diff options
Diffstat (limited to 'utils/GraphUtils.cpp')
| -rw-r--r-- | utils/GraphUtils.cpp | 257 |
1 files changed, 136 insertions, 121 deletions
diff --git a/utils/GraphUtils.cpp b/utils/GraphUtils.cpp index 20e1369e73..7e618c9de5 100644 --- a/utils/GraphUtils.cpp +++ b/utils/GraphUtils.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2021, 2024 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -43,18 +43,21 @@ 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) +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) + if (shape.num_dimensions() > 2) { - perm = (data_layout == arm_compute::DataLayout::NHWC) ? arm_compute::PermutationVector(2U, 0U, 1U) : arm_compute::PermutationVector(1U, 2U, 0U); + 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::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); } @@ -62,17 +65,16 @@ std::pair<arm_compute::TensorShape, arm_compute::PermutationVector> compute_perm } } // namespace -TFPreproccessor::TFPreproccessor(float min_range, float max_range) - : _min_range(min_range), _max_range(max_range) +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) + if (tensor.info()->data_type() == DataType::F32) { preprocess_typed<float>(tensor); } - else if(tensor.info()->data_type() == DataType::F16) + else if (tensor.info()->data_type() == DataType::F16) { preprocess_typed<half>(tensor); } @@ -89,19 +91,20 @@ void TFPreproccessor::preprocess_typed(ITensor &tensor) 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; - }); + 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) + if (_bgr) { std::swap(_mean[0], _mean[2]); } @@ -109,11 +112,11 @@ CaffePreproccessor::CaffePreproccessor(std::array<float, 3> mean, bool bgr, floa void CaffePreproccessor::preprocess(ITensor &tensor) { - if(tensor.info()->data_type() == DataType::F32) + if (tensor.info()->data_type() == DataType::F32) { preprocess_typed<float>(tensor); } - else if(tensor.info()->data_type() == DataType::F16) + else if (tensor.info()->data_type() == DataType::F16) { preprocess_typed<half>(tensor); } @@ -130,15 +133,16 @@ void CaffePreproccessor::preprocess_typed(ITensor &tensor) 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); - }); + 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) +PPMWriter::PPMWriter(std::string name, unsigned int maximum) : _name(std::move(name)), _iterator(0), _maximum(maximum) { } @@ -150,23 +154,27 @@ bool PPMWriter::access_tensor(ITensor &tensor) arm_compute::utils::save_to_ppm(tensor, ss.str()); _iterator++; - if(_maximum == 0) + if (_maximum == 0) { return true; } return _iterator < _maximum; } -DummyAccessor::DummyAccessor(unsigned int maximum) - : _iterator(0), _maximum(maximum) +DummyAccessor::DummyAccessor(unsigned int maximum) : _iterator(0), _maximum(maximum) { } +bool DummyAccessor::access_tensor_data() +{ + return false; +} + bool DummyAccessor::access_tensor(ITensor &tensor) { ARM_COMPUTE_UNUSED(tensor); bool ret = _maximum == 0 || _iterator < _maximum; - if(_iterator == _maximum) + if (_iterator == _maximum) { _iterator = 0; } @@ -177,7 +185,8 @@ bool DummyAccessor::access_tensor(ITensor &tensor) return ret; } -NumPyAccessor::NumPyAccessor(std::string npy_path, TensorShape shape, DataType data_type, DataLayout data_layout, std::ostream &output_stream) +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); @@ -198,8 +207,10 @@ void NumPyAccessor::access_numpy_tensor(ITensor &tensor, T tolerance) 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 + _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; } @@ -208,7 +219,7 @@ 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()) + switch (tensor.info()->data_type()) { case DataType::QASYMM8: access_numpy_tensor<qasymm8_t>(tensor, 0); @@ -257,7 +268,7 @@ ImageAccessor::ImageAccessor(std::string filename, bool bgr, std::unique_ptr<IPr bool ImageAccessor::access_tensor(ITensor &tensor) { - if(!_already_loaded) + if (!_already_loaded) { auto image_loader = utils::ImageLoaderFactory::create(_filename); ARM_COMPUTE_EXIT_ON_MSG(image_loader == nullptr, "Unsupported image type"); @@ -268,27 +279,30 @@ bool ImageAccessor::access_tensor(ITensor &tensor) // Get permutated shape and permutation parameters TensorShape permuted_shape = tensor.info()->tensor_shape(); arm_compute::PermutationVector perm; - if(tensor.info()->data_layout() != DataLayout::NCHW) + if (tensor.info()->data_layout() != DataLayout::NCHW) { - std::tie(permuted_shape, perm) = compute_permutation_parameters(tensor.info()->tensor_shape(), tensor.info()->data_layout()); + std::tie(permuted_shape, perm) = + compute_permutation_parameters(tensor.info()->tensor_shape(), tensor.info()->data_layout()); } #ifdef __arm__ - 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 [%" PRIu32 ",%" PRIu32 "].", - image_loader->width(), image_loader->height(), permuted_shape.x(), permuted_shape.y()); + 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 [%" PRIu32 ",%" PRIu32 "].", + image_loader->width(), image_loader->height(), permuted_shape.x(), permuted_shape.y()); #else // __arm__ - 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 [%" PRIu64 ",%" PRIu64 "].", - image_loader->width(), image_loader->height(), - static_cast<uint64_t>(permuted_shape.x()), static_cast<uint64_t>(permuted_shape.y())); + 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 [%" PRIu64 ",%" PRIu64 "].", + image_loader->width(), image_loader->height(), static_cast<uint64_t>(permuted_shape.x()), + static_cast<uint64_t>(permuted_shape.y())); #endif // __arm__ // Fill the tensor with the PPM content (BGR) image_loader->fill_planar_tensor(tensor, _bgr); // Preprocess tensor - if(_preprocessor) + if (_preprocessor) { _preprocessor->preprocess(tensor); } @@ -305,7 +319,12 @@ ValidationInputAccessor::ValidationInputAccessor(const std::string & 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) + : _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!"); @@ -317,10 +336,10 @@ ValidationInputAccessor::ValidationInputAccessor(const std::string & // Parse image names unsigned int counter = 0; - for(std::string line; !std::getline(ifs, line).fail() && counter <= end; ++counter) + for (std::string line; !std::getline(ifs, line).fail() && counter <= end; ++counter) { // Add image to process if withing range - if(counter >= start) + if (counter >= start) { std::stringstream linestream(line); std::string image_name; @@ -330,7 +349,7 @@ ValidationInputAccessor::ValidationInputAccessor(const std::string & } } } - catch(const std::ifstream::failure &e) + catch (const std::ifstream::failure &e) { ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", image_list.c_str(), e.what()); } @@ -339,7 +358,7 @@ ValidationInputAccessor::ValidationInputAccessor(const std::string & bool ValidationInputAccessor::access_tensor(arm_compute::ITensor &tensor) { bool ret = _offset < _images.size(); - if(ret) + if (ret) { utils::JPEGLoader jpeg; @@ -351,28 +370,30 @@ bool ValidationInputAccessor::access_tensor(arm_compute::ITensor &tensor) // Get permutated shape and permutation parameters TensorShape permuted_shape = tensor.info()->tensor_shape(); arm_compute::PermutationVector perm; - if(tensor.info()->data_layout() != DataLayout::NCHW) + if (tensor.info()->data_layout() != DataLayout::NCHW) { - std::tie(permuted_shape, perm) = compute_permutation_parameters(tensor.info()->tensor_shape(), - tensor.info()->data_layout()); + std::tie(permuted_shape, perm) = + compute_permutation_parameters(tensor.info()->tensor_shape(), tensor.info()->data_layout()); } #ifdef __arm__ 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 [%" PRIu32 ",%" PRIu32 "].", + "Failed to load image file: dimensions [%d,%d] not correct, expected [%" PRIu32 + ",%" PRIu32 "].", jpeg.width(), jpeg.height(), permuted_shape.x(), permuted_shape.y()); #else // __arm__ 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 [%" PRIu64 ",%" PRIu64 "].", - jpeg.width(), jpeg.height(), - static_cast<uint64_t>(permuted_shape.x()), static_cast<uint64_t>(permuted_shape.y())); + "Failed to load image file: dimensions [%d,%d] not correct, expected [%" PRIu64 + ",%" PRIu64 "].", + jpeg.width(), jpeg.height(), static_cast<uint64_t>(permuted_shape.x()), + static_cast<uint64_t>(permuted_shape.y())); #endif // __arm__ // Fill the tensor with the JPEG content (BGR) jpeg.fill_planar_tensor(tensor, _bgr); // Preprocess tensor - if(_preprocessor) + if (_preprocessor) { _preprocessor->preprocess(tensor); } @@ -397,10 +418,10 @@ ValidationOutputAccessor::ValidationOutputAccessor(const std::string &image_list // Parse image correctly classified labels unsigned int counter = 0; - for(std::string line; !std::getline(ifs, line).fail() && counter <= end; ++counter) + for (std::string line; !std::getline(ifs, line).fail() && counter <= end; ++counter) { // Add label if within range - if(counter >= start) + if (counter >= start) { std::stringstream linestream(line); std::string image_name; @@ -411,7 +432,7 @@ ValidationOutputAccessor::ValidationOutputAccessor(const std::string &image_list } } } - catch(const std::ifstream::failure &e) + catch (const std::ifstream::failure &e) { ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", image_list.c_str(), e.what()); } @@ -427,11 +448,11 @@ void ValidationOutputAccessor::reset() bool ValidationOutputAccessor::access_tensor(arm_compute::ITensor &tensor) { bool ret = _offset < _results.size(); - if(ret) + if (ret) { // Get results std::vector<size_t> tensor_results; - switch(tensor.info()->data_type()) + switch (tensor.info()->data_type()) { case DataType::QASYMM8: tensor_results = access_predictions_tensor<uint8_t>(tensor); @@ -454,7 +475,7 @@ bool ValidationOutputAccessor::access_tensor(arm_compute::ITensor &tensor) } // Report top_n accuracy - if(_offset >= _results.size()) + if (_offset >= _results.size()) { report_top_n(1, _results.size(), _positive_samples_top1); report_top_n(5, _results.size(), _positive_samples_top5); @@ -476,23 +497,19 @@ std::vector<size_t> ValidationOutputAccessor::access_predictions_tensor(arm_comp // 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]; - }); + 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) +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; - }; + 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)) + if (std::any_of(std::begin(res), std::begin(res) + top_n, is_valid_label)) { ++positive_samples; } @@ -503,14 +520,15 @@ void ValidationOutputAccessor::report_top_n(size_t top_n, size_t total_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 << "----------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) +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(); @@ -522,12 +540,12 @@ DetectionOutputAccessor::DetectionOutputAccessor(const std::string &labels_path, ifs.exceptions(std::ifstream::badbit); ifs.open(labels_path, std::ios::in | std::ios::binary); - for(std::string line; !std::getline(ifs, line).fail();) + for (std::string line; !std::getline(ifs, line).fail();) { _labels.emplace_back(line); } } - catch(const std::ifstream::failure &e) + catch (const std::ifstream::failure &e) { ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", labels_path.c_str(), e.what()); } @@ -537,26 +555,24 @@ 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()); + const auto output_prt = reinterpret_cast<T *>(tensor.buffer() + tensor.info()->offset_first_element_in_bytes()); - if(num_detection > 0) + if (num_detection > 0) { - _output_stream << "---------------------- Detections ----------------------" << std::endl - << std::endl; + _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 | " + _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) + 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; + _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 @@ -569,7 +585,7 @@ bool DetectionOutputAccessor::access_tensor(ITensor &tensor) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32); - switch(tensor.info()->data_type()) + switch (tensor.info()->data_type()) { case DataType::F32: access_predictions_tensor<float>(tensor); @@ -581,7 +597,9 @@ bool DetectionOutputAccessor::access_tensor(ITensor &tensor) return false; } -TopNPredictionsAccessor::TopNPredictionsAccessor(const std::string &labels_path, size_t top_n, std::ostream &output_stream) +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(); @@ -593,12 +611,12 @@ TopNPredictionsAccessor::TopNPredictionsAccessor(const std::string &labels_path, ifs.exceptions(std::ifstream::badbit); ifs.open(labels_path, std::ios::in | std::ios::binary); - for(std::string line; !std::getline(ifs, line).fail();) + for (std::string line; !std::getline(ifs, line).fail();) { _labels.emplace_back(line); } } - catch(const std::ifstream::failure &e) + catch (const std::ifstream::failure &e) { ARM_COMPUTE_ERROR_VAR("Accessing %s: %s", labels_path.c_str(), e.what()); } @@ -622,18 +640,13 @@ void TopNPredictionsAccessor::access_predictions_tensor(ITensor &tensor) // 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]; - }); + [&](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 << "---------- 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) << "]" + _output_stream << std::fixed << std::setprecision(4) << +classes_prob[index.at(i)] << " - [id = " << index.at(i) + << "]" << ", " << _labels[index.at(i)] << std::endl; } } @@ -643,7 +656,7 @@ 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()) + switch (tensor.info()->data_type()) { case DataType::QASYMM8: access_predictions_tensor<uint8_t>(tensor); @@ -668,9 +681,9 @@ void RandomAccessor::fill(ITensor &tensor, D &&distribution) { std::mt19937 gen(_seed); - if(tensor.info()->padding().empty() && (dynamic_cast<SubTensor *>(&tensor) == nullptr)) + 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()) + 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; @@ -682,28 +695,29 @@ void RandomAccessor::fill(ITensor &tensor, D &&distribution) 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; - }); + 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()) + 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>()); + std::uniform_int_distribution<uint32_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>()); + std::uniform_int_distribution<int32_t> distribution_s8(_lower.get<int8_t>(), _upper.get<int8_t>()); fill<int8_t>(tensor, distribution_s8); break; } @@ -745,7 +759,8 @@ bool RandomAccessor::access_tensor(ITensor &tensor) } case DataType::F16: { - arm_compute::utils::uniform_real_distribution_16bit<half> distribution_f16(_lower.get<float>(), _upper.get<float>()); + arm_compute::utils::uniform_real_distribution_16bit<half> distribution_f16(_lower.get<float>(), + _upper.get<float>()); fill<half>(tensor, distribution_f16); break; } @@ -774,7 +789,7 @@ NumPyBinLoader::NumPyBinLoader(std::string filename, DataLayout file_layout) bool NumPyBinLoader::access_tensor(ITensor &tensor) { - if(!_already_loaded) + if (!_already_loaded) { utils::NPYLoader loader; loader.open(_filename, _file_layout); |