Update README.md

CMakeLists.txt

@@ -4,42 +4,130 @@ project(Air_Ground_CEC)
 ## 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
   std_msgs
   cv_bridge
-  sensor_msgs
   image_transport
 )
 
+## 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 installedsudo sh -c 'echo "/usr/local/lib" > /etc/ld.so.conf.d/opencv.conf'
+## 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
+#   std_msgs  # Or other packages containing 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 Air_Ground_CEC
+#  CATKIN_DEPENDS roscpp
+#  DEPENDS system_lib
 )
 
 ###########
 ## Build ##
 ###########
 
-#set(OpenCV_DIR /usr/local/share/opencv4)
 find_package(OpenCV REQUIRED)
 find_package(Qt5  REQUIRED COMPONENTS  Widgets )
 set(CMAKE_AUTOMOC ON)
 set(CMAKE_AUTOUIC ON)
 set(CMAKE_INCLUDE_CURRENT_DIR ON)
 
-file(GLOB QT_FORMS RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} src/ui/*.ui)
+set(SOURCES
-file(GLOB QT_RESOURCES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} src/resources/*.qrc)
+    src/main.cpp
-file(GLOB_RECURSE QT_MOC RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
+    src/mainwindow.cpp
-    FOLLOW_SYMLINKS src/include/Air_Ground_CEC/*.hpp *.h)
+    include/Air_Ground_CEC/mainwindow.hpp
-file(GLOB_RECURSE QT_SOURCES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
+    src/mainwindow.ui
-    FOLLOW_SYMLINKS src/*.cpp)
+    src/qnode.cpp
-
+    include/Air_Ground_CEC/qnode.hpp
-QT5_ADD_RESOURCES(QT_RESOURCES_CPP ${QT_RESOURCES})
+)
-QT5_WRAP_UI(QT_FORMS_HPP ${QT_FORMS})
-QT5_WRAP_CPP(QT_MOC_HPP ${QT_MOC})
-
 ## Specify additional locations of header files
 ## Your package locations should be listed before other locations
 include_directories(
@@ -48,14 +136,94 @@ include_directories(
     ${OpenCV_INCLUDE_DIRS}
 )
 
-add_executable(Air_Ground_CEC ${QRC_FILES} ${QT_SOURCES}
+add_executable(Air_Ground_CEC ${SOURCES})
-    ${QT_RESOURCES_CPP} ${QT_FORMS_HPP} ${QT_MOC_HPP})
-
 target_link_libraries(Air_Ground_CEC
     Qt5::Widgets
     ${catkin_LIBRARIES}
     ${OpenCV_LIBRARIES}
 )
 
+## Declare a C++ library
+# add_library(${PROJECT_NAME}
+#   src/${PROJECT_NAME}/Air_Ground_CEC.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/Air_Ground_CEC_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_Air_Ground_CEC.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)

README.md

@@ -1,3 +1,3 @@
 # Air-ground-CEC
 
-test
+此分支用于 测试 git 命令 不具有任何其他意义

include/Air_Ground_CEC/mainwindow.hpp

@@ -0,0 +1,28 @@
+#ifndef MAINWINDOW_H
+#define MAINWINDOW_H
+
+#include <QMainWindow>
+#include <QImage>
+#include <QMutex>
+
+namespace Ui {
+class MainWindow;
+}
+
+class MainWindow : public QMainWindow
+{
+    Q_OBJECT
+
+public:
+    explicit MainWindow(QWidget *parent = nullptr);
+    ~MainWindow();
+    void updateLogcamera();
+    void displayCamera(const QImage& image);
+private:
+    Ui::MainWindow *ui;
+    //QNode qnode;
+    QImage qimage_;
+    mutable QMutex qinmage_mutex_;
+};
+
+#endif // MAINWINDOW_H

include/Air_Ground_CEC/qnode.hpp

@@ -0,0 +1,8 @@
+#include <opencv4/opencv2/core.hpp>
+#include <opencv4/opencv2/highgui.hpp>
+#include <opencv4/opencv2/imgproc.hpp>
+#include <image_transport/image_transport.h>
+#include <cv_bridge/cv_bridge.h>
+#include <QImage>
+
+

model/model.txt

@@ -0,0 +1 @@
+create

src/Astar/8puzzle.cpp

@@ -1,803 +0,0 @@
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-// STL A* Search implementation
-// (C)2001 Justin Heyes-Jones
-//
-// This uses my A* code to solve the 8-puzzle
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-#include <iostream>
-#include <stdio.h>
-#include <assert.h>
-#include <new>
-
-#include <ctype.h>
-
-using namespace std;
-
-// Configuration
-
-#define NUM_TIMES_TO_RUN_SEARCH 1
-#define DISPLAY_SOLUTION_FORWARDS 1
-#define DISPLAY_SOLUTION_BACKWARDS 0
-#define DISPLAY_SOLUTION_INFO 1
-#define DEBUG_LISTS 0
-
-// AStar search class
-#include "stlastar.h" // See header for copyright and usage information
-
-// Global data
-
-#define BOARD_WIDTH   (3)
-#define BOARD_HEIGHT  (3)
-
-#define GM_TILE     (-1)
-#define GM_SPACE	 (0)
-#define GM_OFF_BOARD (1)
-
-// Definitions
-
-// To use the search class you must define the following calls...
-
-// Data
-//		Your own state space information
-// Functions
-//		(Optional) Constructor.
-//		Nodes are created by the user, so whether you use a
-//      constructor with parameters as below, or just set the object up after the 
-//      constructor, is up to you.
-//
-//		(Optional) Destructor. 
-//		The destructor will be called if you create one. You 
-//		can rely on the default constructor unless you dynamically allocate something in
-//		your data
-//
-//		float GoalDistanceEstimate( PuzzleState &nodeGoal );
-//		Return the estimated cost to goal from this node (pass reference to goal node)
-//
-//		bool IsGoal( PuzzleState &nodeGoal );
-//		Return true if this node is the goal.
-//
-//		bool GetSuccessors( AStarSearch<PuzzleState> *astarsearch );
-//		For each successor to this state call the AStarSearch's AddSuccessor call to 
-//		add each one to the current search - return false if you are out of memory and the search
-//		will fail
-//
-//		float GetCost( PuzzleState *successor );
-//		Return the cost moving from this state to the state of successor
-//
-//		bool IsSameState( PuzzleState &rhs );
-//		Return true if the provided state is the same as this state
-
-// Here the example is the 8-puzzle state ...
-class PuzzleState
-{
-
-public:
-
-	// defs
-
-	typedef enum
-	{
-		TL_SPACE,
-		TL_1,
-		TL_2,
-		TL_3,
-		TL_4,
-		TL_5,
-		TL_6,
-		TL_7,
-		TL_8
-
-	} TILE;
-
-	// data
-
-	static TILE g_goal[ BOARD_WIDTH*BOARD_HEIGHT];
-	static TILE g_start[ BOARD_WIDTH*BOARD_HEIGHT];
-
-	// the tile data for the 8-puzzle
-	TILE tiles[ BOARD_WIDTH*BOARD_HEIGHT ];
-
-	// member functions
-
-	PuzzleState() {  
-						memcpy( tiles, g_goal, sizeof( TILE ) * BOARD_WIDTH * BOARD_HEIGHT );			
-					}
-
-	PuzzleState( TILE *param_tiles ) 
-					{
-						memcpy( tiles, param_tiles, sizeof( TILE ) * BOARD_WIDTH * BOARD_HEIGHT );			
-					}
-
-	float GoalDistanceEstimate( PuzzleState &nodeGoal );
-	bool IsGoal( PuzzleState &nodeGoal );
-	bool GetSuccessors( AStarSearch<PuzzleState> *astarsearch, PuzzleState *parent_node );
-	float GetCost( PuzzleState &successor );
-	bool IsSameState( PuzzleState &rhs );
-	
-	void PrintNodeInfo(); 
-
-private:
-	// User stuff - Just add what you need to help you write the above functions...
-
-	void GetSpacePosition( PuzzleState *pn, int *rx, int *ry );
-	bool LegalMove( TILE *StartTiles, TILE *TargetTiles, int spx, int spy, int tx, int ty );
-	int GetMap( int x, int y, TILE *tiles );
-
-
-
-
-};
-
-// Goal state
-PuzzleState::TILE PuzzleState::g_goal[] = 
-{
-	TL_1,
-	TL_2,
-	TL_3,
-	TL_8,
-	TL_SPACE,
-	TL_4,
-	TL_7,
-	TL_6,
-	TL_5,
-};
-
-// Some nice Start states
-PuzzleState::TILE PuzzleState::g_start[] = 
-{
-
-	// Three example start states from Bratko's Prolog Programming for Artificial Intelligence
-
-#if 1
-	// ex a - 4 steps
-	 TL_1 ,
-	 TL_3 ,
-	 TL_4 ,
-	 TL_8 ,
-	 TL_SPACE ,
-	 TL_2 ,
-	 TL_7 ,
-	 TL_6 ,
-	 TL_5 ,
-
-#elif 0
-  
-	// ex b - 5 steps
-	 TL_2 ,
-	 TL_8 ,
-	 TL_3 ,
-	 TL_1 ,
-	 TL_6 ,
-	 TL_4 ,
-	 TL_7 ,
-	 TL_SPACE ,
-	 TL_5 ,
-
-#elif 0
-	
-	// ex c - 18 steps
-	 TL_2 ,
-	 TL_1 ,
-	 TL_6 ,
-	 TL_4 ,
-	 TL_SPACE ,
-	 TL_8 ,
-	 TL_7 ,
-	 TL_5 ,
-	 TL_3 ,
-
-#elif 0
-	
-	// nasty one - doesn't solve
-	 TL_6 ,
-	 TL_3 ,	  
-	 TL_SPACE ,
-	 TL_4 ,
-	 TL_8 ,
-	 TL_5 ,
-	 TL_7 ,
-	 TL_2 ,
-	 TL_1 ,
-
-#elif 0
-
-	// sent by email - does work though
-
-	 TL_1 ,	 TL_2 ,  TL_3 ,
-	 TL_4 ,	 TL_5 ,  TL_6 ,
-	 TL_8 ,	 TL_7 ,  TL_SPACE ,
-
-	
-// from http://www.cs.utexas.edu/users/novak/asg-8p.html
-
-//Goal:        Easy:        Medium:        Hard:        Worst:
-
-//1 2 3        1 3 4        2 8 1          2 8 1        5 6 7
-//8   4        8 6 2          4 3          4 6 3        4   8
-//7 6 5        7   5        7 6 5            7 5        3 2 1
-
-
-#elif 0
-
-	// easy 5 
-	 TL_1 ,
-	 TL_3 ,	  
-	 TL_4 ,
-
-	 TL_8 ,
-	 TL_6 ,
-	 TL_2 ,
-	
-	 TL_7 ,
-	 TL_SPACE ,
-	 TL_5 ,
-
-
-#elif 0
-
-	// medium 9
-	 TL_2 ,
-	 TL_8 ,	  
-	 TL_1 ,
-
-	 TL_SPACE ,
-	 TL_4 ,
-	 TL_3 ,
-	
-	 TL_7 ,
-	 TL_6 ,
-	 TL_5 ,
-
-#elif 0
-
-	// hard 12
-	 TL_2 ,
-	 TL_8 ,	  
-	 TL_1 ,
-
-	 TL_4 ,
-	 TL_6 ,
-	 TL_3 ,
-	
-	 TL_SPACE ,
-	 TL_7 ,
-	 TL_5 ,
-
-#elif 0
-
-	// worst 30
-	 TL_5 ,
-	 TL_6 ,	  
-	 TL_7 ,
-
-	 TL_4 ,
-	 TL_SPACE ,
-	 TL_8 ,
-	
-	 TL_3 ,
-	 TL_2 ,
-	 TL_1 ,
-
-#elif 0
-
-	//	123
-	//	784
-	//   65
-
-	// two move simple board
-	 TL_1 ,
-	 TL_2 ,	  
-	 TL_3 ,
-
-	 TL_7 ,
-	 TL_8 ,
-	 TL_4 ,
-	
-	 TL_SPACE ,
-	 TL_6 ,
-	 TL_5 ,
-
-#elif 0
-	//  a1 b2 c3 d4 e5 f6 g7 h8 
-	//C3,Blank,H8,A1,G8,F6,E5,D4,B2
-
-	 TL_3 ,
-	 TL_SPACE ,	  
-	 TL_8 ,
-
-	 TL_1 ,
-	 TL_8 ,
-	 TL_6 ,
-	
-	 TL_5 ,
-	 TL_4 ,
-	 TL_2 ,
-
-
-#endif  
-
-};
-
-bool PuzzleState::IsSameState( PuzzleState &rhs )
-{
-
-	for( int i=0; i<(BOARD_HEIGHT*BOARD_WIDTH); i++ )
-	{
-		if( tiles[i] != rhs.tiles[i] )
-		{
-			return false;
-		}
-	}
-
-	return true;
-
-}
-
-void PuzzleState::PrintNodeInfo()
-{
-	char str[100];
-	sprintf( str, "%c %c %c\n%c %c %c\n%c %c %c\n", 
-			tiles[0] + '0',
-			tiles[1] + '0',
-			tiles[2] + '0',
-			tiles[3] + '0',
-			tiles[4] + '0',
-			tiles[5] + '0',
-			tiles[6] + '0',
-			tiles[7] + '0',
-			tiles[8] + '0'
-		);
-
-	cout << str;
-}
-
-// Here's the heuristic function that estimates the distance from a PuzzleState
-// to the Goal. 
-
-float PuzzleState::GoalDistanceEstimate( PuzzleState &nodeGoal )
-{
-
-	// Nilsson's sequence score
-
-	int i, cx, cy, ax, ay, h = 0, s, t;
-
-	// given a tile this returns the tile that should be clockwise
-	TILE correct_follower_to[ BOARD_WIDTH * BOARD_HEIGHT ] =
-	{
-		TL_SPACE, // always wrong
-		TL_2,
-		TL_3,
-		TL_4,
-		TL_5,
-		TL_6,
-		TL_7,
-		TL_8,
-		TL_1,
-	};
-
-	// given a table index returns the index of the tile that is clockwise to it 3*3 only
-	int clockwise_tile_of[ BOARD_WIDTH * BOARD_HEIGHT ] =
-	{
-		1, 
-		2,  	  // 012	  
-		5,  	  // 345	  
-		0,  	  // 678	  
-		-1,  // never called with center square
-		8, 
-		3, 
-		6, 
-		7 
-	};
-
-	int tile_x[ BOARD_WIDTH * BOARD_HEIGHT ] =
-	{
-		/* TL_SPACE */ 1,
-		/* TL_1 */ 0,    
-		/* TL_2 */ 1,    
-		/* TL_3 */ 2,    
-		/* TL_4 */ 2,    
-		/* TL_5 */ 2,    
-		/* TL_6 */ 1,    
-		/* TL_7 */ 0,    
-		/* TL_8 */ 0,    
-	};
-
-	int tile_y[ BOARD_WIDTH * BOARD_HEIGHT ] =
-	{
-		/* TL_SPACE */ 1,	
-		/* TL_1 */ 0,
-		/* TL_2 */ 0,
-		/* TL_3 */ 0,
-		/* TL_4 */ 1,
-		/* TL_5 */ 2,
-		/* TL_6 */ 2,
-		/* TL_7 */ 2,
-		/* TL_8 */ 1,
-	};
-
-	s=0;
-	
-	// score 1 point if centre is not correct 
-	if( tiles[(BOARD_HEIGHT*BOARD_WIDTH)/2] != nodeGoal.tiles[(BOARD_HEIGHT*BOARD_WIDTH)/2] )
-	{
- 		s = 1;
-	}
-
-	for( i=0; i<(BOARD_HEIGHT*BOARD_WIDTH); i++ )
-	{
-		// this loop adds up the totaldist element in h and
-		// the sequence score in s
-
-		// the space does not count
-		if( tiles[i] == TL_SPACE )
-		{
-			continue;
-		}
-
-		// get correct x and y of this tile
-		cx = tile_x[tiles[i]];
-		cy = tile_y[tiles[i]];
-
-		// get actual
-		ax = i % BOARD_WIDTH;
-		ay = i / BOARD_WIDTH;
-
-		// add manhatten distance to h
-		h += abs( cx-ax );
-		h += abs( cy-ay );
-
-		// no s score for center tile
-		if( (ax == (BOARD_WIDTH/2)) && (ay == (BOARD_HEIGHT/2)) )
-		{
-			continue;
-		}
-
-		// score 2 points if not followed by successor
-		if( correct_follower_to[ tiles[i] ] != tiles[ clockwise_tile_of[ i ] ] )
-		{
-			s += 2;
-		}
-
-
-	}
-
-	// mult by 3 and add to h
-	t = h + (3*s);
-
-	return (float) t;
-
-}
-
-bool PuzzleState::IsGoal( PuzzleState &nodeGoal )
-{
-	return IsSameState( nodeGoal );
-}
-
-// Helper
-// Return the x and y position of the space tile
-void PuzzleState::GetSpacePosition( PuzzleState *pn, int *rx, int *ry )
-{
-	int x,y;
-
-	for( y=0; y<BOARD_HEIGHT; y++ )
-	{
-		for( x=0; x<BOARD_WIDTH; x++ )
-		{
-			if( pn->tiles[(y*BOARD_WIDTH)+x] == TL_SPACE )
-			{
-				*rx = x;
-				*ry = y;
-
-				return;
-			}
-		}
-	}
-
-	assert( false && "Something went wrong. There's no space on the board" );
-
-}
-
-int PuzzleState::GetMap( int x, int y, TILE *tiles )
-{
-
-	if( x < 0 ||
-	    x >= BOARD_WIDTH ||
-		 y < 0 ||
-		 y >= BOARD_HEIGHT
-	  )
-		return GM_OFF_BOARD;	 
-
-	if( tiles[(y*BOARD_WIDTH)+x] == TL_SPACE )
-	{
-		return GM_SPACE;
-	}
-
-	return GM_TILE;
-}
-
-// Given a node set of tiles and a set of tiles to move them into, do the move as if it was on a tile board
-// note : returns false if the board wasn't changed, and simply returns the tiles as they were in the target
-// spx and spy is the space position while tx and ty is the target move from position
-
-bool PuzzleState::LegalMove( TILE *StartTiles, TILE *TargetTiles, int spx, int spy, int tx, int ty )
-{
-
-	int t;
-	
-	if( GetMap( spx, spy, StartTiles ) == GM_SPACE )
-	{
-		if( GetMap( tx, ty, StartTiles ) == GM_TILE )
-		{
-
-			// copy tiles
-			for( t=0; t<(BOARD_HEIGHT*BOARD_WIDTH); t++ )
-			{
-				TargetTiles[t] = StartTiles[t];
-			}
-
-
-			TargetTiles[ (ty*BOARD_WIDTH)+tx ] = StartTiles[ (spy*BOARD_WIDTH)+spx ];
-			TargetTiles[ (spy*BOARD_WIDTH)+spx ] = StartTiles[ (ty*BOARD_WIDTH)+tx ];
-
-			return true;
-		}
-	}
-
-
-	return false;
-
-}
-
-// This generates the successors to the given PuzzleState. It uses a helper function called
-// AddSuccessor to give the successors to the AStar class. The A* specific initialisation
-// is done for each node internally, so here you just set the state information that
-// is specific to the application
-bool PuzzleState::GetSuccessors( AStarSearch<PuzzleState> *astarsearch, PuzzleState *parent_node )
-{
-	PuzzleState NewNode;
-
-	int sp_x,sp_y;
-
-	GetSpacePosition( this, &sp_x, &sp_y );
-
-	bool ret;
-
-	if( LegalMove( tiles, NewNode.tiles, sp_x, sp_y, sp_x, sp_y-1 ) == true )
-	{
-		ret = astarsearch->AddSuccessor( NewNode );
-
-		if( !ret ) return false;
-	}
-
-	if( LegalMove( tiles, NewNode.tiles, sp_x, sp_y, sp_x, sp_y+1 ) == true )
-	{
-		ret = astarsearch->AddSuccessor( NewNode );
-		
-		if( !ret ) return false;
-	}
-
-	if( LegalMove( tiles, NewNode.tiles, sp_x, sp_y, sp_x-1, sp_y ) == true )
-	{
-		ret = astarsearch->AddSuccessor( NewNode );
-
-		if( !ret ) return false;
-	}
-
-	if( LegalMove( tiles, NewNode.tiles, sp_x, sp_y, sp_x+1, sp_y ) == true )
-	{
-		ret = astarsearch->AddSuccessor( NewNode );
-	
-		if( !ret ) return false;
-	}
-
-	return true; 
-}
-
-// given this node, what does it cost to move to successor. In the case
-// of our map the answer is the map terrain value at this node since that is 
-// conceptually where we're moving
-
-float PuzzleState::GetCost( PuzzleState &successor )
-{
-	return 1.0f; // I love it when life is simple
-
-}
-
-
-// Main
-
-int puzzle( int argc, char *argv[] )
-{
-
-	cout << "STL A* 8-puzzle solver implementation\n(C)2001 Justin Heyes-Jones\n";
-
-	if( argc > 1 )
-	{
-		int i = 0;
-		int c;
-
-		while( (c = argv[1][i]) )
-		{
-			if( isdigit( c ) )
-			{
-				int num = (c - '0');
-
-				PuzzleState::g_start[i] = static_cast<PuzzleState::TILE>(num);
-				
-			}
-		
-			i++;
-		}
-
-
-	}
-
-	// Create an instance of the search class...
-
-	AStarSearch<PuzzleState> astarsearch;
-
-	int NumTimesToSearch = NUM_TIMES_TO_RUN_SEARCH;
-
-	while( NumTimesToSearch-- )
-	{
-
-		// Create a start state
-		PuzzleState nodeStart( PuzzleState::g_start );
-
-		// Define the goal state
-		PuzzleState nodeEnd( PuzzleState::g_goal );
-
-		// Set Start and goal states
-		astarsearch.SetStartAndGoalStates( nodeStart, nodeEnd );
-
-		unsigned int SearchState;
-
-		unsigned int SearchSteps = 0;
-
-		do
-		{
-			SearchState = astarsearch.SearchStep();
-
-#if DEBUG_LISTS
-
-			float f,g,h;
-
-			cout << "Search step " << SearchSteps << endl;
-
-			cout << "Open:\n";
-			PuzzleState *p = astarsearch.GetOpenListStart( f,g,h );
-			while( p )
-			{
-				((PuzzleState *)p)->PrintNodeInfo();
-				cout << "f: " << f << " g: " << g << " h: " << h << "\n\n";
-				
-				p = astarsearch.GetOpenListNext( f,g,h );
-				
-			}
-
-			cout << "Closed:\n";
-			p = astarsearch.GetClosedListStart( f,g,h );
-			while( p )
-			{
-				p->PrintNodeInfo();
-				cout << "f: " << f << " g: " << g << " h: " << h << "\n\n";
-				
-				p = astarsearch.GetClosedListNext( f,g,h );
-			}
-
-#endif
-
-// Test cancel search
-#if 0
-			int StepCount = astarsearch.GetStepCount();
-			if( StepCount == 10 )
-			{
-				astarsearch.CancelSearch();
-			}
-#endif
-			SearchSteps++;
-		}
-		while( SearchState == AStarSearch<PuzzleState>::SEARCH_STATE_SEARCHING );
-
-		if( SearchState == AStarSearch<PuzzleState>::SEARCH_STATE_SUCCEEDED )
-		{
-#if DISPLAY_SOLUTION_FORWARDS
-			cout << "Search found goal state\n";
-#endif
-			PuzzleState *node = astarsearch.GetSolutionStart();
-
-#if DISPLAY_SOLUTION_FORWARDS
-			cout << "Displaying solution\n";
-#endif
-			int steps = 0;
-
-#if DISPLAY_SOLUTION_FORWARDS
-			node->PrintNodeInfo();
-			cout << endl;
-#endif
-			for( ;; )
-			{
-				node = astarsearch.GetSolutionNext();
-
-				if( !node )
-				{
-					break;
-				}
-
-#if DISPLAY_SOLUTION_FORWARDS
-				node->PrintNodeInfo();
-				cout << endl;
-#endif
-				steps ++;
-			
-			};
-
-#if DISPLAY_SOLUTION_FORWARDS
-			// todo move step count into main algorithm
-			cout << "Solution steps " << steps << endl;
-#endif
-
-////////////
-
-			node = astarsearch.GetSolutionEnd();
-
-#if DISPLAY_SOLUTION_BACKWARDS
-			cout << "Displaying reverse solution\n";
-#endif
-			steps = 0;
-
-			node->PrintNodeInfo();
-			cout << endl;
-			for( ;; )
-			{
-				node = astarsearch.GetSolutionPrev();
-
-				if( !node )
-				{
-					break;
-				}
-#if DISPLAY_SOLUTION_BACKWARDS
-				node->PrintNodeInfo();
-                cout << endl;
-#endif
-				steps ++;
-			
-			};
-
-#if DISPLAY_SOLUTION_BACKWARDS
-			cout << "Solution steps " << steps << endl;
-#endif
-
-//////////////
-
-			// Once you're done with the solution you can free the nodes up
-			astarsearch.FreeSolutionNodes();
-		
-		}
-		else if( SearchState == AStarSearch<PuzzleState>::SEARCH_STATE_FAILED ) 
-		{
-#if DISPLAY_SOLUTION_INFO
-			cout << "Search terminated. Did not find goal state\n";
-#endif		
-		}
-		else if( SearchState == AStarSearch<PuzzleState>::SEARCH_STATE_OUT_OF_MEMORY ) 
-		{
-#if DISPLAY_SOLUTION_INFO
-			cout << "Search terminated. Out of memory\n";
-#endif		
-		}
-
-		
-
-		// Display the number of loops the search went through
-#if DISPLAY_SOLUTION_INFO
-		cout << "SearchSteps : " << astarsearch.GetStepCount() << endl;
-#endif
-	}
-
-	return 0;
-}
-
-

src/Astar/findpath.cpp

@@ -1,343 +0,0 @@
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-// STL A* Search implementation
-// (C)2001 Justin Heyes-Jones
-//
-// Finding a path on a simple grid maze
-// This shows how to do shortest path finding using A*
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-
-#include "stlastar.h" // See header for copyright and usage information
-
-#include <iostream>
-#include <stdio.h>
-#include <math.h>
-
-#define DEBUG_LISTS 0
-#define DEBUG_LIST_LENGTHS_ONLY 0
-
-using namespace std;
-
-// Global data
-
-// The world map
-
-const int MAP_WIDTH = 20;
-const int MAP_HEIGHT = 20;
-
-int world_map[ MAP_WIDTH * MAP_HEIGHT ] = 
-{
-
-// 0001020304050607080910111213141516171819
-	1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,   // 00
-	1,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,1,   // 01
-	1,9,9,1,1,9,9,9,1,9,1,9,1,9,1,9,9,9,1,1,   // 02
-	1,9,9,1,1,9,9,9,1,9,1,9,1,9,1,9,9,9,1,1,   // 03
-	1,9,1,1,1,1,9,9,1,9,1,9,1,1,1,1,9,9,1,1,   // 04
-	1,9,1,1,9,1,1,1,1,9,1,1,1,1,9,1,1,1,1,1,   // 05
-	1,9,9,9,9,1,1,1,1,1,1,9,9,9,9,1,1,1,1,1,   // 06
-	1,9,9,9,9,9,9,9,9,1,1,1,9,9,9,9,9,9,9,1,   // 07
-	1,9,1,1,1,1,1,1,1,1,1,9,1,1,1,1,1,1,1,1,   // 08
-	1,9,1,9,9,9,9,9,9,9,1,1,9,9,9,9,9,9,9,1,   // 09
-	1,9,1,1,1,1,9,1,1,9,1,1,1,1,1,1,1,1,1,1,   // 10
-	1,9,9,9,9,9,1,9,1,9,1,9,9,9,9,9,1,1,1,1,   // 11
-	1,9,1,9,1,9,9,9,1,9,1,9,1,9,1,9,9,9,1,1,   // 12
-	1,9,1,9,1,9,9,9,1,9,1,9,1,9,1,9,9,9,1,1,   // 13
-	1,9,1,1,1,1,9,9,1,9,1,9,1,1,1,1,9,9,1,1,   // 14
-	1,9,1,1,9,1,1,1,1,9,1,1,1,1,9,1,1,1,1,1,   // 15
-	1,9,9,9,9,1,1,1,1,1,1,9,9,9,9,1,1,1,1,1,   // 16
-	1,1,9,9,9,9,9,9,9,1,1,1,9,9,9,1,9,9,9,9,   // 17
-	1,9,1,1,1,1,1,1,1,1,1,9,1,1,1,1,1,1,1,1,   // 18
-	1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,   // 19
-
-};
-
-// map helper functions
-
-int GetMap( int x, int y )
-{
-	if( x < 0 ||
-	    x >= MAP_WIDTH ||
-		 y < 0 ||
-		 y >= MAP_HEIGHT
-	  )
-	{
-		return 9;	 
-	}
-
-	return world_map[(y*MAP_WIDTH)+x];
-}
-
-
-
-// Definitions
-
-class MapSearchNode
-{
-public:
-	int x;	 // the (x,y) positions of the node
-	int y;	
-	
-	MapSearchNode() { x = y = 0; }
-	MapSearchNode( int px, int py ) { x=px; y=py; }
-
-	float GoalDistanceEstimate( MapSearchNode &nodeGoal );
-	bool IsGoal( MapSearchNode &nodeGoal );
-	bool GetSuccessors( AStarSearch<MapSearchNode> *astarsearch, MapSearchNode *parent_node );
-	float GetCost( MapSearchNode &successor );
-	bool IsSameState( MapSearchNode &rhs );
-
-	void PrintNodeInfo(); 
-
-
-};
-
-bool MapSearchNode::IsSameState( MapSearchNode &rhs )
-{
-
-	// same state in a maze search is simply when (x,y) are the same
-	if( (x == rhs.x) &&
-		(y == rhs.y) )
-	{
-		return true;
-	}
-	else
-	{
-		return false;
-	}
-
-}
-
-void MapSearchNode::PrintNodeInfo()
-{
-	char str[100];
-	sprintf( str, "Node position : (%d,%d)\n", x,y );
-
-	cout << str;
-}
-
-// Here's the heuristic function that estimates the distance from a Node
-// to the Goal. 
-
-float MapSearchNode::GoalDistanceEstimate( MapSearchNode &nodeGoal )
-{
-	return abs(x - nodeGoal.x) + abs(y - nodeGoal.y);
-}
-
-bool MapSearchNode::IsGoal( MapSearchNode &nodeGoal )
-{
-
-	if( (x == nodeGoal.x) &&
-		(y == nodeGoal.y) )
-	{
-		return true;
-	}
-
-	return false;
-}
-
-// This generates the successors to the given Node. It uses a helper function called
-// AddSuccessor to give the successors to the AStar class. The A* specific initialisation
-// is done for each node internally, so here you just set the state information that
-// is specific to the application
-bool MapSearchNode::GetSuccessors( AStarSearch<MapSearchNode> *astarsearch, MapSearchNode *parent_node )
-{
-
-	int parent_x = -1; 
-	int parent_y = -1; 
-
-	if( parent_node )
-	{
-		parent_x = parent_node->x;
-		parent_y = parent_node->y;
-	}
-	
-
-	MapSearchNode NewNode;
-
-	// push each possible move except allowing the search to go backwards
-
-	if( (GetMap( x-1, y ) < 9) 
-		&& !((parent_x == x-1) && (parent_y == y))
-	  ) 
-	{
-		NewNode = MapSearchNode( x-1, y );
-		astarsearch->AddSuccessor( NewNode );
-	}	
-
-	if( (GetMap( x, y-1 ) < 9) 
-		&& !((parent_x == x) && (parent_y == y-1))
-	  ) 
-	{
-		NewNode = MapSearchNode( x, y-1 );
-		astarsearch->AddSuccessor( NewNode );
-	}	
-
-	if( (GetMap( x+1, y ) < 9)
-		&& !((parent_x == x+1) && (parent_y == y))
-	  ) 
-	{
-		NewNode = MapSearchNode( x+1, y );
-		astarsearch->AddSuccessor( NewNode );
-	}	
-
-		
-	if( (GetMap( x, y+1 ) < 9) 
-		&& !((parent_x == x) && (parent_y == y+1))
-		)
-	{
-		NewNode = MapSearchNode( x, y+1 );
-		astarsearch->AddSuccessor( NewNode );
-	}	
-
-	return true;
-}
-
-// given this node, what does it cost to move to successor. In the case
-// of our map the answer is the map terrain value at this node since that is 
-// conceptually where we're moving
-
-float MapSearchNode::GetCost( MapSearchNode &successor )
-{
-	return (float) GetMap( x, y );
-}
-
-
-// Main
-
-int findpath( int argc, char *argv[] )
-{
-
-	cout << "STL A* Search implementation\n(C)2001 Justin Heyes-Jones\n";
-
-	// Our sample problem defines the world as a 2d array representing a terrain
-	// Each element contains an integer from 0 to 5 which indicates the cost 
-	// of travel across the terrain. Zero means the least possible difficulty 
-	// in travelling (think ice rink if you can skate) whilst 5 represents the 
-	// most difficult. 9 indicates that we cannot pass.
-
-	// Create an instance of the search class...
-
-	AStarSearch<MapSearchNode> astarsearch;
-
-	unsigned int SearchCount = 0;
-
-	const unsigned int NumSearches = 1;
-
-	while(SearchCount < NumSearches)
-	{
-
-		// Create a start state
-		MapSearchNode nodeStart;
-		nodeStart.x = rand()%MAP_WIDTH;
-		nodeStart.y = rand()%MAP_HEIGHT; 
-
-		// Define the goal state
-		MapSearchNode nodeEnd;
-		nodeEnd.x = rand()%MAP_WIDTH;						
-		nodeEnd.y = rand()%MAP_HEIGHT; 
-		
-		// Set Start and goal states
-		
-		astarsearch.SetStartAndGoalStates( nodeStart, nodeEnd );
-
-		unsigned int SearchState;
-		unsigned int SearchSteps = 0;
-
-		do
-		{
-			SearchState = astarsearch.SearchStep();
-
-			SearchSteps++;
-
-	#if DEBUG_LISTS
-
-			cout << "Steps:" << SearchSteps << "\n";
-
-			int len = 0;
-
-			cout << "Open:\n";
-			MapSearchNode *p = astarsearch.GetOpenListStart();
-			while( p )
-			{
-				len++;
-	#if !DEBUG_LIST_LENGTHS_ONLY			
-				((MapSearchNode *)p)->PrintNodeInfo();
-	#endif
-				p = astarsearch.GetOpenListNext();
-				
-			}
-
-			cout << "Open list has " << len << " nodes\n";
-
-			len = 0;
-
-			cout << "Closed:\n";
-			p = astarsearch.GetClosedListStart();
-			while( p )
-			{
-				len++;
-	#if !DEBUG_LIST_LENGTHS_ONLY			
-				p->PrintNodeInfo();
-	#endif			
-				p = astarsearch.GetClosedListNext();
-			}
-
-			cout << "Closed list has " << len << " nodes\n";
-	#endif
-
-		}
-		while( SearchState == AStarSearch<MapSearchNode>::SEARCH_STATE_SEARCHING );
-
-		if( SearchState == AStarSearch<MapSearchNode>::SEARCH_STATE_SUCCEEDED )
-		{
-			cout << "Search found goal state\n";
-
-				MapSearchNode *node = astarsearch.GetSolutionStart();
-
-	#if DISPLAY_SOLUTION
-				cout << "Displaying solution\n";
-	#endif
-				int steps = 0;
-
-				node->PrintNodeInfo();
-				for( ;; )
-				{
-					node = astarsearch.GetSolutionNext();
-
-					if( !node )
-					{
-						break;
-					}
-
-					node->PrintNodeInfo();
-					steps ++;
-				
-				};
-
-				cout << "Solution steps " << steps << endl;
-
-				// Once you're done with the solution you can free the nodes up
-				astarsearch.FreeSolutionNodes();
-
-	
-		}
-		else if( SearchState == AStarSearch<MapSearchNode>::SEARCH_STATE_FAILED ) 
-		{
-			cout << "Search terminated. Did not find goal state\n";
-		
-		}
-
-		// Display the number of loops the search went through
-		cout << "SearchSteps : " << SearchSteps << "\n";
-
-		SearchCount ++;
-
-		astarsearch.EnsureMemoryFreed();
-	}
-	
-	return 0;
-}
-
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////

src/Astar/fsa.h

@@ -1,252 +0,0 @@
-/* 
-
-A* Algorithm Implementation using STL is
-Copyright (C)2001-2005 Justin Heyes-Jones
-
-Permission is given by the author to freely redistribute and 
-include this code in any program as long as this credit is 
-given where due.
- 
-  COVERED CODE IS PROVIDED UNDER THIS LICENSE ON AN "AS IS" BASIS, 
-  WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, 
-  INCLUDING, WITHOUT LIMITATION, WARRANTIES THAT THE COVERED CODE 
-  IS FREE OF DEFECTS, MERCHANTABLE, FIT FOR A PARTICULAR PURPOSE
-  OR NON-INFRINGING. THE ENTIRE RISK AS TO THE QUALITY AND 
-  PERFORMANCE OF THE COVERED CODE IS WITH YOU. SHOULD ANY COVERED 
-  CODE PROVE DEFECTIVE IN ANY RESPECT, YOU (NOT THE INITIAL 
-  DEVELOPER OR ANY OTHER CONTRIBUTOR) ASSUME THE COST OF ANY 
-  NECESSARY SERVICING, REPAIR OR CORRECTION. THIS DISCLAIMER OF 
-  WARRANTY CONSTITUTES AN ESSENTIAL PART OF THIS LICENSE. NO USE 
-  OF ANY COVERED CODE IS AUTHORIZED HEREUNDER EXCEPT UNDER
-  THIS DISCLAIMER.
- 
-  Use at your own risk!
-
-
-
-  FixedSizeAllocator class
-  Copyright 2001 Justin Heyes-Jones
-
-  This class is a constant time O(1) memory manager for objects of 
-  a specified type. The type is specified using a template class.
-
-  Memory is allocated from a fixed size buffer which you can specify in the
-  class constructor or use the default.
-
-  Using GetFirst and GetNext it is possible to iterate through the elements
-  one by one, and this would be the most common use for the class. 
-
-  I would suggest using this class when you want O(1) add and delete
-  and you don't do much searching, which would be O(n). Structures such as binary 
-  trees can be used instead to get O(logn) access time.
-
-*/
-
-#ifndef FSA_H
-#define FSA_H
-
-#include <string.h>
-#include <stdio.h>
-
-template <class USER_TYPE> class FixedSizeAllocator
-{
-
-public: 
-	// Constants
-	enum
-	{
-		FSA_DEFAULT_SIZE = 100
-	};
-
-	// This class enables us to transparently manage the extra data 
-	// needed to enable the user class to form part of the double-linked
-	// list class
-	struct FSA_ELEMENT
-	{
-		USER_TYPE UserType;
-		
-		FSA_ELEMENT *pPrev;
-		FSA_ELEMENT *pNext;
-	};
-
-public: // methods
-	FixedSizeAllocator( unsigned int MaxElements = FSA_DEFAULT_SIZE ) :
-	m_pFirstUsed( NULL ),
-	m_MaxElements( MaxElements )
-	{
-		// Allocate enough memory for the maximum number of elements
-
-		char *pMem = new char[ m_MaxElements * sizeof(FSA_ELEMENT) ];
-
-		m_pMemory = (FSA_ELEMENT *) pMem; 
-
-		// Set the free list first pointer
-		m_pFirstFree = m_pMemory;
-
-		// Clear the memory
-		memset( m_pMemory, 0, sizeof( FSA_ELEMENT ) * m_MaxElements );
-
-		// Point at first element
-		FSA_ELEMENT *pElement = m_pFirstFree;
-
-		// Set the double linked free list
-		for( unsigned int i=0; i<m_MaxElements; i++ )
-		{
-			pElement->pPrev = pElement-1;
-			pElement->pNext = pElement+1;
-
-			pElement++;
-		}
-
-		// first element should have a null prev
-		m_pFirstFree->pPrev = NULL;
-		// last element should have a null next
-		(pElement-1)->pNext = NULL;
-
-	}
-
-
-	~FixedSizeAllocator()
-	{
-		// Free up the memory
-		delete [] (char *) m_pMemory;
-	}
-
-	// Allocate a new USER_TYPE and return a pointer to it 
-	USER_TYPE *alloc()
-	{
-
-		FSA_ELEMENT *pNewNode = NULL;
-
-		if( !m_pFirstFree )
-		{
-			return NULL;
-		}
-		else
-		{
-			pNewNode = m_pFirstFree;
-			m_pFirstFree = pNewNode->pNext;
-
-			// if the new node points to another free node then
-			// change that nodes prev free pointer...
-			if( pNewNode->pNext )
-			{
-				pNewNode->pNext->pPrev = NULL;
-			}
-
-			// node is now on the used list
-
-			pNewNode->pPrev = NULL; // the allocated node is always first in the list
-
-			if( m_pFirstUsed == NULL )
-			{
-				pNewNode->pNext = NULL; // no other nodes
-			}
-			else
-			{
-				m_pFirstUsed->pPrev = pNewNode; // insert this at the head of the used list
-				pNewNode->pNext = m_pFirstUsed;
-			}
-
-			m_pFirstUsed = pNewNode;
-		}	
-		
-		return reinterpret_cast<USER_TYPE*>(pNewNode);
-	}
-
-	// Free the given user type
-	// For efficiency I don't check whether the user_data is a valid
-	// pointer that was allocated. I may add some debug only checking
-	// (To add the debug check you'd need to make sure the pointer is in 
-	// the m_pMemory area and is pointing at the start of a node)
-	void free( USER_TYPE *user_data )
-	{
-		FSA_ELEMENT *pNode = reinterpret_cast<FSA_ELEMENT*>(user_data);
-
-		// manage used list, remove this node from it
-		if( pNode->pPrev )
-		{
-			pNode->pPrev->pNext = pNode->pNext;
-		}
-		else
-		{
-			// this handles the case that we delete the first node in the used list
-			m_pFirstUsed = pNode->pNext;
-		}
-
-		if( pNode->pNext )
-		{
-			pNode->pNext->pPrev = pNode->pPrev;
-		}
-
-		// add to free list
-		if( m_pFirstFree == NULL ) 
-		{
-			// free list was empty
-			m_pFirstFree = pNode;
-			pNode->pPrev = NULL;
-			pNode->pNext = NULL;
-		}
-		else
-		{
-			// Add this node at the start of the free list
-			m_pFirstFree->pPrev = pNode;
-			pNode->pNext = m_pFirstFree;
-			m_pFirstFree = pNode;
-		}
-
-	}
-
-	// For debugging this displays both lists (using the prev/next list pointers)
-	void Debug()
-	{
-		printf( "free list " );
-
-		FSA_ELEMENT *p = m_pFirstFree;
-		while( p )
-		{
-			printf( "%x!%x ", p->pPrev, p->pNext );
-			p = p->pNext;
-		}
-		printf( "\n" );
-
-		printf( "used list " );
-
-		p = m_pFirstUsed;
-		while( p )
-		{
-			printf( "%x!%x ", p->pPrev, p->pNext );
-			p = p->pNext;
-		}
-		printf( "\n" );
-	}
-
-	// Iterators
-
-	USER_TYPE *GetFirst()
-	{
-		return reinterpret_cast<USER_TYPE *>(m_pFirstUsed);
-	}
-
-	USER_TYPE *GetNext( USER_TYPE *node )
-	{
-		return reinterpret_cast<USER_TYPE *>
-			(
-				(reinterpret_cast<FSA_ELEMENT *>(node))->pNext
-			);
-	}
-
-public: // data
-
-private: // methods
-
-private: // data
-
-	FSA_ELEMENT *m_pFirstFree;
-	FSA_ELEMENT *m_pFirstUsed;
-	unsigned int m_MaxElements;
-	FSA_ELEMENT *m_pMemory;
-
-};
-
-#endif // defined FSA_H

src/Astar/min_path_to_Bucharest.cpp

@@ -1,315 +0,0 @@
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-// This example code illustrate how to use STL A* Search implementation to find the minimum path between two
-// cities given a map. The example is taken from the book AI: A Modern Approach, 3rd Ed., by Russel, where a map
-// of Romania is given. The target node is Bucharest, and the user can specify the initial city from which the
-// search algorithm will start looking for the minimum path to Bucharest.
-//
-// Usage: min_path_to_Bucharest <start city name>
-//        Example:
-//                min_path_to_Bucharest Arad
-//
-// Thanks to Rasoul  Mojtahedzadeh for this contribution
-// Mojtahedzadeh _atsign_ gmail com
-// 
-// Please note that this exercise is academic in nature and that the distances between the cities may not be
-// correct compared to the latitude and longnitude differences in real life. Thanks to parthi2929 for noticing 
-// this issue. In a real application you would use some kind of exact x,y position of each city in order to get 
-// accurate heuristics. In fact, that is part of the point of this example, in the book you will see Norvig
-// mention that the algorithm does some backtracking because the heuristic is not accurate (yet still admissable).
-////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-#include "stlastar.h"
-#include <iostream>
-#include <string>
-#include <vector>
-#include <stdio.h>
-
-#define DEBUG_LISTS 0
-#define DEBUG_LIST_LENGTHS_ONLY 0
-
-using namespace std;
-
-const int MAX_CITIES = 20;
-
-enum ENUM_CITIES{Arad=0, Bucharest, Craiova, Drobeta, Eforie, Fagaras, Giurgiu, Hirsova, Iasi, Lugoj, Mehadia, Neamt, Oradea, Pitesti, RimnicuVilcea, Sibiu, Timisoara, Urziceni, Vaslui, Zerind};
-vector<string> CityNames(MAX_CITIES);
-float RomaniaMap[MAX_CITIES][MAX_CITIES];
-
-class PathSearchNode
-{
-public:
-
-  ENUM_CITIES city;
-
-	PathSearchNode() { city = Arad; }
-	PathSearchNode( ENUM_CITIES in ) { city = in; }
-
-	float GoalDistanceEstimate( PathSearchNode &nodeGoal );
-	bool IsGoal( PathSearchNode &nodeGoal );
-	bool GetSuccessors( AStarSearch<PathSearchNode> *astarsearch, PathSearchNode *parent_node );
-	float GetCost( PathSearchNode &successor );
-	bool IsSameState( PathSearchNode &rhs );
-
-	void PrintNodeInfo();
-};
-
-// check if "this" node is the same as "rhs" node
-bool PathSearchNode::IsSameState( PathSearchNode &rhs )
-{
-  if(city == rhs.city) return(true);
-  return(false);
-}
-
-// Euclidean distance between "this" node city and Bucharest
-// Note: Numbers are taken from the book
-float PathSearchNode::GoalDistanceEstimate( PathSearchNode &nodeGoal )
-{
-  // goal is always Bucharest!
-  switch(city)
-  {
-    case Arad: return 366; break;
-    case Bucharest: return 0; break;
-    case Craiova: return 160; break;
-    case Drobeta: return 242; break;
-    case Eforie: return 161; break;
-    case Fagaras: return 176; break;
-    case Giurgiu: return 77; break;
-    case Hirsova: return 151; break;
-    case Iasi: return 226; break;
-    case Lugoj: return 244; break;
-    case Mehadia: return 241; break;
-    case Neamt: return 234; break;
-    case Oradea: return 380; break;
-    case Pitesti: return 100; break;
-    case RimnicuVilcea: return 193; break;
-    case Sibiu: return 253; break;
-    case Timisoara: return 329; break;
-    case Urziceni: return 80; break;
-    case Vaslui: return 199; break;
-    case Zerind: return 374; break;
-  }
-  cerr << "ASSERT: city = " << CityNames[city] << endl;
-	return 0;
-}
-
-// check if "this" node is the goal node
-bool PathSearchNode::IsGoal( PathSearchNode &nodeGoal )
-{
-	if( city == Bucharest ) return(true);
-	return(false);
-}
-
-// generates the successor nodes of "this" node
-bool PathSearchNode::GetSuccessors( AStarSearch<PathSearchNode> *astarsearch, PathSearchNode *parent_node )
-{
-  PathSearchNode NewNode;
-  for(int c=0; c<MAX_CITIES; c++)
-  {
-    if(RomaniaMap[city][c] < 0) continue;
-    NewNode = PathSearchNode((ENUM_CITIES)c);
-    astarsearch->AddSuccessor( NewNode );
-  }
-	return true;
-}
-
-// the cost of going from "this" node to the "successor" node
-float PathSearchNode::GetCost( PathSearchNode &successor )
-{
-	return RomaniaMap[city][successor.city];
-}
-
-// prints out information about the node
-void PathSearchNode::PrintNodeInfo()
-{
-	cout << " " << CityNames[city] << "\n";
-}
-
-// Main
-int min_path_to_Bucharest( int argc, char *argv[] )
-{
-  // creating map of Romania
-  for(int i=0; i<MAX_CITIES; i++)
-    for(int j=0; j<MAX_CITIES; j++)
-      RomaniaMap[i][j]=-1.0;
-
-  RomaniaMap[Arad][Sibiu]=140;
-  RomaniaMap[Arad][Zerind]=75;
-  RomaniaMap[Arad][Timisoara]=118;
-  RomaniaMap[Bucharest][Giurgiu]=90;
-  RomaniaMap[Bucharest][Urziceni]=85;
-  RomaniaMap[Bucharest][Fagaras]=211;
-  RomaniaMap[Bucharest][Pitesti]=101;
-  RomaniaMap[Craiova][Drobeta]=120;
-  RomaniaMap[Craiova][RimnicuVilcea]=146;
-  RomaniaMap[Craiova][Pitesti]=138;
-  RomaniaMap[Drobeta][Craiova]=120;
-  RomaniaMap[Drobeta][Mehadia]=75;
-  RomaniaMap[Eforie][Hirsova]=75;
-  RomaniaMap[Fagaras][Bucharest]=211;
-  RomaniaMap[Fagaras][Sibiu]=99;
-  RomaniaMap[Giurgiu][Bucharest]=90;
-  RomaniaMap[Hirsova][Eforie]=86;
-  RomaniaMap[Hirsova][Urziceni]=98;
-  RomaniaMap[Iasi][Vaslui]=92;
-  RomaniaMap[Iasi][Neamt]=87;
-  RomaniaMap[Lugoj][Timisoara]=111;
-  RomaniaMap[Lugoj][Mehadia]=70;
-  RomaniaMap[Mehadia][Lugoj]=70;
-  RomaniaMap[Mehadia][Drobeta]=75;
-  RomaniaMap[Neamt][Iasi]=87;
-  RomaniaMap[Oradea][Zerind]=71;
-  RomaniaMap[Oradea][Sibiu]=151;
-  RomaniaMap[Pitesti][Bucharest]=101;
-  RomaniaMap[Pitesti][RimnicuVilcea]=97;
-  RomaniaMap[Pitesti][Craiova]=138;
-  RomaniaMap[RimnicuVilcea][Pitesti]=97;
-  RomaniaMap[RimnicuVilcea][Craiova]=146;
-  RomaniaMap[RimnicuVilcea][Sibiu]=80;
-  RomaniaMap[Sibiu][RimnicuVilcea]=80;
-  RomaniaMap[Sibiu][Fagaras]=99;
-  RomaniaMap[Sibiu][Oradea]=151;
-  RomaniaMap[Sibiu][Arad]=140;
-  RomaniaMap[Timisoara][Arad]=118;
-  RomaniaMap[Timisoara][Lugoj]=111;
-  RomaniaMap[Urziceni][Bucharest]=85;
-  RomaniaMap[Urziceni][Hirsova]=98;
-  RomaniaMap[Urziceni][Vaslui]=142;
-  RomaniaMap[Vaslui][Urziceni]=142;
-  RomaniaMap[Vaslui][Iasi]=92;
-  RomaniaMap[Zerind][Arad]=75;
-  RomaniaMap[Zerind][Oradea]=71;
-
-  // City names
-  CityNames[Arad].assign("Arad");
-  CityNames[Bucharest].assign("Bucharest");
-  CityNames[Craiova].assign("Craiova");
-  CityNames[Drobeta].assign("Drobeta");
-  CityNames[Eforie].assign("Eforie");
-  CityNames[Fagaras].assign("Fagaras");
-  CityNames[Giurgiu].assign("Giurgiu");
-  CityNames[Hirsova].assign("Hirsova");
-  CityNames[Iasi].assign("Iasi");
-  CityNames[Lugoj].assign("Lugoj");
-  CityNames[Mehadia].assign("Mehadia");
-  CityNames[Neamt].assign("Neamt");
-  CityNames[Oradea].assign("Oradea");
-  CityNames[Pitesti].assign("Pitesti");
-  CityNames[RimnicuVilcea].assign("RimnicuVilcea");
-  CityNames[Sibiu].assign("Sibiu");
-  CityNames[Timisoara].assign("Timisoara");
-  CityNames[Urziceni].assign("Urziceni");
-  CityNames[Vaslui].assign("Vaslui");
-  CityNames[Zerind].assign("Zerind");
-
-  ENUM_CITIES initCity = Arad;
-  if(argc == 2)
-  {
-    bool found = false;
-    for(size_t i=0; i<CityNames.size(); i++)
-      if(CityNames[i].compare(argv[1])==0)
-      {
-        initCity = (ENUM_CITIES)i;
-        found = true;
-        break;
-      }
-    if(not found)
-    {
-      cout << "There is no city named "<<argv[1]<<" in the map!\n";
-      return(1);
-    }
-  }
-
-  // An instance of A* search class
-	AStarSearch<PathSearchNode> astarsearch;
-
-	unsigned int SearchCount = 0;
-	const unsigned int NumSearches = 1;
-
-	while(SearchCount < NumSearches)
-	{
-		// Create a start state
-		PathSearchNode nodeStart;
-		nodeStart.city = initCity;
-
-		// Define the goal state, always Bucharest!
-		PathSearchNode nodeEnd;
-		nodeEnd.city = Bucharest;
-
-		// Set Start and goal states
-		astarsearch.SetStartAndGoalStates( nodeStart, nodeEnd );
-
-		unsigned int SearchState;
-		unsigned int SearchSteps = 0;
-
-		do
-		{
-			SearchState = astarsearch.SearchStep();
-			SearchSteps++;
-
-	#if DEBUG_LISTS
-
-			cout << "Steps:" << SearchSteps << "\n";
-
-			int len = 0;
-
-			cout << "Open:\n";
-			PathSearchNode *p = astarsearch.GetOpenListStart();
-			while( p )
-			{
-				len++;
-	#if !DEBUG_LIST_LENGTHS_ONLY
-				((PathSearchNode *)p)->PrintNodeInfo();
-	#endif
-				p = astarsearch.GetOpenListNext();
-
-			}
-			cout << "Open list has " << len << " nodes\n";
-
-			len = 0;
-
-			cout << "Closed:\n";
-			p = astarsearch.GetClosedListStart();
-			while( p )
-			{
-				len++;
-	#if !DEBUG_LIST_LENGTHS_ONLY
-				p->PrintNodeInfo();
-	#endif
-				p = astarsearch.GetClosedListNext();
-			}
-
-			cout << "Closed list has " << len << " nodes\n";
-	#endif
-
-		}
-		while( SearchState == AStarSearch<PathSearchNode>::SEARCH_STATE_SEARCHING );
-
-		if( SearchState == AStarSearch<PathSearchNode>::SEARCH_STATE_SUCCEEDED )
-		{
-			cout << "Search found the goal state\n";
-      PathSearchNode *node = astarsearch.GetSolutionStart();
-      cout << "Displaying solution\n";
-      int steps = 0;
-      node->PrintNodeInfo();
-      for( ;; )
-      {
-        node = astarsearch.GetSolutionNext();
-        if( !node ) break;
-        node->PrintNodeInfo();
-        steps ++;
-      };
-      cout << "Solution steps " << steps << endl;
-      // Once you're done with the solution you can free the nodes up
-			astarsearch.FreeSolutionNodes();
-		}
-		else if( SearchState == AStarSearch<PathSearchNode>::SEARCH_STATE_FAILED )
-		{
-			cout << "Search terminated. Did not find goal state\n";
-		}
-		// Display the number of loops the search went through
-		cout << "SearchSteps : " << SearchSteps << "\n";
-		SearchCount ++;
-		astarsearch.EnsureMemoryFreed();
-	}
-
-	return 0;
-}

src/Astar/stlastar.h

@@ -1,833 +0,0 @@
-/*
-A* Algorithm Implementation using STL is
-Copyright (C)2001-2005 Justin Heyes-Jones
-
-Permission is given by the author to freely redistribute and 
-include this code in any program as long as this credit is 
-given where due.
- 
-  COVERED CODE IS PROVIDED UNDER THIS LICENSE ON AN "AS IS" BASIS, 
-  WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, 
-  INCLUDING, WITHOUT LIMITATION, WARRANTIES THAT THE COVERED CODE 
-  IS FREE OF DEFECTS, MERCHANTABLE, FIT FOR A PARTICULAR PURPOSE
-  OR NON-INFRINGING. THE ENTIRE RISK AS TO THE QUALITY AND 
-  PERFORMANCE OF THE COVERED CODE IS WITH YOU. SHOULD ANY COVERED 
-  CODE PROVE DEFECTIVE IN ANY RESPECT, YOU (NOT THE INITIAL 
-  DEVELOPER OR ANY OTHER CONTRIBUTOR) ASSUME THE COST OF ANY 
-  NECESSARY SERVICING, REPAIR OR CORRECTION. THIS DISCLAIMER OF 
-  WARRANTY CONSTITUTES AN ESSENTIAL PART OF THIS LICENSE. NO USE 
-  OF ANY COVERED CODE IS AUTHORIZED HEREUNDER EXCEPT UNDER
-  THIS DISCLAIMER.
- 
-  Use at your own risk!
-
-*/
-
-#ifndef STLASTAR_H
-#define STLASTAR_H
-// used for text debugging
-#include <iostream>
-#include <stdio.h>
-//#include <conio.h>
-#include <assert.h>
-
-// stl includes
-#include <algorithm>
-#include <set>
-#include <vector>
-#include <cfloat>
-
-using namespace std;
-
-// fast fixed size memory allocator, used for fast node memory management
-#include "fsa.h"
-
-// Fixed size memory allocator can be disabled to compare performance
-// Uses std new and delete instead if you turn it off
-#define USE_FSA_MEMORY 1
-
-// disable warning that debugging information has lines that are truncated
-// occurs in stl headers
-#if defined(WIN32) && defined(_WINDOWS)
-#pragma warning( disable : 4786 )
-#endif
-
-template <class T> class AStarState;
-
-// The AStar search class. UserState is the users state space type
-template <class UserState> class AStarSearch
-{
-
-public: // data
-
-	enum
-	{
-		SEARCH_STATE_NOT_INITIALISED,
-		SEARCH_STATE_SEARCHING,
-		SEARCH_STATE_SUCCEEDED,
-		SEARCH_STATE_FAILED,
-		SEARCH_STATE_OUT_OF_MEMORY,
-		SEARCH_STATE_INVALID
-	};
-
-
-	// A node represents a possible state in the search
-	// The user provided state type is included inside this type
-
-	public:
-
-	class Node
-	{
-		public:
-
-			Node *parent; // used during the search to record the parent of successor nodes
-			Node *child; // used after the search for the application to view the search in reverse
-			
-			float g; // cost of this node + it's predecessors
-			float h; // heuristic estimate of distance to goal
-			float f; // sum of cumulative cost of predecessors and self and heuristic
-
-			Node() :
-				parent( 0 ),
-				child( 0 ),
-				g( 0.0f ),
-				h( 0.0f ),
-				f( 0.0f )
-			{			
-			}
-
-			UserState m_UserState;
-	};
-
-
-	// For sorting the heap the STL needs compare function that lets us compare
-	// the f value of two nodes
-
-	class HeapCompare_f 
-	{
-		public:
-
-			bool operator() ( const Node *x, const Node *y ) const
-			{
-				return x->f > y->f;
-			}
-	};
-
-
-public: // methods
-
-
-	// constructor just initialises private data
-	AStarSearch() :
-		m_State( SEARCH_STATE_NOT_INITIALISED ),
-		m_CurrentSolutionNode( NULL ),
-#if USE_FSA_MEMORY
-		m_FixedSizeAllocator( 1000 ),
-#endif
-		m_AllocateNodeCount(0),
-		m_CancelRequest( false )
-	{
-	}
-
-	AStarSearch( int MaxNodes ) :
-		m_State( SEARCH_STATE_NOT_INITIALISED ),
-		m_CurrentSolutionNode( NULL ),
-#if USE_FSA_MEMORY
-		m_FixedSizeAllocator( MaxNodes ),
-#endif
-		m_AllocateNodeCount(0),
-		m_CancelRequest( false )
-	{
-	}
-
-	// call at any time to cancel the search and free up all the memory
-	void CancelSearch()
-	{
-		m_CancelRequest = true;
-	}
-
-	// Set Start and goal states
-	void SetStartAndGoalStates( UserState &Start, UserState &Goal )
-	{
-		m_CancelRequest = false;
-
-		m_Start = AllocateNode();
-		m_Goal = AllocateNode();
-
-		assert((m_Start != NULL && m_Goal != NULL));
-		
-		m_Start->m_UserState = Start;
-		m_Goal->m_UserState = Goal;
-
-		m_State = SEARCH_STATE_SEARCHING;
-		
-		// Initialise the AStar specific parts of the Start Node
-		// The user only needs fill out the state information
-
-		m_Start->g = 0; 
-		m_Start->h = m_Start->m_UserState.GoalDistanceEstimate( m_Goal->m_UserState );
-		m_Start->f = m_Start->g + m_Start->h;
-		m_Start->parent = 0;
-
-		// Push the start node on the Open list
-
-		m_OpenList.push_back( m_Start ); // heap now unsorted
-
-		// Sort back element into heap
-		push_heap( m_OpenList.begin(), m_OpenList.end(), HeapCompare_f() );
-
-		// Initialise counter for search steps
-		m_Steps = 0;
-	}
-
-	// Advances search one step 
-	unsigned int SearchStep()
-	{
-		// Firstly break if the user has not initialised the search
-		assert( (m_State > SEARCH_STATE_NOT_INITIALISED) &&
-				(m_State < SEARCH_STATE_INVALID) );
-
-		// Next I want it to be safe to do a searchstep once the search has succeeded...
-		if( (m_State == SEARCH_STATE_SUCCEEDED) ||
-			(m_State == SEARCH_STATE_FAILED) 
-		  )
-		{
-			return m_State; 
-		}
-
-		// Failure is defined as emptying the open list as there is nothing left to 
-		// search...
-		// New: Allow user abort
-		if( m_OpenList.empty() || m_CancelRequest )
-		{
-			FreeAllNodes();
-			m_State = SEARCH_STATE_FAILED;
-			return m_State;
-		}
-		
-		// Incremement step count
-		m_Steps ++;
-
-		// Pop the best node (the one with the lowest f) 
-		Node *n = m_OpenList.front(); // get pointer to the node
-		pop_heap( m_OpenList.begin(), m_OpenList.end(), HeapCompare_f() );
-		m_OpenList.pop_back();
-
-		// Check for the goal, once we pop that we're done
-		if( n->m_UserState.IsGoal( m_Goal->m_UserState ) )
-		{
-			// The user is going to use the Goal Node he passed in 
-			// so copy the parent pointer of n 
-			m_Goal->parent = n->parent;
-			m_Goal->g = n->g;
-
-			// A special case is that the goal was passed in as the start state
-			// so handle that here
-			if( false == n->m_UserState.IsSameState( m_Start->m_UserState ) )
-			{
-				FreeNode( n );
-
-				// set the child pointers in each node (except Goal which has no child)
-				Node *nodeChild = m_Goal;
-				Node *nodeParent = m_Goal->parent;
-
-				do 
-				{
-					nodeParent->child = nodeChild;
-
-					nodeChild = nodeParent;
-					nodeParent = nodeParent->parent;
-				
-				} 
-				while( nodeChild != m_Start ); // Start is always the first node by definition
-
-			}
-
-			// delete nodes that aren't needed for the solution
-			FreeUnusedNodes();
-
-			m_State = SEARCH_STATE_SUCCEEDED;
-
-			return m_State;
-		}
-		else // not goal
-		{
-
-			// We now need to generate the successors of this node
-			// The user helps us to do this, and we keep the new nodes in
-			// m_Successors ...
-
-			m_Successors.clear(); // empty vector of successor nodes to n
-
-			// User provides this functions and uses AddSuccessor to add each successor of
-			// node 'n' to m_Successors
-			bool ret = n->m_UserState.GetSuccessors( this, n->parent ? &n->parent->m_UserState : NULL ); 
-
-			if( !ret )
-			{
-
-			    typename vector< Node * >::iterator successor;
-
-				// free the nodes that may previously have been added 
-				for( successor = m_Successors.begin(); successor != m_Successors.end(); successor ++ )
-				{
-					FreeNode( (*successor) );
-				}
-
-				m_Successors.clear(); // empty vector of successor nodes to n
-
-				// free up everything else we allocated
-				FreeNode( (n) );
-				FreeAllNodes();
-
-				m_State = SEARCH_STATE_OUT_OF_MEMORY;
-				return m_State;
-			}
-			
-			// Now handle each successor to the current node ...
-			for( typename vector< Node * >::iterator successor = m_Successors.begin(); successor != m_Successors.end(); successor ++ )
-			{
-
-				// 	The g value for this successor ...
-				float newg = n->g + n->m_UserState.GetCost( (*successor)->m_UserState );
-
-				// Now we need to find whether the node is on the open or closed lists
-				// If it is but the node that is already on them is better (lower g)
-				// then we can forget about this successor
-
-				// First linear search of open list to find node
-
-				typename vector< Node * >::iterator openlist_result;
-
-				for( openlist_result = m_OpenList.begin(); openlist_result != m_OpenList.end(); openlist_result ++ )
-				{
-					if( (*openlist_result)->m_UserState.IsSameState( (*successor)->m_UserState ) )
-					{
-						break;					
-					}
-				}
-
-				if( openlist_result != m_OpenList.end() )
-				{
-
-					// we found this state on open
-
-					if( (*openlist_result)->g <= newg )
-					{
-						FreeNode( (*successor) );
-
-						// the one on Open is cheaper than this one
-						continue;
-					}
-				}
-
-				typename vector< Node * >::iterator closedlist_result;
-
-				for( closedlist_result = m_ClosedList.begin(); closedlist_result != m_ClosedList.end(); closedlist_result ++ )
-				{
-					if( (*closedlist_result)->m_UserState.IsSameState( (*successor)->m_UserState ) )
-					{
-						break;					
-					}
-				}
-
-				if( closedlist_result != m_ClosedList.end() )
-				{
-
-					// we found this state on closed
-
-					if( (*closedlist_result)->g <= newg )
-					{
-						// the one on Closed is cheaper than this one
-						FreeNode( (*successor) );
-
-						continue;
-					}
-				}
-
-				// This node is the best node so far with this particular state
-				// so lets keep it and set up its AStar specific data ...
-
-				(*successor)->parent = n;
-				(*successor)->g = newg;
-				(*successor)->h = (*successor)->m_UserState.GoalDistanceEstimate( m_Goal->m_UserState );
-				(*successor)->f = (*successor)->g + (*successor)->h;
-
-				// Successor in closed list
-				// 1 - Update old version of this node in closed list
-				// 2 - Move it from closed to open list
-				// 3 - Sort heap again in open list
-
-				if( closedlist_result != m_ClosedList.end() )
-				{
-					// Update closed node with successor node AStar data
-					//*(*closedlist_result) = *(*successor);
-					(*closedlist_result)->parent = (*successor)->parent;
-					(*closedlist_result)->g      = (*successor)->g;
-					(*closedlist_result)->h      = (*successor)->h;
-					(*closedlist_result)->f      = (*successor)->f;
-
-					// Free successor node
-					FreeNode( (*successor) );
-
-					// Push closed node into open list 
-					m_OpenList.push_back( (*closedlist_result) );
-
-					// Remove closed node from closed list
-					m_ClosedList.erase( closedlist_result );
-
-					// Sort back element into heap
-					push_heap( m_OpenList.begin(), m_OpenList.end(), HeapCompare_f() );
-
-					// Fix thanks to ...
-					// Greg Douglas <gregdouglasmail@gmail.com>
-					// who noticed that this code path was incorrect
-					// Here we have found a new state which is already CLOSED
-
-				}
-
-				// Successor in open list
-				// 1 - Update old version of this node in open list
-				// 2 - sort heap again in open list
-
-				else if( openlist_result != m_OpenList.end() )
-				{
-					// Update open node with successor node AStar data
-					//*(*openlist_result) = *(*successor);
-					(*openlist_result)->parent = (*successor)->parent;
-					(*openlist_result)->g      = (*successor)->g;
-					(*openlist_result)->h      = (*successor)->h;
-					(*openlist_result)->f      = (*successor)->f;
-
-					// Free successor node
-					FreeNode( (*successor) );
-
-					// re-make the heap 
-					// make_heap rather than sort_heap is an essential bug fix
-					// thanks to Mike Ryynanen for pointing this out and then explaining
-					// it in detail. sort_heap called on an invalid heap does not work
-					make_heap( m_OpenList.begin(), m_OpenList.end(), HeapCompare_f() );
-				}
-
-				// New successor
-				// 1 - Move it from successors to open list
-				// 2 - sort heap again in open list
-
-				else
-				{
-					// Push successor node into open list
-					m_OpenList.push_back( (*successor) );
-
-					// Sort back element into heap
-					push_heap( m_OpenList.begin(), m_OpenList.end(), HeapCompare_f() );
-				}
-
-			}
-
-			// push n onto Closed, as we have expanded it now
-
-			m_ClosedList.push_back( n );
-
-		} // end else (not goal so expand)
-
- 		return m_State; // Succeeded bool is false at this point. 
-
-	}
-
-	// User calls this to add a successor to a list of successors
-	// when expanding the search frontier
-	bool AddSuccessor( UserState &State )
-	{
-		Node *node = AllocateNode();
-
-		if( node )
-		{
-			node->m_UserState = State;
-
-			m_Successors.push_back( node );
-
-			return true;
-		}
-
-		return false;
-	}
-
-	// Free the solution nodes
-	// This is done to clean up all used Node memory when you are done with the
-	// search
-	void FreeSolutionNodes()
-	{
-		Node *n = m_Start;
-
-		if( m_Start->child )
-		{
-			do
-			{
-				Node *del = n;
-				n = n->child;
-				FreeNode( del );
-
-				del = NULL;
-
-			} while( n != m_Goal );
-
-			FreeNode( n ); // Delete the goal
-
-		}
-		else
-		{
-			// if the start node is the solution we need to just delete the start and goal
-			// nodes
-			FreeNode( m_Start );
-			FreeNode( m_Goal );
-		}
-
-	}
-
-	// Functions for traversing the solution
-
-	// Get start node
-	UserState *GetSolutionStart()
-	{
-		m_CurrentSolutionNode = m_Start;
-		if( m_Start )
-		{
-			return &m_Start->m_UserState;
-		}
-		else
-		{
-			return NULL;
-		}
-	}
-	
-	// Get next node
-	UserState *GetSolutionNext()
-	{
-		if( m_CurrentSolutionNode )
-		{
-			if( m_CurrentSolutionNode->child )
-			{
-
-				Node *child = m_CurrentSolutionNode->child;
-
-				m_CurrentSolutionNode = m_CurrentSolutionNode->child;
-
-				return &child->m_UserState;
-			}
-		}
-
-		return NULL;
-	}
-	
-	// Get end node
-	UserState *GetSolutionEnd()
-	{
-		m_CurrentSolutionNode = m_Goal;
-		if( m_Goal )
-		{
-			return &m_Goal->m_UserState;
-		}
-		else
-		{
-			return NULL;
-		}
-	}
-	
-	// Step solution iterator backwards
-	UserState *GetSolutionPrev()
-	{
-		if( m_CurrentSolutionNode )
-		{
-			if( m_CurrentSolutionNode->parent )
-			{
-
-				Node *parent = m_CurrentSolutionNode->parent;
-
-				m_CurrentSolutionNode = m_CurrentSolutionNode->parent;
-
-				return &parent->m_UserState;
-			}
-		}
-
-		return NULL;
-	}
-
-	// Get final cost of solution
-	// Returns FLT_MAX if goal is not defined or there is no solution
-	float GetSolutionCost()
-	{
-		if( m_Goal && m_State == SEARCH_STATE_SUCCEEDED )
-		{
-			return m_Goal->g;
-		}
-		else
-		{
-			return FLT_MAX;
-		}
-	}
-
-	// For educational use and debugging it is useful to be able to view
-	// the open and closed list at each step, here are two functions to allow that.
-
-	UserState *GetOpenListStart()
-	{
-		float f,g,h;
-		return GetOpenListStart( f,g,h );
-	}
-
-	UserState *GetOpenListStart( float &f, float &g, float &h )
-	{
-		iterDbgOpen = m_OpenList.begin();
-		if( iterDbgOpen != m_OpenList.end() )
-		{
-			f = (*iterDbgOpen)->f;
-			g = (*iterDbgOpen)->g;
-			h = (*iterDbgOpen)->h;
-			return &(*iterDbgOpen)->m_UserState;
-		}
-
-		return NULL;
-	}
-
-	UserState *GetOpenListNext()
-	{
-		float f,g,h;
-		return GetOpenListNext( f,g,h );
-	}
-
-	UserState *GetOpenListNext( float &f, float &g, float &h )
-	{
-		iterDbgOpen++;
-		if( iterDbgOpen != m_OpenList.end() )
-		{
-			f = (*iterDbgOpen)->f;
-			g = (*iterDbgOpen)->g;
-			h = (*iterDbgOpen)->h;
-			return &(*iterDbgOpen)->m_UserState;
-		}
-
-		return NULL;
-	}
-
-	UserState *GetClosedListStart()
-	{
-		float f,g,h;
-		return GetClosedListStart( f,g,h );
-	}
-
-	UserState *GetClosedListStart( float &f, float &g, float &h )
-	{
-		iterDbgClosed = m_ClosedList.begin();
-		if( iterDbgClosed != m_ClosedList.end() )
-		{
-			f = (*iterDbgClosed)->f;
-			g = (*iterDbgClosed)->g;
-			h = (*iterDbgClosed)->h;
-
-			return &(*iterDbgClosed)->m_UserState;
-		}
-
-		return NULL;
-	}
-
-	UserState *GetClosedListNext()
-	{
-		float f,g,h;
-		return GetClosedListNext( f,g,h );
-	}
-
-	UserState *GetClosedListNext( float &f, float &g, float &h )
-	{
-		iterDbgClosed++;
-		if( iterDbgClosed != m_ClosedList.end() )
-		{
-			f = (*iterDbgClosed)->f;
-			g = (*iterDbgClosed)->g;
-			h = (*iterDbgClosed)->h;
-
-			return &(*iterDbgClosed)->m_UserState;
-		}
-
-		return NULL;
-	}
-
-	// Get the number of steps
-
-	int GetStepCount() { return m_Steps; }
-
-	void EnsureMemoryFreed()
-	{
-#if USE_FSA_MEMORY
-		assert(m_AllocateNodeCount == 0);
-#endif
-
-	}
-
-private: // methods
-
-	// This is called when a search fails or is cancelled to free all used
-	// memory 
-	void FreeAllNodes()
-	{
-		// iterate open list and delete all nodes
-		typename vector< Node * >::iterator iterOpen = m_OpenList.begin();
-
-		while( iterOpen != m_OpenList.end() )
-		{
-			Node *n = (*iterOpen);
-			FreeNode( n );
-
-			iterOpen ++;
-		}
-
-		m_OpenList.clear();
-
-		// iterate closed list and delete unused nodes
-		typename vector< Node * >::iterator iterClosed;
-
-		for( iterClosed = m_ClosedList.begin(); iterClosed != m_ClosedList.end(); iterClosed ++ )
-		{
-			Node *n = (*iterClosed);
-			FreeNode( n );
-		}
-
-		m_ClosedList.clear();
-
-		// delete the goal
-
-		FreeNode(m_Goal);
-	}
-
-
-	// This call is made by the search class when the search ends. A lot of nodes may be
-	// created that are still present when the search ends. They will be deleted by this 
-	// routine once the search ends
-	void FreeUnusedNodes()
-	{
-		// iterate open list and delete unused nodes
-		typename vector< Node * >::iterator iterOpen = m_OpenList.begin();
-
-		while( iterOpen != m_OpenList.end() )
-		{
-			Node *n = (*iterOpen);
-
-			if( !n->child )
-			{
-				FreeNode( n );
-
-				n = NULL;
-			}
-
-			iterOpen ++;
-		}
-
-		m_OpenList.clear();
-
-		// iterate closed list and delete unused nodes
-		typename vector< Node * >::iterator iterClosed;
-
-		for( iterClosed = m_ClosedList.begin(); iterClosed != m_ClosedList.end(); iterClosed ++ )
-		{
-			Node *n = (*iterClosed);
-
-			if( !n->child )
-			{
-				FreeNode( n );
-				n = NULL;
-
-			}
-		}
-
-		m_ClosedList.clear();
-
-	}
-
-	// Node memory management
-	Node *AllocateNode()
-	{
-
-#if !USE_FSA_MEMORY
-		m_AllocateNodeCount ++;
-		Node *p = new Node;
-		return p;
-#else
-		Node *address = m_FixedSizeAllocator.alloc();
-
-		if( !address )
-		{
-			return NULL;
-		}
-		m_AllocateNodeCount ++;
-		Node *p = new (address) Node;
-		return p;
-#endif
-	}
-
-	void FreeNode( Node *node )
-	{
-
-		m_AllocateNodeCount --;
-
-#if !USE_FSA_MEMORY
-		delete node;
-#else
-		node->~Node();
-		m_FixedSizeAllocator.free( node );
-#endif
-	}
-
-private: // data
-
-	// Heap (simple vector but used as a heap, cf. Steve Rabin's game gems article)
-	vector< Node *> m_OpenList;
-
-	// Closed list is a vector.
-	vector< Node * > m_ClosedList; 
-
-	// Successors is a vector filled out by the user each type successors to a node
-	// are generated
-	vector< Node * > m_Successors;
-
-	// State
-	unsigned int m_State;
-
-	// Counts steps
-	int m_Steps;
-
-	// Start and goal state pointers
-	Node *m_Start;
-	Node *m_Goal;
-
-	Node *m_CurrentSolutionNode;
-
-#if USE_FSA_MEMORY
-	// Memory
- 	FixedSizeAllocator<Node> m_FixedSizeAllocator;
-#endif
-	
-	//Debug : need to keep these two iterators around
-	// for the user Dbg functions
-	typename vector< Node * >::iterator iterDbgOpen;
-	typename vector< Node * >::iterator iterDbgClosed;
-
-	// debugging : count memory allocation and free's
-	int m_AllocateNodeCount;
-	
-	bool m_CancelRequest;
-
-};
-
-template <class T> class AStarState
-{
-public:
-	virtual ~AStarState() {}
-	virtual float GoalDistanceEstimate( T &nodeGoal ) = 0; // Heuristic function which computes the estimated cost to the goal node
-	virtual bool IsGoal( T &nodeGoal ) = 0; // Returns true if this node is the goal node
-	virtual bool GetSuccessors( AStarSearch<T> *astarsearch, T *parent_node ) = 0; // Retrieves all successors to this node and adds them via astarsearch.addSuccessor()
-	virtual float GetCost( T &successor ) = 0; // Computes the cost of travelling from this node to the successor node
-	virtual bool IsSameState( T &rhs ) = 0; // Returns true if this node is the same as the rhs node
-};
-
-#endif
-
-   

src/YOLOv6/LICENSE

@@ -1,674 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- 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.  By contrast,
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-software for all its users.  We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors.  You can apply it to
-your programs, too.
-
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-price.  Our General Public Licenses are designed to make sure that you
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-  To protect your rights, we need to prevent others from denying you
-these rights or asking you to surrender the rights.  Therefore, you have
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-you modify it: responsibilities to respect the freedom of others.
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-  Developers that use the GNU GPL protect your rights with two steps:
-(1) assert copyright on the software, and (2) offer you this License
-giving you legal permission to copy, distribute and/or modify it.
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src/YOLOv6/README.md

@@ -1,105 +0,0 @@
-# MT-YOLOv6 [About Naming YOLOv6](./docs/About_naming_yolov6.md)
-
-## Introduction
-
-YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.
-
-<img src="assets/picture.png" width="800">
-
-YOLOv6-nano achieves 35.0 mAP on COCO val2017 dataset with 1242 FPS on T4 using TensorRT FP16 for bs32 inference, and YOLOv6-s achieves 43.1 mAP on COCO val2017 dataset with 520 FPS on T4 using TensorRT FP16 for bs32 inference.
-
-YOLOv6 is composed of the following methods:
-
-- Hardware-friendly Design for Backbone and Neck
-- Efficient Decoupled Head with SIoU Loss
-
-
-## Coming soon
-
-- [ ] YOLOv6 m/l/x model.
-- [ ] Deployment for MNN/TNN/NCNN/CoreML...
-- [ ] Quantization tools
-
-
-## Quick Start
-
-### Install
-
-```shell
-git clone https://github.com/meituan/YOLOv6
-cd YOLOv6
-pip install -r requirements.txt
-```
-
-### Inference
-
-First, download a pretrained model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases/tag/0.1.0)
-
-Second, run inference with `tools/infer.py`
-
-```shell
-python tools/infer.py --weights yolov6s.pt --source img.jpg / imgdir
-                                yolov6n.pt
-```
-
-### Training
-
-Single GPU
-
-```shell
-python tools/train.py --batch 32 --conf configs/yolov6s.py --data data/coco.yaml --device 0
-                                        configs/yolov6n.py
-```
-
-Multi GPUs (DDP mode recommended)
-
-```shell
-python -m torch.distributed.launch --nproc_per_node 8 tools/train.py --batch 256 --conf configs/yolov6s.py --data data/coco.yaml --device 0,1,2,3,4,5,6,7
-                                                                                        configs/yolov6n.py
-```
-
-- conf: select config file to specify network/optimizer/hyperparameters
-- data: prepare [COCO](http://cocodataset.org) dataset and specify dataset paths in data.yaml
-
-
-### Evaluation
-
-Reproduce mAP on COCO val2017 dataset
-
-```shell
-python tools/eval.py --data data/coco.yaml --batch 32 --weights yolov6s.pt --task val
-                                                                yolov6n.pt
-```
-
-
-### Deployment
-
-*  [ONNX](./deploy/ONNX)
-*  [OpenVINO](./deploy/OpenVINO)
-
-### Tutorials
-
-*  [Train custom data](./docs/Train_custom_data.md)
-*  [Test speed](./docs/Test_speed.md)
-
-
-## Benchmark
-
-
-| Model           | Size        | mAP<sup>val<br/>0.5:0.95 | Speed<sup>V100<br/>fp16 b32 <br/>(ms) | Speed<sup>V100<br/>fp32 b32 <br/>(ms) | Speed<sup>T4<br/>trt fp16 b1 <br/>(fps) | Speed<sup>T4<br/>trt fp16 b32 <br/>(fps) | Params<br/><sup> (M) | Flops<br/><sup> (G) |
-| :-------------- | ----------- | :----------------------- | :------------------------------------ | :------------------------------------ | ---------------------------------------- | ----------------------------------------- | --------------- | -------------- |
-| [**YOLOv6-n**](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6n.pt)    | 416<br/>640 | 30.8<br/>35.0            | 0.3<br/>0.5                           | 0.4<br/>0.7                           | 1100<br/>788                             | 2716<br/>1242                             | 4.3<br/>4.3     | 4.7<br/>11.1   |
-| [**YOLOv6-tiny**](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6t.pt) | 640         | 41.3                     | 0.9                                   | 1.5                                   | 425                                      | 602                                       | 15.0            | 36.7           |
-| [**YOLOv6-s**](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6s.pt)    | 640         | 43.1                     | 1.0                                   | 1.7                                   | 373                                      | 520                                       | 17.2            | 44.2           |
-
-
-- Comparisons of the mAP and speed of different object detectors are tested on [COCO val2017](https://cocodataset.org/#download) dataset.
-- Refer to [Test speed](./docs/Test_speed.md) tutorial to reproduce the speed results of YOLOv6.
-- Params and Flops of YOLOv6 are estimated on deployed model.
-- Speed results of other methods are tested in our environment using official codebase and model if not found from the corresponding official release.
-
- ## Third-party resources
- * YOLOv6 NCNN Android app demo: [ncnn-android-yolov6](https://github.com/FeiGeChuanShu/ncnn-android-yolov6) from [FeiGeChuanShu](https://github.com/FeiGeChuanShu)
- * YOLOv6 ONNXRuntime/MNN/TNN C++: [YOLOv6-ORT](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ort/cv/yolov6.cpp), [YOLOv6-MNN](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/mnn/cv/mnn_yolov6.cpp) and [YOLOv6-TNN](https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_yolov6.cpp) from [DefTruth](https://github.com/DefTruth)
- * YOLOv6 TensorRT Python: [yolov6-tensorrt-python](https://github.com/Linaom1214/tensorrt-python/blob/main/yolov6/trt.py) from [Linaom1214](https://github.com/Linaom1214)
- * YOLOv6 TensorRT Windows C++: [yolort](https://github.com/zhiqwang/yolov5-rt-stack/tree/main/deployment/tensorrt-yolov6) from [Wei Zeng](https://github.com/Wulingtian)

src/YOLOv6/configs/yolov6_tiny.py

@@ -1,53 +0,0 @@
-# YOLOv6t model
-model = dict(
-    type='YOLOv6t',
-    pretrained=None,
-    depth_multiple=0.25,
-    width_multiple=0.50,
-    backbone=dict(
-        type='EfficientRep',
-        num_repeats=[1, 6, 12, 18, 6],
-        out_channels=[64, 128, 256, 512, 1024],
-        ),
-    neck=dict(
-        type='RepPAN',
-        num_repeats=[12, 12, 12, 12],
-        out_channels=[256, 128, 128, 256, 256, 512],
-        ),
-    head=dict(
-        type='EffiDeHead',
-        in_channels=[128, 256, 512],
-        num_layers=3,
-        begin_indices=24,
-        anchors=1,
-        out_indices=[17, 20, 23],
-        strides=[8, 16, 32],
-        iou_type='ciou'
-    )
-)
-
-solver = dict(
-    optim='SGD',
-    lr_scheduler='Cosine',
-    lr0=0.01,
-    lrf=0.01,
-    momentum=0.937,
-    weight_decay=0.0005,
-    warmup_epochs=3.0,
-    warmup_momentum=0.8,
-    warmup_bias_lr=0.1
-)
-
-data_aug = dict(
-    hsv_h=0.015,
-    hsv_s=0.7,
-    hsv_v=0.4,
-    degrees=0.0,
-    translate=0.1,
-    scale=0.5,
-    shear=0.0,
-    flipud=0.0,
-    fliplr=0.5,
-    mosaic=1.0,
-    mixup=0.0,
-)

src/YOLOv6/configs/yolov6_tiny_finetune.py

@@ -1,53 +0,0 @@
-# YOLOv6t model
-model = dict(
-    type='YOLOv6t',
-    pretrained='./weights/yolov6t.pt',
-    depth_multiple=0.25,
-    width_multiple=0.50,
-    backbone=dict(
-        type='EfficientRep',
-        num_repeats=[1, 6, 12, 18, 6],
-        out_channels=[64, 128, 256, 512, 1024],
-        ),
-    neck=dict(
-        type='RepPAN',
-        num_repeats=[12, 12, 12, 12],
-        out_channels=[256, 128, 128, 256, 256, 512],
-        ),
-    head=dict(
-        type='EffiDeHead',
-        in_channels=[128, 256, 512],
-        num_layers=3,
-        begin_indices=24,
-        anchors=1,
-        out_indices=[17, 20, 23],
-        strides=[8, 16, 32],
-        iou_type='ciou'
-    )
-)
-
-solver = dict(
-    optim='SGD',
-    lr_scheduler='Cosine',
-    lr0=0.0032,
-    lrf=0.12,
-    momentum=0.843,
-    weight_decay=0.00036,
-    warmup_epochs=2.0,
-    warmup_momentum=0.5,
-    warmup_bias_lr=0.05
-)
-
-data_aug = dict(
-    hsv_h=0.0138,
-    hsv_s=0.664,
-    hsv_v=0.464,
-    degrees=0.373,
-    translate=0.245,
-    scale=0.898,
-    shear=0.602,
-    flipud=0.00856,
-    fliplr=0.5,
-    mosaic=1.0,
-    mixup=0.243,
-)

src/YOLOv6/configs/yolov6n.py

@@ -1,53 +0,0 @@
-# YOLOv6n model
-model = dict(
-    type='YOLOv6n',
-    pretrained=None,
-    depth_multiple=0.33,
-    width_multiple=0.25,
-    backbone=dict(
-        type='EfficientRep',
-        num_repeats=[1, 6, 12, 18, 6],
-        out_channels=[64, 128, 256, 512, 1024],
-        ),
-    neck=dict(
-        type='RepPAN',
-        num_repeats=[12, 12, 12, 12],
-        out_channels=[256, 128, 128, 256, 256, 512],
-        ),
-    head=dict(
-        type='EffiDeHead',
-        in_channels=[128, 256, 512],
-        num_layers=3,
-        begin_indices=24,
-        anchors=1,
-        out_indices=[17, 20, 23],
-        strides=[8, 16, 32],
-        iou_type='ciou'
-    )
-)
-
-solver = dict(
-    optim='SGD',
-    lr_scheduler='Cosine',
-    lr0=0.01,
-    lrf=0.01,
-    momentum=0.937,
-    weight_decay=0.0005,
-    warmup_epochs=3.0,
-    warmup_momentum=0.8,
-    warmup_bias_lr=0.1
-)
-
-data_aug = dict(
-    hsv_h=0.015,
-    hsv_s=0.7,
-    hsv_v=0.4,
-    degrees=0.0,
-    translate=0.1,
-    scale=0.5,
-    shear=0.0,
-    flipud=0.0,
-    fliplr=0.5,
-    mosaic=1.0,
-    mixup=0.0,
-)

src/YOLOv6/configs/yolov6n_finetune.py

@@ -1,53 +0,0 @@
-# YOLOv6n model
-model = dict(
-    type='YOLOv6n',
-    pretrained='./weights/yolov6n.pt',
-    depth_multiple=0.33,
-    width_multiple=0.25,
-    backbone=dict(
-        type='EfficientRep',
-        num_repeats=[1, 6, 12, 18, 6],
-        out_channels=[64, 128, 256, 512, 1024],
-        ),
-    neck=dict(
-        type='RepPAN',
-        num_repeats=[12, 12, 12, 12],
-        out_channels=[256, 128, 128, 256, 256, 512],
-        ),
-    head=dict(
-        type='EffiDeHead',
-        in_channels=[128, 256, 512],
-        num_layers=3,
-        begin_indices=24,
-        anchors=1,
-        out_indices=[17, 20, 23],
-        strides=[8, 16, 32],
-        iou_type='ciou'
-    )
-)
-
-solver = dict(
-    optim='SGD',
-    lr_scheduler='Cosine',
-    lr0=0.0032,
-    lrf=0.12,
-    momentum=0.843,
-    weight_decay=0.00036,
-    warmup_epochs=2.0,
-    warmup_momentum=0.5,
-    warmup_bias_lr=0.05
-)
-
-data_aug = dict(
-    hsv_h=0.0138,
-    hsv_s=0.664,
-    hsv_v=0.464,
-    degrees=0.373,
-    translate=0.245,
-    scale=0.898,
-    shear=0.602,
-    flipud=0.00856,
-    fliplr=0.5,
-    mosaic=1.0,
-    mixup=0.243
-)

src/YOLOv6/configs/yolov6s.py

@@ -1,53 +0,0 @@
-# YOLOv6s model
-model = dict(
-    type='YOLOv6s',
-    pretrained=None,
-    depth_multiple=0.33,
-    width_multiple=0.50,
-    backbone=dict(
-        type='EfficientRep',
-        num_repeats=[1, 6, 12, 18, 6],
-        out_channels=[64, 128, 256, 512, 1024],
-        ),
-    neck=dict(
-        type='RepPAN',
-        num_repeats=[12, 12, 12, 12],
-        out_channels=[256, 128, 128, 256, 256, 512],
-        ),
-    head=dict(
-        type='EffiDeHead',
-        in_channels=[128, 256, 512],
-        num_layers=3,
-        begin_indices=24,
-        anchors=1,
-        out_indices=[17, 20, 23],
-        strides=[8, 16, 32],
-        iou_type='siou'
-    )
-)
-
-solver = dict(
-    optim='SGD',
-    lr_scheduler='Cosine',
-    lr0=0.01,
-    lrf=0.01,
-    momentum=0.937,
-    weight_decay=0.0005,
-    warmup_epochs=3.0,
-    warmup_momentum=0.8,
-    warmup_bias_lr=0.1
-)
-
-data_aug = dict(
-    hsv_h=0.015,
-    hsv_s=0.7,
-    hsv_v=0.4,
-    degrees=0.0,
-    translate=0.1,
-    scale=0.5,
-    shear=0.0,
-    flipud=0.0,
-    fliplr=0.5,
-    mosaic=1.0,
-    mixup=0.0,
-)

src/YOLOv6/configs/yolov6s_finetune.py

@@ -1,53 +0,0 @@
-# YOLOv6s model
-model = dict(
-    type='YOLOv6s',
-    pretrained='./weights/yolov6s.pt',
-    depth_multiple=0.33,
-    width_multiple=0.50,
-    backbone=dict(
-        type='EfficientRep',
-        num_repeats=[1, 6, 12, 18, 6],
-        out_channels=[64, 128, 256, 512, 1024],
-        ),
-    neck=dict(
-        type='RepPAN',
-        num_repeats=[12, 12, 12, 12],
-        out_channels=[256, 128, 128, 256, 256, 512],
-        ),
-    head=dict(
-        type='EffiDeHead',
-        in_channels=[128, 256, 512],
-        num_layers=3,
-        begin_indices=24,
-        anchors=1,
-        out_indices=[17, 20, 23],
-        strides=[8, 16, 32],
-        iou_type='siou'
-    )
-)
-
-solver = dict(
-    optim='SGD',
-    lr_scheduler='Cosine',
-    lr0=0.0032,
-    lrf=0.12,
-    momentum=0.843,
-    weight_decay=0.00036,
-    warmup_epochs=2.0,
-    warmup_momentum=0.5,
-    warmup_bias_lr=0.05
-)
-
-data_aug = dict(
-    hsv_h=0.0138,
-    hsv_s=0.664,
-    hsv_v=0.464,
-    degrees=0.373,
-    translate=0.245,
-    scale=0.898,
-    shear=0.602,
-    flipud=0.00856,
-    fliplr=0.5,
-    mosaic=1.0,
-    mixup=0.243,
-)

src/YOLOv6/data/coco.yaml

@@ -1,20 +0,0 @@
-# COCO 2017 dataset http://cocodataset.org
-train: ../coco/images/train2017 # 118287 images
-val: ../coco/images/val2017  # 5000 images
-test: ../coco/images/test2017
-anno_path: ../coco/annotations/instances_val2017.json
-# number of classes
-nc: 80
-# whether it is coco dataset, only coco dataset should be set to True.
-is_coco: True
-
-# 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' ]

src/YOLOv6/data/dataset.yaml

@@ -1,11 +0,0 @@
-# Please insure that your custom_dataset are put in same parent dir with YOLOv6_DIR
-train: ../custom_dataset/images/train # train images
-val: ../custom_dataset/images/val # val images
-test: ../custom_dataset/images/test # test images (optional)
-
-# whether it is coco dataset, only coco dataset should be set to True.
-is_coco: False
-# Classes
-nc: 20  # number of classes
-names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
-        'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']  # class names

src/YOLOv6/deploy/ONNX/README.md

@@ -1,98 +0,0 @@
-# Export ONNX Model
-
-## Check requirements
-```shell
-pip install onnx>=1.10.0
-```
-
-## Export script
-```shell
-python ./deploy/ONNX/export_onnx.py \
-    --weights yolov6s.pt \
-    --img 640 \
-    --batch 1
-```
-
-
-
-#### Description of all arguments
-
-- `--weights` : The path of yolov6 model weights.
-- `--img` : Image size of model inputs.
-- `--batch` : Batch size of model inputs.
-- `--half` : Whether to export half-precision model.
-- `--inplace` : Whether to set Detect() inplace.
-- `--simplify` : Whether to simplify onnx. Not support in end to end export.
-- `--end2end` : Whether to export end to end onnx model. Only support onnxruntime and TensorRT >= 8.0.0 .
-- `--max-wh` : Default is None for TensorRT backend. Set int for onnxruntime backend.
-- `--topk-all` : Topk objects for every image.
-- `--iou-thres` : IoU threshold for NMS algorithm.
-- `--conf-thres` : Confidence threshold for NMS algorithm.
-- `--device` : Export device. Cuda device : 0 or 0,1,2,3 ... , CPU : cpu .
-
-## Download
-
-* [YOLOv6-nano](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6n.onnx)
-* [YOLOv6-tiny](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6t.onnx)
-* [YOLOv6-s](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6s.onnx)
-
-## End2End export
-
-Now YOLOv6 supports end to end detect for onnxruntime and TensorRT !
-
-If you want to deploy in TensorRT, make sure you have installed TensorRT >= 8.0.0 !
-
-### onnxruntime backend
-#### Usage
-
-```bash
-python ./deploy/ONNX/export_onnx.py \
-    --weights yolov6s.pt \
-    --img 640 \
-    --batch 1 \
-    --end2end \
-    --max-wh 7680
-```
-
-You will get an onnx with **NonMaxSuppression** operater .
-
-The onnx outputs shape is ```nums x 7```.
-
-```nums``` means the number of all objects which were detected.
-
-```7```  means [`batch_index`,`x0`,`y0`,`x1`,` y1`,`classid`,`score`]
-
-### TensorRT backend (TensorRT version>= 8.0.0)
-
-#### Usage
-
-```bash
-python ./deploy/ONNX/export_onnx.py \
-    --weights yolov6s.pt \
-    --img 640 \
-    --batch 1 \
-    --end2end
-```
-
-You will get an onnx with **[EfficientNMS_TRT](https://github.com/NVIDIA/TensorRT/tree/main/plugin/efficientNMSPlugin)** plugin .
-The onnx outputs are as shown :
-
-<img src="https://user-images.githubusercontent.com/92794867/176650971-a4fa3d65-10d4-4b65-b8ef-00a2ff13406c.png" height="300px" />
-
-```num_dets``` means the number of object in every image in its batch .
-
-```det_boxes``` means topk(100) object's location about [`x0`,`y0`,`x1`,` y1`] .
-
-```det_scores``` means the confidence score of every topk(100) objects .
-
-```det_classes``` means the category of every topk(100) objects .
-
-
-You can export TensorRT engine use [trtexec](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#trtexec-ovr) tools.
-#### Usage
-``` shell
-/path/to/trtexec \
-    --onnx=yolov6s.onnx \
-    --saveEngine=yolov6s.engine \
-    --fp16 # if export TensorRT fp16 model
-```

src/YOLOv6/deploy/ONNX/export_onnx.py

@@ -1,112 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import argparse
-import time
-import sys
-import os
-import torch
-import torch.nn as nn
-import onnx
-
-ROOT = os.getcwd()
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))
-
-from yolov6.models.yolo import *
-from yolov6.models.effidehead import Detect
-from yolov6.layers.common import *
-from yolov6.utils.events import LOGGER
-from yolov6.utils.checkpoint import load_checkpoint
-from io import BytesIO
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default='./yolov6s.pt', help='weights path')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')  # height, width
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
-    parser.add_argument('--inplace', action='store_true', help='set Detect() inplace=True')
-    parser.add_argument('--simplify', action='store_true', help='simplify onnx model')
-    parser.add_argument('--end2end', action='store_true', help='export end2end onnx')
-    parser.add_argument('--max-wh', type=int, default=None, help='None for trt int for ort')
-    parser.add_argument('--topk-all', type=int, default=100, help='topk objects for every images')
-    parser.add_argument('--iou-thres', type=float, default=0.45, help='iou threshold for NMS')
-    parser.add_argument('--conf-thres', type=float, default=0.25, help='conf threshold for NMS')
-    parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    args = parser.parse_args()
-    args.img_size *= 2 if len(args.img_size) == 1 else 1  # expand
-    print(args)
-    t = time.time()
-
-    # Check device
-    cuda = args.device != 'cpu' and torch.cuda.is_available()
-    device = torch.device('cuda:0' if cuda else 'cpu')
-    assert not (device.type == 'cpu' and args.half), '--half only compatible with GPU export, i.e. use --device 0'
-    # Load PyTorch model
-    model = load_checkpoint(args.weights, map_location=device, inplace=True, fuse=True)  # load FP32 model
-    for layer in model.modules():
-        if isinstance(layer, RepVGGBlock):
-            layer.switch_to_deploy()
-
-    # Input
-    img = torch.zeros(args.batch_size, 3, *args.img_size).to(device)  # image size(1,3,320,192) iDetection
-
-    # Update model
-    if args.half:
-        img, model = img.half(), model.half()  # to FP16
-    model.eval()
-    for k, m in model.named_modules():
-        if isinstance(m, Conv):  # assign export-friendly activations
-            if isinstance(m.act, nn.SiLU):
-                m.act = SiLU()
-        elif isinstance(m, Detect):
-            m.inplace = args.inplace
-    if args.end2end:
-        from yolov6.models.end2end import End2End
-        model = End2End(model, max_obj=args.topk_all, iou_thres=args.iou_thres,
-                        score_thres=args.conf_thres, max_wh=args.max_wh, device=device)
-
-    y = model(img)  # dry run
-
-    # ONNX export
-    try:
-        LOGGER.info('\nStarting to export ONNX...')
-        export_file = args.weights.replace('.pt', '.onnx')  # filename
-        with BytesIO() as f:
-            torch.onnx.export(model, img, f, verbose=False, opset_version=12,
-                              training=torch.onnx.TrainingMode.EVAL,
-                              do_constant_folding=True,
-                              input_names=['image_arrays'],
-                              output_names=['num_dets', 'det_boxes', 'det_scores', 'det_classes']
-                              if args.end2end and args.max_wh is None else ['outputs'],)
-            f.seek(0)
-            # Checks
-            onnx_model = onnx.load(f)  # load onnx model
-            onnx.checker.check_model(onnx_model)  # check onnx model
-            # Fix output shape
-            if args.end2end and args.max_wh is None:
-                shapes = [args.batch_size, 1, args.batch_size, args.topk_all, 4,
-                          args.batch_size, args.topk_all, args.batch_size, args.topk_all]
-                for i in onnx_model.graph.output:
-                    for j in i.type.tensor_type.shape.dim:
-                        j.dim_param = str(shapes.pop(0))
-        if args.simplify:
-            try:
-                import onnxsim
-                LOGGER.info('\nStarting to simplify ONNX...')
-                onnx_model, check = onnxsim.simplify(onnx_model)
-                assert check, 'assert check failed'
-            except Exception as e:
-                LOGGER.info(f'Simplifier failure: {e}')
-        onnx.save(onnx_model, export_file)
-        LOGGER.info(f'ONNX export success, saved as {export_file}')
-    except Exception as e:
-        LOGGER.info(f'ONNX export failure: {e}')
-
-    # Finish
-    LOGGER.info('\nExport complete (%.2fs)' % (time.time() - t))
-    if args.end2end:
-        if args.max_wh is None:
-            LOGGER.info('\nYou can export tensorrt engine use trtexec tools.\nCommand is:')
-            LOGGER.info(f'trtexec --onnx={export_file} --saveEngine={export_file.replace(".onnx",".engine")}')

src/YOLOv6/deploy/OpenVINO/README.md

@@ -1,24 +0,0 @@
-## Export OpenVINO Model
-
-### Check requirements
-```shell
-pip install --upgrade pip
-pip install openvino-dev
-```
-
-### Export script
-```shell
-python deploy/OpenVINO/export_openvino.py --weights yolov6s.pt --img 640 --batch 1
-
-```
-
-### Download
-* [YOLOv6-nano](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6n_openvino.tar.gz)
-* [YOLOv6-tiny](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6n_openvino.tar.gz)
-* [YOLOv6-s](https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6n_openvino.tar.gz)
-
-### Speed test
-```shell
-benchmark_app -m yolov6s_openvino/yolov6s.xml -i data/images/image1.jpg -d CPU -niter 100 -progress
-
-```

src/YOLOv6/deploy/OpenVINO/export_openvino.py

@@ -1,92 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import argparse
-import time
-import sys
-import os
-import torch
-import torch.nn as nn
-import onnx
-import subprocess
-
-ROOT = os.getcwd()
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))
-
-from yolov6.models.yolo import *
-from yolov6.models.effidehead import Detect
-from yolov6.layers.common import *
-from yolov6.utils.events import LOGGER
-from yolov6.utils.checkpoint import load_checkpoint
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', type=str, default='./yolov6s.pt', help='weights path')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')  # height, width
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
-    parser.add_argument('--inplace', action='store_true', help='set Detect() inplace=True')
-    parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    args = parser.parse_args()
-    args.img_size *= 2 if len(args.img_size) == 1 else 1  # expand
-    print(args)
-    t = time.time()
-
-    # Check device
-    cuda = args.device != 'cpu' and torch.cuda.is_available()
-    device = torch.device('cuda:0' if cuda else 'cpu')
-    assert not (device.type == 'cpu' and args.half), '--half only compatible with GPU export, i.e. use --device 0'
-    # Load PyTorch model
-    model = load_checkpoint(args.weights, map_location=device, inplace=True, fuse=True)  # load FP32 model
-    for layer in model.modules():
-        if isinstance(layer, RepVGGBlock):
-            layer.switch_to_deploy()
-
-    # Input
-    img = torch.zeros(args.batch_size, 3, *args.img_size).to(device)  # image size(1,3,320,192) iDetection
-
-    # Update model
-    if args.half:
-        img, model = img.half(), model.half()  # to FP16
-    model.eval()
-    for k, m in model.named_modules():
-        if isinstance(m, Conv):  # assign export-friendly activations
-            if isinstance(m.act, nn.SiLU):
-                m.act = SiLU()
-        elif isinstance(m, Detect):
-            m.inplace = args.inplace
-
-    y = model(img)  # dry run
-
-    # ONNX export
-    try:
-        LOGGER.info('\nStarting to export ONNX...')
-        export_file = args.weights.replace('.pt', '.onnx')  # filename
-        torch.onnx.export(model, img, export_file, verbose=False, opset_version=12,
-                          training=torch.onnx.TrainingMode.EVAL,
-                          do_constant_folding=True,
-                          input_names=['image_arrays'],
-                          output_names=['outputs'],
-                         )
-
-        # Checks
-        onnx_model = onnx.load(export_file)  # load onnx model
-        onnx.checker.check_model(onnx_model)  # check onnx model
-        LOGGER.info(f'ONNX export success, saved as {export_file}')
-    except Exception as e:
-        LOGGER.info(f'ONNX export failure: {e}')
-
-    # OpenVINO export
-    try:
-        LOGGER.info('\nStarting to export OpenVINO...')
-        import_file = args.weights.replace('.pt', '.onnx')
-        export_dir = str(import_file).replace('.onnx', '_openvino')
-        cmd = f"mo --input_model {import_file} --output_dir {export_dir} --data_type {'FP16' if args.half else 'FP32'}"
-        subprocess.check_output(cmd.split())
-        LOGGER.info(f'OpenVINO export success, saved as {export_dir}')
-    except Exception as e:
-        LOGGER.info(f'OpenVINO export failure: {e}')
-
-    # Finish
-    LOGGER.info('\nExport complete (%.2fs)' % (time.time() - t))

src/YOLOv6/docs/About_naming_yolov6.md

@@ -1,9 +0,0 @@
-# About the naming of YOLOv6
-
-### WHY named YOLOv6 ?
-The full name is actually MT-YOLOv6, which is called YOLOv6 for brevity. Our work is majorly inspired by the original idea of the one-stage YOLO detection algorithm and the implementation has leveraged various techniques and tricks of former relevant work . Therefore, we named the project YOLOv6 to pay tribute to the work of YOLO series. Furthermore, we have indeed adopted some novel method and made solid engineering improvements to dedicate the algorithm to industrial applications.
-As for the project, we'll continue to improve and maintain it, contributing more values for industrial applications.
-
-P.S. We are contacting the authors of YOLO series about the naming of YOLOv6.
-
-Thanks for your attention！

src/YOLOv6/docs/Test_speed.md

@@ -1,41 +0,0 @@
-# Test speed
-
-This guidence explains how to reproduce speed results of YOLOv6. For fair comparison, the speed results do not contain the time cost of data pre-processing and NMS post-processing.
-
-## 0. Prepare model
-
-Download the models you want to test from the latest release.
-
-## 1. Prepare testing environment
-
-Refer to README, install packages corresponding to CUDA, CUDNN and TensorRT version.
-
-Here, we use Torch1.8.0 inference on V100 and TensorRT 7.2 on T4.
-
-## 2. Reproduce speed
-
-#### 2.1 Torch Inference on V100
-
-To get inference speed without TensorRT on V100,  you can run the following command:
-
-```shell
-python tools/eval.py --data data/coco.yaml  --batch 32 --weights yolov6n.pt --task speed [--half]
-```
-
-- Speed results with batchsize = 1 are unstable in multiple runs, thus we do not provide the bs1 speed results.
-
-#### 2.2 TensorRT Inference on T4
-
-To  get inference speed with TensorRT in FP16 mode on T4, you can follow the steps below:
-
-First, export pytorch model as onnx format using the  following command:
-
-```shell
-python deploy/ONNX/export_onnx.py --weights yolov6n.pt --device 0 --batch [1 or 32]
-```
-
-Second,  generate an inference  trt engine and test speed using `trtexec`:
-
-```
-trtexec --onnx=yolov6n.onnx --workspace=1024 --avgRuns=1000 --inputIOFormats=fp16:chw --outputIOFormats=fp16:chw
-```

src/YOLOv6/docs/Train_custom_data.md

@@ -1,143 +0,0 @@
-# Train Custom Data
-
-This guidence explains how to train your own custom data with YOLOv6 (take fine-tuning YOLOv6-s model for example).
-
-## 0. Before you start
-
-Clone this repo and follow README.md to install requirements in a Python3.8 environment.
-
-
-## 1. Prepare your own dataset
-
-**Step 1** Prepare your own dataset with images. For labeling images, you can use tools like [Labelme](https://github.com/wkentaro/labelme).
-
-**Step 2** Generate label files in YOLO format.
-
-One image corresponds to one label file, and the label format example is presented as below.
-
-```json
-# class_id center_x center_y bbox_width bbox_height
-0 0.300926 0.617063 0.601852 0.765873
-1 0.575 0.319531 0.4 0.551562
-```
-
-- Each row represents one object.
-- Class id starts from `0`.
-- Boundingbox coordinates must be in normalized `xywh` format (from 0 - 1). If your boxes are in pixels, divide `center_x` and `bbox_width` by image width, and `center_y` and `bbox_height` by image height.
-
-**Step 3** Organize directories.
-
-Organize your directory of custom dataset as follows:
-
-```shell
-custom_dataset
-├── images
-│   ├── train
-│   │   ├── train0.jpg
-│   │   └── train1.jpg
-│   ├── val
-│   │   ├── val0.jpg
-│   │   └── val1.jpg
-│   └── test
-│       ├── test0.jpg
-│       └── test1.jpg
-└── labels
-    ├── train
-    │   ├── train0.txt
-    │   └── train1.txt
-    ├── val
-    │   ├── val0.txt
-    │   └── val1.txt
-    └── test
-        ├── test0.txt
-        └── test1.txt
-```
-
-**Step 4** Create `dataset.yaml` in `$YOLOv6_DIR/data`.
-
-```yaml
-# Please insure that your custom_dataset are put in same parent dir with YOLOv6_DIR
-train: ../custom_dataset/images/train # train images
-val: ../custom_dataset/images/val # val images
-test: ../custom_dataset/images/test # test images (optional)
-
-# whether it is coco dataset, only coco dataset should be set to True.
-is_coco: False
-
-# Classes
-nc: 20  # number of classes
-names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
-        'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']  # class names
-```
-
-## 2. Create a config file
-
-We use a config file to specify the network structure and training setting, including  optimizer and data augmentation hyperparameters.
-
-If you create a new config file, please put it under the configs directory.
-Or just use the provided config file in `$YOLOV6_HOME/configs/*_finetune.py`.
-
-```python
-## YOLOv6s Model config file
-model = dict(
-    type='YOLOv6s',
-    pretrained='./weights/yolov6s.pt', # download pretrain model from YOLOv6 github if use pretrained model
-    depth_multiple = 0.33,
-    width_multiple = 0.50,
-    ...
-)
-solver=dict(
-    optim='SGD',
-    lr_scheduler='Cosine',
-    ...
-)
-
-data_aug = dict(
-    hsv_h=0.015,
-    hsv_s=0.7,
-    hsv_v=0.4,
-    ...
-)
-```
-
-
-
-## 3. Train
-
-Single GPU
-
-```shell
-python tools/train.py --batch 256 --conf configs/yolov6s_finetune.py --data data/data.yaml --device 0
-```
-
-Multi GPUs (DDP mode recommended)
-
-```shell
-python -m torch.distributed.launch --nproc_per_node 4 tools/train.py --batch 256 --conf configs/yolov6s_finetune.py --data data/data.yaml --device 0,1,2,3
-```
-
-
-
-## 4. Evaluation
-
-```shell
-python tools/eval.py --data data/data.yaml  --weights output_dir/name/weights/best_ckpt.pt --device 0
-```
-
-
-
-## 5. Inference
-
-```shell
-python tools/infer.py --weights output_dir/name/weights/best_ckpt.pt --source img.jpg --device 0
-```
-
-
-
-## 6. Deployment
-
-Export as ONNX Format
-
-```shell
-python deploy/ONNX/export_onnx.py --weights output_dir/name/weights/best_ckpt.pt --device 0
-```

src/YOLOv6/requirements.txt

@@ -1,16 +0,0 @@
-# pip install -r requirements.txt
-# python3.8 environment
-
-torch>=1.8.0
-torchvision>=0.9.0
-numpy>=1.18.5
-opencv-python>=4.1.2
-PyYAML>=5.3.1
-scipy>=1.4.1
-tqdm>=4.41.0
-addict>=2.4.0
-tensorboard>=2.7.0
-pycocotools>=2.0
-onnx>=1.10.0  # ONNX export
-onnx-simplifier>=0.3.6 # ONNX simplifier
-thop  # FLOPs computation

src/YOLOv6/tools/eval.py

@@ -1,93 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import argparse
-import os
-import os.path as osp
-import sys
-import torch
-
-ROOT = os.getcwd()
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))
-
-from yolov6.core.evaler import Evaler
-from yolov6.utils.events import LOGGER
-from yolov6.utils.general import increment_name
-
-
-def get_args_parser(add_help=True):
-    parser = argparse.ArgumentParser(description='YOLOv6 PyTorch Evalating', add_help=add_help)
-    parser.add_argument('--data', type=str, default='./data/coco.yaml', help='dataset.yaml path')
-    parser.add_argument('--weights', type=str, default='./weights/yolov6s.pt', help='model.pt path(s)')
-    parser.add_argument('--batch-size', type=int, default=32, help='batch size')
-    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
-    parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
-    parser.add_argument('--iou-thres', type=float, default=0.65, help='NMS IoU threshold')
-    parser.add_argument('--task', default='val', help='val, or speed')
-    parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--half', default=False, action='store_true', help='whether to use fp16 infer')
-    parser.add_argument('--save_dir', type=str, default='runs/val/', help='evaluation save dir')
-    parser.add_argument('--name', type=str, default='exp', help='save evaluation results to save_dir/name')
-    args = parser.parse_args()
-    LOGGER.info(args)
-    return args
-
-
-@torch.no_grad()
-def run(data,
-        weights=None,
-        batch_size=32,
-        img_size=640,
-        conf_thres=0.001,
-        iou_thres=0.65,
-        task='val',
-        device='',
-        half=False,
-        model=None,
-        dataloader=None,
-        save_dir='',
-        name = ''
-        ):
-    """ Run the evaluation process
-
-    This function is the main process of evaluataion, supporting image file and dir containing images.
-    It has tasks of 'val', 'train' and 'speed'. Task 'train' processes the evaluation during training phase.
-    Task 'val' processes the evaluation purely and return the mAP of model.pt. Task 'speed' precesses the
-    evaluation of inference speed of model.pt.
-
-    """
-
-     # task
-    Evaler.check_task(task)
-    if task == 'train':
-        save_dir = save_dir
-    else:
-        save_dir = str(increment_name(osp.join(save_dir, name)))
-        os.makedirs(save_dir, exist_ok=True)
-
-    # reload thres/device/half/data according task
-    conf_thres, iou_thres = Evaler.reload_thres(conf_thres, iou_thres, task)
-    device = Evaler.reload_device(device, model, task)
-    half = device.type != 'cpu' and half
-    data = Evaler.reload_dataset(data) if isinstance(data, str) else data
-
-    # init
-    val = Evaler(data, batch_size, img_size, conf_thres, \
-                iou_thres, device, half, save_dir)
-    model = val.init_model(model, weights, task)
-    dataloader = val.init_data(dataloader, task)
-
-    # eval
-    model.eval()
-    pred_result = val.predict_model(model, dataloader, task)
-    eval_result = val.eval_model(pred_result, model, dataloader, task)
-    return eval_result
-
-
-def main(args):
-    run(**vars(args))
-
-
-if __name__ == "__main__":
-    args = get_args_parser()
-    main(args)

src/YOLOv6/tools/infer.py

@@ -1,108 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import argparse
-import os
-import sys
-import os.path as osp
-
-import torch
-
-ROOT = os.getcwd()
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))
-
-from yolov6.utils.events import LOGGER
-from yolov6.core.inferer import Inferer
-
-
-def get_args_parser(add_help=True):
-    parser = argparse.ArgumentParser(description='YOLOv6 PyTorch Inference.', add_help=add_help)
-    parser.add_argument('--weights', type=str, default='weights/yolov6s.pt', help='model path(s) for inference.')
-    parser.add_argument('--source', type=str, default='data/images', help='the source path, e.g. image-file/dir.')
-    parser.add_argument('--yaml', type=str, default='data/coco.yaml', help='data yaml file.')
-    parser.add_argument('--img-size', type=int, default=640, help='the image-size(h,w) in inference size.')
-    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold for inference.')
-    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold for inference.')
-    parser.add_argument('--max-det', type=int, default=1000, help='maximal inferences per image.')
-    parser.add_argument('--device', default='0', help='device to run our model i.e. 0 or 0,1,2,3 or cpu.')
-    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt.')
-    parser.add_argument('--save-img', action='store_false', help='save visuallized inference results.')
-    parser.add_argument('--classes', nargs='+', type=int, help='filter by classes, e.g. --classes 0, or --classes 0 2 3.')
-    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS.')
-    parser.add_argument('--project', default='runs/inference', help='save inference results to project/name.')
-    parser.add_argument('--name', default='exp', help='save inference results to project/name.')
-    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels.')
-    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences.')
-    parser.add_argument('--half', action='store_true', help='whether to use FP16 half-precision inference.')
-
-    args = parser.parse_args()
-    LOGGER.info(args)
-    return args
-
-@torch.no_grad()
-def run(weights=osp.join(ROOT, 'yolov6s.pt'),
-        source=osp.join(ROOT, 'data/images'),
-        yaml=None,
-        img_size=640,
-        conf_thres=0.25,
-        iou_thres=0.45,
-        max_det=1000,
-        device='',
-        save_txt=False,
-        save_img=True,
-        classes=None,
-        agnostic_nms=False,
-        project=osp.join(ROOT, 'runs/inference'),
-        name='exp',
-        hide_labels=False,
-        hide_conf=False,
-        half=False,
-        ):
-    """ Inference process
-
-    This function is the main process of inference, supporting image files or dirs containing images.
-
-    Args:
-        weights: The path of model.pt, e.g. yolov6s.pt
-        source: Source path, supporting image files or dirs containing images.
-        yaml: Data yaml file, .
-        img_size: Inference image-size, e.g. 640
-        conf_thres: Confidence threshold in inference, e.g. 0.25
-        iou_thres: NMS IOU threshold in inference, e.g. 0.45
-        max_det: Maximal detections per image, e.g. 1000
-        device: Cuda device, e.e. 0, or 0,1,2,3 or cpu
-        save_txt: Save results to *.txt
-        save_img: Save visualized inference results
-        classes: Filter by class: --class 0, or --class 0 2 3
-        agnostic_nms: Class-agnostic NMS
-        project: Save results to project/name
-        name: Save results to project/name, e.g. 'exp'
-        line_thickness: Bounding box thickness (pixels), e.g. 3
-        hide_labels: Hide labels, e.g. False
-        hide_conf: Hide confidences
-        half: Use FP16 half-precision inference, e.g. False
-    """
-    # create save dir
-    save_dir = osp.join(project, name)
-    if (save_img or save_txt) and not osp.exists(save_dir):
-        os.makedirs(save_dir)
-    else:
-        LOGGER.warning('Save directory already existed')
-    if save_txt:
-        os.mkdir(osp.join(save_dir, 'labels'))
-
-    # Inference
-    inferer = Inferer(source, weights, device, yaml, img_size, half)
-    inferer.infer(conf_thres, iou_thres, classes, agnostic_nms, max_det, save_dir, save_txt, save_img, hide_labels, hide_conf)
-
-    if save_txt or save_img:
-        LOGGER.info(f"Results saved to {save_dir}")
-
-
-def main(args):
-    run(**vars(args))
-
-
-if __name__ == "__main__":
-    args = get_args_parser()
-    main(args)

src/YOLOv6/tools/quantization/mnn/README.md

@@ -1 +0,0 @@
-# Coming soon

src/YOLOv6/tools/quantization/tensorrt/post_training/Calibrator.py

@@ -1,210 +0,0 @@
-#
-# Modified by Meituan
-# 2022.6.24
-#
-
-# Copyright 2019 NVIDIA Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import sys
-import glob
-import random
-import logging
-import cv2
-
-import numpy as np
-from PIL import Image
-import tensorrt as trt
-import pycuda.driver as cuda
-import pycuda.autoinit
-
-logging.basicConfig(level=logging.DEBUG,
-                    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-                    datefmt="%Y-%m-%d %H:%M:%S")
-logger = logging.getLogger(__name__)
-
-def preprocess_yolov6(image, channels=3, height=224, width=224):
-    """Pre-processing for YOLOv6-based Object Detection Models
-
-    Parameters
-    ----------
-    image: PIL.Image
-        The image resulting from PIL.Image.open(filename) to preprocess
-    channels: int
-        The number of channels the image has (Usually 1 or 3)
-    height: int
-        The desired height of the image (usually 640)
-    width: int
-        The desired width of the image  (usually 640)
-
-    Returns
-    -------
-    img_data: numpy array
-        The preprocessed image data in the form of a numpy array
-
-    """
-    # Get the image in CHW format
-    resized_image = image.resize((width, height), Image.BILINEAR)
-    img_data = np.asarray(resized_image).astype(np.float32)
-
-    if len(img_data.shape) == 2:
-        # For images without a channel dimension, we stack
-        img_data = np.stack([img_data] * 3)
-        logger.debug("Received grayscale image. Reshaped to {:}".format(img_data.shape))
-    else:
-        img_data = img_data.transpose([2, 0, 1])
-
-    mean_vec = np.array([0.0, 0.0, 0.0])
-    stddev_vec = np.array([1.0, 1.0, 1.0])
-    assert img_data.shape[0] == channels
-
-    for i in range(img_data.shape[0]):
-        # Scale each pixel to [0, 1] and normalize per channel.
-        img_data[i, :, :] = (img_data[i, :, :] / 255.0 - mean_vec[i]) / stddev_vec[i]
-
-    return img_data
-
-def get_int8_calibrator(calib_cache, calib_data, max_calib_size, calib_batch_size):
-    # Use calibration cache if it exists
-    if os.path.exists(calib_cache):
-        logger.info("Skipping calibration files, using calibration cache: {:}".format(calib_cache))
-        calib_files = []
-    # Use calibration files from validation dataset if no cache exists
-    else:
-        if not calib_data:
-            raise ValueError("ERROR: Int8 mode requested, but no calibration data provided. Please provide --calibration-data /path/to/calibration/files")
-
-        calib_files = get_calibration_files(calib_data, max_calib_size)
-
-    # Choose pre-processing function for INT8 calibration
-    preprocess_func = preprocess_yolov6
-
-    int8_calibrator = ImageCalibrator(calibration_files=calib_files,
-                                         batch_size=calib_batch_size,
-                                         cache_file=calib_cache)
-    return int8_calibrator
-
-
-def get_calibration_files(calibration_data, max_calibration_size=None, allowed_extensions=(".jpeg", ".jpg", ".png")):
-    """Returns a list of all filenames ending with `allowed_extensions` found in the `calibration_data` directory.
-
-    Parameters
-    ----------
-    calibration_data: str
-        Path to directory containing desired files.
-    max_calibration_size: int
-        Max number of files to use for calibration. If calibration_data contains more than this number,
-        a random sample of size max_calibration_size will be returned instead. If None, all samples will be used.
-
-    Returns
-    -------
-    calibration_files: List[str]
-         List of filenames contained in the `calibration_data` directory ending with `allowed_extensions`.
-    """
-
-    logger.info("Collecting calibration files from: {:}".format(calibration_data))
-    calibration_files = [path for path in glob.iglob(os.path.join(calibration_data, "**"), recursive=True)
-                         if os.path.isfile(path) and path.lower().endswith(allowed_extensions)]
-    logger.info("Number of Calibration Files found: {:}".format(len(calibration_files)))
-
-    if len(calibration_files) == 0:
-        raise Exception("ERROR: Calibration data path [{:}] contains no files!".format(calibration_data))
-
-    if max_calibration_size:
-        if len(calibration_files) > max_calibration_size:
-            logger.warning("Capping number of calibration images to max_calibration_size: {:}".format(max_calibration_size))
-            random.seed(42)  # Set seed for reproducibility
-            calibration_files = random.sample(calibration_files, max_calibration_size)
-
-    return calibration_files
-
-
-# https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/infer/Int8/EntropyCalibrator2.html
-class ImageCalibrator(trt.IInt8EntropyCalibrator2):
-    """INT8 Calibrator Class for Imagenet-based Image Classification Models.
-
-    Parameters
-    ----------
-    calibration_files: List[str]
-        List of image filenames to use for INT8 Calibration
-    batch_size: int
-        Number of images to pass through in one batch during calibration
-    input_shape: Tuple[int]
-        Tuple of integers defining the shape of input to the model (Default: (3, 224, 224))
-    cache_file: str
-        Name of file to read/write calibration cache from/to.
-    preprocess_func: function -> numpy.ndarray
-        Pre-processing function to run on calibration data. This should match the pre-processing
-        done at inference time. In general, this function should return a numpy array of
-        shape `input_shape`.
-    """
-
-    def __init__(self, calibration_files=[], batch_size=32, input_shape=(3, 224, 224),
-                 cache_file="calibration.cache", use_cv2=False):
-        super().__init__()
-        self.input_shape = input_shape
-        self.cache_file = cache_file
-        self.batch_size = batch_size
-        self.batch = np.zeros((self.batch_size, *self.input_shape), dtype=np.float32)
-        self.device_input = cuda.mem_alloc(self.batch.nbytes)
-
-        self.files = calibration_files
-        self.use_cv2 = use_cv2
-        # Pad the list so it is a multiple of batch_size
-        if len(self.files) % self.batch_size != 0:
-            logger.info("Padding # calibration files to be a multiple of batch_size {:}".format(self.batch_size))
-            self.files += calibration_files[(len(calibration_files) % self.batch_size):self.batch_size]
-
-        self.batches = self.load_batches()
-        self.preprocess_func = preprocess_yolov6
-
-    def load_batches(self):
-        # Populates a persistent self.batch buffer with images.
-        for index in range(0, len(self.files), self.batch_size):
-            for offset in range(self.batch_size):
-                if self.use_cv2:
-                    image = cv2.imread(self.files[index + offset])
-                else:
-                    image = Image.open(self.files[index + offset])
-                self.batch[offset] = self.preprocess_func(image, *self.input_shape)
-            logger.info("Calibration images pre-processed: {:}/{:}".format(index+self.batch_size, len(self.files)))
-            yield self.batch
-
-    def get_batch_size(self):
-        return self.batch_size
-
-    def get_batch(self, names):
-        try:
-            # Assume self.batches is a generator that provides batch data.
-            batch = next(self.batches)
-            # Assume that self.device_input is a device buffer allocated by the constructor.
-            cuda.memcpy_htod(self.device_input, batch)
-            return [int(self.device_input)]
-        except StopIteration:
-            # When we're out of batches, we return either [] or None.
-            # This signals to TensorRT that there is no calibration data remaining.
-            return None
-
-    def read_calibration_cache(self):
-        # If there is a cache, use it instead of calibrating again. Otherwise, implicitly return None.
-        if os.path.exists(self.cache_file):
-            with open(self.cache_file, "rb") as f:
-                logger.info("Using calibration cache to save time: {:}".format(self.cache_file))
-                return f.read()
-
-    def write_calibration_cache(self, cache):
-        with open(self.cache_file, "wb") as f:
-            logger.info("Caching calibration data for future use: {:}".format(self.cache_file))
-            f.write(cache)

src/YOLOv6/tools/quantization/tensorrt/post_training/LICENSE

@@ -1,191 +0,0 @@
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-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
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-   limitations under the License.

src/YOLOv6/tools/quantization/tensorrt/post_training/README.md

@@ -1,83 +0,0 @@
-# ONNX -> TensorRT INT8
-These scripts were last tested using the
-[NGC TensorRT Container Version 20.06-py3](https://ngc.nvidia.com/catalog/containers/nvidia:tensorrt).
-You can see the corresponding framework versions for this container [here](https://docs.nvidia.com/deeplearning/sdk/tensorrt-container-release-notes/rel_20.06.html#rel_20.06).
-
-## Quickstart
-
-> **NOTE**: This INT8 example is only valid for **fixed-shape** ONNX models at the moment.
->
-INT8 Calibration on **dynamic-shape** models is now supported, however this example has not been updated
-to reflect that yet. For more details on INT8 Calibration for **dynamic-shape** models, please
-see the [documentation](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#int8-calib-dynamic-shapes).
-
-### 1. Convert ONNX model to TensorRT INT8
-
-See `./onnx_to_tensorrt.py -h` for full list of command line arguments.
-
-```bash
-./onnx_to_tensorrt.py --explicit-batch \
-                      --onnx resnet50/model.onnx \
-                      --fp16 \
-                      --int8 \
-                      --calibration-cache="caches/yolov6.cache" \
-                      -o resnet50.int8.engine
-```
-
-See the [INT8 Calibration](#int8-calibration) section below for details on calibration
-using your own model or different data, where you don't have an existing calibration cache
-or want to create a new one.
-
-## INT8 Calibration
-
-See [ImagenetCalibrator.py](ImagenetCalibrator.py) for a reference implementation
-of TensorRT's [IInt8EntropyCalibrator2](https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/infer/Int8/EntropyCalibrator2.html).
-
-This class can be tweaked to work for other kinds of models, inputs, etc.
-
-In the [Quickstart](#quickstart) section above, we made use of a pre-existing cache,
-[caches/yolov6.cache](caches/yolov6.cache), to save time for the sake of an example.
-
-However, to calibrate using different data or a different model, you can do so with the `--calibration-data` argument.
-
-* This requires that you've mounted a dataset, such as Imagenet, to use for calibration.
-    * Add something like `-v /imagenet:/imagenet` to your Docker command in Step (1)
-      to mount a dataset found locally at `/imagenet`.
-* You can specify your own `preprocess_func` by defining it inside of `ImageCalibrator.py`
-
-```bash
-# Path to dataset to use for calibration.
-#   **Not necessary if you already have a calibration cache from a previous run.
-CALIBRATION_DATA="/imagenet"
-
-# Truncate calibration images to a random sample of this amount if more are found.
-#   **Not necessary if you already have a calibration cache from a previous run.
-MAX_CALIBRATION_SIZE=512
-
-# Calibration cache to be used instead of calibration data if it already exists,
-# or the cache will be created from the calibration data if it doesn't exist.
-CACHE_FILENAME="caches/yolov6.cache"
-
-# Path to ONNX model
-ONNX_MODEL="model/yolov6.onnx"
-
-# Path to write TensorRT engine to
-OUTPUT="yolov6.int8.engine"
-
-# Creates an int8 engine from your ONNX model, creating ${CACHE_FILENAME} based
-# on your ${CALIBRATION_DATA}, unless ${CACHE_FILENAME} already exists, then
-# it will use simply use that instead.
-python3 onnx_to_tensorrt.py --fp16 --int8 -v \
-        --max_calibration_size=${MAX_CALIBRATION_SIZE} \
-        --calibration-data=${CALIBRATION_DATA} \
-        --calibration-cache=${CACHE_FILENAME} \
-        --preprocess_func=${PREPROCESS_FUNC} \
-        --explicit-batch \
-        --onnx ${ONNX_MODEL} -o ${OUTPUT}
-
-```
-
-### Pre-processing
-
-In order to calibrate your model correctly, you should `pre-process` your data the same way
-that you would during inference.

src/YOLOv6/tools/quantization/tensorrt/post_training/onnx_to_tensorrt.py

@@ -1,220 +0,0 @@
-#!/usr/bin/env python3
-
-#
-# Modified by Meituan
-# 2022.6.24
-#
-
-# Copyright 2019 NVIDIA Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import sys
-import glob
-import math
-import logging
-import argparse
-
-import tensorrt as trt
-#sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
-
-TRT_LOGGER = trt.Logger()
-logging.basicConfig(level=logging.DEBUG,
-                    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
-                    datefmt="%Y-%m-%d %H:%M:%S")
-logger = logging.getLogger(__name__)
-
-
-def add_profiles(config, inputs, opt_profiles):
-    logger.debug("=== Optimization Profiles ===")
-    for i, profile in enumerate(opt_profiles):
-        for inp in inputs:
-            _min, _opt, _max = profile.get_shape(inp.name)
-            logger.debug("{} - OptProfile {} - Min {} Opt {} Max {}".format(inp.name, i, _min, _opt, _max))
-        config.add_optimization_profile(profile)
-
-
-def mark_outputs(network):
-    # Mark last layer's outputs if not already marked
-    # NOTE: This may not be correct in all cases
-    last_layer = network.get_layer(network.num_layers-1)
-    if not last_layer.num_outputs:
-        logger.error("Last layer contains no outputs.")
-        return
-
-    for i in range(last_layer.num_outputs):
-        network.mark_output(last_layer.get_output(i))
-
-
-def check_network(network):
-    if not network.num_outputs:
-        logger.warning("No output nodes found, marking last layer's outputs as network outputs. Correct this if wrong.")
-        mark_outputs(network)
-
-    inputs = [network.get_input(i) for i in range(network.num_inputs)]
-    outputs = [network.get_output(i) for i in range(network.num_outputs)]
-    max_len = max([len(inp.name) for inp in inputs] + [len(out.name) for out in outputs])
-
-    logger.debug("=== Network Description ===")
-    for i, inp in enumerate(inputs):
-        logger.debug("Input  {0} | Name: {1:{2}} | Shape: {3}".format(i, inp.name, max_len, inp.shape))
-    for i, out in enumerate(outputs):
-        logger.debug("Output {0} | Name: {1:{2}} | Shape: {3}".format(i, out.name, max_len, out.shape))
-
-
-def get_batch_sizes(max_batch_size):
-    # Returns powers of 2, up to and including max_batch_size
-    max_exponent = math.log2(max_batch_size)
-    for i in range(int(max_exponent)+1):
-        batch_size = 2**i
-        yield batch_size
-
-    if max_batch_size != batch_size:
-        yield max_batch_size
-
-
-# TODO: This only covers dynamic shape for batch size, not dynamic shape for other dimensions
-def create_optimization_profiles(builder, inputs, batch_sizes=[1,8,16,32,64]):
-    # Check if all inputs are fixed explicit batch to create a single profile and avoid duplicates
-    if all([inp.shape[0] > -1 for inp in inputs]):
-        profile = builder.create_optimization_profile()
-        for inp in inputs:
-            fbs, shape = inp.shape[0], inp.shape[1:]
-            profile.set_shape(inp.name, min=(fbs, *shape), opt=(fbs, *shape), max=(fbs, *shape))
-            return [profile]
-
-    # Otherwise for mixed fixed+dynamic explicit batch inputs, create several profiles
-    profiles = {}
-    for bs in batch_sizes:
-        if not profiles.get(bs):
-            profiles[bs] = builder.create_optimization_profile()
-
-        for inp in inputs:
-            shape = inp.shape[1:]
-            # Check if fixed explicit batch
-            if inp.shape[0] > -1:
-                bs = inp.shape[0]
-
-            profiles[bs].set_shape(inp.name, min=(bs, *shape), opt=(bs, *shape), max=(bs, *shape))
-
-    return list(profiles.values())
-
-def main():
-    parser = argparse.ArgumentParser(description="Creates a TensorRT engine from the provided ONNX file.\n")
-    parser.add_argument("--onnx", required=True, help="The ONNX model file to convert to TensorRT")
-    parser.add_argument("-o", "--output", type=str, default="model.engine", help="The path at which to write the engine")
-    parser.add_argument("-b", "--max-batch-size", type=int, help="The max batch size for the TensorRT engine input")
-    parser.add_argument("-v", "--verbosity", action="count", help="Verbosity for logging. (None) for ERROR, (-v) for INFO/WARNING/ERROR, (-vv) for VERBOSE.")
-    parser.add_argument("--explicit-batch", action='store_true', help="Set trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH.")
-    parser.add_argument("--explicit-precision", action='store_true', help="Set trt.NetworkDefinitionCreationFlag.EXPLICIT_PRECISION.")
-    parser.add_argument("--gpu-fallback", action='store_true', help="Set trt.BuilderFlag.GPU_FALLBACK.")
-    parser.add_argument("--refittable", action='store_true', help="Set trt.BuilderFlag.REFIT.")
-    parser.add_argument("--debug", action='store_true', help="Set trt.BuilderFlag.DEBUG.")
-    parser.add_argument("--strict-types", action='store_true', help="Set trt.BuilderFlag.STRICT_TYPES.")
-    parser.add_argument("--fp16", action="store_true", help="Attempt to use FP16 kernels when possible.")
-    parser.add_argument("--int8", action="store_true", help="Attempt to use INT8 kernels when possible. This should generally be used in addition to the --fp16 flag. \
-                                                             ONLY SUPPORTS RESNET-LIKE MODELS SUCH AS RESNET50/VGG16/INCEPTION/etc.")
-    parser.add_argument("--calibration-cache", help="(INT8 ONLY) The path to read/write from calibration cache.", default="calibration.cache")
-    parser.add_argument("--calibration-data", help="(INT8 ONLY) The directory containing {*.jpg, *.jpeg, *.png} files to use for calibration. (ex: Imagenet Validation Set)", default=None)
-    parser.add_argument("--calibration-batch-size", help="(INT8 ONLY) The batch size to use during calibration.", type=int, default=128)
-    parser.add_argument("--max-calibration-size", help="(INT8 ONLY) The max number of data to calibrate on from --calibration-data.", type=int, default=2048)
-    parser.add_argument("-s", "--simple", action="store_true", help="Use SimpleCalibrator with random data instead of ImagenetCalibrator for INT8 calibration.")
-    args, _ = parser.parse_known_args()
-
-    print(args)
-
-    # Adjust logging verbosity
-    if args.verbosity is None:
-        TRT_LOGGER.min_severity = trt.Logger.Severity.ERROR
-    # -v
-    elif args.verbosity == 1:
-        TRT_LOGGER.min_severity = trt.Logger.Severity.INFO
-    # -vv
-    else:
-        TRT_LOGGER.min_severity = trt.Logger.Severity.VERBOSE
-    logger.info("TRT_LOGGER Verbosity: {:}".format(TRT_LOGGER.min_severity))
-
-    # Network flags
-    network_flags = 0
-    if args.explicit_batch:
-        network_flags |= 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
-    if args.explicit_precision:
-        network_flags |= 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_PRECISION)
-
-    builder_flag_map = {
-            'gpu_fallback': trt.BuilderFlag.GPU_FALLBACK,
-            'refittable': trt.BuilderFlag.REFIT,
-            'debug': trt.BuilderFlag.DEBUG,
-            'strict_types': trt.BuilderFlag.STRICT_TYPES,
-            'fp16': trt.BuilderFlag.FP16,
-            'int8': trt.BuilderFlag.INT8,
-    }
-
-    # Building engine
-    with trt.Builder(TRT_LOGGER) as builder, \
-         builder.create_network(network_flags) as network, \
-         builder.create_builder_config() as config, \
-         trt.OnnxParser(network, TRT_LOGGER) as parser:
-
-        config.max_workspace_size = 2**30 # 1GiB
-
-        # Set Builder Config Flags
-        for flag in builder_flag_map:
-            if getattr(args, flag):
-                logger.info("Setting {}".format(builder_flag_map[flag]))
-                config.set_flag(builder_flag_map[flag])
-
-        # Fill network atrributes with information by parsing model
-        with open(args.onnx, "rb") as f:
-            if not parser.parse(f.read()):
-                print('ERROR: Failed to parse the ONNX file: {}'.format(args.onnx))
-                for error in range(parser.num_errors):
-                    print(parser.get_error(error))
-                sys.exit(1)
-
-        # Display network info and check certain properties
-        check_network(network)
-
-        if args.explicit_batch:
-            # Add optimization profiles
-            batch_sizes = [1, 8, 16, 32, 64]
-            inputs = [network.get_input(i) for i in range(network.num_inputs)]
-            opt_profiles = create_optimization_profiles(builder, inputs, batch_sizes)
-            add_profiles(config, inputs, opt_profiles)
-        # Implicit Batch Network
-        else:
-            builder.max_batch_size = args.max_batch_size
-            opt_profiles = []
-
-        # Precision flags
-        if args.fp16 and not builder.platform_has_fast_fp16:
-            logger.warning("FP16 not supported on this platform.")
-
-        if args.int8 and not builder.platform_has_fast_int8:
-            logger.warning("INT8 not supported on this platform.")
-
-        if args.int8:
-                from Calibrator import ImageCalibrator, get_int8_calibrator # local module
-                config.int8_calibrator = get_int8_calibrator(args.calibration_cache,
-                                                             args.calibration_data,
-                                                             args.max_calibration_size,
-                                                             args.calibration_batch_size)
-
-        logger.info("Building Engine...")
-        with builder.build_engine(network, config) as engine, open(args.output, "wb") as f:
-            logger.info("Serializing engine to file: {:}".format(args.output))
-            f.write(engine.serialize())
-
-if __name__ == "__main__":
-    main()

src/YOLOv6/tools/quantization/tensorrt/post_training/quant.sh

@@ -1,23 +0,0 @@
-# Path to ONNX model
-# ex: ../yolov6.onnx
-ONNX_MODEL=$1
-
-# Path to dataset to use for calibration.
-#   **Not necessary if you already have a calibration cache from a previous run.
-CALIBRATION_DATA=$2
-
-# Path to Cache file to Serving
-# ex: ./caches/demo.cache
-CACHE_FILENAME=$3
-
-# Path to write TensorRT engine to
-OUTPUT=$4
-
-# Creates an int8 engine from your ONNX model, creating ${CACHE_FILENAME} based
-# on your ${CALIBRATION_DATA}, unless ${CACHE_FILENAME} already exists, then
-# it will use simply use that instead.
-python3 onnx_to_tensorrt.py --fp16 --int8 -v \
-        --calibration-data=${CALIBRATION_DATA} \
-        --calibration-cache=${CACHE_FILENAME} \
-        --explicit-batch \
-        --onnx ${ONNX_MODEL} -o ${OUTPUT}

src/YOLOv6/tools/quantization/tensorrt/requirements.txt

@@ -1,7 +0,0 @@
-# pip install -r requirements.txt
-# python3.8 environment
-
-tensorrt # TensorRT 8.0+
-pycuda==2020.1 # CUDA 11.0
-nvidia-pyindex
-pytorch-quantization

src/YOLOv6/tools/quantization/tensorrt/training_aware/QAT_quantizer.py

@@ -1,39 +0,0 @@
-#
-#  QAT_quantizer.py
-#  YOLOv6
-#
-#  Created by Meituan on 2022/06/24.
-#  Copyright © 2022
-#
-
-from absl import logging
-from pytorch_quantization import nn as quant_nn
-from pytorch_quantization import quant_modules
-
-# Call this function before defining the model
-def tensorrt_official_qat():
-    # Quantization Aware Training is based on Straight Through Estimator (STE) derivative approximation.
-    # It is some time known as “quantization aware training”.
-
-    # PyTorch-Quantization is a toolkit for training and evaluating PyTorch models with simulated quantization.
-    # Quantization can be added to the model automatically, or manually, allowing the model to be tuned for accuracy and performance.
-    # Quantization is compatible with NVIDIAs high performance integer kernels which leverage integer Tensor Cores.
-    # The quantized model can be exported to ONNX and imported by TensorRT 8.0 and later.
-    # https://github.com/NVIDIA/TensorRT/blob/main/tools/pytorch-quantization/examples/finetune_quant_resnet50.ipynb
-
-    # The example to export the
-    # model.eval()
-    # quant_nn.TensorQuantizer.use_fb_fake_quant = True # We have to shift to pytorch's fake quant ops before exporting the model to ONNX
-    # opset_version = 13
-
-    # Export ONNX for multiple batch sizes
-    # print("Creating ONNX file: " + onnx_filename)
-    # dummy_input = torch.randn(batch_onnx, 3, 224, 224, device='cuda') #TODO: switch input dims by model
-    # torch.onnx.export(model, dummy_input, onnx_filename, verbose=False, opset_version=opset_version, enable_onnx_checker=False, do_constant_folding=True)
-    try:
-        quant_modules.initialize()
-    except NameError:
-        logging.info("initialzation error for quant_modules")
-
-# def QAT_quantizer():
-# coming soon

src/YOLOv6/tools/train.py

@@ -1,94 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import argparse
-import os
-import os.path as osp
-import torch
-import torch.distributed as dist
-import sys
-
-ROOT = os.getcwd()
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))
-
-from yolov6.core.engine import Trainer
-from yolov6.utils.config import Config
-from yolov6.utils.events import LOGGER, save_yaml
-from yolov6.utils.envs import get_envs, select_device, set_random_seed
-from yolov6.utils.general import increment_name
-
-
-def get_args_parser(add_help=True):
-    parser = argparse.ArgumentParser(description='YOLOv6 PyTorch Training', add_help=add_help)
-    parser.add_argument('--data-path', default='./data/coco.yaml', type=str, help='path of dataset')
-    parser.add_argument('--conf-file', default='./configs/yolov6s.py', type=str, help='experiments description file')
-    parser.add_argument('--img-size', default=640, type=int, help='train, val image size (pixels)')
-    parser.add_argument('--batch-size', default=32, type=int, help='total batch size for all GPUs')
-    parser.add_argument('--epochs', default=400, type=int, help='number of total epochs to run')
-    parser.add_argument('--workers', default=8, type=int, help='number of data loading workers (default: 8)')
-    parser.add_argument('--device', default='0', type=str, help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--eval-interval', default=20, type=int, help='evaluate at every interval epochs')
-    parser.add_argument('--eval-final-only', action='store_true', help='only evaluate at the final epoch')
-    parser.add_argument('--heavy-eval-range', default=50, type=int,
-                        help='evaluating every epoch for last such epochs (can be jointly used with --eval-interval)')
-    parser.add_argument('--check-images', action='store_true', help='check images when initializing datasets')
-    parser.add_argument('--check-labels', action='store_true', help='check label files when initializing datasets')
-    parser.add_argument('--output-dir', default='./runs/train', type=str, help='path to save outputs')
-    parser.add_argument('--name', default='exp', type=str, help='experiment name, saved to output_dir/name')
-    parser.add_argument('--dist_url', default='env://', type=str, help='url used to set up distributed training')
-    parser.add_argument('--gpu_count', type=int, default=0)
-    parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter')
-    parser.add_argument('--resume', type=str, default=None, help='resume the corresponding ckpt')
-
-    return parser
-
-
-def check_and_init(args):
-    '''check config files and device, and initialize '''
-
-    # check files
-    master_process = args.rank == 0 if args.world_size > 1 else args.rank == -1
-    args.save_dir = str(increment_name(osp.join(args.output_dir, args.name), master_process))
-    cfg = Config.fromfile(args.conf_file)
-
-    # check device
-    device = select_device(args.device)
-
-    # set random seed
-    set_random_seed(1+args.rank, deterministic=(args.rank == -1))
-
-    # save args
-    if master_process:
-        os.makedirs(args.save_dir)
-        save_yaml(vars(args), osp.join(args.save_dir, 'args.yaml'))
-
-    return cfg, device
-
-
-def main(args):
-    '''main function of training'''
-    # Setup
-    args.rank, args.local_rank, args.world_size = get_envs()
-    LOGGER.info(f'training args are: {args}\n')
-    cfg, device = check_and_init(args)
-
-    if args.local_rank != -1: # if DDP mode
-        torch.cuda.set_device(args.local_rank)
-        device = torch.device('cuda', args.local_rank)
-        LOGGER.info('Initializing process group... ')
-        dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo", \
-                init_method=args.dist_url, rank=args.local_rank, world_size=args.world_size)
-
-    # Start
-    trainer = Trainer(args, cfg, device)
-    trainer.train()
-
-    # End
-    if args.world_size > 1 and args.rank == 0:
-        LOGGER.info('Destroying process group... ')
-        dist.destroy_process_group()
-
-
-if __name__ == '__main__':
-    args = get_args_parser().parse_args()
-    main(args)

src/YOLOv6/yolov6/core/engine.py

@@ -1,276 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import os
-import time
-from copy import deepcopy
-import os.path as osp
-
-from tqdm import tqdm
-
-import numpy as np
-import torch
-from torch.cuda import amp
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.utils.tensorboard import SummaryWriter
-
-import tools.eval as eval
-from yolov6.data.data_load import create_dataloader
-from yolov6.models.yolo import build_model
-from yolov6.models.loss import ComputeLoss
-from yolov6.utils.events import LOGGER, NCOLS, load_yaml, write_tblog
-from yolov6.utils.ema import ModelEMA, de_parallel
-from yolov6.utils.checkpoint import load_state_dict, save_checkpoint, strip_optimizer
-from yolov6.solver.build import build_optimizer, build_lr_scheduler
-
-
-class Trainer:
-    def __init__(self, args, cfg, device):
-        self.args = args
-        self.cfg = cfg
-        self.device = device
-
-        self.rank = args.rank
-        self.local_rank = args.local_rank
-        self.world_size = args.world_size
-        self.main_process = self.rank in [-1, 0]
-        self.save_dir = args.save_dir
-        # get data loader
-        self.data_dict = load_yaml(args.data_path)
-        self.num_classes = self.data_dict['nc']
-        self.train_loader, self.val_loader = self.get_data_loader(args, cfg, self.data_dict)
-        # get model and optimizer
-        model = self.get_model(args, cfg, self.num_classes, device)
-        self.optimizer = self.get_optimizer(args, cfg, model)
-        self.scheduler, self.lf = self.get_lr_scheduler(args, cfg, self.optimizer)
-        self.ema = ModelEMA(model) if self.main_process else None
-        self.model = self.parallel_model(args, model, device)
-        self.model.nc, self.model.names = self.data_dict['nc'], self.data_dict['names']
-        # tensorboard
-        self.tblogger = SummaryWriter(self.save_dir) if self.main_process else None
-
-        self.start_epoch = 0
-
-        # resume ckpt from user-defined path
-        if args.resume:
-            assert os.path.isfile(args.resume), 'ERROR: --resume checkpoint does not exists'
-            self.ckpt = torch.load(args.resume, map_location='cpu')
-            self.start_epoch = self.ckpt['epoch'] + 1
-
-        self.max_epoch = args.epochs
-        self.max_stepnum = len(self.train_loader)
-        self.batch_size = args.batch_size
-        self.img_size = args.img_size
-
-    # Training Process
-
-    def train(self):
-        try:
-            self.train_before_loop()
-            for self.epoch in range(self.start_epoch, self.max_epoch):
-                self.train_in_loop()
-
-        except Exception as _:
-            LOGGER.error('ERROR in training loop or eval/save model.')
-            raise
-        finally:
-            self.train_after_loop()
-
-    # Training loop for each epoch
-    def train_in_loop(self):
-        try:
-            self.prepare_for_steps()
-            for self.step, self.batch_data in self.pbar:
-                self.train_in_steps()
-                self.print_details()
-        except Exception as _:
-            LOGGER.error('ERROR in training steps.')
-            raise
-        try:
-            self.eval_and_save()
-        except Exception as _:
-            LOGGER.error('ERROR in evaluate and save model.')
-            raise
-
-    # Training loop for batchdata
-    def train_in_steps(self):
-        images, targets = self.prepro_data(self.batch_data, self.device)
-        # forward
-        with amp.autocast(enabled=self.device != 'cpu'):
-            preds = self.model(images)
-            total_loss, loss_items = self.compute_loss(preds, targets)
-            if self.rank != -1:
-                total_loss *= self.world_size
-        # backward
-        self.scaler.scale(total_loss).backward()
-        self.loss_items = loss_items
-        self.update_optimizer()
-
-    def eval_and_save(self):
-        remaining_epochs = self.max_epoch - self.epoch
-        eval_interval = self.args.eval_interval if remaining_epochs > self.args.heavy_eval_range else 1
-        is_val_epoch = (not self.args.eval_final_only or (remaining_epochs == 1)) and (self.epoch % eval_interval == 0)
-        if self.main_process:
-            self.ema.update_attr(self.model, include=['nc', 'names', 'stride']) # update attributes for ema model
-            if is_val_epoch:
-                self.eval_model()
-                self.ap = self.evaluate_results[0] * 0.1 + self.evaluate_results[1] * 0.9
-                self.best_ap = max(self.ap, self.best_ap)
-            # save ckpt
-            ckpt = {
-                    'model': deepcopy(de_parallel(self.model)).half(),
-                    'ema': deepcopy(self.ema.ema).half(),
-                    'updates': self.ema.updates,
-                    'optimizer': self.optimizer.state_dict(),
-                    'epoch': self.epoch,
-                    }
-
-            save_ckpt_dir = osp.join(self.save_dir, 'weights')
-            save_checkpoint(ckpt, (is_val_epoch) and (self.ap == self.best_ap), save_ckpt_dir, model_name='last_ckpt')
-            del ckpt
-            # log for tensorboard
-            write_tblog(self.tblogger, self.epoch, self.evaluate_results, self.mean_loss)
-
-    def eval_model(self):
-        results = eval.run(self.data_dict,
-                           batch_size=self.batch_size // self.world_size * 2,
-                           img_size=self.img_size,
-                           model=self.ema.ema,
-                           dataloader=self.val_loader,
-                           save_dir=self.save_dir,
-                           task='train')
-
-        LOGGER.info(f"Epoch: {self.epoch} | mAP@0.5: {results[0]} | mAP@0.50:0.95: {results[1]}")
-        self.evaluate_results = results[:2]
-
-    def train_before_loop(self):
-        LOGGER.info('Training start...')
-        self.start_time = time.time()
-        self.warmup_stepnum = max(round(self.cfg.solver.warmup_epochs * self.max_stepnum), 1000)
-        self.scheduler.last_epoch = self.start_epoch - 1
-        self.last_opt_step = -1
-        self.scaler = amp.GradScaler(enabled=self.device != 'cpu')
-
-        self.best_ap, self.ap = 0.0, 0.0
-        self.evaluate_results = (0, 0) # AP50, AP50_95
-        self.compute_loss = ComputeLoss(iou_type=self.cfg.model.head.iou_type)
-
-        if hasattr(self, "ckpt"):
-            resume_state_dict = self.ckpt['model'].float().state_dict()  # checkpoint's state_dict as FP32
-            self.model.load_state_dict(resume_state_dict, strict=True)  # load model state dict
-            self.optimizer.load_state_dict(self.ckpt['optimizer']) # load optimizer
-            self.start_epoch = self.ckpt['epoch'] + 1
-            self.ema.ema.load_state_dict(self.ckpt['ema'].float().state_dict()) # load ema state dict
-            self.ema.updates = self.ckpt['updates']
-
-    def prepare_for_steps(self):
-        if self.epoch > self.start_epoch:
-            self.scheduler.step()
-        self.model.train()
-        if self.rank != -1:
-            self.train_loader.sampler.set_epoch(self.epoch)
-        self.mean_loss = torch.zeros(4, device=self.device)
-        self.optimizer.zero_grad()
-
-        LOGGER.info(('\n' + '%10s' * 5) % ('Epoch', 'iou_loss', 'l1_loss', 'obj_loss', 'cls_loss'))
-        self.pbar = enumerate(self.train_loader)
-        if self.main_process:
-            self.pbar = tqdm(self.pbar, total=self.max_stepnum, ncols=NCOLS, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}')
-
-    # Print loss after each steps
-    def print_details(self):
-        if self.main_process:
-            self.mean_loss = (self.mean_loss * self.step + self.loss_items) / (self.step + 1)
-            self.pbar.set_description(('%10s' + '%10.4g' * 4) % (f'{self.epoch}/{self.max_epoch - 1}', \
-                                                                *(self.mean_loss)))
-
-    # Empty cache if training finished
-    def train_after_loop(self):
-        if self.main_process:
-            LOGGER.info(f'\nTraining completed in {(time.time() - self.start_time) / 3600:.3f} hours.')
-            save_ckpt_dir = osp.join(self.save_dir, 'weights')
-            strip_optimizer(save_ckpt_dir, self.epoch)  # strip optimizers for saved pt model
-        if self.device != 'cpu':
-            torch.cuda.empty_cache()
-
-    def update_optimizer(self):
-        curr_step = self.step + self.max_stepnum * self.epoch
-        self.accumulate = max(1, round(64 / self.batch_size))
-        if curr_step <= self.warmup_stepnum:
-            self.accumulate = max(1, np.interp(curr_step, [0, self.warmup_stepnum], [1, 64 / self.batch_size]).round())
-            for k, param in enumerate(self.optimizer.param_groups):
-                warmup_bias_lr = self.cfg.solver.warmup_bias_lr if k == 2 else 0.0
-                param['lr'] = np.interp(curr_step, [0, self.warmup_stepnum], [warmup_bias_lr, param['initial_lr'] * self.lf(self.epoch)])
-                if 'momentum' in param:
-                    param['momentum'] = np.interp(curr_step, [0, self.warmup_stepnum], [self.cfg.solver.warmup_momentum, self.cfg.solver.momentum])
-        if curr_step - self.last_opt_step >= self.accumulate:
-            self.scaler.step(self.optimizer)
-            self.scaler.update()
-            self.optimizer.zero_grad()
-            if self.ema:
-                self.ema.update(self.model)
-            self.last_opt_step = curr_step
-
-    @staticmethod
-    def get_data_loader(args, cfg, data_dict):
-        train_path, val_path = data_dict['train'], data_dict['val']
-        # check data
-        nc = int(data_dict['nc'])
-        class_names = data_dict['names']
-        assert len(class_names) == nc, f'the length of class names does not match the number of classes defined'
-        grid_size = max(int(max(cfg.model.head.strides)), 32)
-        # create train dataloader
-        train_loader = create_dataloader(train_path, args.img_size, args.batch_size // args.world_size, grid_size,
-                                         hyp=dict(cfg.data_aug), augment=True, rect=False, rank=args.local_rank,
-                                         workers=args.workers, shuffle=True, check_images=args.check_images,
-                                         check_labels=args.check_labels, data_dict=data_dict, task='train')[0]
-        # create val dataloader
-        val_loader = None
-        if args.rank in [-1, 0]:
-            val_loader = create_dataloader(val_path, args.img_size, args.batch_size // args.world_size * 2, grid_size,
-                                           hyp=dict(cfg.data_aug), rect=True, rank=-1, pad=0.5,
-                                           workers=args.workers, check_images=args.check_images,
-                                           check_labels=args.check_labels, data_dict=data_dict, task='val')[0]
-
-        return train_loader, val_loader
-
-    @staticmethod
-    def prepro_data(batch_data, device):
-        images = batch_data[0].to(device, non_blocking=True).float() / 255
-        targets = batch_data[1].to(device)
-        return images, targets
-
-    def get_model(self, args, cfg, nc, device):
-        model = build_model(cfg, nc, device)
-        weights = cfg.model.pretrained
-        if weights:  # finetune if pretrained model is set
-            LOGGER.info(f'Loading state_dict from {weights} for fine-tuning...')
-            model = load_state_dict(weights, model, map_location=device)
-        LOGGER.info('Model: {}'.format(model))
-        return model
-
-    @staticmethod
-    def parallel_model(args, model, device):
-        # If DP mode
-        dp_mode = device.type != 'cpu' and args.rank == -1
-        if dp_mode and torch.cuda.device_count() > 1:
-            LOGGER.warning('WARNING: DP not recommended, use DDP instead.\n')
-            model = torch.nn.DataParallel(model)
-
-        # If DDP mode
-        ddp_mode = device.type != 'cpu' and args.rank != -1
-        if ddp_mode:
-            model = DDP(model, device_ids=[args.local_rank], output_device=args.local_rank)
-
-        return model
-
-    def get_optimizer(self, args, cfg, model):
-        accumulate = max(1, round(64 / args.batch_size))
-        cfg.solver.weight_decay *= args.batch_size * accumulate / 64
-        optimizer = build_optimizer(cfg, model)
-        return optimizer
-
-    @staticmethod
-    def get_lr_scheduler(args, cfg, optimizer):
-        epochs = args.epochs
-        lr_scheduler, lf = build_lr_scheduler(cfg, optimizer, epochs)
-        return lr_scheduler, lf

src/YOLOv6/yolov6/core/evaler.py

@@ -1,256 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import os
-from tqdm import tqdm
-import numpy as np
-import json
-import torch
-import yaml
-from pathlib import Path
-
-from pycocotools.coco import COCO
-from pycocotools.cocoeval import COCOeval
-
-from yolov6.data.data_load import create_dataloader
-from yolov6.utils.events import LOGGER, NCOLS
-from yolov6.utils.nms import non_max_suppression
-from yolov6.utils.checkpoint import load_checkpoint
-from yolov6.utils.torch_utils import time_sync, get_model_info
-
-'''
-python tools/eval.py --task 'train'/'val'/'speed'
-'''
-
-
-class Evaler:
-    def __init__(self,
-                 data,
-                 batch_size=32,
-                 img_size=640,
-                 conf_thres=0.001,
-                 iou_thres=0.65,
-                 device='',
-                 half=True,
-                 save_dir=''):
-        self.data = data
-        self.batch_size = batch_size
-        self.img_size = img_size
-        self.conf_thres = conf_thres
-        self.iou_thres = iou_thres
-        self.device = device
-        self.half = half
-        self.save_dir = save_dir
-
-    def init_model(self, model, weights, task):
-        if task != 'train':
-            model = load_checkpoint(weights, map_location=self.device)
-            self.stride = int(model.stride.max())
-            if self.device.type != 'cpu':
-                model(torch.zeros(1, 3, self.img_size, self.img_size).to(self.device).type_as(next(model.parameters())))
-            # switch to deploy
-            from yolov6.layers.common import RepVGGBlock
-            for layer in model.modules():
-                if isinstance(layer, RepVGGBlock):
-                    layer.switch_to_deploy()
-            LOGGER.info("Switch model to deploy modality.")
-            LOGGER.info("Model Summary: {}".format(get_model_info(model, self.img_size)))
-        model.half() if self.half else model.float()
-        return model
-
-    def init_data(self, dataloader, task):
-        '''Initialize dataloader.
-        Returns a dataloader for task val or speed.
-        '''
-        self.is_coco = self.data.get("is_coco", False)
-        self.ids = self.coco80_to_coco91_class() if self.is_coco else list(range(1000))
-        if task != 'train':
-            pad = 0.0 if task == 'speed' else 0.5
-            dataloader = create_dataloader(self.data[task if task in ('train', 'val', 'test') else 'val'],
-                                           self.img_size, self.batch_size, self.stride, check_labels=True, pad=pad, rect=True,
-                                           data_dict=self.data, task=task)[0]
-        return dataloader
-
-    def predict_model(self, model, dataloader, task):
-        '''Model prediction
-        Predicts the whole dataset and gets the prediced results and inference time.
-        '''
-        self.speed_result = torch.zeros(4, device=self.device)
-        pred_results = []
-        pbar = tqdm(dataloader, desc="Inferencing model in val datasets.", ncols=NCOLS)
-        for imgs, targets, paths, shapes in pbar:
-            # pre-process
-            t1 = time_sync()
-            imgs = imgs.to(self.device, non_blocking=True)
-            imgs = imgs.half() if self.half else imgs.float()
-            imgs /= 255
-            self.speed_result[1] += time_sync() - t1  # pre-process time
-
-            # Inference
-            t2 = time_sync()
-            outputs = model(imgs)
-            self.speed_result[2] += time_sync() - t2  # inference time
-
-            # post-process
-            t3 = time_sync()
-            outputs = non_max_suppression(outputs, self.conf_thres, self.iou_thres, multi_label=True)
-            self.speed_result[3] += time_sync() - t3  # post-process time
-            self.speed_result[0] += len(outputs)
-
-            # save result
-            pred_results.extend(self.convert_to_coco_format(outputs, imgs, paths, shapes, self.ids))
-        return pred_results
-
-    def eval_model(self, pred_results, model, dataloader, task):
-        '''Evaluate models
-        For task speed, this function only evaluates the speed of model and outputs inference time.
-        For task val, this function evaluates the speed and mAP by pycocotools, and returns
-        inference time and mAP value.
-        '''
-        LOGGER.info(f'\nEvaluating speed.')
-        self.eval_speed(task)
-
-        LOGGER.info(f'\nEvaluating mAP by pycocotools.')
-        if task != 'speed' and len(pred_results):
-            if 'anno_path' in self.data:
-                anno_json = self.data['anno_path']
-            else:
-                # generated coco format labels in dataset initialization
-                dataset_root = os.path.dirname(os.path.dirname(self.data['val']))
-                base_name = os.path.basename(self.data['val'])
-                anno_json = os.path.join(dataset_root, 'annotations', f'instances_{base_name}.json')
-            pred_json = os.path.join(self.save_dir, "predictions.json")
-            LOGGER.info(f'Saving {pred_json}...')
-            with open(pred_json, 'w') as f:
-                json.dump(pred_results, f)
-
-            anno = COCO(anno_json)
-            pred = anno.loadRes(pred_json)
-            cocoEval = COCOeval(anno, pred, 'bbox')
-            if self.is_coco:
-                imgIds = [int(os.path.basename(x).split(".")[0])
-                            for x in dataloader.dataset.img_paths]
-                cocoEval.params.imgIds = imgIds
-            cocoEval.evaluate()
-            cocoEval.accumulate()
-            cocoEval.summarize()
-            map, map50 = cocoEval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
-            # Return results
-            model.float()  # for training
-            if task != 'train':
-                LOGGER.info(f"Results saved to {self.save_dir}")
-            return (map50, map)
-        return (0.0, 0.0)
-
-    def eval_speed(self, task):
-        '''Evaluate model inference speed.'''
-        if task != 'train':
-            n_samples = self.speed_result[0].item()
-            pre_time, inf_time, nms_time = 1000 * self.speed_result[1:].cpu().numpy() / n_samples
-            for n, v in zip(["pre-process", "inference", "NMS"],[pre_time, inf_time, nms_time]):
-                LOGGER.info("Average {} time: {:.2f} ms".format(n, v))
-
-    def box_convert(self, x):
-        # Convert boxes with shape [n, 4] from [x1, y1, x2, y2] to [x, y, w, h] where x1y1=top-left, x2y2=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 scale_coords(self, 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
-        if isinstance(coords, torch.Tensor):  # faster individually
-            coords[:, 0].clamp_(0, img0_shape[1])  # x1
-            coords[:, 1].clamp_(0, img0_shape[0])  # y1
-            coords[:, 2].clamp_(0, img0_shape[1])  # x2
-            coords[:, 3].clamp_(0, img0_shape[0])  # y2
-        else:  # np.array (faster grouped)
-            coords[:, [0, 2]] = coords[:, [0, 2]].clip(0, img0_shape[1])  # x1, x2
-            coords[:, [1, 3]] = coords[:, [1, 3]].clip(0, img0_shape[0])  # y1, y2
-        return coords
-
-    def convert_to_coco_format(self, outputs, imgs, paths, shapes, ids):
-        pred_results = []
-        for i, pred in enumerate(outputs):
-            if len(pred) == 0:
-                continue
-            path, shape = Path(paths[i]), shapes[i][0]
-            self.scale_coords(imgs[i].shape[1:], pred[:, :4], shape, shapes[i][1])
-            image_id = int(path.stem) if path.stem.isnumeric() else path.stem
-            bboxes = self.box_convert(pred[:, 0:4])
-            bboxes[:, :2] -= bboxes[:, 2:] / 2
-            cls = pred[:, 5]
-            scores = pred[:, 4]
-            for ind in range(pred.shape[0]):
-                category_id = ids[int(cls[ind])]
-                bbox = [round(x, 3) for x in bboxes[ind].tolist()]
-                score = round(scores[ind].item(), 5)
-                pred_data = {
-                    "image_id": image_id,
-                    "category_id": category_id,
-                    "bbox": bbox,
-                    "score": score
-                }
-                pred_results.append(pred_data)
-        return pred_results
-
-    @staticmethod
-    def check_task(task):
-        if task not in ['train','val','speed']:
-            raise Exception("task argument error: only support 'train' / 'val' / 'speed' task.")
-
-    @staticmethod
-    def reload_thres(conf_thres, iou_thres, task):
-        '''Sets conf and iou threshold for task val/speed'''
-        if task != 'train':
-            if task == 'val':
-                conf_thres = 0.001
-            if task == 'speed':
-                conf_thres = 0.25
-                iou_thres = 0.45
-        return conf_thres, iou_thres
-
-    @staticmethod
-    def reload_device(device, model, task):
-        # device = 'cpu' or '0' or '0,1,2,3'
-        if task == 'train':
-            device = next(model.parameters()).device
-        else:
-            if device == 'cpu':
-                os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-            elif device:
-                os.environ['CUDA_VISIBLE_DEVICES'] = device
-                assert torch.cuda.is_available()
-            cuda = device != 'cpu' and torch.cuda.is_available()
-            device = torch.device('cuda:0' if cuda else 'cpu')
-        return device
-
-    @staticmethod
-    def reload_dataset(data):
-        with open(data, errors='ignore') as yaml_file:
-            data = yaml.safe_load(yaml_file)
-        val = data.get('val')
-        if not os.path.exists(val):
-            raise Exception('Dataset not found.')
-        return data
-
-    @staticmethod
-    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/
-        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

src/YOLOv6/yolov6/core/inferer.py

@@ -1,193 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import os
-import os.path as osp
-import math
-from tqdm import tqdm
-import numpy as np
-import cv2
-import torch
-from PIL import ImageFont
-
-from yolov6.utils.events import LOGGER, load_yaml
-from yolov6.layers.common import DetectBackend
-from yolov6.data.data_augment import letterbox
-from yolov6.utils.nms import non_max_suppression
-
-
-class Inferer:
-    def __init__(self, source, weights, device, yaml, img_size, half):
-        import glob
-        from yolov6.data.datasets import IMG_FORMATS
-
-        self.__dict__.update(locals())
-
-        # Init model
-        self.device = device
-        self.img_size = img_size
-        cuda = self.device != 'cpu' and torch.cuda.is_available()
-        self.device = torch.device('cuda:0' if cuda else 'cpu')
-        self.model = DetectBackend(weights, device=self.device)
-        self.stride = self.model.stride
-        self.class_names = load_yaml(yaml)['names']
-        self.img_size = self.check_img_size(self.img_size, s=self.stride)  # check image size
-
-        # Half precision
-        if half & (self.device.type != 'cpu'):
-            self.model.model.half()
-        else:
-            self.model.model.float()
-            half = False
-
-        if self.device.type != 'cpu':
-            self.model(torch.zeros(1, 3, *self.img_size).to(self.device).type_as(next(self.model.model.parameters())))  # warmup
-
-        # Load data
-        if os.path.isdir(source):
-            img_paths = sorted(glob.glob(os.path.join(source, '*.*')))  # dir
-        elif os.path.isfile(source):
-            img_paths = [source]  # files
-        else:
-            raise Exception(f'Invalid path: {source}')
-        self.img_paths = [img_path for img_path in img_paths if img_path.split('.')[-1].lower() in IMG_FORMATS]
-
-    def infer(self, conf_thres, iou_thres, classes, agnostic_nms, max_det, save_dir, save_txt, save_img, hide_labels, hide_conf):
-        ''' Model Inference and results visualization '''
-
-        for img_path in tqdm(self.img_paths):
-            img, img_src = self.precess_image(img_path, self.img_size, self.stride, self.half)
-            img = img.to(self.device)
-            if len(img.shape) == 3:
-                img = img[None]
-                # expand for batch dim
-            pred_results = self.model(img)
-            det = non_max_suppression(pred_results, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)[0]
-
-            save_path = osp.join(save_dir, osp.basename(img_path))  # im.jpg
-            txt_path = osp.join(save_dir, 'labels', osp.splitext(osp.basename(img_path))[0])
-
-            gn = torch.tensor(img_src.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-            img_ori = img_src
-
-            # check image and font
-            assert img_ori.data.contiguous, 'Image needs to be contiguous. Please apply to input images with np.ascontiguousarray(im).'
-            self.font_check()
-
-            if len(det):
-                det[:, :4] = self.rescale(img.shape[2:], det[:, :4], img_src.shape).round()
-
-                for *xyxy, conf, cls in reversed(det):
-                    if save_txt:  # Write to file
-                        xywh = (self.box_convert(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-                        line = (cls, *xywh, conf)
-                        with open(txt_path + '.txt', 'a') as f:
-                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
-                    if save_img:
-                        class_num = int(cls)  # integer class
-                        label = None if hide_labels else (self.class_names[class_num] if hide_conf else f'{self.class_names[class_num]} {conf:.2f}')
-
-                        self.plot_box_and_label(img_ori, max(round(sum(img_ori.shape) / 2 * 0.003), 2), xyxy, label, color=self.generate_colors(class_num, True))
-
-                img_src = np.asarray(img_ori)
-
-                # Save results (image with detections)
-                if save_img:
-                    cv2.imwrite(save_path, img_src)
-
-    @staticmethod
-    def precess_image(path, img_size, stride, half):
-        '''Process image before image inference.'''
-        try:
-            img_src = cv2.imread(path)
-            assert img_src is not None, f'Invalid image: {path}'
-        except Exception as e:
-            LOGGER.Warning(e)
-        image = letterbox(img_src, img_size, stride=stride)[0]
-
-        # Convert
-        image = image.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-        image = torch.from_numpy(np.ascontiguousarray(image))
-        image = image.half() if half else image.float()  # uint8 to fp16/32
-        image /= 255  # 0 - 255 to 0.0 - 1.0
-
-        return image, img_src
-
-    @staticmethod
-    def rescale(ori_shape, boxes, target_shape):
-        '''Rescale the output to the original image shape'''
-        ratio = min(ori_shape[0] / target_shape[0], ori_shape[1] / target_shape[1])
-        padding = (ori_shape[1] - target_shape[1] * ratio) / 2, (ori_shape[0] - target_shape[0] * ratio) / 2
-
-        boxes[:, [0, 2]] -= padding[0]
-        boxes[:, [1, 3]] -= padding[1]
-        boxes[:, :4] /= ratio
-
-        boxes[:, 0].clamp_(0, target_shape[1])  # x1
-        boxes[:, 1].clamp_(0, target_shape[0])  # y1
-        boxes[:, 2].clamp_(0, target_shape[1])  # x2
-        boxes[:, 3].clamp_(0, target_shape[0])  # y2
-
-        return boxes
-
-    def check_img_size(self, img_size, s=32, floor=0):
-        """Make sure image size is a multiple of stride s in each dimension, and return a new shape list of image."""
-        if isinstance(img_size, int):  # integer i.e. img_size=640
-            new_size = max(self.make_divisible(img_size, int(s)), floor)
-        elif isinstance(img_size, list):  # list i.e. img_size=[640, 480]
-            new_size = [max(self.make_divisible(x, int(s)), floor) for x in img_size]
-        else:
-            raise Exception(f"Unsupported type of img_size: {type(img_size)}")
-
-        if new_size != img_size:
-            print(f'WARNING: --img-size {img_size} must be multiple of max stride {s}, updating to {new_size}')
-        return new_size if isinstance(img_size,list) else [new_size]*2
-
-    def make_divisible(self, x, divisor):
-        # Upward revision the value x to make it evenly divisible by the divisor.
-        return math.ceil(x / divisor) * divisor
-
-    @staticmethod
-    def plot_box_and_label(image, lw, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
-        # Add one xyxy box to image with label
-        p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
-        cv2.rectangle(image, p1, p2, color, thickness=lw, lineType=cv2.LINE_AA)
-        if label:
-            tf = max(lw - 1, 1)  # font thickness
-            w, h = cv2.getTextSize(label, 0, fontScale=lw / 3, thickness=tf)[0]  # text width, height
-            outside = p1[1] - h - 3 >= 0  # label fits outside box
-            p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
-            cv2.rectangle(image, p1, p2, color, -1, cv2.LINE_AA)  # filled
-            cv2.putText(image, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, lw / 3, txt_color,
-                        thickness=tf, lineType=cv2.LINE_AA)
-
-    @staticmethod
-    def font_check(font='./yolov6/utils/Arial.ttf', size=10):
-        # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
-        assert osp.exists(font), f'font path not exists: {font}'
-        try:
-            return ImageFont.truetype(str(font) if font.exists() else font.name, size)
-        except Exception as e:  # download if missing
-            return ImageFont.truetype(str(font), size)
-
-    @staticmethod
-    def box_convert(x):
-        # Convert boxes with shape [n, 4] from [x1, y1, x2, y2] to [x, y, w, h] where x1y1=top-left, x2y2=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
-
-    @staticmethod
-    def generate_colors(i, bgr=False):
-        hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
-               '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
-        palette = []
-        for iter in hex:
-            h = '#' + iter
-            palette.append(tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)))
-        num = len(palette)
-        color = palette[int(i) % num]
-        return (color[2], color[1], color[0]) if bgr else color

src/YOLOv6/yolov6/data/data_augment.py

@@ -1,193 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# This code is based on
-# https://github.com/ultralytics/yolov5/blob/master/utils/dataloaders.py
-
-import math
-import random
-
-import cv2
-import numpy as np
-
-
-def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
-    # HSV color-space augmentation
-    if hgain or sgain or vgain:
-        r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains
-        hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
-        dtype = im.dtype  # uint8
-
-        x = np.arange(0, 256, dtype=r.dtype)
-        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)
-
-        im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
-        cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im)  # no return needed
-
-
-def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleup=True, stride=32):
-    # Resize and pad image while meeting stride-multiple constraints
-    shape = im.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 val mAP)
-        r = min(r, 1.0)
-
-    # Compute padding
-    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
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        im = cv2.resize(im, 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))
-    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return im, r, (dw, dh)
-
-
-def mixup(im, labels, im2, labels2):
-    # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
-    r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
-    im = (im * r + im2 * (1 - r)).astype(np.uint8)
-    labels = np.concatenate((labels, labels2), 0)
-    return im, labels
-
-
-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 random_affine(img, labels=(), degrees=10, translate=.1, scale=.1, shear=10,
-                  new_shape=(640, 640)):
-
-    n = len(labels)
-    height, width = new_shape
-
-    M, s = get_transform_matrix(img.shape[:2], (height, width), degrees, scale, shear, translate)
-    if (M != np.eye(3)).any():  # image changed
-        img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
-
-    # Transform label coordinates
-    if n:
-        new = np.zeros((n, 4))
-
-        xy = np.ones((n * 4, 3))
-        xy[:, :2] = labels[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
-        xy = xy @ M.T  # transform
-        xy = 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=labels[:, 1:5].T * s, box2=new.T, area_thr=0.1)
-        labels = labels[i]
-        labels[:, 1:5] = new[i]
-
-    return img, labels
-
-
-def get_transform_matrix(img_shape, new_shape, degrees, scale, shear, translate):
-    new_height, new_width = new_shape
-    # 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)
-
-    # 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 + 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) * new_width  # x translation (pixels)
-    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * new_height  # y transla ion (pixels)
-
-    # Combined rotation matrix
-    M = T @ S @ R @ C  # order of operations (right to left) is IMPORTANT
-    return M, s
-
-
-def mosaic_augmentation(img_size, imgs, hs, ws, labels, hyp):
-
-    assert len(imgs) == 4, "Mosaic augmentation of current version only supports 4 images."
-
-    labels4 = []
-    s = img_size
-    yc, xc = (int(random.uniform(s//2, 3*s//2)) for _ in range(2))  # mosaic center x, y
-    for i in range(len(imgs)):
-        # Load image
-        img, h, w = imgs[i], hs[i], ws[i]
-        # 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_per_img = labels[i].copy()
-        if labels_per_img.size:
-            boxes = np.copy(labels_per_img[:, 1:])
-            boxes[:, 0] = w * (labels_per_img[:, 1] - labels_per_img[:, 3] / 2) + padw  # top left x
-            boxes[:, 1] = h * (labels_per_img[:, 2] - labels_per_img[:, 4] / 2) + padh  # top left y
-            boxes[:, 2] = w * (labels_per_img[:, 1] + labels_per_img[:, 3] / 2) + padw  # bottom right x
-            boxes[:, 3] = h * (labels_per_img[:, 2] + labels_per_img[:, 4] / 2) + padh  # bottom right y
-            labels_per_img[:, 1:] = boxes
-
-        labels4.append(labels_per_img)
-
-    # Concat/clip labels
-    labels4 = np.concatenate(labels4, 0)
-    for x in (labels4[:, 1:]):
-        np.clip(x, 0, 2 * s, out=x)
-
-    # Augment
-    img4, labels4 = random_affine(img4, labels4,
-                                  degrees=hyp['degrees'],
-                                  translate=hyp['translate'],
-                                  scale=hyp['scale'],
-                                  shear=hyp['shear'])
-
-    return img4, labels4

src/YOLOv6/yolov6/data/data_load.py

@@ -1,113 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# This code is based on
-# https://github.com/ultralytics/yolov5/blob/master/utils/dataloaders.py
-
-import os
-from torch.utils.data import dataloader, distributed
-
-from .datasets import TrainValDataset
-from yolov6.utils.events import LOGGER
-from yolov6.utils.torch_utils import torch_distributed_zero_first
-
-
-def create_dataloader(
-    path,
-    img_size,
-    batch_size,
-    stride,
-    hyp=None,
-    augment=False,
-    check_images=False,
-    check_labels=False,
-    pad=0.0,
-    rect=False,
-    rank=-1,
-    workers=8,
-    shuffle=False,
-    data_dict=None,
-    task="Train",
-):
-    """Create general dataloader.
-
-    Returns dataloader and dataset
-    """
-    if rect and shuffle:
-        LOGGER.warning(
-            "WARNING: --rect is incompatible with DataLoader shuffle, setting shuffle=False"
-        )
-        shuffle = False
-    with torch_distributed_zero_first(rank):
-        dataset = TrainValDataset(
-            path,
-            img_size,
-            batch_size,
-            augment=augment,
-            hyp=hyp,
-            rect=rect,
-            check_images=check_images,
-            check_labels=check_labels,
-            stride=int(stride),
-            pad=pad,
-            rank=rank,
-            data_dict=data_dict,
-            task=task,
-        )
-
-    batch_size = min(batch_size, len(dataset))
-    workers = min(
-        [
-            os.cpu_count() // int(os.getenv("WORLD_SIZE", 1)),
-            batch_size if batch_size > 1 else 0,
-            workers,
-        ]
-    )  # number of workers
-    sampler = (
-        None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
-    )
-    return (
-        TrainValDataLoader(
-            dataset,
-            batch_size=batch_size,
-            shuffle=shuffle and sampler is None,
-            num_workers=workers,
-            sampler=sampler,
-            pin_memory=True,
-            collate_fn=TrainValDataset.collate_fn,
-        ),
-        dataset,
-    )
-
-
-class TrainValDataLoader(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)

src/YOLOv6/yolov6/data/datasets.py

@@ -1,550 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-
-import glob
-import os
-import os.path as osp
-import random
-import json
-import time
-import hashlib
-
-from multiprocessing.pool import Pool
-
-import cv2
-import numpy as np
-import torch
-from PIL import ExifTags, Image, ImageOps
-from torch.utils.data import Dataset
-from tqdm import tqdm
-
-from .data_augment import (
-    augment_hsv,
-    letterbox,
-    mixup,
-    random_affine,
-    mosaic_augmentation,
-)
-from yolov6.utils.events import LOGGER
-
-# Parameters
-IMG_FORMATS = ["bmp", "jpg", "jpeg", "png", "tif", "tiff", "dng", "webp", "mpo"]
-# Get orientation exif tag
-for k, v in ExifTags.TAGS.items():
-    if v == "Orientation":
-        ORIENTATION = k
-        break
-
-
-class TrainValDataset(Dataset):
-    # YOLOv6 train_loader/val_loader, loads images and labels for training and validation
-    def __init__(
-        self,
-        img_dir,
-        img_size=640,
-        batch_size=16,
-        augment=False,
-        hyp=None,
-        rect=False,
-        check_images=False,
-        check_labels=False,
-        stride=32,
-        pad=0.0,
-        rank=-1,
-        data_dict=None,
-        task="train",
-    ):
-        assert task.lower() in ("train", "val", "speed"), f"Not supported task: {task}"
-        t1 = time.time()
-        self.__dict__.update(locals())
-        self.main_process = self.rank in (-1, 0)
-        self.task = self.task.capitalize()
-        self.class_names = data_dict["names"]
-        self.img_paths, self.labels = self.get_imgs_labels(self.img_dir)
-        if self.rect:
-            shapes = [self.img_info[p]["shape"] for p in self.img_paths]
-            self.shapes = np.array(shapes, dtype=np.float64)
-            self.batch_indices = np.floor(
-                np.arange(len(shapes)) / self.batch_size
-            ).astype(
-                np.int
-            )  # batch indices of each image
-            self.sort_files_shapes()
-        t2 = time.time()
-        if self.main_process:
-            LOGGER.info(f"%.1fs for dataset initialization." % (t2 - t1))
-
-    def __len__(self):
-        """Get the length of dataset"""
-        return len(self.img_paths)
-
-    def __getitem__(self, index):
-        """Fetching a data sample for a given key.
-        This function applies mosaic and mixup augments during training.
-        During validation, letterbox augment is applied.
-        """
-        # Mosaic Augmentation
-        if self.augment and random.random() < self.hyp["mosaic"]:
-            img, labels = self.get_mosaic(index)
-            shapes = None
-
-            # MixUp augmentation
-            if random.random() < self.hyp["mixup"]:
-                img_other, labels_other = self.get_mosaic(
-                    random.randint(0, len(self.img_paths) - 1)
-                )
-                img, labels = mixup(img, labels, img_other, labels_other)
-
-        else:
-            # Load image
-            img, (h0, w0), (h, w) = self.load_image(index)
-
-            # Letterbox
-            shape = (
-                self.batch_shapes[self.batch_indices[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:
-                w *= ratio
-                h *= ratio
-                # new boxes
-                boxes = np.copy(labels[:, 1:])
-                boxes[:, 0] = (
-                    w * (labels[:, 1] - labels[:, 3] / 2) + pad[0]
-                )  # top left x
-                boxes[:, 1] = (
-                    h * (labels[:, 2] - labels[:, 4] / 2) + pad[1]
-                )  # top left y
-                boxes[:, 2] = (
-                    w * (labels[:, 1] + labels[:, 3] / 2) + pad[0]
-                )  # bottom right x
-                boxes[:, 3] = (
-                    h * (labels[:, 2] + labels[:, 4] / 2) + pad[1]
-                )  # bottom right y
-                labels[:, 1:] = boxes
-
-            if self.augment:
-                img, labels = random_affine(
-                    img,
-                    labels,
-                    degrees=self.hyp["degrees"],
-                    translate=self.hyp["translate"],
-                    scale=self.hyp["scale"],
-                    shear=self.hyp["shear"],
-                    new_shape=(self.img_size, self.img_size),
-                )
-
-        if len(labels):
-            h, w = img.shape[:2]
-
-            labels[:, [1, 3]] = labels[:, [1, 3]].clip(0, w - 1e-3)  # x1, x2
-            labels[:, [2, 4]] = labels[:, [2, 4]].clip(0, h - 1e-3)  # y1, y2
-
-            boxes = np.copy(labels[:, 1:])
-            boxes[:, 0] = ((labels[:, 1] + labels[:, 3]) / 2) / w  # x center
-            boxes[:, 1] = ((labels[:, 2] + labels[:, 4]) / 2) / h  # y center
-            boxes[:, 2] = (labels[:, 3] - labels[:, 1]) / w  # width
-            boxes[:, 3] = (labels[:, 4] - labels[:, 2]) / h  # height
-            labels[:, 1:] = boxes
-
-        if self.augment:
-            img, labels = self.general_augment(img, labels)
-
-        labels_out = torch.zeros((len(labels), 6))
-        if len(labels):
-            labels_out[:, 1:] = torch.from_numpy(labels)
-
-        # Convert
-        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-        img = np.ascontiguousarray(img)
-
-        return torch.from_numpy(img), labels_out, self.img_paths[index], shapes
-
-    def load_image(self, index):
-        """Load image.
-        This function loads image by cv2, resize original image to target shape(img_size) with keeping ratio.
-
-        Returns:
-            Image, original shape of image, resized image shape
-        """
-        path = self.img_paths[index]
-        im = cv2.imread(path)
-        assert im is not None, f"Image Not Found {path}, workdir: {os.getcwd()}"
-
-        h0, w0 = im.shape[:2]  # origin shape
-        r = self.img_size / max(h0, w0)
-        if r != 1:
-            im = cv2.resize(
-                im,
-                (int(w0 * r), int(h0 * r)),
-                interpolation=cv2.INTER_AREA
-                if r < 1 and not self.augment
-                else cv2.INTER_LINEAR,
-            )
-        return im, (h0, w0), im.shape[:2]
-
-    @staticmethod
-    def collate_fn(batch):
-        """Merges a list of samples to form a mini-batch of Tensor(s)"""
-        img, label, path, shapes = zip(*batch)
-        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
-
-    def get_imgs_labels(self, img_dir):
-
-        assert osp.exists(img_dir), f"{img_dir} is an invalid directory path!"
-        valid_img_record = osp.join(
-            osp.dirname(img_dir), "." + osp.basename(img_dir) + ".json"
-        )
-        NUM_THREADS = min(8, os.cpu_count())
-
-        img_paths = glob.glob(osp.join(img_dir, "*"), recursive=True)
-        img_paths = sorted(
-            p for p in img_paths if p.split(".")[-1].lower() in IMG_FORMATS
-        )
-        assert img_paths, f"No images found in {img_dir}."
-
-        img_hash = self.get_hash(img_paths)
-        if osp.exists(valid_img_record):
-            with open(valid_img_record, "r") as f:
-                cache_info = json.load(f)
-                if "image_hash" in cache_info and cache_info["image_hash"] == img_hash:
-                    img_info = cache_info["information"]
-                else:
-                    self.check_images = True
-        else:
-            self.check_images = True
-
-        # check images
-        if self.check_images and self.main_process:
-            img_info = {}
-            nc, msgs = 0, []  # number corrupt, messages
-            LOGGER.info(
-                f"{self.task}: Checking formats of images with {NUM_THREADS} process(es): "
-            )
-            with Pool(NUM_THREADS) as pool:
-                pbar = tqdm(
-                    pool.imap(TrainValDataset.check_image, img_paths),
-                    total=len(img_paths),
-                )
-                for img_path, shape_per_img, nc_per_img, msg in pbar:
-                    if nc_per_img == 0:  # not corrupted
-                        img_info[img_path] = {"shape": shape_per_img}
-                    nc += nc_per_img
-                    if msg:
-                        msgs.append(msg)
-                    pbar.desc = f"{nc} image(s) corrupted"
-            pbar.close()
-            if msgs:
-                LOGGER.info("\n".join(msgs))
-
-            cache_info = {"information": img_info, "image_hash": img_hash}
-            # save valid image paths.
-            with open(valid_img_record, "w") as f:
-                json.dump(cache_info, f)
-
-        # check and load anns
-        label_dir = osp.join(
-            osp.dirname(osp.dirname(img_dir)), "labels", osp.basename(img_dir)
-        )
-        assert osp.exists(label_dir), f"{label_dir} is an invalid directory path!"
-
-        img_paths = list(img_info.keys())
-        label_paths = sorted(
-            osp.join(label_dir, osp.splitext(osp.basename(p))[0] + ".txt")
-            for p in img_paths
-        )
-        label_hash = self.get_hash(label_paths)
-        if "label_hash" not in cache_info or cache_info["label_hash"] != label_hash:
-            self.check_labels = True
-
-        if self.check_labels:
-            cache_info["label_hash"] = label_hash
-            nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number corrupt, messages
-            LOGGER.info(
-                f"{self.task}: Checking formats of labels with {NUM_THREADS} process(es): "
-            )
-            with Pool(NUM_THREADS) as pool:
-                pbar = pool.imap(
-                    TrainValDataset.check_label_files, zip(img_paths, label_paths)
-                )
-                pbar = tqdm(pbar, total=len(label_paths)) if self.main_process else pbar
-                for (
-                    img_path,
-                    labels_per_file,
-                    nc_per_file,
-                    nm_per_file,
-                    nf_per_file,
-                    ne_per_file,
-                    msg,
-                ) in pbar:
-                    if nc_per_file == 0:
-                        img_info[img_path]["labels"] = labels_per_file
-                    else:
-                        img_info.pop(img_path)
-                    nc += nc_per_file
-                    nm += nm_per_file
-                    nf += nf_per_file
-                    ne += ne_per_file
-                    if msg:
-                        msgs.append(msg)
-                    if self.main_process:
-                        pbar.desc = f"{nf} label(s) found, {nm} label(s) missing, {ne} label(s) empty, {nc} invalid label files"
-            if self.main_process:
-                pbar.close()
-                with open(valid_img_record, "w") as f:
-                    json.dump(cache_info, f)
-            if msgs:
-                LOGGER.info("\n".join(msgs))
-            if nf == 0:
-                LOGGER.warning(
-                    f"WARNING: No labels found in {osp.dirname(self.img_paths[0])}. "
-                )
-
-        if self.task.lower() == "val":
-            if self.data_dict.get("is_coco", False): # use original json file when evaluating on coco dataset.
-                assert osp.exists(self.data_dict["anno_path"]), "Eval on coco dataset must provide valid path of the annotation file in config file: data/coco.yaml"
-            else:
-                assert (
-                    self.class_names
-                ), "Class names is required when converting labels to coco format for evaluating."
-                save_dir = osp.join(osp.dirname(osp.dirname(img_dir)), "annotations")
-                if not osp.exists(save_dir):
-                    os.mkdir(save_dir)
-                save_path = osp.join(
-                    save_dir, "instances_" + osp.basename(img_dir) + ".json"
-                )
-                TrainValDataset.generate_coco_format_labels(
-                    img_info, self.class_names, save_path
-                )
-
-        img_paths, labels = list(
-            zip(
-                *[
-                    (
-                        img_path,
-                        np.array(info["labels"], dtype=np.float32)
-                        if info["labels"]
-                        else np.zeros((0, 5), dtype=np.float32),
-                    )
-                    for img_path, info in img_info.items()
-                ]
-            )
-        )
-        self.img_info = img_info
-        LOGGER.info(
-            f"{self.task}: Final numbers of valid images: {len(img_paths)}/ labels: {len(labels)}. "
-        )
-        return img_paths, labels
-
-    def get_mosaic(self, index):
-        """Gets images and labels after mosaic augments"""
-        indices = [index] + random.choices(
-            range(0, len(self.img_paths)), k=3
-        )  # 3 additional image indices
-        random.shuffle(indices)
-        imgs, hs, ws, labels = [], [], [], []
-        for index in indices:
-            img, _, (h, w) = self.load_image(index)
-            labels_per_img = self.labels[index]
-            imgs.append(img)
-            hs.append(h)
-            ws.append(w)
-            labels.append(labels_per_img)
-        img, labels = mosaic_augmentation(self.img_size, imgs, hs, ws, labels, self.hyp)
-        return img, labels
-
-    def general_augment(self, img, labels):
-        """Gets images and labels after general augment
-        This function applies hsv, random ud-flip and random lr-flips augments.
-        """
-        nl = len(labels)
-
-        # HSV color-space
-        augment_hsv(
-            img,
-            hgain=self.hyp["hsv_h"],
-            sgain=self.hyp["hsv_s"],
-            vgain=self.hyp["hsv_v"],
-        )
-
-        # Flip up-down
-        if random.random() < self.hyp["flipud"]:
-            img = np.flipud(img)
-            if nl:
-                labels[:, 2] = 1 - labels[:, 2]
-
-        # Flip left-right
-        if random.random() < self.hyp["fliplr"]:
-            img = np.fliplr(img)
-            if nl:
-                labels[:, 1] = 1 - labels[:, 1]
-
-        return img, labels
-
-    def sort_files_shapes(self):
-        # Sort by aspect ratio
-        batch_num = self.batch_indices[-1] + 1
-        s = self.shapes  # wh
-        ar = s[:, 1] / s[:, 0]  # aspect ratio
-        irect = ar.argsort()
-        self.img_paths = [self.img_paths[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]] * batch_num
-        for i in range(batch_num):
-            ari = ar[self.batch_indices == 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) * self.img_size / self.stride + self.pad).astype(
-                np.int
-            )
-            * self.stride
-        )
-
-    @staticmethod
-    def check_image(im_file):
-        # verify an image.
-        nc, msg = 0, ""
-        try:
-            im = Image.open(im_file)
-            im.verify()  # PIL verify
-            shape = im.size  # (width, height)
-            im_exif = im._getexif()
-            if im_exif and ORIENTATION in im_exif:
-                rotation = im_exif[ORIENTATION]
-                if rotation in (6, 8):
-                    shape = (shape[1], shape[0])
-
-            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}"
-            if im.format.lower() in ("jpg", "jpeg"):
-                with open(im_file, "rb") as f:
-                    f.seek(-2, 2)
-                    if f.read() != b"\xff\xd9":  # corrupt JPEG
-                        ImageOps.exif_transpose(Image.open(im_file)).save(
-                            im_file, "JPEG", subsampling=0, quality=100
-                        )
-                        msg += f"WARNING: {im_file}: corrupt JPEG restored and saved"
-            return im_file, shape, nc, msg
-        except Exception as e:
-            nc = 1
-            msg = f"WARNING: {im_file}: ignoring corrupt image: {e}"
-            return im_file, None, nc, msg
-
-    @staticmethod
-    def check_label_files(args):
-        img_path, lb_path = args
-        nm, nf, ne, nc, msg = 0, 0, 0, 0, ""  # number (missing, found, empty, message
-        try:
-            if osp.exists(lb_path):
-                nf = 1  # label found
-                with open(lb_path, "r") as f:
-                    labels = [
-                        x.split() for x in f.read().strip().splitlines() if len(x)
-                    ]
-                    labels = np.array(labels, dtype=np.float32)
-                if len(labels):
-                    assert all(
-                        len(l) == 5 for l in labels
-                    ), f"{lb_path}: wrong label format."
-                    assert (
-                        labels >= 0
-                    ).all(), f"{lb_path}: Label values error: all values in label file must > 0"
-                    assert (
-                        labels[:, 1:] <= 1
-                    ).all(), f"{lb_path}: Label values error: all coordinates must be normalized"
-
-                    _, indices = np.unique(labels, axis=0, return_index=True)
-                    if len(indices) < len(labels):  # duplicate row check
-                        labels = labels[indices]  # remove duplicates
-                        msg += f"WARNING: {lb_path}: {len(labels) - len(indices)} duplicate labels removed"
-                    labels = labels.tolist()
-                else:
-                    ne = 1  # label empty
-                    labels = []
-            else:
-                nm = 1  # label missing
-                labels = []
-
-            return img_path, labels, nc, nm, nf, ne, msg
-        except Exception as e:
-            nc = 1
-            msg = f"WARNING: {lb_path}: ignoring invalid labels: {e}"
-            return img_path, None, nc, nm, nf, ne, msg
-
-    @staticmethod
-    def generate_coco_format_labels(img_info, class_names, save_path):
-        # for evaluation with pycocotools
-        dataset = {"categories": [], "annotations": [], "images": []}
-        for i, class_name in enumerate(class_names):
-            dataset["categories"].append(
-                {"id": i, "name": class_name, "supercategory": ""}
-            )
-
-        ann_id = 0
-        LOGGER.info(f"Convert to COCO format")
-        for i, (img_path, info) in enumerate(tqdm(img_info.items())):
-            labels = info["labels"] if info["labels"] else []
-            img_id = osp.splitext(osp.basename(img_path))[0]
-            img_id = int(img_id) if img_id.isnumeric() else img_id
-            img_w, img_h = info["shape"]
-            dataset["images"].append(
-                {
-                    "file_name": os.path.basename(img_path),
-                    "id": img_id,
-                    "width": img_w,
-                    "height": img_h,
-                }
-            )
-            if labels:
-                for label in labels:
-                    c, x, y, w, h = label[:5]
-                    # convert x,y,w,h to x1,y1,x2,y2
-                    x1 = (x - w / 2) * img_w
-                    y1 = (y - h / 2) * img_h
-                    x2 = (x + w / 2) * img_w
-                    y2 = (y + h / 2) * img_h
-                    # cls_id starts from 0
-                    cls_id = int(c)
-                    w = max(0, x2 - x1)
-                    h = max(0, y2 - y1)
-                    dataset["annotations"].append(
-                        {
-                            "area": h * w,
-                            "bbox": [x1, y1, w, h],
-                            "category_id": cls_id,
-                            "id": ann_id,
-                            "image_id": img_id,
-                            "iscrowd": 0,
-                            # mask
-                            "segmentation": [],
-                        }
-                    )
-                    ann_id += 1
-
-        with open(save_path, "w") as f:
-            json.dump(dataset, f)
-            LOGGER.info(
-                f"Convert to COCO format finished. Resutls saved in {save_path}"
-            )
-
-    @staticmethod
-    def get_hash(paths):
-        """Get the hash value of paths"""
-        assert isinstance(paths, list), "Only support list currently."
-        h = hashlib.md5("".join(paths).encode())
-        return h.hexdigest()

src/YOLOv6/yolov6/layers/common.py

@@ -1,501 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-
-import warnings
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from yolov6.layers.dbb_transforms import *
-
-
-class SiLU(nn.Module):
-    '''Activation of SiLU'''
-    @staticmethod
-    def forward(x):
-        return x * torch.sigmoid(x)
-
-
-class Conv(nn.Module):
-    '''Normal Conv with SiLU activation'''
-    def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False):
-        super().__init__()
-        padding = kernel_size // 2
-        self.conv = nn.Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            groups=groups,
-            bias=bias,
-        )
-        self.bn = nn.BatchNorm2d(out_channels)
-        self.act = nn.SiLU()
-
-    def forward(self, x):
-        return self.act(self.bn(self.conv(x)))
-
-    def forward_fuse(self, x):
-        return self.act(self.conv(x))
-
-
-class SimConv(nn.Module):
-    '''Normal Conv with ReLU activation'''
-    def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False):
-        super().__init__()
-        padding = kernel_size // 2
-        self.conv = nn.Conv2d(
-            in_channels,
-            out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            groups=groups,
-            bias=bias,
-        )
-        self.bn = nn.BatchNorm2d(out_channels)
-        self.act = nn.ReLU()
-
-    def forward(self, x):
-        return self.act(self.bn(self.conv(x)))
-
-    def forward_fuse(self, x):
-        return self.act(self.conv(x))
-
-
-class SimSPPF(nn.Module):
-    '''Simplified SPPF with ReLU activation'''
-    def __init__(self, in_channels, out_channels, kernel_size=5):
-        super().__init__()
-        c_ = in_channels // 2  # hidden channels
-        self.cv1 = SimConv(in_channels, c_, 1, 1)
-        self.cv2 = SimConv(c_ * 4, out_channels, 1, 1)
-        self.m = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=kernel_size // 2)
-
-    def forward(self, x):
-        x = self.cv1(x)
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore')
-            y1 = self.m(x)
-            y2 = self.m(y1)
-            return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
-
-
-class Transpose(nn.Module):
-    '''Normal Transpose, default for upsampling'''
-    def __init__(self, in_channels, out_channels, kernel_size=2, stride=2):
-        super().__init__()
-        self.upsample_transpose = torch.nn.ConvTranspose2d(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            bias=True
-        )
-
-    def forward(self, x):
-        return self.upsample_transpose(x)
-
-
-class Concat(nn.Module):
-    def __init__(self, dimension=1):
-        super().__init__()
-        self.d = dimension
-
-    def forward(self, x):
-        return torch.cat(x, self.d)
-
-
-def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1):
-    '''Basic cell for rep-style block, including conv and bn'''
-    result = nn.Sequential()
-    result.add_module('conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
-                                                  kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, bias=False))
-    result.add_module('bn', nn.BatchNorm2d(num_features=out_channels))
-    return result
-
-
-class RepBlock(nn.Module):
-    '''
-        RepBlock is a stage block with rep-style basic block
-    '''
-    def __init__(self, in_channels, out_channels, n=1):
-        super().__init__()
-        self.conv1 = RepVGGBlock(in_channels, out_channels)
-        self.block = nn.Sequential(*(RepVGGBlock(out_channels, out_channels) for _ in range(n - 1))) if n > 1 else None
-
-    def forward(self, x):
-        x = self.conv1(x)
-        if self.block is not None:
-            x = self.block(x)
-        return x
-
-
-class RepVGGBlock(nn.Module):
-    '''RepVGGBlock is a basic rep-style block, including training and deploy status
-    This code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
-    '''
-    def __init__(self, in_channels, out_channels, kernel_size=3,
-                 stride=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False):
-        super(RepVGGBlock, self).__init__()
-        """ Initialization of the class.
-        Args:
-            in_channels (int): Number of channels in the input image
-            out_channels (int): Number of channels produced by the convolution
-            kernel_size (int or tuple): Size of the convolving kernel
-            stride (int or tuple, optional): Stride of the convolution. Default: 1
-            padding (int or tuple, optional): Zero-padding added to both sides of
-                the input. Default: 1
-            dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
-            groups (int, optional): Number of blocked connections from input
-                channels to output channels. Default: 1
-            padding_mode (string, optional): Default: 'zeros'
-            deploy: Whether to be deploy status or training status. Default: False
-            use_se: Whether to use se. Default: False
-        """
-        self.deploy = deploy
-        self.groups = groups
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-
-        assert kernel_size == 3
-        assert padding == 1
-
-        padding_11 = padding - kernel_size // 2
-
-        self.nonlinearity = nn.ReLU()
-
-        if use_se:
-            raise NotImplementedError("se block not supported yet")
-        else:
-            self.se = nn.Identity()
-
-        if deploy:
-            self.rbr_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
-                                         padding=padding, dilation=dilation, groups=groups, bias=True, padding_mode=padding_mode)
-
-        else:
-            self.rbr_identity = nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None
-            self.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups)
-            self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride, padding=padding_11, groups=groups)
-
-    def forward(self, inputs):
-        '''Forward process'''
-        if hasattr(self, 'rbr_reparam'):
-            return self.nonlinearity(self.se(self.rbr_reparam(inputs)))
-
-        if self.rbr_identity is None:
-            id_out = 0
-        else:
-            id_out = self.rbr_identity(inputs)
-
-        return self.nonlinearity(self.se(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 torch.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.conv.weight
-            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:
-            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 switch_to_deploy(self):
-        if hasattr(self, 'rbr_reparam'):
-            return
-        kernel, bias = self.get_equivalent_kernel_bias()
-        self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.conv.in_channels, out_channels=self.rbr_dense.conv.out_channels,
-                                     kernel_size=self.rbr_dense.conv.kernel_size, stride=self.rbr_dense.conv.stride,
-                                     padding=self.rbr_dense.conv.padding, dilation=self.rbr_dense.conv.dilation, groups=self.rbr_dense.conv.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')
-        if hasattr(self, 'id_tensor'):
-            self.__delattr__('id_tensor')
-        self.deploy = True
-
-
-def conv_bn_v2(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1,
-               padding_mode='zeros'):
-    conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
-                           stride=stride, padding=padding, dilation=dilation, groups=groups,
-                           bias=False, padding_mode=padding_mode)
-    bn_layer = nn.BatchNorm2d(num_features=out_channels, affine=True)
-    se = nn.Sequential()
-    se.add_module('conv', conv_layer)
-    se.add_module('bn', bn_layer)
-    return se
-
-
-class IdentityBasedConv1x1(nn.Conv2d):
-
-    def __init__(self, channels, groups=1):
-        super(IdentityBasedConv1x1, self).__init__(in_channels=channels, out_channels=channels, kernel_size=1, stride=1, padding=0, groups=groups, bias=False)
-
-        assert channels % groups == 0
-        input_dim = channels // groups
-        id_value = np.zeros((channels, input_dim, 1, 1))
-        for i in range(channels):
-            id_value[i, i % input_dim, 0, 0] = 1
-        self.id_tensor = torch.from_numpy(id_value).type_as(self.weight)
-        nn.init.zeros_(self.weight)
-
-    def forward(self, input):
-        kernel = self.weight + self.id_tensor.to(self.weight.device)
-        result = F.conv2d(input, kernel, None, stride=1, padding=0, dilation=self.dilation, groups=self.groups)
-        return result
-
-    def get_actual_kernel(self):
-        return self.weight + self.id_tensor.to(self.weight.device)
-
-
-class BNAndPadLayer(nn.Module):
-    def __init__(self,
-                 pad_pixels,
-                 num_features,
-                 eps=1e-5,
-                 momentum=0.1,
-                 affine=True,
-                 track_running_stats=True):
-        super(BNAndPadLayer, self).__init__()
-        self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats)
-        self.pad_pixels = pad_pixels
-
-    def forward(self, input):
-        output = self.bn(input)
-        if self.pad_pixels > 0:
-            if self.bn.affine:
-                pad_values = self.bn.bias.detach() - self.bn.running_mean * self.bn.weight.detach() / torch.sqrt(self.bn.running_var + self.bn.eps)
-            else:
-                pad_values = - self.bn.running_mean / torch.sqrt(self.bn.running_var + self.bn.eps)
-            output = F.pad(output, [self.pad_pixels] * 4)
-            pad_values = pad_values.view(1, -1, 1, 1)
-            output[:, :, 0:self.pad_pixels, :] = pad_values
-            output[:, :, -self.pad_pixels:, :] = pad_values
-            output[:, :, :, 0:self.pad_pixels] = pad_values
-            output[:, :, :, -self.pad_pixels:] = pad_values
-        return output
-
-    @property
-    def bn_weight(self):
-        return self.bn.weight
-
-    @property
-    def bn_bias(self):
-        return self.bn.bias
-
-    @property
-    def running_mean(self):
-        return self.bn.running_mean
-
-    @property
-    def running_var(self):
-        return self.bn.running_var
-
-    @property
-    def eps(self):
-        return self.bn.eps
-
-
-class DBBBlock(nn.Module):
-    '''
-        RepBlock is a stage block with rep-style basic block
-    '''
-    def __init__(self, in_channels, out_channels, n=1):
-        super().__init__()
-        self.conv1 = DiverseBranchBlock(in_channels, out_channels)
-        self.block = nn.Sequential(*(DiverseBranchBlock(out_channels, out_channels) for _ in range(n - 1))) if n > 1 else None
-
-    def forward(self, x):
-        x = self.conv1(x)
-        if self.block is not None:
-            x = self.block(x)
-        return x
-
-
-class DiverseBranchBlock(nn.Module):
-
-    def __init__(self, in_channels, out_channels, kernel_size=3,
-                 stride=1, padding=1, dilation=1, groups=1,
-                 internal_channels_1x1_3x3=None,
-                 deploy=False, nonlinear=nn.ReLU(), single_init=False):
-        super(DiverseBranchBlock, self).__init__()
-        self.deploy = deploy
-
-        if nonlinear is None:
-            self.nonlinear = nn.Identity()
-        else:
-            self.nonlinear = nonlinear
-
-        self.kernel_size = kernel_size
-        self.out_channels = out_channels
-        self.groups = groups
-        assert padding == kernel_size // 2
-
-        if deploy:
-            self.dbb_reparam = 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.dbb_origin = conv_bn_v2(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-            self.dbb_avg = nn.Sequential()
-            if groups < out_channels:
-                self.dbb_avg.add_module('conv',
-                                        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1,
-                                                  stride=1, padding=0, groups=groups, bias=False))
-                self.dbb_avg.add_module('bn', BNAndPadLayer(pad_pixels=padding, num_features=out_channels))
-                self.dbb_avg.add_module('avg', nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=0))
-                self.dbb_1x1 = conv_bn_v2(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
-                                       padding=0, groups=groups)
-            else:
-                self.dbb_avg.add_module('avg', nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=padding))
-
-            self.dbb_avg.add_module('avgbn', nn.BatchNorm2d(out_channels))
-
-            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
-
-            self.dbb_1x1_kxk = nn.Sequential()
-            if internal_channels_1x1_3x3 == in_channels:
-                self.dbb_1x1_kxk.add_module('idconv1', IdentityBasedConv1x1(channels=in_channels, groups=groups))
-            else:
-                self.dbb_1x1_kxk.add_module('conv1', nn.Conv2d(in_channels=in_channels, out_channels=internal_channels_1x1_3x3,
-                                                            kernel_size=1, stride=1, padding=0, groups=groups, bias=False))
-            self.dbb_1x1_kxk.add_module('bn1', BNAndPadLayer(pad_pixels=padding, num_features=internal_channels_1x1_3x3, affine=True))
-            self.dbb_1x1_kxk.add_module('conv2', nn.Conv2d(in_channels=internal_channels_1x1_3x3, out_channels=out_channels,
-                                                            kernel_size=kernel_size, stride=stride, padding=0, groups=groups, bias=False))
-            self.dbb_1x1_kxk.add_module('bn2', nn.BatchNorm2d(out_channels))
-
-        #   The experiments reported in the paper used the default initialization of bn.weight (all as 1). But changing the initialization may be useful in some cases.
-        if single_init:
-            #   Initialize the bn.weight of dbb_origin as 1 and others as 0. This is not the default setting.
-            self.single_init()
-
-    def get_equivalent_kernel_bias(self):
-        k_origin, b_origin = transI_fusebn(self.dbb_origin.conv.weight, self.dbb_origin.bn)
-
-        if hasattr(self, 'dbb_1x1'):
-            k_1x1, b_1x1 = transI_fusebn(self.dbb_1x1.conv.weight, self.dbb_1x1.bn)
-            k_1x1 = transVI_multiscale(k_1x1, self.kernel_size)
-        else:
-            k_1x1, b_1x1 = 0, 0
-
-        if hasattr(self.dbb_1x1_kxk, 'idconv1'):
-            k_1x1_kxk_first = self.dbb_1x1_kxk.idconv1.get_actual_kernel()
-        else:
-            k_1x1_kxk_first = self.dbb_1x1_kxk.conv1.weight
-        k_1x1_kxk_first, b_1x1_kxk_first = transI_fusebn(k_1x1_kxk_first, self.dbb_1x1_kxk.bn1)
-        k_1x1_kxk_second, b_1x1_kxk_second = transI_fusebn(self.dbb_1x1_kxk.conv2.weight, self.dbb_1x1_kxk.bn2)
-        k_1x1_kxk_merged, b_1x1_kxk_merged = transIII_1x1_kxk(k_1x1_kxk_first, b_1x1_kxk_first, k_1x1_kxk_second, b_1x1_kxk_second, groups=self.groups)
-
-        k_avg = transV_avg(self.out_channels, self.kernel_size, self.groups)
-        k_1x1_avg_second, b_1x1_avg_second = transI_fusebn(k_avg.to(self.dbb_avg.avgbn.weight.device), self.dbb_avg.avgbn)
-        if hasattr(self.dbb_avg, 'conv'):
-            k_1x1_avg_first, b_1x1_avg_first = transI_fusebn(self.dbb_avg.conv.weight, self.dbb_avg.bn)
-            k_1x1_avg_merged, b_1x1_avg_merged = transIII_1x1_kxk(k_1x1_avg_first, b_1x1_avg_first, k_1x1_avg_second, b_1x1_avg_second, groups=self.groups)
-        else:
-            k_1x1_avg_merged, b_1x1_avg_merged = k_1x1_avg_second, b_1x1_avg_second
-
-        return transII_addbranch((k_origin, k_1x1, k_1x1_kxk_merged, k_1x1_avg_merged), (b_origin, b_1x1, b_1x1_kxk_merged, b_1x1_avg_merged))
-
-    def switch_to_deploy(self):
-        if hasattr(self, 'dbb_reparam'):
-            return
-        kernel, bias = self.get_equivalent_kernel_bias()
-        self.dbb_reparam = nn.Conv2d(in_channels=self.dbb_origin.conv.in_channels, out_channels=self.dbb_origin.conv.out_channels,
-                                     kernel_size=self.dbb_origin.conv.kernel_size, stride=self.dbb_origin.conv.stride,
-                                     padding=self.dbb_origin.conv.padding, dilation=self.dbb_origin.conv.dilation, groups=self.dbb_origin.conv.groups, bias=True)
-        self.dbb_reparam.weight.data = kernel
-        self.dbb_reparam.bias.data = bias
-        for para in self.parameters():
-            para.detach_()
-        self.__delattr__('dbb_origin')
-        self.__delattr__('dbb_avg')
-        if hasattr(self, 'dbb_1x1'):
-            self.__delattr__('dbb_1x1')
-        self.__delattr__('dbb_1x1_kxk')
-
-    def forward(self, inputs):
-
-        if hasattr(self, 'dbb_reparam'):
-            return self.nonlinear(self.dbb_reparam(inputs))
-
-        out = self.dbb_origin(inputs)
-        if hasattr(self, 'dbb_1x1'):
-            out += self.dbb_1x1(inputs)
-        out += self.dbb_avg(inputs)
-        out += self.dbb_1x1_kxk(inputs)
-        return self.nonlinear(out)
-
-    def init_gamma(self, gamma_value):
-        if hasattr(self, "dbb_origin"):
-            torch.nn.init.constant_(self.dbb_origin.bn.weight, gamma_value)
-        if hasattr(self, "dbb_1x1"):
-            torch.nn.init.constant_(self.dbb_1x1.bn.weight, gamma_value)
-        if hasattr(self, "dbb_avg"):
-            torch.nn.init.constant_(self.dbb_avg.avgbn.weight, gamma_value)
-        if hasattr(self, "dbb_1x1_kxk"):
-            torch.nn.init.constant_(self.dbb_1x1_kxk.bn2.weight, gamma_value)
-
-    def single_init(self):
-        self.init_gamma(0.0)
-        if hasattr(self, "dbb_origin"):
-            torch.nn.init.constant_(self.dbb_origin.bn.weight, 1.0)
-
-
-class DetectBackend(nn.Module):
-    def __init__(self, weights='yolov6s.pt', device=None, dnn=True):
-
-        super().__init__()
-        assert isinstance(weights, str) and Path(weights).suffix == '.pt', f'{Path(weights).suffix} format is not supported.'
-        from yolov6.utils.checkpoint import load_checkpoint
-        model = load_checkpoint(weights, map_location=device)
-        stride = int(model.stride.max())
-        self.__dict__.update(locals())  # assign all variables to self
-
-    def forward(self, im, val=False):
-        y = self.model(im)
-        if isinstance(y, np.ndarray):
-            y = torch.tensor(y, device=self.device)
-        return y

src/YOLOv6/yolov6/layers/dbb_transforms.py

@@ -1,50 +0,0 @@
-import torch
-import numpy as np
-import torch.nn.functional as F
-
-
-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
-
-
-def transII_addbranch(kernels, biases):
-    return sum(kernels), sum(biases)
-
-
-def transIII_1x1_kxk(k1, b1, k2, b2, groups):
-    if groups == 1:
-        k = F.conv2d(k2, k1.permute(1, 0, 2, 3))      #
-        b_hat = (k2 * b1.reshape(1, -1, 1, 1)).sum((1, 2, 3))
-    else:
-        k_slices = []
-        b_slices = []
-        k1_T = k1.permute(1, 0, 2, 3)
-        k1_group_width = k1.size(0) // groups
-        k2_group_width = k2.size(0) // groups
-        for g in range(groups):
-            k1_T_slice = k1_T[:, g*k1_group_width:(g+1)*k1_group_width, :, :]
-            k2_slice = k2[g*k2_group_width:(g+1)*k2_group_width, :, :, :]
-            k_slices.append(F.conv2d(k2_slice, k1_T_slice))
-            b_slices.append((k2_slice * b1[g*k1_group_width:(g+1)*k1_group_width].reshape(1, -1, 1, 1)).sum((1, 2, 3)))
-        k, b_hat = transIV_depthconcat(k_slices, b_slices)
-    return k, b_hat + b2
-
-
-def transIV_depthconcat(kernels, biases):
-    return torch.cat(kernels, dim=0), torch.cat(biases)
-
-
-def transV_avg(channels, kernel_size, groups):
-    input_dim = channels // groups
-    k = torch.zeros((channels, input_dim, kernel_size, kernel_size))
-    k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2
-    return k
-
-
-#   This has not been tested with non-square kernels (kernel.size(2) != kernel.size(3)) nor even-size kernels
-def transVI_multiscale(kernel, target_kernel_size):
-    H_pixels_to_pad = (target_kernel_size - kernel.size(2)) // 2
-    W_pixels_to_pad = (target_kernel_size - kernel.size(3)) // 2
-    return F.pad(kernel, [H_pixels_to_pad, H_pixels_to_pad, W_pixels_to_pad, W_pixels_to_pad])

src/YOLOv6/yolov6/models/efficientrep.py

@@ -1,102 +0,0 @@
-from torch import nn
-from yolov6.layers.common import RepVGGBlock, RepBlock, SimSPPF
-
-
-class EfficientRep(nn.Module):
-    '''EfficientRep Backbone
-    EfficientRep is handcrafted by hardware-aware neural network design.
-    With rep-style struct, EfficientRep is friendly to high-computation hardware(e.g. GPU).
-    '''
-
-    def __init__(
-        self,
-        in_channels=3,
-        channels_list=None,
-        num_repeats=None,
-    ):
-        super().__init__()
-
-        assert channels_list is not None
-        assert num_repeats is not None
-
-        self.stem = RepVGGBlock(
-            in_channels=in_channels,
-            out_channels=channels_list[0],
-            kernel_size=3,
-            stride=2
-        )
-
-        self.ERBlock_2 = nn.Sequential(
-            RepVGGBlock(
-                in_channels=channels_list[0],
-                out_channels=channels_list[1],
-                kernel_size=3,
-                stride=2
-            ),
-            RepBlock(
-                in_channels=channels_list[1],
-                out_channels=channels_list[1],
-                n=num_repeats[1]
-            )
-        )
-
-        self.ERBlock_3 = nn.Sequential(
-            RepVGGBlock(
-                in_channels=channels_list[1],
-                out_channels=channels_list[2],
-                kernel_size=3,
-                stride=2
-            ),
-            RepBlock(
-                in_channels=channels_list[2],
-                out_channels=channels_list[2],
-                n=num_repeats[2]
-            )
-        )
-
-        self.ERBlock_4 = nn.Sequential(
-            RepVGGBlock(
-                in_channels=channels_list[2],
-                out_channels=channels_list[3],
-                kernel_size=3,
-                stride=2
-            ),
-            RepBlock(
-                in_channels=channels_list[3],
-                out_channels=channels_list[3],
-                n=num_repeats[3]
-            )
-        )
-
-        self.ERBlock_5 = nn.Sequential(
-            RepVGGBlock(
-                in_channels=channels_list[3],
-                out_channels=channels_list[4],
-                kernel_size=3,
-                stride=2,
-            ),
-            RepBlock(
-                in_channels=channels_list[4],
-                out_channels=channels_list[4],
-                n=num_repeats[4]
-            ),
-            SimSPPF(
-                in_channels=channels_list[4],
-                out_channels=channels_list[4],
-                kernel_size=5
-            )
-        )
-
-    def forward(self, x):
-
-        outputs = []
-        x = self.stem(x)
-        x = self.ERBlock_2(x)
-        x = self.ERBlock_3(x)
-        outputs.append(x)
-        x = self.ERBlock_4(x)
-        outputs.append(x)
-        x = self.ERBlock_5(x)
-        outputs.append(x)
-
-        return tuple(outputs)

src/YOLOv6/yolov6/models/effidehead.py

@@ -1,211 +0,0 @@
-import torch
-import torch.nn as nn
-import math
-from yolov6.layers.common import *
-
-
-class Detect(nn.Module):
-    '''Efficient Decoupled Head
-    With hardware-aware degisn, the decoupled head is optimized with
-    hybridchannels methods.
-    '''
-    def __init__(self, num_classes=80, anchors=1, num_layers=3, inplace=True, head_layers=None):  # detection layer
-        super().__init__()
-        assert head_layers is not None
-        self.nc = num_classes  # number of classes
-        self.no = num_classes + 5  # number of outputs per anchor
-        self.nl = num_layers  # number of detection layers
-        if isinstance(anchors, (list, tuple)):
-            self.na = len(anchors[0]) // 2
-        else:
-            self.na = anchors
-        self.anchors = anchors
-        self.grid = [torch.zeros(1)] * num_layers
-        self.prior_prob = 1e-2
-        self.inplace = inplace
-        stride = [8, 16, 32]  # strides computed during build
-        self.stride = torch.tensor(stride)
-
-        # Init decouple head
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        self.cls_preds = nn.ModuleList()
-        self.reg_preds = nn.ModuleList()
-        self.obj_preds = nn.ModuleList()
-        self.stems = nn.ModuleList()
-
-        # Efficient decoupled head layers
-        for i in range(num_layers):
-            idx = i*6
-            self.stems.append(head_layers[idx])
-            self.cls_convs.append(head_layers[idx+1])
-            self.reg_convs.append(head_layers[idx+2])
-            self.cls_preds.append(head_layers[idx+3])
-            self.reg_preds.append(head_layers[idx+4])
-            self.obj_preds.append(head_layers[idx+5])
-
-    def initialize_biases(self):
-        for conv in self.cls_preds:
-            b = conv.bias.view(self.na, -1)
-            b.data.fill_(-math.log((1 - self.prior_prob) / self.prior_prob))
-            conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-        for conv in self.obj_preds:
-            b = conv.bias.view(self.na, -1)
-            b.data.fill_(-math.log((1 - self.prior_prob) / self.prior_prob))
-            conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-    def forward(self, x):
-        z = []
-        for i in range(self.nl):
-            x[i] = self.stems[i](x[i])
-            cls_x = x[i]
-            reg_x = x[i]
-            cls_feat = self.cls_convs[i](cls_x)
-            cls_output = self.cls_preds[i](cls_feat)
-            reg_feat = self.reg_convs[i](reg_x)
-            reg_output = self.reg_preds[i](reg_feat)
-            obj_output = self.obj_preds[i](reg_feat)
-            if self.training:
-                x[i] = torch.cat([reg_output, obj_output, cls_output], 1)
-                bs, _, ny, nx = x[i].shape
-                x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-            else:
-                y = torch.cat([reg_output, obj_output.sigmoid(), cls_output.sigmoid()], 1)
-                bs, _, ny, nx = y.shape
-                y = y.view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-                if self.grid[i].shape[2:4] != y.shape[2:4]:
-                    d = self.stride.device
-                    yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)])
-                    self.grid[i] = torch.stack((xv, yv), 2).view(1, self.na, ny, nx, 2).float()
-                if self.inplace:
-                    y[..., 0:2] = (y[..., 0:2] + self.grid[i]) * self.stride[i]  # xy
-                    y[..., 2:4] = torch.exp(y[..., 2:4]) * self.stride[i] # wh
-                else:
-                    xy = (y[..., 0:2] + self.grid[i]) * self.stride[i]  # xy
-                    wh = torch.exp(y[..., 2:4]) * self.stride[i]  # wh
-                    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)
-
-
-def build_effidehead_layer(channels_list, num_anchors, num_classes):
-    head_layers = nn.Sequential(
-        # stem0
-        Conv(
-            in_channels=channels_list[6],
-            out_channels=channels_list[6],
-            kernel_size=1,
-            stride=1
-        ),
-        # cls_conv0
-        Conv(
-            in_channels=channels_list[6],
-            out_channels=channels_list[6],
-            kernel_size=3,
-            stride=1
-        ),
-        # reg_conv0
-        Conv(
-            in_channels=channels_list[6],
-            out_channels=channels_list[6],
-            kernel_size=3,
-            stride=1
-        ),
-        # cls_pred0
-        nn.Conv2d(
-            in_channels=channels_list[6],
-            out_channels=num_classes * num_anchors,
-            kernel_size=1
-        ),
-        # reg_pred0
-        nn.Conv2d(
-            in_channels=channels_list[6],
-            out_channels=4 * num_anchors,
-            kernel_size=1
-        ),
-        # obj_pred0
-        nn.Conv2d(
-            in_channels=channels_list[6],
-            out_channels=1 * num_anchors,
-            kernel_size=1
-        ),
-        # stem1
-        Conv(
-            in_channels=channels_list[8],
-            out_channels=channels_list[8],
-            kernel_size=1,
-            stride=1
-        ),
-        # cls_conv1
-        Conv(
-            in_channels=channels_list[8],
-            out_channels=channels_list[8],
-            kernel_size=3,
-            stride=1
-        ),
-        # reg_conv1
-        Conv(
-            in_channels=channels_list[8],
-            out_channels=channels_list[8],
-            kernel_size=3,
-            stride=1
-        ),
-        # cls_pred1
-        nn.Conv2d(
-            in_channels=channels_list[8],
-            out_channels=num_classes * num_anchors,
-            kernel_size=1
-        ),
-        # reg_pred1
-        nn.Conv2d(
-            in_channels=channels_list[8],
-            out_channels=4 * num_anchors,
-            kernel_size=1
-        ),
-        # obj_pred1
-        nn.Conv2d(
-            in_channels=channels_list[8],
-            out_channels=1 * num_anchors,
-            kernel_size=1
-        ),
-        # stem2
-        Conv(
-            in_channels=channels_list[10],
-            out_channels=channels_list[10],
-            kernel_size=1,
-            stride=1
-        ),
-        # cls_conv2
-        Conv(
-            in_channels=channels_list[10],
-            out_channels=channels_list[10],
-            kernel_size=3,
-            stride=1
-        ),
-        # reg_conv2
-        Conv(
-            in_channels=channels_list[10],
-            out_channels=channels_list[10],
-            kernel_size=3,
-            stride=1
-        ),
-        # cls_pred2
-        nn.Conv2d(
-            in_channels=channels_list[10],
-            out_channels=num_classes * num_anchors,
-            kernel_size=1
-        ),
-        # reg_pred2
-        nn.Conv2d(
-            in_channels=channels_list[10],
-            out_channels=4 * num_anchors,
-            kernel_size=1
-        ),
-        # obj_pred2
-        nn.Conv2d(
-            in_channels=channels_list[10],
-            out_channels=1 * num_anchors,
-            kernel_size=1
-        )
-    )
-    return head_layers

src/YOLOv6/yolov6/models/end2end.py

@@ -1,151 +0,0 @@
-import torch
-import torch.nn as nn
-import random
-
-class ORT_NMS(torch.autograd.Function):
-
-    @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):
-    @staticmethod
-    def forward(
-        ctx,
-        boxes,
-        scores,
-        background_class=-1,
-        box_coding=0,
-        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=0,
-                 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):
-
-    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=640, device=None):
-        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
-        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)
-
-    def forward(self, x):
-        box = x[:, :, :4]
-        conf = x[:, :, 4:5]
-        score = x[:, :, 5:]
-        score *= conf
-        box @= self.convert_matrix
-        objScore, objCls = score.max(2, keepdim=True)
-        dis = objCls.float() * self.max_wh
-        nmsbox = box + dis
-        objScore1 = objScore.transpose(1, 2).contiguous()
-        selected_indices = ORT_NMS.apply(nmsbox, objScore1, self.max_obj, self.iou_threshold, self.score_threshold)
-        X, Y = selected_indices[:, 0], selected_indices[:, 2]
-        resBoxes = box[X, Y, :]
-        resClasses = objCls[X, Y, :].float()
-        resScores = objScore[X, Y, :]
-        X = X.unsqueeze(1).float()
-        return torch.concat([X, resBoxes, resClasses, resScores], 1)
-
-class ONNX_TRT(nn.Module):
-
-    def __init__(self, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None ,device=None):
-        super().__init__()
-        assert max_wh is None
-        self.device = device if device else torch.device('cpu')
-        self.background_class = -1,
-        self.box_coding = 0,
-        self.iou_threshold = iou_thres
-        self.max_obj = max_obj
-        self.plugin_version = '1'
-        self.score_activation = 0
-        self.score_threshold = score_thres
-        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)
-
-    def forward(self, x):
-        box = x[:, :, :4]
-        conf = x[:, :, 4:5]
-        score = x[:, :, 5:]
-        score *= conf
-        box @= self.convert_matrix
-        num_det, det_boxes, det_scores, det_classes = TRT_NMS.apply(box, score, 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):
-
-    def __init__(self, model, max_obj=100, iou_thres=0.45, score_thres=0.25, max_wh=None, device=None):
-        super().__init__()
-        device = device if device else torch.device('cpu')
-        self.model = model.to(device)
-        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)
-        self.end2end.eval()
-
-    def forward(self, x):
-        x = self.model(x)
-        x = self.end2end(x)
-        return x

src/YOLOv6/yolov6/models/loss.py

@@ -1,411 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-
-# The code is based on
-# https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/models/yolo_head.py
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import torch
-import torch.nn as nn
-import numpy as np
-import torch.nn.functional as F
-from yolov6.utils.figure_iou import IOUloss, pairwise_bbox_iou
-
-
-class ComputeLoss:
-    '''Loss computation func.
-    This func contains SimOTA and siou loss.
-    '''
-    def __init__(self,
-                 reg_weight=5.0,
-                 iou_weight=3.0,
-                 cls_weight=1.0,
-                 center_radius=2.5,
-                 eps=1e-7,
-                 in_channels=[256, 512, 1024],
-                 strides=[8, 16, 32],
-                 n_anchors=1,
-                 iou_type='ciou'
-                 ):
-
-        self.reg_weight = reg_weight
-        self.iou_weight = iou_weight
-        self.cls_weight = cls_weight
-
-        self.center_radius = center_radius
-        self.eps = eps
-        self.n_anchors = n_anchors
-        self.strides = strides
-        self.grids = [torch.zeros(1)] * len(in_channels)
-
-        # Define criteria
-        self.l1_loss = nn.L1Loss(reduction="none")
-        self.bcewithlog_loss = nn.BCEWithLogitsLoss(reduction="none")
-        self.iou_loss = IOUloss(iou_type=iou_type, reduction="none")
-
-    def __call__(
-        self,
-        outputs,
-        targets
-    ):
-        dtype = outputs[0].type()
-        device = targets.device
-        loss_cls, loss_obj, loss_iou, loss_l1 = torch.zeros(1, device=device), torch.zeros(1, device=device), \
-            torch.zeros(1, device=device), torch.zeros(1, device=device)
-        num_classes = outputs[0].shape[-1] - 5
-
-        outputs, outputs_origin, gt_bboxes_scale, xy_shifts, expanded_strides = self.get_outputs_and_grids(
-            outputs, self.strides, dtype, device)
-
-        total_num_anchors = outputs.shape[1]
-        bbox_preds = outputs[:, :, :4]  # [batch, n_anchors_all, 4]
-        bbox_preds_org = outputs_origin[:, :, :4]  # [batch, n_anchors_all, 4]
-        obj_preds = outputs[:, :, 4].unsqueeze(-1)  # [batch, n_anchors_all, 1]
-        cls_preds = outputs[:, :, 5:]  # [batch, n_anchors_all, n_cls]
-
-        # targets
-        batch_size = bbox_preds.shape[0]
-        targets_list = np.zeros((batch_size, 1, 5)).tolist()
-        for i, item in enumerate(targets.cpu().numpy().tolist()):
-            targets_list[int(item[0])].append(item[1:])
-        max_len = max((len(l) for l in targets_list))
-
-        targets = torch.from_numpy(np.array(list(map(lambda l:l + [[-1,0,0,0,0]]*(max_len - len(l)), targets_list)))[:,1:,:]).to(targets.device)
-        num_targets_list = (targets.sum(dim=2) > 0).sum(dim=1)  # number of objects
-
-        num_fg, num_gts = 0, 0
-        cls_targets, reg_targets, l1_targets, obj_targets, fg_masks = [], [], [], [], []
-
-        for batch_idx in range(batch_size):
-            num_gt = int(num_targets_list[batch_idx])
-            num_gts += num_gt
-            if num_gt == 0:
-                cls_target = outputs.new_zeros((0, num_classes))
-                reg_target = outputs.new_zeros((0, 4))
-                l1_target = outputs.new_zeros((0, 4))
-                obj_target = outputs.new_zeros((total_num_anchors, 1))
-                fg_mask = outputs.new_zeros(total_num_anchors).bool()
-            else:
-
-                gt_bboxes_per_image = targets[batch_idx, :num_gt, 1:5].mul_(gt_bboxes_scale)
-                gt_classes = targets[batch_idx, :num_gt, 0]
-                bboxes_preds_per_image = bbox_preds[batch_idx]
-                cls_preds_per_image = cls_preds[batch_idx]
-                obj_preds_per_image = obj_preds[batch_idx]
-
-                try:
-                    (
-                        gt_matched_classes,
-                        fg_mask,
-                        pred_ious_this_matching,
-                        matched_gt_inds,
-                        num_fg_img,
-                    ) = self.get_assignments(
-                        batch_idx,
-                        num_gt,
-                        total_num_anchors,
-                        gt_bboxes_per_image,
-                        gt_classes,
-                        bboxes_preds_per_image,
-                        cls_preds_per_image,
-                        obj_preds_per_image,
-                        expanded_strides,
-                        xy_shifts,
-                        num_classes
-                    )
-
-                except RuntimeError:
-                    print(
-                        "OOM RuntimeError is raised due to the huge memory cost during label assignment. \
-                           CPU mode is applied in this batch. If you want to avoid this issue, \
-                           try to reduce the batch size or image size."
-                    )
-                    torch.cuda.empty_cache()
-                    print("------------CPU Mode for This Batch-------------")
-
-                    _gt_bboxes_per_image = gt_bboxes_per_image.cpu().float()
-                    _gt_classes = gt_classes.cpu().float()
-                    _bboxes_preds_per_image = bboxes_preds_per_image.cpu().float()
-                    _cls_preds_per_image = cls_preds_per_image.cpu().float()
-                    _obj_preds_per_image = obj_preds_per_image.cpu().float()
-
-                    _expanded_strides = expanded_strides.cpu().float()
-                    _xy_shifts = xy_shifts.cpu()
-
-                    (
-                        gt_matched_classes,
-                        fg_mask,
-                        pred_ious_this_matching,
-                        matched_gt_inds,
-                        num_fg_img,
-                    ) = self.get_assignments(
-                        batch_idx,
-                        num_gt,
-                        total_num_anchors,
-                        _gt_bboxes_per_image,
-                        _gt_classes,
-                        _bboxes_preds_per_image,
-                        _cls_preds_per_image,
-                        _obj_preds_per_image,
-                        _expanded_strides,
-                        _xy_shifts,
-                        num_classes
-                    )
-
-                    gt_matched_classes = gt_matched_classes.cuda()
-                    fg_mask = fg_mask.cuda()
-                    pred_ious_this_matching = pred_ious_this_matching.cuda()
-                    matched_gt_inds = matched_gt_inds.cuda()
-
-                torch.cuda.empty_cache()
-                num_fg += num_fg_img
-                if num_fg_img > 0:
-                    cls_target = F.one_hot(
-                        gt_matched_classes.to(torch.int64), num_classes
-                    ) * pred_ious_this_matching.unsqueeze(-1)
-                    obj_target = fg_mask.unsqueeze(-1)
-                    reg_target = gt_bboxes_per_image[matched_gt_inds]
-
-                    l1_target = self.get_l1_target(
-                        outputs.new_zeros((num_fg_img, 4)),
-                        gt_bboxes_per_image[matched_gt_inds],
-                        expanded_strides[0][fg_mask],
-                        xy_shifts=xy_shifts[0][fg_mask],
-                    )
-
-            cls_targets.append(cls_target)
-            reg_targets.append(reg_target)
-            obj_targets.append(obj_target)
-            l1_targets.append(l1_target)
-            fg_masks.append(fg_mask)
-
-        cls_targets = torch.cat(cls_targets, 0)
-        reg_targets = torch.cat(reg_targets, 0)
-        obj_targets = torch.cat(obj_targets, 0)
-        l1_targets = torch.cat(l1_targets, 0)
-        fg_masks = torch.cat(fg_masks, 0)
-
-        num_fg = max(num_fg, 1)
-        # loss
-        loss_iou += (self.iou_loss(bbox_preds.view(-1, 4)[fg_masks].T, reg_targets)).sum() / num_fg
-        loss_l1 += (self.l1_loss(bbox_preds_org.view(-1, 4)[fg_masks], l1_targets)).sum() / num_fg
-
-        loss_obj += (self.bcewithlog_loss(obj_preds.view(-1, 1), obj_targets*1.0)).sum() / num_fg
-        loss_cls += (self.bcewithlog_loss(cls_preds.view(-1, num_classes)[fg_masks], cls_targets)).sum() / num_fg
-
-        total_losses = self.reg_weight * loss_iou + loss_l1 + loss_obj + loss_cls
-        return total_losses, torch.cat((self.reg_weight * loss_iou, loss_l1, loss_obj, loss_cls)).detach()
-
-    def decode_output(self, output, k, stride, dtype, device):
-        grid = self.grids[k].to(device)
-        batch_size = output.shape[0]
-        hsize, wsize = output.shape[2:4]
-        if grid.shape[2:4] != output.shape[2:4]:
-            yv, xv = torch.meshgrid([torch.arange(hsize), torch.arange(wsize)])
-            grid = torch.stack((xv, yv), 2).view(1, 1, hsize, wsize, 2).type(dtype).to(device)
-            self.grids[k] = grid
-
-        output = output.reshape(batch_size, self.n_anchors * hsize * wsize, -1)
-        output_origin = output.clone()
-        grid = grid.view(1, -1, 2)
-
-        output[..., :2] = (output[..., :2] + grid) * stride
-        output[..., 2:4] = torch.exp(output[..., 2:4]) * stride
-
-        return output, output_origin, grid, hsize, wsize
-
-    def get_outputs_and_grids(self, outputs, strides, dtype, device):
-        xy_shifts = []
-        expanded_strides = []
-        outputs_new = []
-        outputs_origin = []
-
-        for k, output in enumerate(outputs):
-            output, output_origin, grid, feat_h, feat_w = self.decode_output(
-                output, k, strides[k], dtype, device)
-
-            xy_shift = grid
-            expanded_stride = torch.full((1, grid.shape[1], 1), strides[k], dtype=grid.dtype, device=grid.device)
-
-            xy_shifts.append(xy_shift)
-            expanded_strides.append(expanded_stride)
-            outputs_new.append(output)
-            outputs_origin.append(output_origin)
-
-        xy_shifts = torch.cat(xy_shifts, 1)  # [1, n_anchors_all, 2]
-        expanded_strides = torch.cat(expanded_strides, 1) # [1, n_anchors_all, 1]
-        outputs_origin = torch.cat(outputs_origin, 1)
-        outputs = torch.cat(outputs_new, 1)
-
-        feat_h *= strides[-1]
-        feat_w *= strides[-1]
-        gt_bboxes_scale = torch.Tensor([[feat_w, feat_h, feat_w, feat_h]]).type_as(outputs)
-
-        return outputs, outputs_origin, gt_bboxes_scale, xy_shifts, expanded_strides
-
-    def get_l1_target(self, l1_target, gt, stride, xy_shifts, eps=1e-8):
-
-        l1_target[:, 0:2] = gt[:, 0:2] / stride - xy_shifts
-        l1_target[:, 2:4] = torch.log(gt[:, 2:4] / stride + eps)
-        return l1_target
-
-    @torch.no_grad()
-    def get_assignments(
-        self,
-        batch_idx,
-        num_gt,
-        total_num_anchors,
-        gt_bboxes_per_image,
-        gt_classes,
-        bboxes_preds_per_image,
-        cls_preds_per_image,
-        obj_preds_per_image,
-        expanded_strides,
-        xy_shifts,
-        num_classes
-    ):
-
-        fg_mask, is_in_boxes_and_center = self.get_in_boxes_info(
-            gt_bboxes_per_image,
-            expanded_strides,
-            xy_shifts,
-            total_num_anchors,
-            num_gt,
-        )
-
-        bboxes_preds_per_image = bboxes_preds_per_image[fg_mask]
-        cls_preds_ = cls_preds_per_image[fg_mask]
-        obj_preds_ = obj_preds_per_image[fg_mask]
-        num_in_boxes_anchor = bboxes_preds_per_image.shape[0]
-
-        # cost
-        pair_wise_ious = pairwise_bbox_iou(gt_bboxes_per_image, bboxes_preds_per_image, box_format='xywh')
-        pair_wise_ious_loss = -torch.log(pair_wise_ious + 1e-8)
-
-        gt_cls_per_image = (
-            F.one_hot(gt_classes.to(torch.int64), num_classes)
-            .float()
-            .unsqueeze(1)
-            .repeat(1, num_in_boxes_anchor, 1)
-        )
-
-        with torch.cuda.amp.autocast(enabled=False):
-            cls_preds_ = (
-                cls_preds_.float().sigmoid_().unsqueeze(0).repeat(num_gt, 1, 1)
-                * obj_preds_.float().sigmoid_().unsqueeze(0).repeat(num_gt, 1, 1)
-            )
-            pair_wise_cls_loss = F.binary_cross_entropy(
-                cls_preds_.sqrt_(), gt_cls_per_image, reduction="none"
-            ).sum(-1)
-        del cls_preds_, obj_preds_
-
-        cost = (
-            self.cls_weight * pair_wise_cls_loss
-            + self.iou_weight * pair_wise_ious_loss
-            + 100000.0 * (~is_in_boxes_and_center)
-        )
-
-        (
-            num_fg,
-            gt_matched_classes,
-            pred_ious_this_matching,
-            matched_gt_inds,
-        ) = self.dynamic_k_matching(cost, pair_wise_ious, gt_classes, num_gt, fg_mask)
-
-        del pair_wise_cls_loss, cost, pair_wise_ious, pair_wise_ious_loss
-
-        return (
-            gt_matched_classes,
-            fg_mask,
-            pred_ious_this_matching,
-            matched_gt_inds,
-            num_fg,
-        )
-
-    def get_in_boxes_info(
-        self,
-        gt_bboxes_per_image,
-        expanded_strides,
-        xy_shifts,
-        total_num_anchors,
-        num_gt,
-    ):
-        expanded_strides_per_image = expanded_strides[0]
-        xy_shifts_per_image = xy_shifts[0] * expanded_strides_per_image
-        xy_centers_per_image = (
-            (xy_shifts_per_image + 0.5 * expanded_strides_per_image)
-            .unsqueeze(0)
-            .repeat(num_gt, 1, 1)
-        )  # [n_anchor, 2] -> [n_gt, n_anchor, 2]
-
-        gt_bboxes_per_image_lt = (
-            (gt_bboxes_per_image[:, 0:2] - 0.5 * gt_bboxes_per_image[:, 2:4])
-            .unsqueeze(1)
-            .repeat(1, total_num_anchors, 1)
-        )
-        gt_bboxes_per_image_rb = (
-            (gt_bboxes_per_image[:, 0:2] + 0.5 * gt_bboxes_per_image[:, 2:4])
-            .unsqueeze(1)
-            .repeat(1, total_num_anchors, 1)
-        )  # [n_gt, 2] -> [n_gt, n_anchor, 2]
-
-        b_lt = xy_centers_per_image - gt_bboxes_per_image_lt
-        b_rb = gt_bboxes_per_image_rb - xy_centers_per_image
-        bbox_deltas = torch.cat([b_lt, b_rb], 2)
-
-        is_in_boxes = bbox_deltas.min(dim=-1).values > 0.0
-        is_in_boxes_all = is_in_boxes.sum(dim=0) > 0
-
-        # in fixed center
-        gt_bboxes_per_image_lt = (gt_bboxes_per_image[:, 0:2]).unsqueeze(1).repeat(
-            1, total_num_anchors, 1
-        ) - self.center_radius * expanded_strides_per_image.unsqueeze(0)
-        gt_bboxes_per_image_rb = (gt_bboxes_per_image[:, 0:2]).unsqueeze(1).repeat(
-            1, total_num_anchors, 1
-        ) + self.center_radius * expanded_strides_per_image.unsqueeze(0)
-
-        c_lt = xy_centers_per_image - gt_bboxes_per_image_lt
-        c_rb = gt_bboxes_per_image_rb - xy_centers_per_image
-        center_deltas = torch.cat([c_lt, c_rb], 2)
-        is_in_centers = center_deltas.min(dim=-1).values > 0.0
-        is_in_centers_all = is_in_centers.sum(dim=0) > 0
-
-        # in boxes and in centers
-        is_in_boxes_anchor = is_in_boxes_all | is_in_centers_all
-
-        is_in_boxes_and_center = (
-            is_in_boxes[:, is_in_boxes_anchor] & is_in_centers[:, is_in_boxes_anchor]
-        )
-        return is_in_boxes_anchor, is_in_boxes_and_center
-
-    def dynamic_k_matching(self, cost, pair_wise_ious, gt_classes, num_gt, fg_mask):
-        matching_matrix = torch.zeros_like(cost, dtype=torch.uint8)
-        ious_in_boxes_matrix = pair_wise_ious
-        n_candidate_k = min(10, ious_in_boxes_matrix.size(1))
-        topk_ious, _ = torch.topk(ious_in_boxes_matrix, n_candidate_k, dim=1)
-        dynamic_ks = torch.clamp(topk_ious.sum(1).int(), min=1)
-        dynamic_ks = dynamic_ks.tolist()
-
-        for gt_idx in range(num_gt):
-            _, pos_idx = torch.topk(
-                cost[gt_idx], k=dynamic_ks[gt_idx], largest=False
-            )
-            matching_matrix[gt_idx][pos_idx] = 1
-        del topk_ious, dynamic_ks, pos_idx
-
-        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
-            matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1
-        fg_mask_inboxes = matching_matrix.sum(0) > 0
-        num_fg = fg_mask_inboxes.sum().item()
-        fg_mask[fg_mask.clone()] = fg_mask_inboxes
-        matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-        gt_matched_classes = gt_classes[matched_gt_inds]
-
-        pred_ious_this_matching = (matching_matrix * pair_wise_ious).sum(0)[
-            fg_mask_inboxes
-        ]
-
-        return num_fg, gt_matched_classes, pred_ious_this_matching, matched_gt_inds

src/YOLOv6/yolov6/models/reppan.py

@@ -1,108 +0,0 @@
-import torch
-from torch import nn
-from yolov6.layers.common import RepBlock, SimConv, Transpose
-
-
-class RepPANNeck(nn.Module):
-    """RepPANNeck Module
-    EfficientRep is the default backbone of this model.
-    RepPANNeck has the balance of feature fusion ability and hardware efficiency.
-    """
-
-    def __init__(
-        self,
-        channels_list=None,
-        num_repeats=None
-    ):
-        super().__init__()
-
-        assert channels_list is not None
-        assert num_repeats is not None
-
-        self.Rep_p4 = RepBlock(
-            in_channels=channels_list[3] + channels_list[5],
-            out_channels=channels_list[5],
-            n=num_repeats[5],
-        )
-
-        self.Rep_p3 = RepBlock(
-            in_channels=channels_list[2] + channels_list[6],
-            out_channels=channels_list[6],
-            n=num_repeats[6]
-        )
-
-        self.Rep_n3 = RepBlock(
-            in_channels=channels_list[6] + channels_list[7],
-            out_channels=channels_list[8],
-            n=num_repeats[7],
-        )
-
-        self.Rep_n4 = RepBlock(
-            in_channels=channels_list[5] + channels_list[9],
-            out_channels=channels_list[10],
-            n=num_repeats[8]
-        )
-
-        self.reduce_layer0 = SimConv(
-            in_channels=channels_list[4],
-            out_channels=channels_list[5],
-            kernel_size=1,
-            stride=1
-        )
-
-        self.upsample0 = Transpose(
-            in_channels=channels_list[5],
-            out_channels=channels_list[5],
-        )
-
-        self.reduce_layer1 = SimConv(
-            in_channels=channels_list[5],
-            out_channels=channels_list[6],
-            kernel_size=1,
-            stride=1
-        )
-
-        self.upsample1 = Transpose(
-            in_channels=channels_list[6],
-            out_channels=channels_list[6]
-        )
-
-        self.downsample2 = SimConv(
-            in_channels=channels_list[6],
-            out_channels=channels_list[7],
-            kernel_size=3,
-            stride=2
-        )
-
-        self.downsample1 = SimConv(
-            in_channels=channels_list[8],
-            out_channels=channels_list[9],
-            kernel_size=3,
-            stride=2
-        )
-
-    def forward(self, input):
-
-        (x2, x1, x0) = input
-
-        fpn_out0 = self.reduce_layer0(x0)
-        upsample_feat0 = self.upsample0(fpn_out0)
-        f_concat_layer0 = torch.cat([upsample_feat0, x1], 1)
-        f_out0 = self.Rep_p4(f_concat_layer0)
-
-        fpn_out1 = self.reduce_layer1(f_out0)
-        upsample_feat1 = self.upsample1(fpn_out1)
-        f_concat_layer1 = torch.cat([upsample_feat1, x2], 1)
-        pan_out2 = self.Rep_p3(f_concat_layer1)
-
-        down_feat1 = self.downsample2(pan_out2)
-        p_concat_layer1 = torch.cat([down_feat1, fpn_out1], 1)
-        pan_out1 = self.Rep_n3(p_concat_layer1)
-
-        down_feat0 = self.downsample1(pan_out1)
-        p_concat_layer2 = torch.cat([down_feat0, fpn_out0], 1)
-        pan_out0 = self.Rep_n4(p_concat_layer2)
-
-        outputs = [pan_out2, pan_out1, pan_out0]
-
-        return outputs

src/YOLOv6/yolov6/models/yolo.py

@@ -1,83 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import math
-import torch.nn as nn
-from yolov6.layers.common import *
-from yolov6.utils.torch_utils import initialize_weights
-from yolov6.models.efficientrep import EfficientRep
-from yolov6.models.reppan import RepPANNeck
-from yolov6.models.effidehead import Detect, build_effidehead_layer
-
-
-class Model(nn.Module):
-    '''YOLOv6 model with backbone, neck and head.
-    The default parts are EfficientRep Backbone, Rep-PAN and
-    Efficient Decoupled Head.
-    '''
-    def __init__(self, config, channels=3, num_classes=None, anchors=None):  # model, input channels, number of classes
-        super().__init__()
-        # Build network
-        num_layers = config.model.head.num_layers
-        self.backbone, self.neck, self.detect = build_network(config, channels, num_classes, anchors, num_layers)
-
-        # Init Detect head
-        begin_indices = config.model.head.begin_indices
-        out_indices_head = config.model.head.out_indices
-        self.stride = self.detect.stride
-        self.detect.i = begin_indices
-        self.detect.f = out_indices_head
-        self.detect.initialize_biases()
-
-        # Init weights
-        initialize_weights(self)
-
-    def forward(self, x):
-        x = self.backbone(x)
-        x = self.neck(x)
-        x = self.detect(x)
-        return x
-
-    def _apply(self, fn):
-        self = super()._apply(fn)
-        self.detect.stride = fn(self.detect.stride)
-        self.detect.grid = list(map(fn, self.detect.grid))
-        return self
-
-
-def make_divisible(x, divisor):
-    # Upward revision the value x to make it evenly divisible by the divisor.
-    return math.ceil(x / divisor) * divisor
-
-
-def build_network(config, channels, num_classes, anchors, num_layers):
-    depth_mul = config.model.depth_multiple
-    width_mul = config.model.width_multiple
-    num_repeat_backbone = config.model.backbone.num_repeats
-    channels_list_backbone = config.model.backbone.out_channels
-    num_repeat_neck = config.model.neck.num_repeats
-    channels_list_neck = config.model.neck.out_channels
-    num_anchors = config.model.head.anchors
-    num_repeat = [(max(round(i * depth_mul), 1) if i > 1 else i) for i in (num_repeat_backbone + num_repeat_neck)]
-    channels_list = [make_divisible(i * width_mul, 8) for i in (channels_list_backbone + channels_list_neck)]
-
-    backbone = EfficientRep(
-        in_channels=channels,
-        channels_list=channels_list,
-        num_repeats=num_repeat
-    )
-
-    neck = RepPANNeck(
-        channels_list=channels_list,
-        num_repeats=num_repeat
-    )
-
-    head_layers = build_effidehead_layer(channels_list, num_anchors, num_classes)
-
-    head = Detect(num_classes, anchors, num_layers, head_layers=head_layers)
-
-    return backbone, neck, head
-
-
-def build_model(cfg, num_classes, device):
-    model = Model(cfg, channels=3, num_classes=num_classes, anchors=cfg.model.head.anchors).to(device)
-    return model

src/YOLOv6/yolov6/solver/build.py

@@ -1,42 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import os
-import math
-
-import torch
-import torch.nn as nn
-
-
-def build_optimizer(cfg, model):
-    """ Build optimizer from cfg file."""
-    g_bnw, g_w, g_b = [], [], []
-    for v in model.modules():
-        if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
-            g_b.append(v.bias)
-        if isinstance(v, nn.BatchNorm2d):
-            g_bnw.append(v.weight)
-        elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
-            g_w.append(v.weight)
-
-    assert cfg.solver.optim == 'SGD' or 'Adam', 'ERROR: unknown optimizer, use SGD defaulted'
-    if cfg.solver.optim == 'SGD':
-        optimizer = torch.optim.SGD(g_bnw, lr=cfg.solver.lr0, momentum=cfg.solver.momentum, nesterov=True)
-    elif cfg.solver.optim == 'Adam':
-        optimizer = torch.optim.Adam(g_bnw, lr=cfg.solver.lr0, betas=(cfg.solver.momentum, 0.999))
-
-    optimizer.add_param_group({'params': g_w, 'weight_decay': cfg.solver.weight_decay})
-    optimizer.add_param_group({'params': g_b})
-
-    del g_bnw, g_w, g_b
-    return optimizer
-
-
-def build_lr_scheduler(cfg, optimizer, epochs):
-    """Build learning rate scheduler from cfg file."""
-    if cfg.solver.lr_scheduler == 'Cosine':
-        lf = lambda x: ((1 - math.cos(x * math.pi / epochs)) / 2) * (cfg.solver.lrf - 1) + 1
-    else:
-        LOGGER.error('unknown lr scheduler, use Cosine defaulted')
-
-    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
-    return scheduler, lf

src/YOLOv6/yolov6/utils/checkpoint.py

@@ -1,60 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import os
-import shutil
-import torch
-import os.path as osp
-from yolov6.utils.events import LOGGER
-from yolov6.utils.torch_utils import fuse_model
-
-
-def load_state_dict(weights, model, map_location=None):
-    """Load weights from checkpoint file, only assign weights those layers' name and shape are match."""
-    ckpt = torch.load(weights, map_location=map_location)
-    state_dict = ckpt['model'].float().state_dict()
-    model_state_dict = model.state_dict()
-    state_dict = {k: v for k, v in state_dict.items() if k in model_state_dict and v.shape == model_state_dict[k].shape}
-    model.load_state_dict(state_dict, strict=False)
-    del ckpt, state_dict, model_state_dict
-    return model
-
-
-def load_checkpoint(weights, map_location=None, inplace=True, fuse=True):
-    """Load model from checkpoint file."""
-    LOGGER.info("Loading checkpoint from {}".format(weights))
-    ckpt = torch.load(weights, map_location=map_location)  # load
-    model = ckpt['ema' if ckpt.get('ema') else 'model'].float()
-    if fuse:
-        LOGGER.info("\nFusing model...")
-        model = fuse_model(model).eval()
-    else:
-        model = model.eval()
-    return model
-
-
-def save_checkpoint(ckpt, is_best, save_dir, model_name=""):
-    """ Save checkpoint to the disk."""
-    if not osp.exists(save_dir):
-        os.makedirs(save_dir)
-    filename = osp.join(save_dir, model_name + '.pt')
-    torch.save(ckpt, filename)
-    if is_best:
-        best_filename = osp.join(save_dir, 'best_ckpt.pt')
-        shutil.copyfile(filename, best_filename)
-
-
-def strip_optimizer(ckpt_dir, epoch):
-    for s in ['best', 'last']:
-        ckpt_path = osp.join(ckpt_dir, '{}_ckpt.pt'.format(s))
-        if not osp.exists(ckpt_path):
-            continue
-        ckpt = torch.load(ckpt_path, map_location=torch.device('cpu'))
-        if ckpt.get('ema'):
-            ckpt['model'] = ckpt['ema']  # replace model with ema
-        for k in ['optimizer', 'ema', 'updates']:  # keys
-            ckpt[k] = None
-        ckpt['epoch'] = epoch
-        ckpt['model'].half()  # to FP16
-        for p in ckpt['model'].parameters():
-            p.requires_grad = False
-        torch.save(ckpt, ckpt_path)

src/YOLOv6/yolov6/utils/config.py

@@ -1,101 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# The code is based on
-# https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/config.py
-# Copyright (c) OpenMMLab.
-
-import os.path as osp
-import shutil
-import sys
-import tempfile
-from importlib import import_module
-from addict import Dict
-
-
-class ConfigDict(Dict):
-
-    def __missing__(self, name):
-        raise KeyError(name)
-
-    def __getattr__(self, name):
-        try:
-            value = super(ConfigDict, self).__getattr__(name)
-        except KeyError:
-            ex = AttributeError("'{}' object has no attribute '{}'".format(
-                self.__class__.__name__, name))
-        except Exception as e:
-            ex = e
-        else:
-            return value
-        raise ex
-
-
-class Config(object):
-
-    @staticmethod
-    def _file2dict(filename):
-        filename = str(filename)
-        if filename.endswith('.py'):
-            with tempfile.TemporaryDirectory() as temp_config_dir:
-                shutil.copyfile(filename,
-                                osp.join(temp_config_dir, '_tempconfig.py'))
-                sys.path.insert(0, temp_config_dir)
-                mod = import_module('_tempconfig')
-                sys.path.pop(0)
-                cfg_dict = {
-                    name: value
-                    for name, value in mod.__dict__.items()
-                    if not name.startswith('__')
-                }
-                # delete imported module
-                del sys.modules['_tempconfig']
-        else:
-            raise IOError('Only .py type are supported now!')
-        cfg_text = filename + '\n'
-        with open(filename, 'r') as f:
-            cfg_text += f.read()
-
-        return cfg_dict, cfg_text
-
-    @staticmethod
-    def fromfile(filename):
-        cfg_dict, cfg_text = Config._file2dict(filename)
-        return Config(cfg_dict, cfg_text=cfg_text, filename=filename)
-
-    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
-        if cfg_dict is None:
-            cfg_dict = dict()
-        elif not isinstance(cfg_dict, dict):
-            raise TypeError('cfg_dict must be a dict, but got {}'.format(
-                type(cfg_dict)))
-
-        super(Config, self).__setattr__('_cfg_dict', ConfigDict(cfg_dict))
-        super(Config, self).__setattr__('_filename', filename)
-        if cfg_text:
-            text = cfg_text
-        elif filename:
-            with open(filename, 'r') as f:
-                text = f.read()
-        else:
-            text = ''
-        super(Config, self).__setattr__('_text', text)
-
-    @property
-    def filename(self):
-        return self._filename
-
-    @property
-    def text(self):
-        return self._text
-
-    def __repr__(self):
-        return 'Config (path: {}): {}'.format(self.filename,
-                                              self._cfg_dict.__repr__())
-
-    def __getattr__(self, name):
-        return getattr(self._cfg_dict, name)
-
-    def __setattr__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setattr__(name, value)

src/YOLOv6/yolov6/utils/ema.py

@@ -1,59 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# The code is based on
-# https://github.com/ultralytics/yolov5/blob/master/utils/torch_utils.py
-import math
-from copy import deepcopy
-import torch
-import torch.nn as nn
-
-
-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):
-        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
-        self.updates = updates
-        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))
-        for param in self.ema.parameters():
-            param.requires_grad_(False)
-
-    def update(self, model):
-        with torch.no_grad():
-            self.updates += 1
-            decay = self.decay(self.updates)
-
-            state_dict = model.module.state_dict() if is_parallel(model) else model.state_dict()  # model state_dict
-            for k, item in self.ema.state_dict().items():
-                if item.dtype.is_floating_point:
-                    item *= decay
-                    item += (1 - decay) * state_dict[k].detach()
-
-    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
-        copy_attr(self.ema, model, include, exclude)
-
-
-def copy_attr(a, b, include=(), exclude=()):
-    """Copy attributes from one instance and set them to another instance."""
-    for k, item in b.__dict__.items():
-        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
-            continue
-        else:
-            setattr(a, k, item)
-
-
-def is_parallel(model):
-    # Return True if model's type is DP or DDP, else False.
-    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
-
-
-def de_parallel(model):
-    # De-parallelize a model. Return single-GPU model if model's type is DP or DDP.
-    return model.module if is_parallel(model) else model

src/YOLOv6/yolov6/utils/envs.py

@@ -1,54 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-import os
-import random
-import numpy as np
-
-import torch
-import torch.backends.cudnn as cudnn
-from yolov6.utils.events import LOGGER
-
-
-def get_envs():
-    """Get PyTorch needed environments from system envirionments."""
-    local_rank = int(os.getenv('LOCAL_RANK', -1))
-    rank = int(os.getenv('RANK', -1))
-    world_size = int(os.getenv('WORLD_SIZE', 1))
-    return local_rank, rank, world_size
-
-
-def select_device(device):
-    """Set devices' information to the program.
-    Args:
-        device: a string, like 'cpu' or '1,2,3,4'
-    Returns:
-        torch.device
-    """
-    if device == 'cpu':
-        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
-        LOGGER.info('Using CPU for training... ')
-    elif device:
-        os.environ['CUDA_VISIBLE_DEVICES'] = device
-        assert torch.cuda.is_available()
-        nd = len(device.strip().split(','))
-        LOGGER.info(f'Using {nd} GPU for training... ')
-    cuda = device != 'cpu' and torch.cuda.is_available()
-    device = torch.device('cuda:0' if cuda else 'cpu')
-    return device
-
-
-def set_random_seed(seed, deterministic=False):
-    """ Set random state to random libray, numpy, torch and cudnn.
-    Args:
-        seed: int value.
-        deterministic: bool value.
-    """
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    if deterministic:
-        cudnn.deterministic = True
-        cudnn.benchmark = False
-    else:
-        cudnn.deterministic = False
-        cudnn.benchmark = True

src/YOLOv6/yolov6/utils/events.py

@@ -1,41 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-import os
-import yaml
-import logging
-import shutil
-
-
-def set_logging(name=None):
-    rank = int(os.getenv('RANK', -1))
-    logging.basicConfig(format="%(message)s", level=logging.INFO if (rank in (-1, 0)) else logging.WARNING)
-    return logging.getLogger(name)
-
-
-LOGGER = set_logging(__name__)
-NCOLS = shutil.get_terminal_size().columns
-
-
-def load_yaml(file_path):
-    """Load data from yaml file."""
-    if isinstance(file_path, str):
-        with open(file_path, errors='ignore') as f:
-            data_dict = yaml.safe_load(f)
-    return data_dict
-
-
-def save_yaml(data_dict, save_path):
-    """Save data to yaml file"""
-    with open(save_path, 'w') as f:
-        yaml.safe_dump(data_dict, f, sort_keys=False)
-
-
-def write_tblog(tblogger, epoch, results, losses):
-    """Display mAP and loss information to log."""
-    tblogger.add_scalar("val/mAP@0.5", results[0], epoch + 1)
-    tblogger.add_scalar("val/mAP@0.50:0.95", results[1], epoch + 1)
-
-    tblogger.add_scalar("train/iou_loss", losses[0], epoch + 1)
-    tblogger.add_scalar("train/l1_loss", losses[1], epoch + 1)
-    tblogger.add_scalar("train/obj_loss", losses[2], epoch + 1)
-    tblogger.add_scalar("train/cls_loss", losses[3], epoch + 1)

src/YOLOv6/yolov6/utils/figure_iou.py

@@ -1,114 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import math
-import torch
-
-
-class IOUloss:
-    """ Calculate IoU loss.
-    """
-    def __init__(self, box_format='xywh', iou_type='ciou', reduction='none', eps=1e-7):
-        """ Setting of the class.
-        Args:
-            box_format: (string), must be one of 'xywh' or 'xyxy'.
-            iou_type: (string), can be one of 'ciou', 'diou', 'giou' or 'siou'
-            reduction: (string), specifies the reduction to apply to the output, must be one of 'none', 'mean','sum'.
-            eps: (float), a value to avoid divide by zero error.
-        """
-        self.box_format = box_format
-        self.iou_type = iou_type.lower()
-        self.reduction = reduction
-        self.eps = eps
-
-    def __call__(self, box1, box2):
-        """ calculate iou. box1 and box2 are torch tensor with shape [M, 4] and [Nm 4].
-        """
-        box2 = box2.T
-        if self.box_format == 'xyxy':
-            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]
-        elif self.box_format == 'xywh':
-            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 + self.eps
-        w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + self.eps
-        union = w1 * h1 + w2 * h2 - inter + self.eps
-        iou = inter / union
-
-        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex width
-        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
-        if self.iou_type == 'giou':
-            c_area = cw * ch + self.eps  # convex area
-            iou = iou - (c_area - union) / c_area
-        elif self.iou_type in ['diou', 'ciou']:
-            c2 = cw ** 2 + ch ** 2 + self.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 self.iou_type == 'diou':
-                iou = iou - rho2 / c2
-            elif self.iou_type == 'ciou':
-                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
-                with torch.no_grad():
-                    alpha = v / (v - iou + (1 + self.eps))
-                iou = iou - (rho2 / c2 + v * alpha)
-        elif self.iou_type == 'siou':
-            # SIoU Loss https://arxiv.org/pdf/2205.12740.pdf
-            s_cw = (b2_x1 + b2_x2 - b1_x1 - b1_x2) * 0.5
-            s_ch = (b2_y1 + b2_y2 - b1_y1 - b1_y2) * 0.5
-            sigma = torch.pow(s_cw ** 2 + s_ch ** 2, 0.5)
-            sin_alpha_1 = torch.abs(s_cw) / sigma
-            sin_alpha_2 = torch.abs(s_ch) / sigma
-            threshold = pow(2, 0.5) / 2
-            sin_alpha = torch.where(sin_alpha_1 > threshold, sin_alpha_2, sin_alpha_1)
-            angle_cost = torch.cos(torch.arcsin(sin_alpha) * 2 - math.pi / 2)
-            rho_x = (s_cw / cw) ** 2
-            rho_y = (s_ch / ch) ** 2
-            gamma = angle_cost - 2
-            distance_cost = 2 - torch.exp(gamma * rho_x) - torch.exp(gamma * rho_y)
-            omiga_w = torch.abs(w1 - w2) / torch.max(w1, w2)
-            omiga_h = torch.abs(h1 - h2) / torch.max(h1, h2)
-            shape_cost = torch.pow(1 - torch.exp(-1 * omiga_w), 4) + torch.pow(1 - torch.exp(-1 * omiga_h), 4)
-            iou = iou - 0.5 * (distance_cost + shape_cost)
-        loss = 1.0 - iou
-
-        if self.reduction == 'sum':
-            loss = loss.sum()
-        elif self.reduction == 'mean':
-            loss = loss.mean()
-
-        return loss
-
-
-def pairwise_bbox_iou(box1, box2, box_format='xywh'):
-    """Calculate iou.
-    This code is based on https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/utils/boxes.py
-    """
-    if box_format == 'xyxy':
-        lt = torch.max(box1[:, None, :2], box2[:, :2])
-        rb = torch.min(box1[:, None, 2:], box2[:, 2:])
-        area_1 = torch.prod(box1[:, 2:] - box1[:, :2], 1)
-        area_2 = torch.prod(box2[:, 2:] - box2[:, :2], 1)
-
-    elif box_format == 'xywh':
-        lt = torch.max(
-            (box1[:, None, :2] - box1[:, None, 2:] / 2),
-            (box2[:, :2] - box2[:, 2:] / 2),
-        )
-        rb = torch.min(
-            (box1[:, None, :2] + box1[:, None, 2:] / 2),
-            (box2[:, :2] + box2[:, 2:] / 2),
-        )
-
-        area_1 = torch.prod(box1[:, 2:], 1)
-        area_2 = torch.prod(box2[:, 2:], 1)
-    valid = (lt < rb).type(lt.type()).prod(dim=2)
-    inter = torch.prod(rb - lt, 2) * valid
-    return inter / (area_1[:, None] + area_2 - inter)

src/YOLOv6/yolov6/utils/general.py

@@ -1,17 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import os
-from pathlib import Path
-
-def increment_name(path, master_process):
-    "increase save directory's id"
-    path = Path(path)
-    sep = ''
-    if path.exists() and master_process:
-        path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '')
-        for n in range(1, 9999):
-            p = f'{path}{sep}{n}{suffix}'
-            if not os.path.exists(p):
-                break
-        path = Path(p)
-    return path

src/YOLOv6/yolov6/utils/nms.py

@@ -1,106 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# The code is based on
-# https://github.com/ultralytics/yolov5/blob/master/utils/general.py
-
-import os
-import time
-import numpy as np
-import cv2
-import torch
-import torchvision
-
-
-# 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
-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 xywh2xyxy(x):
-    # Convert boxes with shape [n, 4] from [x, y, w, h] to [x1, y1, x2, y2] where x1y1 is top-left, x2y2=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 non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, max_det=300):
-    """Runs Non-Maximum Suppression (NMS) on inference results.
-    This code is borrowed from: https://github.com/ultralytics/yolov5/blob/47233e1698b89fc437a4fb9463c815e9171be955/utils/general.py#L775
-    Args:
-        prediction: (tensor), with shape [N, 5 + num_classes], N is the number of bboxes.
-        conf_thres: (float) confidence threshold.
-        iou_thres: (float) iou threshold.
-        classes: (None or list[int]), if a list is provided, nms only keep the classes you provide.
-        agnostic: (bool), when it is set to True, we do class-independent nms, otherwise, different class would do nms respectively.
-        multi_label: (bool), when it is set to True, one box can have multi labels, otherwise, one box only huave one label.
-        max_det:(int), max number of output bboxes.
-
-    Returns:
-         list of detections, echo item is one tensor with shape (num_boxes, 6), 6 is for [xyxy, conf, cls].
-    """
-
-    num_classes = prediction.shape[2] - 5  # number of classes
-    pred_candidates = prediction[..., 4] > conf_thres  # candidates
-
-    # Check the parameters.
-    assert 0 <= conf_thres <= 1, f'conf_thresh must be in 0.0 to 1.0, however {conf_thres} is provided.'
-    assert 0 <= iou_thres <= 1, f'iou_thres must be in 0.0 to 1.0, however {iou_thres} is provided.'
-
-    # Function settings.
-    max_wh = 4096  # maximum box width and height
-    max_nms = 30000  # maximum number of boxes put into torchvision.ops.nms()
-    time_limit = 10.0  # quit the function when nms cost time exceed the limit time.
-    multi_label &= num_classes > 1  # multiple labels per box
-
-    tik = time.time()
-    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
-    for img_idx, x in enumerate(prediction):  # image index, image inference
-        x = x[pred_candidates[img_idx]]  # confidence
-
-        # If no box remains, skip the next process.
-        if not x.shape[0]:
-            continue
-
-        # confidence multiply the objectness
-        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf
-
-        # (center x, center y, width, height) to (x1, y1, x2, y2)
-        box = xywh2xyxy(x[:, :4])
-
-        # Detections matrix's shape is  (n,6), each row represents (xyxy, conf, cls)
-        if multi_label:
-            box_idx, class_idx = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
-            x = torch.cat((box[box_idx], x[box_idx, class_idx + 5, None], class_idx[:, None].float()), 1)
-        else:  # Only keep the class with highest scores.
-            conf, class_idx = x[:, 5:].max(1, keepdim=True)
-            x = torch.cat((box, conf, class_idx.float()), 1)[conf.view(-1) > conf_thres]
-
-        # Filter by class, only keep boxes whose category is in classes.
-        if classes is not None:
-            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
-
-        # Check shape
-        num_box = x.shape[0]  # number of boxes
-        if not num_box:  # no boxes kept.
-            continue
-        elif num_box > max_nms:  # excess max boxes' number.
-            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
-
-        # Batched NMS
-        class_offset = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
-        boxes, scores = x[:, :4] + class_offset, x[:, 4]  # boxes (offset by class), scores
-        keep_box_idx = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-        if keep_box_idx.shape[0] > max_det:  # limit detections
-            keep_box_idx = keep_box_idx[:max_det]
-
-        output[img_idx] = x[keep_box_idx]
-        if (time.time() - tik) > time_limit:
-            print(f'WARNING: NMS cost time exceed the limited {time_limit}s.')
-            break  # time limit exceeded
-
-    return output

src/YOLOv6/yolov6/utils/torch_utils.py

@@ -1,109 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-
-import time
-from contextlib import contextmanager
-from copy import deepcopy
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-import torch.nn.functional as F
-from yolov6.utils.events import LOGGER
-
-try:
-    import thop  # for FLOPs computation
-except ImportError:
-    thop = None
-
-
-@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]:
-        dist.barrier(device_ids=[local_rank])
-    yield
-    if local_rank == 0:
-        dist.barrier(device_ids=[0])
-
-
-def time_sync():
-    # Waits for all kernels in all streams on a CUDA device to complete if cuda is available.
-    if torch.cuda.is_available():
-        torch.cuda.synchronize()
-    return time.time()
-
-
-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, nn.SiLU]:
-            m.inplace = True
-
-
-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 fuse_model(model):
-    from yolov6.layers.common import Conv
-
-    for m in model.modules():
-        if 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.forward_fuse  # update forward
-    return model
-
-
-def get_model_info(model, img_size=640):
-    """Get model Params and GFlops.
-    Code base on https://github.com/Megvii-BaseDetection/YOLOX/blob/main/yolox/utils/model_utils.py
-    """
-    from thop import profile
-    stride = 32
-    img = torch.zeros((1, 3, stride, stride), device=next(model.parameters()).device)
-    flops, params = profile(deepcopy(model), inputs=(img,), verbose=False)
-    params /= 1e6
-    flops /= 1e9
-    img_size = img_size if isinstance(img_size, list) else [img_size, img_size]
-    flops *= img_size[0] * img_size[1] / stride / stride * 2  # Gflops
-    info = "Params: {:.2f}M, Gflops: {:.2f}".format(params, flops)
-    return info