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GraphUtils.cpp
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801 lines (694 loc) · 27.7 KB
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/*
* Copyright (c) 2017-2021, 2024 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_data()
{
return false;
}
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());
}
#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());
#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()));
#endif // __arm__
// 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());
}
#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 "].",
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()));
#endif // __arm__
// 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<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<int32_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:
{
arm_compute::utils::uniform_real_distribution_16bit<half> distribution_f16(_lower.get<float>(),
_upper.get<float>());
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;
}