| /* |
| * Copyright (c) 2017-2020 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. |
| */ |
| |
| #include "utils/GraphUtils.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/graph/Logger.h" |
| #include "arm_compute/runtime/SubTensor.h" |
| |
| #pragma GCC diagnostic push |
| #pragma GCC diagnostic ignored "-Wunused-parameter" |
| #include "utils/ImageLoader.h" |
| #pragma GCC diagnostic pop |
| #include "utils/Utils.h" |
| |
| #include <inttypes.h> |
| #include <iomanip> |
| #include <limits> |
| |
| using namespace arm_compute::graph_utils; |
| |
| namespace |
| { |
| std::pair<arm_compute::TensorShape, arm_compute::PermutationVector> compute_permutation_parameters(const arm_compute::TensorShape &shape, |
| arm_compute::DataLayout data_layout) |
| { |
| // Set permutation parameters if needed |
| arm_compute::TensorShape permuted_shape = shape; |
| arm_compute::PermutationVector perm; |
| // Permute only if num_dimensions greater than 2 |
| if(shape.num_dimensions() > 2) |
| { |
| perm = (data_layout == arm_compute::DataLayout::NHWC) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U); |
| |
| arm_compute::PermutationVector perm_shape = (data_layout == arm_compute::DataLayout::NCHW) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U); |
| arm_compute::permute(permuted_shape, perm_shape); |
| } |
| |
| return std::make_pair(permuted_shape, perm); |
| } |
| } // namespace |
| |
| TFPreproccessor::TFPreproccessor(float min_range, float max_range) |
| : _min_range(min_range), _max_range(max_range) |
| { |
| } |
| void TFPreproccessor::preprocess(ITensor &tensor) |
| { |
| if(tensor.info()->data_type() == DataType::F32) |
| { |
| preprocess_typed<float>(tensor); |
| } |
| else if(tensor.info()->data_type() == DataType::F16) |
| { |
| preprocess_typed<half>(tensor); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| } |
| |
| template <typename T> |
| void TFPreproccessor::preprocess_typed(ITensor &tensor) |
| { |
| Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| |
| const float range = _max_range - _min_range; |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const T value = *reinterpret_cast<T *>(tensor.ptr_to_element(id)); |
| float res = value / 255.f; // Normalize to [0, 1] |
| res = res * range + _min_range; // Map to [min_range, max_range] |
| *reinterpret_cast<T *>(tensor.ptr_to_element(id)) = res; |
| }); |
| } |
| |
| CaffePreproccessor::CaffePreproccessor(std::array<float, 3> mean, bool bgr, float scale) |
| : _mean(mean), _bgr(bgr), _scale(scale) |
| { |
| if(_bgr) |
| { |
| std::swap(_mean[0], _mean[2]); |
| } |
| } |
| |
| void CaffePreproccessor::preprocess(ITensor &tensor) |
| { |
| if(tensor.info()->data_type() == DataType::F32) |
| { |
| preprocess_typed<float>(tensor); |
| } |
| else if(tensor.info()->data_type() == DataType::F16) |
| { |
| preprocess_typed<half>(tensor); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| } |
| |
| template <typename T> |
| void CaffePreproccessor::preprocess_typed(ITensor &tensor) |
| { |
| Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| const int channel_idx = get_data_layout_dimension_index(tensor.info()->data_layout(), DataLayoutDimension::CHANNEL); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const T value = *reinterpret_cast<T *>(tensor.ptr_to_element(id)) - T(_mean[id[channel_idx]]); |
| *reinterpret_cast<T *>(tensor.ptr_to_element(id)) = value * T(_scale); |
| }); |
| } |
| |
| PPMWriter::PPMWriter(std::string name, unsigned int maximum) |
| : _name(std::move(name)), _iterator(0), _maximum(maximum) |
| { |
| } |
| |
| bool PPMWriter::access_tensor(ITensor &tensor) |
| { |
| std::stringstream ss; |
| ss << _name << _iterator << ".ppm"; |
| |
| arm_compute::utils::save_to_ppm(tensor, ss.str()); |
| |
| _iterator++; |
| if(_maximum == 0) |
| { |
| return true; |
| } |
| return _iterator < _maximum; |
| } |
| |
| DummyAccessor::DummyAccessor(unsigned int maximum) |
| : _iterator(0), _maximum(maximum) |
| { |
| } |
| |
| bool DummyAccessor::access_tensor(ITensor &tensor) |
| { |
| ARM_COMPUTE_UNUSED(tensor); |
| bool ret = _maximum == 0 || _iterator < _maximum; |
| if(_iterator == _maximum) |
| { |
| _iterator = 0; |
| } |
| else |
| { |
| _iterator++; |
| } |
| return ret; |
| } |
| |
| NumPyAccessor::NumPyAccessor(std::string npy_path, TensorShape shape, DataType data_type, DataLayout data_layout, std::ostream &output_stream) |
| : _npy_tensor(), _filename(std::move(npy_path)), _output_stream(output_stream) |
| { |
| NumPyBinLoader loader(_filename, data_layout); |
| |
| TensorInfo info(shape, 1, data_type); |
| info.set_data_layout(data_layout); |
| |
| _npy_tensor.allocator()->init(info); |
| _npy_tensor.allocator()->allocate(); |
| |
| loader.access_tensor(_npy_tensor); |
| } |
| |
| template <typename T> |
| void NumPyAccessor::access_numpy_tensor(ITensor &tensor, T tolerance) |
| { |
| const int num_elements = tensor.info()->tensor_shape().total_size(); |
| int num_mismatches = utils::compare_tensor<T>(tensor, _npy_tensor, tolerance); |
| float percentage_mismatches = static_cast<float>(num_mismatches) / num_elements; |
| |
| _output_stream << "Results: " << 100.f - (percentage_mismatches * 100) << " % matches with the provided output[" << _filename << "]." << std::endl; |
| _output_stream << " " << num_elements - num_mismatches << " out of " << num_elements << " matches with the provided output[" << _filename << "]." << std::endl |
| << std::endl; |
| } |
| |
| bool NumPyAccessor::access_tensor(ITensor &tensor) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32, DataType::QASYMM8); |
| ARM_COMPUTE_ERROR_ON(_npy_tensor.info()->dimension(0) != tensor.info()->dimension(0)); |
| |
| switch(tensor.info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| access_numpy_tensor<qasymm8_t>(tensor, 0); |
| break; |
| case DataType::F32: |
| access_numpy_tensor<float>(tensor, 0.0001f); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| |
| return false; |
| } |
| |
| #ifdef ARM_COMPUTE_ASSERTS_ENABLED |
| PrintAccessor::PrintAccessor(std::ostream &output_stream, IOFormatInfo io_fmt) |
| : _output_stream(output_stream), _io_fmt(io_fmt) |
| { |
| } |
| |
| bool PrintAccessor::access_tensor(ITensor &tensor) |
| { |
| tensor.print(_output_stream, _io_fmt); |
| return false; |
| } |
| #endif /* ARM_COMPUTE_ASSERTS_ENABLED */ |
| |
| SaveNumPyAccessor::SaveNumPyAccessor(std::string npy_name, const bool is_fortran) |
| : _npy_name(std::move(npy_name)), _is_fortran(is_fortran) |
| { |
| } |
| |
| bool SaveNumPyAccessor::access_tensor(ITensor &tensor) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32); |
| |
| utils::save_to_npy(tensor, _npy_name, _is_fortran); |
| |
| return false; |
| } |
| |
| ImageAccessor::ImageAccessor(std::string filename, bool bgr, std::unique_ptr<IPreprocessor> preprocessor) |
| : _already_loaded(false), _filename(std::move(filename)), _bgr(bgr), _preprocessor(std::move(preprocessor)) |
| { |
| } |
| |
| bool ImageAccessor::access_tensor(ITensor &tensor) |
| { |
| if(!_already_loaded) |
| { |
| auto image_loader = utils::ImageLoaderFactory::create(_filename); |
| ARM_COMPUTE_EXIT_ON_MSG(image_loader == nullptr, "Unsupported image type"); |
| |
| // Open image file |
| image_loader->open(_filename); |
| |
| // Get permutated shape and permutation parameters |
| TensorShape permuted_shape = tensor.info()->tensor_shape(); |
| arm_compute::PermutationVector perm; |
| if(tensor.info()->data_layout() != DataLayout::NCHW) |
| { |
| std::tie(permuted_shape, perm) = compute_permutation_parameters(tensor.info()->tensor_shape(), tensor.info()->data_layout()); |
| } |
| |
| ARM_COMPUTE_EXIT_ON_MSG_VAR(image_loader->width() != permuted_shape.x() || image_loader->height() != permuted_shape.y(), |
| "Failed to load image file: dimensions [%d,%d] not correct, expected [%zu ,%zu ].", |
| image_loader->width(), image_loader->height(), permuted_shape.x(), permuted_shape.y()); |
| |
| // Fill the tensor with the PPM content (BGR) |
| image_loader->fill_planar_tensor(tensor, _bgr); |
| |
| // Preprocess tensor |
| if(_preprocessor) |
| { |
| _preprocessor->preprocess(tensor); |
| } |
| } |
| |
| _already_loaded = !_already_loaded; |
| return _already_loaded; |
| } |
| |
| ValidationInputAccessor::ValidationInputAccessor(const std::string &image_list, |
| std::string images_path, |
| std::unique_ptr<IPreprocessor> preprocessor, |
| bool bgr, |
| unsigned int start, |
| unsigned int end, |
| std::ostream &output_stream) |
| : _path(std::move(images_path)), _images(), _preprocessor(std::move(preprocessor)), _bgr(bgr), _offset(0), _output_stream(output_stream) |
| { |
| ARM_COMPUTE_EXIT_ON_MSG(start > end, "Invalid validation range!"); |
| |
| std::ifstream ifs; |
| try |
| { |
| ifs.exceptions(std::ifstream::badbit); |
| ifs.open(image_list, std::ios::in | std::ios::binary); |
| |
| // Parse image names |
| unsigned int counter = 0; |
| for(std::string line; !std::getline(ifs, line).fail() && counter <= end; ++counter) |
| { |
| // Add image to process if withing range |
| if(counter >= start) |
| { |
| std::stringstream linestream(line); |
| std::string image_name; |
| |
| linestream >> image_name; |
| _images.emplace_back(std::move(image_name)); |
| } |
| } |
| } |
| catch(const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", image_list.c_str(), e.what()); |
| } |
| } |
| |
| bool ValidationInputAccessor::access_tensor(arm_compute::ITensor &tensor) |
| { |
| bool ret = _offset < _images.size(); |
| if(ret) |
| { |
| utils::JPEGLoader jpeg; |
| |
| // Open JPEG file |
| std::string image_name = _path + _images[_offset++]; |
| jpeg.open(image_name); |
| _output_stream << "[" << _offset << "/" << _images.size() << "] Validating " << image_name << std::endl; |
| |
| // Get permutated shape and permutation parameters |
| TensorShape permuted_shape = tensor.info()->tensor_shape(); |
| arm_compute::PermutationVector perm; |
| if(tensor.info()->data_layout() != DataLayout::NCHW) |
| { |
| std::tie(permuted_shape, perm) = compute_permutation_parameters(tensor.info()->tensor_shape(), |
| tensor.info()->data_layout()); |
| } |
| |
| ARM_COMPUTE_EXIT_ON_MSG_VAR(jpeg.width() != permuted_shape.x() || jpeg.height() != permuted_shape.y(), |
| "Failed to load image file: dimensions [%d,%d] not correct, expected [%zu,%zu ].", |
| jpeg.width(), jpeg.height(), permuted_shape.x(), permuted_shape.y()); |
| |
| // Fill the tensor with the JPEG content (BGR) |
| jpeg.fill_planar_tensor(tensor, _bgr); |
| |
| // Preprocess tensor |
| if(_preprocessor) |
| { |
| _preprocessor->preprocess(tensor); |
| } |
| } |
| |
| return ret; |
| } |
| |
| ValidationOutputAccessor::ValidationOutputAccessor(const std::string &image_list, |
| std::ostream &output_stream, |
| unsigned int start, |
| unsigned int end) |
| : _results(), _output_stream(output_stream), _offset(0), _positive_samples_top1(0), _positive_samples_top5(0) |
| { |
| ARM_COMPUTE_EXIT_ON_MSG(start > end, "Invalid validation range!"); |
| |
| std::ifstream ifs; |
| try |
| { |
| ifs.exceptions(std::ifstream::badbit); |
| ifs.open(image_list, std::ios::in | std::ios::binary); |
| |
| // Parse image correctly classified labels |
| unsigned int counter = 0; |
| for(std::string line; !std::getline(ifs, line).fail() && counter <= end; ++counter) |
| { |
| // Add label if within range |
| if(counter >= start) |
| { |
| std::stringstream linestream(line); |
| std::string image_name; |
| int result; |
| |
| linestream >> image_name >> result; |
| _results.emplace_back(result); |
| } |
| } |
| } |
| catch(const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", image_list.c_str(), e.what()); |
| } |
| } |
| |
| void ValidationOutputAccessor::reset() |
| { |
| _offset = 0; |
| _positive_samples_top1 = 0; |
| _positive_samples_top5 = 0; |
| } |
| |
| bool ValidationOutputAccessor::access_tensor(arm_compute::ITensor &tensor) |
| { |
| bool ret = _offset < _results.size(); |
| if(ret) |
| { |
| // Get results |
| std::vector<size_t> tensor_results; |
| switch(tensor.info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| tensor_results = access_predictions_tensor<uint8_t>(tensor); |
| break; |
| case DataType::F16: |
| tensor_results = access_predictions_tensor<half>(tensor); |
| break; |
| case DataType::F32: |
| tensor_results = access_predictions_tensor<float>(tensor); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| |
| // Check if tensor results are within top-n accuracy |
| size_t correct_label = _results[_offset++]; |
| |
| aggregate_sample(tensor_results, _positive_samples_top1, 1, correct_label); |
| aggregate_sample(tensor_results, _positive_samples_top5, 5, correct_label); |
| } |
| |
| // Report top_n accuracy |
| if(_offset >= _results.size()) |
| { |
| report_top_n(1, _results.size(), _positive_samples_top1); |
| report_top_n(5, _results.size(), _positive_samples_top5); |
| } |
| |
| return ret; |
| } |
| |
| template <typename T> |
| std::vector<size_t> ValidationOutputAccessor::access_predictions_tensor(arm_compute::ITensor &tensor) |
| { |
| // Get the predicted class |
| std::vector<size_t> index; |
| |
| const auto output_net = reinterpret_cast<T *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes()); |
| const size_t num_classes = tensor.info()->dimension(0); |
| |
| index.resize(num_classes); |
| |
| // Sort results |
| std::iota(std::begin(index), std::end(index), static_cast<size_t>(0)); |
| std::sort(std::begin(index), std::end(index), |
| [&](size_t a, size_t b) |
| { |
| return output_net[a] > output_net[b]; |
| }); |
| |
| return index; |
| } |
| |
| void ValidationOutputAccessor::aggregate_sample(const std::vector<size_t> &res, size_t &positive_samples, size_t top_n, size_t correct_label) |
| { |
| auto is_valid_label = [correct_label](size_t label) |
| { |
| return label == correct_label; |
| }; |
| |
| if(std::any_of(std::begin(res), std::begin(res) + top_n, is_valid_label)) |
| { |
| ++positive_samples; |
| } |
| } |
| |
| void ValidationOutputAccessor::report_top_n(size_t top_n, size_t total_samples, size_t positive_samples) |
| { |
| size_t negative_samples = total_samples - positive_samples; |
| float accuracy = positive_samples / static_cast<float>(total_samples); |
| |
| _output_stream << "----------Top " << top_n << " accuracy ----------" << std::endl |
| << std::endl; |
| _output_stream << "Positive samples : " << positive_samples << std::endl; |
| _output_stream << "Negative samples : " << negative_samples << std::endl; |
| _output_stream << "Accuracy : " << accuracy << std::endl; |
| } |
| |
| DetectionOutputAccessor::DetectionOutputAccessor(const std::string &labels_path, std::vector<TensorShape> &imgs_tensor_shapes, std::ostream &output_stream) |
| : _labels(), _tensor_shapes(std::move(imgs_tensor_shapes)), _output_stream(output_stream) |
| { |
| _labels.clear(); |
| |
| std::ifstream ifs; |
| |
| try |
| { |
| ifs.exceptions(std::ifstream::badbit); |
| ifs.open(labels_path, std::ios::in | std::ios::binary); |
| |
| for(std::string line; !std::getline(ifs, line).fail();) |
| { |
| _labels.emplace_back(line); |
| } |
| } |
| catch(const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", labels_path.c_str(), e.what()); |
| } |
| } |
| |
| template <typename T> |
| void DetectionOutputAccessor::access_predictions_tensor(ITensor &tensor) |
| { |
| const size_t num_detection = tensor.info()->valid_region().shape.y(); |
| const auto output_prt = reinterpret_cast<T *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes()); |
| |
| if(num_detection > 0) |
| { |
| _output_stream << "---------------------- Detections ----------------------" << std::endl |
| << std::endl; |
| |
| _output_stream << std::left << std::setprecision(4) << std::setw(8) << "Image | " << std::setw(8) << "Label | " << std::setw(12) << "Confidence | " |
| << "[ xmin, ymin, xmax, ymax ]" << std::endl; |
| |
| for(size_t i = 0; i < num_detection; ++i) |
| { |
| auto im = static_cast<const int>(output_prt[i * 7]); |
| _output_stream << std::setw(8) << im << std::setw(8) |
| << _labels[output_prt[i * 7 + 1]] << std::setw(12) << output_prt[i * 7 + 2] |
| << " [" << (output_prt[i * 7 + 3] * _tensor_shapes[im].x()) |
| << ", " << (output_prt[i * 7 + 4] * _tensor_shapes[im].y()) |
| << ", " << (output_prt[i * 7 + 5] * _tensor_shapes[im].x()) |
| << ", " << (output_prt[i * 7 + 6] * _tensor_shapes[im].y()) |
| << "]" << std::endl; |
| } |
| } |
| else |
| { |
| _output_stream << "No detection found." << std::endl; |
| } |
| } |
| |
| bool DetectionOutputAccessor::access_tensor(ITensor &tensor) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32); |
| |
| switch(tensor.info()->data_type()) |
| { |
| case DataType::F32: |
| access_predictions_tensor<float>(tensor); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| |
| return false; |
| } |
| |
| TopNPredictionsAccessor::TopNPredictionsAccessor(const std::string &labels_path, size_t top_n, std::ostream &output_stream) |
| : _labels(), _output_stream(output_stream), _top_n(top_n) |
| { |
| _labels.clear(); |
| |
| std::ifstream ifs; |
| |
| try |
| { |
| ifs.exceptions(std::ifstream::badbit); |
| ifs.open(labels_path, std::ios::in | std::ios::binary); |
| |
| for(std::string line; !std::getline(ifs, line).fail();) |
| { |
| _labels.emplace_back(line); |
| } |
| } |
| catch(const std::ifstream::failure &e) |
| { |
| ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", labels_path.c_str(), e.what()); |
| } |
| } |
| |
| template <typename T> |
| void TopNPredictionsAccessor::access_predictions_tensor(ITensor &tensor) |
| { |
| // Get the predicted class |
| std::vector<T> classes_prob; |
| std::vector<size_t> index; |
| |
| const auto output_net = reinterpret_cast<T *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes()); |
| const size_t num_classes = tensor.info()->dimension(0); |
| |
| classes_prob.resize(num_classes); |
| index.resize(num_classes); |
| |
| std::copy(output_net, output_net + num_classes, classes_prob.begin()); |
| |
| // Sort results |
| std::iota(std::begin(index), std::end(index), static_cast<size_t>(0)); |
| std::sort(std::begin(index), std::end(index), |
| [&](size_t a, size_t b) |
| { |
| return classes_prob[a] > classes_prob[b]; |
| }); |
| |
| _output_stream << "---------- Top " << _top_n << " predictions ----------" << std::endl |
| << std::endl; |
| for(size_t i = 0; i < _top_n; ++i) |
| { |
| _output_stream << std::fixed << std::setprecision(4) |
| << +classes_prob[index.at(i)] |
| << " - [id = " << index.at(i) << "]" |
| << ", " << _labels[index.at(i)] << std::endl; |
| } |
| } |
| |
| bool TopNPredictionsAccessor::access_tensor(ITensor &tensor) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32, DataType::QASYMM8); |
| ARM_COMPUTE_ERROR_ON(_labels.size() != tensor.info()->dimension(0)); |
| |
| switch(tensor.info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| access_predictions_tensor<uint8_t>(tensor); |
| break; |
| case DataType::F32: |
| access_predictions_tensor<float>(tensor); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| |
| return false; |
| } |
| |
| RandomAccessor::RandomAccessor(PixelValue lower, PixelValue upper, std::random_device::result_type seed) |
| : _lower(lower), _upper(upper), _seed(seed) |
| { |
| } |
| |
| template <typename T, typename D> |
| void RandomAccessor::fill(ITensor &tensor, D &&distribution) |
| { |
| std::mt19937 gen(_seed); |
| |
| if(tensor.info()->padding().empty() && (dynamic_cast<SubTensor *>(&tensor) == nullptr)) |
| { |
| for(size_t offset = 0; offset < tensor.info()->total_size(); offset += tensor.info()->element_size()) |
| { |
| const auto value = static_cast<T>(distribution(gen)); |
| *reinterpret_cast<T *>(tensor.buffer() + offset) = value; |
| } |
| } |
| else |
| { |
| // If tensor has padding accessing tensor elements through execution window. |
| Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const auto value = static_cast<T>(distribution(gen)); |
| *reinterpret_cast<T *>(tensor.ptr_to_element(id)) = value; |
| }); |
| } |
| } |
| |
| bool RandomAccessor::access_tensor(ITensor &tensor) |
| { |
| switch(tensor.info()->data_type()) |
| { |
| case DataType::QASYMM8: |
| case DataType::U8: |
| { |
| std::uniform_int_distribution<uint8_t> distribution_u8(_lower.get<uint8_t>(), _upper.get<uint8_t>()); |
| fill<uint8_t>(tensor, distribution_u8); |
| break; |
| } |
| case DataType::S8: |
| { |
| std::uniform_int_distribution<int8_t> distribution_s8(_lower.get<int8_t>(), _upper.get<int8_t>()); |
| fill<int8_t>(tensor, distribution_s8); |
| break; |
| } |
| case DataType::U16: |
| { |
| std::uniform_int_distribution<uint16_t> distribution_u16(_lower.get<uint16_t>(), _upper.get<uint16_t>()); |
| fill<uint16_t>(tensor, distribution_u16); |
| break; |
| } |
| case DataType::S16: |
| { |
| std::uniform_int_distribution<int16_t> distribution_s16(_lower.get<int16_t>(), _upper.get<int16_t>()); |
| fill<int16_t>(tensor, distribution_s16); |
| break; |
| } |
| case DataType::U32: |
| { |
| std::uniform_int_distribution<uint32_t> distribution_u32(_lower.get<uint32_t>(), _upper.get<uint32_t>()); |
| fill<uint32_t>(tensor, distribution_u32); |
| break; |
| } |
| case DataType::S32: |
| { |
| std::uniform_int_distribution<int32_t> distribution_s32(_lower.get<int32_t>(), _upper.get<int32_t>()); |
| fill<int32_t>(tensor, distribution_s32); |
| break; |
| } |
| case DataType::U64: |
| { |
| std::uniform_int_distribution<uint64_t> distribution_u64(_lower.get<uint64_t>(), _upper.get<uint64_t>()); |
| fill<uint64_t>(tensor, distribution_u64); |
| break; |
| } |
| case DataType::S64: |
| { |
| std::uniform_int_distribution<int64_t> distribution_s64(_lower.get<int64_t>(), _upper.get<int64_t>()); |
| fill<int64_t>(tensor, distribution_s64); |
| break; |
| } |
| case DataType::F16: |
| { |
| std::uniform_real_distribution<float> distribution_f16(_lower.get<half>(), _upper.get<half>()); |
| fill<half>(tensor, distribution_f16); |
| break; |
| } |
| case DataType::F32: |
| { |
| std::uniform_real_distribution<float> distribution_f32(_lower.get<float>(), _upper.get<float>()); |
| fill<float>(tensor, distribution_f32); |
| break; |
| } |
| case DataType::F64: |
| { |
| std::uniform_real_distribution<double> distribution_f64(_lower.get<double>(), _upper.get<double>()); |
| fill<double>(tensor, distribution_f64); |
| break; |
| } |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| return true; |
| } |
| |
| NumPyBinLoader::NumPyBinLoader(std::string filename, DataLayout file_layout) |
| : _already_loaded(false), _filename(std::move(filename)), _file_layout(file_layout) |
| { |
| } |
| |
| bool NumPyBinLoader::access_tensor(ITensor &tensor) |
| { |
| if(!_already_loaded) |
| { |
| utils::NPYLoader loader; |
| loader.open(_filename, _file_layout); |
| loader.fill_tensor(tensor); |
| } |
| |
| _already_loaded = !_already_loaded; |
| return _already_loaded; |
| } |
