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Diffstat (limited to 'tests/networks/AlexNetNetwork.h')
-rw-r--r-- | tests/networks/AlexNetNetwork.h | 622 |
1 files changed, 622 insertions, 0 deletions
diff --git a/tests/networks/AlexNetNetwork.h b/tests/networks/AlexNetNetwork.h new file mode 100644 index 0000000000..d41e1b676c --- /dev/null +++ b/tests/networks/AlexNetNetwork.h @@ -0,0 +1,622 @@ +/* + * Copyright (c) 2017 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef __ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__ +#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__ + +#include "arm_compute/runtime/Tensor.h" + +#include "tests/AssetsLibrary.h" +#include "tests/Globals.h" +#include "tests/Utils.h" + +#include <memory> + +namespace arm_compute +{ +namespace test +{ +namespace networks +{ +/** AlexNet model object */ +template <typename ITensorType, + typename TensorType, + typename SubTensorType, + typename Accessor, + typename ActivationLayerFunction, + typename ConvolutionLayerFunction, + typename DirectConvolutionLayerFunction, + typename FullyConnectedLayerFunction, + typename NormalizationLayerFunction, + typename PoolingLayerFunction, + typename SoftmaxLayerFunction> +class AlexNetNetwork +{ +public: + void init(DataType data_type, int fixed_point_position, int batches, bool reshaped_weights = false) + { + _data_type = data_type; + _fixed_point_position = fixed_point_position; + _batches = batches; + _reshaped_weights = reshaped_weights; + + // Initialize weights and biases + if(!_reshaped_weights) + { + w[0].allocator()->init(TensorInfo(TensorShape(11U, 11U, 3U, 96U), 1, _data_type, _fixed_point_position)); + b[0].allocator()->init(TensorInfo(TensorShape(96U), 1, _data_type, _fixed_point_position)); + w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type, _fixed_point_position)); + b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position)); + w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type, _fixed_point_position)); + b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position)); + w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type, _fixed_point_position)); + b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position)); + w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type, _fixed_point_position)); + b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position)); + w[5].allocator()->init(TensorInfo(TensorShape(9216U, 4096U), 1, _data_type, _fixed_point_position)); + b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position)); + w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type, _fixed_point_position)); + b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position)); + w[7].allocator()->init(TensorInfo(TensorShape(4096U, 1000U), 1, _data_type, _fixed_point_position)); + b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type, _fixed_point_position)); + + w21 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates())); + w22 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128))); + b21 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates())); + b22 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128))); + + w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates())); + w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192))); + b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates())); + b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192))); + + w51 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates())); + w52 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128))); + b51 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates())); + b52 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128))); + } + else + { + auto reshape = [&](unsigned int width, unsigned int height) -> TensorShape + { + const int interleave_width = 16 / arm_compute::data_size_from_type(_data_type); + + return TensorShape{ width * interleave_width, static_cast<unsigned int>(std::ceil(static_cast<float>(height) / interleave_width)) }; + }; + + // Create tensor for the reshaped weights + w[0].allocator()->init(TensorInfo(reshape(366U, 96U), 1, _data_type, _fixed_point_position)); + + // Configure the direct convolution's weights. Direct convolution doesn't need reshape weights + if(!_is_direct_conv) + { + auto w21_tensor = std::unique_ptr<TensorType>(new TensorType()); + auto w22_tensor = std::unique_ptr<TensorType>(new TensorType()); + auto w41_tensor = std::unique_ptr<TensorType>(new TensorType()); + auto w42_tensor = std::unique_ptr<TensorType>(new TensorType()); + auto w51_tensor = std::unique_ptr<TensorType>(new TensorType()); + auto w52_tensor = std::unique_ptr<TensorType>(new TensorType()); + w21_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U), 1, _data_type, _fixed_point_position)); + w22_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U), 1, _data_type, _fixed_point_position)); + w41_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U), 1, _data_type, _fixed_point_position)); + w42_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U), 1, _data_type, _fixed_point_position)); + w51_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U), 1, _data_type, _fixed_point_position)); + w52_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U), 1, _data_type, _fixed_point_position)); + w[2].allocator()->init(TensorInfo(reshape(2560U, 384U), 1, _data_type, _fixed_point_position)); + w21 = std::move(w21_tensor); + w22 = std::move(w22_tensor); + w41 = std::move(w41_tensor); + w42 = std::move(w42_tensor); + w51 = std::move(w51_tensor); + w52 = std::move(w52_tensor); + } + else + { + w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type, _fixed_point_position)); + b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position)); + w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type, _fixed_point_position)); + b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position)); + w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type, _fixed_point_position)); + b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position)); + w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type, _fixed_point_position)); + b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position)); + w21 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates())); + w22 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128))); + b21 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates())); + b22 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128))); + + w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates())); + w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192))); + b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates())); + b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192))); + + w51 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates())); + w52 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128))); + b51 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates())); + b52 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128))); + } + + b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position)); + b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position)); + b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type, _fixed_point_position)); + + if(_batches > 1 && std::is_same<TensorType, Tensor>::value) + { + w[5].allocator()->init(TensorInfo(reshape(9216U, 4096U), 1, _data_type, _fixed_point_position)); + w[6].allocator()->init(TensorInfo(reshape(4096U, 4096U), 1, _data_type, _fixed_point_position)); + w[7].allocator()->init(TensorInfo(reshape(4096U, 1000U), 1, _data_type, _fixed_point_position)); + } + else + { + w[5].allocator()->init(TensorInfo(TensorShape(4096U, 9216U), 1, _data_type, _fixed_point_position)); + w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type, _fixed_point_position)); + w[7].allocator()->init(TensorInfo(TensorShape(1000U, 4096U), 1, _data_type, _fixed_point_position)); + } + } + } + + void build() + { + input.allocator()->init(TensorInfo(TensorShape(227U, 227U, 3U, _batches), 1, _data_type, _fixed_point_position)); + output.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type, _fixed_point_position)); + + // Initialize intermediate tensors + // Layer 1 + conv1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position)); + act1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position)); + norm1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position)); + pool1_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 96U, _batches), 1, _data_type, _fixed_point_position)); + pool11_out = std::unique_ptr<SubTensorType>(new SubTensorType(&pool1_out, TensorShape(27U, 27U, 48U, _batches), Coordinates())); + pool12_out = std::unique_ptr<SubTensorType>(new SubTensorType(&pool1_out, TensorShape(27U, 27U, 48U, _batches), Coordinates(0, 0, 48))); + // Layer 2 + conv2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type, _fixed_point_position)); + conv21_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv2_out, TensorShape(27U, 27U, 128U, _batches), Coordinates())); + conv22_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv2_out, TensorShape(27U, 27U, 128U, _batches), Coordinates(0, 0, 128))); + act2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type, _fixed_point_position)); + norm2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type, _fixed_point_position)); + pool2_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type, _fixed_point_position)); + // Layer 3 + conv3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position)); + act3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position)); + act31_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act3_out, TensorShape(13U, 13U, 192U, _batches), Coordinates())); + act32_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act3_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192))); + // Layer 4 + conv4_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position)); + conv41_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates())); + conv42_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192))); + act4_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position)); + act41_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates())); + act42_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192))); + // Layer 5 + conv5_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type, _fixed_point_position)); + conv51_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv5_out, TensorShape(13U, 13U, 128U, _batches), Coordinates())); + conv52_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv5_out, TensorShape(13U, 13U, 128U, _batches), Coordinates(0, 0, 128))); + act5_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type, _fixed_point_position)); + pool5_out.allocator()->init(TensorInfo(TensorShape(6U, 6U, 256U, _batches), 1, _data_type, _fixed_point_position)); + // Layer 6 + fc6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position)); + act6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position)); + // Layer 7 + fc7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position)); + act7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position)); + // Layer 8 + fc8_out.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type, _fixed_point_position)); + + // Configure Layers + // Layer 1 + TensorType *b0 = _reshaped_weights ? nullptr : &b[0]; + conv1.configure(&input, &w[0], b0, &conv1_out, PadStrideInfo(4, 4, 0, 0), WeightsInfo(_reshaped_weights, 11U, 11U, 96U)); + act1.configure(&conv1_out, &act1_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + norm1.configure(&act1_out, &norm1_out, NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)); + pool1.configure(&norm1_out, &pool1_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))); + // Layer 2 + conv21.configure(pool11_out.get(), w21.get(), b21.get(), conv21_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 5U, 128U)); + conv22.configure(pool12_out.get(), w22.get(), b22.get(), conv22_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 5U, 128U)); + act2.configure(&conv2_out, &act2_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + norm2.configure(&act2_out, &norm2_out, NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)); + pool2.configure(&norm2_out, &pool2_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))); + // Layer 3 + TensorType *b2 = (_reshaped_weights && !_is_direct_conv) ? nullptr : &b[2]; + conv3.configure(&pool2_out, &w[2], b2, &conv3_out, PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 384U)); + act3.configure(&conv3_out, &act3_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + // Layer 4 + conv41.configure(act31_out.get(), w41.get(), b41.get(), conv41_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 192U)); + conv42.configure(act32_out.get(), w42.get(), b42.get(), conv42_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 192U)); + act4.configure(&conv4_out, &act4_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + // Layer 5 + conv51.configure(act41_out.get(), w51.get(), b51.get(), conv51_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 128U)); + conv52.configure(act42_out.get(), w52.get(), b52.get(), conv52_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 128U)); + act5.configure(&conv5_out, &act5_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + pool5.configure(&act5_out, &pool5_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))); + // Layer 6 + fc6.configure(&pool5_out, &w[5], &b[5], &fc6_out, true, _reshaped_weights); + act6.configure(&fc6_out, &act6_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + // Layer 7 + fc7.configure(&act6_out, &w[6], &b[6], &fc7_out, true, _reshaped_weights); + act7.configure(&fc7_out, &act7_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + // Layer 8 + fc8.configure(&act7_out, &w[7], &b[7], &fc8_out, true, _reshaped_weights); + // Softmax + smx.configure(&fc8_out, &output); + } + + void allocate() + { + input.allocator()->allocate(); + output.allocator()->allocate(); + + if(!_reshaped_weights) + { + for(auto &wi : w) + { + wi.allocator()->allocate(); + } + + for(auto &bi : b) + { + bi.allocator()->allocate(); + } + } + else + { + w[0].allocator()->allocate(); + w[2].allocator()->allocate(); + w[5].allocator()->allocate(); + w[6].allocator()->allocate(); + w[7].allocator()->allocate(); + + b[5].allocator()->allocate(); + b[6].allocator()->allocate(); + b[7].allocator()->allocate(); + + if(!_is_direct_conv) + { + dynamic_cast<TensorType *>(w21.get())->allocator()->allocate(); + dynamic_cast<TensorType *>(w22.get())->allocator()->allocate(); + dynamic_cast<TensorType *>(w41.get())->allocator()->allocate(); + dynamic_cast<TensorType *>(w42.get())->allocator()->allocate(); + dynamic_cast<TensorType *>(w51.get())->allocator()->allocate(); + dynamic_cast<TensorType *>(w52.get())->allocator()->allocate(); + } + else + { + b[1].allocator()->allocate(); + b[2].allocator()->allocate(); + b[3].allocator()->allocate(); + b[4].allocator()->allocate(); + w[1].allocator()->allocate(); + w[3].allocator()->allocate(); + w[4].allocator()->allocate(); + } + } + + conv1_out.allocator()->allocate(); + act1_out.allocator()->allocate(); + norm1_out.allocator()->allocate(); + pool1_out.allocator()->allocate(); + conv2_out.allocator()->allocate(); + act2_out.allocator()->allocate(); + norm2_out.allocator()->allocate(); + pool2_out.allocator()->allocate(); + conv3_out.allocator()->allocate(); + act3_out.allocator()->allocate(); + conv4_out.allocator()->allocate(); + act4_out.allocator()->allocate(); + conv5_out.allocator()->allocate(); + act5_out.allocator()->allocate(); + pool5_out.allocator()->allocate(); + fc6_out.allocator()->allocate(); + act6_out.allocator()->allocate(); + fc7_out.allocator()->allocate(); + act7_out.allocator()->allocate(); + fc8_out.allocator()->allocate(); + } + + /** Fills the trainable parameters and input with random data. */ + void fill_random() + { + library->fill_tensor_uniform(Accessor(input), 0); + + if(!_reshaped_weights) + { + for(unsigned int i = 0; i < w.size(); ++i) + { + library->fill_tensor_uniform(Accessor(w[i]), i + 1); + library->fill_tensor_uniform(Accessor(b[i]), i + 10); + } + } + else + { + library->fill_tensor_uniform(Accessor(w[0]), 1); + library->fill_tensor_uniform(Accessor(w[2]), 2); + + library->fill_tensor_uniform(Accessor(w[5]), 3); + library->fill_tensor_uniform(Accessor(b[5]), 4); + library->fill_tensor_uniform(Accessor(w[6]), 5); + library->fill_tensor_uniform(Accessor(b[6]), 6); + library->fill_tensor_uniform(Accessor(w[7]), 7); + library->fill_tensor_uniform(Accessor(b[7]), 8); + + if(!_is_direct_conv) + { + library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w21.get())), 9); + library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w22.get())), 10); + library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w41.get())), 11); + library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w42.get())), 12); + library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w51.get())), 13); + library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w52.get())), 14); + } + else + { + library->fill_tensor_uniform(Accessor(w[1]), 9); + library->fill_tensor_uniform(Accessor(b[1]), 10); + library->fill_tensor_uniform(Accessor(w[3]), 11); + library->fill_tensor_uniform(Accessor(b[3]), 12); + library->fill_tensor_uniform(Accessor(w[4]), 13); + library->fill_tensor_uniform(Accessor(b[4]), 14); + } + } + } + +#ifdef INTERNAL_ONLY + /** Fills the trainable parameters from binary files + * + * @param weights Files names containing the weights data + * @param biases Files names containing the bias data + */ + void fill(std::vector<std::string> weights, std::vector<std::string> biases) + { + ARM_COMPUTE_ERROR_ON(weights.size() != w.size()); + ARM_COMPUTE_ERROR_ON(biases.size() != b.size()); + ARM_COMPUTE_ERROR_ON(_reshaped_weights); + + for(unsigned int i = 0; i < weights.size(); ++i) + { + library->fill_layer_data(Accessor(w[i]), weights[i]); + library->fill_layer_data(Accessor(b[i]), biases[i]); + } + } + + /** Feed input to network from file. + * + * @param name File name of containing the input data. + */ + void feed(std::string name) + { + library->fill_layer_data(Accessor(input), name); + } +#endif /* INTERNAL_ONLY */ + + /** Get the classification results. + * + * @return Vector containing the classified labels + */ + std::vector<unsigned int> get_classifications() + { + std::vector<unsigned int> classified_labels; + Accessor output_accessor(output); + + Window window; + window.set(Window::DimX, Window::Dimension(0, 1, 1)); + for(unsigned int d = 1; d < output_accessor.shape().num_dimensions(); ++d) + { + window.set(d, Window::Dimension(0, output_accessor.shape()[d], 1)); + } + + execute_window_loop(window, [&](const Coordinates & id) + { + int max_idx = 0; + float val = 0; + const void *const out_ptr = output_accessor(id); + for(unsigned int l = 0; l < output_accessor.shape().x(); ++l) + { + float curr_val = reinterpret_cast<const float *>(out_ptr)[l]; + if(curr_val > val) + { + max_idx = l; + val = curr_val; + } + } + classified_labels.push_back(max_idx); + }); + return classified_labels; + } + + /** Clear all allocated memory from the tensor objects */ + void clear() + { + // Free allocations + input.allocator()->free(); + output.allocator()->free(); + + if(!_reshaped_weights) + { + for(auto &wi : w) + { + wi.allocator()->free(); + } + + for(auto &bi : b) + { + bi.allocator()->free(); + } + } + else + { + w[0].allocator()->free(); + w[2].allocator()->free(); + w[5].allocator()->free(); + w[6].allocator()->free(); + w[7].allocator()->free(); + + b[5].allocator()->free(); + b[6].allocator()->free(); + b[7].allocator()->free(); + + if(_is_direct_conv) + { + w[3].allocator()->free(); + w[4].allocator()->free(); + b[2].allocator()->free(); + b[3].allocator()->free(); + b[4].allocator()->free(); + } + } + + w21.reset(); + w22.reset(); + b21.reset(); + b21.reset(); + w41.reset(); + w42.reset(); + b41.reset(); + b42.reset(); + w51.reset(); + w52.reset(); + b51.reset(); + b52.reset(); + + conv1_out.allocator()->free(); + act1_out.allocator()->free(); + norm1_out.allocator()->free(); + pool1_out.allocator()->free(); + conv2_out.allocator()->free(); + act2_out.allocator()->free(); + norm2_out.allocator()->free(); + pool2_out.allocator()->free(); + conv3_out.allocator()->free(); + act3_out.allocator()->free(); + conv4_out.allocator()->free(); + act4_out.allocator()->free(); + conv5_out.allocator()->free(); + act5_out.allocator()->free(); + pool5_out.allocator()->free(); + fc6_out.allocator()->free(); + act6_out.allocator()->free(); + fc7_out.allocator()->free(); + act7_out.allocator()->free(); + fc8_out.allocator()->free(); + } + + /** Runs the model */ + void run() + { + // Layer 1 + conv1.run(); + act1.run(); + norm1.run(); + pool1.run(); + // Layer 2 + conv21.run(); + conv22.run(); + act2.run(); + norm2.run(); + pool2.run(); + // Layer 3 + conv3.run(); + act3.run(); + // Layer 4 + conv41.run(); + conv42.run(); + act4.run(); + // Layer 5 + conv51.run(); + conv52.run(); + act5.run(); + pool5.run(); + // Layer 6 + fc6.run(); + act6.run(); + // Layer 7 + fc7.run(); + act7.run(); + // Layer 8 + fc8.run(); + // Softmax + smx.run(); + } + +private: + struct DirectConv + { + template <typename ConvolutionLayerFunction1 = ConvolutionLayerFunction, typename DirectConvolutionLayerFunction1 = DirectConvolutionLayerFunction> + typename std::enable_if < !std::is_same<ConvolutionLayerFunction1, DirectConvolutionLayerFunction1>::value, void >::type + configure(ITensorType *input, const ITensorType *weights, const ITensorType *biases, ITensorType *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo()) + { + _func.configure(input, weights, biases, output, conv_info); + } + + template <typename ConvolutionLayerFunction1 = ConvolutionLayerFunction, typename DirectConvolutionLayerFunction1 = DirectConvolutionLayerFunction> + typename std::enable_if<std::is_same<ConvolutionLayerFunction1, DirectConvolutionLayerFunction1>::value, void>::type + configure(ITensorType *input, const ITensorType *weights, const ITensorType *biases, ITensorType *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo()) + { + _func.configure(input, weights, biases, output, conv_info, weights_info); + } + + void run() + { + _func.run(); + } + + DirectConvolutionLayerFunction _func{}; + }; + + DataType _data_type{ DataType::UNKNOWN }; + int _fixed_point_position{ 0 }; + unsigned int _batches{ 0 }; + bool _reshaped_weights{ false }; + bool _is_direct_conv{ !std::is_same<ConvolutionLayerFunction, DirectConvolutionLayerFunction>::value }; + + ActivationLayerFunction act1{}, act2{}, act3{}, act4{}, act5{}, act6{}, act7{}; + ConvolutionLayerFunction conv1{}; + DirectConv conv21{}, conv22{}, conv3{}, conv41{}, conv42{}, conv51{}, conv52{}; + FullyConnectedLayerFunction fc6{}, fc7{}, fc8{}; + NormalizationLayerFunction norm1{}, norm2{}; + PoolingLayerFunction pool1{}, pool2{}, pool5{}; + SoftmaxLayerFunction smx{}; + + TensorType input{}, output{}; + std::array<TensorType, 8> w{ {} }, b{ {} }; + std::unique_ptr<ITensorType> w21{ nullptr }, w22{ nullptr }, b21{ nullptr }, b22{ nullptr }; + std::unique_ptr<ITensorType> w41{ nullptr }, w42{ nullptr }, b41{ nullptr }, b42{ nullptr }; + std::unique_ptr<ITensorType> w51{ nullptr }, w52{ nullptr }, b51{ nullptr }, b52{ nullptr }; + + TensorType conv1_out{}, act1_out{}, norm1_out{}, pool1_out{}; + TensorType conv2_out{}, act2_out{}, pool2_out{}, norm2_out{}; + TensorType conv3_out{}, act3_out{}; + TensorType conv4_out{}, act4_out{}; + TensorType conv5_out{}, act5_out{}, pool5_out{}; + TensorType fc6_out{}, act6_out{}; + TensorType fc7_out{}, act7_out{}; + TensorType fc8_out{}; + + std::unique_ptr<SubTensorType> pool11_out{}, pool12_out{}; + std::unique_ptr<SubTensorType> conv21_out{}, conv22_out{}; + std::unique_ptr<SubTensorType> act31_out{}, act32_out{}; + std::unique_ptr<SubTensorType> conv41_out{}, conv42_out{}, act41_out{}, act42_out{}; + std::unique_ptr<SubTensorType> conv51_out{}, conv52_out{}; +}; +} // namespace networks +} // namespace test +} // namespace arm_compute +#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__ |