mirror of
https://github.com/RapidAI/RapidOCR.git
synced 2026-11-11 13:30:51 +08:00
181 lines
9.4 KiB
C++
181 lines
9.4 KiB
C++
#include "onnx_lite.hpp"
|
|
#include "ppocr/ppocr.hpp"
|
|
|
|
#include <algorithm>
|
|
#include <cmath>
|
|
#include <iostream>
|
|
#include <stdexcept>
|
|
#include <string>
|
|
#include <unordered_map>
|
|
#include <vector>
|
|
|
|
namespace {
|
|
|
|
ppocr::detail::Tensor CpuDetectorInput(const ppocr::Image& image, int width, int height) {
|
|
ppocr::detail::Tensor output{{1, 3, height, width},
|
|
std::vector<float>(std::size_t(3) * width * height)};
|
|
constexpr float scale[3]{1.F / (255.F * .229F), 1.F / (255.F * .224F), 1.F / (255.F * .225F)};
|
|
constexpr float offset[3]{-.485F / .229F, -.456F / .224F, -.406F / .225F};
|
|
for (int y = 0; y < height; ++y) for (int x = 0; x < width; ++x) {
|
|
const float fx = (float(x) + .5F) * image.width / width - .5F;
|
|
const float fy = (float(y) + .5F) * image.height / height - .5F;
|
|
const int xf = int(std::floor(fx)), yf = int(std::floor(fy));
|
|
const int x0 = std::clamp(xf, 0, image.width - 1), x1 = std::min(x0 + 1, image.width - 1);
|
|
const int y0 = std::clamp(yf, 0, image.height - 1), y1 = std::min(y0 + 1, image.height - 1);
|
|
const float dx = fx - xf, dy = fy - yf;
|
|
for (int channel = 0; channel < 3; ++channel) {
|
|
const int component = 2 - channel;
|
|
const auto pixel = [&](int py, int px) {
|
|
return float(image.rgb[(std::size_t(py) * image.width + px) * 3 + component]);
|
|
};
|
|
const float upper = pixel(y0, x0) * (1.F - dx) + pixel(y0, x1) * dx;
|
|
const float lower = pixel(y1, x0) * (1.F - dx) + pixel(y1, x1) * dx;
|
|
const float sample = float(std::clamp(std::lround(upper * (1.F - dy) + lower * dy), 0L, 255L));
|
|
output.data[(std::size_t(channel) * height + y) * width + x] = sample * scale[channel] + offset[channel];
|
|
}
|
|
}
|
|
return output;
|
|
}
|
|
|
|
void CheckFinite(const ppocr::detail::Tensor& tensor, const char* source) {
|
|
for (std::size_t index = 0; index < tensor.data.size(); ++index) {
|
|
if (!std::isfinite(tensor.data[index])) {
|
|
throw std::runtime_error(std::string(source) + " produced non-finite value at " + std::to_string(index));
|
|
}
|
|
}
|
|
}
|
|
|
|
float MaximumAbsoluteError(const ppocr::detail::Tensor& actual,
|
|
const ppocr::detail::Tensor& expected,
|
|
const char* source) {
|
|
if (actual.shape != expected.shape || actual.data.size() != expected.data.size()) {
|
|
throw std::runtime_error(std::string(source) + " output shape mismatch");
|
|
}
|
|
float maximum{};
|
|
for (std::size_t index = 0; index < actual.data.size(); ++index) {
|
|
maximum = std::max(maximum, std::abs(actual.data[index] - expected.data[index]));
|
|
}
|
|
return maximum;
|
|
}
|
|
|
|
} // namespace
|
|
|
|
// Regression for the actual GPU-only detector boundary. It proves that a
|
|
// Vulkan RGB resize/normalization output can feed the strict graph directly,
|
|
// then compares the device result with the portable graph's exact image input.
|
|
int main(int argc, char** argv) {
|
|
if (argc != 3) {
|
|
std::cerr << "usage: ppocr_gpu_pipeline_smoke DETECTOR.onnx IMAGE.ppm\n";
|
|
return 2;
|
|
}
|
|
try {
|
|
const auto image = ppocr::LoadPPM(argv[2]);
|
|
if (image.empty()) throw std::runtime_error("invalid image");
|
|
const int width = std::max(32, (image.width / 32) * 32);
|
|
const int height = std::max(32, (image.height / 32) * 32);
|
|
|
|
ppocr::detail::OnnxLite cpu(argv[1], ppocr::Backend::cpu_only);
|
|
ppocr::detail::OnnxLite gpu_host(argv[1], ppocr::Backend::gpu_only);
|
|
ppocr::detail::OnnxLite gpu_slot(argv[1], ppocr::Backend::gpu_only);
|
|
const auto cpu_input = CpuDetectorInput(image, width, height);
|
|
const auto expected = cpu.Run({{"x", cpu_input}});
|
|
if (expected.size() != 1) throw std::runtime_error("unexpected detector output count");
|
|
ppocr::detail::OnnxLite gpu_cpu_input(argv[1], ppocr::Backend::gpu_only);
|
|
const auto cpu_input_gpu_output = gpu_cpu_input.Run({{"x", cpu_input}});
|
|
if (cpu_input_gpu_output.size() != 1) throw std::runtime_error("CPU-input Vulkan graph output count mismatch");
|
|
CheckFinite(cpu_input_gpu_output.begin()->second, "Vulkan graph with CPU preprocessing");
|
|
const float cpu_input_graph_error = MaximumAbsoluteError(cpu_input_gpu_output.begin()->second,
|
|
expected.begin()->second, "Vulkan graph with CPU preprocessing");
|
|
|
|
auto& arena = gpu_slot.gpu_arena();
|
|
auto rgb = arena.Acquire(image.rgb.size(), "pipeline-rgb");
|
|
auto input = arena.Acquire(std::size_t(3) * height * width, "pipeline-input");
|
|
if (!rgb.resident || !input.resident || !arena.UploadRgb8(rgb, image.rgb.data(), image.rgb.size()) ||
|
|
!arena.ResizeRgbToNchw(rgb, input, image.width, image.height, 0, 0, image.width, image.height,
|
|
width, height, 0, 0, 0, 0, 0, 0)) {
|
|
throw std::runtime_error("Vulkan detector input failed");
|
|
}
|
|
ppocr::detail::Tensor preprocessed{{1, 3, height, width},
|
|
std::vector<float>(input.live_elements)};
|
|
if (!arena.Download(preprocessed.data.data(), input, preprocessed.data.size())) {
|
|
arena.Release(input); arena.Release(rgb);
|
|
throw std::runtime_error("Vulkan detector input download failed");
|
|
}
|
|
CheckFinite(preprocessed, "Vulkan preprocessing");
|
|
float preprocess_error{};
|
|
std::size_t preprocess_mismatch_count{};
|
|
std::size_t first_preprocess_mismatch = preprocessed.data.size();
|
|
std::size_t maximum_preprocess_error_index{};
|
|
for (std::size_t index = 0; index < preprocessed.data.size(); ++index) {
|
|
const float error = std::abs(preprocessed.data[index] - cpu_input.data[index]);
|
|
if (error > preprocess_error) {
|
|
preprocess_error = error;
|
|
maximum_preprocess_error_index = index;
|
|
}
|
|
if (error != 0.F) {
|
|
++preprocess_mismatch_count;
|
|
first_preprocess_mismatch = std::min(first_preprocess_mismatch, index);
|
|
}
|
|
}
|
|
const auto [input_minimum, input_maximum] = std::minmax_element(
|
|
preprocessed.data.begin(), preprocessed.data.end());
|
|
std::cout << "GPU preprocessing range=" << *input_minimum << ',' << *input_maximum
|
|
<< " max_abs_error=" << preprocess_error << '\n';
|
|
if (first_preprocess_mismatch != preprocessed.data.size()) {
|
|
const std::size_t plane = std::size_t(width) * height;
|
|
const std::size_t channel = first_preprocess_mismatch / plane;
|
|
const std::size_t spatial = first_preprocess_mismatch % plane;
|
|
std::cout << "GPU preprocessing mismatches=" << preprocess_mismatch_count
|
|
<< " first=(c=" << channel << ",y=" << spatial / width
|
|
<< ",x=" << spatial % width << ") cpu="
|
|
<< cpu_input.data[first_preprocess_mismatch] << " gpu="
|
|
<< preprocessed.data[first_preprocess_mismatch] << '\n';
|
|
const std::size_t maximum_plane = std::size_t(width) * height;
|
|
const std::size_t maximum_channel = maximum_preprocess_error_index / maximum_plane;
|
|
const std::size_t maximum_spatial = maximum_preprocess_error_index % maximum_plane;
|
|
std::cout << "GPU preprocessing maximum=(c=" << maximum_channel << ",y="
|
|
<< maximum_spatial / width << ",x=" << maximum_spatial % width
|
|
<< ") cpu=" << cpu_input.data[maximum_preprocess_error_index]
|
|
<< " gpu=" << preprocessed.data[maximum_preprocess_error_index] << '\n';
|
|
}
|
|
// This isolates numerical preprocessing from slot handoff. If this strict
|
|
// host-boundary run fails, the image values themselves are invalid; if it
|
|
// passes while the live-slot run below fails, the device-slot path is the
|
|
// only remaining suspect.
|
|
const auto host_boundary = gpu_host.Run({{"x", preprocessed}});
|
|
if (host_boundary.size() != 1) throw std::runtime_error("host-boundary graph output count mismatch");
|
|
CheckFinite(host_boundary.begin()->second, "Vulkan graph after input download/upload");
|
|
std::unordered_map<std::string, ppocr::detail::OnnxLite::GpuTensor> output_slots;
|
|
if (!gpu_slot.RunGpuOnlyDevice({{"x", {{1, 3, height, width}, input}}}, output_slots) ||
|
|
output_slots.size() != 1) {
|
|
arena.Release(rgb);
|
|
throw std::runtime_error("strict Vulkan detector graph failed");
|
|
}
|
|
auto output = std::move(output_slots.begin()->second);
|
|
ppocr::detail::Tensor actual{output.shape, std::vector<float>(output.slot.live_elements)};
|
|
const bool downloaded = arena.Download(actual.data.data(), output.slot, actual.data.size());
|
|
arena.Release(output.slot);
|
|
arena.Release(rgb);
|
|
if (!downloaded) throw std::runtime_error("Vulkan detector output download failed");
|
|
CheckFinite(actual, "Vulkan live-slot pipeline");
|
|
|
|
const auto& expected_tensor = expected.begin()->second;
|
|
if (actual.shape != expected_tensor.shape || actual.data.size() != expected_tensor.data.size()) {
|
|
throw std::runtime_error("detector output shape mismatch");
|
|
}
|
|
float maximum_error{};
|
|
for (std::size_t index = 0; index < actual.data.size(); ++index) {
|
|
maximum_error = std::max(maximum_error, std::abs(actual.data[index] - expected_tensor.data[index]));
|
|
}
|
|
std::cout << "GPU front-end to detector graph smoke passed shape=1x3x" << height << 'x' << width
|
|
<< " output_elements=" << actual.data.size()
|
|
<< " cpu_input_graph_max_abs_error=" << cpu_input_graph_error
|
|
<< " frontend_graph_max_abs_error=" << MaximumAbsoluteError(host_boundary.begin()->second,
|
|
expected_tensor, "Vulkan graph after input download/upload")
|
|
<< " live_slot_max_abs_error=" << maximum_error << '\n';
|
|
} catch (const std::exception& error) {
|
|
std::cerr << "GPU pipeline smoke failed: " << error.what() << '\n';
|
|
return 1;
|
|
}
|
|
}
|