| /* |
| * 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. |
| */ |
| |
| #include "utils/GraphUtils.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/SubTensor.h" |
| #include "utils/Utils.h" |
| |
| #include <iomanip> |
| |
| using namespace arm_compute::graph_utils; |
| |
| namespace |
| { |
| std::pair<arm_compute::TensorShape, arm_compute::PermutationVector> compute_permutation_paramaters(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 |
| |
| void TFPreproccessor::preprocess(ITensor &tensor) |
| { |
| Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const float value = *reinterpret_cast<float *>(tensor.ptr_to_element(id)); |
| float res = value / 255.f; // Normalize to [0, 1] |
| res = (res - 0.5f) * 2.f; // Map to [-1, 1] |
| *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = res; |
| }); |
| } |
| |
| CaffePreproccessor::CaffePreproccessor(std::array<float, 3> mean, bool bgr) |
| : _mean(mean), _bgr(bgr) |
| { |
| if(_bgr) |
| { |
| std::swap(_mean[0], _mean[2]); |
| } |
| } |
| |
| void CaffePreproccessor::preprocess(ITensor &tensor) |
| { |
| Window window; |
| window.use_tensor_dimensions(tensor.info()->tensor_shape()); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| const float value = *reinterpret_cast<float *>(tensor.ptr_to_element(id)) - _mean[id.z()]; |
| *reinterpret_cast<float *>(tensor.ptr_to_element(id)) = value; |
| }); |
| } |
| |
| 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, std::ostream &output_stream) |
| : _npy_tensor(), _filename(std::move(npy_path)), _output_stream(output_stream) |
| { |
| NumPyBinLoader loader(_filename); |
| |
| TensorInfo info(shape, 1, data_type); |
| _npy_tensor.allocator()->init(info); |
| _npy_tensor.allocator()->allocate(); |
| |
| loader.access_tensor(_npy_tensor); |
| } |
| |
| template <typename T> |
| void NumPyAccessor::access_numpy_tensor(ITensor &tensor) |
| { |
| const int num_elements = tensor.info()->total_size(); |
| int num_mismatches = utils::compare_tensor<T>(tensor, _npy_tensor); |
| 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; |
| } |
| |
| bool NumPyAccessor::access_tensor(ITensor &tensor) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32); |
| ARM_COMPUTE_ERROR_ON(_npy_tensor.info()->dimension(0) != tensor.info()->dimension(0)); |
| |
| switch(tensor.info()->data_type()) |
| { |
| case DataType::F32: |
| access_numpy_tensor<float>(tensor); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("NOT SUPPORTED!"); |
| } |
| |
| return false; |
| } |
| |
| PPMAccessor::PPMAccessor(std::string ppm_path, bool bgr, std::unique_ptr<IPreprocessor> preprocessor) |
| : _ppm_path(std::move(ppm_path)), _bgr(bgr), _preprocessor(std::move(preprocessor)) |
| { |
| } |
| |
| bool PPMAccessor::access_tensor(ITensor &tensor) |
| { |
| utils::PPMLoader ppm; |
| |
| // Open PPM file |
| ppm.open(_ppm_path); |
| |
| // 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_paramaters(tensor.info()->tensor_shape(), tensor.info()->data_layout()); |
| } |
| ARM_COMPUTE_ERROR_ON_MSG(ppm.width() != permuted_shape.x() || ppm.height() != permuted_shape.y(), |
| "Failed to load image file: dimensions [%d,%d] not correct, expected [%d,%d].", ppm.width(), ppm.height(), permuted_shape.x(), permuted_shape.y()); |
| |
| // Fill the tensor with the PPM content (BGR) |
| ppm.fill_planar_tensor(tensor, _bgr); |
| |
| // Preprocess tensor |
| if(_preprocessor) |
| { |
| _preprocessor->preprocess(tensor); |
| } |
| |
| return true; |
| } |
| |
| 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("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 T value = 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 T value = distribution(gen); |
| *reinterpret_cast<T *>(tensor.ptr_to_element(id)) = value; |
| }); |
| } |
| } |
| |
| bool RandomAccessor::access_tensor(ITensor &tensor) |
| { |
| switch(tensor.info()->data_type()) |
| { |
| 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: |
| case DataType::QS8: |
| { |
| 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: |
| case DataType::QS16: |
| { |
| 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<float>(), _upper.get<float>()); |
| fill<float>(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) |
| : _filename(std::move(filename)), _file_layout(file_layout) |
| { |
| } |
| |
| bool NumPyBinLoader::access_tensor(ITensor &tensor) |
| { |
| const TensorShape tensor_shape = tensor.info()->tensor_shape(); |
| std::vector<unsigned long> shape; |
| |
| // Open file |
| std::ifstream stream(_filename, std::ios::in | std::ios::binary); |
| ARM_COMPUTE_ERROR_ON_MSG(!stream.good(), "Failed to load binary data"); |
| std::string header = npy::read_header(stream); |
| |
| // Parse header |
| bool fortran_order = false; |
| std::string typestr; |
| npy::parse_header(header, typestr, fortran_order, shape); |
| |
| // Check if the typestring matches the given one |
| std::string expect_typestr = arm_compute::utils::get_typestring(tensor.info()->data_type()); |
| ARM_COMPUTE_ERROR_ON_MSG(typestr != expect_typestr, "Typestrings mismatch"); |
| |
| // Reverse vector in case of non fortran order |
| if(!fortran_order) |
| { |
| std::reverse(shape.begin(), shape.end()); |
| } |
| |
| // Correct dimensions (Needs to match TensorShape dimension corrections) |
| if(shape.size() != tensor_shape.num_dimensions()) |
| { |
| for(int i = static_cast<int>(shape.size()) - 1; i > 0; --i) |
| { |
| if(shape[i] == 1) |
| { |
| shape.pop_back(); |
| } |
| else |
| { |
| break; |
| } |
| } |
| } |
| |
| bool are_layouts_different = (_file_layout != tensor.info()->data_layout()); |
| |
| // Validate tensor ranks |
| ARM_COMPUTE_ERROR_ON_MSG(shape.size() != tensor_shape.num_dimensions(), "Tensor ranks mismatch"); |
| |
| // Set permutation parameters if needed |
| TensorShape permuted_shape = tensor_shape; |
| arm_compute::PermutationVector perm; |
| if(are_layouts_different) |
| { |
| std::tie(permuted_shape, perm) = compute_permutation_paramaters(tensor_shape, tensor.info()->data_layout()); |
| } |
| |
| // Validate shapes |
| for(size_t i = 0; i < shape.size(); ++i) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(permuted_shape[i] != shape[i], "Tensor dimensions mismatch"); |
| } |
| |
| // Validate shapes and copy tensor |
| if(!are_layouts_different || perm.num_dimensions() <= 2) |
| { |
| // Read data |
| if(tensor.info()->padding().empty() && (dynamic_cast<SubTensor *>(&tensor) == nullptr)) |
| { |
| // If tensor has no padding read directly from stream. |
| stream.read(reinterpret_cast<char *>(tensor.buffer()), tensor.info()->total_size()); |
| } |
| else |
| { |
| // If tensor has padding accessing tensor elements through execution window. |
| Window window; |
| window.use_tensor_dimensions(tensor_shape); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| stream.read(reinterpret_cast<char *>(tensor.ptr_to_element(id)), tensor.info()->element_size()); |
| }); |
| } |
| } |
| else |
| { |
| // If tensor has padding accessing tensor elements through execution window. |
| Window window; |
| window.use_tensor_dimensions(permuted_shape); |
| |
| execute_window_loop(window, [&](const Coordinates & id) |
| { |
| Coordinates coords(id); |
| arm_compute::permute(coords, perm); |
| stream.read(reinterpret_cast<char *>(tensor.ptr_to_element(coords)), tensor.info()->element_size()); |
| }); |
| } |
| return true; |
| } |