COMPMID-1188: Remove graph system tests

Change-Id: I429087f8aa436cf0877c3abec8fd7201bec1b81c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/141661
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

4 files changed, 0 insertions, 1615 deletions

diff --git a/tests/networks/AlexNetNetwork.h b/tests/networks/AlexNetNetwork.h
deleted file mode 100644
index e15db2a110..0000000000
--- a/tests/networks/AlexNetNetwork.h
+++ /dev/null
@@ -1,646 +0,0 @@
-/*
- * Copyright (c) 2017-2018 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/NEON/NEScheduler.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:
-    /** Initialize the network.
-     *
-     * @param[in] data_type        Data type.
-     * @param[in] batches          Number of batches.
-     * @param[in] reshaped_weights Whether the weights need reshaping or not. Default: false.
-     */
-    void init(DataType data_type, int batches, bool reshaped_weights = false)
-    {
-        _data_type        = data_type;
-        _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));
-            b[0].allocator()->init(TensorInfo(TensorShape(96U), 1, _data_type));
-            w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type));
-            b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type));
-            w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type));
-            b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type));
-            w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type));
-            b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type));
-            w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type));
-            b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type));
-            w[5].allocator()->init(TensorInfo(TensorShape(9216U, 4096U), 1, _data_type));
-            b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type));
-            w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type));
-            b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type));
-            w[7].allocator()->init(TensorInfo(TensorShape(4096U, 1000U), 1, _data_type));
-            b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type));
-
-            w11 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates()));
-            w12 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128)));
-            b11 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(), true));
-            b12 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128), true));
-
-            w31 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates()));
-            w32 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192)));
-            b31 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(), true));
-            b32 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192), true));
-
-            w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates()));
-            w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128)));
-            b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(), true));
-            b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128), true));
-        }
-        else
-        {
-            auto reshape = [&](unsigned int width, unsigned int height, bool convolution_layer) -> TensorShape
-            {
-                const bool is_optimised = std::is_same<ITensorType, ITensor>::value && data_type == DataType::F32;
-
-                if(convolution_layer && is_optimised)
-                {
-                    return TensorShape{ height, width };
-                }
-                else
-                {
-                    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, true), 1, _data_type));
-
-            // Configure the direct convolution's weights. Direct convolution doesn't need reshape weights
-            if(!_is_direct_conv)
-            {
-                auto w11_tensor = std::unique_ptr<TensorType>(new TensorType());
-                auto w12_tensor = std::unique_ptr<TensorType>(new TensorType());
-                auto w31_tensor = std::unique_ptr<TensorType>(new TensorType());
-                auto w32_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());
-                w11_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U, true), 1, _data_type));
-                w12_tensor->allocator()->init(TensorInfo(reshape(1248U, 128U, true), 1, _data_type));
-                w31_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U, true), 1, _data_type));
-                w32_tensor->allocator()->init(TensorInfo(reshape(1920U, 192U, true), 1, _data_type));
-                w41_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U, true), 1, _data_type));
-                w42_tensor->allocator()->init(TensorInfo(reshape(1920U, 128U, true), 1, _data_type));
-                w[2].allocator()->init(TensorInfo(reshape(2560U, 384U, true), 1, _data_type));
-                w11 = std::move(w11_tensor);
-                w12 = std::move(w12_tensor);
-                w31 = std::move(w31_tensor);
-                w32 = std::move(w32_tensor);
-                w41 = std::move(w41_tensor);
-                w42 = std::move(w42_tensor);
-            }
-            else
-            {
-                w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type));
-                b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type));
-                w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type));
-                b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type));
-                w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type));
-                b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type));
-                w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type));
-                b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type));
-                w11 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates()));
-                w12 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128)));
-                b11 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates()));
-                b12 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128)));
-
-                w31 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates()));
-                w32 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192)));
-                b31 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates()));
-                b32 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192)));
-
-                w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates()));
-                w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128)));
-                b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates()));
-                b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128)));
-            }
-
-            b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type));
-            b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type));
-            b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type));
-
-            if(_batches > 1 && std::is_same<TensorType, Tensor>::value)
-            {
-                w[5].allocator()->init(TensorInfo(reshape(9216U, 4096U, false), 1, _data_type));
-                w[6].allocator()->init(TensorInfo(reshape(4096U, 4096U, false), 1, _data_type));
-                w[7].allocator()->init(TensorInfo(reshape(4096U, 1000U, false), 1, _data_type));
-            }
-            else
-            {
-                w[5].allocator()->init(TensorInfo(TensorShape(4096U, 9216U), 1, _data_type));
-                w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type));
-                w[7].allocator()->init(TensorInfo(TensorShape(1000U, 4096U), 1, _data_type));
-            }
-        }
-    }
-
-    /** Build the network */
-    void build()
-    {
-        FullyConnectedLayerInfo fc_info;
-        fc_info.are_weights_reshaped = _reshaped_weights;
-
-        input.allocator()->init(TensorInfo(TensorShape(227U, 227U, 3U, _batches), 1, _data_type));
-        output.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type));
-
-        // Initialize intermediate tensors
-        // Layer 1
-        conv1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type));
-        act1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type));
-        norm1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type));
-        pool1_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 96U, _batches), 1, _data_type));
-        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));
-        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));
-        norm2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type));
-        pool2_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type));
-        // Layer 3
-        conv3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type));
-        act3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type));
-        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));
-        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));
-        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));
-        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));
-        pool5_out.allocator()->init(TensorInfo(TensorShape(6U, 6U, 256U, _batches), 1, _data_type));
-        // Layer 6
-        fc6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type));
-        act6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type));
-        // Layer 7
-        fc7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type));
-        act7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type));
-        // Layer 8
-        fc8_out.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type));
-
-        // 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(), w11.get(), b11.get(), conv21_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 5U, 128U));
-        conv22.configure(pool12_out.get(), w12.get(), b12.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(), w31.get(), b31.get(), conv41_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 192U));
-        conv42.configure(act32_out.get(), w32.get(), b32.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(), w41.get(), b41.get(), conv51_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U, 128U));
-        conv52.configure(act42_out.get(), w42.get(), b42.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, fc_info);
-        act6.configure(&fc6_out, &act6_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-        // Layer 7
-        fc7.configure(&act6_out, &w[6], &b[6], &fc7_out, fc_info);
-        act7.configure(&fc7_out, &act7_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-        // Layer 8
-        fc8.configure(&act7_out, &w[7], &b[7], &fc8_out, fc_info);
-        // Softmax
-        smx.configure(&fc8_out, &output);
-    }
-
-    /** Allocate the network */
-    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 *>(w11.get())->allocator()->allocate();
-                dynamic_cast<TensorType *>(w12.get())->allocator()->allocate();
-                dynamic_cast<TensorType *>(w31.get())->allocator()->allocate();
-                dynamic_cast<TensorType *>(w32.get())->allocator()->allocate();
-                dynamic_cast<TensorType *>(w41.get())->allocator()->allocate();
-                dynamic_cast<TensorType *>(w42.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 *>(w11.get())), 9);
-                library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w12.get())), 10);
-                library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w31.get())), 11);
-                library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w32.get())), 12);
-                library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w41.get())), 13);
-                library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w42.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);
-            }
-        }
-    }
-
-    /** 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);
-    }
-
-    /** 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();
-            }
-        }
-
-        w11.reset();
-        w12.reset();
-        b11.reset();
-        b11.reset();
-        w31.reset();
-        w32.reset();
-        b31.reset();
-        b32.reset();
-        w41.reset();
-        w42.reset();
-        b41.reset();
-        b42.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();
-    }
-
-    /** Sync the results */
-    void sync()
-    {
-        sync_if_necessary<TensorType>();
-        sync_tensor_if_necessary<TensorType>(output);
-    }
-
-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 };
-    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> w11{ nullptr }, w12{ nullptr }, b11{ nullptr }, b12{ nullptr };
-    std::unique_ptr<ITensorType> w31{ nullptr }, w32{ nullptr }, b31{ nullptr }, b32{ nullptr };
-    std::unique_ptr<ITensorType> w41{ nullptr }, w42{ nullptr }, b41{ nullptr }, b42{ 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__
diff --git a/tests/networks/LeNet5Network.h b/tests/networks/LeNet5Network.h
deleted file mode 100644
index 9cfd59284c..0000000000
--- a/tests/networks/LeNet5Network.h
+++ /dev/null
@@ -1,265 +0,0 @@
-/*
- * Copyright (c) 2017-2018 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_LENET5_H__
-#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__
-
-#include "tests/AssetsLibrary.h"
-#include "tests/Globals.h"
-#include "tests/Utils.h"
-
-#include <memory>
-
-using namespace arm_compute;
-using namespace arm_compute::test;
-
-namespace arm_compute
-{
-namespace test
-{
-namespace networks
-{
-/** Lenet5 model object */
-template <typename TensorType,
-          typename Accessor,
-          typename ActivationLayerFunction,
-          typename ConvolutionLayerFunction,
-          typename FullyConnectedLayerFunction,
-          typename PoolingLayerFunction,
-          typename SoftmaxLayerFunction>
-class LeNet5Network
-{
-public:
-    /** Initialize the network.
-     *
-     * @param[in] batches Number of batches.
-     */
-    void init(int batches)
-    {
-        _batches = batches;
-
-        // Initialize input, output, weights and biases
-        input.allocator()->init(TensorInfo(TensorShape(28U, 28U, 1U, _batches), 1, DataType::F32));
-        output.allocator()->init(TensorInfo(TensorShape(10U, _batches), 1, DataType::F32));
-        w[0].allocator()->init(TensorInfo(TensorShape(5U, 5U, 1U, 20U), 1, DataType::F32));
-        b[0].allocator()->init(TensorInfo(TensorShape(20U), 1, DataType::F32));
-        w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 20U, 50U), 1, DataType::F32));
-        b[1].allocator()->init(TensorInfo(TensorShape(50U), 1, DataType::F32));
-        w[2].allocator()->init(TensorInfo(TensorShape(800U, 500U), 1, DataType::F32));
-        b[2].allocator()->init(TensorInfo(TensorShape(500U), 1, DataType::F32));
-        w[3].allocator()->init(TensorInfo(TensorShape(500U, 10U), 1, DataType::F32));
-        b[3].allocator()->init(TensorInfo(TensorShape(10U), 1, DataType::F32));
-    }
-
-    /** Build the model. */
-    void build()
-    {
-        // Initialize intermediate tensors
-        // Layer 1
-        conv1_out.allocator()->init(TensorInfo(TensorShape(24U, 24U, 20U, _batches), 1, DataType::F32));
-        pool1_out.allocator()->init(TensorInfo(TensorShape(12U, 12U, 20U, _batches), 1, DataType::F32));
-        // Layer 2
-        conv2_out.allocator()->init(TensorInfo(TensorShape(8U, 8U, 50U, _batches), 1, DataType::F32));
-        pool2_out.allocator()->init(TensorInfo(TensorShape(4U, 4U, 50U, _batches), 1, DataType::F32));
-        // Layer 3
-        fc1_out.allocator()->init(TensorInfo(TensorShape(500U, _batches), 1, DataType::F32));
-        act1_out.allocator()->init(TensorInfo(TensorShape(500U, _batches), 1, DataType::F32));
-        // Layer 6
-        fc2_out.allocator()->init(TensorInfo(TensorShape(10U, _batches), 1, DataType::F32));
-
-        // Configure Layers
-        conv1.configure(&input, &w[0], &b[0], &conv1_out, PadStrideInfo(1, 1, 0, 0));
-        pool1.configure(&conv1_out, &pool1_out, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)));
-        conv2.configure(&pool1_out, &w[1], &b[1], &conv2_out, PadStrideInfo(1, 1, 0, 0));
-        pool2.configure(&conv2_out, &pool2_out, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)));
-        fc1.configure(&pool2_out, &w[2], &b[2], &fc1_out);
-        act1.configure(&fc1_out, &act1_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
-        fc2.configure(&act1_out, &w[3], &b[3], &fc2_out);
-        smx.configure(&fc2_out, &output);
-    }
-
-    /** Allocate the network */
-    void allocate()
-    {
-        // Allocate tensors
-        input.allocator()->allocate();
-        output.allocator()->allocate();
-        for(auto &wi : w)
-        {
-            wi.allocator()->allocate();
-        }
-        for(auto &bi : b)
-        {
-            bi.allocator()->allocate();
-        }
-        conv1_out.allocator()->allocate();
-        pool1_out.allocator()->allocate();
-        conv2_out.allocator()->allocate();
-        pool2_out.allocator()->allocate();
-        fc1_out.allocator()->allocate();
-        act1_out.allocator()->allocate();
-        fc2_out.allocator()->allocate();
-    }
-
-    /** Fills the trainable parameters and input with random data. */
-    void fill_random()
-    {
-        std::uniform_real_distribution<> distribution(-1, 1);
-        library->fill(Accessor(input), distribution, 0);
-        for(unsigned int i = 0; i < w.size(); ++i)
-        {
-            library->fill(Accessor(w[i]), distribution, i + 1);
-            library->fill(Accessor(b[i]), distribution, i + 10);
-        }
-    }
-
-    /** 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());
-
-        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);
-    }
-
-    /** 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()
-    {
-        input.allocator()->free();
-        output.allocator()->free();
-        for(auto &wi : w)
-        {
-            wi.allocator()->free();
-        }
-        for(auto &bi : b)
-        {
-            bi.allocator()->free();
-        }
-
-        conv1_out.allocator()->free();
-        pool1_out.allocator()->free();
-        conv2_out.allocator()->free();
-        pool2_out.allocator()->free();
-        fc1_out.allocator()->free();
-        act1_out.allocator()->free();
-        fc2_out.allocator()->free();
-    }
-
-    /** Runs the model */
-    void run()
-    {
-        // Layer 1
-        conv1.run();
-        pool1.run();
-        // Layer 2
-        conv2.run();
-        pool2.run();
-        // Layer 3
-        fc1.run();
-        act1.run();
-        // Layer 4
-        fc2.run();
-        // Softmax
-        smx.run();
-    }
-
-    /** Sync the results */
-    void sync()
-    {
-        sync_if_necessary<TensorType>();
-        sync_tensor_if_necessary<TensorType>(output);
-    }
-
-private:
-    unsigned int _batches{ 0 };
-
-    ActivationLayerFunction     act1{};
-    ConvolutionLayerFunction    conv1{}, conv2{};
-    FullyConnectedLayerFunction fc1{}, fc2{};
-    PoolingLayerFunction        pool1{}, pool2{};
-    SoftmaxLayerFunction        smx{};
-
-    TensorType input{}, output{};
-    std::array<TensorType, 4> w{ {} }, b{ {} };
-
-    TensorType conv1_out{}, pool1_out{};
-    TensorType conv2_out{}, pool2_out{};
-    TensorType fc1_out{}, act1_out{};
-    TensorType fc2_out{};
-};
-} // namespace networks
-} // namespace test
-} // namespace arm_compute
-#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__
diff --git a/tests/networks/MobileNetNetwork.h b/tests/networks/MobileNetNetwork.h
deleted file mode 100644
index ec054b237e..0000000000
--- a/tests/networks/MobileNetNetwork.h
+++ /dev/null
@@ -1,314 +0,0 @@
-/*
- * Copyright (c) 2017-2018 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_MOBILENET_H__
-#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENET_H__
-
-#include "tests/AssetsLibrary.h"
-#include "tests/Globals.h"
-#include "tests/Utils.h"
-
-#include "utils/Utils.h"
-
-#include <memory>
-
-using namespace arm_compute;
-using namespace arm_compute::test;
-
-namespace arm_compute
-{
-namespace test
-{
-namespace networks
-{
-/** MobileNet model object */
-template <typename TensorType,
-          typename Accessor,
-          typename ActivationLayerFunction,
-          typename ConvolutionLayerFunction,
-          typename DirectConvolutionLayerFunction,
-          typename DepthwiseConvolutionLayerFunction,
-          typename ReshapeFunction,
-          typename PoolingLayerFunction>
-class MobileNetNetwork
-{
-public:
-    /** Initialize the network.
-     *
-     * @param[in] batches Number of batches.
-     */
-    void init(int batches)
-    {
-        _batches = batches;
-
-        // Initialize input, output
-        input.allocator()->init(TensorInfo(TensorShape(224U, 224U, 3U, _batches), 1, DataType::F32));
-        output.allocator()->init(TensorInfo(TensorShape(11U, _batches), 1, DataType::F32));
-        // Initialize weights and biases
-        w_conv3x3.allocator()->init(TensorInfo(TensorShape(3U, 3U, 3U, 16U), 1, DataType::F32));
-        b_conv3x3.allocator()->init(TensorInfo(TensorShape(16U), 1, DataType::F32));
-        depthwise_conv_block_init(0, 16, 16);
-        depthwise_conv_block_init(1, 16, 32);
-        depthwise_conv_block_init(2, 32, 32);
-        depthwise_conv_block_init(3, 32, 64);
-        depthwise_conv_block_init(4, 64, 64);
-        depthwise_conv_block_init(5, 64, 128);
-        depthwise_conv_block_init(6, 128, 128);
-        depthwise_conv_block_init(7, 128, 128);
-        depthwise_conv_block_init(8, 128, 128);
-        depthwise_conv_block_init(9, 128, 128);
-        depthwise_conv_block_init(10, 128, 128);
-        depthwise_conv_block_init(11, 128, 256);
-        depthwise_conv_block_init(12, 256, 256);
-        w_conv[13].allocator()->init(TensorInfo(TensorShape(1U, 1U, 256U, 11U), 1, DataType::F32));
-        b_conv[13].allocator()->init(TensorInfo(TensorShape(11U), 1, DataType::F32));
-    }
-
-    /** Build the model. */
-    void build()
-    {
-        // Configure Layers
-        conv3x3.configure(&input, &w_conv3x3, &b_conv3x3, &conv_out[0], PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR));
-        conv3x3_act.configure(&conv_out[0], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
-        depthwise_conv_block_build(0, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(1, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(2, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(4, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(5, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(6, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(7, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(8, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(9, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(10, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(11, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(12, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        pool.configure(&conv_out[13], &pool_out, PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(2, 2, 0, 0)));
-        conv1x1[13].configure(&pool_out, &w_conv[13], &b_conv[13], &conv_out[14], PadStrideInfo(1, 1, 0, 0));
-        logistic.configure(&conv_out[14], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
-        reshape.configure(&conv_out[14], &output);
-    }
-
-    /** Allocate the network. */
-    void allocate()
-    {
-        input.allocator()->allocate();
-        output.allocator()->allocate();
-
-        w_conv3x3.allocator()->allocate();
-        b_conv3x3.allocator()->allocate();
-        for(unsigned int i = 0; i < w_conv.size(); ++i)
-        {
-            w_conv[i].allocator()->allocate();
-            b_conv[i].allocator()->allocate();
-        }
-        for(unsigned int i = 0; i < w_dwc.size(); ++i)
-        {
-            w_dwc[i].allocator()->allocate();
-            b_dwc[i].allocator()->allocate();
-        }
-        for(auto &o : conv_out)
-        {
-            o.allocator()->allocate();
-        }
-        for(auto &o : dwc_out)
-        {
-            o.allocator()->allocate();
-        }
-        pool_out.allocator()->allocate();
-    }
-
-    /** Fills the trainable parameters and input with random data. */
-    void fill_random()
-    {
-        unsigned int                     seed_idx = 0;
-        std::uniform_real_distribution<> distribution(-1, 1);
-        library->fill(Accessor(input), distribution, seed_idx++);
-
-        library->fill(Accessor(w_conv3x3), distribution, seed_idx++);
-        library->fill(Accessor(b_conv3x3), distribution, seed_idx++);
-        for(unsigned int i = 0; i < w_conv.size(); ++i)
-        {
-            library->fill(Accessor(w_conv[i]), distribution, seed_idx++);
-            library->fill(Accessor(b_conv[i]), distribution, seed_idx++);
-        }
-        for(unsigned int i = 0; i < w_dwc.size(); ++i)
-        {
-            library->fill(Accessor(w_dwc[i]), distribution, seed_idx++);
-            library->fill(Accessor(b_dwc[i]), distribution, seed_idx++);
-        }
-    }
-
-    /** 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);
-    }
-
-    /** 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()
-    {
-        input.allocator()->free();
-        output.allocator()->free();
-
-        w_conv3x3.allocator()->free();
-        b_conv3x3.allocator()->free();
-        for(unsigned int i = 0; i < w_conv.size(); ++i)
-        {
-            w_conv[i].allocator()->free();
-            b_conv[i].allocator()->free();
-        }
-        for(unsigned int i = 0; i < w_dwc.size(); ++i)
-        {
-            w_dwc[i].allocator()->free();
-            b_dwc[i].allocator()->free();
-        }
-        for(auto &o : conv_out)
-        {
-            o.allocator()->free();
-        }
-        for(auto &o : dwc_out)
-        {
-            o.allocator()->free();
-        }
-        pool_out.allocator()->free();
-    }
-
-    /** Runs the model */
-    void run()
-    {
-        conv3x3.run();
-        conv3x3_act.run();
-        depthwise_conv_block_run(0);
-        depthwise_conv_block_run(1);
-        depthwise_conv_block_run(2);
-        depthwise_conv_block_run(3);
-        depthwise_conv_block_run(4);
-        depthwise_conv_block_run(5);
-        depthwise_conv_block_run(6);
-        depthwise_conv_block_run(7);
-        depthwise_conv_block_run(8);
-        depthwise_conv_block_run(9);
-        depthwise_conv_block_run(10);
-        depthwise_conv_block_run(11);
-        depthwise_conv_block_run(12);
-        pool.run();
-        conv1x1[13].run();
-        logistic.run();
-        reshape.run();
-    }
-
-    /** Sync the results */
-    void sync()
-    {
-        sync_if_necessary<TensorType>();
-        sync_tensor_if_necessary<TensorType>(output);
-    }
-
-private:
-    void depthwise_conv_block_init(unsigned int idx, unsigned int ifm, unsigned int ofm)
-    {
-        w_dwc[idx].allocator()->init(TensorInfo(TensorShape(3U, 3U, ifm), 1, DataType::F32));
-        b_dwc[idx].allocator()->init(TensorInfo(TensorShape(ifm), 1, DataType::F32));
-        w_conv[idx].allocator()->init(TensorInfo(TensorShape(1U, 1U, ifm, ofm), 1, DataType::F32));
-        b_conv[idx].allocator()->init(TensorInfo(TensorShape(ofm), 1, DataType::F32));
-    }
-    void depthwise_conv_block_build(unsigned int idx, PadStrideInfo dwc_ps, PadStrideInfo conv_ps)
-    {
-        dwc3x3[idx].configure(&conv_out[idx], &w_dwc[idx], &b_dwc[idx], &dwc_out[idx], dwc_ps);
-        act[2 * idx].configure(&dwc_out[idx], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
-        conv1x1[idx].configure(&dwc_out[idx], &w_conv[idx], &b_conv[idx], &conv_out[idx + 1], conv_ps);
-        act[2 * idx + 1].configure(&conv_out[idx], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
-    }
-    void depthwise_conv_block_run(unsigned int idx)
-    {
-        dwc3x3[idx].run();
-        act[2 * idx].run();
-        conv1x1[idx].run();
-        act[2 * idx + 1].run();
-    }
-
-private:
-    unsigned int _batches{ 0 };
-
-    ConvolutionLayerFunction conv3x3{};
-    ActivationLayerFunction  conv3x3_act{};
-    std::array<ActivationLayerFunction, 26>           act{ {} };
-    std::array<DirectConvolutionLayerFunction, 14>    conv1x1{ {} };
-    std::array<DepthwiseConvolutionLayerFunction, 13> dwc3x3{ {} };
-    PoolingLayerFunction    pool{};
-    ActivationLayerFunction logistic{};
-    ReshapeFunction         reshape{};
-
-    TensorType w_conv3x3{}, b_conv3x3{};
-    std::array<TensorType, 14> w_conv{ {} }, b_conv{ {} };
-    std::array<TensorType, 13> w_dwc{ {} }, b_dwc{ {} };
-
-    TensorType input{}, output{};
-
-    std::array<TensorType, 15> conv_out{ {} };
-    std::array<TensorType, 13> dwc_out{ {} };
-    TensorType pool_out{};
-};
-} // namespace networks
-} // namespace test
-} // namespace arm_compute
-#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENET_H__
diff --git a/tests/networks/MobileNetV1Network.h b/tests/networks/MobileNetV1Network.h
deleted file mode 100644
index aea5c113e8..0000000000
--- a/tests/networks/MobileNetV1Network.h
+++ /dev/null
@@ -1,390 +0,0 @@
-/*
- * Copyright (c) 2017-2018 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_MOBILENETV1_H__
-#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENETV1_H__
-
-#include "tests/AssetsLibrary.h"
-#include "tests/Globals.h"
-#include "tests/Utils.h"
-
-#include "utils/Utils.h"
-
-#include <memory>
-
-using namespace arm_compute;
-using namespace arm_compute::test;
-
-namespace arm_compute
-{
-namespace test
-{
-namespace networks
-{
-/** MobileNet model object */
-template <typename TensorType,
-          typename Accessor,
-          typename ActivationLayerFunction,
-          typename BatchNormalizationLayerFunction,
-          typename ConvolutionLayerFunction,
-          typename DirectConvolutionLayerFunction,
-          typename DepthwiseConvolutionFunction,
-          typename ReshapeFunction,
-          typename PoolingLayerFunction,
-          typename SoftmaxLayerFunction>
-class MobileNetV1Network
-{
-public:
-    /** Initialize the network.
-     *
-     * @param[in] input_spatial_size Size of the spatial input.
-     * @param[in] batches            Number of batches.
-     */
-    void init(unsigned int input_spatial_size, int batches)
-    {
-        _batches            = batches;
-        _input_spatial_size = input_spatial_size;
-
-        // Currently supported sizes
-        ARM_COMPUTE_ERROR_ON(input_spatial_size != 128 && input_spatial_size != 224);
-
-        // Initialize input, output
-        input.allocator()->init(TensorInfo(TensorShape(input_spatial_size, input_spatial_size, 3U, _batches), 1, DataType::F32));
-        output.allocator()->init(TensorInfo(TensorShape(1001U, _batches), 1, DataType::F32));
-        // Initialize weights and biases
-        w_conv3x3.allocator()->init(TensorInfo(TensorShape(3U, 3U, 3U, 32U), 1, DataType::F32));
-        mean_conv3x3.allocator()->init(TensorInfo(TensorShape(32U), 1, DataType::F32));
-        var_conv3x3.allocator()->init(TensorInfo(TensorShape(32U), 1, DataType::F32));
-        beta_conv3x3.allocator()->init(TensorInfo(TensorShape(32U), 1, DataType::F32));
-        gamma_conv3x3.allocator()->init(TensorInfo(TensorShape(32U), 1, DataType::F32));
-        depthwise_conv_block_init(0, 32, 32);
-        depthwise_conv_block_init(1, 32, 64);
-        depthwise_conv_block_init(2, 64, 64);
-        depthwise_conv_block_init(3, 64, 128);
-        depthwise_conv_block_init(4, 128, 256);
-        depthwise_conv_block_init(5, 256, 512);
-        depthwise_conv_block_init(6, 512, 512);
-        depthwise_conv_block_init(7, 512, 512);
-        depthwise_conv_block_init(8, 512, 512);
-        depthwise_conv_block_init(9, 512, 512);
-        depthwise_conv_block_init(10, 512, 512);
-        depthwise_conv_block_init(11, 512, 1024);
-        depthwise_conv_block_init(12, 1024, 1024);
-        w_conv1c.allocator()->init(TensorInfo(TensorShape(1U, 1U, 1024U, 1001U), 1, DataType::F32));
-        b_conv1c.allocator()->init(TensorInfo(TensorShape(1001U), 1, DataType::F32));
-        // Init reshaped output
-        reshape_out.allocator()->init(TensorInfo(TensorShape(1001U, _batches), 1, DataType::F32));
-    }
-
-    /** Build the model. */
-    void build()
-    {
-        // Configure Layers
-        conv3x3.configure(&input, &w_conv3x3, nullptr, &conv_out[0], PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR));
-        conv3x3_bn.configure(&conv_out[0], nullptr, &mean_conv3x3, &var_conv3x3, &beta_conv3x3, &gamma_conv3x3, 0.001f);
-        conv3x3_act.configure(&conv_out[0], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
-        depthwise_conv_block_build(0, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(1, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(2, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(4, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(5, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(6, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(7, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(8, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(9, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(10, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(11, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        depthwise_conv_block_build(12, PadStrideInfo(1, 1, 1, 1, 1, 1, DimensionRoundingType::FLOOR), PadStrideInfo(1, 1, 0, 0));
-        pool.configure(&conv_out[13], &pool_out, PoolingLayerInfo(PoolingType::AVG));
-        conv1c.configure(&pool_out, &w_conv1c, &b_conv1c, &conv_out[14], PadStrideInfo(1, 1, 0, 0));
-        reshape.configure(&conv_out[14], &reshape_out);
-        smx.configure(&reshape_out, &output);
-    }
-
-    /** Allocate the network. */
-    void allocate()
-    {
-        input.allocator()->allocate();
-        output.allocator()->allocate();
-
-        w_conv3x3.allocator()->allocate();
-        mean_conv3x3.allocator()->allocate();
-        var_conv3x3.allocator()->allocate();
-        beta_conv3x3.allocator()->allocate();
-        gamma_conv3x3.allocator()->allocate();
-
-        ARM_COMPUTE_ERROR_ON(w_conv.size() != w_dwc.size());
-        for(unsigned int i = 0; i < w_conv.size(); ++i)
-        {
-            w_dwc[i].allocator()->allocate();
-            bn_mean[2 * i].allocator()->allocate();
-            bn_var[2 * i].allocator()->allocate();
-            bn_beta[2 * i].allocator()->allocate();
-            bn_gamma[2 * i].allocator()->allocate();
-            w_conv[i].allocator()->allocate();
-            bn_mean[2 * i + 1].allocator()->allocate();
-            bn_var[2 * i + 1].allocator()->allocate();
-            bn_beta[2 * i + 1].allocator()->allocate();
-            bn_gamma[2 * i + 1].allocator()->allocate();
-        }
-        w_conv1c.allocator()->allocate();
-        b_conv1c.allocator()->allocate();
-
-        // Allocate intermediate buffers
-        for(auto &o : conv_out)
-        {
-            o.allocator()->allocate();
-        }
-        for(auto &o : dwc_out)
-        {
-            o.allocator()->allocate();
-        }
-        pool_out.allocator()->allocate();
-        reshape_out.allocator()->allocate();
-    }
-
-    /** Fills the trainable parameters and input with random data. */
-    void fill_random()
-    {
-        unsigned int                     seed_idx = 0;
-        std::uniform_real_distribution<> distribution(-1, 1);
-        library->fill(Accessor(input), distribution, seed_idx++);
-
-        library->fill(Accessor(w_conv3x3), distribution, seed_idx++);
-        library->fill(Accessor(mean_conv3x3), distribution, seed_idx++);
-        library->fill(Accessor(var_conv3x3), distribution, seed_idx++);
-        library->fill(Accessor(beta_conv3x3), distribution, seed_idx++);
-        library->fill(Accessor(gamma_conv3x3), distribution, seed_idx++);
-
-        ARM_COMPUTE_ERROR_ON(w_conv.size() != w_dwc.size());
-        for(unsigned int i = 0; i < w_conv.size(); ++i)
-        {
-            library->fill(Accessor(w_dwc[i]), distribution, seed_idx++);
-            library->fill(Accessor(bn_mean[2 * i]), distribution, seed_idx++);
-            library->fill(Accessor(bn_var[2 * i]), distribution, seed_idx++);
-            library->fill(Accessor(bn_beta[2 * i]), distribution, seed_idx++);
-            library->fill(Accessor(bn_gamma[2 * i]), distribution, seed_idx++);
-            library->fill(Accessor(w_conv[i]), distribution, seed_idx++);
-            library->fill(Accessor(bn_mean[2 * i + 1]), distribution, seed_idx++);
-            library->fill(Accessor(bn_var[2 * i + 1]), distribution, seed_idx++);
-            library->fill(Accessor(bn_beta[2 * i + 1]), distribution, seed_idx++);
-            library->fill(Accessor(bn_gamma[2 * i + 1]), distribution, seed_idx++);
-        }
-        library->fill(Accessor(w_conv1c), distribution, seed_idx++);
-        library->fill(Accessor(b_conv1c), distribution, seed_idx++);
-    }
-
-    /** 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);
-    }
-
-    /** 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()
-    {
-        input.allocator()->free();
-        output.allocator()->free();
-
-        w_conv3x3.allocator()->free();
-        mean_conv3x3.allocator()->free();
-        var_conv3x3.allocator()->free();
-        beta_conv3x3.allocator()->free();
-        gamma_conv3x3.allocator()->free();
-
-        ARM_COMPUTE_ERROR_ON(w_conv.size() != w_dwc.size());
-        for(unsigned int i = 0; i < w_conv.size(); ++i)
-        {
-            w_dwc[i].allocator()->free();
-            bn_mean[2 * i].allocator()->free();
-            bn_var[2 * i].allocator()->free();
-            bn_beta[2 * i].allocator()->free();
-            bn_gamma[2 * i].allocator()->free();
-            w_conv[i].allocator()->free();
-            bn_mean[2 * i + 1].allocator()->free();
-            bn_var[2 * i + 1].allocator()->free();
-            bn_beta[2 * i + 1].allocator()->free();
-            bn_gamma[2 * i + 1].allocator()->free();
-        }
-        w_conv1c.allocator()->free();
-        b_conv1c.allocator()->free();
-
-        // Free intermediate buffers
-        for(auto &o : conv_out)
-        {
-            o.allocator()->free();
-        }
-        for(auto &o : dwc_out)
-        {
-            o.allocator()->free();
-        }
-        pool_out.allocator()->free();
-        reshape_out.allocator()->free();
-    }
-
-    /** Runs the model */
-    void run()
-    {
-        conv3x3.run();
-        conv3x3_bn.run();
-        conv3x3_act.run();
-        depthwise_conv_block_run(0);
-        depthwise_conv_block_run(1);
-        depthwise_conv_block_run(2);
-        depthwise_conv_block_run(3);
-        depthwise_conv_block_run(4);
-        depthwise_conv_block_run(5);
-        depthwise_conv_block_run(6);
-        depthwise_conv_block_run(7);
-        depthwise_conv_block_run(8);
-        depthwise_conv_block_run(9);
-        depthwise_conv_block_run(10);
-        depthwise_conv_block_run(11);
-        depthwise_conv_block_run(12);
-        pool.run();
-        conv1c.run();
-        reshape.run();
-        smx.run();
-    }
-
-    /** Sync the results */
-    void sync()
-    {
-        sync_if_necessary<TensorType>();
-        sync_tensor_if_necessary<TensorType>(output);
-    }
-
-private:
-    void depthwise_conv_block_init(unsigned int idx, unsigned int ifm, unsigned int ofm)
-    {
-        // Depthwise Convolution weights
-        w_dwc[idx].allocator()->init(TensorInfo(TensorShape(3U, 3U, ifm), 1, DataType::F32));
-        // Batch normalization parameters
-        bn_mean[2 * idx].allocator()->init(TensorInfo(TensorShape(ifm), 1, DataType::F32));
-        bn_var[2 * idx].allocator()->init(TensorInfo(TensorShape(ifm), 1, DataType::F32));
-        bn_beta[2 * idx].allocator()->init(TensorInfo(TensorShape(ifm), 1, DataType::F32));
-        bn_gamma[2 * idx].allocator()->init(TensorInfo(TensorShape(ifm), 1, DataType::F32));
-        // Convolution weights
-        w_conv[idx].allocator()->init(TensorInfo(TensorShape(1U, 1U, ifm, ofm), 1, DataType::F32));
-        // Batch normalization parameters
-        bn_mean[2 * idx + 1].allocator()->init(TensorInfo(TensorShape(ofm), 1, DataType::F32));
-        bn_var[2 * idx + 1].allocator()->init(TensorInfo(TensorShape(ofm), 1, DataType::F32));
-        bn_beta[2 * idx + 1].allocator()->init(TensorInfo(TensorShape(ofm), 1, DataType::F32));
-        bn_gamma[2 * idx + 1].allocator()->init(TensorInfo(TensorShape(ofm), 1, DataType::F32));
-    }
-    void depthwise_conv_block_build(unsigned int idx, PadStrideInfo dwc_ps, PadStrideInfo conv_ps)
-    {
-        // Configure depthwise convolution block
-        dwc3x3[idx].configure(&conv_out[idx], &w_dwc[idx], nullptr, &dwc_out[idx], dwc_ps);
-        bn[2 * idx].configure(&dwc_out[idx], nullptr, &bn_mean[2 * idx], &bn_var[2 * idx], &bn_beta[2 * idx], &bn_gamma[2 * idx], 0.001f);
-        act[2 * idx].configure(&dwc_out[idx], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
-        // Configure pointwise convolution block
-        conv1x1[idx].configure(&dwc_out[idx], &w_conv[idx], nullptr, &conv_out[idx + 1], conv_ps);
-        bn[2 * idx + 1].configure(&conv_out[idx + 1], nullptr, &bn_mean[2 * idx + 1], &bn_var[2 * idx + 1], &bn_beta[2 * idx + 1], &bn_gamma[2 * idx + 1], 0.001f);
-        act[2 * idx + 1].configure(&conv_out[idx], nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f));
-    }
-    void depthwise_conv_block_run(unsigned int idx)
-    {
-        dwc3x3[idx].run();
-        bn[2 * idx].run();
-        act[2 * idx].run();
-        conv1x1[idx].run();
-        bn[2 * idx + 1].run();
-        act[2 * idx + 1].run();
-    }
-
-private:
-    unsigned int _batches{ 0 };
-    unsigned int _input_spatial_size{ 0 };
-
-    ConvolutionLayerFunction        conv3x3{};
-    BatchNormalizationLayerFunction conv3x3_bn{};
-    ActivationLayerFunction         conv3x3_act{};
-    std::array<ActivationLayerFunction, 26>         act{ {} };
-    std::array<BatchNormalizationLayerFunction, 26> bn{ {} };
-    std::array<DepthwiseConvolutionFunction, 13>    dwc3x3{ {} };
-    std::array<DirectConvolutionLayerFunction, 13>  conv1x1{ {} };
-    DirectConvolutionLayerFunction conv1c{};
-    PoolingLayerFunction           pool{};
-    ReshapeFunction                reshape{};
-    SoftmaxLayerFunction           smx{};
-
-    TensorType w_conv3x3{}, mean_conv3x3{}, var_conv3x3{}, beta_conv3x3{}, gamma_conv3x3{};
-    std::array<TensorType, 13> w_conv{ {} };
-    std::array<TensorType, 13> w_dwc{ {} };
-    std::array<TensorType, 26> bn_mean{ {} };
-    std::array<TensorType, 26> bn_var{ {} };
-    std::array<TensorType, 26> bn_beta{ {} };
-    std::array<TensorType, 26> bn_gamma{ {} };
-    TensorType w_conv1c{}, b_conv1c{};
-
-    TensorType input{}, output{};
-
-    std::array<TensorType, 15> conv_out{ {} };
-    std::array<TensorType, 13> dwc_out{ {} };
-    TensorType pool_out{};
-    TensorType reshape_out{};
-};
-} // namespace networks
-} // namespace test
-} // namespace arm_compute
-#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_MOBILENETV1_H__