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diff --git a/tests/networks/AlexNetNetwork.h b/tests/networks/AlexNetNetwork.h
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+/*
+ * 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__
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-24 21:38:20 +0000