| /* |
| * 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. |
| */ |
| |
| #include "utils/GraphUtils.h" |
| #include "utils/Utils.h" |
| |
| #ifdef ARM_COMPUTE_CL |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/runtime/CL/CLTensor.h" |
| #endif /* ARM_COMPUTE_CL */ |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/PixelValue.h" |
| #include "libnpy/npy.hpp" |
| |
| #include <algorithm> |
| #include <iomanip> |
| #include <ostream> |
| #include <random> |
| |
| using namespace arm_compute::graph_utils; |
| |
| 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; |
| } |
| |
| PPMAccessor::PPMAccessor(const std::string &ppm_path, bool bgr, float mean_r, float mean_g, float mean_b) |
| : _ppm_path(ppm_path), _bgr(bgr), _mean_r(mean_r), _mean_g(mean_g), _mean_b(mean_b) |
| { |
| } |
| |
| bool PPMAccessor::access_tensor(ITensor &tensor) |
| { |
| utils::PPMLoader ppm; |
| const float mean[3] = |
| { |
| _bgr ? _mean_b : _mean_r, |
| _mean_g, |
| _bgr ? _mean_r : _mean_b |
| }; |
| |
| // Open PPM file |
| ppm.open(_ppm_path); |
| |
| // Fill the tensor with the PPM content (BGR) |
| ppm.fill_planar_tensor(tensor, _bgr); |
| |
| // Subtract the mean value from each channel |
| 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; |
| }); |
| |
| 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()); |
| } |
| } |
| |
| bool TopNPredictionsAccessor::access_tensor(ITensor &tensor) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&tensor, 1, DataType::F32); |
| ARM_COMPUTE_ERROR_ON(_labels.size() != tensor.info()->dimension(0)); |
| |
| // Get the predicted class |
| std::vector<float> classes_prob; |
| std::vector<size_t> index; |
| |
| const auto output_net = reinterpret_cast<float *>(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; |
| } |
| |
| 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()) |
| { |
| 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) |
| : _filename(std::move(filename)) |
| { |
| } |
| |
| 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"); |
| // Check magic bytes and version number |
| unsigned char v_major = 0; |
| unsigned char v_minor = 0; |
| npy::read_magic(stream, &v_major, &v_minor); |
| |
| // Read header |
| std::string header; |
| if(v_major == 1 && v_minor == 0) |
| { |
| header = npy::read_header_1_0(stream); |
| } |
| else if(v_major == 2 && v_minor == 0) |
| { |
| header = npy::read_header_2_0(stream); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR("Unsupported file format version"); |
| } |
| |
| // Parse header |
| bool fortran_order = false; |
| std::string typestr; |
| npy::ParseHeader(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"); |
| |
| // Validate tensor shape |
| ARM_COMPUTE_ERROR_ON_MSG(shape.size() != tensor_shape.num_dimensions(), "Tensor ranks mismatch"); |
| if(fortran_order) |
| { |
| for(size_t i = 0; i < shape.size(); ++i) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(tensor_shape[i] != shape[i], "Tensor dimensions mismatch"); |
| } |
| } |
| else |
| { |
| for(size_t i = 0; i < shape.size(); ++i) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(tensor_shape[i] != shape[shape.size() - i - 1], "Tensor dimensions mismatch"); |
| } |
| } |
| |
| // Read data |
| if(tensor.info()->padding().empty()) |
| { |
| // 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()); |
| }); |
| } |
| return true; |
| } |
