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Align fastdeploy prediction precision with yolov5 (#11)

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* Align fastdeploy prediction precision with yolov5

* Change name of Sort function to SortDetectionResult

* Add stride max_wh is_mini_pad property in __init__.py and unify format of getting image width and length

* Change mergesort.cc to sort_det_res.cc
This commit is contained in:
huangjianhui
2022-07-08 15:52:22 +08:00
committed by GitHub
parent 2b51f0efbc
commit 7d13491879
11 changed files with 151 additions and 65 deletions
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0
fastdeploy/libs/__init__.py Normal file
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7
fastdeploy/version.py
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@@ -1,7 +0,0 @@
# This file is generated by setup.py. DO NOT EDIT!
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
version = '0.3.0'
git_version = 'b69f13b26846f2e13f6ad3c81f1d4ad3ad81fdbb'

29
fastdeploy/vision/common/result.cc
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@@ -59,31 +59,6 @@ void DetectionResult::Resize(int size) {
label_ids.resize(size);
}
void DetectionResult::Sort() {
for (size_t i = 0; i < scores.size(); ++i) {
float max_score = scores[i];
float index = i;
for (size_t j = i + 1; j < scores.size(); ++j) {
if (max_score < scores[j]) {
max_score = scores[j];
index = j;
}
}
if (i == index) {
continue;
}
float tmp_score = scores[i];
scores[i] = scores[index];
scores[index] = tmp_score;
int32_t tmp_label_id = label_ids[i];
label_ids[i] = label_ids[index];
label_ids[index] = tmp_label_id;
std::array<float, 4> tmp_box = boxes[i];
boxes[i] = boxes[index];
boxes[index] = tmp_box;
}
}
std::string DetectionResult::Str() {
std::string out;
out = "DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]\n";
@@ -97,5 +72,5 @@ std::string DetectionResult::Str() {
return out;
}
} // namespace vision
} // namespace fastdeploy
} // namespace vision
} // namespace fastdeploy

2
fastdeploy/vision/common/result.h
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@@ -53,8 +53,6 @@ struct FASTDEPLOY_DECL DetectionResult : public BaseResult {
void Resize(int size);
void Sort();
std::string Str();
};

31
fastdeploy/vision/ultralytics/__init__.py
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@@ -51,6 +51,10 @@ class YOLOv5(FastDeployModel):
def is_no_pad(self):
return self.model.is_no_pad
@property
def is_mini_pad(self):
return self.model.is_mini_pad
@property
def is_scale_up(self):
return self.model.is_scale_up
@@ -59,14 +63,16 @@ class YOLOv5(FastDeployModel):
def stride(self):
return self.model.stride
@property
def max_wh(self):
return self.model.max_wh
@size.setter
def size(self, wh):
assert isinstance(wh, [
list, tuple
]), "The value to set `size` must be type of tuple or list."
assert len(
wh
) == 2, "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
assert isinstance(wh, [list, tuple]),\
"The value to set `size` must be type of tuple or list."
assert len(wh) == 2,\
"The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
len(wh))
self.model.size = wh
@@ -83,6 +89,13 @@ class YOLOv5(FastDeployModel):
value, bool), "The value to set `is_no_pad` must be type of bool."
self.model.is_no_pad = value
@is_mini_pad.setter
def is_mini_pad(self, value):
assert isinstance(
value,
bool), "The value to set `is_mini_pad` must be type of bool."
self.model.is_mini_pad = value
@is_scale_up.setter
def is_scale_up(self, value):
assert isinstance(
@@ -95,3 +108,9 @@ class YOLOv5(FastDeployModel):
assert isinstance(
value, int), "The value to set `stride` must be type of int."
self.model.stride = value
@max_wh.setter
def max_wh(self, value):
assert isinstance(
value, float), "The value to set `max_wh` must be type of float."
self.model.max_wh = value

4
fastdeploy/vision/ultralytics/ultralytics_pybind.cc
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@@ -34,6 +34,8 @@ void BindUltralytics(pybind11::module& m) {
&vision::ultralytics::YOLOv5::padding_value)
.def_readwrite("is_mini_pad", &vision::ultralytics::YOLOv5::is_mini_pad)
.def_readwrite("is_no_pad", &vision::ultralytics::YOLOv5::is_no_pad)
.def_readwrite("is_scale_up", &vision::ultralytics::YOLOv5::stride);
.def_readwrite("is_scale_up", &vision::ultralytics::YOLOv5::is_scale_up)
.def_readwrite("stride", &vision::ultralytics::YOLOv5::stride)
.def_readwrite("max_wh", &vision::ultralytics::YOLOv5::max_wh);
}
} // namespace fastdeploy

32
fastdeploy/vision/ultralytics/yolov5.cc
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@@ -64,8 +64,9 @@ bool YOLOv5::Initialize() {
padding_value = {114.0, 114.0, 114.0};
is_mini_pad = false;
is_no_pad = false;
is_scale_up = true;
is_scale_up = false;
stride = 32;
max_wh = 7680.0;
if (!InitRuntime()) {
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
@@ -76,6 +77,18 @@ bool YOLOv5::Initialize() {
bool YOLOv5::Preprocess(Mat* mat, FDTensor* output,
std::map<std::string, std::array<float, 2>>* im_info) {
// process after image load
double ratio = (size[0] * 1.0) / std::max(static_cast<float>(mat->Height()),
static_cast<float>(mat->Width()));
if (ratio != 1.0) {
int interp = cv::INTER_AREA;
if (ratio > 1.0) {
interp = cv::INTER_LINEAR;
}
int resize_h = int(mat->Height() * ratio);
int resize_w = int(mat->Width() * ratio);
Resize::Run(mat, resize_w, resize_h, -1, -1, interp);
}
// yolov5's preprocess steps
// 1. letterbox
// 2. BGR->RGB
@@ -119,11 +132,14 @@ bool YOLOv5::Postprocess(
if (confidence <= conf_threshold) {
continue;
}
int32_t label_id = std::distance(data + s + 5, max_class_score);
// convert from [x, y, w, h] to [x1, y1, x2, y2]
result->boxes.emplace_back(std::array<float, 4>{
data[s] - data[s + 2] / 2, data[s + 1] - data[s + 3] / 2,
data[s + 0] + data[s + 2] / 2, data[s + 1] + data[s + 3] / 2});
result->label_ids.push_back(std::distance(data + s + 5, max_class_score));
data[s] - data[s + 2] / 2.0f + label_id * max_wh,
data[s + 1] - data[s + 3] / 2.0f + label_id * max_wh,
data[s + 0] + data[s + 2] / 2.0f + label_id * max_wh,
data[s + 1] + data[s + 3] / 2.0f + label_id * max_wh});
result->label_ids.push_back(label_id);
result->scores.push_back(confidence);
}
utils::NMS(result, nms_iou_threshold);
@@ -141,8 +157,12 @@ bool YOLOv5::Postprocess(
for (size_t i = 0; i < result->boxes.size(); ++i) {
float pad_h = (out_h - ipt_h * scale) / 2;
float pad_w = (out_w - ipt_w * scale) / 2;
int32_t label_id = (result->label_ids)[i];
// clip box
result->boxes[i][0] = result->boxes[i][0] - max_wh * label_id;
result->boxes[i][1] = result->boxes[i][1] - max_wh * label_id;
result->boxes[i][2] = result->boxes[i][2] - max_wh * label_id;
result->boxes[i][3] = result->boxes[i][3] - max_wh * label_id;
result->boxes[i][0] = std::max((result->boxes[i][0] - pad_w) / scale, 0.0f);
result->boxes[i][1] = std::max((result->boxes[i][1] - pad_h) / scale, 0.0f);
result->boxes[i][2] = std::max((result->boxes[i][2] - pad_w) / scale, 0.0f);
@@ -183,13 +203,11 @@ bool YOLOv5::Predict(cv::Mat* im, DetectionResult* result, float conf_threshold,
#endif
input_tensors[0].name = InputInfoOfRuntime(0).name;
std::vector<FDTensor> output_tensors;
if (!Infer(input_tensors, &output_tensors)) {
FDERROR << "Failed to inference." << std::endl;
return false;
}
#ifdef FASTDEPLOY_DEBUG
TIMERECORD_END(1, "Inference")
TIMERECORD_START(2)

2
fastdeploy/vision/ultralytics/yolov5.h
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@@ -79,6 +79,8 @@ class FASTDEPLOY_DECL YOLOv5 : public FastDeployModel {
bool is_scale_up;
// padding stride, for is_mini_pad
int stride;
// for offseting the boxes by classes when using NMS
float max_wh;
};
} // namespace ultralytics
} // namespace vision

19
fastdeploy/vision/utils/nms.cc
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@@ -22,13 +22,13 @@ namespace utils {
// The implementation refers to
// https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/cpp/src/utils.cc
void NMS(DetectionResult* result, float iou_threshold) {
result->Sort();
utils::SortDetectionResult(result);
std::vector<float> area_of_boxes(result->boxes.size());
std::vector<int> suppressed(result->boxes.size(), 0);
for (size_t i = 0; i < result->boxes.size(); ++i) {
area_of_boxes[i] = (result->boxes[i][2] - result->boxes[i][0] + 1) *
(result->boxes[i][3] - result->boxes[i][1] + 1);
area_of_boxes[i] = (result->boxes[i][2] - result->boxes[i][0]) *
(result->boxes[i][3] - result->boxes[i][1]);
}
for (size_t i = 0; i < result->boxes.size(); ++i) {
@@ -43,12 +43,11 @@ void NMS(DetectionResult* result, float iou_threshold) {
float ymin = std::max(result->boxes[i][1], result->boxes[j][1]);
float xmax = std::min(result->boxes[i][2], result->boxes[j][2]);
float ymax = std::min(result->boxes[i][3], result->boxes[j][3]);
float overlap_w = std::max(0.0f, xmax - xmin + 1);
float overlap_h = std::max(0.0f, ymax - ymin + 1);
float overlap_w = std::max(0.0f, xmax - xmin);
float overlap_h = std::max(0.0f, ymax - ymin);
float overlap_area = overlap_w * overlap_h;
float overlap_ratio =
overlap_area /
(area_of_boxes[i] + area_of_boxes[j] - overlap_area + 1e-06);
overlap_area / (area_of_boxes[i] + area_of_boxes[j] - overlap_area);
if (overlap_ratio > iou_threshold) {
suppressed[j] = 1;
}
@@ -67,6 +66,6 @@ void NMS(DetectionResult* result, float iou_threshold) {
}
}
} // namespace utils
} // namespace vision
} // namespace fastdeploy
} // namespace utils
} // namespace vision
} // namespace fastdeploy

77
fastdeploy/vision/utils/sort_det_res.cc Normal file
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@@ -0,0 +1,77 @@
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "fastdeploy/vision/utils/utils.h"
namespace fastdeploy {
namespace vision {
namespace utils {
void Merge(DetectionResult* result, size_t low, size_t mid, size_t high) {
std::vector<std::array<float, 4>>& boxes = result->boxes;
std::vector<float>& scores = result->scores;
std::vector<int32_t>& label_ids = result->label_ids;
std::vector<std::array<float, 4>> temp_boxes(boxes);
std::vector<float> temp_scores(scores);
std::vector<int32_t> temp_label_ids(label_ids);
size_t i = low;
size_t j = mid + 1;
size_t k = i;
for (; i <= mid && j <= high; k++) {
if (temp_scores[i] >= temp_scores[j]) {
scores[k] = temp_scores[i];
label_ids[k] = temp_label_ids[i];
boxes[k] = temp_boxes[i];
i++;
} else {
scores[k] = temp_scores[j];
label_ids[k] = temp_label_ids[j];
boxes[k] = temp_boxes[j];
j++;
}
}
while (i <= mid) {
scores[k] = temp_scores[i];
label_ids[k] = temp_label_ids[i];
boxes[k] = temp_boxes[i];
k++;
i++;
}
while (j <= high) {
scores[k] = temp_scores[j];
label_ids[k] = temp_label_ids[j];
boxes[k] = temp_boxes[j];
k++;
j++;
}
}
void MergeSort(DetectionResult* result, size_t low, size_t high) {
if (low < high) {
size_t mid = (high - low) / 2 + low;
MergeSort(result, low, mid);
MergeSort(result, mid + 1, high);
Merge(result, low, mid, high);
}
}
void SortDetectionResult(DetectionResult* result) {
size_t low = 0;
size_t high = result->scores.size() - 1;
MergeSort(result, low, high);
}
} // namespace utils
} // namespace vision
} // namespace fastdeploy

13
fastdeploy/vision/utils/utils.h
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@@ -14,11 +14,11 @@
#pragma once
#include <set>
#include <vector>
#include "fastdeploy/core/fd_tensor.h"
#include "fastdeploy/utils/utils.h"
#include "fastdeploy/vision/common/result.h"
#include <set>
#include <vector>
namespace fastdeploy {
namespace vision {
@@ -53,6 +53,9 @@ std::vector<int32_t> TopKIndices(const T* array, int array_size, int topk) {
void NMS(DetectionResult* output, float iou_threshold = 0.5);
} // namespace utils
} // namespace vision
} // namespace fastdeploy
// MergeSort
void SortDetectionResult(DetectionResult* output);
} // namespace utils
} // namespace vision
} // namespace fastdeploy