COMPMID-645: MobileNet network and benchmark
Change-Id: Id9c50abdbd28e7e95fd460240cec51af1d6f1036
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/95580
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
diff --git a/tests/networks/MobileNetNetwork.h b/tests/networks/MobileNetNetwork.h
new file mode 100644
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+++ b/tests/networks/MobileNetNetwork.h
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+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef __ARM_COMPUTE_TEST_MODEL_OBJECTS_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 DepthwiseConvolutionFunction,
+ typename ReshapeFunction,
+ typename PoolingLayerFunction>
+class MobileNetNetwork
+{
+public:
+ 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);
+ }
+
+ 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();
+ }
+
+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<DepthwiseConvolutionFunction, 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__