diff --git a/.gitmodules b/.gitmodules index 74f0ceae1..d5bd0a6c4 100644 --- a/.gitmodules +++ b/.gitmodules @@ -23,3 +23,6 @@ [submodule "mil_common/perception/yolov7-ros"] path = mil_common/perception/yolov7-ros url = https://github.com/uf-mil/yolov7-ros.git +[submodule "mil_common/perception/vision_stack"] + path = mil_common/perception/vision_stack + url = https://github.com/uf-mil/vision_stack.git diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5db81ae41..ddf9c5ff3 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -14,6 +14,7 @@ repos: rev: v1.32.0 hooks: - id: yamllint + exclude: mil_common/perception/yoloros - repo: https://github.com/psf/black rev: 23.7.0 hooks: @@ -31,7 +32,7 @@ repos: rev: v0.9.0.5 hooks: - id: shellcheck - exclude: ^docker|deprecated|NaviGator/simulation/VRX + exclude: ^docker|deprecated|NaviGator/simulation/VRX|mil_common/perception/yoloros args: [--severity=warning, --exclude=SC1090] - repo: https://github.com/scop/pre-commit-shfmt rev: v3.7.0-1 @@ -44,6 +45,7 @@ repos: hooks: - id: ruff args: [--fix, --exit-non-zero-on-fix] + exclude: mil_common/perception/yoloros - repo: https://github.com/codespell-project/codespell rev: v2.2.5 hooks: diff --git a/SubjuGator/command/subjugator_launch/launch/subsystems/perception.launch b/SubjuGator/command/subjugator_launch/launch/subsystems/perception.launch index 0d93e4ae9..34f9432d7 100644 --- a/SubjuGator/command/subjugator_launch/launch/subsystems/perception.launch +++ b/SubjuGator/command/subjugator_launch/launch/subsystems/perception.launch @@ -19,6 +19,7 @@ --> + diff --git a/SubjuGator/perception/subjugator_perception/nodes/symbols_detect.py b/SubjuGator/perception/subjugator_perception/nodes/symbols_detect.py new file mode 100644 index 000000000..6920f9591 --- /dev/null +++ b/SubjuGator/perception/subjugator_perception/nodes/symbols_detect.py @@ -0,0 +1,3 @@ +from yoloros import Detector + +Detector("robosub24").test_detection(conf_thres=0.77) diff --git a/SubjuGator/perception/subjugator_perception/nodes/vision_pipeline_test.py b/SubjuGator/perception/subjugator_perception/nodes/vision_pipeline_test.py new file mode 100755 index 000000000..7854b576f --- /dev/null +++ b/SubjuGator/perception/subjugator_perception/nodes/vision_pipeline_test.py @@ -0,0 +1,34 @@ +#!/usr/bin/env python3 +import rospy +from image_geometry import PinholeCameraModel +from mil_ros_tools import ( + Image_Subscriber, +) +from yoloros import Detector + +# from vision_stack import VisionStack + +__author__ = "Daniel Parra" + + +class ObjectDetectionTest: + def __init__(self): + camera = rospy.get_param("~image_topic", "/camera/front/right/image_rect_color") + self.detector = Detector("robosub24", device="cpu") + + self.image_sub = Image_Subscriber(camera, self.detection_callback) + self.camera_info = self.image_sub.wait_for_camera_info() + assert self.camera_info is not None + self.cam = PinholeCameraModel() + self.cam.fromCameraInfo(self.camera_info) + + def detection_callback(self, msg): + # Create Image from array + print("Detecting...") + self.detector.display_detection_ros_msg(msg, conf_thres=0.85) + + +if __name__ == "__main__": + rospy.init_node("vision_pipeline_test") + ObjectDetectionTest() + rospy.spin() diff --git a/docs/software/index.rst b/docs/software/index.rst index 9e779a0eb..48b6acb36 100644 --- a/docs/software/index.rst +++ b/docs/software/index.rst @@ -11,6 +11,7 @@ Various documentation related to practices followed by the MIL Software team. documentation_syntax adding_documentation help + vrx_2023 zobelisk asyncio rqt diff --git a/docs/software/vrx_2023.md b/docs/software/vrx_2023.md new file mode 100644 index 000000000..5c454a5be --- /dev/null +++ b/docs/software/vrx_2023.md @@ -0,0 +1,64 @@ +# VRX 2023 + +Thanks for your interest in working with the VRX tasks for 2023! We're happy to +have you participate in the competition! + +There are some design changes for the competition this year that you need to be +aware of. First, VRX now defaults to supporting ROS 2, which is unfortunate for +us since we use ROS 1. But, don't be afraid! Thankfully, the VRX staff have built +a bridge between ROS 1 and ROS 2 that we can use to connect our submission with +their competition environment. This guide will help you get familiar with that +setup and how to work with VRX. + +## Working with ROS 2 + +Your machine will have both ROS 1 and ROS 2 running on it. To work with two versions +of ROS, it's best to only source one version of ROS at a time. + +### Installing ROS 2 + +To install ROS 2, run the `./scripts/install_ros2.sh` script. This will install +ROS 2, the ROS 1 to 2 bridge, and the bridge VRX messages packages. + +### Working with ROS 1 and 2 + +Currently, the MIL installation will source `./scripts/setup.bash` each time you +open your terminal. While this works for most members and most applications, for +VRX this is no longer ideal. We recommend changing that line to this (or +something similar) such that you will be able to choose a version to run with each +new terminal: + +```bash +noetic() { + source ~/catkin_ws/src/mil/scripts/setup.bash +} + +humble() { + source ./ros2_humble/install/local_setup.bash +} +``` + +Then, in terminals where you want to run ROS 2, you can type `humble`. Likewise, +in terminals where you want to run ROS 1, run `noetic`. + +## Bridging VRX + +Because the VRX platform is built for ROS 2 and our current software focuses on +ROS 1, we will create a ROS 1 to 2 bridge to allow for communication. The bridge, +our ROS 1 code, and the VRX platform (in ROS 2) will all run on your host machine. + +## Launching Missions + +To run the entire the entire VRX simulation, you can `roslaunch`. This launches +the example world (a basic setup). + +```bash +roslaunch navigator_launch vrx.launch --screen +``` + +Some arguments you may want to provide include: + +* `run_task` - This flag allows you to run and get scored for a certain task. + An example argument includes `Navigation` (to run the navigation task). The + task runs the mission named `Vrx` (so in our case, it would + run `VrxNavigation`). diff --git a/mil_common/axros b/mil_common/axros index 1b0399935..8cdf13186 160000 --- a/mil_common/axros +++ b/mil_common/axros @@ -1 +1 @@ -Subproject commit 1b03999351fb5a61b202ff125f493229c2a1676b +Subproject commit 8cdf131866af08edf0bbe3ac9020e1da47b3e432 diff --git a/mil_common/perception/vision_stack b/mil_common/perception/vision_stack new file mode 160000 index 000000000..00ac8b966 --- /dev/null +++ b/mil_common/perception/vision_stack @@ -0,0 +1 @@ +Subproject commit 00ac8b966a702c5c779b7a64988bec24a79240ce diff --git a/mil_common/perception/yoloros/CMakeLists.txt b/mil_common/perception/yoloros/CMakeLists.txt new file mode 100644 index 000000000..d775924b3 --- /dev/null +++ b/mil_common/perception/yoloros/CMakeLists.txt @@ -0,0 +1,208 @@ +cmake_minimum_required(VERSION 3.0.2) +project(mil_yolov7_pkg) + +## Compile as C++11, supported in ROS Kinetic and newer +# add_compile_options(-std=c++11) + +## Find catkin macros and libraries +## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz) +## is used, also find other catkin packages +find_package(catkin REQUIRED COMPONENTS + roscpp + rospy + sensor_msgs + std_msgs + vision_msgs +) + +## System dependencies are found with CMake's conventions +# find_package(Boost REQUIRED COMPONENTS system) + + +## Uncomment this if the package has a setup.py. This macro ensures +## modules and global scripts declared therein get installed +## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html +catkin_python_setup() + +################################################ +## Declare ROS messages, services and actions ## +################################################ + +## To declare and build messages, services or actions from within this +## package, follow these steps: +## * Let MSG_DEP_SET be the set of packages whose message types you use in +## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...). +## * In the file package.xml: +## * add a build_depend tag for "message_generation" +## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET +## * If MSG_DEP_SET isn't empty the following dependency has been pulled in +## but can be declared for certainty nonetheless: +## * add a exec_depend tag for "message_runtime" +## * In this file (CMakeLists.txt): +## * add "message_generation" and every package in MSG_DEP_SET to +## find_package(catkin REQUIRED COMPONENTS ...) +## * add "message_runtime" and every package in MSG_DEP_SET to +## catkin_package(CATKIN_DEPENDS ...) +## * uncomment the add_*_files sections below as needed +## and list every .msg/.srv/.action file to be processed +## * uncomment the generate_messages entry below +## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...) + +## Generate messages in the 'msg' folder +# add_message_files( +# FILES +# Message1.msg +# Message2.msg +# ) + +## Generate services in the 'srv' folder +# add_service_files( +# FILES +# Service1.srv +# Service2.srv +# ) + +## Generate actions in the 'action' folder +# add_action_files( +# FILES +# Action1.action +# Action2.action +# ) + +## Generate added messages and services with any dependencies listed here +# generate_messages( +# DEPENDENCIES +# sensor_msgs# std_msgs# vision_msgs +# ) + +################################################ +## Declare ROS dynamic reconfigure parameters ## +################################################ + +## To declare and build dynamic reconfigure parameters within this +## package, follow these steps: +## * In the file package.xml: +## * add a build_depend and a exec_depend tag for "dynamic_reconfigure" +## * In this file (CMakeLists.txt): +## * add "dynamic_reconfigure" to +## find_package(catkin REQUIRED COMPONENTS ...) +## * uncomment the "generate_dynamic_reconfigure_options" section below +## and list every .cfg file to be processed + +## Generate dynamic reconfigure parameters in the 'cfg' folder +# generate_dynamic_reconfigure_options( +# cfg/DynReconf1.cfg +# cfg/DynReconf2.cfg +# ) + +################################### +## catkin specific configuration ## +################################### +## The catkin_package macro generates cmake config files for your package +## Declare things to be passed to dependent projects +## INCLUDE_DIRS: uncomment this if your package contains header files +## LIBRARIES: libraries you create in this project that dependent projects also need +## CATKIN_DEPENDS: catkin_packages dependent projects also need +## DEPENDS: system dependencies of this project that dependent projects also need +catkin_package( +# INCLUDE_DIRS include +# LIBRARIES mil_yolov7_pkg +# CATKIN_DEPENDS roscpp rospy sensor_msgs std_msgs vision_msgs +# DEPENDS system_lib +) + +########### +## Build ## +########### + +## Specify additional locations of header files +## Your package locations should be listed before other locations +include_directories( +# include + ${catkin_INCLUDE_DIRS} +) + +## Declare a C++ library +# add_library(${PROJECT_NAME} +# src/${PROJECT_NAME}/mil_yolov7_pkg.cpp +# ) + +## Add cmake target dependencies of the library +## as an example, code may need to be generated before libraries +## either from message generation or dynamic reconfigure +# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) + +## Declare a C++ executable +## With catkin_make all packages are built within a single CMake context +## The recommended prefix ensures that target names across packages don't collide +# add_executable(${PROJECT_NAME}_node src/mil_yolov7_pkg_node.cpp) + +## Rename C++ executable without prefix +## The above recommended prefix causes long target names, the following renames the +## target back to the shorter version for ease of user use +## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node" +# set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "") + +## Add cmake target dependencies of the executable +## same as for the library above +# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS}) + +## Specify libraries to link a library or executable target against +# target_link_libraries(${PROJECT_NAME}_node +# ${catkin_LIBRARIES} +# ) + +############# +## Install ## +############# + +# all install targets should use catkin DESTINATION variables +# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html + +## Mark executable scripts (Python etc.) for installation +## in contrast to setup.py, you can choose the destination +# catkin_install_python(PROGRAMS +# scripts/my_python_script +# DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} +# ) + +## Mark executables for installation +## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_executables.html +# install(TARGETS ${PROJECT_NAME}_node +# RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION} +# ) + +## Mark libraries for installation +## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_libraries.html +# install(TARGETS ${PROJECT_NAME} +# ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} +# LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION} +# RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION} +# ) + +## Mark cpp header files for installation +# install(DIRECTORY include/${PROJECT_NAME}/ +# DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION} +# FILES_MATCHING PATTERN "*.h" +# PATTERN ".svn" EXCLUDE +# ) + +## Mark other files for installation (e.g. launch and bag files, etc.) +# install(FILES +# # myfile1 +# # myfile2 +# DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION} +# ) + +############# +## Testing ## +############# + +## Add gtest based cpp test target and link libraries +# catkin_add_gtest(${PROJECT_NAME}-test test/test_mil_yolov7_pkg.cpp) +# if(TARGET ${PROJECT_NAME}-test) +# target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME}) +# endif() + +## Add folders to be run by python nosetests +# catkin_add_nosetests(test) diff --git a/mil_common/perception/yoloros/package.xml b/mil_common/perception/yoloros/package.xml new file mode 100644 index 000000000..fd593a6b3 --- /dev/null +++ b/mil_common/perception/yoloros/package.xml @@ -0,0 +1,68 @@ + + + mil_yolov7_pkg + 0.0.0 + The mil_yolov7_pkg package + + + + + zobelisk + + + + + TODO + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + catkin + roscpp + rospy + sensor_msgs + std_msgs + vision_msgs + roscpp + rospy + sensor_msgs + std_msgs + vision_msgs + roscpp + rospy + sensor_msgs + std_msgs + vision_msgs + + + + + + diff --git a/mil_common/perception/yoloros/setup.py b/mil_common/perception/yoloros/setup.py new file mode 100644 index 000000000..c57adfaf1 --- /dev/null +++ b/mil_common/perception/yoloros/setup.py @@ -0,0 +1,13 @@ +# ! DO NOT MANUALLY INVOKE THIS setup.py, USE CATKIN INSTEAD + +from catkin_pkg.python_setup import generate_distutils_setup +from setuptools import setup + +# fetch values from package.xml +setup_args = generate_distutils_setup( + packages=["yoloros"], + package_dir={"": "src"}, + requires=[], # TODO +) + +setup(**setup_args) diff --git a/mil_common/perception/yoloros/src/yoloros/LICENSE.md b/mil_common/perception/yoloros/src/yoloros/LICENSE.md new file mode 100644 index 000000000..f288702d2 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/LICENSE.md @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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But first, please read +. diff --git a/mil_common/perception/yoloros/src/yoloros/README.md b/mil_common/perception/yoloros/src/yoloros/README.md new file mode 100644 index 000000000..d0cbe7de2 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/README.md @@ -0,0 +1,310 @@ +# Official YOLOv7 + +Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696) + +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/yolov7-trainable-bag-of-freebies-sets-new/real-time-object-detection-on-coco)](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=yolov7-trainable-bag-of-freebies-sets-new) +[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7) +Open In Colab +[![arxiv.org](http://img.shields.io/badge/cs.CV-arXiv%3A2207.02696-B31B1B.svg)](https://arxiv.org/abs/2207.02696) + + + +## Web Demo + +- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces/akhaliq/yolov7) using Gradio. Try out the Web Demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/yolov7) + +## Performance + +MS COCO + +| Model | Test Size | APtest | AP50test | AP75test | batch 1 fps | batch 32 average time | +| :-- | :-: | :-: | :-: | :-: | :-: | :-: | +| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* | +| [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* | +| | | | | | | | +| [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* | +| [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* | +| [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* | +| [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* | + +## Installation + +Docker environment (recommended) +
Expand + +``` shell +# create the docker container, you can change the share memory size if you have more. +nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3 + +# apt install required packages +apt update +apt install -y zip htop screen libgl1-mesa-glx + +# pip install required packages +pip install seaborn thop + +# go to code folder +cd /yolov7 +``` + +
+ +## Testing + +[`yolov7.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) [`yolov7x.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) [`yolov7-w6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) [`yolov7-e6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) [`yolov7-d6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) [`yolov7-e6e.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) + +``` shell +python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val +``` + +You will get the results: + +``` + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51206 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.69730 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.55521 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38453 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.63765 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.68772 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868 +``` + +To measure accuracy, download [COCO-annotations for Pycocotools](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) to the `./coco/annotations/instances_val2017.json` + +## Training + +Data preparation + +``` shell +bash scripts/get_coco.sh +``` + +* Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). If you have previously used a different version of YOLO, we strongly recommend that you delete `train2017.cache` and `val2017.cache` files, and redownload [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip) + +Single GPU training + +``` shell +# train p5 models +python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml + +# train p6 models +python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml +``` + +Multiple GPU training + +``` shell +# train p5 models +python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml + +# train p6 models +python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_aux.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch-size 128 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml +``` + +## Transfer learning + +[`yolov7_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt) [`yolov7x_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x_training.pt) [`yolov7-w6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6_training.pt) [`yolov7-e6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6_training.pt) [`yolov7-d6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6_training.pt) [`yolov7-e6e_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e_training.pt) + +Single GPU finetuning for custom dataset + +``` shell +# finetune p5 models +python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml + +# finetune p6 models +python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights 'yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml +``` + +## Re-parameterization + +See [reparameterization.ipynb](tools/reparameterization.ipynb) + +## Inference + +On video: +``` shell +python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4 +``` + +On image: +``` shell +python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg +``` + + + + +## Export + +**Pytorch to CoreML (and inference on MacOS/iOS)** Open In Colab + +**Pytorch to ONNX with NMS (and inference)** Open In Colab +```shell +python export.py --weights yolov7-tiny.pt --grid --end2end --simplify \ + --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640 +``` + +**Pytorch to TensorRT with NMS (and inference)** Open In Colab + +```shell +wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt +python export.py --weights ./yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 +git clone https://github.com/Linaom1214/tensorrt-python.git +python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16 +``` + +**Pytorch to TensorRT another way** Open In Colab
Expand + + +```shell +wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt +python export.py --weights yolov7-tiny.pt --grid --include-nms +git clone https://github.com/Linaom1214/tensorrt-python.git +python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16 + +# Or use trtexec to convert ONNX to TensorRT engine +/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16 +``` + +
+ +Tested with: Python 3.7.13, Pytorch 1.12.0+cu113 + +## Pose estimation + +[`code`](https://github.com/WongKinYiu/yolov7/tree/pose) [`yolov7-w6-pose.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6-pose.pt) + +See [keypoint.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/keypoint.ipynb). + + + + +## Instance segmentation (with NTU) + +[`code`](https://github.com/WongKinYiu/yolov7/tree/mask) [`yolov7-mask.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-mask.pt) + +See [instance.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/instance.ipynb). + + + +## Instance segmentation + +[`code`](https://github.com/WongKinYiu/yolov7/tree/u7/seg) [`yolov7-seg.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-seg.pt) + +YOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT) + +| Model | Test Size | APbox | AP50box | AP75box | APmask | AP50mask | AP75mask | +| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | +| **YOLOv7-seg** | 640 | **51.4%** | **69.4%** | **55.8%** | **41.5%** | **65.5%** | **43.7%** | + +## Anchor free detection head + +[`code`](https://github.com/WongKinYiu/yolov7/tree/u6) [`yolov7-u6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-u6.pt) + +YOLOv7 with decoupled TAL head (YOLOR + YOLOv5 + YOLOv6) + +| Model | Test Size | APval | AP50val | AP75val | +| :-- | :-: | :-: | :-: | :-: | +| **YOLOv7-u6** | 640 | **52.6%** | **69.7%** | **57.3%** | + + +## Citation + +``` +@inproceedings{wang2023yolov7, + title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors}, + author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2023} +} +``` + +``` +@article{wang2023designing, + title={Designing Network Design Strategies Through Gradient Path Analysis}, + author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau}, + journal={Journal of Information Science and Engineering}, + year={2023} +} +``` + + +## Teaser + +YOLOv7-semantic & YOLOv7-panoptic & YOLOv7-caption + + + +YOLOv7-semantic & YOLOv7-detection & YOLOv7-depth (with NTUT) + + + +YOLOv7-3d-detection & YOLOv7-lidar & YOLOv7-road (with NTUT) + + + + +## Acknowledgements + +
Expand + +* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet) +* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor) +* [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) +* [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) +* [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) +* [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3) +* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5) +* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG) +* [https://github.com/JUGGHM/OREPA_CVPR2022](https://github.com/JUGGHM/OREPA_CVPR2022) +* [https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose](https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose) + +
diff --git a/mil_common/perception/yoloros/src/yoloros/__init__.py b/mil_common/perception/yoloros/src/yoloros/__init__.py new file mode 100644 index 000000000..92b96e0e3 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/__init__.py @@ -0,0 +1 @@ +from .detect import Detector diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/r50-csp.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/r50-csp.yaml new file mode 100644 index 000000000..94559f7d0 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/r50-csp.yaml @@ -0,0 +1,49 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# CSP-ResNet backbone +backbone: + # [from, number, module, args] + [[-1, 1, Stem, [128]], # 0-P1/2 + [-1, 3, ResCSPC, [128]], + [-1, 1, Conv, [256, 3, 2]], # 2-P3/8 + [-1, 4, ResCSPC, [256]], + [-1, 1, Conv, [512, 3, 2]], # 4-P3/8 + [-1, 6, ResCSPC, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 6-P3/8 + [-1, 3, ResCSPC, [1024]], # 7 + ] + +# CSP-Res-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 8 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [5, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 2, ResCSPB, [256]], # 13 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [3, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 2, ResCSPB, [128]], # 18 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 13], 1, Concat, [1]], # cat + [-1, 2, ResCSPB, [256]], # 22 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 8], 1, Concat, [1]], # cat + [-1, 2, ResCSPB, [512]], # 26 + [-1, 1, Conv, [1024, 3, 1]], + + [[19,23,27], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/x50-csp.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/x50-csp.yaml new file mode 100644 index 000000000..8de14f81a --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/x50-csp.yaml @@ -0,0 +1,49 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# CSP-ResNeXt backbone +backbone: + # [from, number, module, args] + [[-1, 1, Stem, [128]], # 0-P1/2 + [-1, 3, ResXCSPC, [128]], + [-1, 1, Conv, [256, 3, 2]], # 2-P3/8 + [-1, 4, ResXCSPC, [256]], + [-1, 1, Conv, [512, 3, 2]], # 4-P3/8 + [-1, 6, ResXCSPC, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 6-P3/8 + [-1, 3, ResXCSPC, [1024]], # 7 + ] + +# CSP-ResX-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 8 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [5, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 2, ResXCSPB, [256]], # 13 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [3, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 2, ResXCSPB, [128]], # 18 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 13], 1, Concat, [1]], # cat + [-1, 2, ResXCSPB, [256]], # 22 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 8], 1, Concat, [1]], # cat + [-1, 2, ResXCSPB, [512]], # 26 + [-1, 1, Conv, [1024, 3, 1]], + + [[19,23,27], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-csp-x.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-csp-x.yaml new file mode 100644 index 000000000..82496bb75 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-csp-x.yaml @@ -0,0 +1,52 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, BottleneckCSPC, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, BottleneckCSPC, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, BottleneckCSPC, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, BottleneckCSPC, [1024]], # 10 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 11 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [8, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [256]], # 16 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [6, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [128]], # 21 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [256]], # 25 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 11], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [512]], # 29 + [-1, 1, Conv, [1024, 3, 1]], + + [[22,26,30], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-csp.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-csp.yaml new file mode 100644 index 000000000..92413ff12 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-csp.yaml @@ -0,0 +1,52 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, BottleneckCSPC, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, BottleneckCSPC, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, BottleneckCSPC, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, BottleneckCSPC, [1024]], # 10 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 11 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [8, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [256]], # 16 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [6, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [128]], # 21 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [256]], # 25 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 11], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [512]], # 29 + [-1, 1, Conv, [1024, 3, 1]], + + [[22,26,30], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-d6.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-d6.yaml new file mode 100644 index 000000000..b67732c4e --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-d6.yaml @@ -0,0 +1,63 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # expand model depth +width_multiple: 1.25 # expand layer channels + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + [-1, 1, DownC, [128]], # 2-P2/4 + [-1, 3, BottleneckCSPA, [128]], + [-1, 1, DownC, [256]], # 4-P3/8 + [-1, 15, BottleneckCSPA, [256]], + [-1, 1, DownC, [512]], # 6-P4/16 + [-1, 15, BottleneckCSPA, [512]], + [-1, 1, DownC, [768]], # 8-P5/32 + [-1, 7, BottleneckCSPA, [768]], + [-1, 1, DownC, [1024]], # 10-P6/64 + [-1, 7, BottleneckCSPA, [1024]], # 11 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 12 + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-6, 1, Conv, [384, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [384]], # 17 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-13, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [256]], # 22 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-20, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [128]], # 27 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, DownC, [256]], + [[-1, 22], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [256]], # 31 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, DownC, [384]], + [[-1, 17], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [384]], # 35 + [-1, 1, Conv, [768, 3, 1]], + [-2, 1, DownC, [512]], + [[-1, 12], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [512]], # 39 + [-1, 1, Conv, [1024, 3, 1]], + + [[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-e6.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-e6.yaml new file mode 100644 index 000000000..75e07bd69 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-e6.yaml @@ -0,0 +1,63 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # expand model depth +width_multiple: 1.25 # expand layer channels + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + [-1, 1, DownC, [128]], # 2-P2/4 + [-1, 3, BottleneckCSPA, [128]], + [-1, 1, DownC, [256]], # 4-P3/8 + [-1, 7, BottleneckCSPA, [256]], + [-1, 1, DownC, [512]], # 6-P4/16 + [-1, 7, BottleneckCSPA, [512]], + [-1, 1, DownC, [768]], # 8-P5/32 + [-1, 3, BottleneckCSPA, [768]], + [-1, 1, DownC, [1024]], # 10-P6/64 + [-1, 3, BottleneckCSPA, [1024]], # 11 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 12 + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-6, 1, Conv, [384, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [384]], # 17 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-13, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [256]], # 22 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-20, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [128]], # 27 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, DownC, [256]], + [[-1, 22], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [256]], # 31 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, DownC, [384]], + [[-1, 17], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [384]], # 35 + [-1, 1, Conv, [768, 3, 1]], + [-2, 1, DownC, [512]], + [[-1, 12], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [512]], # 39 + [-1, 1, Conv, [1024, 3, 1]], + + [[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-p6.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-p6.yaml new file mode 100644 index 000000000..cf35f6c17 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-p6.yaml @@ -0,0 +1,63 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # expand model depth +width_multiple: 1.0 # expand layer channels + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 2-P2/4 + [-1, 3, BottleneckCSPA, [128]], + [-1, 1, Conv, [256, 3, 2]], # 4-P3/8 + [-1, 7, BottleneckCSPA, [256]], + [-1, 1, Conv, [384, 3, 2]], # 6-P4/16 + [-1, 7, BottleneckCSPA, [384]], + [-1, 1, Conv, [512, 3, 2]], # 8-P5/32 + [-1, 3, BottleneckCSPA, [512]], + [-1, 1, Conv, [640, 3, 2]], # 10-P6/64 + [-1, 3, BottleneckCSPA, [640]], # 11 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [320]], # 12 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-6, 1, Conv, [256, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [256]], # 17 + [-1, 1, Conv, [192, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-13, 1, Conv, [192, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [192]], # 22 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-20, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [128]], # 27 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [192, 3, 2]], + [[-1, 22], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [192]], # 31 + [-1, 1, Conv, [384, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 17], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [256]], # 35 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [320, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [320]], # 39 + [-1, 1, Conv, [640, 3, 1]], + + [[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-w6.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-w6.yaml new file mode 100644 index 000000000..674d4a16f --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolor-w6.yaml @@ -0,0 +1,63 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # expand model depth +width_multiple: 1.0 # expand layer channels + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 2-P2/4 + [-1, 3, BottleneckCSPA, [128]], + [-1, 1, Conv, [256, 3, 2]], # 4-P3/8 + [-1, 7, BottleneckCSPA, [256]], + [-1, 1, Conv, [512, 3, 2]], # 6-P4/16 + [-1, 7, BottleneckCSPA, [512]], + [-1, 1, Conv, [768, 3, 2]], # 8-P5/32 + [-1, 3, BottleneckCSPA, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 10-P6/64 + [-1, 3, BottleneckCSPA, [1024]], # 11 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 12 + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-6, 1, Conv, [384, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [384]], # 17 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-13, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [256]], # 22 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [-20, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 3, BottleneckCSPB, [128]], # 27 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 22], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [256]], # 31 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [384, 3, 2]], + [[-1, 17], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [384]], # 35 + [-1, 1, Conv, [768, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat + [-1, 3, BottleneckCSPB, [512]], # 39 + [-1, 1, Conv, [1024, 3, 1]], + + [[28,32,36,40], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolov3-spp.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolov3-spp.yaml new file mode 100644 index 000000000..38dcc449f --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolov3-spp.yaml @@ -0,0 +1,51 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3-SPP head +head: + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, SPP, [512, [5, 9, 13]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolov3.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolov3.yaml new file mode 100644 index 000000000..f2e761355 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolov3.yaml @@ -0,0 +1,51 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3 head +head: + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, Conv, [512, [1, 1]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolov4-csp.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolov4-csp.yaml new file mode 100644 index 000000000..0e32104e9 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/baseline/yolov4-csp.yaml @@ -0,0 +1,52 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# CSP-Darknet backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, BottleneckCSPC, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, BottleneckCSPC, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, BottleneckCSPC, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, BottleneckCSPC, [1024]], # 10 + ] + +# CSP-Dark-PAN head +head: + [[-1, 1, SPPCSPC, [512]], # 11 + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [8, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [256]], # 16 + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [6, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + [-1, 2, BottleneckCSPB, [128]], # 21 + [-1, 1, Conv, [256, 3, 1]], + [-2, 1, Conv, [256, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [256]], # 25 + [-1, 1, Conv, [512, 3, 1]], + [-2, 1, Conv, [512, 3, 2]], + [[-1, 11], 1, Concat, [1]], # cat + [-1, 2, BottleneckCSPB, [512]], # 29 + [-1, 1, Conv, [1024, 3, 1]], + + [[22,26,30], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-d6.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-d6.yaml new file mode 100644 index 000000000..4072f470b --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-d6.yaml @@ -0,0 +1,202 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7-d6 backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [96, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [192]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [192, 1, 1]], # 14 + + [-1, 1, DownC, [384]], # 15-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 27 + + [-1, 1, DownC, [768]], # 28-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 40 + + [-1, 1, DownC, [1152]], # 41-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [1152, 1, 1]], # 53 + + [-1, 1, DownC, [1536]], # 54-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [1536, 1, 1]], # 66 + ] + +# yolov7-d6 head +head: + [[-1, 1, SPPCSPC, [768]], # 67 + + [-1, 1, Conv, [576, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [53, 1, Conv, [576, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [576, 1, 1]], # 83 + + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [40, 1, Conv, [384, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 99 + + [-1, 1, Conv, [192, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [27, 1, Conv, [192, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [192, 1, 1]], # 115 + + [-1, 1, DownC, [384]], + [[-1, 99], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 129 + + [-1, 1, DownC, [576]], + [[-1, 83], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [576, 1, 1]], # 143 + + [-1, 1, DownC, [768]], + [[-1, 67], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 157 + + [115, 1, Conv, [384, 3, 1]], + [129, 1, Conv, [768, 3, 1]], + [143, 1, Conv, [1152, 3, 1]], + [157, 1, Conv, [1536, 3, 1]], + + [[158,159,160,161], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-e6.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-e6.yaml new file mode 100644 index 000000000..6e961abee --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-e6.yaml @@ -0,0 +1,180 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7-e6 backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [80, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [160]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 12 + + [-1, 1, DownC, [320]], # 13-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 23 + + [-1, 1, DownC, [640]], # 24-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 34 + + [-1, 1, DownC, [960]], # 35-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 45 + + [-1, 1, DownC, [1280]], # 46-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 56 + ] + +# yolov7-e6 head +head: + [[-1, 1, SPPCSPC, [640]], # 57 + + [-1, 1, Conv, [480, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [45, 1, Conv, [480, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 71 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [34, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 85 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [23, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 99 + + [-1, 1, DownC, [320]], + [[-1, 85], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 111 + + [-1, 1, DownC, [480]], + [[-1, 71], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 123 + + [-1, 1, DownC, [640]], + [[-1, 57], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 135 + + [99, 1, Conv, [320, 3, 1]], + [111, 1, Conv, [640, 3, 1]], + [123, 1, Conv, [960, 3, 1]], + [135, 1, Conv, [1280, 3, 1]], + + [[136,137,138,139], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-e6e.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-e6e.yaml new file mode 100644 index 000000000..8a628dedf --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-e6e.yaml @@ -0,0 +1,301 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7-e6e backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [80, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [160]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 12 + [-11, 1, Conv, [64, 1, 1]], + [-12, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 22 + [[-1, -11], 1, Shortcut, [1]], # 23 + + [-1, 1, DownC, [320]], # 24-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 34 + [-11, 1, Conv, [128, 1, 1]], + [-12, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 44 + [[-1, -11], 1, Shortcut, [1]], # 45 + + [-1, 1, DownC, [640]], # 46-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 56 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 66 + [[-1, -11], 1, Shortcut, [1]], # 67 + + [-1, 1, DownC, [960]], # 68-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 78 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 88 + [[-1, -11], 1, Shortcut, [1]], # 89 + + [-1, 1, DownC, [1280]], # 90-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 100 + [-11, 1, Conv, [512, 1, 1]], + [-12, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 110 + [[-1, -11], 1, Shortcut, [1]], # 111 + ] + +# yolov7-e6e head +head: + [[-1, 1, SPPCSPC, [640]], # 112 + + [-1, 1, Conv, [480, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [89, 1, Conv, [480, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 126 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 136 + [[-1, -11], 1, Shortcut, [1]], # 137 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [67, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 151 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 161 + [[-1, -11], 1, Shortcut, [1]], # 162 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [45, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 176 + [-11, 1, Conv, [128, 1, 1]], + [-12, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 186 + [[-1, -11], 1, Shortcut, [1]], # 187 + + [-1, 1, DownC, [320]], + [[-1, 162], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 199 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 209 + [[-1, -11], 1, Shortcut, [1]], # 210 + + [-1, 1, DownC, [480]], + [[-1, 137], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 222 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 232 + [[-1, -11], 1, Shortcut, [1]], # 233 + + [-1, 1, DownC, [640]], + [[-1, 112], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 245 + [-11, 1, Conv, [512, 1, 1]], + [-12, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 255 + [[-1, -11], 1, Shortcut, [1]], # 256 + + [187, 1, Conv, [320, 3, 1]], + [210, 1, Conv, [640, 3, 1]], + [233, 1, Conv, [960, 3, 1]], + [256, 1, Conv, [1280, 3, 1]], + + [[257,258,259,260], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-tiny-silu.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-tiny-silu.yaml new file mode 100644 index 000000000..f3f7d5f3b --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-tiny-silu.yaml @@ -0,0 +1,112 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv7-tiny backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 2]], # 0-P1/2 + + [-1, 1, Conv, [64, 3, 2]], # 1-P2/4 + + [-1, 1, Conv, [32, 1, 1]], + [-2, 1, Conv, [32, 1, 1]], + [-1, 1, Conv, [32, 3, 1]], + [-1, 1, Conv, [32, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1]], # 7 + + [-1, 1, MP, []], # 8-P3/8 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 14 + + [-1, 1, MP, []], # 15-P4/16 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 21 + + [-1, 1, MP, []], # 22-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 28 + ] + +# YOLOv7-tiny head +head: + [[-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, SP, [5]], + [-2, 1, SP, [9]], + [-3, 1, SP, [13]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], + [[-1, -7], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 37 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [21, 1, Conv, [128, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 47 + + [-1, 1, Conv, [64, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [14, 1, Conv, [64, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [32, 1, 1]], + [-2, 1, Conv, [32, 1, 1]], + [-1, 1, Conv, [32, 3, 1]], + [-1, 1, Conv, [32, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1]], # 57 + + [-1, 1, Conv, [128, 3, 2]], + [[-1, 47], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 65 + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 37], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 73 + + [57, 1, Conv, [128, 3, 1]], + [65, 1, Conv, [256, 3, 1]], + [73, 1, Conv, [512, 3, 1]], + + [[74,75,76], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-tiny.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-tiny.yaml new file mode 100644 index 000000000..862ab22ef --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-tiny.yaml @@ -0,0 +1,112 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# yolov7-tiny backbone +backbone: + # [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True + [[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 0-P1/2 + + [-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 1-P2/4 + + [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7 + + [-1, 1, MP, []], # 8-P3/8 + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 14 + + [-1, 1, MP, []], # 15-P4/16 + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 21 + + [-1, 1, MP, []], # 22-P5/32 + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 28 + ] + +# yolov7-tiny head +head: + [[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, SP, [5]], + [-2, 1, SP, [9]], + [-3, 1, SP, [13]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -7], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 37 + + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 47 + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 57 + + [-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]], + [[-1, 47], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 65 + + [-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]], + [[-1, 37], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 73 + + [57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [65, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [73, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + + [[74,75,76], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-w6.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-w6.yaml new file mode 100644 index 000000000..4d2819445 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7-w6.yaml @@ -0,0 +1,158 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7-w6 backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + + [-1, 1, Conv, [128, 3, 2]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 10 + + [-1, 1, Conv, [256, 3, 2]], # 11-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 19 + + [-1, 1, Conv, [512, 3, 2]], # 20-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 28 + + [-1, 1, Conv, [768, 3, 2]], # 29-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 37 + + [-1, 1, Conv, [1024, 3, 2]], # 38-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 46 + ] + +# yolov7-w6 head +head: + [[-1, 1, SPPCSPC, [512]], # 47 + + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [37, 1, Conv, [384, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 59 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [28, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 71 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [19, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 83 + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 71], 1, Concat, [1]], # cat + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 93 + + [-1, 1, Conv, [384, 3, 2]], + [[-1, 59], 1, Concat, [1]], # cat + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 103 + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 47], 1, Concat, [1]], # cat + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 113 + + [83, 1, Conv, [256, 3, 1]], + [93, 1, Conv, [512, 3, 1]], + [103, 1, Conv, [768, 3, 1]], + [113, 1, Conv, [1024, 3, 1]], + + [[114,115,116,117], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7.yaml new file mode 100644 index 000000000..81ddbd1af --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7.yaml @@ -0,0 +1,140 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# yolov7 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Conv, [64, 3, 1]], + + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 11 + + [-1, 1, MP, []], + [-1, 1, Conv, [128, 1, 1]], + [-3, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 16-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 24 + + [-1, 1, MP, []], + [-1, 1, Conv, [256, 1, 1]], + [-3, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 29-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 37 + + [-1, 1, MP, []], + [-1, 1, Conv, [512, 1, 1]], + [-3, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 42-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 50 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [512]], # 51 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [37, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 63 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [24, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 75 + + [-1, 1, MP, []], + [-1, 1, Conv, [128, 1, 1]], + [-3, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 2]], + [[-1, -3, 63], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 88 + + [-1, 1, MP, []], + [-1, 1, Conv, [256, 1, 1]], + [-3, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 2]], + [[-1, -3, 51], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 101 + + [75, 1, RepConv, [256, 3, 1]], + [88, 1, RepConv, [512, 3, 1]], + [101, 1, RepConv, [1024, 3, 1]], + + [[102,103,104], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7x.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7x.yaml new file mode 100644 index 000000000..8c6b7a8d2 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/deploy/yolov7x.yaml @@ -0,0 +1,156 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# yolov7x backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [40, 3, 1]], # 0 + + [-1, 1, Conv, [80, 3, 2]], # 1-P1/2 + [-1, 1, Conv, [80, 3, 1]], + + [-1, 1, Conv, [160, 3, 2]], # 3-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 13 + + [-1, 1, MP, []], + [-1, 1, Conv, [160, 1, 1]], + [-3, 1, Conv, [160, 1, 1]], + [-1, 1, Conv, [160, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 18-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 28 + + [-1, 1, MP, []], + [-1, 1, Conv, [320, 1, 1]], + [-3, 1, Conv, [320, 1, 1]], + [-1, 1, Conv, [320, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 33-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 43 + + [-1, 1, MP, []], + [-1, 1, Conv, [640, 1, 1]], + [-3, 1, Conv, [640, 1, 1]], + [-1, 1, Conv, [640, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 48-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 58 + ] + +# yolov7x head +head: + [[-1, 1, SPPCSPC, [640]], # 59 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [43, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 73 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [28, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 87 + + [-1, 1, MP, []], + [-1, 1, Conv, [160, 1, 1]], + [-3, 1, Conv, [160, 1, 1]], + [-1, 1, Conv, [160, 3, 2]], + [[-1, -3, 73], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 102 + + [-1, 1, MP, []], + [-1, 1, Conv, [320, 1, 1]], + [-3, 1, Conv, [320, 1, 1]], + [-1, 1, Conv, [320, 3, 2]], + [[-1, -3, 59], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 117 + + [87, 1, Conv, [320, 3, 1]], + [102, 1, Conv, [640, 3, 1]], + [117, 1, Conv, [1280, 3, 1]], + + [[118,119,120], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-d6.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-d6.yaml new file mode 100644 index 000000000..8e74825d6 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-d6.yaml @@ -0,0 +1,207 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7 backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [96, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [192]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [192, 1, 1]], # 14 + + [-1, 1, DownC, [384]], # 15-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 27 + + [-1, 1, DownC, [768]], # 28-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 40 + + [-1, 1, DownC, [1152]], # 41-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [1152, 1, 1]], # 53 + + [-1, 1, DownC, [1536]], # 54-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [1536, 1, 1]], # 66 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [768]], # 67 + + [-1, 1, Conv, [576, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [53, 1, Conv, [576, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [576, 1, 1]], # 83 + + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [40, 1, Conv, [384, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 99 + + [-1, 1, Conv, [192, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [27, 1, Conv, [192, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [192, 1, 1]], # 115 + + [-1, 1, DownC, [384]], + [[-1, 99], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 129 + + [-1, 1, DownC, [576]], + [[-1, 83], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [576, 1, 1]], # 143 + + [-1, 1, DownC, [768]], + [[-1, 67], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 157 + + [115, 1, Conv, [384, 3, 1]], + [129, 1, Conv, [768, 3, 1]], + [143, 1, Conv, [1152, 3, 1]], + [157, 1, Conv, [1536, 3, 1]], + + [115, 1, Conv, [384, 3, 1]], + [99, 1, Conv, [768, 3, 1]], + [83, 1, Conv, [1152, 3, 1]], + [67, 1, Conv, [1536, 3, 1]], + + [[158,159,160,161,162,163,164,165], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-e6.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-e6.yaml new file mode 100644 index 000000000..faf3c75bb --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-e6.yaml @@ -0,0 +1,185 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7 backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [80, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [160]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 12 + + [-1, 1, DownC, [320]], # 13-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 23 + + [-1, 1, DownC, [640]], # 24-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 34 + + [-1, 1, DownC, [960]], # 35-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 45 + + [-1, 1, DownC, [1280]], # 46-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 56 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [640]], # 57 + + [-1, 1, Conv, [480, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [45, 1, Conv, [480, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 71 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [34, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 85 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [23, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 99 + + [-1, 1, DownC, [320]], + [[-1, 85], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 111 + + [-1, 1, DownC, [480]], + [[-1, 71], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 123 + + [-1, 1, DownC, [640]], + [[-1, 57], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 135 + + [99, 1, Conv, [320, 3, 1]], + [111, 1, Conv, [640, 3, 1]], + [123, 1, Conv, [960, 3, 1]], + [135, 1, Conv, [1280, 3, 1]], + + [99, 1, Conv, [320, 3, 1]], + [85, 1, Conv, [640, 3, 1]], + [71, 1, Conv, [960, 3, 1]], + [57, 1, Conv, [1280, 3, 1]], + + [[136,137,138,139,140,141,142,143], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-e6e.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-e6e.yaml new file mode 100644 index 000000000..ec5b19437 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-e6e.yaml @@ -0,0 +1,306 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7 backbone +backbone: + # [from, number, module, args], + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [80, 3, 1]], # 1-P1/2 + + [-1, 1, DownC, [160]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 12 + [-11, 1, Conv, [64, 1, 1]], + [-12, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 22 + [[-1, -11], 1, Shortcut, [1]], # 23 + + [-1, 1, DownC, [320]], # 24-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 34 + [-11, 1, Conv, [128, 1, 1]], + [-12, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 44 + [[-1, -11], 1, Shortcut, [1]], # 45 + + [-1, 1, DownC, [640]], # 46-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 56 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 66 + [[-1, -11], 1, Shortcut, [1]], # 67 + + [-1, 1, DownC, [960]], # 68-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 78 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [960, 1, 1]], # 88 + [[-1, -11], 1, Shortcut, [1]], # 89 + + [-1, 1, DownC, [1280]], # 90-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 100 + [-11, 1, Conv, [512, 1, 1]], + [-12, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 110 + [[-1, -11], 1, Shortcut, [1]], # 111 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [640]], # 112 + + [-1, 1, Conv, [480, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [89, 1, Conv, [480, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 126 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 136 + [[-1, -11], 1, Shortcut, [1]], # 137 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [67, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 151 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 161 + [[-1, -11], 1, Shortcut, [1]], # 162 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [45, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 176 + [-11, 1, Conv, [128, 1, 1]], + [-12, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 186 + [[-1, -11], 1, Shortcut, [1]], # 187 + + [-1, 1, DownC, [320]], + [[-1, 162], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 199 + [-11, 1, Conv, [256, 1, 1]], + [-12, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 209 + [[-1, -11], 1, Shortcut, [1]], # 210 + + [-1, 1, DownC, [480]], + [[-1, 137], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 222 + [-11, 1, Conv, [384, 1, 1]], + [-12, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [480, 1, 1]], # 232 + [[-1, -11], 1, Shortcut, [1]], # 233 + + [-1, 1, DownC, [640]], + [[-1, 112], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 245 + [-11, 1, Conv, [512, 1, 1]], + [-12, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 255 + [[-1, -11], 1, Shortcut, [1]], # 256 + + [187, 1, Conv, [320, 3, 1]], + [210, 1, Conv, [640, 3, 1]], + [233, 1, Conv, [960, 3, 1]], + [256, 1, Conv, [1280, 3, 1]], + + [186, 1, Conv, [320, 3, 1]], + [161, 1, Conv, [640, 3, 1]], + [136, 1, Conv, [960, 3, 1]], + [112, 1, Conv, [1280, 3, 1]], + + [[257,258,259,260,261,262,263,264], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-tiny.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-tiny.yaml new file mode 100644 index 000000000..a409d9941 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-tiny.yaml @@ -0,0 +1,112 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# yolov7-tiny backbone +backbone: + # [from, number, module, args] c2, k=1, s=1, p=None, g=1, act=True + [[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 0-P1/2 + + [-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]], # 1-P2/4 + + [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 7 + + [-1, 1, MP, []], # 8-P3/8 + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 14 + + [-1, 1, MP, []], # 15-P4/16 + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 21 + + [-1, 1, MP, []], # 22-P5/32 + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 28 + ] + +# yolov7-tiny head +head: + [[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, SP, [5]], + [-2, 1, SP, [9]], + [-3, 1, SP, [13]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -7], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 37 + + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 47 + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 57 + + [-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]], + [[-1, 47], 1, Concat, [1]], + + [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 65 + + [-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]], + [[-1, 37], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [[-1, -2, -3, -4], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # 73 + + [57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [65, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + [73, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]], + + [[74,75,76], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-w6.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-w6.yaml new file mode 100644 index 000000000..88c2118a9 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7-w6.yaml @@ -0,0 +1,163 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [ 19,27, 44,40, 38,94 ] # P3/8 + - [ 96,68, 86,152, 180,137 ] # P4/16 + - [ 140,301, 303,264, 238,542 ] # P5/32 + - [ 436,615, 739,380, 925,792 ] # P6/64 + +# yolov7 backbone +backbone: + # [from, number, module, args] + [[-1, 1, ReOrg, []], # 0 + [-1, 1, Conv, [64, 3, 1]], # 1-P1/2 + + [-1, 1, Conv, [128, 3, 2]], # 2-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 10 + + [-1, 1, Conv, [256, 3, 2]], # 11-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 19 + + [-1, 1, Conv, [512, 3, 2]], # 20-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 28 + + [-1, 1, Conv, [768, 3, 2]], # 29-P5/32 + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [-1, 1, Conv, [384, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [768, 1, 1]], # 37 + + [-1, 1, Conv, [1024, 3, 2]], # 38-P6/64 + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 46 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [512]], # 47 + + [-1, 1, Conv, [384, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [37, 1, Conv, [384, 1, 1]], # route backbone P5 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 59 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [28, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 71 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [19, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 83 + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 71], 1, Concat, [1]], # cat + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 93 + + [-1, 1, Conv, [384, 3, 2]], + [[-1, 59], 1, Concat, [1]], # cat + + [-1, 1, Conv, [384, 1, 1]], + [-2, 1, Conv, [384, 1, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [-1, 1, Conv, [192, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [384, 1, 1]], # 103 + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 47], 1, Concat, [1]], # cat + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 113 + + [83, 1, Conv, [256, 3, 1]], + [93, 1, Conv, [512, 3, 1]], + [103, 1, Conv, [768, 3, 1]], + [113, 1, Conv, [1024, 3, 1]], + + [83, 1, Conv, [320, 3, 1]], + [71, 1, Conv, [640, 3, 1]], + [59, 1, Conv, [960, 3, 1]], + [47, 1, Conv, [1280, 3, 1]], + + [[114,115,116,117,118,119,120,121], 1, IAuxDetect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7.yaml new file mode 100644 index 000000000..59ff339b8 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7.yaml @@ -0,0 +1,140 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# yolov7 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Conv, [64, 3, 1]], + + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 11 + + [-1, 1, MP, []], + [-1, 1, Conv, [128, 1, 1]], + [-3, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 16-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 24 + + [-1, 1, MP, []], + [-1, 1, Conv, [256, 1, 1]], + [-3, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 29-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 37 + + [-1, 1, MP, []], + [-1, 1, Conv, [512, 1, 1]], + [-3, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 42-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [1024, 1, 1]], # 50 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [512]], # 51 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [37, 1, Conv, [256, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 63 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [24, 1, Conv, [128, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [128, 1, 1]], # 75 + + [-1, 1, MP, []], + [-1, 1, Conv, [128, 1, 1]], + [-3, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 2]], + [[-1, -3, 63], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [256, 1, 1]], # 88 + + [-1, 1, MP, []], + [-1, 1, Conv, [256, 1, 1]], + [-3, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 2]], + [[-1, -3, 51], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]], + [-1, 1, Conv, [512, 1, 1]], # 101 + + [75, 1, RepConv, [256, 3, 1]], + [88, 1, RepConv, [512, 3, 1]], + [101, 1, RepConv, [1024, 3, 1]], + + [[102,103,104], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7x.yaml b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7x.yaml new file mode 100644 index 000000000..ff1565074 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/cfg/training/yolov7x.yaml @@ -0,0 +1,156 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [12,16, 19,36, 40,28] # P3/8 + - [36,75, 76,55, 72,146] # P4/16 + - [142,110, 192,243, 459,401] # P5/32 + +# yolov7 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [40, 3, 1]], # 0 + + [-1, 1, Conv, [80, 3, 2]], # 1-P1/2 + [-1, 1, Conv, [80, 3, 1]], + + [-1, 1, Conv, [160, 3, 2]], # 3-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 13 + + [-1, 1, MP, []], + [-1, 1, Conv, [160, 1, 1]], + [-3, 1, Conv, [160, 1, 1]], + [-1, 1, Conv, [160, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 18-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 28 + + [-1, 1, MP, []], + [-1, 1, Conv, [320, 1, 1]], + [-3, 1, Conv, [320, 1, 1]], + [-1, 1, Conv, [320, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 33-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 43 + + [-1, 1, MP, []], + [-1, 1, Conv, [640, 1, 1]], + [-3, 1, Conv, [640, 1, 1]], + [-1, 1, Conv, [640, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 48-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 58 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [640]], # 59 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [43, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 73 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [28, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 87 + + [-1, 1, MP, []], + [-1, 1, Conv, [160, 1, 1]], + [-3, 1, Conv, [160, 1, 1]], + [-1, 1, Conv, [160, 3, 2]], + [[-1, -3, 73], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 102 + + [-1, 1, MP, []], + [-1, 1, Conv, [320, 1, 1]], + [-3, 1, Conv, [320, 1, 1]], + [-1, 1, Conv, [320, 3, 2]], + [[-1, -3, 59], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 117 + + [87, 1, Conv, [320, 3, 1]], + [102, 1, Conv, [640, 3, 1]], + [117, 1, Conv, [1280, 3, 1]], + + [[118,119,120], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/mil_common/perception/yoloros/src/yoloros/data/coco.yaml b/mil_common/perception/yoloros/src/yoloros/data/coco.yaml new file mode 100644 index 000000000..a1d126c90 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/data/coco.yaml @@ -0,0 +1,23 @@ +# COCO 2017 dataset http://cocodataset.org + +# download command/URL (optional) +download: bash ./scripts/get_coco.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ./coco/train2017.txt # 118287 images +val: ./coco/val2017.txt # 5000 images +test: ./coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 + +# number of classes +nc: 80 + +# class names +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush' ] diff --git a/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.custom.yaml b/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.custom.yaml new file mode 100644 index 000000000..1c290da2d --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.custom.yaml @@ -0,0 +1,31 @@ +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.2 # image translation (+/- fraction) +scale: 0.5 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) +copy_paste: 0.0 # image copy paste (probability) +paste_in: 0.0 # image copy paste (probability), use 0 for faster training +loss_ota: 1 # use ComputeLossOTA, use 0 for faster training diff --git a/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.p5.yaml b/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.p5.yaml new file mode 100644 index 000000000..b81587b85 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.p5.yaml @@ -0,0 +1,31 @@ +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.2 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.15 # image mixup (probability) +copy_paste: 0.0 # image copy paste (probability) +paste_in: 0.15 # image copy paste (probability), use 0 for faster training +loss_ota: 1 # use ComputeLossOTA, use 0 for faster training diff --git a/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.p6.yaml b/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.p6.yaml new file mode 100644 index 000000000..0254d0b3c --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.p6.yaml @@ -0,0 +1,31 @@ +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.2 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.15 # image mixup (probability) +copy_paste: 0.0 # image copy paste (probability) +paste_in: 0.15 # image copy paste (probability), use 0 for faster training +loss_ota: 1 # use ComputeLossOTA, use 0 for faster training diff --git a/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.tiny.yaml b/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.tiny.yaml new file mode 100644 index 000000000..b0dc14ae1 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/data/hyp.scratch.tiny.yaml @@ -0,0 +1,31 @@ +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.5 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 1.0 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.5 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.05 # image mixup (probability) +copy_paste: 0.0 # image copy paste (probability) +paste_in: 0.05 # image copy paste (probability), use 0 for faster training +loss_ota: 1 # use ComputeLossOTA, use 0 for faster training diff --git a/mil_common/perception/yoloros/src/yoloros/detect.py b/mil_common/perception/yoloros/src/yoloros/detect.py new file mode 100644 index 000000000..b50995c9b --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/detect.py @@ -0,0 +1,112 @@ +from __future__ import annotations + +import os + +import numpy as np +import torch +from models.experimental import attempt_load +from PIL import Image +from torchvision import transforms +from utils.general import non_max_suppression +from utils.plots import plot_one_box +from visualizer import load_visuals + +from mil_ros_tools import Image_Publisher + + +class Detector: + def __init__(self, weights, device="cuda"): + self.image_pub = Image_Publisher("~yolo_detections_display") + self.device = device + if weights == "robosub24": + print("Weights loaded for Robosub24") + self.model_name = weights + absolute_file_path = os.path.abspath( + os.path.join(os.path.dirname(__file__), "weights/robosub24.pt"), + ) + self.__MODEL = attempt_load( + absolute_file_path, + map_location=torch.device(device), + ) + self.__CLASSES, self.__COLORS = load_visuals(weights) + else: + print("Invalid Model") + + def test_detection(self, conf_thres, iou_thres=0.5): + image_path = "" + if self.model_name == "robosub24": + image_path = os.path.abspath( + os.path.join( + os.path.dirname(__file__), + "tests/RoboSub-2023-Dataset-Cover_png.rf.ffda25ca7a57ac74ee37bc85707cf784.jpg", + ), + ) + self.__MODEL.eval() + img = Image.open(image_path).convert("RGB") + + img_transform = transforms.Compose([transforms.ToTensor()]) + + img_tensor = img_transform(img).to(self.device).unsqueeze(0) + pred_results = self.__MODEL(img_tensor)[0] + detections = non_max_suppression( + pred_results, + conf_thres=conf_thres, + iou_thres=iou_thres, + ) + + arr_image = np.array(img) + + if detections: + detections = detections[0] + for x1, y1, x2, y2, conf, cls in detections: + class_index = int(cls.cpu().item()) + print(f"{self.__CLASSES[class_index]} => {conf}") + plot_one_box( + [x1, y1, x2, y2], + arr_image, + label=f"{self.__CLASSES[class_index]}", + color=self.__COLORS[class_index], + line_thickness=2, + ) + else: + print("No Detections Made") + + Image.fromarray(arr_image).show() + + def display_detection_ros_msg(self, ros_msg, conf_thres=0.85, iou_thres=0.5): + img = Image.fromarray(ros_msg.astype("uint8"), "RGB") + + img = img.resize((960, 608)) + print(img.size) + + img_transform = transforms.Compose([transforms.ToTensor()]) + + img_tensor = img_transform(img).to(self.device).unsqueeze(0) + + pred_results = self.__MODEL(img_tensor)[0] + detections = non_max_suppression( + pred_results, + conf_thres=conf_thres, + iou_thres=iou_thres, + ) + + arr_image = np.array(img) + + if detections: + detections = detections[0] + for x1, y1, x2, y2, conf, cls in detections: + class_index = int(cls.cpu().item()) + print(f"{self.__CLASSES[class_index]} => {conf}") + plot_one_box( + [x1, y1, x2, y2], + arr_image, + label=f"{self.__CLASSES[class_index]}", + color=self.__COLORS[class_index], + line_thickness=2, + ) + + self.image_pub.publish(arr_image) + + +if __name__ == "__main__": + Detector("robosub24").test_detection(conf_thres=0.77) diff --git a/mil_common/perception/yoloros/src/yoloros/inference/images/bus.jpg 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b/mil_common/perception/yoloros/src/yoloros/models/__init__.py @@ -0,0 +1 @@ +# init diff --git a/mil_common/perception/yoloros/src/yoloros/models/common.py b/mil_common/perception/yoloros/src/yoloros/models/common.py new file mode 100644 index 000000000..34df92af3 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/models/common.py @@ -0,0 +1,3022 @@ +import math +from copy import copy +from pathlib import Path + +import numpy as np +import pandas as pd +import requests +import torch +import torch.nn as nn +import torch.nn.functional as F +from PIL import Image +from torch.cuda import amp +from utils.datasets import letterbox +from utils.general import ( + increment_path, + make_divisible, + non_max_suppression, + scale_coords, + xyxy2xywh, +) +from utils.plots import color_list, plot_one_box +from utils.torch_utils import time_synchronized + +##### basic #### + + +def autopad(k, p=None): # kernel, padding + # Pad to 'same' + if p is None: + p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad + return p + + +class MP(nn.Module): + def __init__(self, k=2): + super().__init__() + self.m = nn.MaxPool2d(kernel_size=k, stride=k) + + def forward(self, x): + return self.m(x) + + +class SP(nn.Module): + def __init__(self, k=3, s=1): + super().__init__() + self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2) + + def forward(self, x): + return self.m(x) + + +class ReOrg(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) + return torch.cat( + [ + x[..., ::2, ::2], + x[..., 1::2, ::2], + x[..., ::2, 1::2], + x[..., 1::2, 1::2], + ], + 1, + ) + + +class Concat(nn.Module): + def __init__(self, dimension=1): + super().__init__() + self.d = dimension + + def forward(self, x): + return torch.cat(x, self.d) + + +class Chuncat(nn.Module): + def __init__(self, dimension=1): + super().__init__() + self.d = dimension + + def forward(self, x): + x1 = [] + x2 = [] + for xi in x: + xi1, xi2 = xi.chunk(2, self.d) + x1.append(xi1) + x2.append(xi2) + return torch.cat(x1 + x2, self.d) + + +class Shortcut(nn.Module): + def __init__(self, dimension=0): + super().__init__() + self.d = dimension + + def forward(self, x): + return x[0] + x[1] + + +class Foldcut(nn.Module): + def __init__(self, dimension=0): + super().__init__() + self.d = dimension + + def forward(self, x): + x1, x2 = x.chunk(2, self.d) + return x1 + x2 + + +class Conv(nn.Module): + # Standard convolution + def __init__( + self, + c1, + c2, + k=1, + s=1, + p=None, + g=1, + act=True, + ): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) + self.bn = nn.BatchNorm2d(c2) + self.act = ( + nn.SiLU() + if act is True + else (act if isinstance(act, nn.Module) else nn.Identity()) + ) + + def forward(self, x): + return self.act(self.bn(self.conv(x))) + + def fuseforward(self, x): + return self.act(self.conv(x)) + + +class RobustConv(nn.Module): + # Robust convolution (use high kernel size 7-11 for: downsampling and other layers). Train for 300 - 450 epochs. + def __init__( + self, + c1, + c2, + k=7, + s=1, + p=None, + g=1, + act=True, + layer_scale_init_value=1e-6, + ): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.conv_dw = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act) + self.conv1x1 = nn.Conv2d(c1, c2, 1, 1, 0, groups=1, bias=True) + self.gamma = ( + nn.Parameter(layer_scale_init_value * torch.ones(c2)) + if layer_scale_init_value > 0 + else None + ) + + def forward(self, x): + x = x.to(memory_format=torch.channels_last) + x = self.conv1x1(self.conv_dw(x)) + if self.gamma is not None: + x = x.mul(self.gamma.reshape(1, -1, 1, 1)) + return x + + +class RobustConv2(nn.Module): + # Robust convolution 2 (use [32, 5, 2] or [32, 7, 4] or [32, 11, 8] for one of the paths in CSP). + def __init__( + self, + c1, + c2, + k=7, + s=4, + p=None, + g=1, + act=True, + layer_scale_init_value=1e-6, + ): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.conv_strided = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act) + self.conv_deconv = nn.ConvTranspose2d( + in_channels=c1, + out_channels=c2, + kernel_size=s, + stride=s, + padding=0, + bias=True, + dilation=1, + groups=1, + ) + self.gamma = ( + nn.Parameter(layer_scale_init_value * torch.ones(c2)) + if layer_scale_init_value > 0 + else None + ) + + def forward(self, x): + x = self.conv_deconv(self.conv_strided(x)) + if self.gamma is not None: + x = x.mul(self.gamma.reshape(1, -1, 1, 1)) + return x + + +def DWConv(c1, c2, k=1, s=1, act=True): + # Depthwise convolution + return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) + + +class GhostConv(nn.Module): + # Ghost Convolution https://github.com/huawei-noah/ghostnet + def __init__( + self, + c1, + c2, + k=1, + s=1, + g=1, + act=True, + ): # ch_in, ch_out, kernel, stride, groups + super().__init__() + c_ = c2 // 2 # hidden channels + self.cv1 = Conv(c1, c_, k, s, None, g, act) + self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) + + def forward(self, x): + y = self.cv1(x) + return torch.cat([y, self.cv2(y)], 1) + + +class Stem(nn.Module): + # Stem + def __init__( + self, + c1, + c2, + k=1, + s=1, + p=None, + g=1, + act=True, + ): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + c_ = int(c2 / 2) # hidden channels + self.cv1 = Conv(c1, c_, 3, 2) + self.cv2 = Conv(c_, c_, 1, 1) + self.cv3 = Conv(c_, c_, 3, 2) + self.pool = torch.nn.MaxPool2d(2, stride=2) + self.cv4 = Conv(2 * c_, c2, 1, 1) + + def forward(self, x): + x = self.cv1(x) + return self.cv4(torch.cat((self.cv3(self.cv2(x)), self.pool(x)), dim=1)) + + +class DownC(nn.Module): + # Spatial pyramid pooling layer used in YOLOv3-SPP + def __init__(self, c1, c2, n=1, k=2): + super().__init__() + c_ = int(c1) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c2 // 2, 3, k) + self.cv3 = Conv(c1, c2 // 2, 1, 1) + self.mp = nn.MaxPool2d(kernel_size=k, stride=k) + + def forward(self, x): + return torch.cat((self.cv2(self.cv1(x)), self.cv3(self.mp(x))), dim=1) + + +class SPP(nn.Module): + # Spatial pyramid pooling layer used in YOLOv3-SPP + def __init__(self, c1, c2, k=(5, 9, 13)): + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) + self.m = nn.ModuleList( + [nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k], + ) + + def forward(self, x): + x = self.cv1(x) + return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) + + +class Bottleneck(nn.Module): + # Darknet bottleneck + def __init__( + self, + c1, + c2, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c2, 3, 1, g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class Res(nn.Module): + # ResNet bottleneck + def __init__( + self, + c1, + c2, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c_, 3, 1, g=g) + self.cv3 = Conv(c_, c2, 1, 1) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return ( + x + self.cv3(self.cv2(self.cv1(x))) + if self.add + else self.cv3(self.cv2(self.cv1(x))) + ) + + +class ResX(Res): + # ResNet bottleneck + def __init__( + self, + c1, + c2, + shortcut=True, + g=32, + e=0.5, + ): # ch_in, ch_out, shortcut, groups, expansion + super().__init__(c1, c2, shortcut, g, e) + int(c2 * e) # hidden channels + + +class Ghost(nn.Module): + # Ghost Bottleneck https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride + super().__init__() + c_ = c2 // 2 + self.conv = nn.Sequential( + GhostConv(c1, c_, 1, 1), # pw + DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw + GhostConv(c_, c2, 1, 1, act=False), + ) # pw-linear + self.shortcut = ( + nn.Sequential( + DWConv(c1, c1, k, s, act=False), + Conv(c1, c2, 1, 1, act=False), + ) + if s == 2 + else nn.Identity() + ) + + def forward(self, x): + return self.conv(x) + self.shortcut(x) + + +##### end of basic ##### + + +##### cspnet ##### + + +class SPPCSPC(nn.Module): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): + super().__init__() + c_ = int(2 * c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(c_, c_, 3, 1) + self.cv4 = Conv(c_, c_, 1, 1) + self.m = nn.ModuleList( + [nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k], + ) + self.cv5 = Conv(4 * c_, c_, 1, 1) + self.cv6 = Conv(c_, c_, 3, 1) + self.cv7 = Conv(2 * c_, c2, 1, 1) + + def forward(self, x): + x1 = self.cv4(self.cv3(self.cv1(x))) + y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1))) + y2 = self.cv2(x) + return self.cv7(torch.cat((y1, y2), dim=1)) + + +class GhostSPPCSPC(SPPCSPC): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): + super().__init__(c1, c2, n, shortcut, g, e, k) + c_ = int(2 * c2 * e) # hidden channels + self.cv1 = GhostConv(c1, c_, 1, 1) + self.cv2 = GhostConv(c1, c_, 1, 1) + self.cv3 = GhostConv(c_, c_, 3, 1) + self.cv4 = GhostConv(c_, c_, 1, 1) + self.cv5 = GhostConv(4 * c_, c_, 1, 1) + self.cv6 = GhostConv(c_, c_, 3, 1) + self.cv7 = GhostConv(2 * c_, c2, 1, 1) + + +class GhostStem(Stem): + # Stem + def __init__( + self, + c1, + c2, + k=1, + s=1, + p=None, + g=1, + act=True, + ): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__(c1, c2, k, s, p, g, act) + c_ = int(c2 / 2) # hidden channels + self.cv1 = GhostConv(c1, c_, 3, 2) + self.cv2 = GhostConv(c_, c_, 1, 1) + self.cv3 = GhostConv(c_, c_, 3, 2) + self.cv4 = GhostConv(2 * c_, c2, 1, 1) + + +class BottleneckCSPA(nn.Module): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + self.m = nn.Sequential( + *[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)], + ) + + def forward(self, x): + y1 = self.m(self.cv1(x)) + y2 = self.cv2(x) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class BottleneckCSPB(nn.Module): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=False, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + self.m = nn.Sequential( + *[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)], + ) + + def forward(self, x): + x1 = self.cv1(x) + y1 = self.m(x1) + y2 = self.cv2(x1) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class BottleneckCSPC(nn.Module): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(c_, c_, 1, 1) + self.cv4 = Conv(2 * c_, c2, 1, 1) + self.m = nn.Sequential( + *[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)], + ) + + def forward(self, x): + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(torch.cat((y1, y2), dim=1)) + + +class ResCSPA(BottleneckCSPA): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class ResCSPB(BottleneckCSPB): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class ResCSPC(BottleneckCSPC): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class ResXCSPA(ResCSPA): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=32, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + +class ResXCSPB(ResCSPB): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=32, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + +class ResXCSPC(ResCSPC): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=32, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + +class GhostCSPA(BottleneckCSPA): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) + + +class GhostCSPB(BottleneckCSPB): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) + + +class GhostCSPC(BottleneckCSPC): + # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) + + +##### end of cspnet ##### + + +##### yolor ##### + + +class ImplicitA(nn.Module): + def __init__(self, channel, mean=0.0, std=0.02): + super().__init__() + self.channel = channel + self.mean = mean + self.std = std + self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1)) + nn.init.normal_(self.implicit, mean=self.mean, std=self.std) + + def forward(self, x): + return self.implicit + x + + +class ImplicitM(nn.Module): + def __init__(self, channel, mean=1.0, std=0.02): + super().__init__() + self.channel = channel + self.mean = mean + self.std = std + self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1)) + nn.init.normal_(self.implicit, mean=self.mean, std=self.std) + + def forward(self, x): + return self.implicit * x + + +##### end of yolor ##### + + +##### repvgg ##### + + +class RepConv(nn.Module): + # Represented convolution + # https://arxiv.org/abs/2101.03697 + + def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False): + super().__init__() + + self.deploy = deploy + self.groups = g + self.in_channels = c1 + self.out_channels = c2 + + assert k == 3 + assert autopad(k, p) == 1 + + padding_11 = autopad(k, p) - k // 2 + + self.act = ( + nn.SiLU() + if act is True + else (act if isinstance(act, nn.Module) else nn.Identity()) + ) + + if deploy: + self.rbr_reparam = nn.Conv2d( + c1, + c2, + k, + s, + autopad(k, p), + groups=g, + bias=True, + ) + + else: + self.rbr_identity = ( + nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None + ) + + self.rbr_dense = nn.Sequential( + nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False), + nn.BatchNorm2d(num_features=c2), + ) + + self.rbr_1x1 = nn.Sequential( + nn.Conv2d(c1, c2, 1, s, padding_11, groups=g, bias=False), + nn.BatchNorm2d(num_features=c2), + ) + + def forward(self, inputs): + if hasattr(self, "rbr_reparam"): + return self.act(self.rbr_reparam(inputs)) + + id_out = 0 if self.rbr_identity is None else self.rbr_identity(inputs) + + return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out) + + def get_equivalent_kernel_bias(self): + kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense) + kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1) + kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity) + return ( + kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, + bias3x3 + bias1x1 + biasid, + ) + + def _pad_1x1_to_3x3_tensor(self, kernel1x1): + if kernel1x1 is None: + return 0 + else: + return nn.functional.pad(kernel1x1, [1, 1, 1, 1]) + + def _fuse_bn_tensor(self, branch): + if branch is None: + return 0, 0 + if isinstance(branch, nn.Sequential): + kernel = branch[0].weight + running_mean = branch[1].running_mean + running_var = branch[1].running_var + gamma = branch[1].weight + beta = branch[1].bias + eps = branch[1].eps + else: + assert isinstance(branch, nn.BatchNorm2d) + if not hasattr(self, "id_tensor"): + input_dim = self.in_channels // self.groups + kernel_value = np.zeros( + (self.in_channels, input_dim, 3, 3), + dtype=np.float32, + ) + for i in range(self.in_channels): + kernel_value[i, i % input_dim, 1, 1] = 1 + self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) + kernel = self.id_tensor + running_mean = branch.running_mean + running_var = branch.running_var + gamma = branch.weight + beta = branch.bias + eps = branch.eps + std = (running_var + eps).sqrt() + t = (gamma / std).reshape(-1, 1, 1, 1) + return kernel * t, beta - running_mean * gamma / std + + def repvgg_convert(self): + kernel, bias = self.get_equivalent_kernel_bias() + return ( + kernel.detach().cpu().numpy(), + bias.detach().cpu().numpy(), + ) + + def fuse_conv_bn(self, conv, bn): + std = (bn.running_var + bn.eps).sqrt() + bias = bn.bias - bn.running_mean * bn.weight / std + + t = (bn.weight / std).reshape(-1, 1, 1, 1) + weights = conv.weight * t + + bn = nn.Identity() + conv = nn.Conv2d( + in_channels=conv.in_channels, + out_channels=conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + dilation=conv.dilation, + groups=conv.groups, + bias=True, + padding_mode=conv.padding_mode, + ) + + conv.weight = torch.nn.Parameter(weights) + conv.bias = torch.nn.Parameter(bias) + return conv + + def fuse_repvgg_block(self): + if self.deploy: + return + print("RepConv.fuse_repvgg_block") + + self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1]) + + self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1]) + rbr_1x1_bias = self.rbr_1x1.bias + weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1]) + + # Fuse self.rbr_identity + if isinstance( + self.rbr_identity, + (nn.BatchNorm2d, nn.modules.batchnorm.SyncBatchNorm), + ): + # print(f"fuse: rbr_identity == BatchNorm2d or SyncBatchNorm") + identity_conv_1x1 = nn.Conv2d( + in_channels=self.in_channels, + out_channels=self.out_channels, + kernel_size=1, + stride=1, + padding=0, + groups=self.groups, + bias=False, + ) + identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to( + self.rbr_1x1.weight.data.device, + ) + identity_conv_1x1.weight.data = ( + identity_conv_1x1.weight.data.squeeze().squeeze() + ) + # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}") + identity_conv_1x1.weight.data.fill_(0.0) + identity_conv_1x1.weight.data.fill_diagonal_(1.0) + identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze( + 2, + ).unsqueeze(3) + # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}") + + identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity) + bias_identity_expanded = identity_conv_1x1.bias + weight_identity_expanded = torch.nn.functional.pad( + identity_conv_1x1.weight, + [1, 1, 1, 1], + ) + else: + # print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}") + bias_identity_expanded = torch.nn.Parameter(torch.zeros_like(rbr_1x1_bias)) + weight_identity_expanded = torch.nn.Parameter( + torch.zeros_like(weight_1x1_expanded), + ) + + # print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ") + # print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ") + # print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ") + + self.rbr_dense.weight = torch.nn.Parameter( + self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded, + ) + self.rbr_dense.bias = torch.nn.Parameter( + self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded, + ) + + self.rbr_reparam = self.rbr_dense + self.deploy = True + + if self.rbr_identity is not None: + del self.rbr_identity + self.rbr_identity = None + + if self.rbr_1x1 is not None: + del self.rbr_1x1 + self.rbr_1x1 = None + + if self.rbr_dense is not None: + del self.rbr_dense + self.rbr_dense = None + + +class RepBottleneck(Bottleneck): + # Standard bottleneck + def __init__( + self, + c1, + c2, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, shortcut, groups, expansion + super().__init__(c1, c2, shortcut=True, g=1, e=0.5) + c_ = int(c2 * e) # hidden channels + self.cv2 = RepConv(c_, c2, 3, 1, g=g) + + +class RepBottleneckCSPA(BottleneckCSPA): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential( + *[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)], + ) + + +class RepBottleneckCSPB(BottleneckCSPB): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=False, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential( + *[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)], + ) + + +class RepBottleneckCSPC(BottleneckCSPC): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential( + *[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)], + ) + + +class RepRes(Res): + # Standard bottleneck + def __init__( + self, + c1, + c2, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, shortcut, groups, expansion + super().__init__(c1, c2, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.cv2 = RepConv(c_, c_, 3, 1, g=g) + + +class RepResCSPA(ResCSPA): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class RepResCSPB(ResCSPB): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=False, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class RepResCSPC(ResCSPC): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class RepResX(ResX): + # Standard bottleneck + def __init__( + self, + c1, + c2, + shortcut=True, + g=32, + e=0.5, + ): # ch_in, ch_out, shortcut, groups, expansion + super().__init__(c1, c2, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.cv2 = RepConv(c_, c_, 3, 1, g=g) + + +class RepResXCSPA(ResXCSPA): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=32, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class RepResXCSPB(ResXCSPB): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=False, + g=32, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2) # hidden channels + self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +class RepResXCSPC(ResXCSPC): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=32, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) + + +##### end of repvgg ##### + + +##### transformer ##### + + +class TransformerLayer(nn.Module): + # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) + def __init__(self, c, num_heads): + super().__init__() + self.q = nn.Linear(c, c, bias=False) + self.k = nn.Linear(c, c, bias=False) + self.v = nn.Linear(c, c, bias=False) + self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) + self.fc1 = nn.Linear(c, c, bias=False) + self.fc2 = nn.Linear(c, c, bias=False) + + def forward(self, x): + x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x + x = self.fc2(self.fc1(x)) + x + return x + + +class TransformerBlock(nn.Module): + # Vision Transformer https://arxiv.org/abs/2010.11929 + def __init__(self, c1, c2, num_heads, num_layers): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + self.linear = nn.Linear(c2, c2) # learnable position embedding + self.tr = nn.Sequential( + *[TransformerLayer(c2, num_heads) for _ in range(num_layers)], + ) + self.c2 = c2 + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + b, _, w, h = x.shape + p = x.flatten(2) + p = p.unsqueeze(0) + p = p.transpose(0, 3) + p = p.squeeze(3) + e = self.linear(p) + x = p + e + + x = self.tr(x) + x = x.unsqueeze(3) + x = x.transpose(0, 3) + x = x.reshape(b, self.c2, w, h) + return x + + +##### end of transformer ##### + + +##### yolov5 ##### + + +class Focus(nn.Module): + # Focus wh information into c-space + def __init__( + self, + c1, + c2, + k=1, + s=1, + p=None, + g=1, + act=True, + ): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.conv = Conv(c1 * 4, c2, k, s, p, g, act) + # self.contract = Contract(gain=2) + + def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) + return self.conv( + torch.cat( + [ + x[..., ::2, ::2], + x[..., 1::2, ::2], + x[..., ::2, 1::2], + x[..., 1::2, 1::2], + ], + 1, + ), + ) + # return self.conv(self.contract(x)) + + +class SPPF(nn.Module): + # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher + def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * 4, c2, 1, 1) + self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) + + def forward(self, x): + x = self.cv1(x) + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) + + +class Contract(nn.Module): + # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) + def __init__(self, gain=2): + super().__init__() + self.gain = gain + + def forward(self, x): + ( + N, + C, + H, + W, + ) = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain' + s = self.gain + x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2) + x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) + return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40) + + +class Expand(nn.Module): + # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) + def __init__(self, gain=2): + super().__init__() + self.gain = gain + + def forward(self, x): + N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' + s = self.gain + x = x.view(N, s, s, C // s**2, H, W) # x(1,2,2,16,80,80) + x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) + return x.view(N, C // s**2, H * s, W * s) # x(1,16,160,160) + + +class NMS(nn.Module): + # Non-Maximum Suppression (NMS) module + conf = 0.25 # confidence threshold + iou = 0.45 # IoU threshold + classes = None # (optional list) filter by class + + def __init__(self): + super().__init__() + + def forward(self, x): + return non_max_suppression( + x[0], + conf_thres=self.conf, + iou_thres=self.iou, + classes=self.classes, + ) + + +class autoShape(nn.Module): + # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS + conf = 0.25 # NMS confidence threshold + iou = 0.45 # NMS IoU threshold + classes = None # (optional list) filter by class + + def __init__(self, model): + super().__init__() + self.model = model.eval() + + def autoshape(self): + print( + "autoShape already enabled, skipping... ", + ) # model already converted to model.autoshape() + return self + + @torch.no_grad() + def forward(self, imgs, size=640, augment=False, profile=False): + # Inference from various sources. For height=640, width=1280, RGB images example inputs are: + # filename: imgs = 'data/samples/zidane.jpg' + # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' + # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) + # PIL: = Image.open('image.jpg') # HWC x(640,1280,3) + # numpy: = np.zeros((640,1280,3)) # HWC + # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) + # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images + + t = [time_synchronized()] + p = next(self.model.parameters()) # for device and type + if isinstance(imgs, torch.Tensor): # torch + with amp.autocast(enabled=p.device.type != "cpu"): + return self.model( + imgs.to(p.device).type_as(p), + augment, + profile, + ) # inference + + # Pre-process + n, imgs = ( + (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) + ) # number of images, list of images + shape0, shape1, files = [], [], [] # image and inference shapes, filenames + for i, im in enumerate(imgs): + f = f"image{i}" # filename + if isinstance(im, str): # filename or uri + im, f = ( + np.asarray( + Image.open( + requests.get(im, stream=True).raw + if im.startswith("http") + else im, + ), + ), + im, + ) + elif isinstance(im, Image.Image): # PIL Image + im, f = np.asarray(im), getattr(im, "filename", f) or f + files.append(Path(f).with_suffix(".jpg").name) + if im.shape[0] < 5: # image in CHW + im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) + im = ( + im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) + ) # enforce 3ch input + s = im.shape[:2] # HWC + shape0.append(s) # image shape + g = size / max(s) # gain + shape1.append([y * g for y in s]) + imgs[i] = im # update + shape1 = [ + make_divisible(x, int(self.stride.max())) + for x in np.stack(shape1, 0).max(0) + ] # inference shape + x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad + x = np.stack(x, 0) if n > 1 else x[0][None] # stack + x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW + x = torch.from_numpy(x).to(p.device).type_as(p) / 255.0 # uint8 to fp16/32 + t.append(time_synchronized()) + + with amp.autocast(enabled=p.device.type != "cpu"): + # Inference + y = self.model(x, augment, profile)[0] # forward + t.append(time_synchronized()) + + # Post-process + y = non_max_suppression( + y, + conf_thres=self.conf, + iou_thres=self.iou, + classes=self.classes, + ) # NMS + for i in range(n): + scale_coords(shape1, y[i][:, :4], shape0[i]) + + t.append(time_synchronized()) + return Detections(imgs, y, files, t, self.names, x.shape) + + +class Detections: + # detections class for YOLOv5 inference results + def __init__(self, imgs, pred, files, times=None, names=None, shape=None): + super().__init__() + d = pred[0].device # device + gn = [ + torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1.0, 1.0], device=d) + for im in imgs + ] # normalizations + self.imgs = imgs # list of images as numpy arrays + self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) + self.names = names # class names + self.files = files # image filenames + self.xyxy = pred # xyxy pixels + self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels + self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized + self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized + self.n = len(self.pred) # number of images (batch size) + self.t = tuple( + (times[i + 1] - times[i]) * 1000 / self.n for i in range(3) + ) # timestamps (ms) + self.s = shape # inference BCHW shape + + def display(self, pprint=False, show=False, save=False, render=False, save_dir=""): + colors = color_list() + for i, (img, pred) in enumerate(zip(self.imgs, self.pred)): + str = f"image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} " + if pred is not None: + for c in pred[:, -1].unique(): + n = (pred[:, -1] == c).sum() # detections per class + str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string + if show or save or render: + for *box, conf, cls in pred: # xyxy, confidence, class + label = f"{self.names[int(cls)]} {conf:.2f}" + plot_one_box(box, img, label=label, color=colors[int(cls) % 10]) + img = ( + Image.fromarray(img.astype(np.uint8)) + if isinstance(img, np.ndarray) + else img + ) # from np + if pprint: + print(str.rstrip(", ")) + if show: + img.show(self.files[i]) # show + if save: + f = self.files[i] + img.save(Path(save_dir) / f) # save + print( + f"{'Saved' * (i == 0)} {f}", + end="," if i < self.n - 1 else f" to {save_dir}\n", + ) + if render: + self.imgs[i] = np.asarray(img) + + def print(self): + self.display(pprint=True) # print results + print( + f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}" + % self.t, + ) + + def show(self): + self.display(show=True) # show results + + def save(self, save_dir="runs/hub/exp"): + save_dir = increment_path( + save_dir, + exist_ok=save_dir != "runs/hub/exp", + ) # increment save_dir + Path(save_dir).mkdir(parents=True, exist_ok=True) + self.display(save=True, save_dir=save_dir) # save results + + def render(self): + self.display(render=True) # render results + return self.imgs + + def pandas(self): + # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) + new = copy(self) # return copy + ca = ( + "xmin", + "ymin", + "xmax", + "ymax", + "confidence", + "class", + "name", + ) # xyxy columns + cb = ( + "xcenter", + "ycenter", + "width", + "height", + "confidence", + "class", + "name", + ) # xywh columns + for k, c in zip(["xyxy", "xyxyn", "xywh", "xywhn"], [ca, ca, cb, cb]): + a = [ + [x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] + for x in getattr(self, k) + ] # update + setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) + return new + + def tolist(self): + # return a list of Detections objects, i.e. 'for result in results.tolist():' + x = [ + Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) + for i in range(self.n) + ] + for d in x: + for k in ["imgs", "pred", "xyxy", "xyxyn", "xywh", "xywhn"]: + setattr(d, k, getattr(d, k)[0]) # pop out of list + return x + + def __len__(self): + return self.n + + +class Classify(nn.Module): + # Classification head, i.e. x(b,c1,20,20) to x(b,c2) + def __init__( + self, + c1, + c2, + k=1, + s=1, + p=None, + g=1, + ): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) + self.flat = nn.Flatten() + + def forward(self, x): + z = torch.cat( + [self.aap(y) for y in (x if isinstance(x, list) else [x])], + 1, + ) # cat if list + return self.flat(self.conv(z)) # flatten to x(b,c2) + + +##### end of yolov5 ###### + + +##### orepa ##### + + +def transI_fusebn(kernel, bn): + gamma = bn.weight + std = (bn.running_var + bn.eps).sqrt() + return ( + kernel * ((gamma / std).reshape(-1, 1, 1, 1)), + bn.bias - bn.running_mean * gamma / std, + ) + + +class ConvBN(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + deploy=False, + nonlinear=None, + ): + super().__init__() + if nonlinear is None: + self.nonlinear = nn.Identity() + else: + self.nonlinear = nonlinear + if deploy: + self.conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=True, + ) + else: + self.conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=False, + ) + self.bn = nn.BatchNorm2d(num_features=out_channels) + + def forward(self, x): + if hasattr(self, "bn"): + return self.nonlinear(self.bn(self.conv(x))) + else: + return self.nonlinear(self.conv(x)) + + def switch_to_deploy(self): + kernel, bias = transI_fusebn(self.conv.weight, self.bn) + conv = nn.Conv2d( + in_channels=self.conv.in_channels, + out_channels=self.conv.out_channels, + kernel_size=self.conv.kernel_size, + stride=self.conv.stride, + padding=self.conv.padding, + dilation=self.conv.dilation, + groups=self.conv.groups, + bias=True, + ) + conv.weight.data = kernel + conv.bias.data = bias + for para in self.parameters(): + para.detach_() + self.__delattr__("conv") + self.__delattr__("bn") + self.conv = conv + + +class OREPA_3x3_RepConv(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + internal_channels_1x1_3x3=None, + deploy=False, + nonlinear=None, + single_init=False, + ): + super().__init__() + self.deploy = deploy + + if nonlinear is None: + self.nonlinear = nn.Identity() + else: + self.nonlinear = nonlinear + + self.kernel_size = kernel_size + self.in_channels = in_channels + self.out_channels = out_channels + self.groups = groups + assert padding == kernel_size // 2 + + self.stride = stride + self.padding = padding + self.dilation = dilation + + self.branch_counter = 0 + + self.weight_rbr_origin = nn.Parameter( + torch.Tensor( + out_channels, + int(in_channels / self.groups), + kernel_size, + kernel_size, + ), + ) + nn.init.kaiming_uniform_(self.weight_rbr_origin, a=math.sqrt(1.0)) + self.branch_counter += 1 + + if groups < out_channels: + self.weight_rbr_avg_conv = nn.Parameter( + torch.Tensor(out_channels, int(in_channels / self.groups), 1, 1), + ) + self.weight_rbr_pfir_conv = nn.Parameter( + torch.Tensor(out_channels, int(in_channels / self.groups), 1, 1), + ) + nn.init.kaiming_uniform_(self.weight_rbr_avg_conv, a=1.0) + nn.init.kaiming_uniform_(self.weight_rbr_pfir_conv, a=1.0) + self.weight_rbr_avg_conv.data + self.weight_rbr_pfir_conv.data + self.register_buffer( + "weight_rbr_avg_avg", + torch.ones(kernel_size, kernel_size).mul( + 1.0 / kernel_size / kernel_size, + ), + ) + self.branch_counter += 1 + + else: + raise NotImplementedError + self.branch_counter += 1 + + if internal_channels_1x1_3x3 is None: + internal_channels_1x1_3x3 = ( + in_channels if groups < out_channels else 2 * in_channels + ) # For mobilenet, it is better to have 2X internal channels + + if internal_channels_1x1_3x3 == in_channels: + self.weight_rbr_1x1_kxk_idconv1 = nn.Parameter( + torch.zeros(in_channels, int(in_channels / self.groups), 1, 1), + ) + id_value = np.zeros((in_channels, int(in_channels / self.groups), 1, 1)) + for i in range(in_channels): + id_value[i, i % int(in_channels / self.groups), 0, 0] = 1 + id_tensor = torch.from_numpy(id_value).type_as( + self.weight_rbr_1x1_kxk_idconv1, + ) + self.register_buffer("id_tensor", id_tensor) + + else: + self.weight_rbr_1x1_kxk_conv1 = nn.Parameter( + torch.Tensor( + internal_channels_1x1_3x3, + int(in_channels / self.groups), + 1, + 1, + ), + ) + nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv1, a=math.sqrt(1.0)) + self.weight_rbr_1x1_kxk_conv2 = nn.Parameter( + torch.Tensor( + out_channels, + int(internal_channels_1x1_3x3 / self.groups), + kernel_size, + kernel_size, + ), + ) + nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv2, a=math.sqrt(1.0)) + self.branch_counter += 1 + + expand_ratio = 8 + self.weight_rbr_gconv_dw = nn.Parameter( + torch.Tensor(in_channels * expand_ratio, 1, kernel_size, kernel_size), + ) + self.weight_rbr_gconv_pw = nn.Parameter( + torch.Tensor(out_channels, in_channels * expand_ratio, 1, 1), + ) + nn.init.kaiming_uniform_(self.weight_rbr_gconv_dw, a=math.sqrt(1.0)) + nn.init.kaiming_uniform_(self.weight_rbr_gconv_pw, a=math.sqrt(1.0)) + self.branch_counter += 1 + + if out_channels == in_channels and stride == 1: + self.branch_counter += 1 + + self.vector = nn.Parameter(torch.Tensor(self.branch_counter, self.out_channels)) + self.bn = nn.BatchNorm2d(out_channels) + + self.fre_init() + + nn.init.constant_(self.vector[0, :], 0.25) # origin + nn.init.constant_(self.vector[1, :], 0.25) # avg + nn.init.constant_(self.vector[2, :], 0.0) # prior + nn.init.constant_(self.vector[3, :], 0.5) # 1x1_kxk + nn.init.constant_(self.vector[4, :], 0.5) # dws_conv + + def fre_init(self): + prior_tensor = torch.Tensor( + self.out_channels, + self.kernel_size, + self.kernel_size, + ) + half_fg = self.out_channels / 2 + for i in range(self.out_channels): + for h in range(3): + for w in range(3): + if i < half_fg: + prior_tensor[i, h, w] = math.cos( + math.pi * (h + 0.5) * (i + 1) / 3, + ) + else: + prior_tensor[i, h, w] = math.cos( + math.pi * (w + 0.5) * (i + 1 - half_fg) / 3, + ) + + self.register_buffer("weight_rbr_prior", prior_tensor) + + def weight_gen(self): + weight_rbr_origin = torch.einsum( + "oihw,o->oihw", + self.weight_rbr_origin, + self.vector[0, :], + ) + + weight_rbr_avg = torch.einsum( + "oihw,o->oihw", + torch.einsum( + "oihw,hw->oihw", + self.weight_rbr_avg_conv, + self.weight_rbr_avg_avg, + ), + self.vector[1, :], + ) + + weight_rbr_pfir = torch.einsum( + "oihw,o->oihw", + torch.einsum( + "oihw,ohw->oihw", + self.weight_rbr_pfir_conv, + self.weight_rbr_prior, + ), + self.vector[2, :], + ) + + weight_rbr_1x1_kxk_conv1 = None + if hasattr(self, "weight_rbr_1x1_kxk_idconv1"): + weight_rbr_1x1_kxk_conv1 = ( + self.weight_rbr_1x1_kxk_idconv1 + self.id_tensor + ).squeeze() + elif hasattr(self, "weight_rbr_1x1_kxk_conv1"): + weight_rbr_1x1_kxk_conv1 = self.weight_rbr_1x1_kxk_conv1.squeeze() + else: + raise NotImplementedError + weight_rbr_1x1_kxk_conv2 = self.weight_rbr_1x1_kxk_conv2 + + if self.groups > 1: + g = self.groups + t, ig = weight_rbr_1x1_kxk_conv1.size() + o, tg, h, w = weight_rbr_1x1_kxk_conv2.size() + weight_rbr_1x1_kxk_conv1 = weight_rbr_1x1_kxk_conv1.view(g, int(t / g), ig) + weight_rbr_1x1_kxk_conv2 = weight_rbr_1x1_kxk_conv2.view( + g, + int(o / g), + tg, + h, + w, + ) + weight_rbr_1x1_kxk = torch.einsum( + "gti,gothw->goihw", + weight_rbr_1x1_kxk_conv1, + weight_rbr_1x1_kxk_conv2, + ).view(o, ig, h, w) + else: + weight_rbr_1x1_kxk = torch.einsum( + "ti,othw->oihw", + weight_rbr_1x1_kxk_conv1, + weight_rbr_1x1_kxk_conv2, + ) + + weight_rbr_1x1_kxk = torch.einsum( + "oihw,o->oihw", + weight_rbr_1x1_kxk, + self.vector[3, :], + ) + + weight_rbr_gconv = self.dwsc2full( + self.weight_rbr_gconv_dw, + self.weight_rbr_gconv_pw, + self.in_channels, + ) + weight_rbr_gconv = torch.einsum( + "oihw,o->oihw", + weight_rbr_gconv, + self.vector[4, :], + ) + + weight = ( + weight_rbr_origin + + weight_rbr_avg + + weight_rbr_1x1_kxk + + weight_rbr_pfir + + weight_rbr_gconv + ) + + return weight + + def dwsc2full(self, weight_dw, weight_pw, groups): + t, ig, h, w = weight_dw.size() + o, _, _, _ = weight_pw.size() + tg = int(t / groups) + i = int(ig * groups) + weight_dw = weight_dw.view(groups, tg, ig, h, w) + weight_pw = weight_pw.squeeze().view(o, groups, tg) + + weight_dsc = torch.einsum("gtihw,ogt->ogihw", weight_dw, weight_pw) + return weight_dsc.view(o, i, h, w) + + def forward(self, inputs): + weight = self.weight_gen() + out = F.conv2d( + inputs, + weight, + bias=None, + stride=self.stride, + padding=self.padding, + dilation=self.dilation, + groups=self.groups, + ) + + return self.nonlinear(self.bn(out)) + + +class RepConv_OREPA(nn.Module): + def __init__( + self, + c1, + c2, + k=3, + s=1, + padding=1, + dilation=1, + groups=1, + padding_mode="zeros", + deploy=False, + use_se=False, + nonlinear=nn.SiLU(), + ): + super().__init__() + self.deploy = deploy + self.groups = groups + self.in_channels = c1 + self.out_channels = c2 + + self.padding = padding + self.dilation = dilation + self.groups = groups + + assert k == 3 + assert padding == 1 + + padding_11 = padding - k // 2 + + if nonlinear is None: + self.nonlinearity = nn.Identity() + else: + self.nonlinearity = nonlinear + + if use_se: + self.se = SEBlock( + self.out_channels, + internal_neurons=self.out_channels // 16, + ) + else: + self.se = nn.Identity() + + if deploy: + self.rbr_reparam = nn.Conv2d( + in_channels=self.in_channels, + out_channels=self.out_channels, + kernel_size=k, + stride=s, + padding=padding, + dilation=dilation, + groups=groups, + bias=True, + padding_mode=padding_mode, + ) + + else: + self.rbr_identity = ( + nn.BatchNorm2d(num_features=self.in_channels) + if self.out_channels == self.in_channels and s == 1 + else None + ) + self.rbr_dense = OREPA_3x3_RepConv( + in_channels=self.in_channels, + out_channels=self.out_channels, + kernel_size=k, + stride=s, + padding=padding, + groups=groups, + dilation=1, + ) + self.rbr_1x1 = ConvBN( + in_channels=self.in_channels, + out_channels=self.out_channels, + kernel_size=1, + stride=s, + padding=padding_11, + groups=groups, + dilation=1, + ) + print("RepVGG Block, identity = ", self.rbr_identity) + + def forward(self, inputs): + if hasattr(self, "rbr_reparam"): + return self.nonlinearity(self.se(self.rbr_reparam(inputs))) + + id_out = 0 if self.rbr_identity is None else self.rbr_identity(inputs) + + out1 = self.rbr_dense(inputs) + out2 = self.rbr_1x1(inputs) + out3 = id_out + out = out1 + out2 + out3 + + return self.nonlinearity(self.se(out)) + + # Optional. This improves the accuracy and facilitates quantization. + # 1. Cancel the original weight decay on rbr_dense.conv.weight and rbr_1x1.conv.weight. + # 2. Use like this. + # loss = criterion(....) + # for every RepVGGBlock blk: + # loss += weight_decay_coefficient * 0.5 * blk.get_cust_L2() + # optimizer.zero_grad() + # loss.backward() + + # Not used for OREPA + def get_custom_L2(self): + K3 = self.rbr_dense.weight_gen() + K1 = self.rbr_1x1.conv.weight + t3 = ( + ( + self.rbr_dense.bn.weight + / ((self.rbr_dense.bn.running_var + self.rbr_dense.bn.eps).sqrt()) + ) + .reshape(-1, 1, 1, 1) + .detach() + ) + t1 = ( + ( + self.rbr_1x1.bn.weight + / ((self.rbr_1x1.bn.running_var + self.rbr_1x1.bn.eps).sqrt()) + ) + .reshape(-1, 1, 1, 1) + .detach() + ) + + l2_loss_circle = (K3**2).sum() - ( + K3[:, :, 1:2, 1:2] ** 2 + ).sum() # The L2 loss of the "circle" of weights in 3x3 kernel. Use regular L2 on them. + eq_kernel = ( + K3[:, :, 1:2, 1:2] * t3 + K1 * t1 + ) # The equivalent resultant central point of 3x3 kernel. + l2_loss_eq_kernel = ( + eq_kernel**2 / (t3**2 + t1**2) + ).sum() # Normalize for an L2 coefficient comparable to regular L2. + return l2_loss_eq_kernel + l2_loss_circle + + def get_equivalent_kernel_bias(self): + kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense) + kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1) + kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity) + return ( + kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, + bias3x3 + bias1x1 + biasid, + ) + + def _pad_1x1_to_3x3_tensor(self, kernel1x1): + if kernel1x1 is None: + return 0 + else: + return torch.nn.functional.pad(kernel1x1, [1, 1, 1, 1]) + + def _fuse_bn_tensor(self, branch): + if branch is None: + return 0, 0 + if not isinstance(branch, nn.BatchNorm2d): + if isinstance(branch, OREPA_3x3_RepConv): + kernel = branch.weight_gen() + elif isinstance(branch, ConvBN): + kernel = branch.conv.weight + else: + raise NotImplementedError + running_mean = branch.bn.running_mean + running_var = branch.bn.running_var + gamma = branch.bn.weight + beta = branch.bn.bias + eps = branch.bn.eps + else: + if not hasattr(self, "id_tensor"): + input_dim = self.in_channels // self.groups + kernel_value = np.zeros( + (self.in_channels, input_dim, 3, 3), + dtype=np.float32, + ) + for i in range(self.in_channels): + kernel_value[i, i % input_dim, 1, 1] = 1 + self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) + kernel = self.id_tensor + running_mean = branch.running_mean + running_var = branch.running_var + gamma = branch.weight + beta = branch.bias + eps = branch.eps + std = (running_var + eps).sqrt() + t = (gamma / std).reshape(-1, 1, 1, 1) + return kernel * t, beta - running_mean * gamma / std + + def switch_to_deploy(self): + if hasattr(self, "rbr_reparam"): + return + print("RepConv_OREPA.switch_to_deploy") + kernel, bias = self.get_equivalent_kernel_bias() + self.rbr_reparam = nn.Conv2d( + in_channels=self.rbr_dense.in_channels, + out_channels=self.rbr_dense.out_channels, + kernel_size=self.rbr_dense.kernel_size, + stride=self.rbr_dense.stride, + padding=self.rbr_dense.padding, + dilation=self.rbr_dense.dilation, + groups=self.rbr_dense.groups, + bias=True, + ) + self.rbr_reparam.weight.data = kernel + self.rbr_reparam.bias.data = bias + for para in self.parameters(): + para.detach_() + self.__delattr__("rbr_dense") + self.__delattr__("rbr_1x1") + if hasattr(self, "rbr_identity"): + self.__delattr__("rbr_identity") + + +##### end of orepa ##### + + +##### swin transformer ##### + + +class WindowAttention(nn.Module): + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads), + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, + 2, + 0, + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + nn.init.normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + B_, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B_, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, + 0, + 1, + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( + 1, + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + # print(attn.dtype, v.dtype) + try: + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + except: + # print(attn.dtype, v.dtype) + x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.SiLU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + B, H, W, C = x.shape + assert H % window_size == 0, "feature map h and w can not divide by window size" + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + ) + return windows + + +def window_reverse(windows, window_size, H, W): + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view( + B, + H // window_size, + W // window_size, + window_size, + window_size, + -1, + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class SwinTransformerLayer(nn.Module): + def __init__( + self, + dim, + num_heads, + window_size=8, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.SiLU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + # if min(self.input_resolution) <= self.window_size: + # # if window size is larger than input resolution, we don't partition windows + # self.shift_size = 0 + # self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=(self.window_size, self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + def create_mask(self, H, W): + # calculate attention mask for SW-MSA + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, + self.window_size, + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, + 0.0, + ) + + return attn_mask + + def forward(self, x): + # reshape x[b c h w] to x[b l c] + _, _, H_, W_ = x.shape + + Padding = False + if ( + min(H_, W_) < self.window_size + or H_ % self.window_size != 0 + or W_ % self.window_size != 0 + ): + Padding = True + # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.') + pad_r = (self.window_size - W_ % self.window_size) % self.window_size + pad_b = (self.window_size - H_ % self.window_size) % self.window_size + x = F.pad(x, (0, pad_r, 0, pad_b)) + + # print('2', x.shape) + B, C, H, W = x.shape + L = H * W + x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c + + # create mask from init to forward + attn_mask = self.create_mask(H, W).to(x.device) if self.shift_size > 0 else None + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, + shifts=(-self.shift_size, -self.shift_size), + dims=(1, 2), + ) + else: + shifted_x = x + + # partition windows + x_windows = window_partition( + shifted_x, + self.window_size, + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, + self.window_size * self.window_size, + C, + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn( + x_windows, + mask=attn_mask, + ) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll( + shifted_x, + shifts=(self.shift_size, self.shift_size), + dims=(1, 2), + ) + else: + x = shifted_x + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w + + if Padding: + x = x[:, :, :H_, :W_] # reverse padding + + return x + + +class SwinTransformerBlock(nn.Module): + def __init__(self, c1, c2, num_heads, num_layers, window_size=8): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + + # remove input_resolution + self.blocks = nn.Sequential( + *[ + SwinTransformerLayer( + dim=c2, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + ) + for i in range(num_layers) + ], + ) + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + x = self.blocks(x) + return x + + +class STCSPA(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformerBlock(c_, c_, num_heads, n) + # self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.m(self.cv1(x)) + y2 = self.cv2(x) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class STCSPB(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=False, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformerBlock(c_, c_, num_heads, n) + # self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + x1 = self.cv1(x) + y1 = self.m(x1) + y2 = self.cv2(x1) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class STCSPC(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(c_, c_, 1, 1) + self.cv4 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformerBlock(c_, c_, num_heads, n) + # self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(torch.cat((y1, y2), dim=1)) + + +##### end of swin transformer ##### + + +##### swin transformer v2 ##### + + +class WindowAttention_v2(nn.Module): + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + attn_drop=0.0, + proj_drop=0.0, + pretrained_window_size=[0, 0], + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.pretrained_window_size = pretrained_window_size + self.num_heads = num_heads + + self.logit_scale = nn.Parameter( + torch.log(10 * torch.ones((num_heads, 1, 1))), + requires_grad=True, + ) + + # mlp to generate continuous relative position bias + self.cpb_mlp = nn.Sequential( + nn.Linear(2, 512, bias=True), + nn.ReLU(inplace=True), + nn.Linear(512, num_heads, bias=False), + ) + + # get relative_coords_table + relative_coords_h = torch.arange( + -(self.window_size[0] - 1), + self.window_size[0], + dtype=torch.float32, + ) + relative_coords_w = torch.arange( + -(self.window_size[1] - 1), + self.window_size[1], + dtype=torch.float32, + ) + relative_coords_table = ( + torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) + .permute(1, 2, 0) + .contiguous() + .unsqueeze(0) + ) # 1, 2*Wh-1, 2*Ww-1, 2 + if pretrained_window_size[0] > 0: + relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1 + else: + relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1 + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = ( + torch.sign(relative_coords_table) + * torch.log2(torch.abs(relative_coords_table) + 1.0) + / np.log2(8) + ) + + self.register_buffer("relative_coords_table", relative_coords_table) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, + 2, + 0, + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(dim)) + self.v_bias = nn.Parameter(torch.zeros(dim)) + else: + self.q_bias = None + self.v_bias = None + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + B_, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat( + ( + self.q_bias, + torch.zeros_like(self.v_bias, requires_grad=False), + self.v_bias, + ), + ) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + # cosine attention + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + logit_scale = torch.clamp( + self.logit_scale, + max=torch.log(torch.tensor(1.0 / 0.01)), + ).exp() + attn = attn * logit_scale + + relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view( + -1, + self.num_heads, + ) + relative_position_bias = relative_position_bias_table[ + self.relative_position_index.view(-1) + ].view( + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, + 0, + 1, + ).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( + 1, + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + try: + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + except: + x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, window_size={self.window_size}, " + f"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}" + ) + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + +class Mlp_v2(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.SiLU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition_v2(x, window_size): + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + ) + return windows + + +def window_reverse_v2(windows, window_size, H, W): + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view( + B, + H // window_size, + W // window_size, + window_size, + window_size, + -1, + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class SwinTransformerLayer_v2(nn.Module): + def __init__( + self, + dim, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.SiLU, + norm_layer=nn.LayerNorm, + pretrained_window_size=0, + ): + super().__init__() + self.dim = dim + # self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + # if min(self.input_resolution) <= self.window_size: + # # if window size is larger than input resolution, we don't partition windows + # self.shift_size = 0 + # self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention_v2( + dim, + window_size=(self.window_size, self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop, + pretrained_window_size=(pretrained_window_size, pretrained_window_size), + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp_v2( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + def create_mask(self, H, W): + # calculate attention mask for SW-MSA + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, + self.window_size, + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, + 0.0, + ) + + return attn_mask + + def forward(self, x): + # reshape x[b c h w] to x[b l c] + _, _, H_, W_ = x.shape + + Padding = False + if ( + min(H_, W_) < self.window_size + or H_ % self.window_size != 0 + or W_ % self.window_size != 0 + ): + Padding = True + # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.') + pad_r = (self.window_size - W_ % self.window_size) % self.window_size + pad_b = (self.window_size - H_ % self.window_size) % self.window_size + x = F.pad(x, (0, pad_r, 0, pad_b)) + + # print('2', x.shape) + B, C, H, W = x.shape + L = H * W + x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c + + # create mask from init to forward + attn_mask = self.create_mask(H, W).to(x.device) if self.shift_size > 0 else None + + shortcut = x + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, + shifts=(-self.shift_size, -self.shift_size), + dims=(1, 2), + ) + else: + shifted_x = x + + # partition windows + x_windows = window_partition_v2( + shifted_x, + self.window_size, + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, + self.window_size * self.window_size, + C, + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA + attn_windows = self.attn( + x_windows, + mask=attn_mask, + ) # nW*B, window_size*window_size, C + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse_v2(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll( + shifted_x, + shifts=(self.shift_size, self.shift_size), + dims=(1, 2), + ) + else: + x = shifted_x + x = x.view(B, H * W, C) + x = shortcut + self.drop_path(self.norm1(x)) + + # FFN + x = x + self.drop_path(self.norm2(self.mlp(x))) + x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w + + if Padding: + x = x[:, :, :H_, :W_] # reverse padding + + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + ) + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class SwinTransformer2Block(nn.Module): + def __init__(self, c1, c2, num_heads, num_layers, window_size=7): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + + # remove input_resolution + self.blocks = nn.Sequential( + *[ + SwinTransformerLayer_v2( + dim=c2, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + ) + for i in range(num_layers) + ], + ) + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + x = self.blocks(x) + return x + + +class ST2CSPA(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformer2Block(c_, c_, num_heads, n) + # self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.m(self.cv1(x)) + y2 = self.cv2(x) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class ST2CSPB(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=False, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformer2Block(c_, c_, num_heads, n) + # self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + x1 = self.cv1(x) + y1 = self.m(x1) + y2 = self.cv2(x1) + return self.cv3(torch.cat((y1, y2), dim=1)) + + +class ST2CSPC(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__( + self, + c1, + c2, + n=1, + shortcut=True, + g=1, + e=0.5, + ): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(c_, c_, 1, 1) + self.cv4 = Conv(2 * c_, c2, 1, 1) + num_heads = c_ // 32 + self.m = SwinTransformer2Block(c_, c_, num_heads, n) + # self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(torch.cat((y1, y2), dim=1)) + + +##### end of swin transformer v2 ##### diff --git a/mil_common/perception/yoloros/src/yoloros/models/experimental.py b/mil_common/perception/yoloros/src/yoloros/models/experimental.py new file mode 100644 index 000000000..cd755cc4b --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/models/experimental.py @@ -0,0 +1,357 @@ +import random + +import numpy as np +import torch +import torch.nn as nn +from models.common import Conv +from utils.google_utils import attempt_download + + +class CrossConv(nn.Module): + # Cross Convolution Downsample + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): + # ch_in, ch_out, kernel, stride, groups, expansion, shortcut + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, (1, k), (1, s)) + self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class Sum(nn.Module): + # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 + def __init__(self, n, weight=False): # n: number of inputs + super().__init__() + self.weight = weight # apply weights boolean + self.iter = range(n - 1) # iter object + if weight: + self.w = nn.Parameter( + -torch.arange(1.0, n) / 2, + requires_grad=True, + ) # layer weights + + def forward(self, x): + y = x[0] # no weight + if self.weight: + w = torch.sigmoid(self.w) * 2 + for i in self.iter: + y = y + x[i + 1] * w[i] + else: + for i in self.iter: + y = y + x[i + 1] + return y + + +class MixConv2d(nn.Module): + # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595 + def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): + super().__init__() + groups = len(k) + if equal_ch: # equal c_ per group + i = torch.linspace(0, groups - 1e-6, c2).floor() # c2 indices + c_ = [(i == g).sum() for g in range(groups)] # intermediate channels + else: # equal weight.numel() per group + b = [c2] + [0] * groups + a = np.eye(groups + 1, groups, k=-1) + a -= np.roll(a, 1, axis=1) + a *= np.array(k) ** 2 + a[0] = 1 + c_ = np.linalg.lstsq(a, b, rcond=None)[ + 0 + ].round() # solve for equal weight indices, ax = b + + self.m = nn.ModuleList( + [ + nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) + for g in range(groups) + ], + ) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.LeakyReLU(0.1, inplace=True) + + def forward(self, x): + return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) + + +class Ensemble(nn.ModuleList): + # Ensemble of models + def __init__(self): + super().__init__() + + def forward(self, x, augment=False): + y = [] + for module in self: + y.append(module(x, augment)[0]) + # y = torch.stack(y).max(0)[0] # max ensemble + # y = torch.stack(y).mean(0) # mean ensemble + y = torch.cat(y, 1) # nms ensemble + return y, None # inference, train output + + +class ORT_NMS(torch.autograd.Function): + """ONNX-Runtime NMS operation""" + + @staticmethod + def forward( + ctx, + boxes, + scores, + max_output_boxes_per_class=torch.tensor([100]), + iou_threshold=torch.tensor([0.45]), + score_threshold=torch.tensor([0.25]), + ): + device = boxes.device + batch = scores.shape[0] + num_det = random.randint(0, 100) + batches = torch.randint(0, batch, (num_det,)).sort()[0].to(device) + idxs = torch.arange(100, 100 + num_det).to(device) + zeros = torch.zeros((num_det,), dtype=torch.int64).to(device) + selected_indices = torch.cat( + [batches[None], zeros[None], idxs[None]], + 0, + ).T.contiguous() + selected_indices = selected_indices.to(torch.int64) + return selected_indices + + @staticmethod + def symbolic( + g, + boxes, + scores, + max_output_boxes_per_class, + iou_threshold, + score_threshold, + ): + return g.op( + "NonMaxSuppression", + boxes, + scores, + max_output_boxes_per_class, + iou_threshold, + score_threshold, + ) + + +class TRT_NMS(torch.autograd.Function): + """TensorRT NMS operation""" + + @staticmethod + def forward( + ctx, + boxes, + scores, + background_class=-1, + box_coding=1, + iou_threshold=0.45, + max_output_boxes=100, + plugin_version="1", + score_activation=0, + score_threshold=0.25, + ): + batch_size, num_boxes, num_classes = scores.shape + num_det = torch.randint(0, max_output_boxes, (batch_size, 1), dtype=torch.int32) + det_boxes = torch.randn(batch_size, max_output_boxes, 4) + det_scores = torch.randn(batch_size, max_output_boxes) + det_classes = torch.randint( + 0, + num_classes, + (batch_size, max_output_boxes), + dtype=torch.int32, + ) + return num_det, det_boxes, det_scores, det_classes + + @staticmethod + def symbolic( + g, + boxes, + scores, + background_class=-1, + box_coding=1, + iou_threshold=0.45, + max_output_boxes=100, + plugin_version="1", + score_activation=0, + score_threshold=0.25, + ): + out = g.op( + "TRT::EfficientNMS_TRT", + boxes, + scores, + background_class_i=background_class, + box_coding_i=box_coding, + iou_threshold_f=iou_threshold, + max_output_boxes_i=max_output_boxes, + plugin_version_s=plugin_version, + score_activation_i=score_activation, + score_threshold_f=score_threshold, + outputs=4, + ) + nums, boxes, scores, classes = out + return nums, boxes, scores, classes + + +class ONNX_ORT(nn.Module): + """onnx module with ONNX-Runtime NMS operation.""" + + def __init__( + self, + max_obj=100, + iou_thres=0.45, + score_thres=0.25, + max_wh=640, + device=None, + n_classes=80, + ): + super().__init__() + self.device = device if device else torch.device("cpu") + self.max_obj = torch.tensor([max_obj]).to(device) + self.iou_threshold = torch.tensor([iou_thres]).to(device) + self.score_threshold = torch.tensor([score_thres]).to(device) + self.max_wh = max_wh # if max_wh != 0 : non-agnostic else : agnostic + self.convert_matrix = torch.tensor( + [[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], + dtype=torch.float32, + device=self.device, + ) + self.n_classes = n_classes + + def forward(self, x): + boxes = x[:, :, :4] + conf = x[:, :, 4:5] + scores = x[:, :, 5:] + if self.n_classes == 1: + scores = conf # for models with one class, cls_loss is 0 and cls_conf is always 0.5, + # so there is no need to multiplicate. + else: + scores *= conf # conf = obj_conf * cls_conf + boxes @= self.convert_matrix + max_score, category_id = scores.max(2, keepdim=True) + dis = category_id.float() * self.max_wh + nmsbox = boxes + dis + max_score_tp = max_score.transpose(1, 2).contiguous() + selected_indices = ORT_NMS.apply( + nmsbox, + max_score_tp, + self.max_obj, + self.iou_threshold, + self.score_threshold, + ) + X, Y = selected_indices[:, 0], selected_indices[:, 2] + selected_boxes = boxes[X, Y, :] + selected_categories = category_id[X, Y, :].float() + selected_scores = max_score[X, Y, :] + X = X.unsqueeze(1).float() + return torch.cat([X, selected_boxes, selected_categories, selected_scores], 1) + + +class ONNX_TRT(nn.Module): + """onnx module with TensorRT NMS operation.""" + + def __init__( + self, + max_obj=100, + iou_thres=0.45, + score_thres=0.25, + max_wh=None, + device=None, + n_classes=80, + ): + super().__init__() + assert max_wh is None + self.device = device if device else torch.device("cpu") + self.background_class = (-1,) + self.box_coding = (1,) + self.iou_threshold = iou_thres + self.max_obj = max_obj + self.plugin_version = "1" + self.score_activation = 0 + self.score_threshold = score_thres + self.n_classes = n_classes + + def forward(self, x): + boxes = x[:, :, :4] + conf = x[:, :, 4:5] + scores = x[:, :, 5:] + if self.n_classes == 1: + scores = conf # for models with one class, cls_loss is 0 and cls_conf is always 0.5, + # so there is no need to multiplicate. + else: + scores *= conf # conf = obj_conf * cls_conf + num_det, det_boxes, det_scores, det_classes = TRT_NMS.apply( + boxes, + scores, + self.background_class, + self.box_coding, + self.iou_threshold, + self.max_obj, + self.plugin_version, + self.score_activation, + self.score_threshold, + ) + return num_det, det_boxes, det_scores, det_classes + + +class End2End(nn.Module): + """export onnx or tensorrt model with NMS operation.""" + + def __init__( + self, + model, + max_obj=100, + iou_thres=0.45, + score_thres=0.25, + max_wh=None, + device=None, + n_classes=80, + ): + super().__init__() + device = device if device else torch.device("cpu") + assert isinstance(max_wh, (int)) or max_wh is None + self.model = model.to(device) + self.model.model[-1].end2end = True + self.patch_model = ONNX_TRT if max_wh is None else ONNX_ORT + self.end2end = self.patch_model( + max_obj, + iou_thres, + score_thres, + max_wh, + device, + n_classes, + ) + self.end2end.eval() + + def forward(self, x): + x = self.model(x) + x = self.end2end(x) + return x + + +def attempt_load(weights, map_location=None): + # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a + model = Ensemble() + for w in weights if isinstance(weights, list) else [weights]: + attempt_download(w) + ckpt = torch.load(w, map_location=map_location) # load + model.append( + ckpt["ema" if ckpt.get("ema") else "model"].float().fuse().eval(), + ) # FP32 model + + # Compatibility updates + for m in model.modules(): + if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: + m.inplace = True # pytorch 1.7.0 compatibility + elif type(m) is nn.Upsample: + m.recompute_scale_factor = None # torch 1.11.0 compatibility + elif type(m) is Conv: + m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility + + if len(model) == 1: + return model[-1] # return model + else: + print("Ensemble created with %s\n" % weights) + for k in ["names", "stride"]: + setattr(model, k, getattr(model[-1], k)) + return model # return ensemble diff --git a/mil_common/perception/yoloros/src/yoloros/models/yolo.py b/mil_common/perception/yoloros/src/yoloros/models/yolo.py new file mode 100644 index 000000000..0e80320ed --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/models/yolo.py @@ -0,0 +1,1191 @@ +import argparse +import logging +import sys +from copy import deepcopy + +sys.path.append("./") # to run '$ python *.py' files in subdirectories +logger = logging.getLogger(__name__) +import torch +from models.common import * +from models.experimental import * +from utils.autoanchor import check_anchor_order +from utils.general import check_file, make_divisible, set_logging +from utils.loss import SigmoidBin +from utils.torch_utils import ( + copy_attr, + fuse_conv_and_bn, + initialize_weights, + model_info, + scale_img, + select_device, + time_synchronized, +) + +try: + import thop # for FLOPS computation +except ImportError: + thop = None + + +class Detect(nn.Module): + stride = None # strides computed during build + export = False # onnx export + end2end = False + include_nms = False + concat = False + + def __init__(self, nc=80, anchors=(), ch=()): # detection layer + super().__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer("anchors", a) # shape(nl,na,2) + self.register_buffer( + "anchor_grid", + a.clone().view(self.nl, 1, -1, 1, 1, 2), + ) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList( + nn.Conv2d(x, self.no * self.na, 1) for x in ch + ) # output conv + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = ( + x[i] + .view(bs, self.na, self.no, ny, nx) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + y = x[i].sigmoid() + if not torch.onnx.is_in_onnx_export(): + y[..., 0:2] = ( + y[..., 0:2] * 2.0 - 0.5 + self.grid[i] + ) * self.stride[ + i + ] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + else: + xy, wh, conf = y.split( + (2, 2, self.nc + 1), + 4, + ) # y.tensor_split((2, 4, 5), 4) # torch 1.8.0 + xy = xy * (2.0 * self.stride[i]) + ( + self.stride[i] * (self.grid[i] - 0.5) + ) # new xy + wh = wh**2 * (4 * self.anchor_grid[i].data) # new wh + y = torch.cat((xy, wh, conf), 4) + z.append(y.view(bs, -1, self.no)) + + if self.training: + out = x + elif self.end2end: + out = torch.cat(z, 1) + elif self.include_nms: + z = self.convert(z) + out = (z,) + elif self.concat: + out = torch.cat(z, 1) + else: + out = (torch.cat(z, 1), x) + + return out + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + def convert(self, z): + z = torch.cat(z, 1) + box = z[:, :, :4] + conf = z[:, :, 4:5] + score = z[:, :, 5:] + score *= conf + convert_matrix = torch.tensor( + [[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], + dtype=torch.float32, + device=z.device, + ) + box @= convert_matrix + return (box, score) + + +class IDetect(nn.Module): + stride = None # strides computed during build + export = False # onnx export + end2end = False + include_nms = False + concat = False + + def __init__(self, nc=80, anchors=(), ch=()): # detection layer + super().__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer("anchors", a) # shape(nl,na,2) + self.register_buffer( + "anchor_grid", + a.clone().view(self.nl, 1, -1, 1, 1, 2), + ) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList( + nn.Conv2d(x, self.no * self.na, 1) for x in ch + ) # output conv + + self.ia = nn.ModuleList(ImplicitA(x) for x in ch) + self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch) + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](self.ia[i](x[i])) # conv + x[i] = self.im[i](x[i]) + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = ( + x[i] + .view(bs, self.na, self.no, ny, nx) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[ + i + ] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + z.append(y.view(bs, -1, self.no)) + + return x if self.training else (torch.cat(z, 1), x) + + def fuseforward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = ( + x[i] + .view(bs, self.na, self.no, ny, nx) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + if not torch.onnx.is_in_onnx_export(): + y[..., 0:2] = ( + y[..., 0:2] * 2.0 - 0.5 + self.grid[i] + ) * self.stride[ + i + ] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + else: + xy, wh, conf = y.split( + (2, 2, self.nc + 1), + 4, + ) # y.tensor_split((2, 4, 5), 4) # torch 1.8.0 + xy = xy * (2.0 * self.stride[i]) + ( + self.stride[i] * (self.grid[i] - 0.5) + ) # new xy + wh = wh**2 * (4 * self.anchor_grid[i].data) # new wh + y = torch.cat((xy, wh, conf), 4) + z.append(y.view(bs, -1, self.no)) + + if self.training: + out = x + elif self.end2end: + out = torch.cat(z, 1) + elif self.include_nms: + z = self.convert(z) + out = (z,) + elif self.concat: + out = torch.cat(z, 1) + else: + out = (torch.cat(z, 1), x) + + return out + + def fuse(self): + print("IDetect.fuse") + # fuse ImplicitA and Convolution + for i in range(len(self.m)): + c1, c2, _, _ = self.m[i].weight.shape + c1_, c2_, _, _ = self.ia[i].implicit.shape + self.m[i].bias += torch.matmul( + self.m[i].weight.reshape(c1, c2), + self.ia[i].implicit.reshape(c2_, c1_), + ).squeeze(1) + + # fuse ImplicitM and Convolution + for i in range(len(self.m)): + c1, c2, _, _ = self.im[i].implicit.shape + self.m[i].bias *= self.im[i].implicit.reshape(c2) + self.m[i].weight *= self.im[i].implicit.transpose(0, 1) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + def convert(self, z): + z = torch.cat(z, 1) + box = z[:, :, :4] + conf = z[:, :, 4:5] + score = z[:, :, 5:] + score *= conf + convert_matrix = torch.tensor( + [[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], + dtype=torch.float32, + device=z.device, + ) + box @= convert_matrix + return (box, score) + + +class IKeypoint(nn.Module): + stride = None # strides computed during build + export = False # onnx export + + def __init__( + self, + nc=80, + anchors=(), + nkpt=17, + ch=(), + inplace=True, + dw_conv_kpt=False, + ): # detection layer + super().__init__() + self.nc = nc # number of classes + self.nkpt = nkpt + self.dw_conv_kpt = dw_conv_kpt + self.no_det = nc + 5 # number of outputs per anchor for box and class + self.no_kpt = 3 * self.nkpt ## number of outputs per anchor for keypoints + self.no = self.no_det + self.no_kpt + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + self.flip_test = False + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer("anchors", a) # shape(nl,na,2) + self.register_buffer( + "anchor_grid", + a.clone().view(self.nl, 1, -1, 1, 1, 2), + ) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList( + nn.Conv2d(x, self.no_det * self.na, 1) for x in ch + ) # output conv + + self.ia = nn.ModuleList(ImplicitA(x) for x in ch) + self.im = nn.ModuleList(ImplicitM(self.no_det * self.na) for _ in ch) + + if self.nkpt is not None: + if self.dw_conv_kpt: # keypoint head is slightly more complex + self.m_kpt = nn.ModuleList( + nn.Sequential( + DWConv(x, x, k=3), + Conv(x, x), + DWConv(x, x, k=3), + Conv(x, x), + DWConv(x, x, k=3), + Conv(x, x), + DWConv(x, x, k=3), + Conv(x, x), + DWConv(x, x, k=3), + Conv(x, x), + DWConv(x, x, k=3), + nn.Conv2d(x, self.no_kpt * self.na, 1), + ) + for x in ch + ) + else: # keypoint head is a single convolution + self.m_kpt = nn.ModuleList( + nn.Conv2d(x, self.no_kpt * self.na, 1) for x in ch + ) + + self.inplace = inplace # use in-place ops (e.g. slice assignment) + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + if self.nkpt is None or self.nkpt == 0: + x[i] = self.im[i](self.m[i](self.ia[i](x[i]))) # conv + else: + x[i] = torch.cat( + (self.im[i](self.m[i](self.ia[i](x[i]))), self.m_kpt[i](x[i])), + axis=1, + ) + + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = ( + x[i] + .view(bs, self.na, self.no, ny, nx) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) + x_det = x[i][..., :6] + x_kpt = x[i][..., 6:] + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + kpt_grid_x = self.grid[i][..., 0:1] + kpt_grid_y = self.grid[i][..., 1:2] + + y = x[i].sigmoid() if self.nkpt == 0 else x_det.sigmoid() + + if self.inplace: + xy = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[i] # xy + wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view( + 1, + self.na, + 1, + 1, + 2, + ) # wh + if self.nkpt != 0: + x_kpt[..., 0::3] = ( + x_kpt[..., ::3] * 2.0 + - 0.5 + + kpt_grid_x.repeat(1, 1, 1, 1, 17) + ) * self.stride[ + i + ] # xy + x_kpt[..., 1::3] = ( + x_kpt[..., 1::3] * 2.0 + - 0.5 + + kpt_grid_y.repeat(1, 1, 1, 1, 17) + ) * self.stride[ + i + ] # xy + # x_kpt[..., 0::3] = (x_kpt[..., ::3] + kpt_grid_x.repeat(1,1,1,1,17)) * self.stride[i] # xy + # x_kpt[..., 1::3] = (x_kpt[..., 1::3] + kpt_grid_y.repeat(1,1,1,1,17)) * self.stride[i] # xy + # print('=============') + # print(self.anchor_grid[i].shape) + # print(self.anchor_grid[i][...,0].unsqueeze(4).shape) + # print(x_kpt[..., 0::3].shape) + # x_kpt[..., 0::3] = ((x_kpt[..., 0::3].tanh() * 2.) ** 3 * self.anchor_grid[i][...,0].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_x.repeat(1,1,1,1,17) * self.stride[i] # xy + # x_kpt[..., 1::3] = ((x_kpt[..., 1::3].tanh() * 2.) ** 3 * self.anchor_grid[i][...,1].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_y.repeat(1,1,1,1,17) * self.stride[i] # xy + # x_kpt[..., 0::3] = (((x_kpt[..., 0::3].sigmoid() * 4.) ** 2 - 8.) * self.anchor_grid[i][...,0].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_x.repeat(1,1,1,1,17) * self.stride[i] # xy + # x_kpt[..., 1::3] = (((x_kpt[..., 1::3].sigmoid() * 4.) ** 2 - 8.) * self.anchor_grid[i][...,1].unsqueeze(4).repeat(1,1,1,1,self.nkpt)) + kpt_grid_y.repeat(1,1,1,1,17) * self.stride[i] # xy + x_kpt[..., 2::3] = x_kpt[..., 2::3].sigmoid() + + y = torch.cat((xy, wh, y[..., 4:], x_kpt), dim=-1) + + else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953 + xy = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[i] # xy + wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + if self.nkpt != 0: + y[..., 6:] = ( + y[..., 6:] * 2.0 + - 0.5 + + self.grid[i].repeat((1, 1, 1, 1, self.nkpt)) + ) * self.stride[ + i + ] # xy + y = torch.cat((xy, wh, y[..., 4:]), -1) + + z.append(y.view(bs, -1, self.no)) + + return x if self.training else (torch.cat(z, 1), x) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + +class IAuxDetect(nn.Module): + stride = None # strides computed during build + export = False # onnx export + end2end = False + include_nms = False + concat = False + + def __init__(self, nc=80, anchors=(), ch=()): # detection layer + super().__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer("anchors", a) # shape(nl,na,2) + self.register_buffer( + "anchor_grid", + a.clone().view(self.nl, 1, -1, 1, 1, 2), + ) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList( + nn.Conv2d(x, self.no * self.na, 1) for x in ch[: self.nl] + ) # output conv + self.m2 = nn.ModuleList( + nn.Conv2d(x, self.no * self.na, 1) for x in ch[self.nl :] + ) # output conv + + self.ia = nn.ModuleList(ImplicitA(x) for x in ch[: self.nl]) + self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch[: self.nl]) + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](self.ia[i](x[i])) # conv + x[i] = self.im[i](x[i]) + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = ( + x[i] + .view(bs, self.na, self.no, ny, nx) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) + + x[i + self.nl] = self.m2[i](x[i + self.nl]) + x[i + self.nl] = ( + x[i + self.nl] + .view(bs, self.na, self.no, ny, nx) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + if not torch.onnx.is_in_onnx_export(): + y[..., 0:2] = ( + y[..., 0:2] * 2.0 - 0.5 + self.grid[i] + ) * self.stride[ + i + ] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + else: + xy, wh, conf = y.split( + (2, 2, self.nc + 1), + 4, + ) # y.tensor_split((2, 4, 5), 4) # torch 1.8.0 + xy = xy * (2.0 * self.stride[i]) + ( + self.stride[i] * (self.grid[i] - 0.5) + ) # new xy + wh = wh**2 * (4 * self.anchor_grid[i].data) # new wh + y = torch.cat((xy, wh, conf), 4) + z.append(y.view(bs, -1, self.no)) + + return x if self.training else (torch.cat(z, 1), x[: self.nl]) + + def fuseforward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = ( + x[i] + .view(bs, self.na, self.no, ny, nx) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + if not torch.onnx.is_in_onnx_export(): + y[..., 0:2] = ( + y[..., 0:2] * 2.0 - 0.5 + self.grid[i] + ) * self.stride[ + i + ] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + else: + xy = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[i] # xy + wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].data # wh + y = torch.cat((xy, wh, y[..., 4:]), -1) + z.append(y.view(bs, -1, self.no)) + + if self.training: + out = x + elif self.end2end: + out = torch.cat(z, 1) + elif self.include_nms: + z = self.convert(z) + out = (z,) + elif self.concat: + out = torch.cat(z, 1) + else: + out = (torch.cat(z, 1), x) + + return out + + def fuse(self): + print("IAuxDetect.fuse") + # fuse ImplicitA and Convolution + for i in range(len(self.m)): + c1, c2, _, _ = self.m[i].weight.shape + c1_, c2_, _, _ = self.ia[i].implicit.shape + self.m[i].bias += torch.matmul( + self.m[i].weight.reshape(c1, c2), + self.ia[i].implicit.reshape(c2_, c1_), + ).squeeze(1) + + # fuse ImplicitM and Convolution + for i in range(len(self.m)): + c1, c2, _, _ = self.im[i].implicit.shape + self.m[i].bias *= self.im[i].implicit.reshape(c2) + self.m[i].weight *= self.im[i].implicit.transpose(0, 1) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + def convert(self, z): + z = torch.cat(z, 1) + box = z[:, :, :4] + conf = z[:, :, 4:5] + score = z[:, :, 5:] + score *= conf + convert_matrix = torch.tensor( + [[1, 0, 1, 0], [0, 1, 0, 1], [-0.5, 0, 0.5, 0], [0, -0.5, 0, 0.5]], + dtype=torch.float32, + device=z.device, + ) + box @= convert_matrix + return (box, score) + + +class IBin(nn.Module): + stride = None # strides computed during build + export = False # onnx export + + def __init__(self, nc=80, anchors=(), ch=(), bin_count=21): # detection layer + super().__init__() + self.nc = nc # number of classes + self.bin_count = bin_count + + self.w_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0) + self.h_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0) + # classes, x,y,obj + self.no = ( + nc + 3 + self.w_bin_sigmoid.get_length() + self.h_bin_sigmoid.get_length() + ) # w-bce, h-bce + # + self.x_bin_sigmoid.get_length() + self.y_bin_sigmoid.get_length() + + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer("anchors", a) # shape(nl,na,2) + self.register_buffer( + "anchor_grid", + a.clone().view(self.nl, 1, -1, 1, 1, 2), + ) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList( + nn.Conv2d(x, self.no * self.na, 1) for x in ch + ) # output conv + + self.ia = nn.ModuleList(ImplicitA(x) for x in ch) + self.im = nn.ModuleList(ImplicitM(self.no * self.na) for _ in ch) + + def forward(self, x): + # self.x_bin_sigmoid.use_fw_regression = True + # self.y_bin_sigmoid.use_fw_regression = True + self.w_bin_sigmoid.use_fw_regression = True + self.h_bin_sigmoid.use_fw_regression = True + + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](self.ia[i](x[i])) # conv + x[i] = self.im[i](x[i]) + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = ( + x[i] + .view(bs, self.na, self.no, ny, nx) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i]) * self.stride[ + i + ] # xy + # y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + + # px = (self.x_bin_sigmoid.forward(y[..., 0:12]) + self.grid[i][..., 0]) * self.stride[i] + # py = (self.y_bin_sigmoid.forward(y[..., 12:24]) + self.grid[i][..., 1]) * self.stride[i] + + pw = ( + self.w_bin_sigmoid.forward(y[..., 2:24]) + * self.anchor_grid[i][..., 0] + ) + ph = ( + self.h_bin_sigmoid.forward(y[..., 24:46]) + * self.anchor_grid[i][..., 1] + ) + + # y[..., 0] = px + # y[..., 1] = py + y[..., 2] = pw + y[..., 3] = ph + + y = torch.cat((y[..., 0:4], y[..., 46:]), dim=-1) + + z.append(y.view(bs, -1, y.shape[-1])) + + return x if self.training else (torch.cat(z, 1), x) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + +class Model(nn.Module): + def __init__( + self, + cfg="yolor-csp-c.yaml", + ch=3, + nc=None, + anchors=None, + ): # model, input channels, number of classes + super().__init__() + self.traced = False + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + + self.yaml_file = Path(cfg).name + with open(cfg) as f: + self.yaml = yaml.load(f, Loader=yaml.SafeLoader) # model dict + + # Define model + ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels + if nc and nc != self.yaml["nc"]: + logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") + self.yaml["nc"] = nc # override yaml value + if anchors: + logger.info(f"Overriding model.yaml anchors with anchors={anchors}") + self.yaml["anchors"] = round(anchors) # override yaml value + self.model, self.save = parse_model( + deepcopy(self.yaml), + ch=[ch], + ) # model, savelist + self.names = [str(i) for i in range(self.yaml["nc"])] # default names + # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) + + # Build strides, anchors + m = self.model[-1] # Detect() + if isinstance(m, Detect): + s = 256 # 2x min stride + m.stride = torch.tensor( + [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))], + ) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases() # only run once + # print('Strides: %s' % m.stride.tolist()) + if isinstance(m, IDetect): + s = 256 # 2x min stride + m.stride = torch.tensor( + [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))], + ) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases() # only run once + # print('Strides: %s' % m.stride.tolist()) + if isinstance(m, IAuxDetect): + s = 256 # 2x min stride + m.stride = torch.tensor( + [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))[:4]], + ) # forward + # print(m.stride) + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_aux_biases() # only run once + # print('Strides: %s' % m.stride.tolist()) + if isinstance(m, IBin): + s = 256 # 2x min stride + m.stride = torch.tensor( + [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))], + ) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases_bin() # only run once + # print('Strides: %s' % m.stride.tolist()) + if isinstance(m, IKeypoint): + s = 256 # 2x min stride + m.stride = torch.tensor( + [s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))], + ) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases_kpt() # only run once + # print('Strides: %s' % m.stride.tolist()) + + # Init weights, biases + initialize_weights(self) + self.info() + logger.info("") + + def forward(self, x, augment=False, profile=False): + if augment: + img_size = x.shape[-2:] # height, width + s = [1, 0.83, 0.67] # scales + f = [None, 3, None] # flips (2-ud, 3-lr) + y = [] # outputs + for si, fi in zip(s, f): + xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) + yi = self.forward_once(xi)[0] # forward + # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save + yi[..., :4] /= si # de-scale + if fi == 2: + yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud + elif fi == 3: + yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr + y.append(yi) + return torch.cat(y, 1), None # augmented inference, train + else: + return self.forward_once(x, profile) # single-scale inference, train + + def forward_once(self, x, profile=False): + y, dt = [], [] # outputs + for m in self.model: + if m.f != -1: # if not from previous layer + x = ( + y[m.f] + if isinstance(m.f, int) + else [x if j == -1 else y[j] for j in m.f] + ) # from earlier layers + + if not hasattr(self, "traced"): + self.traced = False + + if self.traced and ( + isinstance(m, (Detect, IDetect, IAuxDetect, IKeypoint)) + ): + break + + if profile: + c = isinstance(m, (Detect, IDetect, IAuxDetect, IBin)) + o = ( + thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] + / 1e9 + * 2 + if thop + else 0 + ) # FLOPS + for _ in range(10): + m(x.copy() if c else x) + t = time_synchronized() + for _ in range(10): + m(x.copy() if c else x) + dt.append((time_synchronized() - t) * 100) + print("%10.1f%10.0f%10.1fms %-40s" % (o, m.np, dt[-1], m.type)) + + x = m(x) # run + + y.append(x if m.i in self.save else None) # save output + + if profile: + print("%.1fms total" % sum(dt)) + return x + + def _initialize_biases( + self, + cf=None, + ): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log( + 8 / (640 / s) ** 2, + ) # obj (8 objects per 640 image) + b.data[:, 5:] += ( + math.log(0.6 / (m.nc - 0.99)) + if cf is None + else torch.log(cf / cf.sum()) + ) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + def _initialize_aux_biases( + self, + cf=None, + ): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, mi2, s in zip(m.m, m.m2, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log( + 8 / (640 / s) ** 2, + ) # obj (8 objects per 640 image) + b.data[:, 5:] += ( + math.log(0.6 / (m.nc - 0.99)) + if cf is None + else torch.log(cf / cf.sum()) + ) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + b2 = mi2.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b2.data[:, 4] += math.log( + 8 / (640 / s) ** 2, + ) # obj (8 objects per 640 image) + b2.data[:, 5:] += ( + math.log(0.6 / (m.nc - 0.99)) + if cf is None + else torch.log(cf / cf.sum()) + ) # cls + mi2.bias = torch.nn.Parameter(b2.view(-1), requires_grad=True) + + def _initialize_biases_bin( + self, + cf=None, + ): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Bin() module + bc = m.bin_count + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + old = b[:, (0, 1, 2, bc + 3)].data + obj_idx = 2 * bc + 4 + b[:, :obj_idx].data += math.log(0.6 / (bc + 1 - 0.99)) + b[:, obj_idx].data += math.log( + 8 / (640 / s) ** 2, + ) # obj (8 objects per 640 image) + b[:, (obj_idx + 1) :].data += ( + math.log(0.6 / (m.nc - 0.99)) + if cf is None + else torch.log(cf / cf.sum()) + ) # cls + b[:, (0, 1, 2, bc + 3)].data = old + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + def _initialize_biases_kpt( + self, + cf=None, + ): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log( + 8 / (640 / s) ** 2, + ) # obj (8 objects per 640 image) + b.data[:, 5:] += ( + math.log(0.6 / (m.nc - 0.99)) + if cf is None + else torch.log(cf / cf.sum()) + ) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + def _print_biases(self): + m = self.model[-1] # Detect() module + for mi in m.m: # from + b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) + print( + ("%6g Conv2d.bias:" + "%10.3g" * 6) + % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()), + ) + + # def _print_weights(self): + # for m in self.model.modules(): + # if type(m) is Bottleneck: + # print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights + + def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers + print("Fusing layers... ") + for m in self.model.modules(): + if isinstance(m, RepConv): + # print(f" fuse_repvgg_block") + m.fuse_repvgg_block() + elif isinstance(m, RepConv_OREPA): + # print(f" switch_to_deploy") + m.switch_to_deploy() + elif type(m) is Conv and hasattr(m, "bn"): + m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv + delattr(m, "bn") # remove batchnorm + m.forward = m.fuseforward # update forward + elif isinstance(m, (IDetect, IAuxDetect)): + m.fuse() + m.forward = m.fuseforward + self.info() + return self + + def nms(self, mode=True): # add or remove NMS module + present = type(self.model[-1]) is NMS # last layer is NMS + if mode and not present: + print("Adding NMS... ") + m = NMS() # module + m.f = -1 # from + m.i = self.model[-1].i + 1 # index + self.model.add_module(name="%s" % m.i, module=m) # add + self.eval() + elif not mode and present: + print("Removing NMS... ") + self.model = self.model[:-1] # remove + return self + + def autoshape(self): # add autoShape module + print("Adding autoShape... ") + m = autoShape(self) # wrap model + copy_attr( + m, + self, + include=("yaml", "nc", "hyp", "names", "stride"), + exclude=(), + ) # copy attributes + return m + + def info(self, verbose=False, img_size=640): # print model information + model_info(self, verbose, img_size) + + +def parse_model(d, ch): # model_dict, input_channels(3) + logger.info( + "\n%3s%18s%3s%10s %-40s%-30s" + % ("", "from", "n", "params", "module", "arguments"), + ) + anchors, nc, gd, gw = ( + d["anchors"], + d["nc"], + d["depth_multiple"], + d["width_multiple"], + ) + na = ( + (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors + ) # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate( + d["backbone"] + d["head"], + ): # from, number, module, args + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except: + pass + + n = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in [ + nn.Conv2d, + Conv, + RobustConv, + RobustConv2, + DWConv, + GhostConv, + RepConv, + RepConv_OREPA, + DownC, + SPP, + SPPF, + SPPCSPC, + GhostSPPCSPC, + MixConv2d, + Focus, + Stem, + GhostStem, + CrossConv, + Bottleneck, + BottleneckCSPA, + BottleneckCSPB, + BottleneckCSPC, + RepBottleneck, + RepBottleneckCSPA, + RepBottleneckCSPB, + RepBottleneckCSPC, + Res, + ResCSPA, + ResCSPB, + ResCSPC, + RepRes, + RepResCSPA, + RepResCSPB, + RepResCSPC, + ResX, + ResXCSPA, + ResXCSPB, + ResXCSPC, + RepResX, + RepResXCSPA, + RepResXCSPB, + RepResXCSPC, + Ghost, + GhostCSPA, + GhostCSPB, + GhostCSPC, + SwinTransformerBlock, + STCSPA, + STCSPB, + STCSPC, + SwinTransformer2Block, + ST2CSPA, + ST2CSPB, + ST2CSPC, + ]: + c1, c2 = ch[f], args[0] + if c2 != no: # if not output + c2 = make_divisible(c2 * gw, 8) + + args = [c1, c2, *args[1:]] + if m in [ + DownC, + SPPCSPC, + GhostSPPCSPC, + BottleneckCSPA, + BottleneckCSPB, + BottleneckCSPC, + RepBottleneckCSPA, + RepBottleneckCSPB, + RepBottleneckCSPC, + ResCSPA, + ResCSPB, + ResCSPC, + RepResCSPA, + RepResCSPB, + RepResCSPC, + ResXCSPA, + ResXCSPB, + ResXCSPC, + RepResXCSPA, + RepResXCSPB, + RepResXCSPC, + GhostCSPA, + GhostCSPB, + GhostCSPC, + STCSPA, + STCSPB, + STCSPC, + ST2CSPA, + ST2CSPB, + ST2CSPC, + ]: + args.insert(2, n) # number of repeats + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum([ch[x] for x in f]) + elif m is Chuncat: + c2 = sum([ch[x] for x in f]) + elif m is Shortcut: + c2 = ch[f[0]] + elif m is Foldcut: + c2 = ch[f] // 2 + elif m in [Detect, IDetect, IAuxDetect, IBin, IKeypoint]: + args.append([ch[x] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + elif m is ReOrg: + c2 = ch[f] * 4 + elif m is Contract: + c2 = ch[f] * args[0] ** 2 + elif m is Expand: + c2 = ch[f] // args[0] ** 2 + else: + c2 = ch[f] + + m_ = ( + nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) + ) # module + t = str(m)[8:-2].replace("__main__.", "") # module type + np = sum([x.numel() for x in m_.parameters()]) # number params + m_.i, m_.f, m_.type, m_.np = ( + i, + f, + t, + np, + ) # attach index, 'from' index, type, number params + logger.info("%3s%18s%3s%10.0f %-40s%-30s" % (i, f, n, np, t, args)) # print + save.extend( + x % i for x in ([f] if isinstance(f, int) else f) if x != -1 + ) # append to savelist + layers.append(m_) + if i == 0: + ch = [] + ch.append(c2) + return nn.Sequential(*layers), sorted(save) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--cfg", + type=str, + default="yolor-csp-c.yaml", + help="model.yaml", + ) + parser.add_argument( + "--device", + default="", + help="cuda device, i.e. 0 or 0,1,2,3 or cpu", + ) + parser.add_argument("--profile", action="store_true", help="profile model speed") + opt = parser.parse_args() + opt.cfg = check_file(opt.cfg) # check file + set_logging() + device = select_device(opt.device) + + # Create model + model = Model(opt.cfg).to(device) + model.train() + + if opt.profile: + img = torch.rand(1, 3, 640, 640).to(device) + y = model(img, profile=True) + + # Profile + # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device) + # y = model(img, profile=True) + + # Tensorboard + # from torch.utils.tensorboard import SummaryWriter + # tb_writer = SummaryWriter() + # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/") + # tb_writer.add_graph(model.model, img) # add model to tensorboard + # tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard diff --git a/mil_common/perception/yoloros/src/yoloros/requirements.txt b/mil_common/perception/yoloros/src/yoloros/requirements.txt new file mode 100644 index 000000000..f4d218218 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/requirements.txt @@ -0,0 +1,39 @@ +# Usage: pip install -r requirements.txt + +# Base ---------------------------------------- +matplotlib>=3.2.2 +numpy>=1.18.5,<1.24.0 +opencv-python>=4.1.1 +Pillow>=7.1.2 +PyYAML>=5.3.1 +requests>=2.23.0 +scipy>=1.4.1 +torch>=1.7.0,!=1.12.0 +torchvision>=0.8.1,!=0.13.0 +tqdm>=4.41.0 +protobuf<4.21.3 + +# Logging ------------------------------------- +tensorboard>=2.4.1 +# wandb + +# Plotting ------------------------------------ +pandas>=1.1.4 +seaborn>=0.11.0 + +# Export -------------------------------------- +# coremltools>=4.1 # CoreML export +# onnx>=1.9.0 # ONNX export +# onnx-simplifier>=0.3.6 # ONNX simplifier +# scikit-learn==0.19.2 # CoreML quantization +# tensorflow>=2.4.1 # TFLite export +# tensorflowjs>=3.9.0 # TF.js export +# openvino-dev # OpenVINO export + +# Extras -------------------------------------- +ipython # interactive notebook +psutil # system utilization +thop # FLOPs computation +# albumentations>=1.0.3 +# pycocotools>=2.0 # COCO mAP +# roboflow diff --git a/mil_common/perception/yoloros/src/yoloros/scripts/get_coco.sh b/mil_common/perception/yoloros/src/yoloros/scripts/get_coco.sh new file mode 100644 index 000000000..6d16285c4 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/scripts/get_coco.sh @@ -0,0 +1,22 @@ +#!/bin/bash +# COCO 2017 dataset http://cocodataset.org +# Download command: bash ./scripts/get_coco.sh + +# Download/unzip labels +d='./' # unzip directory +url=https://github.com/ultralytics/yolov5/releases/download/v1.0/ +f='coco2017labels-segments.zip' # or 'coco2017labels.zip', 68 MB +echo 'Downloading' $url$f ' ...' +curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background + +# Download/unzip images +d='./coco/images' # unzip directory +url=http://images.cocodataset.org/zips/ +f1='train2017.zip' # 19G, 118k images +f2='val2017.zip' # 1G, 5k images +f3='test2017.zip' # 7G, 41k images (optional) +for f in $f1 $f2 $f3; do + echo 'Downloading' $url$f '...' + curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background +done +wait # finish background tasks diff --git a/mil_common/perception/yoloros/src/yoloros/testing.py b/mil_common/perception/yoloros/src/yoloros/testing.py new file mode 100644 index 000000000..9597aaa8d --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/testing.py @@ -0,0 +1,63 @@ +import numpy as np +import torch +from models.experimental import attempt_load +from PIL import Image +from torchvision import transforms +from utils.general import non_max_suppression +from utils.plots import plot_one_box + +# TODO: Place this file into the yolov7 folder when implementing it in mil_commmons + +MODEL = attempt_load( + "yolopy/runs/train/exp8/weights/last.pt", + map_location=torch.device("cuda"), +) +CLASSES = [ + "buoy_abydos_serpenscaput", + "buoy_abydos_taurus", + "buoy_earth_auriga", + "buoy_earth_cetus", + "gate_abydos", + "gate_earth", +] +COLORS = [ + (255, 0, 0), + (0, 255, 0), + (0, 0, 255), + (255, 155, 0), + (255, 0, 255), + (0, 255, 255), +] +CONFIDENCE_THRESHOLD = 0.85 + +MODEL.eval() + +image_path = "yolopy/tests/RoboSub-2023-Dataset-Cover_png.rf.ffda25ca7a57ac74ee37bc85707cf784.jpg" +img = Image.open(image_path).convert("RGB") + +img_transform = transforms.Compose([transforms.ToTensor()]) + +img_tensor = img_transform(img).to("cuda").unsqueeze(0) +pred_results = MODEL(img_tensor)[0] +detections = non_max_suppression(pred_results, conf_thres=0.5, iou_thres=0.5) + +arr_image = np.array(img) + +if detections: + detections = detections[0] + for x1, y1, x2, y2, conf, cls in detections: + class_index = int(cls.cpu().item()) + print(f"{CLASSES[class_index]} => {conf}") + if conf < CONFIDENCE_THRESHOLD: + continue + plot_one_box( + [x1, y1, x2, y2], + arr_image, + label=f"{CLASSES[class_index]}", + color=COLORS[class_index], + line_thickness=2, + ) +else: + print("No Detections Made") + +Image.fromarray(arr_image).show() diff --git a/mil_common/perception/yoloros/src/yoloros/tests/RoboSub-2023-Dataset-Cover_png.rf.ffda25ca7a57ac74ee37bc85707cf784.jpg b/mil_common/perception/yoloros/src/yoloros/tests/RoboSub-2023-Dataset-Cover_png.rf.ffda25ca7a57ac74ee37bc85707cf784.jpg new file mode 100644 index 000000000..5a0fa67c1 Binary files /dev/null and b/mil_common/perception/yoloros/src/yoloros/tests/RoboSub-2023-Dataset-Cover_png.rf.ffda25ca7a57ac74ee37bc85707cf784.jpg differ diff --git a/mil_common/perception/yoloros/src/yoloros/utils/__init__.py b/mil_common/perception/yoloros/src/yoloros/utils/__init__.py new file mode 100644 index 000000000..a6131c10e --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/__init__.py @@ -0,0 +1 @@ +# init diff --git a/mil_common/perception/yoloros/src/yoloros/utils/activations.py b/mil_common/perception/yoloros/src/yoloros/utils/activations.py new file mode 100644 index 000000000..da6d9c19c --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/activations.py @@ -0,0 +1,72 @@ +# Activation functions + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +# SiLU https://arxiv.org/pdf/1606.08415.pdf ---------------------------------------------------------------------------- +class SiLU(nn.Module): # export-friendly version of nn.SiLU() + @staticmethod + def forward(x): + return x * torch.sigmoid(x) + + +class Hardswish(nn.Module): # export-friendly version of nn.Hardswish() + @staticmethod + def forward(x): + # return x * F.hardsigmoid(x) # for torchscript and CoreML + return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for torchscript, CoreML and ONNX + + +class MemoryEfficientSwish(nn.Module): + class F(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x * torch.sigmoid(x) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + return grad_output * (sx * (1 + x * (1 - sx))) + + def forward(self, x): + return self.F.apply(x) + + +# Mish https://github.com/digantamisra98/Mish -------------------------------------------------------------------------- +class Mish(nn.Module): + @staticmethod + def forward(x): + return x * F.softplus(x).tanh() + + +class MemoryEfficientMish(nn.Module): + class F(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + fx = F.softplus(x).tanh() + return grad_output * (fx + x * sx * (1 - fx * fx)) + + def forward(self, x): + return self.F.apply(x) + + +# FReLU https://arxiv.org/abs/2007.11824 ------------------------------------------------------------------------------- +class FReLU(nn.Module): + def __init__(self, c1, k=3): # ch_in, kernel + super().__init__() + self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) + self.bn = nn.BatchNorm2d(c1) + + def forward(self, x): + return torch.max(x, self.bn(self.conv(x))) diff --git a/mil_common/perception/yoloros/src/yoloros/utils/add_nms.py b/mil_common/perception/yoloros/src/yoloros/utils/add_nms.py new file mode 100644 index 000000000..158807040 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/add_nms.py @@ -0,0 +1,167 @@ +import numpy as np +import onnx +from onnx import shape_inference + +try: + import onnx_graphsurgeon as gs +except Exception as e: + print("Import onnx_graphsurgeon failure: %s" % e) + +import logging + +LOGGER = logging.getLogger(__name__) + + +class RegisterNMS: + def __init__( + self, + onnx_model_path: str, + precision: str = "fp32", + ): + self.graph = gs.import_onnx(onnx.load(onnx_model_path)) + assert self.graph + LOGGER.info("ONNX graph created successfully") + # Fold constants via ONNX-GS that PyTorch2ONNX may have missed + self.graph.fold_constants() + self.precision = precision + self.batch_size = 1 + + def infer(self): + """ + Sanitize the graph by cleaning any unconnected nodes, do a topological resort, + and fold constant inputs values. When possible, run shape inference on the + ONNX graph to determine tensor shapes. + """ + for _ in range(3): + count_before = len(self.graph.nodes) + + self.graph.cleanup().toposort() + try: + for node in self.graph.nodes: + for o in node.outputs: + o.shape = None + model = gs.export_onnx(self.graph) + model = shape_inference.infer_shapes(model) + self.graph = gs.import_onnx(model) + except Exception as e: + LOGGER.info( + f"Shape inference could not be performed at this time:\n{e}", + ) + try: + self.graph.fold_constants(fold_shapes=True) + except TypeError as e: + LOGGER.error( + "This version of ONNX GraphSurgeon does not support folding shapes, " + f"please upgrade your onnx_graphsurgeon module. Error:\n{e}", + ) + raise + + count_after = len(self.graph.nodes) + if count_before == count_after: + # No new folding occurred in this iteration, so we can stop for now. + break + + def save(self, output_path): + """ + Save the ONNX model to the given location. + Args: + output_path: Path pointing to the location where to write + out the updated ONNX model. + """ + self.graph.cleanup().toposort() + model = gs.export_onnx(self.graph) + onnx.save(model, output_path) + LOGGER.info(f"Saved ONNX model to {output_path}") + + def register_nms( + self, + *, + score_thresh: float = 0.25, + nms_thresh: float = 0.45, + detections_per_img: int = 100, + ): + """ + Register the ``EfficientNMS_TRT`` plugin node. + NMS expects these shapes for its input tensors: + - box_net: [batch_size, number_boxes, 4] + - class_net: [batch_size, number_boxes, number_labels] + Args: + score_thresh (float): The scalar threshold for score (low scoring boxes are removed). + nms_thresh (float): The scalar threshold for IOU (new boxes that have high IOU + overlap with previously selected boxes are removed). + detections_per_img (int): Number of best detections to keep after NMS. + """ + + self.infer() + # Find the concat node at the end of the network + op_inputs = self.graph.outputs + op = "EfficientNMS_TRT" + attrs = { + "plugin_version": "1", + "background_class": -1, # no background class + "max_output_boxes": detections_per_img, + "score_threshold": score_thresh, + "iou_threshold": nms_thresh, + "score_activation": False, + "box_coding": 0, + } + + if self.precision == "fp32": + dtype_output = np.float32 + elif self.precision == "fp16": + dtype_output = np.float16 + else: + raise NotImplementedError( + f"Currently not supports precision: {self.precision}", + ) + + # NMS Outputs + output_num_detections = gs.Variable( + name="num_dets", + dtype=np.int32, + shape=[self.batch_size, 1], + ) # A scalar indicating the number of valid detections per batch image. + output_boxes = gs.Variable( + name="det_boxes", + dtype=dtype_output, + shape=[self.batch_size, detections_per_img, 4], + ) + output_scores = gs.Variable( + name="det_scores", + dtype=dtype_output, + shape=[self.batch_size, detections_per_img], + ) + output_labels = gs.Variable( + name="det_classes", + dtype=np.int32, + shape=[self.batch_size, detections_per_img], + ) + + op_outputs = [output_num_detections, output_boxes, output_scores, output_labels] + + # Create the NMS Plugin node with the selected inputs. The outputs of the node will also + # become the final outputs of the graph. + self.graph.layer( + op=op, + name="batched_nms", + inputs=op_inputs, + outputs=op_outputs, + attrs=attrs, + ) + LOGGER.info(f"Created NMS plugin '{op}' with attributes: {attrs}") + + self.graph.outputs = op_outputs + + self.infer() + + def save(self, output_path): + """ + Save the ONNX model to the given location. + Args: + output_path: Path pointing to the location where to write + out the updated ONNX model. + """ + self.graph.cleanup().toposort() + model = gs.export_onnx(self.graph) + onnx.save(model, output_path) + LOGGER.info(f"Saved ONNX model to {output_path}") diff --git a/mil_common/perception/yoloros/src/yoloros/utils/autoanchor.py b/mil_common/perception/yoloros/src/yoloros/utils/autoanchor.py new file mode 100644 index 000000000..760d23e26 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/autoanchor.py @@ -0,0 +1,215 @@ +# Auto-anchor utils + +import numpy as np +import torch +import yaml +from scipy.cluster.vq import kmeans +from tqdm import tqdm +from utils.general import colorstr + + +def check_anchor_order(m): + # Check anchor order against stride order for YOLO Detect() module m, and correct if necessary + a = m.anchor_grid.prod(-1).view(-1) # anchor area + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + if da.sign() != ds.sign(): # same order + print("Reversing anchor order") + m.anchors[:] = m.anchors.flip(0) + m.anchor_grid[:] = m.anchor_grid.flip(0) + + +def check_anchors(dataset, model, thr=4.0, imgsz=640): + # Check anchor fit to data, recompute if necessary + prefix = colorstr("autoanchor: ") + print(f"\n{prefix}Analyzing anchors... ", end="") + m = ( + model.module.model[-1] if hasattr(model, "module") else model.model[-1] + ) # Detect() + shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) + scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale + wh = torch.tensor( + np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)]), + ).float() # wh + + def metric(k): # compute metric + r = wh[:, None] / k[None] + x = torch.min(r, 1.0 / r).min(2)[0] # ratio metric + best = x.max(1)[0] # best_x + aat = (x > 1.0 / thr).float().sum(1).mean() # anchors above threshold + bpr = (best > 1.0 / thr).float().mean() # best possible recall + return bpr, aat + + anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors + bpr, aat = metric(anchors) + print(f"anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}", end="") + if bpr < 0.98: # threshold to recompute + print(". Attempting to improve anchors, please wait...") + na = m.anchor_grid.numel() // 2 # number of anchors + try: + anchors = kmean_anchors( + dataset, + n=na, + img_size=imgsz, + thr=thr, + gen=1000, + verbose=False, + ) + except Exception as e: + print(f"{prefix}ERROR: {e}") + new_bpr = metric(anchors)[0] + if new_bpr > bpr: # replace anchors + anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) + m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference + check_anchor_order(m) + m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to( + m.anchors.device, + ).view( + -1, + 1, + 1, + ) # loss + print( + f"{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.", + ) + else: + print( + f"{prefix}Original anchors better than new anchors. Proceeding with original anchors.", + ) + print("") # newline + + +def kmean_anchors( + path="./data/coco.yaml", + n=9, + img_size=640, + thr=4.0, + gen=1000, + verbose=True, +): + """Creates kmeans-evolved anchors from training dataset + + Arguments: + path: path to dataset *.yaml, or a loaded dataset + n: number of anchors + img_size: image size used for training + thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 + gen: generations to evolve anchors using genetic algorithm + verbose: print all results + + Return: + k: kmeans evolved anchors + + Usage: + from utils.autoanchor import *; _ = kmean_anchors() + """ + thr = 1.0 / thr + prefix = colorstr("autoanchor: ") + + def metric(k, wh): # compute metrics + r = wh[:, None] / k[None] + x = torch.min(r, 1.0 / r).min(2)[0] # ratio metric + # x = wh_iou(wh, torch.tensor(k)) # iou metric + return x, x.max(1)[0] # x, best_x + + def anchor_fitness(k): # mutation fitness + _, best = metric(torch.tensor(k, dtype=torch.float32), wh) + return (best * (best > thr).float()).mean() # fitness + + def print_results(k): + k = k[np.argsort(k.prod(1))] # sort small to large + x, best = metric(k, wh0) + bpr, aat = (best > thr).float().mean(), ( + x > thr + ).float().mean() * n # best possible recall, anch > thr + print( + f"{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr", + ) + print( + f"{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, " + f"past_thr={x[x > thr].mean():.3f}-mean: ", + end="", + ) + for i, x in enumerate(k): + print( + "%i,%i" % (round(x[0]), round(x[1])), + end=", " if i < len(k) - 1 else "\n", + ) # use in *.cfg + return k + + if isinstance(path, str): # *.yaml file + with open(path) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # model dict + from utils.datasets import LoadImagesAndLabels + + dataset = LoadImagesAndLabels(data_dict["train"], augment=True, rect=True) + else: + dataset = path # dataset + + # Get label wh + shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) + wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh + + # Filter + i = (wh0 < 3.0).any(1).sum() + if i: + print( + f"{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.", + ) + wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels + # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 + + # Kmeans calculation + print(f"{prefix}Running kmeans for {n} anchors on {len(wh)} points...") + s = wh.std(0) # sigmas for whitening + k, dist = kmeans(wh / s, n, iter=30) # points, mean distance + assert len(k) == n, print( + f"{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}", + ) + k *= s + wh = torch.tensor(wh, dtype=torch.float32) # filtered + wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered + k = print_results(k) + + # Plot + # k, d = [None] * 20, [None] * 20 + # for i in tqdm(range(1, 21)): + # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance + # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) + # ax = ax.ravel() + # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') + # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh + # ax[0].hist(wh[wh[:, 0]<100, 0],400) + # ax[1].hist(wh[wh[:, 1]<100, 1],400) + # fig.savefig('wh.png', dpi=200) + + # Evolve + npr = np.random + f, sh, mp, s = ( + anchor_fitness(k), + k.shape, + 0.9, + 0.1, + ) # fitness, generations, mutation prob, sigma + pbar = tqdm( + range(gen), + desc=f"{prefix}Evolving anchors with Genetic Algorithm:", + ) # progress bar + for _ in pbar: + v = np.ones(sh) + while (v == 1).all(): # mutate until a change occurs (prevent duplicates) + v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip( + 0.3, + 3.0, + ) + kg = (k.copy() * v).clip(min=2.0) + fg = anchor_fitness(kg) + if fg > f: + f, k = fg, kg.copy() + pbar.desc = ( + f"{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}" + ) + if verbose: + print_results(k) + + return print_results(k) diff --git a/mil_common/perception/yoloros/src/yoloros/utils/aws/__init__.py b/mil_common/perception/yoloros/src/yoloros/utils/aws/__init__.py new file mode 100644 index 000000000..a6131c10e --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/aws/__init__.py @@ -0,0 +1 @@ +# init diff --git a/mil_common/perception/yoloros/src/yoloros/utils/aws/mime.sh b/mil_common/perception/yoloros/src/yoloros/utils/aws/mime.sh new file mode 100644 index 000000000..3113d45ff --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/aws/mime.sh @@ -0,0 +1,31 @@ +# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ +# This script will run on every instance restart, not only on first start +# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- + +Content-Type: multipart/mixed +boundary="//" +MIME-Version: 1.0 + +--// +Content-Type: text/cloud-config +charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment +filename="cloud-config.txt" + +#cloud-config +cloud_final_modules: +- [scripts-user, always] + +--// +Content-Type: text/x-shellscript +charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment +filename="userdata.txt" + +#!/bin/bash +# --- paste contents of userdata.sh here --- +--// diff --git a/mil_common/perception/yoloros/src/yoloros/utils/aws/resume.py b/mil_common/perception/yoloros/src/yoloros/utils/aws/resume.py new file mode 100644 index 000000000..c39c5c5b7 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/aws/resume.py @@ -0,0 +1,39 @@ +# Resume all interrupted trainings in yolor/ dir including DDP trainings +# Usage: $ python utils/aws/resume.py + +import os +import sys +from pathlib import Path + +import torch +import yaml + +sys.path.append("./") # to run '$ python *.py' files in subdirectories + +port = 0 # --master_port +path = Path("").resolve() +for last in path.rglob("*/**/last.pt"): + ckpt = torch.load(last) + if ckpt["optimizer"] is None: + continue + + # Load opt.yaml + with open(last.parent.parent / "opt.yaml") as f: + opt = yaml.load(f, Loader=yaml.SafeLoader) + + # Get device count + d = opt["device"].split(",") # devices + nd = len(d) # number of devices + ddp = nd > 1 or ( + nd == 0 and torch.cuda.device_count() > 1 + ) # distributed data parallel + + if ddp: # multi-GPU + port += 1 + cmd = f"python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}" + else: # single-GPU + cmd = f"python train.py --resume {last}" + + cmd += " > /dev/null 2>&1 &" # redirect output to dev/null and run in daemon thread + print(cmd) + os.system(cmd) diff --git a/mil_common/perception/yoloros/src/yoloros/utils/aws/userdata.sh b/mil_common/perception/yoloros/src/yoloros/utils/aws/userdata.sh new file mode 100644 index 000000000..582aeeca2 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/aws/userdata.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html +# This script will run only once on first instance start (for a re-start script see mime.sh) +# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir +# Use >300 GB SSD + +cd home/ubuntu +if [ ! -d yolor ]; then + echo "Running first-time script." # install dependencies, download COCO, pull Docker + git clone -b main https://github.com/WongKinYiu/yolov7 && sudo chmod -R 777 yolov7 + cd yolov7 + bash data/scripts/get_coco.sh && echo "Data done." & + sudo docker pull nvcr.io/nvidia/pytorch:21.08-py3 && echo "Docker done." & + python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & + wait && echo "All tasks done." # finish background tasks +else + echo "Running re-start script." # resume interrupted runs + i=0 + list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' + while IFS= read -r id; do + ((i++)) + echo "restarting container $i: $id" + sudo docker start $id + # sudo docker exec -it $id python train.py --resume # single-GPU + sudo docker exec -d $id python utils/aws/resume.py # multi-scenario + done <<<"$list" +fi diff --git a/mil_common/perception/yoloros/src/yoloros/utils/datasets.py b/mil_common/perception/yoloros/src/yoloros/utils/datasets.py new file mode 100644 index 000000000..2ea317534 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/datasets.py @@ -0,0 +1,1750 @@ +# Dataset utils and dataloaders + +import glob +import logging +import math +import os +import random +import shutil +import time +from itertools import repeat +from multiprocessing.pool import ThreadPool +from pathlib import Path +from threading import Thread + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from PIL import ExifTags, Image +from torch.utils.data import Dataset + +# from pycocotools import mask as maskUtils +from tqdm import tqdm +from utils.general import ( + check_requirements, + clean_str, + resample_segments, + segment2box, + segments2boxes, + xyn2xy, + xywh2xyxy, + xywhn2xyxy, + xyxy2xywh, +) +from utils.torch_utils import torch_distributed_zero_first + +# Parameters +help_url = "https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data" +img_formats = [ + "bmp", + "jpg", + "jpeg", + "png", + "tif", + "tiff", + "dng", + "webp", + "mpo", +] # acceptable image suffixes +vid_formats = [ + "mov", + "avi", + "mp4", + "mpg", + "mpeg", + "m4v", + "wmv", + "mkv", +] # acceptable video suffixes +logger = logging.getLogger(__name__) + +# Get orientation exif tag +for orientation in ExifTags.TAGS: + if ExifTags.TAGS[orientation] == "Orientation": + break + + +def get_hash(files): + # Returns a single hash value of a list of files + return sum(os.path.getsize(f) for f in files if os.path.isfile(f)) + + +def exif_size(img): + # Returns exif-corrected PIL size + s = img.size # (width, height) + try: + rotation = dict(img._getexif().items())[orientation] + if rotation == 6: # rotation 270 + s = (s[1], s[0]) + elif rotation == 8: # rotation 90 + s = (s[1], s[0]) + except: + pass + + return s + + +def create_dataloader( + path, + imgsz, + batch_size, + stride, + opt, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + world_size=1, + workers=8, + image_weights=False, + quad=False, + prefix="", +): + # Make sure only the first process in DDP process the dataset first, and the following others can use the cache + with torch_distributed_zero_first(rank): + dataset = LoadImagesAndLabels( + path, + imgsz, + batch_size, + augment=augment, # augment images + hyp=hyp, # augmentation hyperparameters + rect=rect, # rectangular training + cache_images=cache, + single_cls=opt.single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix, + ) + + batch_size = min(batch_size, len(dataset)) + nw = min( + [os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers], + ) # number of workers + sampler = ( + torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None + ) + loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader + # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader() + dataloader = loader( + dataset, + batch_size=batch_size, + num_workers=nw, + sampler=sampler, + pin_memory=True, + collate_fn=LoadImagesAndLabels.collate_fn4 + if quad + else LoadImagesAndLabels.collate_fn, + ) + return dataloader, dataset + + +class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader): + """Dataloader that reuses workers + + Uses same syntax as vanilla DataLoader + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler)) + self.iterator = super().__iter__() + + def __len__(self): + return len(self.batch_sampler.sampler) + + def __iter__(self): + for i in range(len(self)): + yield next(self.iterator) + + +class _RepeatSampler: + """Sampler that repeats forever + + Args: + sampler (Sampler) + """ + + def __init__(self, sampler): + self.sampler = sampler + + def __iter__(self): + while True: + yield from iter(self.sampler) + + +class LoadImages: # for inference + def __init__(self, path, img_size=640, stride=32): + p = str(Path(path).absolute()) # os-agnostic absolute path + if "*" in p: + files = sorted(glob.glob(p, recursive=True)) # glob + elif os.path.isdir(p): + files = sorted(glob.glob(os.path.join(p, "*.*"))) # dir + elif os.path.isfile(p): + files = [p] # files + else: + raise Exception(f"ERROR: {p} does not exist") + + images = [x for x in files if x.split(".")[-1].lower() in img_formats] + videos = [x for x in files if x.split(".")[-1].lower() in vid_formats] + ni, nv = len(images), len(videos) + + self.img_size = img_size + self.stride = stride + self.files = images + videos + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv + self.mode = "image" + if any(videos): + self.new_video(videos[0]) # new video + else: + self.cap = None + assert self.nf > 0, ( + f"No images or videos found in {p}. " + f"Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}" + ) + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = "video" + ret_val, img0 = self.cap.read() + if not ret_val: + self.count += 1 + self.cap.release() + if self.count == self.nf: # last video + raise StopIteration + else: + path = self.files[self.count] + self.new_video(path) + ret_val, img0 = self.cap.read() + + self.frame += 1 + print( + f"video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ", + end="", + ) + + else: + # Read image + self.count += 1 + img0 = cv2.imread(path) # BGR + assert img0 is not None, "Image Not Found " + path + # print(f'image {self.count}/{self.nf} {path}: ', end='') + + # Padded resize + img = letterbox(img0, self.img_size, stride=self.stride)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return path, img, img0, self.cap + + def new_video(self, path): + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + def __len__(self): + return self.nf # number of files + + +class LoadWebcam: # for inference + def __init__(self, pipe="0", img_size=640, stride=32): + self.img_size = img_size + self.stride = stride + + if pipe.isnumeric(): + pipe = eval(pipe) # local camera + # pipe = 'rtsp://192.168.1.64/1' # IP camera + # pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login + # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera + + self.pipe = pipe + self.cap = cv2.VideoCapture(pipe) # video capture object + self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + if cv2.waitKey(1) == ord("q"): # q to quit + self.cap.release() + cv2.destroyAllWindows() + raise StopIteration + + # Read frame + if self.pipe == 0: # local camera + ret_val, img0 = self.cap.read() + img0 = cv2.flip(img0, 1) # flip left-right + else: # IP camera + n = 0 + while True: + n += 1 + self.cap.grab() + if n % 30 == 0: # skip frames + ret_val, img0 = self.cap.retrieve() + if ret_val: + break + + # Print + assert ret_val, f"Camera Error {self.pipe}" + img_path = "webcam.jpg" + print(f"webcam {self.count}: ", end="") + + # Padded resize + img = letterbox(img0, self.img_size, stride=self.stride)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return img_path, img, img0, None + + def __len__(self): + return 0 + + +class LoadStreams: # multiple IP or RTSP cameras + def __init__(self, sources="streams.txt", img_size=640, stride=32): + self.mode = "stream" + self.img_size = img_size + self.stride = stride + + if os.path.isfile(sources): + with open(sources) as f: + sources = [ + x.strip() for x in f.read().strip().splitlines() if len(x.strip()) + ] + else: + sources = [sources] + + n = len(sources) + self.imgs = [None] * n + self.sources = [clean_str(x) for x in sources] # clean source names for later + for i, s in enumerate(sources): + # Start the thread to read frames from the video stream + print(f"{i + 1}/{n}: {s}... ", end="") + url = eval(s) if s.isnumeric() else s + if "youtube.com/" in str(url) or "youtu.be/" in str( + url, + ): # if source is YouTube video + check_requirements(("pafy", "youtube_dl")) + import pafy + + url = pafy.new(url).getbest(preftype="mp4").url + cap = cv2.VideoCapture(url) + assert cap.isOpened(), f"Failed to open {s}" + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + self.fps = cap.get(cv2.CAP_PROP_FPS) % 100 + + _, self.imgs[i] = cap.read() # guarantee first frame + thread = Thread(target=self.update, args=([i, cap]), daemon=True) + print(f" success ({w}x{h} at {self.fps:.2f} FPS).") + thread.start() + print("") # newline + + # check for common shapes + s = np.stack( + [ + letterbox(x, self.img_size, stride=self.stride)[0].shape + for x in self.imgs + ], + 0, + ) # shapes + self.rect = ( + np.unique(s, axis=0).shape[0] == 1 + ) # rect inference if all shapes equal + if not self.rect: + print( + "WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.", + ) + + def update(self, index, cap): + # Read next stream frame in a daemon thread + n = 0 + while cap.isOpened(): + n += 1 + # _, self.imgs[index] = cap.read() + cap.grab() + if n == 4: # read every 4th frame + success, im = cap.retrieve() + self.imgs[index] = im if success else self.imgs[index] * 0 + n = 0 + time.sleep(1 / self.fps) # wait time + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + img0 = self.imgs.copy() + if cv2.waitKey(1) == ord("q"): # q to quit + cv2.destroyAllWindows() + raise StopIteration + + # Letterbox + img = [ + letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] + for x in img0 + ] + + # Stack + img = np.stack(img, 0) + + # Convert + img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416 + img = np.ascontiguousarray(img) + + return self.sources, img, img0, None + + def __len__(self): + return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years + + +def img2label_paths(img_paths): + # Define label paths as a function of image paths + sa, sb = ( + os.sep + "images" + os.sep, + os.sep + "labels" + os.sep, + ) # /images/, /labels/ substrings + return [ + "txt".join(x.replace(sa, sb, 1).rsplit(x.split(".")[-1], 1)) for x in img_paths + ] + + +class LoadImagesAndLabels(Dataset): # for training/testing + def __init__( + self, + path, + img_size=640, + batch_size=16, + augment=False, + hyp=None, + rect=False, + image_weights=False, + cache_images=False, + single_cls=False, + stride=32, + pad=0.0, + prefix="", + ): + self.img_size = img_size + self.augment = augment + self.hyp = hyp + self.image_weights = image_weights + self.rect = False if image_weights else rect + self.mosaic = ( + self.augment and not self.rect + ) # load 4 images at a time into a mosaic (only during training) + self.mosaic_border = [-img_size // 2, -img_size // 2] + self.stride = stride + self.path = path + # self.albumentations = Albumentations() if augment else None + + try: + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = Path(p) # os-agnostic + if p.is_dir(): # dir + f += glob.glob(str(p / "**" / "*.*"), recursive=True) + # f = list(p.rglob('**/*.*')) # pathlib + elif p.is_file(): # file + with open(p) as t: + t = t.read().strip().splitlines() + parent = str(p.parent) + os.sep + f += [ + x.replace("./", parent) if x.startswith("./") else x + for x in t + ] # local to global path + # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) + else: + raise Exception(f"{prefix}{p} does not exist") + self.img_files = sorted( + [ + x.replace("/", os.sep) + for x in f + if x.split(".")[-1].lower() in img_formats + ], + ) + # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats]) # pathlib + assert self.img_files, f"{prefix}No images found" + except Exception as e: + raise Exception( + f"{prefix}Error loading data from {path}: {e}\nSee {help_url}", + ) + + # Check cache + self.label_files = img2label_paths(self.img_files) # labels + cache_path = ( + p if p.is_file() else Path(self.label_files[0]).parent + ).with_suffix( + ".cache", + ) # cached labels + if cache_path.is_file(): + cache, exists = torch.load(cache_path), True # load + # if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache: # changed + # cache, exists = self.cache_labels(cache_path, prefix), False # re-cache + else: + cache, exists = self.cache_labels(cache_path, prefix), False # cache + + # Display cache + nf, nm, ne, nc, n = cache.pop( + "results", + ) # found, missing, empty, corrupted, total + if exists: + d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" + tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results + assert ( + nf > 0 or not augment + ), f"{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}" + + # Read cache + cache.pop("hash") # remove hash + cache.pop("version") # remove version + labels, shapes, self.segments = zip(*cache.values()) + self.labels = list(labels) + self.shapes = np.array(shapes, dtype=np.float64) + self.img_files = list(cache.keys()) # update + self.label_files = img2label_paths(cache.keys()) # update + if single_cls: + for x in self.labels: + x[:, 0] = 0 + + n = len(shapes) # number of images + bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index + nb = bi[-1] + 1 # number of batches + self.batch = bi # batch index of image + self.n = n + self.indices = range(n) + + # Rectangular Training + if self.rect: + # Sort by aspect ratio + s = self.shapes # wh + ar = s[:, 1] / s[:, 0] # aspect ratio + irect = ar.argsort() + self.img_files = [self.img_files[i] for i in irect] + self.label_files = [self.label_files[i] for i in irect] + self.labels = [self.labels[i] for i in irect] + self.shapes = s[irect] # wh + ar = ar[irect] + + # Set training image shapes + shapes = [[1, 1]] * nb + for i in range(nb): + ari = ar[bi == i] + mini, maxi = ari.min(), ari.max() + if maxi < 1: + shapes[i] = [maxi, 1] + elif mini > 1: + shapes[i] = [1, 1 / mini] + + self.batch_shapes = ( + np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride + ) + + # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) + self.imgs = [None] * n + if cache_images: + if cache_images == "disk": + self.im_cache_dir = Path( + Path(self.img_files[0]).parent.as_posix() + "_npy", + ) + self.img_npy = [ + self.im_cache_dir / Path(f).with_suffix(".npy").name + for f in self.img_files + ] + self.im_cache_dir.mkdir(parents=True, exist_ok=True) + gb = 0 # Gigabytes of cached images + self.img_hw0, self.img_hw = [None] * n, [None] * n + results = ThreadPool(8).imap( + lambda x: load_image(*x), + zip(repeat(self), range(n)), + ) + pbar = tqdm(enumerate(results), total=n) + for i, x in pbar: + if cache_images == "disk": + if not self.img_npy[i].exists(): + np.save(self.img_npy[i].as_posix(), x[0]) + gb += self.img_npy[i].stat().st_size + else: + self.imgs[i], self.img_hw0[i], self.img_hw[i] = x + gb += self.imgs[i].nbytes + pbar.desc = f"{prefix}Caching images ({gb / 1E9:.1f}GB)" + pbar.close() + + def cache_labels(self, path=Path("./labels.cache"), prefix=""): + # Cache dataset labels, check images and read shapes + x = {} # dict + nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate + pbar = tqdm( + zip(self.img_files, self.label_files), + desc="Scanning images", + total=len(self.img_files), + ) + for i, (im_file, lb_file) in enumerate(pbar): + try: + # verify images + im = Image.open(im_file) + im.verify() # PIL verify + shape = exif_size(im) # image size + segments = [] # instance segments + assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels" + assert ( + im.format.lower() in img_formats + ), f"invalid image format {im.format}" + + # verify labels + if os.path.isfile(lb_file): + nf += 1 # label found + with open(lb_file) as f: + l = [x.split() for x in f.read().strip().splitlines()] + if any(len(x) > 8 for x in l): # is segment + classes = np.array([x[0] for x in l], dtype=np.float32) + segments = [ + np.array(x[1:], dtype=np.float32).reshape(-1, 2) + for x in l + ] # (cls, xy1...) + l = np.concatenate( + (classes.reshape(-1, 1), segments2boxes(segments)), + 1, + ) # (cls, xywh) + l = np.array(l, dtype=np.float32) + if len(l): + assert l.shape[1] == 5, "labels require 5 columns each" + assert (l >= 0).all(), "negative labels" + assert ( + l[:, 1:] <= 1 + ).all(), "non-normalized or out of bounds coordinate labels" + assert ( + np.unique(l, axis=0).shape[0] == l.shape[0] + ), "duplicate labels" + else: + ne += 1 # label empty + l = np.zeros((0, 5), dtype=np.float32) + else: + nm += 1 # label missing + l = np.zeros((0, 5), dtype=np.float32) + x[im_file] = [l, shape, segments] + except Exception as e: + nc += 1 + print( + f"{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}", + ) + + pbar.desc = ( + f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " + f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" + ) + pbar.close() + + if nf == 0: + print(f"{prefix}WARNING: No labels found in {path}. See {help_url}") + + x["hash"] = get_hash(self.label_files + self.img_files) + x["results"] = nf, nm, ne, nc, i + 1 + x["version"] = 0.1 # cache version + torch.save(x, path) # save for next time + logging.info(f"{prefix}New cache created: {path}") + return x + + def __len__(self): + return len(self.img_files) + + # def __iter__(self): + # self.count = -1 + # print('ran dataset iter') + # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) + # return self + + def __getitem__(self, index): + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp["mosaic"] + if mosaic: + # Load mosaic + if random.random() < 0.8: + img, labels = load_mosaic(self, index) + else: + img, labels = load_mosaic9(self, index) + shapes = None + + # MixUp https://arxiv.org/pdf/1710.09412.pdf + if random.random() < hyp["mixup"]: + if random.random() < 0.8: + img2, labels2 = load_mosaic( + self, + random.randint(0, len(self.labels) - 1), + ) + else: + img2, labels2 = load_mosaic9( + self, + random.randint(0, len(self.labels) - 1), + ) + r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0 + img = (img * r + img2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + + else: + # Load image + img, (h0, w0), (h, w) = load_image(self, index) + + # Letterbox + shape = ( + self.batch_shapes[self.batch[index]] if self.rect else self.img_size + ) # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy( + labels[:, 1:], + ratio[0] * w, + ratio[1] * h, + padw=pad[0], + padh=pad[1], + ) + + if self.augment: + # Augment imagespace + if not mosaic: + img, labels = random_perspective( + img, + labels, + degrees=hyp["degrees"], + translate=hyp["translate"], + scale=hyp["scale"], + shear=hyp["shear"], + perspective=hyp["perspective"], + ) + + # img, labels = self.albumentations(img, labels) + + # Augment colorspace + augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"]) + + # Apply cutouts + # if random.random() < 0.9: + # labels = cutout(img, labels) + + if random.random() < hyp["paste_in"]: + sample_labels, sample_images, sample_masks = [], [], [] + while len(sample_labels) < 30: + sample_labels_, sample_images_, sample_masks_ = load_samples( + self, + random.randint(0, len(self.labels) - 1), + ) + sample_labels += sample_labels_ + sample_images += sample_images_ + sample_masks += sample_masks_ + # print(len(sample_labels)) + if len(sample_labels) == 0: + break + labels = pastein( + img, + labels, + sample_labels, + sample_images, + sample_masks, + ) + + nL = len(labels) # number of labels + if nL: + labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh + labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1 + labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1 + + if self.augment: + # flip up-down + if random.random() < hyp["flipud"]: + img = np.flipud(img) + if nL: + labels[:, 2] = 1 - labels[:, 2] + + # flip left-right + if random.random() < hyp["fliplr"]: + img = np.fliplr(img) + if nL: + labels[:, 1] = 1 - labels[:, 1] + + labels_out = torch.zeros((nL, 6)) + if nL: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return torch.from_numpy(img), labels_out, self.img_files[index], shapes + + @staticmethod + def collate_fn(batch): + img, label, path, shapes = zip(*batch) # transposed + for i, l in enumerate(label): + l[:, 0] = i # add target image index for build_targets() + return torch.stack(img, 0), torch.cat(label, 0), path, shapes + + @staticmethod + def collate_fn4(batch): + img, label, path, shapes = zip(*batch) # transposed + n = len(shapes) // 4 + img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] + + ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]]) + wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]]) + s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale + for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW + i *= 4 + if random.random() < 0.5: + im = F.interpolate( + img[i].unsqueeze(0).float(), + scale_factor=2.0, + mode="bilinear", + align_corners=False, + )[0].type(img[i].type()) + l = label[i] + else: + im = torch.cat( + ( + torch.cat((img[i], img[i + 1]), 1), + torch.cat((img[i + 2], img[i + 3]), 1), + ), + 2, + ) + l = ( + torch.cat( + ( + label[i], + label[i + 1] + ho, + label[i + 2] + wo, + label[i + 3] + ho + wo, + ), + 0, + ) + * s + ) + img4.append(im) + label4.append(l) + + for i, l in enumerate(label4): + l[:, 0] = i # add target image index for build_targets() + + return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4 + + +# Ancillary functions -------------------------------------------------------------------------------------------------- +def load_image(self, index): + # loads 1 image from dataset, returns img, original hw, resized hw + img = self.imgs[index] + if img is None: # not cached + path = self.img_files[index] + img = cv2.imread(path) # BGR + assert img is not None, "Image Not Found " + path + h0, w0 = img.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # resize image to img_size + if r != 1: # always resize down, only resize up if training with augmentation + interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR + img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp) + return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized + else: + return ( + self.imgs[index], + self.img_hw0[index], + self.img_hw[index], + ) # img, hw_original, hw_resized + + +def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) + dtype = img.dtype # uint8 + + x = np.arange(0, 256, dtype=np.int16) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + img_hsv = cv2.merge( + (cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)), + ).astype(dtype) + cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed + + +def hist_equalize(img, clahe=True, bgr=False): + # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255 + yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) + if clahe: + c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + yuv[:, :, 0] = c.apply(yuv[:, :, 0]) + else: + yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram + return cv2.cvtColor( + yuv, + cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB, + ) # convert YUV image to RGB + + +def load_mosaic(self, index): + # loads images in a 4-mosaic + + labels4, segments4 = [], [] + s = self.img_size + yc, xc = ( + int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border + ) # mosaic center x, y + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img4 + if i == 0: # top left + img4 = np.full( + (s * 2, s * 2, img.shape[2]), + 114, + dtype=np.uint8, + ) # base image with 4 tiles + x1a, y1a, x2a, y2a = ( + max(xc - w, 0), + max(yc - h, 0), + xc, + yc, + ) # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = ( + w - (x2a - x1a), + h - (y2a - y1a), + w, + h, + ) # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy( + labels[:, 1:], + w, + h, + padw, + padh, + ) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + # img4, labels4, segments4 = remove_background(img4, labels4, segments4) + # sample_segments(img4, labels4, segments4, probability=self.hyp['copy_paste']) + img4, labels4, segments4 = copy_paste( + img4, + labels4, + segments4, + probability=self.hyp["copy_paste"], + ) + img4, labels4 = random_perspective( + img4, + labels4, + segments4, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove + + return img4, labels4 + + +def load_mosaic9(self, index): + # loads images in a 9-mosaic + + labels9, segments9 = [], [] + s = self.img_size + indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img9 + if i == 0: # center + img9 = np.full( + (s * 3, s * 3, img.shape[2]), + 114, + dtype=np.uint8, + ) # base image with 4 tiles + h0, w0 = h, w + c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates + elif i == 1: # top + c = s, s - h, s + w, s + elif i == 2: # top right + c = s + wp, s - h, s + wp + w, s + elif i == 3: # right + c = s + w0, s, s + w0 + w, s + h + elif i == 4: # bottom right + c = s + w0, s + hp, s + w0 + w, s + hp + h + elif i == 5: # bottom + c = s + w0 - w, s + h0, s + w0, s + h0 + h + elif i == 6: # bottom left + c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h + elif i == 7: # left + c = s - w, s + h0 - h, s, s + h0 + elif i == 8: # top left + c = s - w, s + h0 - hp - h, s, s + h0 - hp + + padx, pady = c[:2] + x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy( + labels[:, 1:], + w, + h, + padx, + pady, + ) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padx, pady) for x in segments] + labels9.append(labels) + segments9.extend(segments) + + # Image + img9[y1:y2, x1:x2] = img[y1 - pady :, x1 - padx :] # img9[ymin:ymax, xmin:xmax] + hp, wp = h, w # height, width previous + + # Offset + yc, xc = ( + int(random.uniform(0, s)) for _ in self.mosaic_border + ) # mosaic center x, y + img9 = img9[yc : yc + 2 * s, xc : xc + 2 * s] + + # Concat/clip labels + labels9 = np.concatenate(labels9, 0) + labels9[:, [1, 3]] -= xc + labels9[:, [2, 4]] -= yc + c = np.array([xc, yc]) # centers + segments9 = [x - c for x in segments9] + + for x in (labels9[:, 1:], *segments9): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img9, labels9 = replicate(img9, labels9) # replicate + + # Augment + # img9, labels9, segments9 = remove_background(img9, labels9, segments9) + img9, labels9, segments9 = copy_paste( + img9, + labels9, + segments9, + probability=self.hyp["copy_paste"], + ) + img9, labels9 = random_perspective( + img9, + labels9, + segments9, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove + + return img9, labels9 + + +def load_samples(self, index): + # loads images in a 4-mosaic + + labels4, segments4 = [], [] + s = self.img_size + yc, xc = ( + int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border + ) # mosaic center x, y + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img4 + if i == 0: # top left + img4 = np.full( + (s * 2, s * 2, img.shape[2]), + 114, + dtype=np.uint8, + ) # base image with 4 tiles + x1a, y1a, x2a, y2a = ( + max(xc - w, 0), + max(yc - h, 0), + xc, + yc, + ) # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = ( + w - (x2a - x1a), + h - (y2a - y1a), + w, + h, + ) # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy( + labels[:, 1:], + w, + h, + padw, + padh, + ) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + # img4, labels4, segments4 = remove_background(img4, labels4, segments4) + sample_labels, sample_images, sample_masks = sample_segments( + img4, + labels4, + segments4, + probability=0.5, + ) + + return sample_labels, sample_images, sample_masks + + +def copy_paste(img, labels, segments, probability=0.5): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + if probability and n: + h, w, c = img.shape # height, width, channels + im_new = np.zeros(img.shape, np.uint8) + for j in random.sample(range(n), k=round(probability * n)): + l, s = labels[j], segments[j] + box = w - l[3], l[2], w - l[1], l[4] + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + if (ioa < 0.30).all(): # allow 30% obscuration of existing labels + labels = np.concatenate((labels, [[l[0], *box]]), 0) + segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) + cv2.drawContours( + im_new, + [segments[j].astype(np.int32)], + -1, + (255, 255, 255), + cv2.FILLED, + ) + + result = cv2.bitwise_and(src1=img, src2=im_new) + result = cv2.flip(result, 1) # augment segments (flip left-right) + i = result > 0 # pixels to replace + # i[:, :] = result.max(2).reshape(h, w, 1) # act over ch + img[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug + + return img, labels, segments + + +def remove_background(img, labels, segments): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + h, w, c = img.shape # height, width, channels + im_new = np.zeros(img.shape, np.uint8) + img_new = np.ones(img.shape, np.uint8) * 114 + for j in range(n): + cv2.drawContours( + im_new, + [segments[j].astype(np.int32)], + -1, + (255, 255, 255), + cv2.FILLED, + ) + + result = cv2.bitwise_and(src1=img, src2=im_new) + + i = result > 0 # pixels to replace + img_new[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug + + return img_new, labels, segments + + +def sample_segments(img, labels, segments, probability=0.5): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + sample_labels = [] + sample_images = [] + sample_masks = [] + if probability and n: + h, w, c = img.shape # height, width, channels + for j in random.sample(range(n), k=round(probability * n)): + l, s = labels[j], segments[j] + box = ( + l[1].astype(int).clip(0, w - 1), + l[2].astype(int).clip(0, h - 1), + l[3].astype(int).clip(0, w - 1), + l[4].astype(int).clip(0, h - 1), + ) + + # print(box) + if (box[2] <= box[0]) or (box[3] <= box[1]): + continue + + sample_labels.append(l[0]) + + mask = np.zeros(img.shape, np.uint8) + + cv2.drawContours( + mask, + [segments[j].astype(np.int32)], + -1, + (255, 255, 255), + cv2.FILLED, + ) + sample_masks.append(mask[box[1] : box[3], box[0] : box[2], :]) + + result = cv2.bitwise_and(src1=img, src2=mask) + i = result > 0 # pixels to replace + mask[i] = result[i] # cv2.imwrite('debug.jpg', img) # debug + # print(box) + sample_images.append(mask[box[1] : box[3], box[0] : box[2], :]) + + return sample_labels, sample_images, sample_masks + + +def replicate(img, labels): + # Replicate labels + h, w = img.shape[:2] + boxes = labels[:, 1:].astype(int) + x1, y1, x2, y2 = boxes.T + s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) + for i in s.argsort()[: round(s.size * 0.5)]: # smallest indices + x1b, y1b, x2b, y2b = boxes[i] + bh, bw = y2b - y1b, x2b - x1b + yc, xc = int(random.uniform(0, h - bh)), int( + random.uniform(0, w - bw), + ) # offset x, y + x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] + img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) + + return img, labels + + +def letterbox( + img, + new_shape=(640, 640), + color=(114, 114, 114), + auto=True, + scaleFill=False, + scaleup=True, + stride=32, +): + # Resize and pad image while meeting stride-multiple constraints + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder( + img, + top, + bottom, + left, + right, + cv2.BORDER_CONSTANT, + value=color, + ) # add border + return img, ratio, (dw, dh) + + +def random_perspective( + img, + targets=(), + segments=(), + degrees=10, + translate=0.1, + scale=0.1, + shear=10, + perspective=0.0, + border=(0, 0), +): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + height = img.shape[0] + border[0] * 2 # shape(h,w,c) + width = img.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -img.shape[1] / 2 # x translation (pixels) + C[1, 2] = -img.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1.1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = ( + random.uniform(0.5 - translate, 0.5 + translate) * width + ) # x translation (pixels) + T[1, 2] = ( + random.uniform(0.5 - translate, 0.5 + translate) * height + ) # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (np.eye(3) != M).any(): # image changed + if perspective: + img = cv2.warpPerspective( + img, + M, + dsize=(width, height), + borderValue=(114, 114, 114), + ) + else: # affine + img = cv2.warpAffine( + img, + M[:2], + dsize=(width, height), + borderValue=(114, 114, 114), + ) + + # Visualize + # import matplotlib.pyplot as plt + # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() + # ax[0].imshow(img[:, :, ::-1]) # base + # ax[1].imshow(img2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + if n: + use_segments = any(x.any() for x in segments) + new = np.zeros((n, 4)) + if use_segments: # warp segments + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = ( + xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] + ) # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + + else: # warp boxes + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape( + n * 4, + 2, + ) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape( + n, + 8, + ) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + new = ( + np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + ) + + # clip + new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) + new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) + + # filter candidates + i = box_candidates( + box1=targets[:, 1:5].T * s, + box2=new.T, + area_thr=0.01 if use_segments else 0.10, + ) + targets = targets[i] + targets[:, 1:5] = new[i] + + return img, targets + + +def box_candidates( + box1, + box2, + wh_thr=2, + ar_thr=20, + area_thr=0.1, + eps=1e-16, +): # box1(4,n), box2(4,n) + # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio + return ( + (w2 > wh_thr) + & (h2 > wh_thr) + & (w2 * h2 / (w1 * h1 + eps) > area_thr) + & (ar < ar_thr) + ) # candidates + + +def bbox_ioa(box1, box2): + # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 + box2 = box2.transpose() + + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + + # Intersection area + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * ( + np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1) + ).clip(0) + + # box2 area + box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 + + # Intersection over box2 area + return inter_area / box2_area + + +def cutout(image, labels): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + h, w = image.shape[:2] + + # create random masks + scales = ( + [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 + ) # image size fraction + for s in scales: + mask_h = random.randint(1, int(h * s)) + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] + + # return unobscured labels + if len(labels) and s > 0.03: + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + labels = labels[ioa < 0.60] # remove >60% obscured labels + + return labels + + +def pastein(image, labels, sample_labels, sample_images, sample_masks): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + h, w = image.shape[:2] + + # create random masks + scales = ( + [0.75] * 2 + [0.5] * 4 + [0.25] * 4 + [0.125] * 4 + [0.0625] * 6 + ) # image size fraction + for s in scales: + if random.random() < 0.2: + continue + mask_h = random.randint(1, int(h * s)) + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + if len(labels): + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + else: + ioa = np.zeros(1) + + if ( + (ioa < 0.30).all() + and len(sample_labels) + and (xmax > xmin + 20) + and (ymax > ymin + 20) + ): # allow 30% obscuration of existing labels + sel_ind = random.randint(0, len(sample_labels) - 1) + # print(len(sample_labels)) + # print(sel_ind) + # print((xmax-xmin, ymax-ymin)) + # print(image[ymin:ymax, xmin:xmax].shape) + # print([[sample_labels[sel_ind], *box]]) + # print(labels.shape) + hs, ws, cs = sample_images[sel_ind].shape + r_scale = min((ymax - ymin) / hs, (xmax - xmin) / ws) + r_w = int(ws * r_scale) + r_h = int(hs * r_scale) + + if (r_w > 10) and (r_h > 10): + r_mask = cv2.resize(sample_masks[sel_ind], (r_w, r_h)) + r_image = cv2.resize(sample_images[sel_ind], (r_w, r_h)) + temp_crop = image[ymin : ymin + r_h, xmin : xmin + r_w] + m_ind = r_mask > 0 + if m_ind.astype(np.int32).sum() > 60: + temp_crop[m_ind] = r_image[m_ind] + # print(sample_labels[sel_ind]) + # print(sample_images[sel_ind].shape) + # print(temp_crop.shape) + box = np.array( + [xmin, ymin, xmin + r_w, ymin + r_h], + dtype=np.float32, + ) + if len(labels): + labels = np.concatenate( + (labels, [[sample_labels[sel_ind], *box]]), + 0, + ) + else: + labels = np.array([[sample_labels[sel_ind], *box]]) + + image[ymin : ymin + r_h, xmin : xmin + r_w] = temp_crop + + return labels + + +class Albumentations: + # YOLOv5 Albumentations class (optional, only used if package is installed) + def __init__(self): + self.transform = None + import albumentations as A + + self.transform = A.Compose( + [ + A.CLAHE(p=0.01), + A.RandomBrightnessContrast( + brightness_limit=0.2, + contrast_limit=0.2, + p=0.01, + ), + A.RandomGamma(gamma_limit=[80, 120], p=0.01), + A.Blur(p=0.01), + A.MedianBlur(p=0.01), + A.ToGray(p=0.01), + A.ImageCompression(quality_lower=75, p=0.01), + ], + bbox_params=A.BboxParams( + format="pascal_voc", + label_fields=["class_labels"], + ), + ) + + # logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p)) + + def __call__(self, im, labels, p=1.0): + if self.transform and random.random() < p: + new = self.transform( + image=im, + bboxes=labels[:, 1:], + class_labels=labels[:, 0], + ) # transformed + im, labels = new["image"], np.array( + [[c, *b] for c, b in zip(new["class_labels"], new["bboxes"])], + ) + return im, labels + + +def create_folder(path="./new"): + # Create folder + if os.path.exists(path): + shutil.rmtree(path) # delete output folder + os.makedirs(path) # make new output folder + + +def flatten_recursive(path="../coco"): + # Flatten a recursive directory by bringing all files to top level + new_path = Path(path + "_flat") + create_folder(new_path) + for file in tqdm(glob.glob(str(Path(path)) + "/**/*.*", recursive=True)): + shutil.copyfile(file, new_path / Path(file).name) + + +def extract_boxes( + path="../coco/", +): # from utils.datasets import *; extract_boxes('../coco128') + # Convert detection dataset into classification dataset, with one directory per class + + path = Path(path) # images dir + shutil.rmtree(path / "classifier") if ( + path / "classifier" + ).is_dir() else None # remove existing + files = list(path.rglob("*.*")) + n = len(files) # number of files + for im_file in tqdm(files, total=n): + if im_file.suffix[1:] in img_formats: + # image + im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB + h, w = im.shape[:2] + + # labels + lb_file = Path(img2label_paths([str(im_file)])[0]) + if Path(lb_file).exists(): + with open(lb_file) as f: + lb = np.array( + [x.split() for x in f.read().strip().splitlines()], + dtype=np.float32, + ) # labels + + for j, x in enumerate(lb): + c = int(x[0]) # class + f = ( + (path / "classifier") + / f"{c}" + / f"{path.stem}_{im_file.stem}_{j}.jpg" + ) # new filename + if not f.parent.is_dir(): + f.parent.mkdir(parents=True) + + b = x[1:] * [w, h, w, h] # box + # b[2:] = b[2:].max() # rectangle to square + b[2:] = b[2:] * 1.2 + 3 # pad + b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int) + + b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image + b[[1, 3]] = np.clip(b[[1, 3]], 0, h) + assert cv2.imwrite( + str(f), + im[b[1] : b[3], b[0] : b[2]], + ), f"box failure in {f}" + + +def autosplit(path="../coco", weights=(0.9, 0.1, 0.0), annotated_only=False): + """Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files + Usage: from utils.datasets import *; autosplit('../coco') + Arguments + path: Path to images directory + weights: Train, val, test weights (list) + annotated_only: Only use images with an annotated txt file + """ + path = Path(path) # images dir + files = sum( + [list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], + [], + ) # image files only + n = len(files) # number of files + indices = random.choices( + [0, 1, 2], + weights=weights, + k=n, + ) # assign each image to a split + + txt = [ + "autosplit_train.txt", + "autosplit_val.txt", + "autosplit_test.txt", + ] # 3 txt files + [(path / x).unlink() for x in txt if (path / x).exists()] # remove existing + + print( + f"Autosplitting images from {path}" + + ", using *.txt labeled images only" * annotated_only, + ) + for i, img in tqdm(zip(indices, files), total=n): + if ( + not annotated_only or Path(img2label_paths([str(img)])[0]).exists() + ): # check label + with open(path / txt[i], "a") as f: + f.write(str(img) + "\n") # add image to txt file + + +def load_segmentations(self, index): + key = "/work/handsomejw66/coco17/" + self.img_files[index] + # print(key) + # /work/handsomejw66/coco17/ + return self.segs[key] diff --git a/mil_common/perception/yoloros/src/yoloros/utils/general.py b/mil_common/perception/yoloros/src/yoloros/utils/general.py new file mode 100644 index 000000000..fb74b448c --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/general.py @@ -0,0 +1,1160 @@ +# YOLOR general utils + +import glob +import logging +import math +import os +import platform +import random +import re +import subprocess +import time +from pathlib import Path + +import cv2 +import numpy as np +import pandas as pd +import torch +import torchvision +import yaml +from utils.google_utils import gsutil_getsize +from utils.metrics import fitness +from utils.torch_utils import init_torch_seeds + +# Settings +torch.set_printoptions(linewidth=320, precision=5, profile="long") +np.set_printoptions( + linewidth=320, + formatter={"float_kind": "{:11.5g}".format}, +) # format short g, %precision=5 +pd.options.display.max_columns = 10 +cv2.setNumThreads( + 0, +) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) +os.environ["NUMEXPR_MAX_THREADS"] = str(min(os.cpu_count(), 8)) # NumExpr max threads + + +def set_logging(rank=-1): + logging.basicConfig( + format="%(message)s", + level=logging.INFO if rank in [-1, 0] else logging.WARN, + ) + + +def init_seeds(seed=0): + # Initialize random number generator (RNG) seeds + random.seed(seed) + np.random.seed(seed) + init_torch_seeds(seed) + + +def get_latest_run(search_dir="."): + # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) + last_list = glob.glob(f"{search_dir}/**/last*.pt", recursive=True) + return max(last_list, key=os.path.getctime) if last_list else "" + + +def isdocker(): + # Is environment a Docker container + return Path("/workspace").exists() # or Path('/.dockerenv').exists() + + +def emojis(str=""): + # Return platform-dependent emoji-safe version of string + return ( + str.encode().decode("ascii", "ignore") + if platform.system() == "Windows" + else str + ) + + +def check_online(): + # Check internet connectivity + import socket + + try: + socket.create_connection(("1.1.1.1", 443), 5) # check host accesability + return True + except OSError: + return False + + +def check_git_status(): + # Recommend 'git pull' if code is out of date + print(colorstr("github: "), end="") + try: + assert Path(".git").exists(), "skipping check (not a git repository)" + assert not isdocker(), "skipping check (Docker image)" + assert check_online(), "skipping check (offline)" + + cmd = "git fetch && git config --get remote.origin.url" + url = ( + subprocess.check_output(cmd, shell=True).decode().strip().rstrip(".git") + ) # github repo url + branch = ( + subprocess.check_output("git rev-parse --abbrev-ref HEAD", shell=True) + .decode() + .strip() + ) # checked out + n = int( + subprocess.check_output( + f"git rev-list {branch}..origin/master --count", + shell=True, + ), + ) # commits behind + if n > 0: + s = ( + f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " + f"Use 'git pull' to update or 'git clone {url}' to download latest." + ) + else: + s = f"up to date with {url} ✅" + print(emojis(s)) # emoji-safe + except Exception as e: + print(e) + + +def check_requirements(requirements="requirements.txt", exclude=()): + # Check installed dependencies meet requirements (pass *.txt file or list of packages) + import pkg_resources as pkg + + prefix = colorstr("red", "bold", "requirements:") + if isinstance(requirements, (str, Path)): # requirements.txt file + file = Path(requirements) + if not file.exists(): + print(f"{prefix} {file.resolve()} not found, check failed.") + return + requirements = [ + f"{x.name}{x.specifier}" + for x in pkg.parse_requirements(file.open()) + if x.name not in exclude + ] + else: # list or tuple of packages + requirements = [x for x in requirements if x not in exclude] + + n = 0 # number of packages updates + for r in requirements: + try: + pkg.require(r) + except ( + Exception + ) as e: # DistributionNotFound or VersionConflict if requirements not met + n += 1 + print( + f"{prefix} {e.req} not found and is required by YOLOR, attempting auto-update...", + ) + print( + subprocess.check_output(f"pip install '{e.req}'", shell=True).decode(), + ) + + if n: # if packages updated + source = file.resolve() if "file" in locals() else requirements + s = ( + f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" + f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" + ) + print(emojis(s)) # emoji-safe + + +def check_img_size(img_size, s=32): + # Verify img_size is a multiple of stride s + new_size = make_divisible(img_size, int(s)) # ceil gs-multiple + if new_size != img_size: + print( + "WARNING: --img-size {:g} must be multiple of max stride {:g}, updating to {:g}".format( + img_size, + s, + new_size, + ), + ) + return new_size + + +def check_imshow(): + # Check if environment supports image displays + try: + assert not isdocker(), "cv2.imshow() is disabled in Docker environments" + cv2.imshow("test", np.zeros((1, 1, 3))) + cv2.waitKey(1) + cv2.destroyAllWindows() + cv2.waitKey(1) + return True + except Exception as e: + print( + f"WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}", + ) + return False + + +def check_file(file): + # Search for file if not found + if Path(file).is_file() or file == "": + return file + else: + files = glob.glob("./**/" + file, recursive=True) # find file + assert len(files), f"File Not Found: {file}" # assert file was found + assert ( + len(files) == 1 + ), f"Multiple files match '{file}', specify exact path: {files}" # assert unique + return files[0] # return file + + +def check_dataset(dict): + # Download dataset if not found locally + val, s = dict.get("val"), dict.get("download") + if val and len(val): + val = [ + Path(x).resolve() for x in (val if isinstance(val, list) else [val]) + ] # val path + if not all(x.exists() for x in val): + print( + "\nWARNING: Dataset not found, nonexistent paths: %s" + % [str(x) for x in val if not x.exists()], + ) + if s and len(s): # download script + print("Downloading %s ..." % s) + if s.startswith("http") and s.endswith(".zip"): # URL + f = Path(s).name # filename + torch.hub.download_url_to_file(s, f) + r = os.system(f"unzip -q {f} -d ../ && rm {f}") # unzip + else: # bash script + r = os.system(s) + print( + "Dataset autodownload %s\n" % ("success" if r == 0 else "failure"), + ) # analyze return value + else: + raise Exception("Dataset not found.") + + +def make_divisible(x, divisor): + # Returns x evenly divisible by divisor + return math.ceil(x / divisor) * divisor + + +def clean_str(s): + # Cleans a string by replacing special characters with underscore _ + return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨`><+]", repl="_", string=s) + + +def one_cycle(y1=0.0, y2=1.0, steps=100): + # lambda function for sinusoidal ramp from y1 to y2 + return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 + + +def colorstr(*input): + # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') + *args, string = ( + input if len(input) > 1 else ("blue", "bold", input[0]) + ) # color arguments, string + colors = { + "black": "\033[30m", # basic colors + "red": "\033[31m", + "green": "\033[32m", + "yellow": "\033[33m", + "blue": "\033[34m", + "magenta": "\033[35m", + "cyan": "\033[36m", + "white": "\033[37m", + "bright_black": "\033[90m", # bright colors + "bright_red": "\033[91m", + "bright_green": "\033[92m", + "bright_yellow": "\033[93m", + "bright_blue": "\033[94m", + "bright_magenta": "\033[95m", + "bright_cyan": "\033[96m", + "bright_white": "\033[97m", + "end": "\033[0m", # misc + "bold": "\033[1m", + "underline": "\033[4m", + } + return "".join(colors[x] for x in args) + f"{string}" + colors["end"] + + +def labels_to_class_weights(labels, nc=80): + # Get class weights (inverse frequency) from training labels + if labels[0] is None: # no labels loaded + return torch.Tensor() + + labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO + classes = labels[:, 0].astype(np.int32) # labels = [class xywh] + weights = np.bincount(classes, minlength=nc) # occurrences per class + + # Prepend gridpoint count (for uCE training) + # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image + # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start + + weights[weights == 0] = 1 # replace empty bins with 1 + weights = 1 / weights # number of targets per class + weights /= weights.sum() # normalize + return torch.from_numpy(weights) + + +def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): + # Produces image weights based on class_weights and image contents + class_counts = np.array( + [np.bincount(x[:, 0].astype(np.int32), minlength=nc) for x in labels], + ) + image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1) + # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample + return image_weights + + +def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) + # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ + # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') + # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') + # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco + # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet + x = [ + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 27, + 28, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 67, + 70, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + ] + return x + + +def xyxy2xywh(x): + # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center + y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center + y[:, 2] = x[:, 2] - x[:, 0] # width + y[:, 3] = x[:, 3] - x[:, 1] # height + return y + + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): + # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x + y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y + y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x + y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y + return y + + +def xyn2xy(x, w=640, h=640, padw=0, padh=0): + # Convert normalized segments into pixel segments, shape (n,2) + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * x[:, 0] + padw # top left x + y[:, 1] = h * x[:, 1] + padh # top left y + return y + + +def segment2box(segment, width=640, height=640): + # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) + x, y = segment.T # segment xy + inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) + ( + x, + y, + ) = ( + x[inside], + y[inside], + ) + return ( + np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) + ) # xyxy + + +def segments2boxes(segments): + # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) + boxes = [] + for s in segments: + x, y = s.T # segment xy + boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy + return xyxy2xywh(np.array(boxes)) # cls, xywh + + +def resample_segments(segments, n=1000): + # Up-sample an (n,2) segment + for i, s in enumerate(segments): + s = np.concatenate((s, s[0:1, :]), axis=0) + x = np.linspace(0, len(s) - 1, n) + xp = np.arange(len(s)) + segments[i] = ( + np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]) + .reshape(2, -1) + .T + ) # segment xy + return segments + + +def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min( + img1_shape[0] / img0_shape[0], + img1_shape[1] / img0_shape[1], + ) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, ( + img1_shape[0] - img0_shape[0] * gain + ) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2]] -= pad[0] # x padding + coords[:, [1, 3]] -= pad[1] # y padding + coords[:, :4] /= gain + clip_coords(coords, img0_shape) + return coords + + +def clip_coords(boxes, img_shape): + # Clip bounding xyxy bounding boxes to image shape (height, width) + boxes[:, 0].clamp_(0, img_shape[1]) # x1 + boxes[:, 1].clamp_(0, img_shape[0]) # y1 + boxes[:, 2].clamp_(0, img_shape[1]) # x2 + boxes[:, 3].clamp_(0, img_shape[0]) # y2 + + +def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # Intersection area + inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * ( + torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1) + ).clamp(0) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + iou = inter / union + + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min( + b1_x1, + b2_x1, + ) # convex (smallest enclosing box) width + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw**2 + ch**2 + eps # convex diagonal squared + rho2 = ( + (b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2 + ) / 4 # center distance squared + if DIoU: + return iou - rho2 / c2 # DIoU + elif ( + CIoU + ): # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi**2) * torch.pow( + torch.atan(w2 / (h2 + eps)) - torch.atan(w1 / (h1 + eps)), + 2, + ) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU + else: + return iou # IoU + + +def bbox_alpha_iou( + box1, + box2, + x1y1x2y2=False, + GIoU=False, + DIoU=False, + CIoU=False, + alpha=2, + eps=1e-9, +): + # Returns tsqrt_he IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # Intersection area + inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * ( + torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1) + ).clamp(0) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + # change iou into pow(iou+eps) + # iou = inter / union + iou = torch.pow(inter / union + eps, alpha) + # beta = 2 * alpha + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min( + b1_x1, + b2_x1, + ) # convex (smallest enclosing box) width + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = (cw**2 + ch**2) ** alpha + eps # convex diagonal + rho_x = torch.abs(b2_x1 + b2_x2 - b1_x1 - b1_x2) + rho_y = torch.abs(b2_y1 + b2_y2 - b1_y1 - b1_y2) + rho2 = ((rho_x**2 + rho_y**2) / 4) ** alpha # center distance + if DIoU: + return iou - rho2 / c2 # DIoU + elif ( + CIoU + ): # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi**2) * torch.pow( + torch.atan(w2 / h2) - torch.atan(w1 / h1), + 2, + ) + with torch.no_grad(): + alpha_ciou = v / ((1 + eps) - inter / union + v) + # return iou - (rho2 / c2 + v * alpha_ciou) # CIoU + return iou - ( + rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha) + ) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + # c_area = cw * ch + eps # convex area + # return iou - (c_area - union) / c_area # GIoU + c_area = torch.max(cw * ch + eps, union) # convex area + return iou - torch.pow((c_area - union) / c_area + eps, alpha) # GIoU + else: + return iou # torch.log(iou+eps) or iou + + +def box_iou(box1, box2): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + inter = ( + ( + torch.min(box1[:, None, 2:], box2[:, 2:]) + - torch.max(box1[:, None, :2], box2[:, :2]) + ) + .clamp(0) + .prod(2) + ) + return inter / ( + area1[:, None] + area2 - inter + ) # iou = inter / (area1 + area2 - inter) + + +def wh_iou(wh1, wh2): + # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 + wh1 = wh1[:, None] # [N,1,2] + wh2 = wh2[None] # [1,M,2] + inter = torch.min(wh1, wh2).prod(2) # [N,M] + return inter / ( + wh1.prod(2) + wh2.prod(2) - inter + ) # iou = inter / (area1 + area2 - inter) + + +def box_giou(box1, box2): + """ + Return generalized intersection-over-union (Jaccard index) between two sets of boxes. + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise generalized IoU values + for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + inter = ( + ( + torch.min(box1[:, None, 2:], box2[:, 2:]) + - torch.max(box1[:, None, :2], box2[:, :2]) + ) + .clamp(0) + .prod(2) + ) + union = area1[:, None] + area2 - inter + + iou = inter / union + + lti = torch.min(box1[:, None, :2], box2[:, :2]) + rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) + + whi = (rbi - lti).clamp(min=0) # [N,M,2] + areai = whi[:, :, 0] * whi[:, :, 1] + + return iou - (areai - union) / areai + + +def box_ciou(box1, box2, eps: float = 1e-7): + """ + Return complete intersection-over-union (Jaccard index) between two sets of boxes. + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + eps (float, optional): small number to prevent division by zero. Default: 1e-7 + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise complete IoU values + for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + inter = ( + ( + torch.min(box1[:, None, 2:], box2[:, 2:]) + - torch.max(box1[:, None, :2], box2[:, :2]) + ) + .clamp(0) + .prod(2) + ) + union = area1[:, None] + area2 - inter + + iou = inter / union + + lti = torch.min(box1[:, None, :2], box2[:, :2]) + rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) + + whi = (rbi - lti).clamp(min=0) # [N,M,2] + diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps + + # centers of boxes + x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2 + y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2 + x_g = (box2[:, 0] + box2[:, 2]) / 2 + y_g = (box2[:, 1] + box2[:, 3]) / 2 + # The distance between boxes' centers squared. + centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2 + + w_pred = box1[:, None, 2] - box1[:, None, 0] + h_pred = box1[:, None, 3] - box1[:, None, 1] + + w_gt = box2[:, 2] - box2[:, 0] + h_gt = box2[:, 3] - box2[:, 1] + + v = (4 / (torch.pi**2)) * torch.pow( + (torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), + 2, + ) + with torch.no_grad(): + alpha = v / (1 - iou + v + eps) + return iou - (centers_distance_squared / diagonal_distance_squared) - alpha * v + + +def box_diou(box1, box2, eps: float = 1e-7): + """ + Return distance intersection-over-union (Jaccard index) between two sets of boxes. + Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with + ``0 <= x1 < x2`` and ``0 <= y1 < y2``. + Args: + boxes1 (Tensor[N, 4]): first set of boxes + boxes2 (Tensor[M, 4]): second set of boxes + eps (float, optional): small number to prevent division by zero. Default: 1e-7 + Returns: + Tensor[N, M]: the NxM matrix containing the pairwise distance IoU values + for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + inter = ( + ( + torch.min(box1[:, None, 2:], box2[:, 2:]) + - torch.max(box1[:, None, :2], box2[:, :2]) + ) + .clamp(0) + .prod(2) + ) + union = area1[:, None] + area2 - inter + + iou = inter / union + + lti = torch.min(box1[:, None, :2], box2[:, :2]) + rbi = torch.max(box1[:, None, 2:], box2[:, 2:]) + + whi = (rbi - lti).clamp(min=0) # [N,M,2] + diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps + + # centers of boxes + x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2 + y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2 + x_g = (box2[:, 0] + box2[:, 2]) / 2 + y_g = (box2[:, 1] + box2[:, 3]) / 2 + # The distance between boxes' centers squared. + centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2 + + # The distance IoU is the IoU penalized by a normalized + # distance between boxes' centers squared. + return iou - (centers_distance_squared / diagonal_distance_squared) + + +def non_max_suppression( + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + multi_label=False, + labels=(), +): + """Runs Non-Maximum Suppression (NMS) on inference results + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + + nc = prediction.shape[2] - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_det = 300 # maximum number of detections per image + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + if nc == 1: + x[:, 5:] = x[ + :, + 4:5, + ] # for models with one class, cls_loss is 0 and cls_conf is always 0.5, + # so there is no need to multiplicate. + else: + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum( + 1, + keepdim=True, + ) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if (time.time() - t) > time_limit: + print(f"WARNING: NMS time limit {time_limit}s exceeded") + break # time limit exceeded + + return output + + +def non_max_suppression_kpt( + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + multi_label=False, + labels=(), + kpt_label=False, + nc=None, + nkpt=None, +): + """Runs Non-Maximum Suppression (NMS) on inference results + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + if nc is None: + nc = ( + prediction.shape[2] - 5 if not kpt_label else prediction.shape[2] - 56 + ) # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_det = 300 # maximum number of detections per image + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5 : 5 + nc] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + if not kpt_label: + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + else: + kpts = x[:, 6:] + conf, j = x[:, 5:6].max(1, keepdim=True) + x = torch.cat((box, conf, j.float(), kpts), 1)[ + conf.view(-1) > conf_thres + ] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum( + 1, + keepdim=True, + ) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if (time.time() - t) > time_limit: + print(f"WARNING: NMS time limit {time_limit}s exceeded") + break # time limit exceeded + + return output + + +def strip_optimizer( + f="best.pt", + s="", +): # from utils.general import *; strip_optimizer() + # Strip optimizer from 'f' to finalize training, optionally save as 's' + x = torch.load(f, map_location=torch.device("cpu")) + if x.get("ema"): + x["model"] = x["ema"] # replace model with ema + for k in "optimizer", "training_results", "wandb_id", "ema", "updates": # keys + x[k] = None + x["epoch"] = -1 + x["model"].half() # to FP16 + for p in x["model"].parameters(): + p.requires_grad = False + torch.save(x, s or f) + mb = os.path.getsize(s or f) / 1e6 # filesize + print( + f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB", + ) + + +def print_mutation(hyp, results, yaml_file="hyp_evolved.yaml", bucket=""): + # Print mutation results to evolve.txt (for use with train.py --evolve) + a = "%10s" * len(hyp) % tuple(hyp.keys()) # hyperparam keys + b = "%10.3g" * len(hyp) % tuple(hyp.values()) # hyperparam values + c = ( + "%10.4g" * len(results) % results + ) # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) + print(f"\n{a}\n{b}\nEvolved fitness: {c}\n") + + if bucket: + url = "gs://%s/evolve.txt" % bucket + if gsutil_getsize(url) > ( + os.path.getsize("evolve.txt") if os.path.exists("evolve.txt") else 0 + ): + os.system( + "gsutil cp %s ." % url, + ) # download evolve.txt if larger than local + + with open("evolve.txt", "a") as f: # append result + f.write(c + b + "\n") + x = np.unique(np.loadtxt("evolve.txt", ndmin=2), axis=0) # load unique rows + x = x[np.argsort(-fitness(x))] # sort + np.savetxt("evolve.txt", x, "%10.3g") # save sort by fitness + + # Save yaml + for i, k in enumerate(hyp.keys()): + hyp[k] = float(x[0, i + 7]) + with open(yaml_file, "w") as f: + results = tuple(x[0, :7]) + c = ( + "%10.4g" * len(results) % results + ) # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) + f.write( + "# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: " + % len(x) + + c + + "\n\n", + ) + yaml.dump(hyp, f, sort_keys=False) + + if bucket: + os.system(f"gsutil cp evolve.txt {yaml_file} gs://{bucket}") # upload + + +def apply_classifier(x, model, img, im0): + # applies a second stage classifier to yolo outputs + im0 = [im0] if isinstance(im0, np.ndarray) else im0 + for i, d in enumerate(x): # per image + if d is not None and len(d): + d = d.clone() + + # Reshape and pad cutouts + b = xyxy2xywh(d[:, :4]) # boxes + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square + b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad + d[:, :4] = xywh2xyxy(b).long() + + # Rescale boxes from img_size to im0 size + scale_coords(img.shape[2:], d[:, :4], im0[i].shape) + + # Classes + pred_cls1 = d[:, 5].long() + ims = [] + for j, a in enumerate(d): # per item + cutout = im0[i][int(a[1]) : int(a[3]), int(a[0]) : int(a[2])] + im = cv2.resize(cutout, (224, 224)) # BGR + # cv2.imwrite('test%i.jpg' % j, cutout) + + im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 + im /= 255.0 # 0 - 255 to 0.0 - 1.0 + ims.append(im) + + pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax( + 1, + ) # classifier prediction + x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections + + return x + + +def increment_path(path, exist_ok=True, sep=""): + # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc. + path = Path(path) # os-agnostic + if (path.exists() and exist_ok) or (not path.exists()): + return str(path) + else: + dirs = glob.glob(f"{path}{sep}*") # similar paths + matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs] + i = [int(m.groups()[0]) for m in matches if m] # indices + n = max(i) + 1 if i else 2 # increment number + return f"{path}{sep}{n}" # update path diff --git a/mil_common/perception/yoloros/src/yoloros/utils/google_app_engine/Dockerfile b/mil_common/perception/yoloros/src/yoloros/utils/google_app_engine/Dockerfile new file mode 100644 index 000000000..0155618f4 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/google_app_engine/Dockerfile @@ -0,0 +1,25 @@ +FROM gcr.io/google-appengine/python + +# Create a virtualenv for dependencies. This isolates these packages from +# system-level packages. +# Use -p python3 or -p python3.7 to select python version. Default is version 2. +RUN virtualenv /env -p python3 + +# Setting these environment variables are the same as running +# source /env/bin/activate. +ENV VIRTUAL_ENV /env +ENV PATH /env/bin:$PATH + +RUN apt-get update && apt-get install -y python-opencv + +# Copy the application's requirements.txt and run pip to install all +# dependencies into the virtualenv. +ADD requirements.txt /app/requirements.txt +RUN pip install -r /app/requirements.txt + +# Add the application source code. +ADD . /app + +# Run a WSGI server to serve the application. gunicorn must be declared as +# a dependency in requirements.txt. +CMD gunicorn -b :$PORT main:app diff --git a/mil_common/perception/yoloros/src/yoloros/utils/google_app_engine/additional_requirements.txt b/mil_common/perception/yoloros/src/yoloros/utils/google_app_engine/additional_requirements.txt new file mode 100644 index 000000000..5fcc30524 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/google_app_engine/additional_requirements.txt @@ -0,0 +1,4 @@ +# add these requirements in your app on top of the existing ones +pip==18.1 +Flask==1.0.2 +gunicorn==19.9.0 diff --git a/mil_common/perception/yoloros/src/yoloros/utils/google_app_engine/app.yaml b/mil_common/perception/yoloros/src/yoloros/utils/google_app_engine/app.yaml new file mode 100644 index 000000000..61bfcdfe7 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/google_app_engine/app.yaml @@ -0,0 +1,14 @@ +runtime: custom +env: flex + +service: yolorapp + +liveness_check: + initial_delay_sec: 600 + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 4 + disk_size_gb: 20 diff --git a/mil_common/perception/yoloros/src/yoloros/utils/google_utils.py b/mil_common/perception/yoloros/src/yoloros/utils/google_utils.py new file mode 100644 index 000000000..9a0898f12 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/google_utils.py @@ -0,0 +1,140 @@ +# Google utils: https://cloud.google.com/storage/docs/reference/libraries + +import os +import platform +import subprocess +import time +from pathlib import Path + +import requests +import torch + + +def gsutil_getsize(url=""): + # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du + s = subprocess.check_output(f"gsutil du {url}", shell=True).decode("utf-8") + return eval(s.split(" ")[0]) if len(s) else 0 # bytes + + +def attempt_download(file, repo="WongKinYiu/yolov7"): + # Attempt file download if does not exist + file = Path(str(file).strip().replace("'", "").lower()) + + if not file.exists(): + try: + response = requests.get( + f"https://api.github.com/repos/{repo}/releases/latest", + ).json() # github api + assets = [x["name"] for x in response["assets"]] # release assets + tag = response["tag_name"] # i.e. 'v1.0' + except: # fallback plan + assets = [ + "yolov7.pt", + "yolov7-tiny.pt", + "yolov7x.pt", + "yolov7-d6.pt", + "yolov7-e6.pt", + "yolov7-e6e.pt", + "yolov7-w6.pt", + ] + tag = subprocess.check_output("git tag", shell=True).decode().split()[-1] + + name = file.name + if name in assets: + msg = f"{file} missing, try downloading from https://github.com/{repo}/releases/" + redundant = False # second download option + try: # GitHub + url = f"https://github.com/{repo}/releases/download/{tag}/{name}" + print(f"Downloading {url} to {file}...") + torch.hub.download_url_to_file(url, file) + assert file.exists() and file.stat().st_size > 1e6 # check + except Exception as e: # GCP + print(f"Download error: {e}") + assert redundant, "No secondary mirror" + url = f"https://storage.googleapis.com/{repo}/ckpt/{name}" + print(f"Downloading {url} to {file}...") + os.system( + f"curl -L {url} -o {file}", + ) # torch.hub.download_url_to_file(url, weights) + finally: + if not file.exists() or file.stat().st_size < 1e6: # check + file.unlink(missing_ok=True) # remove partial downloads + print(f"ERROR: Download failure: {msg}") + print("") + return + + +def gdrive_download(id="", file="tmp.zip"): + # Downloads a file from Google Drive. from yolov7.utils.google_utils import *; gdrive_download() + t = time.time() + file = Path(file) + cookie = Path("cookie") # gdrive cookie + print( + f"Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ", + end="", + ) + file.unlink(missing_ok=True) # remove existing file + cookie.unlink(missing_ok=True) # remove existing cookie + + # Attempt file download + out = "NUL" if platform.system() == "Windows" else "/dev/null" + os.system( + f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}', + ) + if os.path.exists("cookie"): # large file + s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' + else: # small file + s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' + r = os.system(s) # execute, capture return + cookie.unlink(missing_ok=True) # remove existing cookie + + # Error check + if r != 0: + file.unlink(missing_ok=True) # remove partial + print("Download error ") # raise Exception('Download error') + return r + + # Unzip if archive + if file.suffix == ".zip": + print("unzipping... ", end="") + os.system(f"unzip -q {file}") # unzip + file.unlink() # remove zip to free space + + print(f"Done ({time.time() - t:.1f}s)") + return r + + +def get_token(cookie="./cookie"): + with open(cookie) as f: + for line in f: + if "download" in line: + return line.split()[-1] + return "" + + +# def upload_blob(bucket_name, source_file_name, destination_blob_name): +# # Uploads a file to a bucket +# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python +# +# storage_client = storage.Client() +# bucket = storage_client.get_bucket(bucket_name) +# blob = bucket.blob(destination_blob_name) +# +# blob.upload_from_filename(source_file_name) +# +# print('File {} uploaded to {}.'.format( +# source_file_name, +# destination_blob_name)) +# +# +# def download_blob(bucket_name, source_blob_name, destination_file_name): +# # Uploads a blob from a bucket +# storage_client = storage.Client() +# bucket = storage_client.get_bucket(bucket_name) +# blob = bucket.blob(source_blob_name) +# +# blob.download_to_filename(destination_file_name) +# +# print('Blob {} downloaded to {}.'.format( +# source_blob_name, +# destination_file_name)) diff --git a/mil_common/perception/yoloros/src/yoloros/utils/loss.py b/mil_common/perception/yoloros/src/yoloros/utils/loss.py new file mode 100644 index 000000000..4e9665489 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/loss.py @@ -0,0 +1,2097 @@ +# Loss functions + +import torch +import torch.nn as nn +import torch.nn.functional as F +from utils.general import ( + bbox_iou, + box_iou, + xywh2xyxy, +) +from utils.torch_utils import is_parallel + + +def smooth_BCE( + eps=0.1, +): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 + # return positive, negative label smoothing BCE targets + return 1.0 - 0.5 * eps, 0.5 * eps + + +class BCEBlurWithLogitsLoss(nn.Module): + # BCEwithLogitLoss() with reduced missing label effects. + def __init__(self, alpha=0.05): + super().__init__() + self.loss_fcn = nn.BCEWithLogitsLoss( + reduction="none", + ) # must be nn.BCEWithLogitsLoss() + self.alpha = alpha + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + pred = torch.sigmoid(pred) # prob from logits + dx = pred - true # reduce only missing label effects + # dx = (pred - true).abs() # reduce missing label and false label effects + alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) + loss *= alpha_factor + return loss.mean() + + +class SigmoidBin(nn.Module): + stride = None # strides computed during build + export = False # onnx export + + def __init__( + self, + bin_count=10, + min=0.0, + max=1.0, + reg_scale=2.0, + use_loss_regression=True, + use_fw_regression=True, + BCE_weight=1.0, + smooth_eps=0.0, + ): + super().__init__() + + self.bin_count = bin_count + self.length = bin_count + 1 + self.min = min + self.max = max + self.scale = float(max - min) + self.shift = self.scale / 2.0 + + self.use_loss_regression = use_loss_regression + self.use_fw_regression = use_fw_regression + self.reg_scale = reg_scale + self.BCE_weight = BCE_weight + + start = min + (self.scale / 2.0) / self.bin_count + end = max - (self.scale / 2.0) / self.bin_count + step = self.scale / self.bin_count + self.step = step + # print(f" start = {start}, end = {end}, step = {step} ") + + bins = torch.range(start, end + 0.0001, step).float() + self.register_buffer("bins", bins) + + self.cp = 1.0 - 0.5 * smooth_eps + self.cn = 0.5 * smooth_eps + + self.BCEbins = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([BCE_weight])) + self.MSELoss = nn.MSELoss() + + def get_length(self): + return self.length + + def forward(self, pred): + assert ( + pred.shape[-1] == self.length + ), "pred.shape[-1]=%d is not equal to self.length=%d" % ( + pred.shape[-1], + self.length, + ) + + pred_reg = (pred[..., 0] * self.reg_scale - self.reg_scale / 2.0) * self.step + pred_bin = pred[..., 1 : (1 + self.bin_count)] + + _, bin_idx = torch.max(pred_bin, dim=-1) + bin_bias = self.bins[bin_idx] + + result = pred_reg + bin_bias if self.use_fw_regression else bin_bias + result = result.clamp(min=self.min, max=self.max) + + return result + + def training_loss(self, pred, target): + assert ( + pred.shape[-1] == self.length + ), "pred.shape[-1]=%d is not equal to self.length=%d" % ( + pred.shape[-1], + self.length, + ) + assert ( + pred.shape[0] == target.shape[0] + ), "pred.shape=%d is not equal to the target.shape=%d" % ( + pred.shape[0], + target.shape[0], + ) + device = pred.device + + pred_reg = ( + pred[..., 0].sigmoid() * self.reg_scale - self.reg_scale / 2.0 + ) * self.step + pred_bin = pred[..., 1 : (1 + self.bin_count)] + + diff_bin_target = torch.abs(target[..., None] - self.bins) + _, bin_idx = torch.min(diff_bin_target, dim=-1) + + bin_bias = self.bins[bin_idx] + bin_bias.requires_grad = False + result = pred_reg + bin_bias + + target_bins = torch.full_like(pred_bin, self.cn, device=device) # targets + n = pred.shape[0] + target_bins[range(n), bin_idx] = self.cp + + loss_bin = self.BCEbins(pred_bin, target_bins) # BCE + + if self.use_loss_regression: + loss_regression = self.MSELoss(result, target) # MSE + loss = loss_bin + loss_regression + else: + loss = loss_bin + + out_result = result.clamp(min=self.min, max=self.max) + + return loss, out_result + + +class FocalLoss(nn.Module): + # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super().__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = "none" # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + # p_t = torch.exp(-loss) + # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability + + # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py + pred_prob = torch.sigmoid(pred) # prob from logits + p_t = true * pred_prob + (1 - true) * (1 - pred_prob) + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = (1.0 - p_t) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == "mean": + return loss.mean() + elif self.reduction == "sum": + return loss.sum() + else: # 'none' + return loss + + +class QFocalLoss(nn.Module): + # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super().__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = "none" # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + + pred_prob = torch.sigmoid(pred) # prob from logits + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = torch.abs(true - pred_prob) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == "mean": + return loss.mean() + elif self.reduction == "sum": + return loss.sum() + else: # 'none' + return loss + + +class RankSort(torch.autograd.Function): + @staticmethod + def forward(ctx, logits, targets, delta_RS=0.50, eps=1e-10): + classification_grads = torch.zeros(logits.shape).cuda() + + # Filter fg logits + fg_labels = targets > 0.0 + fg_logits = logits[fg_labels] + fg_targets = targets[fg_labels] + fg_num = len(fg_logits) + + # Do not use bg with scores less than minimum fg logit + # since changing its score does not have an effect on precision + threshold_logit = torch.min(fg_logits) - delta_RS + relevant_bg_labels = (targets == 0) & (logits >= threshold_logit) + + relevant_bg_logits = logits[relevant_bg_labels] + relevant_bg_grad = torch.zeros(len(relevant_bg_logits)).cuda() + sorting_error = torch.zeros(fg_num).cuda() + ranking_error = torch.zeros(fg_num).cuda() + fg_grad = torch.zeros(fg_num).cuda() + + # sort the fg logits + order = torch.argsort(fg_logits) + # Loops over each positive following the order + for ii in order: + # Difference Transforms (x_ij) + fg_relations = fg_logits - fg_logits[ii] + bg_relations = relevant_bg_logits - fg_logits[ii] + + if delta_RS > 0: + fg_relations = torch.clamp( + fg_relations / (2 * delta_RS) + 0.5, + min=0, + max=1, + ) + bg_relations = torch.clamp( + bg_relations / (2 * delta_RS) + 0.5, + min=0, + max=1, + ) + else: + fg_relations = (fg_relations >= 0).float() + bg_relations = (bg_relations >= 0).float() + + # Rank of ii among pos and false positive number (bg with larger scores) + rank_pos = torch.sum(fg_relations) + FP_num = torch.sum(bg_relations) + + # Rank of ii among all examples + rank = rank_pos + FP_num + + # Ranking error of example ii. target_ranking_error is always 0. (Eq. 7) + ranking_error[ii] = FP_num / rank + + # Current sorting error of example ii. (Eq. 7) + current_sorting_error = ( + torch.sum(fg_relations * (1 - fg_targets)) / rank_pos + ) + + # Find examples in the target sorted order for example ii + iou_relations = fg_targets >= fg_targets[ii] + target_sorted_order = iou_relations * fg_relations + + # The rank of ii among positives in sorted order + rank_pos_target = torch.sum(target_sorted_order) + + # Compute target sorting error. (Eq. 8) + # Since target ranking error is 0, this is also total target error + target_sorting_error = ( + torch.sum(target_sorted_order * (1 - fg_targets)) / rank_pos_target + ) + + # Compute sorting error on example ii + sorting_error[ii] = current_sorting_error - target_sorting_error + + # Identity Update for Ranking Error + if FP_num > eps: + # For ii the update is the ranking error + fg_grad[ii] -= ranking_error[ii] + # For negatives, distribute error via ranking pmf (i.e. bg_relations/FP_num) + relevant_bg_grad += bg_relations * (ranking_error[ii] / FP_num) + + # Find the positives that are misranked (the cause of the error) + # These are the ones with smaller IoU but larger logits + missorted_examples = (~iou_relations) * fg_relations + + # Denominotor of sorting pmf + sorting_pmf_denom = torch.sum(missorted_examples) + + # Identity Update for Sorting Error + if sorting_pmf_denom > eps: + # For ii the update is the sorting error + fg_grad[ii] -= sorting_error[ii] + # For positives, distribute error via sorting pmf (i.e. missorted_examples/sorting_pmf_denom) + fg_grad += missorted_examples * (sorting_error[ii] / sorting_pmf_denom) + + # Normalize gradients by number of positives + classification_grads[fg_labels] = fg_grad / fg_num + classification_grads[relevant_bg_labels] = relevant_bg_grad / fg_num + + ctx.save_for_backward(classification_grads) + + return ranking_error.mean(), sorting_error.mean() + + @staticmethod + def backward(ctx, out_grad1, out_grad2): + (g1,) = ctx.saved_tensors + return g1 * out_grad1, None, None, None + + +class aLRPLoss(torch.autograd.Function): + @staticmethod + def forward(ctx, logits, targets, regression_losses, delta=1.0, eps=1e-5): + classification_grads = torch.zeros(logits.shape).cuda() + + # Filter fg logits + fg_labels = targets == 1 + fg_logits = logits[fg_labels] + fg_num = len(fg_logits) + + # Do not use bg with scores less than minimum fg logit + # since changing its score does not have an effect on precision + threshold_logit = torch.min(fg_logits) - delta + + # Get valid bg logits + relevant_bg_labels = (targets == 0) & (logits >= threshold_logit) + relevant_bg_logits = logits[relevant_bg_labels] + relevant_bg_grad = torch.zeros(len(relevant_bg_logits)).cuda() + rank = torch.zeros(fg_num).cuda() + prec = torch.zeros(fg_num).cuda() + fg_grad = torch.zeros(fg_num).cuda() + + # sort the fg logits + order = torch.argsort(fg_logits) + # Loops over each positive following the order + for ii in order: + # x_ij s as score differences with fgs + fg_relations = fg_logits - fg_logits[ii] + # Apply piecewise linear function and determine relations with fgs + fg_relations = torch.clamp(fg_relations / (2 * delta) + 0.5, min=0, max=1) + # Discard i=j in the summation in rank_pos + fg_relations[ii] = 0 + + # x_ij s as score differences with bgs + bg_relations = relevant_bg_logits - fg_logits[ii] + # Apply piecewise linear function and determine relations with bgs + bg_relations = torch.clamp(bg_relations / (2 * delta) + 0.5, min=0, max=1) + + # Compute the rank of the example within fgs and number of bgs with larger scores + rank_pos = 1 + torch.sum(fg_relations) + FP_num = torch.sum(bg_relations) + # Store the total since it is normalizer also for aLRP Regression error + rank[ii] = rank_pos + FP_num + + # Compute precision for this example to compute classification loss + prec[ii] = rank_pos / rank[ii] + # For stability, set eps to a infinitesmall value (e.g. 1e-6), then compute grads + if FP_num > eps: + fg_grad[ii] = ( + -(torch.sum(fg_relations * regression_losses) + FP_num) / rank[ii] + ) + relevant_bg_grad += bg_relations * (-fg_grad[ii] / FP_num) + + # aLRP with grad formulation fg gradient + classification_grads[fg_labels] = fg_grad + # aLRP with grad formulation bg gradient + classification_grads[relevant_bg_labels] = relevant_bg_grad + + classification_grads /= fg_num + + cls_loss = 1 - prec.mean() + ctx.save_for_backward(classification_grads) + + return cls_loss, rank, order + + @staticmethod + def backward(ctx, out_grad1, out_grad2, out_grad3): + (g1,) = ctx.saved_tensors + return g1 * out_grad1, None, None, None, None + + +class APLoss(torch.autograd.Function): + @staticmethod + def forward(ctx, logits, targets, delta=1.0): + classification_grads = torch.zeros(logits.shape).cuda() + + # Filter fg logits + fg_labels = targets == 1 + fg_logits = logits[fg_labels] + fg_num = len(fg_logits) + + # Do not use bg with scores less than minimum fg logit + # since changing its score does not have an effect on precision + threshold_logit = torch.min(fg_logits) - delta + + # Get valid bg logits + relevant_bg_labels = (targets == 0) & (logits >= threshold_logit) + relevant_bg_logits = logits[relevant_bg_labels] + relevant_bg_grad = torch.zeros(len(relevant_bg_logits)).cuda() + rank = torch.zeros(fg_num).cuda() + prec = torch.zeros(fg_num).cuda() + fg_grad = torch.zeros(fg_num).cuda() + + max_prec = 0 + # sort the fg logits + order = torch.argsort(fg_logits) + # Loops over each positive following the order + for ii in order: + # x_ij s as score differences with fgs + fg_relations = fg_logits - fg_logits[ii] + # Apply piecewise linear function and determine relations with fgs + fg_relations = torch.clamp(fg_relations / (2 * delta) + 0.5, min=0, max=1) + # Discard i=j in the summation in rank_pos + fg_relations[ii] = 0 + + # x_ij s as score differences with bgs + bg_relations = relevant_bg_logits - fg_logits[ii] + # Apply piecewise linear function and determine relations with bgs + bg_relations = torch.clamp(bg_relations / (2 * delta) + 0.5, min=0, max=1) + + # Compute the rank of the example within fgs and number of bgs with larger scores + rank_pos = 1 + torch.sum(fg_relations) + FP_num = torch.sum(bg_relations) + # Store the total since it is normalizer also for aLRP Regression error + rank[ii] = rank_pos + FP_num + + # Compute precision for this example + current_prec = rank_pos / rank[ii] + + # Compute interpolated AP and store gradients for relevant bg examples + if max_prec <= current_prec: + max_prec = current_prec + relevant_bg_grad += bg_relations / rank[ii] + else: + relevant_bg_grad += (bg_relations / rank[ii]) * ( + (1 - max_prec) / (1 - current_prec) + ) + + # Store fg gradients + fg_grad[ii] = -(1 - max_prec) + prec[ii] = max_prec + + # aLRP with grad formulation fg gradient + classification_grads[fg_labels] = fg_grad + # aLRP with grad formulation bg gradient + classification_grads[relevant_bg_labels] = relevant_bg_grad + + classification_grads /= fg_num + + cls_loss = 1 - prec.mean() + ctx.save_for_backward(classification_grads) + + return cls_loss + + @staticmethod + def backward(ctx, out_grad1): + (g1,) = ctx.saved_tensors + return g1 * out_grad1, None, None + + +class ComputeLoss: + # Compute losses + def __init__(self, model, autobalance=False): + super().__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss( + pos_weight=torch.tensor([h["cls_pw"]], device=device), + ) + BCEobj = nn.BCEWithLogitsLoss( + pos_weight=torch.tensor([h["obj_pw"]], device=device), + ) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE( + eps=h.get("label_smoothing", 0.0), + ) # positive, negative BCE targets + + # Focal loss + g = h["fl_gamma"] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = ( + model.module.model[-1] if is_parallel(model) else model.model[-1] + ) # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get( + det.nl, + [4.0, 1.0, 0.25, 0.06, 0.02], + ) # P3-P7 + # self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.1, .05]) # P3-P7 + # self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.5, 0.4, .1]) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = ( + BCEcls, + BCEobj, + model.gr, + h, + autobalance, + ) + for k in "na", "nc", "nl", "anchors": + setattr(self, k, getattr(det, k)) + + def __call__(self, p, targets): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = ( + torch.zeros(1, device=device), + torch.zeros(1, device=device), + torch.zeros(1, device=device), + ) + tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + pxy = ps[:, :2].sigmoid() * 2.0 - 0.5 + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou( + pbox.T, + tbox[i], + x1y1x2y2=False, + CIoU=True, + ) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp( + 0, + ).type( + tobj.dtype, + ) # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), tcls[i]] = self.cp + # t[t==self.cp] = iou.detach().clamp(0).type(t.dtype) + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = ( + self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + ) + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp["box"] + lobj *= self.hyp["obj"] + lcls *= self.hyp["cls"] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch = [], [], [], [] + gain = torch.ones( + 7, + device=targets.device, + ).long() # normalized to gridspace gain + ai = ( + torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) + ) # same as .repeat_interleave(nt) + targets = torch.cat( + (targets.repeat(na, 1, 1), ai[:, :, None]), + 2, + ) # append anchor indices + + g = 0.5 # bias + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=targets.device, + ).float() + * g + ) # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1.0 / r).max(2)[0] < self.hyp["anchor_t"] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1.0 < g) & (gxy > 1.0)).T + l, m = ((gxi % 1.0 < g) & (gxi > 1.0)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append( + (b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)), + ) # image, anchor, grid indices + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + return tcls, tbox, indices, anch + + +class ComputeLossOTA: + # Compute losses + def __init__(self, model, autobalance=False): + super().__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss( + pos_weight=torch.tensor([h["cls_pw"]], device=device), + ) + BCEobj = nn.BCEWithLogitsLoss( + pos_weight=torch.tensor([h["obj_pw"]], device=device), + ) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE( + eps=h.get("label_smoothing", 0.0), + ) # positive, negative BCE targets + + # Focal loss + g = h["fl_gamma"] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = ( + model.module.model[-1] if is_parallel(model) else model.model[-1] + ) # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get( + det.nl, + [4.0, 1.0, 0.25, 0.06, 0.02], + ) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = ( + BCEcls, + BCEobj, + model.gr, + h, + autobalance, + ) + for k in "na", "nc", "nl", "anchors", "stride": + setattr(self, k, getattr(det, k)) + + def __call__(self, p, targets, imgs): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = ( + torch.zeros(1, device=device), + torch.zeros(1, device=device), + torch.zeros(1, device=device), + ) + bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs) + pre_gen_gains = [ + torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p + ] + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + grid = torch.stack([gi, gj], dim=1) + pxy = ps[:, :2].sigmoid() * 2.0 - 0.5 + # pxy = ps[:, :2].sigmoid() * 3. - 1. + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] + selected_tbox[:, :2] -= grid + iou = bbox_iou( + pbox.T, + selected_tbox, + x1y1x2y2=False, + CIoU=True, + ) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp( + 0, + ).type( + tobj.dtype, + ) # iou ratio + + # Classification + selected_tcls = targets[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), selected_tcls] = self.cp + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = ( + self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + ) + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp["box"] + lobj *= self.hyp["obj"] + lcls *= self.hyp["cls"] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets, imgs): + # indices, anch = self.find_positive(p, targets) + indices, anch = self.find_3_positive(p, targets) + # indices, anch = self.find_4_positive(p, targets) + # indices, anch = self.find_5_positive(p, targets) + # indices, anch = self.find_9_positive(p, targets) + device = torch.device(targets.device) + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + b_idx = targets[:, 0] == batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + b, a, gj, gi = indices[i] + idx = b == batch_idx + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append((torch.ones(size=(len(b),)) * i).to(device)) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, 4:5]) + p_cls.append(fg_pred[:, 5:]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2.0 - 0.5 + grid) * self.stride[ + i + ] # / 8. + # pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] + pwh = ( + (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] + ) # / 8. + pxywh = torch.cat([pxy, pwh], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y / (1 - y)), + gt_cls_per_image, + reduction="none", + ).sum(-1) + del cls_preds_ + + cost = pair_wise_cls_loss + 3.0 * pair_wise_iou_loss + + matching_matrix = torch.zeros_like(cost, device=device) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], + k=dynamic_ks[gt_idx].item(), + largest=False, + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = (matching_matrix.sum(0) > 0.0).to(device) + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_as[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_gis[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_targets[i] = torch.tensor( + [], + device="cuda:0", + dtype=torch.int64, + ) + matching_anchs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + + return ( + matching_bs, + matching_as, + matching_gjs, + matching_gis, + matching_targets, + matching_anchs, + ) + + def find_3_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones( + 7, + device=targets.device, + ).long() # normalized to gridspace gain + ai = ( + torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) + ) # same as .repeat_interleave(nt) + targets = torch.cat( + (targets.repeat(na, 1, 1), ai[:, :, None]), + 2, + ) # append anchor indices + + g = 0.5 # bias + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=targets.device, + ).float() + * g + ) # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1.0 / r).max(2)[0] < self.hyp["anchor_t"] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1.0 < g) & (gxy > 1.0)).T + l, m = ((gxi % 1.0 < g) & (gxi > 1.0)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append( + (b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)), + ) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch + + +class ComputeLossBinOTA: + # Compute losses + def __init__(self, model, autobalance=False): + super().__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss( + pos_weight=torch.tensor([h["cls_pw"]], device=device), + ) + BCEobj = nn.BCEWithLogitsLoss( + pos_weight=torch.tensor([h["obj_pw"]], device=device), + ) + # MSEangle = nn.MSELoss().to(device) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE( + eps=h.get("label_smoothing", 0.0), + ) # positive, negative BCE targets + + # Focal loss + g = h["fl_gamma"] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = ( + model.module.model[-1] if is_parallel(model) else model.model[-1] + ) # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get( + det.nl, + [4.0, 1.0, 0.25, 0.06, 0.02], + ) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = ( + BCEcls, + BCEobj, + model.gr, + h, + autobalance, + ) + for k in "na", "nc", "nl", "anchors", "stride", "bin_count": + setattr(self, k, getattr(det, k)) + + # xy_bin_sigmoid = SigmoidBin(bin_count=11, min=-0.5, max=1.5, use_loss_regression=False).to(device) + wh_bin_sigmoid = SigmoidBin( + bin_count=self.bin_count, + min=0.0, + max=4.0, + use_loss_regression=False, + ).to(device) + # angle_bin_sigmoid = SigmoidBin(bin_count=31, min=-1.1, max=1.1, use_loss_regression=False).to(device) + self.wh_bin_sigmoid = wh_bin_sigmoid + + def __call__(self, p, targets, imgs): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = ( + torch.zeros(1, device=device), + torch.zeros(1, device=device), + torch.zeros(1, device=device), + ) + bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs) + pre_gen_gains = [ + torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p + ] + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + obj_idx = ( + self.wh_bin_sigmoid.get_length() * 2 + 2 + ) # x,y, w-bce, h-bce # xy_bin_sigmoid.get_length()*2 + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + grid = torch.stack([gi, gj], dim=1) + selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] + selected_tbox[:, :2] -= grid + + # pxy = ps[:, :2].sigmoid() * 2. - 0.5 + ##pxy = ps[:, :2].sigmoid() * 3. - 1. + # pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + # pbox = torch.cat((pxy, pwh), 1) # predicted box + + # x_loss, px = xy_bin_sigmoid.training_loss(ps[..., 0:12], tbox[i][..., 0]) + # y_loss, py = xy_bin_sigmoid.training_loss(ps[..., 12:24], tbox[i][..., 1]) + w_loss, pw = self.wh_bin_sigmoid.training_loss( + ps[..., 2 : (3 + self.bin_count)], + selected_tbox[..., 2] / anchors[i][..., 0], + ) + h_loss, ph = self.wh_bin_sigmoid.training_loss( + ps[..., (3 + self.bin_count) : obj_idx], + selected_tbox[..., 3] / anchors[i][..., 1], + ) + + pw *= anchors[i][..., 0] + ph *= anchors[i][..., 1] + + px = ps[:, 0].sigmoid() * 2.0 - 0.5 + py = ps[:, 1].sigmoid() * 2.0 - 0.5 + + lbox += w_loss + h_loss # + x_loss + y_loss + + # print(f"\n px = {px.shape}, py = {py.shape}, pw = {pw.shape}, ph = {ph.shape} \n") + + pbox = torch.cat( + ( + px.unsqueeze(1), + py.unsqueeze(1), + pw.unsqueeze(1), + ph.unsqueeze(1), + ), + 1, + ).to( + device, + ) # predicted box + + iou = bbox_iou( + pbox.T, + selected_tbox, + x1y1x2y2=False, + CIoU=True, + ) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp( + 0, + ).type( + tobj.dtype, + ) # iou ratio + + # Classification + selected_tcls = targets[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like( + ps[:, (1 + obj_idx) :], + self.cn, + device=device, + ) # targets + t[range(n), selected_tcls] = self.cp + lcls += self.BCEcls(ps[:, (1 + obj_idx) :], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., obj_idx], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = ( + self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + ) + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp["box"] + lobj *= self.hyp["obj"] + lcls *= self.hyp["cls"] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets, imgs): + # indices, anch = self.find_positive(p, targets) + indices, anch = self.find_3_positive(p, targets) + # indices, anch = self.find_4_positive(p, targets) + # indices, anch = self.find_5_positive(p, targets) + # indices, anch = self.find_9_positive(p, targets) + + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + b_idx = targets[:, 0] == batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + obj_idx = self.wh_bin_sigmoid.get_length() * 2 + 2 + + b, a, gj, gi = indices[i] + idx = b == batch_idx + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append(torch.ones(size=(len(b),)) * i) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, obj_idx : (obj_idx + 1)]) + p_cls.append(fg_pred[:, (obj_idx + 1) :]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2.0 - 0.5 + grid) * self.stride[ + i + ] # / 8. + # pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8. + pw = ( + self.wh_bin_sigmoid.forward( + fg_pred[..., 2 : (3 + self.bin_count)].sigmoid(), + ) + * anch[i][idx][:, 0] + * self.stride[i] + ) + ph = ( + self.wh_bin_sigmoid.forward( + fg_pred[..., (3 + self.bin_count) : obj_idx].sigmoid(), + ) + * anch[i][idx][:, 1] + * self.stride[i] + ) + + pxywh = torch.cat([pxy, pw.unsqueeze(1), ph.unsqueeze(1)], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y / (1 - y)), + gt_cls_per_image, + reduction="none", + ).sum(-1) + del cls_preds_ + + cost = pair_wise_cls_loss + 3.0 * pair_wise_iou_loss + + matching_matrix = torch.zeros_like(cost) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], + k=dynamic_ks[gt_idx].item(), + largest=False, + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_as[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_gis[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_targets[i] = torch.tensor( + [], + device="cuda:0", + dtype=torch.int64, + ) + matching_anchs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + + return ( + matching_bs, + matching_as, + matching_gjs, + matching_gis, + matching_targets, + matching_anchs, + ) + + def find_3_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones( + 7, + device=targets.device, + ).long() # normalized to gridspace gain + ai = ( + torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) + ) # same as .repeat_interleave(nt) + targets = torch.cat( + (targets.repeat(na, 1, 1), ai[:, :, None]), + 2, + ) # append anchor indices + + g = 0.5 # bias + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=targets.device, + ).float() + * g + ) # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1.0 / r).max(2)[0] < self.hyp["anchor_t"] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1.0 < g) & (gxy > 1.0)).T + l, m = ((gxi % 1.0 < g) & (gxi > 1.0)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append( + (b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)), + ) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch + + +class ComputeLossAuxOTA: + # Compute losses + def __init__(self, model, autobalance=False): + super().__init__() + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss( + pos_weight=torch.tensor([h["cls_pw"]], device=device), + ) + BCEobj = nn.BCEWithLogitsLoss( + pos_weight=torch.tensor([h["obj_pw"]], device=device), + ) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE( + eps=h.get("label_smoothing", 0.0), + ) # positive, negative BCE targets + + # Focal loss + g = h["fl_gamma"] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + det = ( + model.module.model[-1] if is_parallel(model) else model.model[-1] + ) # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get( + det.nl, + [4.0, 1.0, 0.25, 0.06, 0.02], + ) # P3-P7 + self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = ( + BCEcls, + BCEobj, + model.gr, + h, + autobalance, + ) + for k in "na", "nc", "nl", "anchors", "stride": + setattr(self, k, getattr(det, k)) + + def __call__(self, p, targets, imgs): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = ( + torch.zeros(1, device=device), + torch.zeros(1, device=device), + torch.zeros(1, device=device), + ) + ( + bs_aux, + as_aux_, + gjs_aux, + gis_aux, + targets_aux, + anchors_aux, + ) = self.build_targets2(p[: self.nl], targets, imgs) + bs, as_, gjs, gis, targets, anchors = self.build_targets( + p[: self.nl], + targets, + imgs, + ) + pre_gen_gains_aux = [ + torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[: self.nl] + ] + pre_gen_gains = [ + torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[: self.nl] + ] + + # Losses + for i in range(self.nl): # layer index, layer predictions + pi = p[i] + pi_aux = p[i + self.nl] + b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i] # image, anchor, gridy, gridx + b_aux, a_aux, gj_aux, gi_aux = ( + bs_aux[i], + as_aux_[i], + gjs_aux[i], + gis_aux[i], + ) # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + tobj_aux = torch.zeros_like(pi_aux[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + grid = torch.stack([gi, gj], dim=1) + pxy = ps[:, :2].sigmoid() * 2.0 - 0.5 + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i] + selected_tbox[:, :2] -= grid + iou = bbox_iou( + pbox.T, + selected_tbox, + x1y1x2y2=False, + CIoU=True, + ) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp( + 0, + ).type( + tobj.dtype, + ) # iou ratio + + # Classification + selected_tcls = targets[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), selected_tcls] = self.cp + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + n_aux = b_aux.shape[0] # number of targets + if n_aux: + ps_aux = pi_aux[ + b_aux, + a_aux, + gj_aux, + gi_aux, + ] # prediction subset corresponding to targets + grid_aux = torch.stack([gi_aux, gj_aux], dim=1) + pxy_aux = ps_aux[:, :2].sigmoid() * 2.0 - 0.5 + # pxy_aux = ps_aux[:, :2].sigmoid() * 3. - 1. + pwh_aux = (ps_aux[:, 2:4].sigmoid() * 2) ** 2 * anchors_aux[i] + pbox_aux = torch.cat((pxy_aux, pwh_aux), 1) # predicted box + selected_tbox_aux = targets_aux[i][:, 2:6] * pre_gen_gains_aux[i] + selected_tbox_aux[:, :2] -= grid_aux + iou_aux = bbox_iou( + pbox_aux.T, + selected_tbox_aux, + x1y1x2y2=False, + CIoU=True, + ) # iou(prediction, target) + lbox += 0.25 * (1.0 - iou_aux).mean() # iou loss + + # Objectness + tobj_aux[b_aux, a_aux, gj_aux, gi_aux] = ( + 1.0 - self.gr + ) + self.gr * iou_aux.detach().clamp(0).type( + tobj_aux.dtype, + ) # iou ratio + + # Classification + selected_tcls_aux = targets_aux[i][:, 1].long() + if self.nc > 1: # cls loss (only if multiple classes) + t_aux = torch.full_like( + ps_aux[:, 5:], + self.cn, + device=device, + ) # targets + t_aux[range(n_aux), selected_tcls_aux] = self.cp + lcls += 0.25 * self.BCEcls(ps_aux[:, 5:], t_aux) # BCE + + obji = self.BCEobj(pi[..., 4], tobj) + obji_aux = self.BCEobj(pi_aux[..., 4], tobj_aux) + lobj += ( + obji * self.balance[i] + 0.25 * obji_aux * self.balance[i] + ) # obj loss + if self.autobalance: + self.balance[i] = ( + self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + ) + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp["box"] + lobj *= self.hyp["obj"] + lcls *= self.hyp["cls"] + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + def build_targets(self, p, targets, imgs): + indices, anch = self.find_3_positive(p, targets) + + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + b_idx = targets[:, 0] == batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + b, a, gj, gi = indices[i] + idx = b == batch_idx + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append(torch.ones(size=(len(b),)) * i) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, 4:5]) + p_cls.append(fg_pred[:, 5:]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2.0 - 0.5 + grid) * self.stride[ + i + ] # / 8. + # pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] + pwh = ( + (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] + ) # / 8. + pxywh = torch.cat([pxy, pwh], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y / (1 - y)), + gt_cls_per_image, + reduction="none", + ).sum(-1) + del cls_preds_ + + cost = pair_wise_cls_loss + 3.0 * pair_wise_iou_loss + + matching_matrix = torch.zeros_like(cost) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], + k=dynamic_ks[gt_idx].item(), + largest=False, + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_as[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_gis[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_targets[i] = torch.tensor( + [], + device="cuda:0", + dtype=torch.int64, + ) + matching_anchs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + + return ( + matching_bs, + matching_as, + matching_gjs, + matching_gis, + matching_targets, + matching_anchs, + ) + + def build_targets2(self, p, targets, imgs): + indices, anch = self.find_5_positive(p, targets) + + matching_bs = [[] for pp in p] + matching_as = [[] for pp in p] + matching_gjs = [[] for pp in p] + matching_gis = [[] for pp in p] + matching_targets = [[] for pp in p] + matching_anchs = [[] for pp in p] + + nl = len(p) + + for batch_idx in range(p[0].shape[0]): + b_idx = targets[:, 0] == batch_idx + this_target = targets[b_idx] + if this_target.shape[0] == 0: + continue + + txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1] + txyxy = xywh2xyxy(txywh) + + pxyxys = [] + p_cls = [] + p_obj = [] + from_which_layer = [] + all_b = [] + all_a = [] + all_gj = [] + all_gi = [] + all_anch = [] + + for i, pi in enumerate(p): + b, a, gj, gi = indices[i] + idx = b == batch_idx + b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx] + all_b.append(b) + all_a.append(a) + all_gj.append(gj) + all_gi.append(gi) + all_anch.append(anch[i][idx]) + from_which_layer.append(torch.ones(size=(len(b),)) * i) + + fg_pred = pi[b, a, gj, gi] + p_obj.append(fg_pred[:, 4:5]) + p_cls.append(fg_pred[:, 5:]) + + grid = torch.stack([gi, gj], dim=1) + pxy = (fg_pred[:, :2].sigmoid() * 2.0 - 0.5 + grid) * self.stride[ + i + ] # / 8. + # pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i] + pwh = ( + (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] + ) # / 8. + pxywh = torch.cat([pxy, pwh], dim=-1) + pxyxy = xywh2xyxy(pxywh) + pxyxys.append(pxyxy) + + pxyxys = torch.cat(pxyxys, dim=0) + if pxyxys.shape[0] == 0: + continue + p_obj = torch.cat(p_obj, dim=0) + p_cls = torch.cat(p_cls, dim=0) + from_which_layer = torch.cat(from_which_layer, dim=0) + all_b = torch.cat(all_b, dim=0) + all_a = torch.cat(all_a, dim=0) + all_gj = torch.cat(all_gj, dim=0) + all_gi = torch.cat(all_gi, dim=0) + all_anch = torch.cat(all_anch, dim=0) + + pair_wise_iou = box_iou(txyxy, pxyxys) + + pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8) + + top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1) + dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1) + + gt_cls_per_image = ( + F.one_hot(this_target[:, 1].to(torch.int64), self.nc) + .float() + .unsqueeze(1) + .repeat(1, pxyxys.shape[0], 1) + ) + + num_gt = this_target.shape[0] + cls_preds_ = ( + p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_() + ) + + y = cls_preds_.sqrt_() + pair_wise_cls_loss = F.binary_cross_entropy_with_logits( + torch.log(y / (1 - y)), + gt_cls_per_image, + reduction="none", + ).sum(-1) + del cls_preds_ + + cost = pair_wise_cls_loss + 3.0 * pair_wise_iou_loss + + matching_matrix = torch.zeros_like(cost) + + for gt_idx in range(num_gt): + _, pos_idx = torch.topk( + cost[gt_idx], + k=dynamic_ks[gt_idx].item(), + largest=False, + ) + matching_matrix[gt_idx][pos_idx] = 1.0 + + del top_k, dynamic_ks + anchor_matching_gt = matching_matrix.sum(0) + if (anchor_matching_gt > 1).sum() > 0: + _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0) + matching_matrix[:, anchor_matching_gt > 1] *= 0.0 + matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0 + fg_mask_inboxes = matching_matrix.sum(0) > 0.0 + matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0) + + from_which_layer = from_which_layer[fg_mask_inboxes] + all_b = all_b[fg_mask_inboxes] + all_a = all_a[fg_mask_inboxes] + all_gj = all_gj[fg_mask_inboxes] + all_gi = all_gi[fg_mask_inboxes] + all_anch = all_anch[fg_mask_inboxes] + + this_target = this_target[matched_gt_inds] + + for i in range(nl): + layer_idx = from_which_layer == i + matching_bs[i].append(all_b[layer_idx]) + matching_as[i].append(all_a[layer_idx]) + matching_gjs[i].append(all_gj[layer_idx]) + matching_gis[i].append(all_gi[layer_idx]) + matching_targets[i].append(this_target[layer_idx]) + matching_anchs[i].append(all_anch[layer_idx]) + + for i in range(nl): + if matching_targets[i] != []: + matching_bs[i] = torch.cat(matching_bs[i], dim=0) + matching_as[i] = torch.cat(matching_as[i], dim=0) + matching_gjs[i] = torch.cat(matching_gjs[i], dim=0) + matching_gis[i] = torch.cat(matching_gis[i], dim=0) + matching_targets[i] = torch.cat(matching_targets[i], dim=0) + matching_anchs[i] = torch.cat(matching_anchs[i], dim=0) + else: + matching_bs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_as[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_gjs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_gis[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + matching_targets[i] = torch.tensor( + [], + device="cuda:0", + dtype=torch.int64, + ) + matching_anchs[i] = torch.tensor([], device="cuda:0", dtype=torch.int64) + + return ( + matching_bs, + matching_as, + matching_gjs, + matching_gis, + matching_targets, + matching_anchs, + ) + + def find_5_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones( + 7, + device=targets.device, + ).long() # normalized to gridspace gain + ai = ( + torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) + ) # same as .repeat_interleave(nt) + targets = torch.cat( + (targets.repeat(na, 1, 1), ai[:, :, None]), + 2, + ) # append anchor indices + + g = 1.0 # bias + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=targets.device, + ).float() + * g + ) # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1.0 / r).max(2)[0] < self.hyp["anchor_t"] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1.0 < g) & (gxy > 1.0)).T + l, m = ((gxi % 1.0 < g) & (gxi > 1.0)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append( + (b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)), + ) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch + + def find_3_positive(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + indices, anch = [], [] + gain = torch.ones( + 7, + device=targets.device, + ).long() # normalized to gridspace gain + ai = ( + torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) + ) # same as .repeat_interleave(nt) + targets = torch.cat( + (targets.repeat(na, 1, 1), ai[:, :, None]), + 2, + ) # append anchor indices + + g = 0.5 # bias + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=targets.device, + ).float() + * g + ) # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1.0 / r).max(2)[0] < self.hyp["anchor_t"] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1.0 < g) & (gxy > 1.0)).T + l, m = ((gxi % 1.0 < g) & (gxi > 1.0)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append( + (b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)), + ) # image, anchor, grid indices + anch.append(anchors[a]) # anchors + + return indices, anch diff --git a/mil_common/perception/yoloros/src/yoloros/utils/metrics.py b/mil_common/perception/yoloros/src/yoloros/utils/metrics.py new file mode 100644 index 000000000..48d5daac9 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/metrics.py @@ -0,0 +1,285 @@ +# Model validation metrics + +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch + +from . import general + + +def fitness(x): + # Model fitness as a weighted combination of metrics + w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] + return (x[:, :4] * w).sum(1) + + +def ap_per_class( + tp, + conf, + pred_cls, + target_cls, + v5_metric=False, + plot=False, + save_dir=".", + names=(), +): + """Compute the average precision, given the recall and precision curves. + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. + # Arguments + tp: True positives (nparray, nx1 or nx10). + conf: Objectness value from 0-1 (nparray). + pred_cls: Predicted object classes (nparray). + target_cls: True object classes (nparray). + plot: Plot precision-recall curve at mAP@0.5 + save_dir: Plot save directory + # Returns + The average precision as computed in py-faster-rcnn. + """ + + # Sort by objectness + i = np.argsort(-conf) + tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] + + # Find unique classes + unique_classes = np.unique(target_cls) + nc = unique_classes.shape[0] # number of classes, number of detections + + # Create Precision-Recall curve and compute AP for each class + px, py = np.linspace(0, 1, 1000), [] # for plotting + ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) + for ci, c in enumerate(unique_classes): + i = pred_cls == c + n_l = (target_cls == c).sum() # number of labels + n_p = i.sum() # number of predictions + + if n_p == 0 or n_l == 0: + continue + else: + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_l + 1e-16) # recall curve + r[ci] = np.interp( + -px, + -conf[i], + recall[:, 0], + left=0, + ) # negative x, xp because xp decreases + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): + ap[ci, j], mpre, mrec = compute_ap( + recall[:, j], + precision[:, j], + v5_metric=v5_metric, + ) + if plot and j == 0: + py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 + + # Compute F1 (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + 1e-16) + if plot: + plot_pr_curve(px, py, ap, Path(save_dir) / "PR_curve.png", names) + plot_mc_curve(px, f1, Path(save_dir) / "F1_curve.png", names, ylabel="F1") + plot_mc_curve(px, p, Path(save_dir) / "P_curve.png", names, ylabel="Precision") + plot_mc_curve(px, r, Path(save_dir) / "R_curve.png", names, ylabel="Recall") + + i = f1.mean(0).argmax() # max F1 index + return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype("int32") + + +def compute_ap(recall, precision, v5_metric=False): + """Compute the average precision, given the recall and precision curves + # Arguments + recall: The recall curve (list) + precision: The precision curve (list) + v5_metric: Assume maximum recall to be 1.0, as in YOLOv5, MMDetetion etc. + # Returns + Average precision, precision curve, recall curve + """ + + # Append sentinel values to beginning and end + if v5_metric: # New YOLOv5 metric, same as MMDetection and Detectron2 repositories + mrec = np.concatenate(([0.0], recall, [1.0])) + else: # Old YOLOv5 metric, i.e. default YOLOv7 metric + mrec = np.concatenate(([0.0], recall, [recall[-1] + 0.01])) + mpre = np.concatenate(([1.0], precision, [0.0])) + + # Compute the precision envelope + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + + # Integrate area under curve + method = "interp" # methods: 'continuous', 'interp' + if method == "interp": + x = np.linspace(0, 1, 101) # 101-point interp (COCO) + ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate + else: # 'continuous' + i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve + + return ap, mpre, mrec + + +class ConfusionMatrix: + # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix + def __init__(self, nc, conf=0.25, iou_thres=0.45): + self.matrix = np.zeros((nc + 1, nc + 1)) + self.nc = nc # number of classes + self.conf = conf + self.iou_thres = iou_thres + + def process_batch(self, detections, labels): + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + detections (Array[N, 6]), x1, y1, x2, y2, conf, class + labels (Array[M, 5]), class, x1, y1, x2, y2 + Returns: + None, updates confusion matrix accordingly + """ + detections = detections[detections[:, 4] > self.conf] + gt_classes = labels[:, 0].int() + detection_classes = detections[:, 5].int() + iou = general.box_iou(labels[:, 1:], detections[:, :4]) + + x = torch.where(iou > self.iou_thres) + if x[0].shape[0]: + matches = ( + torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1) + .cpu() + .numpy() + ) + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + else: + matches = np.zeros((0, 3)) + + n = matches.shape[0] > 0 + m0, m1, _ = matches.transpose().astype(np.int16) + for i, gc in enumerate(gt_classes): + j = m0 == i + if n and sum(j) == 1: + self.matrix[gc, detection_classes[m1[j]]] += 1 # correct + else: + self.matrix[self.nc, gc] += 1 # background FP + + if n: + for i, dc in enumerate(detection_classes): + if not any(m1 == i): + self.matrix[dc, self.nc] += 1 # background FN + + def matrix(self): + return self.matrix + + def plot(self, save_dir="", names=()): + try: + import seaborn as sn + + array = self.matrix / ( + self.matrix.sum(0).reshape(1, self.nc + 1) + 1e-6 + ) # normalize + array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) + + fig = plt.figure(figsize=(12, 9), tight_layout=True) + sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size + labels = (0 < len(names) < 99) and len( + names, + ) == self.nc # apply names to ticklabels + sn.heatmap( + array, + annot=self.nc < 30, + annot_kws={"size": 8}, + cmap="Blues", + fmt=".2f", + square=True, + xticklabels=[*names, "background FP"] if labels else "auto", + yticklabels=[*names, "background FN"] if labels else "auto", + ).set_facecolor((1, 1, 1)) + fig.axes[0].set_xlabel("True") + fig.axes[0].set_ylabel("Predicted") + fig.savefig(Path(save_dir) / "confusion_matrix.png", dpi=250) + except Exception: + pass + + def print(self): + for i in range(self.nc + 1): + print(" ".join(map(str, self.matrix[i]))) + + +# Plots ---------------------------------------------------------------------------------------------------------------- + + +def plot_pr_curve(px, py, ap, save_dir="pr_curve.png", names=()): + # Precision-recall curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = np.stack(py, axis=1) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py.T): + ax.plot( + px, + y, + linewidth=1, + label=f"{names[i]} {ap[i, 0]:.3f}", + ) # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision) + + ax.plot( + px, + py.mean(1), + linewidth=3, + color="blue", + label="all classes %.3f mAP@0.5" % ap[:, 0].mean(), + ) + ax.set_xlabel("Recall") + ax.set_ylabel("Precision") + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + fig.savefig(Path(save_dir), dpi=250) + + +def plot_mc_curve( + px, + py, + save_dir="mc_curve.png", + names=(), + xlabel="Confidence", + ylabel="Metric", +): + # Metric-confidence curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric) + + y = py.mean(0) + ax.plot( + px, + y, + linewidth=3, + color="blue", + label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}", + ) + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + fig.savefig(Path(save_dir), dpi=250) diff --git a/mil_common/perception/yoloros/src/yoloros/utils/plots.py b/mil_common/perception/yoloros/src/yoloros/utils/plots.py new file mode 100644 index 000000000..8d47df09e --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/plots.py @@ -0,0 +1,683 @@ +# Plotting utils + +import glob +import math +import os +import random +from copy import copy +from pathlib import Path + +import cv2 +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +import torch +import yaml +from PIL import Image, ImageDraw, ImageFont +from scipy.signal import butter, filtfilt +from utils.general import xywh2xyxy, xyxy2xywh +from utils.metrics import fitness + +# Settings +matplotlib.rc("font", **{"size": 11}) +matplotlib.use("Agg") # for writing to files only + + +def color_list(): + # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb + def hex2rgb(h): + return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4)) + + return [ + hex2rgb(h) for h in matplotlib.colors.TABLEAU_COLORS.values() + ] # or BASE_ (8), CSS4_ (148), XKCD_ (949) + + +def hist2d(x, y, n=100): + # 2d histogram used in labels.png and evolve.png + xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) + hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) + xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) + yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) + return np.log(hist[xidx, yidx]) + + +def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): + # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy + def butter_lowpass(cutoff, fs, order): + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + return butter(order, normal_cutoff, btype="low", analog=False) + + b, a = butter_lowpass(cutoff, fs, order=order) + return filtfilt(b, a, data) # forward-backward filter + + +def plot_one_box(x, img, color=None, label=None, line_thickness=3): + # Plots one bounding box on image img + tl = ( + line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 + ) # line/font thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) + cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 + cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled + cv2.putText( + img, + label, + (c1[0], c1[1] - 2), + 0, + tl / 3, + [225, 255, 255], + thickness=tf, + lineType=cv2.LINE_AA, + ) + + +def plot_one_box_PIL(box, img, color=None, label=None, line_thickness=None): + img = Image.fromarray(img) + draw = ImageDraw.Draw(img) + line_thickness = line_thickness or max(int(min(img.size) / 200), 2) + draw.rectangle(box, width=line_thickness, outline=tuple(color)) # plot + if label: + fontsize = max(round(max(img.size) / 40), 12) + font = ImageFont.truetype("Arial.ttf", fontsize) + txt_width, txt_height = font.getsize(label) + draw.rectangle( + [box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], + fill=tuple(color), + ) + draw.text( + (box[0], box[1] - txt_height + 1), + label, + fill=(255, 255, 255), + font=font, + ) + return np.asarray(img) + + +def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() + # Compares the two methods for width-height anchor multiplication + # https://github.com/ultralytics/yolov3/issues/168 + x = np.arange(-4.0, 4.0, 0.1) + ya = np.exp(x) + yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 + + fig = plt.figure(figsize=(6, 3), tight_layout=True) + plt.plot(x, ya, ".-", label="YOLOv3") + plt.plot(x, yb**2, ".-", label="YOLOR ^2") + plt.plot(x, yb**1.6, ".-", label="YOLOR ^1.6") + plt.xlim(left=-4, right=4) + plt.ylim(bottom=0, top=6) + plt.xlabel("input") + plt.ylabel("output") + plt.grid() + plt.legend() + fig.savefig("comparison.png", dpi=200) + + +def output_to_target(output): + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] + targets = [] + for i, o in enumerate(output): + for *box, conf, cls in o.cpu().numpy(): + targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf]) + return np.array(targets) + + +def plot_images( + images, + targets, + paths=None, + fname="images.jpg", + names=None, + max_size=640, + max_subplots=16, +): + # Plot image grid with labels + + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + + # un-normalise + if np.max(images[0]) <= 1: + images *= 255 + + tl = 3 # line thickness + tf = max(tl - 1, 1) # font thickness + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs**0.5) # number of subplots (square) + + # Check if we should resize + scale_factor = max_size / max(h, w) + if scale_factor < 1: + h = math.ceil(scale_factor * h) + w = math.ceil(scale_factor * w) + + colors = color_list() # list of colors + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, img in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + + block_x = int(w * (i // ns)) + block_y = int(h * (i % ns)) + + img = img.transpose(1, 2, 0) + if scale_factor < 1: + img = cv2.resize(img, (w, h)) + + mosaic[block_y : block_y + h, block_x : block_x + w, :] = img + if len(targets) > 0: + image_targets = targets[targets[:, 0] == i] + boxes = xywh2xyxy(image_targets[:, 2:6]).T + classes = image_targets[:, 1].astype("int") + labels = image_targets.shape[1] == 6 # labels if no conf column + conf = ( + None if labels else image_targets[:, 6] + ) # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale_factor < 1: # absolute coords need scale if image scales + boxes *= scale_factor + boxes[[0, 2]] += block_x + boxes[[1, 3]] += block_y + for j, box in enumerate(boxes.T): + cls = int(classes[j]) + color = colors[cls % len(colors)] + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = "%s" % cls if labels else f"{cls} {conf[j]:.1f}" + plot_one_box( + box, + mosaic, + label=label, + color=color, + line_thickness=tl, + ) + + # Draw image filename labels + if paths: + label = Path(paths[i]).name[:40] # trim to 40 char + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + cv2.putText( + mosaic, + label, + (block_x + 5, block_y + t_size[1] + 5), + 0, + tl / 3, + [220, 220, 220], + thickness=tf, + lineType=cv2.LINE_AA, + ) + + # Image border + cv2.rectangle( + mosaic, + (block_x, block_y), + (block_x + w, block_y + h), + (255, 255, 255), + thickness=3, + ) + + if fname: + r = min(1280.0 / max(h, w) / ns, 1.0) # ratio to limit image size + mosaic = cv2.resize( + mosaic, + (int(ns * w * r), int(ns * h * r)), + interpolation=cv2.INTER_AREA, + ) + # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save + Image.fromarray(mosaic).save(fname) # PIL save + return mosaic + + +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=""): + # Plot LR simulating training for full epochs + optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals + y = [] + for _ in range(epochs): + scheduler.step() + y.append(optimizer.param_groups[0]["lr"]) + plt.plot(y, ".-", label="LR") + plt.xlabel("epoch") + plt.ylabel("LR") + plt.grid() + plt.xlim(0, epochs) + plt.ylim(0) + plt.savefig(Path(save_dir) / "LR.png", dpi=200) + plt.close() + + +def plot_test_txt(): # from utils.plots import *; plot_test() + # Plot test.txt histograms + x = np.loadtxt("test.txt", dtype=np.float32) + box = xyxy2xywh(x[:, :4]) + cx, cy = box[:, 0], box[:, 1] + + fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) + ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) + ax.set_aspect("equal") + plt.savefig("hist2d.png", dpi=300) + + fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) + ax[0].hist(cx, bins=600) + ax[1].hist(cy, bins=600) + plt.savefig("hist1d.png", dpi=200) + + +def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() + # Plot targets.txt histograms + x = np.loadtxt("targets.txt", dtype=np.float32).T + s = ["x targets", "y targets", "width targets", "height targets"] + fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) + ax = ax.ravel() + for i in range(4): + ax[i].hist(x[i], bins=100, label=f"{x[i].mean():.3g} +/- {x[i].std():.3g}") + ax[i].legend() + ax[i].set_title(s[i]) + plt.savefig("targets.jpg", dpi=200) + + +def plot_study_txt(path="", x=None): # from utils.plots import *; plot_study_txt() + # Plot study.txt generated by test.py + fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) + # ax = ax.ravel() + + fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) + # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolor-p6', 'yolor-w6', 'yolor-e6', 'yolor-d6']]: + for f in sorted(Path(path).glob("study*.txt")): + y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T + x = np.arange(y.shape[1]) if x is None else np.array(x) + # for i in range(7): + # ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + # ax[i].set_title(s[i]) + + j = y[3].argmax() + 1 + ax2.plot( + y[6, 1:j], + y[3, 1:j] * 1e2, + ".-", + linewidth=2, + markersize=8, + label=f.stem.replace("study_coco_", "").replace("yolo", "YOLO"), + ) + + ax2.plot( + 1e3 / np.array([209, 140, 97, 58, 35, 18]), + [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], + "k.-", + linewidth=2, + markersize=8, + alpha=0.25, + label="EfficientDet", + ) + + ax2.grid(alpha=0.2) + ax2.set_yticks(np.arange(20, 60, 5)) + ax2.set_xlim(0, 57) + ax2.set_ylim(30, 55) + ax2.set_xlabel("GPU Speed (ms/img)") + ax2.set_ylabel("COCO AP val") + ax2.legend(loc="lower right") + plt.savefig(str(Path(path).name) + ".png", dpi=300) + + +def plot_labels(labels, names=(), save_dir=Path(""), loggers=None): + # plot dataset labels + print("Plotting labels... ") + c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes + nc = int(c.max() + 1) # number of classes + colors = color_list() + x = pd.DataFrame(b.transpose(), columns=["x", "y", "width", "height"]) + + # seaborn correlogram + sns.pairplot( + x, + corner=True, + diag_kind="auto", + kind="hist", + diag_kws={"bins": 50}, + plot_kws={"pmax": 0.9}, + ) + plt.savefig(save_dir / "labels_correlogram.jpg", dpi=200) + plt.close() + + # matplotlib labels + matplotlib.use("svg") # faster + ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() + ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) + ax[0].set_ylabel("instances") + if 0 < len(names) < 30: + ax[0].set_xticks(range(len(names))) + ax[0].set_xticklabels(names, rotation=90, fontsize=10) + else: + ax[0].set_xlabel("classes") + sns.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9) + sns.histplot(x, x="width", y="height", ax=ax[3], bins=50, pmax=0.9) + + # rectangles + labels[:, 1:3] = 0.5 # center + labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 + img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) + for cls, *box in labels[:1000]: + ImageDraw.Draw(img).rectangle( + box, + width=1, + outline=colors[int(cls) % 10], + ) # plot + ax[1].imshow(img) + ax[1].axis("off") + + for a in [0, 1, 2, 3]: + for s in ["top", "right", "left", "bottom"]: + ax[a].spines[s].set_visible(False) + + plt.savefig(save_dir / "labels.jpg", dpi=200) + matplotlib.use("Agg") + plt.close() + + # loggers + for k, v in loggers.items() or {}: + if k == "wandb" and v: + v.log( + { + "Labels": [ + v.Image(str(x), caption=x.name) + for x in save_dir.glob("*labels*.jpg") + ], + }, + commit=False, + ) + + +def plot_evolution( + yaml_file="data/hyp.finetune.yaml", +): # from utils.plots import *; plot_evolution() + # Plot hyperparameter evolution results in evolve.txt + with open(yaml_file) as f: + hyp = yaml.load(f, Loader=yaml.SafeLoader) + x = np.loadtxt("evolve.txt", ndmin=2) + f = fitness(x) + # weights = (f - f.min()) ** 2 # for weighted results + plt.figure(figsize=(10, 12), tight_layout=True) + matplotlib.rc("font", **{"size": 8}) + for i, (k, v) in enumerate(hyp.items()): + y = x[:, i + 7] + # mu = (y * weights).sum() / weights.sum() # best weighted result + mu = y[f.argmax()] # best single result + plt.subplot(6, 5, i + 1) + plt.scatter( + y, + f, + c=hist2d(y, f, 20), + cmap="viridis", + alpha=0.8, + edgecolors="none", + ) + plt.plot(mu, f.max(), "k+", markersize=15) + plt.title(f"{k} = {mu:.3g}", fontdict={"size": 9}) # limit to 40 characters + if i % 5 != 0: + plt.yticks([]) + print("%15s: %.3g" % (k, mu)) + plt.savefig("evolve.png", dpi=200) + print("\nPlot saved as evolve.png") + + +def profile_idetection(start=0, stop=0, labels=(), save_dir=""): + # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() + ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() + s = [ + "Images", + "Free Storage (GB)", + "RAM Usage (GB)", + "Battery", + "dt_raw (ms)", + "dt_smooth (ms)", + "real-world FPS", + ] + files = list(Path(save_dir).glob("frames*.txt")) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows + n = results.shape[1] # number of rows + x = np.arange(start, min(stop, n) if stop else n) + results = results[:, x] + t = results[0] - results[0].min() # set t0=0s + results[0] = x + for i, a in enumerate(ax): + if i < len(results): + label = labels[fi] if len(labels) else f.stem.replace("frames_", "") + a.plot( + t, + results[i], + marker=".", + label=label, + linewidth=1, + markersize=5, + ) + a.set_title(s[i]) + a.set_xlabel("time (s)") + # if fi == len(files) - 1: + # a.set_ylim(bottom=0) + for side in ["top", "right"]: + a.spines[side].set_visible(False) + else: + a.remove() + except Exception as e: + print(f"Warning: Plotting error for {f}; {e}") + + ax[1].legend() + plt.savefig(Path(save_dir) / "idetection_profile.png", dpi=200) + + +def plot_results_overlay( + start=0, + stop=0, +): # from utils.plots import *; plot_results_overlay() + # Plot training 'results*.txt', overlaying train and val losses + s = [ + "train", + "train", + "train", + "Precision", + "mAP@0.5", + "val", + "val", + "val", + "Recall", + "mAP@0.5:0.95", + ] # legends + t = ["Box", "Objectness", "Classification", "P-R", "mAP-F1"] # titles + for f in sorted( + glob.glob("results*.txt") + glob.glob("../../Downloads/results*.txt"), + ): + results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T + n = results.shape[1] # number of rows + x = range(start, min(stop, n) if stop else n) + fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True) + ax = ax.ravel() + for i in range(5): + for j in [i, i + 5]: + y = results[j, x] + ax[i].plot(x, y, marker=".", label=s[j]) + # y_smooth = butter_lowpass_filtfilt(y) + # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j]) + + ax[i].set_title(t[i]) + ax[i].legend() + ax[i].set_ylabel(f) if i == 0 else None # add filename + fig.savefig(f.replace(".txt", ".png"), dpi=200) + + +def plot_results(start=0, stop=0, bucket="", id=(), labels=(), save_dir=""): + # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp') + fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) + ax = ax.ravel() + s = [ + "Box", + "Objectness", + "Classification", + "Precision", + "Recall", + "val Box", + "val Objectness", + "val Classification", + "mAP@0.5", + "mAP@0.5:0.95", + ] + if bucket: + # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id] + files = ["results%g.txt" % x for x in id] + c = ("gsutil cp " + "%s " * len(files) + ".") % tuple( + f"gs://{bucket}/results{x:g}.txt" for x in id + ) + os.system(c) + else: + files = list(Path(save_dir).glob("results*.txt")) + assert len( + files, + ), "No results.txt files found in %s, nothing to plot." % os.path.abspath(save_dir) + for fi, f in enumerate(files): + try: + results = np.loadtxt( + f, + usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], + ndmin=2, + ).T + n = results.shape[1] # number of rows + x = range(start, min(stop, n) if stop else n) + for i in range(10): + y = results[i, x] + if i in [0, 1, 2, 5, 6, 7]: + y[y == 0] = np.nan # don't show zero loss values + # y /= y[0] # normalize + label = labels[fi] if len(labels) else f.stem + ax[i].plot(x, y, marker=".", label=label, linewidth=2, markersize=8) + ax[i].set_title(s[i]) + # if i in [5, 6, 7]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + print(f"Warning: Plotting error for {f}; {e}") + + ax[1].legend() + fig.savefig(Path(save_dir) / "results.png", dpi=200) + + +def output_to_keypoint(output): + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] + targets = [] + for i, o in enumerate(output): + kpts = o[:, 6:] + o = o[:, :6] + for index, (*box, conf, cls) in enumerate(o.detach().cpu().numpy()): + targets.append( + [ + i, + cls, + *list(*xyxy2xywh(np.array(box)[None])), + conf, + *list(kpts.detach().cpu().numpy()[index]), + ], + ) + return np.array(targets) + + +def plot_skeleton_kpts(im, kpts, steps, orig_shape=None): + # Plot the skeleton and keypointsfor coco dataset + palette = np.array( + [ + [255, 128, 0], + [255, 153, 51], + [255, 178, 102], + [230, 230, 0], + [255, 153, 255], + [153, 204, 255], + [255, 102, 255], + [255, 51, 255], + [102, 178, 255], + [51, 153, 255], + [255, 153, 153], + [255, 102, 102], + [255, 51, 51], + [153, 255, 153], + [102, 255, 102], + [51, 255, 51], + [0, 255, 0], + [0, 0, 255], + [255, 0, 0], + [255, 255, 255], + ], + ) + + skeleton = [ + [16, 14], + [14, 12], + [17, 15], + [15, 13], + [12, 13], + [6, 12], + [7, 13], + [6, 7], + [6, 8], + [7, 9], + [8, 10], + [9, 11], + [2, 3], + [1, 2], + [1, 3], + [2, 4], + [3, 5], + [4, 6], + [5, 7], + ] + + pose_limb_color = palette[ + [9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16] + ] + pose_kpt_color = palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]] + radius = 5 + num_kpts = len(kpts) // steps + + for kid in range(num_kpts): + r, g, b = pose_kpt_color[kid] + x_coord, y_coord = kpts[steps * kid], kpts[steps * kid + 1] + if not (x_coord % 640 == 0 or y_coord % 640 == 0): + if steps == 3: + conf = kpts[steps * kid + 2] + if conf < 0.5: + continue + cv2.circle( + im, + (int(x_coord), int(y_coord)), + radius, + (int(r), int(g), int(b)), + -1, + ) + + for sk_id, sk in enumerate(skeleton): + r, g, b = pose_limb_color[sk_id] + pos1 = (int(kpts[(sk[0] - 1) * steps]), int(kpts[(sk[0] - 1) * steps + 1])) + pos2 = (int(kpts[(sk[1] - 1) * steps]), int(kpts[(sk[1] - 1) * steps + 1])) + if steps == 3: + conf1 = kpts[(sk[0] - 1) * steps + 2] + conf2 = kpts[(sk[1] - 1) * steps + 2] + if conf1 < 0.5 or conf2 < 0.5: + continue + if pos1[0] % 640 == 0 or pos1[1] % 640 == 0 or pos1[0] < 0 or pos1[1] < 0: + continue + if pos2[0] % 640 == 0 or pos2[1] % 640 == 0 or pos2[0] < 0 or pos2[1] < 0: + continue + cv2.line(im, pos1, pos2, (int(r), int(g), int(b)), thickness=2) diff --git a/mil_common/perception/yoloros/src/yoloros/utils/torch_utils.py b/mil_common/perception/yoloros/src/yoloros/utils/torch_utils.py new file mode 100644 index 000000000..d15fd406c --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/torch_utils.py @@ -0,0 +1,463 @@ +# YOLOR PyTorch utils + +import datetime +import logging +import math +import os +import platform +import subprocess +import time +from contextlib import contextmanager +from copy import deepcopy +from pathlib import Path + +import torch +import torch.backends.cudnn as cudnn +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +try: + import thop # for FLOPS computation +except ImportError: + thop = None +logger = logging.getLogger(__name__) + + +@contextmanager +def torch_distributed_zero_first(local_rank: int): + """ + Decorator to make all processes in distributed training wait for each local_master to do something. + """ + if local_rank not in [-1, 0]: + torch.distributed.barrier() + yield + if local_rank == 0: + torch.distributed.barrier() + + +def init_torch_seeds(seed=0): + # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html + torch.manual_seed(seed) + if seed == 0: # slower, more reproducible + cudnn.benchmark, cudnn.deterministic = False, True + else: # faster, less reproducible + cudnn.benchmark, cudnn.deterministic = True, False + + +def date_modified(path=__file__): + # return human-readable file modification date, i.e. '2021-3-26' + t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime) + return f"{t.year}-{t.month}-{t.day}" + + +def git_describe(path=Path(__file__).parent): # path must be a directory + # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe + s = f"git -C {path} describe --tags --long --always" + try: + return subprocess.check_output( + s, + shell=True, + stderr=subprocess.STDOUT, + ).decode()[:-1] + except subprocess.CalledProcessError: + return "" # not a git repository + + +def select_device(device="", batch_size=None): + # device = 'cpu' or '0' or '0,1,2,3' + s = f"YOLOR 🚀 {git_describe() or date_modified()} torch {torch.__version__} " # string + cpu = device.lower() == "cpu" + if cpu: + os.environ[ + "CUDA_VISIBLE_DEVICES" + ] = "-1" # force torch.cuda.is_available() = False + elif device: # non-cpu device requested + os.environ["CUDA_VISIBLE_DEVICES"] = device # set environment variable + assert ( + torch.cuda.is_available() + ), f"CUDA unavailable, invalid device {device} requested" # check availability + + cuda = not cpu and torch.cuda.is_available() + if cuda: + n = torch.cuda.device_count() + if ( + n > 1 and batch_size + ): # check that batch_size is compatible with device_count + assert ( + batch_size % n == 0 + ), f"batch-size {batch_size} not multiple of GPU count {n}" + space = " " * len(s) + for i, d in enumerate(device.split(",") if device else range(n)): + p = torch.cuda.get_device_properties(i) + s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" # bytes to MB + else: + s += "CPU\n" + + logger.info( + s.encode().decode("ascii", "ignore") if platform.system() == "Windows" else s, + ) # emoji-safe + return torch.device("cuda:0" if cuda else "cpu") + + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + + +def profile(x, ops, n=100, device=None): + # profile a pytorch module or list of modules. Example usage: + # x = torch.randn(16, 3, 640, 640) # input + # m1 = lambda x: x * torch.sigmoid(x) + # m2 = nn.SiLU() + # profile(x, [m1, m2], n=100) # profile speed over 100 iterations + + device = device or torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + x = x.to(device) + x.requires_grad = True + print( + torch.__version__, + device.type, + torch.cuda.get_device_properties(0) if device.type == "cuda" else "", + ) + print( + f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}", + ) + for m in ops if isinstance(ops, list) else [ops]: + m = m.to(device) if hasattr(m, "to") else m # device + m = ( + m.half() + if hasattr(m, "half") + and isinstance(x, torch.Tensor) + and x.dtype is torch.float16 + else m + ) # type + dtf, dtb, t = 0.0, 0.0, [0.0, 0.0, 0.0] # dt forward, backward + try: + flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1e9 * 2 # GFLOPS + except: + flops = 0 + + for _ in range(n): + t[0] = time_synchronized() + y = m(x) + t[1] = time_synchronized() + try: + _ = y.sum().backward() + t[2] = time_synchronized() + except: # no backward method + t[2] = float("nan") + dtf += (t[1] - t[0]) * 1000 / n # ms per op forward + dtb += (t[2] - t[1]) * 1000 / n # ms per op backward + + s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else "list" + s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else "list" + p = ( + sum([x.numel() for x in m.parameters()]) if isinstance(m, nn.Module) else 0 + ) # parameters + print( + f"{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{s_in!s:>24s}{s_out!s:>24s}", + ) + + +def is_parallel(model): + return type(model) in ( + nn.parallel.DataParallel, + nn.parallel.DistributedDataParallel, + ) + + +def intersect_dicts(da, db, exclude=()): + # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values + return { + k: v + for k, v in da.items() + if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape + } + + +def initialize_weights(model): + for m in model.modules(): + t = type(m) + if t is nn.Conv2d: + pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif t is nn.BatchNorm2d: + m.eps = 1e-3 + m.momentum = 0.03 + elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: + m.inplace = True + + +def find_modules(model, mclass=nn.Conv2d): + # Finds layer indices matching module class 'mclass' + return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] + + +def sparsity(model): + # Return global model sparsity + a, b = 0.0, 0.0 + for p in model.parameters(): + a += p.numel() + b += (p == 0).sum() + return b / a + + +def prune(model, amount=0.3): + # Prune model to requested global sparsity + import torch.nn.utils.prune as prune + + print("Pruning model... ", end="") + for name, m in model.named_modules(): + if isinstance(m, nn.Conv2d): + prune.l1_unstructured(m, name="weight", amount=amount) # prune + prune.remove(m, "weight") # make permanent + print(" %.3g global sparsity" % sparsity(model)) + + +def fuse_conv_and_bn(conv, bn): + # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ + fusedconv = ( + nn.Conv2d( + conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + groups=conv.groups, + bias=True, + ) + .requires_grad_(False) + .to(conv.weight.device) + ) + + # prepare filters + w_conv = conv.weight.clone().view(conv.out_channels, -1) + w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) + + # prepare spatial bias + b_conv = ( + torch.zeros(conv.weight.size(0), device=conv.weight.device) + if conv.bias is None + else conv.bias + ) + b_bn = bn.bias - bn.weight.mul(bn.running_mean).div( + torch.sqrt(bn.running_var + bn.eps), + ) + fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) + + return fusedconv + + +def model_info(model, verbose=False, img_size=640): + # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] + n_p = sum(x.numel() for x in model.parameters()) # number parameters + n_g = sum( + x.numel() for x in model.parameters() if x.requires_grad + ) # number gradients + if verbose: + print( + "%5s %40s %9s %12s %20s %10s %10s" + % ("layer", "name", "gradient", "parameters", "shape", "mu", "sigma"), + ) + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace("module_list.", "") + print( + "%5g %40s %9s %12g %20s %10.3g %10.3g" + % ( + i, + name, + p.requires_grad, + p.numel(), + list(p.shape), + p.mean(), + p.std(), + ), + ) + + try: # FLOPS + from thop import profile + + stride = max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32 + img = torch.zeros( + (1, model.yaml.get("ch", 3), stride, stride), + device=next(model.parameters()).device, + ) # input + flops = ( + profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1e9 * 2 + ) # stride GFLOPS + img_size = ( + img_size if isinstance(img_size, list) else [img_size, img_size] + ) # expand if int/float + fs = ", %.1f GFLOPS" % ( + flops * img_size[0] / stride * img_size[1] / stride + ) # 640x640 GFLOPS + except (ImportError, Exception): + fs = "" + + logger.info( + f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}", + ) + + +def load_classifier(name="resnet101", n=2): + # Loads a pretrained model reshaped to n-class output + model = torchvision.models.__dict__[name](pretrained=True) + + # ResNet model properties + # input_size = [3, 224, 224] + # input_space = 'RGB' + # input_range = [0, 1] + # mean = [0.485, 0.456, 0.406] + # std = [0.229, 0.224, 0.225] + + # Reshape output to n classes + filters = model.fc.weight.shape[1] + model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True) + model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True) + model.fc.out_features = n + return model + + +def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) + # scales img(bs,3,y,x) by ratio constrained to gs-multiple + if ratio == 1.0: + return img + else: + h, w = img.shape[2:] + s = (int(h * ratio), int(w * ratio)) # new size + img = F.interpolate(img, size=s, mode="bilinear", align_corners=False) # resize + if not same_shape: # pad/crop img + h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w)) + return F.pad( + img, + [0, w - s[1], 0, h - s[0]], + value=0.447, + ) # value = imagenet mean + + +def copy_attr(a, b, include=(), exclude=()): + # Copy attributes from b to a, options to only include [...] and to exclude [...] + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith("_") or k in exclude: + continue + else: + setattr(a, k, v) + + +class ModelEMA: + """Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models + Keep a moving average of everything in the model state_dict (parameters and buffers). + This is intended to allow functionality like + https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + A smoothed version of the weights is necessary for some training schemes to perform well. + This class is sensitive where it is initialized in the sequence of model init, + GPU assignment and distributed training wrappers. + """ + + def __init__(self, model, decay=0.9999, updates=0): + # Create EMA + self.ema = deepcopy( + model.module if is_parallel(model) else model, + ).eval() # FP32 EMA + # if next(model.parameters()).device.type != 'cpu': + # self.ema.half() # FP16 EMA + self.updates = updates # number of EMA updates + self.decay = lambda x: decay * ( + 1 - math.exp(-x / 2000) + ) # decay exponential ramp (to help early epochs) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + # Update EMA parameters + with torch.no_grad(): + self.updates += 1 + d = self.decay(self.updates) + + msd = ( + model.module.state_dict() if is_parallel(model) else model.state_dict() + ) # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: + v *= d + v += (1.0 - d) * msd[k].detach() + + def update_attr(self, model, include=(), exclude=("process_group", "reducer")): + # Update EMA attributes + copy_attr(self.ema, model, include, exclude) + + +class BatchNormXd(torch.nn.modules.batchnorm._BatchNorm): + def _check_input_dim(self, input): + # The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc + # is this method that is overwritten by the sub-class + # This original goal of this method was for tensor sanity checks + # If you're ok bypassing those sanity checks (eg. if you trust your inference + # to provide the right dimensional inputs), then you can just use this method + # for easy conversion from SyncBatchNorm + # (unfortunately, SyncBatchNorm does not store the original class - if it did + # we could return the one that was originally created) + return + + +def revert_sync_batchnorm(module): + # this is very similar to the function that it is trying to revert: + # https://github.com/pytorch/pytorch/blob/c8b3686a3e4ba63dc59e5dcfe5db3430df256833/torch/nn/modules/batchnorm.py#L679 + module_output = module + if isinstance(module, torch.nn.modules.batchnorm.SyncBatchNorm): + module_output = BatchNormXd( + module.num_features, + module.eps, + module.momentum, + module.affine, + module.track_running_stats, + ) + if module.affine: + with torch.no_grad(): + module_output.weight = module.weight + module_output.bias = module.bias + module_output.running_mean = module.running_mean + module_output.running_var = module.running_var + module_output.num_batches_tracked = module.num_batches_tracked + if hasattr(module, "qconfig"): + module_output.qconfig = module.qconfig + for name, child in module.named_children(): + module_output.add_module(name, revert_sync_batchnorm(child)) + del module + return module_output + + +class TracedModel(nn.Module): + def __init__(self, model=None, device=None, img_size=(640, 640)): + super().__init__() + + print(" Convert model to Traced-model... ") + self.stride = model.stride + self.names = model.names + self.model = model + + self.model = revert_sync_batchnorm(self.model) + self.model.to("cpu") + self.model.eval() + + self.detect_layer = self.model.model[-1] + self.model.traced = True + + rand_example = torch.rand(1, 3, img_size, img_size) + + traced_script_module = torch.jit.trace(self.model, rand_example, strict=False) + # traced_script_module = torch.jit.script(self.model) + traced_script_module.save("traced_model.pt") + print(" traced_script_module saved! ") + self.model = traced_script_module + self.model.to(device) + self.detect_layer.to(device) + print(" model is traced! \n") + + def forward(self, x, augment=False, profile=False): + out = self.model(x) + out = self.detect_layer(out) + return out diff --git a/mil_common/perception/yoloros/src/yoloros/utils/wandb_logging/__init__.py b/mil_common/perception/yoloros/src/yoloros/utils/wandb_logging/__init__.py new file mode 100644 index 000000000..a6131c10e --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/wandb_logging/__init__.py @@ -0,0 +1 @@ +# init diff --git a/mil_common/perception/yoloros/src/yoloros/utils/wandb_logging/log_dataset.py b/mil_common/perception/yoloros/src/yoloros/utils/wandb_logging/log_dataset.py new file mode 100644 index 000000000..51689df2d --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/wandb_logging/log_dataset.py @@ -0,0 +1,37 @@ +import argparse + +import yaml +from wandb_utils import WandbLogger + +WANDB_ARTIFACT_PREFIX = "wandb-artifact://" + + +def create_dataset_artifact(opt): + with open(opt.data) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + WandbLogger(opt, "", None, data, job_type="Dataset Creation") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "--data", + type=str, + default="data/coco.yaml", + help="data.yaml path", + ) + parser.add_argument( + "--single-cls", + action="store_true", + help="train as single-class dataset", + ) + parser.add_argument( + "--project", + type=str, + default="YOLOR", + help="name of W&B Project", + ) + opt = parser.parse_args() + opt.resume = False # Explicitly disallow resume check for dataset upload job + + create_dataset_artifact(opt) diff --git a/mil_common/perception/yoloros/src/yoloros/utils/wandb_logging/wandb_utils.py b/mil_common/perception/yoloros/src/yoloros/utils/wandb_logging/wandb_utils.py new file mode 100644 index 000000000..4ee1412b1 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/utils/wandb_logging/wandb_utils.py @@ -0,0 +1,465 @@ +import json +import sys +from pathlib import Path + +import torch +import yaml +from tqdm import tqdm + +sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path +from utils.datasets import LoadImagesAndLabels, img2label_paths +from utils.general import check_dataset, colorstr, xywh2xyxy + +try: + import wandb + from wandb import finish, init +except ImportError: + wandb = None + +WANDB_ARTIFACT_PREFIX = "wandb-artifact://" + + +def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): + return from_string[len(prefix) :] + + +def check_wandb_config_file(data_config_file): + wandb_config = "_wandb.".join( + data_config_file.rsplit(".", 1), + ) # updated data.yaml path + if Path(wandb_config).is_file(): + return wandb_config + return data_config_file + + +def get_run_info(run_path): + run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) + run_id = run_path.stem + project = run_path.parent.stem + model_artifact_name = "run_" + run_id + "_model" + return run_id, project, model_artifact_name + + +def check_wandb_resume(opt): + process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None + if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + if opt.global_rank not in [-1, 0]: # For resuming DDP runs + run_id, project, model_artifact_name = get_run_info(opt.resume) + api = wandb.Api() + artifact = api.artifact(project + "/" + model_artifact_name + ":latest") + modeldir = artifact.download() + opt.weights = str(Path(modeldir) / "last.pt") + return True + return None + + +def process_wandb_config_ddp_mode(opt): + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict + train_dir, val_dir = None, None + if isinstance(data_dict["train"], str) and data_dict["train"].startswith( + WANDB_ARTIFACT_PREFIX, + ): + api = wandb.Api() + train_artifact = api.artifact( + remove_prefix(data_dict["train"]) + ":" + opt.artifact_alias, + ) + train_dir = train_artifact.download() + train_path = Path(train_dir) / "data/images/" + data_dict["train"] = str(train_path) + + if isinstance(data_dict["val"], str) and data_dict["val"].startswith( + WANDB_ARTIFACT_PREFIX, + ): + api = wandb.Api() + val_artifact = api.artifact( + remove_prefix(data_dict["val"]) + ":" + opt.artifact_alias, + ) + val_dir = val_artifact.download() + val_path = Path(val_dir) / "data/images/" + data_dict["val"] = str(val_path) + if train_dir or val_dir: + ddp_data_path = str(Path(val_dir) / "wandb_local_data.yaml") + with open(ddp_data_path, "w") as f: + yaml.dump(data_dict, f) + opt.data = ddp_data_path + + +class WandbLogger: + def __init__(self, opt, name, run_id, data_dict, job_type="Training"): + # Pre-training routine -- + self.job_type = job_type + self.wandb, self.wandb_run, self.data_dict = ( + wandb, + None if not wandb else wandb.run, + data_dict, + ) + # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call + if isinstance(opt.resume, str): # checks resume from artifact + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + run_id, project, model_artifact_name = get_run_info(opt.resume) + model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name + assert wandb, "install wandb to resume wandb runs" + # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config + self.wandb_run = wandb.init(id=run_id, project=project, resume="allow") + opt.resume = model_artifact_name + elif self.wandb: + self.wandb_run = ( + wandb.init( + config=opt, + resume="allow", + project="YOLOR" + if opt.project == "runs/train" + else Path(opt.project).stem, + name=name, + job_type=job_type, + id=run_id, + ) + if not wandb.run + else wandb.run + ) + if self.wandb_run: + if self.job_type == "Training": + if not opt.resume: + wandb_data_dict = ( + self.check_and_upload_dataset(opt) + if opt.upload_dataset + else data_dict + ) + # Info useful for resuming from artifacts + self.wandb_run.config.opt = vars(opt) + self.wandb_run.config.data_dict = wandb_data_dict + self.data_dict = self.setup_training(opt, data_dict) + if self.job_type == "Dataset Creation": + self.data_dict = self.check_and_upload_dataset(opt) + else: + prefix = colorstr("wandb: ") + print( + f"{prefix}Install Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)", + ) + + def check_and_upload_dataset(self, opt): + assert wandb, "Install wandb to upload dataset" + check_dataset(self.data_dict) + config_path = self.log_dataset_artifact( + opt.data, + opt.single_cls, + "YOLOR" if opt.project == "runs/train" else Path(opt.project).stem, + ) + print("Created dataset config file ", config_path) + with open(config_path) as f: + wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader) + return wandb_data_dict + + def setup_training(self, opt, data_dict): + self.log_dict, self.current_epoch, self.log_imgs = ( + {}, + 0, + 16, + ) # Logging Constants + self.bbox_interval = opt.bbox_interval + if isinstance(opt.resume, str): + modeldir, _ = self.download_model_artifact(opt) + if modeldir: + self.weights = Path(modeldir) / "last.pt" + config = self.wandb_run.config + ( + opt.weights, + opt.save_period, + opt.batch_size, + opt.bbox_interval, + opt.epochs, + opt.hyp, + ) = ( + str(self.weights), + config.save_period, + config.total_batch_size, + config.bbox_interval, + config.epochs, + config.opt["hyp"], + ) + data_dict = dict( + self.wandb_run.config.data_dict, + ) # eliminates the need for config file to resume + if ( + "val_artifact" not in self.__dict__ + ): # If --upload_dataset is set, use the existing artifact, don't download + ( + self.train_artifact_path, + self.train_artifact, + ) = self.download_dataset_artifact( + data_dict.get("train"), + opt.artifact_alias, + ) + self.val_artifact_path, self.val_artifact = self.download_dataset_artifact( + data_dict.get("val"), + opt.artifact_alias, + ) + self.result_artifact, self.result_table, self.val_table, self.weights = ( + None, + None, + None, + None, + ) + if self.train_artifact_path is not None: + train_path = Path(self.train_artifact_path) / "data/images/" + data_dict["train"] = str(train_path) + if self.val_artifact_path is not None: + val_path = Path(self.val_artifact_path) / "data/images/" + data_dict["val"] = str(val_path) + self.val_table = self.val_artifact.get("val") + self.map_val_table_path() + if self.val_artifact is not None: + self.result_artifact = wandb.Artifact( + "run_" + wandb.run.id + "_progress", + "evaluation", + ) + self.result_table = wandb.Table( + ["epoch", "id", "prediction", "avg_confidence"], + ) + if opt.bbox_interval == -1: + self.bbox_interval = opt.bbox_interval = ( + (opt.epochs // 10) if opt.epochs > 10 else 1 + ) + return data_dict + + def download_dataset_artifact(self, path, alias): + if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): + dataset_artifact = wandb.use_artifact( + remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias, + ) + assert ( + dataset_artifact is not None + ), "'Error: W&B dataset artifact doesn't exist'" + datadir = dataset_artifact.download() + return datadir, dataset_artifact + return None, None + + def download_model_artifact(self, opt): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + model_artifact = wandb.use_artifact( + remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest", + ) + assert model_artifact is not None, "Error: W&B model artifact doesn't exist" + modeldir = model_artifact.download() + epochs_trained = model_artifact.metadata.get("epochs_trained") + total_epochs = model_artifact.metadata.get("total_epochs") + assert ( + epochs_trained < total_epochs + ), "training to %g epochs is finished, nothing to resume." % (total_epochs) + return modeldir, model_artifact + return None, None + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + model_artifact = wandb.Artifact( + "run_" + wandb.run.id + "_model", + type="model", + metadata={ + "original_url": str(path), + "epochs_trained": epoch + 1, + "save period": opt.save_period, + "project": opt.project, + "total_epochs": opt.epochs, + "fitness_score": fitness_score, + }, + ) + model_artifact.add_file(str(path / "last.pt"), name="last.pt") + wandb.log_artifact( + model_artifact, + aliases=[ + "latest", + "epoch " + str(self.current_epoch), + "best" if best_model else "", + ], + ) + print("Saving model artifact on epoch ", epoch + 1) + + def log_dataset_artifact( + self, + data_file, + single_cls, + project, + overwrite_config=False, + ): + with open(data_file) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + nc, names = (1, ["item"]) if single_cls else (int(data["nc"]), data["names"]) + names = dict(enumerate(names)) # to index dictionary + self.train_artifact = ( + self.create_dataset_table( + LoadImagesAndLabels(data["train"]), + names, + name="train", + ) + if data.get("train") + else None + ) + self.val_artifact = ( + self.create_dataset_table( + LoadImagesAndLabels(data["val"]), + names, + name="val", + ) + if data.get("val") + else None + ) + if data.get("train"): + data["train"] = WANDB_ARTIFACT_PREFIX + str(Path(project) / "train") + if data.get("val"): + data["val"] = WANDB_ARTIFACT_PREFIX + str(Path(project) / "val") + path = ( + data_file if overwrite_config else "_wandb.".join(data_file.rsplit(".", 1)) + ) # updated data.yaml path + data.pop("download", None) + with open(path, "w") as f: + yaml.dump(data, f) + + if self.job_type == "Training": # builds correct artifact pipeline graph + self.wandb_run.use_artifact(self.val_artifact) + self.wandb_run.use_artifact(self.train_artifact) + self.val_artifact.wait() + self.val_table = self.val_artifact.get("val") + self.map_val_table_path() + else: + self.wandb_run.log_artifact(self.train_artifact) + self.wandb_run.log_artifact(self.val_artifact) + return path + + def map_val_table_path(self): + self.val_table_map = {} + print("Mapping dataset") + for i, data in enumerate(tqdm(self.val_table.data)): + self.val_table_map[data[3]] = data[0] + + def create_dataset_table(self, dataset, class_to_id, name="dataset"): + # TODO: Explore multiprocessing to slpit this loop parallelly| This is essential for speeding up the the logging + artifact = wandb.Artifact(name=name, type="dataset") + img_files = ( + tqdm([dataset.path]) + if isinstance(dataset.path, str) and Path(dataset.path).is_dir() + else None + ) + img_files = img_files if img_files else tqdm(dataset.img_files) + for img_file in img_files: + if Path(img_file).is_dir(): + artifact.add_dir(img_file, name="data/images") + labels_path = "labels".join(dataset.path.rsplit("images", 1)) + artifact.add_dir(labels_path, name="data/labels") + else: + artifact.add_file(img_file, name="data/images/" + Path(img_file).name) + label_file = Path(img2label_paths([img_file])[0]) + artifact.add_file( + str(label_file), + name="data/labels/" + label_file.name, + ) if label_file.exists() else None + table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) + class_set = wandb.Classes( + [{"id": id, "name": name} for id, name in class_to_id.items()], + ) + for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): + height, width = shapes[0] + labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor( + [width, height, width, height], + ) + box_data, img_classes = [], {} + for cls, *xyxy in labels[:, 1:].tolist(): + cls = int(cls) + box_data.append( + { + "position": { + "minX": xyxy[0], + "minY": xyxy[1], + "maxX": xyxy[2], + "maxY": xyxy[3], + }, + "class_id": cls, + "box_caption": "%s" % (class_to_id[cls]), + "scores": {"acc": 1}, + "domain": "pixel", + }, + ) + img_classes[cls] = class_to_id[cls] + boxes = { + "ground_truth": {"box_data": box_data, "class_labels": class_to_id}, + } # inference-space + table.add_data( + si, + wandb.Image(paths, classes=class_set, boxes=boxes), + json.dumps(img_classes), + Path(paths).name, + ) + artifact.add(table, name) + return artifact + + def log_training_progress(self, predn, path, names): + if self.val_table and self.result_table: + class_set = wandb.Classes( + [{"id": id, "name": name} for id, name in names.items()], + ) + box_data = [] + total_conf = 0 + for *xyxy, conf, cls in predn.tolist(): + if conf >= 0.25: + box_data.append( + { + "position": { + "minX": xyxy[0], + "minY": xyxy[1], + "maxX": xyxy[2], + "maxY": xyxy[3], + }, + "class_id": int(cls), + "box_caption": f"{names[cls]} {conf:.3f}", + "scores": {"class_score": conf}, + "domain": "pixel", + }, + ) + total_conf = total_conf + conf + boxes = { + "predictions": {"box_data": box_data, "class_labels": names}, + } # inference-space + id = self.val_table_map[Path(path).name] + self.result_table.add_data( + self.current_epoch, + id, + wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), + total_conf / max(1, len(box_data)), + ) + + def log(self, log_dict): + if self.wandb_run: + for key, value in log_dict.items(): + self.log_dict[key] = value + + def end_epoch(self, best_result=False): + if self.wandb_run: + wandb.log(self.log_dict) + self.log_dict = {} + if self.result_artifact: + train_results = wandb.JoinedTable( + self.val_table, + self.result_table, + "id", + ) + self.result_artifact.add(train_results, "result") + wandb.log_artifact( + self.result_artifact, + aliases=[ + "latest", + "epoch " + str(self.current_epoch), + ("best" if best_result else ""), + ], + ) + self.result_table = wandb.Table( + ["epoch", "id", "prediction", "avg_confidence"], + ) + self.result_artifact = wandb.Artifact( + "run_" + wandb.run.id + "_progress", + "evaluation", + ) + + def finish_run(self): + if self.wandb_run: + if self.log_dict: + wandb.log(self.log_dict) + wandb.run.finish() diff --git a/mil_common/perception/yoloros/src/yoloros/visualizer.py b/mil_common/perception/yoloros/src/yoloros/visualizer.py new file mode 100644 index 000000000..f1ada0276 --- /dev/null +++ b/mil_common/perception/yoloros/src/yoloros/visualizer.py @@ -0,0 +1,52 @@ +from utils.plots import plot_one_box + + +def load_visuals(weights): + CLASSES = [] + COLORS = [] + if weights == "robosub24": + CLASSES = [ + "buoy_abydos_serpenscaput", + "buoy_abydos_taurus", + "buoy_earth_auriga", + "buoy_earth_cetus", + "gate_abydos", + "gate_earth", + ] + COLORS = [ + (255, 0, 0), + (0, 255, 0), + (0, 0, 255), + (255, 155, 0), + (255, 0, 255), + (0, 255, 255), + ] + return CLASSES, COLORS + + +def draw_detections(CLASSES, COLORS, detections, frame): + processed_detections = [] + if detections: + detections = detections[0] + + for x1, y1, x2, y2, conf, cls in detections: + class_index = int(cls.cpu().item()) + try: + class_name = CLASSES[class_index] + except: + print("ERROR: (Internal Error) Index out of range, please check") + return None + print(f"{class_name} => {conf}") + plot_one_box( + [x1, y1, x2, y2], + frame, + label=f"{CLASSES[class_index]}", + color=COLORS[class_index], + line_thickness=2, + ) + processed_detections.append((x1, y1, x2, y2, class_name)) + + return processed_detections, frame + else: + print("No Detections Made") + return None diff --git a/scripts/setup.bash b/scripts/setup.bash index 97d54f7da..b4790647b 100755 --- a/scripts/setup.bash +++ b/scripts/setup.bash @@ -102,4 +102,4 @@ startxbox() { alias xbox=startxbox # PYTHONPATH modifications -export PYTHONPATH="${HOME}/catkin_ws/src/mil/mil_common/axros/axros/src:${PYTHONPATH}" +export PYTHONPATH="${HOME}/catkin_ws/src/mil/mil_common/perception/vision_stack:${HOME}/catkin_ws/src/mil/mil_common/perception/yoloros/src/yoloros:${HOME}/catkin_ws/src/mil/mil_common/axros/axros/src:${PYTHONPATH}"