| /* |
| * Copyright (c) 2016, 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 "arm_compute/core/Error.h" |
| #include "arm_compute/core/Validate.h" |
| |
| #include <cmath> |
| #include <numeric> |
| |
| namespace arm_compute |
| { |
| inline uint8_t pixel_area_c1u8_clamp(const uint8_t *first_pixel_ptr, size_t stride, size_t width, size_t height, float wr, float hr, int x, int y) |
| { |
| ARM_COMPUTE_ERROR_ON(first_pixel_ptr == nullptr); |
| |
| // Calculate sampling position |
| float in_x = (x + 0.5f) * wr - 0.5f; |
| float in_y = (y + 0.5f) * hr - 0.5f; |
| |
| // Get bounding box offsets |
| int x_from = std::floor(x * wr - 0.5f - in_x); |
| int y_from = std::floor(y * hr - 0.5f - in_y); |
| int x_to = std::ceil((x + 1) * wr - 0.5f - in_x); |
| int y_to = std::ceil((y + 1) * hr - 0.5f - in_y); |
| |
| // Clamp position to borders |
| in_x = std::max(-1.f, std::min(in_x, static_cast<float>(width))); |
| in_y = std::max(-1.f, std::min(in_y, static_cast<float>(height))); |
| |
| // Clamp bounding box offsets to borders |
| x_from = ((in_x + x_from) < -1) ? -1 : x_from; |
| y_from = ((in_y + y_from) < -1) ? -1 : y_from; |
| x_to = ((in_x + x_to) > width) ? (width - in_x) : x_to; |
| y_to = ((in_y + y_to) > height) ? (height - in_y) : y_to; |
| |
| // Get pixel index |
| const int xi = std::floor(in_x); |
| const int yi = std::floor(in_y); |
| |
| // Bounding box elements in each dimension |
| const int x_elements = (x_to - x_from + 1); |
| const int y_elements = (y_to - y_from + 1); |
| ARM_COMPUTE_ERROR_ON(x_elements == 0 || y_elements == 0); |
| |
| // Sum pixels in area |
| int sum = 0; |
| for(int j = yi + y_from, je = yi + y_to; j <= je; ++j) |
| { |
| const uint8_t *ptr = first_pixel_ptr + j * stride + xi + x_from; |
| sum = std::accumulate(ptr, ptr + x_elements, sum); |
| } |
| |
| // Return average |
| return sum / (x_elements * y_elements); |
| } |
| |
| template <size_t dimension> |
| struct IncrementIterators |
| { |
| template <typename T, typename... Ts> |
| static void unroll(T &&it, Ts &&... iterators) |
| { |
| auto increment = [](T && it) |
| { |
| it.increment(dimension); |
| }; |
| utility::for_each(increment, std::forward<T>(it), std::forward<Ts>(iterators)...); |
| } |
| static void unroll() |
| { |
| // End of recursion |
| } |
| }; |
| |
| template <size_t dim> |
| struct ForEachDimension |
| { |
| template <typename L, typename... Ts> |
| static void unroll(const Window &w, Coordinates &id, L &&lambda_function, Ts &&... iterators) |
| { |
| const auto &d = w[dim - 1]; |
| |
| for(auto v = d.start(); v < d.end(); v += d.step(), IncrementIterators < dim - 1 >::unroll(iterators...)) |
| { |
| id.set(dim - 1, v); |
| ForEachDimension < dim - 1 >::unroll(w, id, lambda_function, iterators...); |
| } |
| } |
| }; |
| |
| template <> |
| struct ForEachDimension<0> |
| { |
| template <typename L, typename... Ts> |
| static void unroll(const Window &w, Coordinates &id, L &&lambda_function, Ts &&... iterators) |
| { |
| lambda_function(id); |
| } |
| }; |
| |
| template <typename L, typename... Ts> |
| inline void execute_window_loop(const Window &w, L &&lambda_function, Ts &&... iterators) |
| { |
| w.validate(); |
| |
| Coordinates id; |
| ForEachDimension<Coordinates::num_max_dimensions>::unroll(w, id, std::forward<L>(lambda_function), std::forward<Ts>(iterators)...); |
| } |
| |
| inline constexpr Iterator::Iterator() |
| : _ptr(nullptr), _dims() |
| { |
| } |
| |
| inline Iterator::Iterator(const ITensor *tensor, const Window &win) |
| : Iterator() |
| { |
| ARM_COMPUTE_ERROR_ON(tensor == nullptr); |
| const ITensorInfo *info = tensor->info(); |
| ARM_COMPUTE_ERROR_ON(info == nullptr); |
| const Strides &strides = info->strides_in_bytes(); |
| |
| _ptr = tensor->buffer() + info->offset_first_element_in_bytes(); |
| |
| //Initialize the stride for each dimension and calculate the position of the first element of the iteration: |
| for(unsigned int n = 0; n < info->num_dimensions(); ++n) |
| { |
| _dims[n]._stride = win[n].step() * strides[n]; |
| std::get<0>(_dims)._dim_start += strides[n] * win[n].start(); |
| } |
| |
| //Copy the starting point to all the dimensions: |
| for(unsigned int n = 1; n < Coordinates::num_max_dimensions; ++n) |
| { |
| _dims[n]._dim_start = std::get<0>(_dims)._dim_start; |
| } |
| |
| ARM_COMPUTE_ERROR_ON_WINDOW_DIMENSIONS_GTE(win, info->num_dimensions()); |
| } |
| |
| inline void Iterator::increment(const size_t dimension) |
| { |
| ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions); |
| |
| _dims[dimension]._dim_start += _dims[dimension]._stride; |
| |
| for(unsigned int n = 0; n < dimension; ++n) |
| { |
| _dims[n]._dim_start = _dims[dimension]._dim_start; |
| } |
| } |
| |
| inline constexpr int Iterator::offset() const |
| { |
| return _dims.at(0)._dim_start; |
| } |
| |
| inline constexpr uint8_t *Iterator::ptr() const |
| { |
| return _ptr + _dims.at(0)._dim_start; |
| } |
| |
| inline void Iterator::reset(const size_t dimension) |
| { |
| ARM_COMPUTE_ERROR_ON(dimension >= Coordinates::num_max_dimensions - 1); |
| |
| _dims[dimension]._dim_start = _dims[dimension + 1]._dim_start; |
| |
| for(unsigned int n = 0; n < dimension; ++n) |
| { |
| _dims[n]._dim_start = _dims[dimension]._dim_start; |
| } |
| } |
| |
| inline bool auto_init_if_empty(ITensorInfo &info, |
| const TensorShape &shape, |
| int num_channels, |
| DataType data_type, |
| int fixed_point_position, |
| QuantizationInfo quantization_info) |
| { |
| if(info.tensor_shape().total_size() == 0) |
| { |
| info.set_data_type(data_type); |
| info.set_num_channels(num_channels); |
| info.set_tensor_shape(shape); |
| info.set_fixed_point_position(fixed_point_position); |
| info.set_quantization_info(quantization_info); |
| return true; |
| } |
| |
| return false; |
| } |
| |
| inline bool auto_init_if_empty(ITensorInfo &info_sink, const ITensorInfo &info_source) |
| { |
| if(info_sink.tensor_shape().total_size() == 0) |
| { |
| info_sink.set_data_type(info_source.data_type()); |
| info_sink.set_num_channels(info_source.num_channels()); |
| info_sink.set_tensor_shape(info_source.tensor_shape()); |
| info_sink.set_fixed_point_position(info_source.fixed_point_position()); |
| info_sink.set_quantization_info(info_source.quantization_info()); |
| return true; |
| } |
| |
| return false; |
| } |
| |
| inline bool set_shape_if_empty(ITensorInfo &info, const TensorShape &shape) |
| { |
| if(info.tensor_shape().total_size() == 0) |
| { |
| info.set_tensor_shape(shape); |
| return true; |
| } |
| |
| return false; |
| } |
| |
| inline bool set_format_if_unknown(ITensorInfo &info, Format format) |
| { |
| if(info.data_type() == DataType::UNKNOWN) |
| { |
| info.set_format(format); |
| return true; |
| } |
| |
| return false; |
| } |
| |
| inline bool set_data_type_if_unknown(ITensorInfo &info, DataType data_type) |
| { |
| if(info.data_type() == DataType::UNKNOWN) |
| { |
| info.set_data_type(data_type); |
| return true; |
| } |
| |
| return false; |
| } |
| |
| inline bool set_fixed_point_position_if_zero(ITensorInfo &info, int fixed_point_position) |
| { |
| if(info.fixed_point_position() == 0 && (info.data_type() == DataType::QS8 || info.data_type() == DataType::QS16)) |
| { |
| info.set_fixed_point_position(fixed_point_position); |
| return true; |
| } |
| |
| return false; |
| } |
| |
| inline bool set_quantization_info_if_empty(ITensorInfo &info, QuantizationInfo quantization_info) |
| { |
| if(info.quantization_info().empty() && (is_data_type_quantized_asymmetric(info.data_type()))) |
| { |
| info.set_quantization_info(quantization_info); |
| return true; |
| } |
| |
| return false; |
| } |
| |
| inline ValidRegion calculate_valid_region_scale(const ITensorInfo &src_info, const TensorShape &dst_shape, InterpolationPolicy policy, BorderSize border_size, bool border_undefined) |
| { |
| const auto wr = static_cast<float>(dst_shape[0]) / static_cast<float>(src_info.tensor_shape()[0]); |
| const auto hr = static_cast<float>(dst_shape[1]) / static_cast<float>(src_info.tensor_shape()[1]); |
| Coordinates anchor; |
| anchor.set_num_dimensions(src_info.tensor_shape().num_dimensions()); |
| TensorShape new_dst_shape(dst_shape); |
| anchor.set(0, (policy == InterpolationPolicy::BILINEAR |
| && border_undefined) ? |
| ((static_cast<int>(src_info.valid_region().anchor[0]) + border_size.left + 0.5f) * wr - 0.5f) : |
| ((static_cast<int>(src_info.valid_region().anchor[0]) + 0.5f) * wr - 0.5f)); |
| anchor.set(1, (policy == InterpolationPolicy::BILINEAR |
| && border_undefined) ? |
| ((static_cast<int>(src_info.valid_region().anchor[1]) + border_size.top + 0.5f) * hr - 0.5f) : |
| ((static_cast<int>(src_info.valid_region().anchor[1]) + 0.5f) * hr - 0.5f)); |
| float shape_out_x = (policy == InterpolationPolicy::BILINEAR |
| && border_undefined) ? |
| ((static_cast<int>(src_info.valid_region().anchor[0]) + static_cast<int>(src_info.valid_region().shape[0]) - 1) - 1 + 0.5f) * wr - 0.5f : |
| ((static_cast<int>(src_info.valid_region().anchor[0]) + static_cast<int>(src_info.valid_region().shape[0])) + 0.5f) * wr - 0.5f; |
| float shape_out_y = (policy == InterpolationPolicy::BILINEAR |
| && border_undefined) ? |
| ((static_cast<int>(src_info.valid_region().anchor[1]) + static_cast<int>(src_info.valid_region().shape[1]) - 1) - 1 + 0.5f) * hr - 0.5f : |
| ((static_cast<int>(src_info.valid_region().anchor[1]) + static_cast<int>(src_info.valid_region().shape[1])) + 0.5f) * hr - 0.5f; |
| |
| new_dst_shape.set(0, shape_out_x - anchor[0]); |
| new_dst_shape.set(1, shape_out_y - anchor[1]); |
| |
| return ValidRegion(std::move(anchor), std::move(new_dst_shape)); |
| } |
| |
| inline Coordinates index2coords(const TensorShape &shape, int index) |
| { |
| int num_elements = shape.total_size(); |
| |
| ARM_COMPUTE_ERROR_ON_MSG(index < 0 || index >= num_elements, "Index has to be in [0, num_elements]!"); |
| ARM_COMPUTE_ERROR_ON_MSG(num_elements == 0, "Cannot create coordinate from empty shape!"); |
| |
| Coordinates coord{ 0 }; |
| |
| for(int d = shape.num_dimensions() - 1; d >= 0; --d) |
| { |
| num_elements /= shape[d]; |
| coord.set(d, index / num_elements); |
| index %= num_elements; |
| } |
| |
| return coord; |
| } |
| |
| inline int coords2index(const TensorShape &shape, const Coordinates &coord) |
| { |
| int num_elements = shape.total_size(); |
| ARM_COMPUTE_UNUSED(num_elements); |
| ARM_COMPUTE_ERROR_ON_MSG(num_elements == 0, "Cannot create linear index from empty shape!"); |
| |
| int index = 0; |
| int stride = 1; |
| |
| for(unsigned int d = 0; d < coord.num_dimensions(); ++d) |
| { |
| index += coord[d] * stride; |
| stride *= shape[d]; |
| } |
| |
| return index; |
| } |
| } // namespace arm_compute |