COMPMID-344 Updated doxygen
Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae
diff --git a/tests/model_objects/AlexNet.h b/tests/model_objects/AlexNet.h
new file mode 100644
index 0000000..d49ef06
--- /dev/null
+++ b/tests/model_objects/AlexNet.h
@@ -0,0 +1,582 @@
+/*
+ * 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 "TensorLibrary.h"
+#include "Utils.h"
+
+#include <memory>
+
+using namespace arm_compute;
+using namespace arm_compute::test;
+
+namespace arm_compute
+{
+namespace test
+{
+namespace model_objects
+{
+/** AlexNet model object */
+template <typename ITensorType,
+ typename TensorType,
+ typename SubTensorType,
+ typename Accessor,
+ typename ActivationLayerFunction,
+ typename ConvolutionLayerFunction,
+ typename FullyConnectedLayerFunction,
+ typename NormalizationLayerFunction,
+ typename PoolingLayerFunction,
+ typename SoftmaxLayerFunction,
+ DataType dt = DataType::F32,
+ int fixed_point_position = 4>
+class AlexNet
+{
+public:
+ AlexNet()
+ : _batches(1), _reshaped_weights(false)
+ {
+ }
+
+ void init_weights(unsigned int batches, bool reshaped_weights = false)
+ {
+ _batches = batches;
+ _reshaped_weights = reshaped_weights;
+
+ // Initialize weights and biases
+ if(!_reshaped_weights)
+ {
+ for(auto &wi : w)
+ {
+ wi = std::unique_ptr<TensorType>(new TensorType());
+ }
+ for(auto &bi : b)
+ {
+ bi = std::unique_ptr<TensorType>(new TensorType());
+ }
+ w[0]->allocator()->init(TensorInfo(TensorShape(11U, 11U, 3U, 96U), 1, dt, fixed_point_position));
+ b[0]->allocator()->init(TensorInfo(TensorShape(96U), 1, dt, fixed_point_position));
+ w[1]->allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, dt, fixed_point_position));
+ b[1]->allocator()->init(TensorInfo(TensorShape(256U), 1, dt, fixed_point_position));
+ w[2]->allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, dt, fixed_point_position));
+ b[2]->allocator()->init(TensorInfo(TensorShape(384U), 1, dt, fixed_point_position));
+ w[3]->allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, dt, fixed_point_position));
+ b[3]->allocator()->init(TensorInfo(TensorShape(384U), 1, dt, fixed_point_position));
+ w[4]->allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, dt, fixed_point_position));
+ b[4]->allocator()->init(TensorInfo(TensorShape(256U), 1, dt, fixed_point_position));
+ w[5]->allocator()->init(TensorInfo(TensorShape(9216U, 4096U), 1, dt, fixed_point_position));
+ b[5]->allocator()->init(TensorInfo(TensorShape(4096U), 1, dt, fixed_point_position));
+ w[6]->allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, dt, fixed_point_position));
+ b[6]->allocator()->init(TensorInfo(TensorShape(4096U), 1, dt, fixed_point_position));
+ w[7]->allocator()->init(TensorInfo(TensorShape(4096U, 1000U), 1, dt, fixed_point_position));
+ b[7]->allocator()->init(TensorInfo(TensorShape(1000U), 1, dt, fixed_point_position));
+
+ w21 = std::unique_ptr<SubTensorType>(new SubTensorType(w[1].get(), TensorShape(5U, 5U, 48U, 128U), Coordinates()));
+ w22 = std::unique_ptr<SubTensorType>(new SubTensorType(w[1].get(), TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128)));
+ b21 = std::unique_ptr<SubTensorType>(new SubTensorType(b[1].get(), TensorShape(128U), Coordinates()));
+ b22 = std::unique_ptr<SubTensorType>(new SubTensorType(b[1].get(), TensorShape(128U), Coordinates(128)));
+
+ w41 = std::unique_ptr<SubTensorType>(new SubTensorType(w[3].get(), TensorShape(3U, 3U, 192U, 192U), Coordinates()));
+ w42 = std::unique_ptr<SubTensorType>(new SubTensorType(w[3].get(), TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192)));
+ b41 = std::unique_ptr<SubTensorType>(new SubTensorType(b[3].get(), TensorShape(192U), Coordinates()));
+ b42 = std::unique_ptr<SubTensorType>(new SubTensorType(b[3].get(), TensorShape(192U), Coordinates(192)));
+
+ w51 = std::unique_ptr<SubTensorType>(new SubTensorType(w[4].get(), TensorShape(3U, 3U, 192U, 128U), Coordinates()));
+ w52 = std::unique_ptr<SubTensorType>(new SubTensorType(w[4].get(), TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128)));
+ b51 = std::unique_ptr<SubTensorType>(new SubTensorType(b[4].get(), TensorShape(128U), Coordinates()));
+ b52 = std::unique_ptr<SubTensorType>(new SubTensorType(b[4].get(), TensorShape(128U), Coordinates(128)));
+ }
+ else
+ {
+ const unsigned int dt_size = 16 / arm_compute::data_size_from_type(dt);
+
+ // Create tensor for the reshaped weights
+ w[0] = std::unique_ptr<TensorType>(new TensorType());
+ auto w21_tensor = std::unique_ptr<TensorType>(new TensorType());
+ auto w22_tensor = std::unique_ptr<TensorType>(new TensorType());
+ w[2] = 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());
+
+ w[0]->allocator()->init(TensorInfo(TensorShape(366U * dt_size, 96U / dt_size), 1, dt, fixed_point_position));
+ w21_tensor->allocator()->init(TensorInfo(TensorShape(1248U * dt_size, 128U / dt_size), 1, dt, fixed_point_position));
+ w22_tensor->allocator()->init(TensorInfo(TensorShape(1248U * dt_size, 128U / dt_size), 1, dt, fixed_point_position));
+ w[2]->allocator()->init(TensorInfo(TensorShape(2560U * dt_size, 384U / dt_size), 1, dt, fixed_point_position));
+ w41_tensor->allocator()->init(TensorInfo(TensorShape(1920U * dt_size, 192U / dt_size), 1, dt, fixed_point_position));
+ w42_tensor->allocator()->init(TensorInfo(TensorShape(1920U * dt_size, 192U / dt_size), 1, dt, fixed_point_position));
+ w51_tensor->allocator()->init(TensorInfo(TensorShape(1920U * dt_size, 128U / dt_size), 1, dt, fixed_point_position));
+ w52_tensor->allocator()->init(TensorInfo(TensorShape(1920U * dt_size, 128U / dt_size), 1, dt, 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);
+
+ w[5] = std::unique_ptr<TensorType>(new TensorType());
+ w[6] = std::unique_ptr<TensorType>(new TensorType());
+ w[7] = std::unique_ptr<TensorType>(new TensorType());
+ b[5] = std::unique_ptr<TensorType>(new TensorType());
+ b[6] = std::unique_ptr<TensorType>(new TensorType());
+ b[7] = std::unique_ptr<TensorType>(new TensorType());
+
+ b[5]->allocator()->init(TensorInfo(TensorShape(4096U), 1, dt, fixed_point_position));
+ b[6]->allocator()->init(TensorInfo(TensorShape(4096U), 1, dt, fixed_point_position));
+ b[7]->allocator()->init(TensorInfo(TensorShape(1000U), 1, dt, fixed_point_position));
+
+ if(_batches > 1)
+ {
+ w[5]->allocator()->init(TensorInfo(TensorShape(9216U * dt_size, 4096U / dt_size), 1, dt, fixed_point_position));
+ w[6]->allocator()->init(TensorInfo(TensorShape(4096U * dt_size, 4096U / dt_size), 1, dt, fixed_point_position));
+ w[7]->allocator()->init(TensorInfo(TensorShape(4096U * dt_size, 1000U / dt_size), 1, dt, fixed_point_position));
+ }
+ else
+ {
+ w[5]->allocator()->init(TensorInfo(TensorShape(4096U, 9216U), 1, dt, fixed_point_position));
+ w[6]->allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, dt, fixed_point_position));
+ w[7]->allocator()->init(TensorInfo(TensorShape(1000U, 4096U), 1, dt, fixed_point_position));
+ }
+ }
+ }
+
+ void build()
+ {
+ input.allocator()->init(TensorInfo(TensorShape(227U, 227U, 3U, _batches), 1, dt, fixed_point_position));
+ output.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, dt, fixed_point_position));
+
+ // Initialize intermediate tensors
+ // Layer 1
+ conv1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, dt, fixed_point_position));
+ act1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, dt, fixed_point_position));
+ norm1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, dt, fixed_point_position));
+ pool1_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 96U, _batches), 1, dt, 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, dt, 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, dt, fixed_point_position));
+ norm2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, dt, fixed_point_position));
+ pool2_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, dt, fixed_point_position));
+ // Layer 3
+ conv3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, dt, fixed_point_position));
+ act3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, dt, 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, dt, 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, dt, 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, dt, 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, dt, fixed_point_position));
+ pool5_out.allocator()->init(TensorInfo(TensorShape(6U, 6U, 256U, _batches), 1, dt, fixed_point_position));
+ // Layer 6
+ fc6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, dt, fixed_point_position));
+ act6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, dt, fixed_point_position));
+ // Layer 7
+ fc7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, dt, fixed_point_position));
+ act7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, dt, fixed_point_position));
+ // Layer 8
+ fc8_out.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, dt, fixed_point_position));
+
+ // Allocate layers
+ {
+ // Layer 1
+ conv1 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction());
+ act1 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction());
+ norm1 = std::unique_ptr<NormalizationLayerFunction>(new NormalizationLayerFunction());
+ pool1 = std::unique_ptr<PoolingLayerFunction>(new PoolingLayerFunction());
+ // Layer 2
+ conv21 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction());
+ conv22 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction());
+ act2 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction());
+ norm2 = std::unique_ptr<NormalizationLayerFunction>(new NormalizationLayerFunction());
+ pool2 = std::unique_ptr<PoolingLayerFunction>(new PoolingLayerFunction());
+ // Layer 3
+ conv3 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction());
+ act3 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction());
+ // Layer 4
+ conv41 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction());
+ conv42 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction());
+ act4 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction());
+ // Layer 5
+ conv51 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction());
+ conv52 = std::unique_ptr<ConvolutionLayerFunction>(new ConvolutionLayerFunction());
+ act5 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction());
+ pool5 = std::unique_ptr<PoolingLayerFunction>(new PoolingLayerFunction());
+ // Layer 6
+ fc6 = std::unique_ptr<FullyConnectedLayerFunction>(new FullyConnectedLayerFunction());
+ act6 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction());
+ // Layer 7
+ fc7 = std::unique_ptr<FullyConnectedLayerFunction>(new FullyConnectedLayerFunction());
+ act7 = std::unique_ptr<ActivationLayerFunction>(new ActivationLayerFunction());
+ // Layer 8
+ fc8 = std::unique_ptr<FullyConnectedLayerFunction>(new FullyConnectedLayerFunction());
+ // Softmax
+ smx = std::unique_ptr<SoftmaxLayerFunction>(new SoftmaxLayerFunction());
+ }
+
+ // Configure Layers
+ {
+ // Layer 1
+ conv1->configure(&input, w[0].get(), b[0].get(), &conv1_out, PadStrideInfo(4, 4, 0, 0), WeightsInfo(_reshaped_weights, 11U));
+ 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));
+ conv22->configure(pool12_out.get(), w22.get(), b22.get(), conv22_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U));
+ 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
+ conv3->configure(&pool2_out, w[2].get(), b[2].get(), &conv3_out, PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U));
+ 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));
+ conv42->configure(act32_out.get(), w42.get(), b42.get(), conv42_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U));
+ 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));
+ conv52->configure(act42_out.get(), w52.get(), b52.get(), conv52_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U));
+ 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].get(), b[5].get(), &fc6_out, true, _reshaped_weights);
+ act6->configure(&fc6_out, &act6_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ // Layer 7
+ fc7->configure(&act6_out, w[6].get(), b[6].get(), &fc7_out, true, _reshaped_weights);
+ act7->configure(&fc7_out, &act7_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+ // Layer 8
+ fc8->configure(&act7_out, w[7].get(), b[7].get(), &fc8_out, true, _reshaped_weights);
+ // Softmax
+ smx->configure(&fc8_out, &output);
+ }
+ }
+
+ void allocate()
+ {
+ input.allocator()->allocate();
+ output.allocator()->allocate();
+ for(auto &wi : w)
+ {
+ if(wi.get())
+ {
+ wi->allocator()->allocate();
+ }
+ }
+ for(auto &bi : b)
+ {
+ if(bi.get())
+ {
+ bi->allocator()->allocate();
+ }
+ }
+ if(_reshaped_weights)
+ {
+ 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();
+ }
+ 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);
+
+ 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);
+ }
+ }
+
+#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()
+ {
+ conv1.reset();
+ act1.reset();
+ norm1.reset();
+ pool1.reset();
+ conv21.reset();
+ conv22.reset();
+ act2.reset();
+ norm2.reset();
+ pool2.reset();
+ conv3.reset();
+ act3.reset();
+ conv41.reset();
+ conv42.reset();
+ act4.reset();
+ conv51.reset();
+ conv52.reset();
+ act5.reset();
+ pool5.reset();
+ fc6.reset();
+ act6.reset();
+ fc7.reset();
+ act7.reset();
+ fc8.reset();
+ smx.reset();
+
+ // Free allocations
+ input.allocator()->free();
+ output.allocator()->free();
+ for(auto &wi : w)
+ {
+ wi.reset();
+ }
+ for(auto &bi : b)
+ {
+ bi.reset();
+ }
+
+ 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:
+ unsigned int _batches;
+ bool _reshaped_weights;
+
+ std::unique_ptr<ActivationLayerFunction> act1{ nullptr }, act2{ nullptr }, act3{ nullptr }, act4{ nullptr }, act5{ nullptr }, act6{ nullptr }, act7{ nullptr };
+ std::unique_ptr<ConvolutionLayerFunction> conv1{ nullptr }, conv21{ nullptr }, conv22{ nullptr }, conv3{ nullptr }, conv41{ nullptr }, conv42{ nullptr }, conv51{ nullptr }, conv52{ nullptr };
+ std::unique_ptr<FullyConnectedLayerFunction> fc6{ nullptr }, fc7{ nullptr }, fc8{};
+ std::unique_ptr<NormalizationLayerFunction> norm1{ nullptr }, norm2{ nullptr };
+ std::unique_ptr<PoolingLayerFunction> pool1{ nullptr }, pool2{ nullptr }, pool5{ nullptr };
+ std::unique_ptr<SoftmaxLayerFunction> smx{ nullptr };
+
+ TensorType input{}, output{};
+ std::array<std::unique_ptr<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{ nullptr }, pool12_out{ nullptr };
+ std::unique_ptr<SubTensorType> conv21_out{ nullptr }, conv22_out{ nullptr };
+ std::unique_ptr<SubTensorType> act31_out{ nullptr }, act32_out{ nullptr };
+ std::unique_ptr<SubTensorType> conv41_out{ nullptr }, conv42_out{ nullptr }, act41_out{ nullptr }, act42_out{ nullptr };
+ std::unique_ptr<SubTensorType> conv51_out{ nullptr }, conv52_out{ nullptr };
+};
+} // namespace model_objects
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__