, 3U, 3U, 64U), TensorShape(64U), TensorShape(111U, 111U, 64U), PadStrideInfo(2, 2, 0, 0));
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// fire2/squeeze1x1
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add_config(TensorShape(55U, 55U, 64U), TensorShape(1U, 1U, 64U, 16U), TensorShape(16U), TensorShape(55U, 55U, 16U), PadStrideInfo(1, 1, 0, 0));
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// fire2/expand1x1, fire3/expand1x1
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add_config(TensorShape(55U, 55U, 16U), TensorShape(1U, 1U, 16U, 64U), TensorShape(64U), TensorShape(55U, 55U, 64U), PadStrideInfo(1, 1, 0, 0));
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// fire2/expand3x3, fire3/expand3x3
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add_config(TensorShape(55U, 55U, 16U), TensorShape(3U, 3U, 16U, 64U), TensorShape(64U), TensorShape(55U, 55U, 64U), PadStrideInfo(1, 1, 1, 1));
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// fire3/squeeze1x1
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add_config(TensorShape(55U, 55U, 128U), TensorShape(1U, 1U, 128U, 16U), TensorShape(16U), TensorShape(55U, 55U, 16U), PadStrideInfo(1, 1, 0, 0));
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// fire4/squeeze1x1
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add_config(TensorShape(27U, 27U, 128U), TensorShape(1U, 1U, 128U, 32U), TensorShape(32U), TensorShape(27U, 27U, 32U), PadStrideInfo(1, 1, 0, 0));
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// fire4/expand1x1, fire5/expand1x1
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add_config(TensorShape(27U, 27U, 32U), TensorShape(1U, 1U, 32U, 128U), TensorShape(128U), TensorShape(27U, 27U, 128U), PadStrideInfo(1, 1, 0, 0));
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// fire4/expand3x3, fire5/expand3x3
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add_config(TensorShape(27U, 27U, 32U), TensorShape(3U, 3U, 32U, 128U), TensorShape(128U), TensorShape(27U, 27U, 128U), PadStrideInfo(1, 1, 1, 1));
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// fire5/squeeze1x1
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add_config(TensorShape(27U, 27U, 256U), TensorShape(1U, 1U, 256U, 32U), TensorShape(32U), TensorShape(27U, 27U, 32U), PadStrideInfo(1, 1, 0, 0));
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// fire6/squeeze1x1
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add_config(TensorShape(13U, 13U, 256U), TensorShape(1U, 1U, 256U, 48U), TensorShape(48U), TensorShape(13U, 13U, 48U), PadStrideInfo(1, 1, 0, 0));
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// fire6/expand1x1, fire7/expand1x1
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add_config(TensorShape(13U, 13U, 48U), TensorShape(1U, 1U, 48U, 192U), TensorShape(192U), TensorShape(13U, 13U, 192U), PadStrideInfo(1, 1, 0, 0));
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// fire6/expand3x3, fire7/expand3x3
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add_config(TensorShape(13U, 13U, 48U), TensorShape(3U, 3U, 48U, 192U), TensorShape(192U), TensorShape(13U, 13U, 192U), PadStrideInfo(1, 1, 1, 1));
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// fire7/squeeze1x1
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add_config(TensorShape(13U, 13U, 384U), TensorShape(1U, 1U, 384U, 48U), TensorShape(48U), TensorShape(13U, 13U, 48U), PadStrideInfo(1, 1, 0, 0));
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// fire8/squeeze1x1
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add_config(TensorShape(13U, 13U, 384U), TensorShape(1U, 1U, 384U, 64U), TensorShape(64U), TensorShape(13U, 13U, 64U), PadStrideInfo(1, 1, 0, 0));
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// fire8/expand1x1, fire9/expand1x1
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add_config(TensorShape(13U, 13U, 64U), TensorShape(1U, 1U, 64U, 256U), TensorShape(256U), TensorShape(13U, 13U, 256U), PadStrideInfo(1, 1, 0, 0));
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// fire8/expand3x3, fire9/expand3x3
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add_config(TensorShape(13U, 13U, 64U), TensorShape(3U, 3U, 64U, 256U), TensorShape(256U), TensorShape(13U, 13U, 256U), PadStrideInfo(1, 1, 1, 1));
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// fire9/squeeze1x1
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add_config(TensorShape(13U, 13U, 512U), TensorShape(1U, 1U, 512U, 64U), TensorShape(64U), TensorShape(13U, 13U, 64U), PadStrideInfo(1, 1, 0, 0));
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// conv10
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add_config(TensorShape(13U, 13U, 512U), TensorShape(1U, 1U, 512U, 1000U), TensorShape(1000U), TensorShape(13U, 13U, 1000U), PadStrideInfo(1, 1, 0, 0));
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}
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};
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} // namespace datasets
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} // namespace test
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_TEST_SQUEEZENET_CONVOLUTION_LAYER_DATASET */
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