first commit

.idea/MPKFNet.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.6 (py36pt18)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.6 (py36pt15)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">

.idea/misc.xml

@@ -1,4 +1,4 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (py36pt18)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (py36pt15)" project-jdk-type="Python SDK" />
 </project>

.vscode/launch.json

@@ -0,0 +1,24 @@
+{
+    // 使用 IntelliSense 了解相关属性。 
+    // 悬停以查看现有属性的描述。
+    // 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Remote Attach",
+            "type": "python",
+            "request": "attach",
+            "connect": {
+                "host": "172.16.0.254",
+                "port": 22
+            },
+            "pathMappings": [
+                {
+                    "localRoot": "${workspaceFolder}",
+                    "remoteRoot": "."
+                }
+            ],
+            "justMyCode": true
+        }
+    ]
+}

CalDatasetMeanVar.py

@@ -0,0 +1,50 @@
+import os
+from PIL import Image
+import matplotlib.pyplot as plt
+import numpy as np
+#from scipy.misc import imread,imresize
+import cv2,argparse
+parser1 = argparse.ArgumentParser(description="CUI")
+parser1.add_argument("root", default='/data1/Hatcher/model/project/data_MPKFNet/rawdata/竹林嘴-3月3日候鸟视频.mp4', type=str,
+                    help='root.')
+parser1.add_argument("imgsize", default='640', type=str,
+                    help='imgsize')
+args = parser1.parse_args()
+filepath = args.root
+imageSize = int(args.imgsize)
+pathDir = os.listdir(filepath)
+
+R_channel = 0
+G_channel = 0
+B_channel = 0
+for idx in range(len(pathDir)):
+    filename = pathDir[idx]
+    print(filename)
+    img = cv2.imread(os.path.join(filepath, filename))
+    img = cv2.resize(img,(imageSize,imageSize))/ 255.0
+    R_channel = R_channel + np.sum(img[:, :, 0])
+    G_channel = G_channel + np.sum(img[:, :, 1])
+    B_channel = B_channel + np.sum(img[:, :, 2])
+
+num = len(pathDir) * 512 * 512  # 这里（512,512）是每幅图片的大小，所有图片尺寸都一样
+R_mean = R_channel / num
+G_mean = G_channel / num
+B_mean = B_channel / num
+
+R_channel = 0
+G_channel = 0
+B_channel = 0
+for idx in range(len(pathDir)):
+    filename = pathDir[idx]
+    print(filename)
+    img = cv2.imread(os.path.join(filepath, filename))
+    img = cv2.resize(img, (imageSize, imageSize)) / 255.0
+    R_channel = R_channel + np.sum((img[:, :, 0] - R_mean) ** 2)
+    G_channel = G_channel + np.sum((img[:, :, 1] - G_mean) ** 2)
+    B_channel = B_channel + np.sum((img[:, :, 2] - B_mean) ** 2)
+
+R_var = np.sqrt(R_channel / num)
+G_var = np.sqrt(G_channel / num)
+B_var = np.sqrt(B_channel / num)
+print("R_mean is %f, G_mean is %f, B_mean is %f" % (R_mean, G_mean, B_mean))
+print("R_var is %f, G_var is %f, B_var is %f" % (R_var, G_var, B_var))

DclTest.py

@@ -44,7 +44,7 @@ from Dcl.models.LoadModel import MainModel
 from Dcl.DclConfig import LoadConfig, load_data_transformers
 from Dcl.util.test_tool import set_text, save_multi_img, cls_base_acc
 
-os.environ['CUDA_VISIBLE_DEVICES'] = '6'
+#os.environ['CUDA_VISIBLE_DEVICES'] = '6'
 devices = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 class DclInference:

DetResizeForTrain.py

@@ -0,0 +1,37 @@
+import os
+import cv2
+import shutil
+from tqdm import tqdm
+anno_pathRoot = "/data/DataManagement/Project/CAS_ProjectBird/CDB_ProjectBird/Annotations_Original/"
+image_pathRoot = anno_pathRoot.replace("/Annotations_Original/","/JPEGImages_Original/")
+w = 1024
+h = 768
+
+def DetResizeForTrain(anno_pathRoot,image_pathRoot,w,h):
+    annoResultPath = anno_pathRoot.replace("/Annotations_Original/","/Annotations/")
+    annoJPEGPath = anno_pathRoot.replace("/JPEGImages_Original/", "/JPEGImages/")
+    for anno in tqdm(os.listdir(anno_pathRoot)):
+        anno = GEN_Annotations(anno.replace(".xml", ".jpg"))
+        dom = xml.dom.minidom.parse(os.path.join(anno_pathRoot, xmls))
+        size = dom.getElementsByTagName("size")
+        # print(size[0].getElementsByTagName('width')[0].childNodes[0].data)
+        width = size[0].getElementsByTagName('width')[0].childNodes[0].data
+        height = size[0].getElementsByTagName('height')[0].childNodes[0].data
+        depth = size[0].getElementsByTagName('depth')[0].childNodes[0].data
+        anno.set_size(int(w), int(h), int(depth))
+        objects = dom.getElementsByTagName("object")
+        for object in objects:
+            bndbox = object.getElementsByTagName('bndbox')[0]
+            xmin = bndbox.getElementsByTagName('xmin')[0]
+            ymin = bndbox.getElementsByTagName('ymin')[0]
+            xmax = bndbox.getElementsByTagName('xmax')[0]
+            ymax = bndbox.getElementsByTagName('ymax')[0]
+
+            xmin_data = int(xmin.childNodes[0].data)*(w/width)
+            ymin_data = int(ymin.childNodes[0].data)*(h/height)
+            xmax_data = int(xmax.childNodes[0].data)*(w/width)
+            ymax_data = int(ymax.childNodes[0].data)*(h/height)
+            label_name = object.getElementsByTagName('name')[0].childNodes[0].data
+            anno.add_pic_attr(label=label_name, xmin=xmin_data, xmax=xmax_data, ymin=ymin_data, ymax=ymax_data)
+        anno.savefile(os.path.join(annoResultPath, xmls))
+        image = cv2.imread

DetectFinePopulationSortVideo.py

@@ -0,0 +1,273 @@
+# -*- coding:utf-8 -*-
+import cv2,os,torch,argparse
+import queue
+import time
+import threading
+from System_I import Check_start_fordataselect,Density,Detection_Y5,Fine_grained,Fine_grained_T,shishi,save_sigle_image,RealDeal
+from common import global_para as gp
+r_db = None
+import torch.multiprocessing as multiprocessing
+import sort.sort as SORT
+from imatovideo import images_to_video
+from multiprocessing import Array
+
+
+
+class DetFineVid():
+    def __init__(self, VideoPath,SavePath):
+        super(DetFineVid,self).__init__()
+        # name
+        self.VideoPath = VideoPath
+        self.VideoResultSavePathroot = "/data1/Hatcher/model/project/data_MPKFNet/result_vedio/"
+        self.VideoName = os.path.basename(self.VideoPath).split(".")[0]
+        self.VideoResultSavePath = os.path.join(self.VideoResultSavePathroot,"{}.avi".format(self.VideoName))
+        self.SavePath = os.path.join(SavePath,self.VideoName)
+        if not os.path.exists(self.SavePath):
+            os.makedirs(self.SavePath)
+
+        self.root = "/home/wanghc/output_jiankong"
+        self.DensityClass =  Density(gp.devices, self.root)
+        self.DetectionClass = Detection_Y5(gp.devices, self.root)
+        self.Fine_grianedClass = Fine_grained_T(gp.devices)
+        self.FineRestartFlag = True
+        self.numrestart = 0
+
+    def conVideo(self):
+        images_to_video( self.SavePath,self.VideoResultSavePath)
+    def StartRec(self):
+
+
+        t1 = time.time()
+        cap = cv2.VideoCapture(self.VideoPath)
+        t2 = time.time()
+        print("capture is ",(t2-t1)*1000)
+        num_detection = 0
+        num_cuntupian=0
+        SortRestart = SORT.Sort(max_age=gp.max_age,
+                       min_hits=gp.min_hits,
+                       iou_threshold=gp.sortiou_threshold)
+        realtimedeal = RealDeal("check_start",self.DetectionClass,self.Fine_grianedClass,self.DensityClass)
+        if cap.isOpened():
+            res, frame = cap.read()
+            if res:
+                shapex = frame.shape[1]
+                shapey = frame.shape[0]
+                gp.radioShowX = shapex / gp.radioShowX
+                gp.radioShowY = shapey / gp.radioShowY
+                gp.radioShowtext = gp.radioShowX
+                if frame.shape[1] == 3840:
+                    gp.fontScale = 3
+                elif frame.shape[1] == 2560:
+                    gp.fontScale = 1
+                else:
+                    gp.fontScale = 1
+        for i in range(20):
+            res, frame = cap.read()
+            print("skip{}".format(i))
+
+        num_image = 0
+        while True:
+            if cap.isOpened():
+                res, frame = cap.read()
+                if res:
+                    closed = False
+                    failCnt = 0
+                    num_image += 1
+                    data_today = 8
+                    print(num_detection)
+                    # if num_detection <12974:
+                    #     a=0
+                    #     num_detection+=1
+                    #     continue
+                        #cv2.imwrite("./test.jpg",frame)
+                    if (int(data_today) > 1) & (int(data_today) < 20):
+                        if (0xFF == ord('q')):
+                            print("restart")
+                            self.FineRestartFlag = True
+                            self.numrestart = 0
+                            SortRestart = SORT.Sort(max_age=gp.max_age,
+                                                    min_hits=gp.min_hits,
+                                                    iou_threshold=gp.sortiou_threshold)
+                        if self.FineRestartFlag :
+                            realtimedeal.realtimerestart(frame,num_image=num_image,num_detection=num_detection,SortRestart = SortRestart)
+                            self.numrestart += 1
+                            if self.numrestart >= 5:
+                                self.numrestart = 0
+                                self.FineRestartFlag = False
+                                FirstDetFineResult = realtimedeal.getrestartresult(gp.IndexMax)
+                                print("µÚÒ»´Îʶ±ðÄ¿±êÊýÁ¿ ",len(FirstDetFineResult))
+                                gp.SortLabel_Index = FirstDetFineResult
+                                # SortRealTime = SORT.Sort(max_age=gp.max_age,
+                                #                          min_hits=gp.min_hits,
+                                #                          iou_threshold=gp.sortiou_threshold)
+
+                        else:
+                            tm1 = time.time()
+                            #frame = cv2.imread("./test.jpg")
+                            img_show,num_image = realtimedeal.realtime(frame,num_image=num_image,num_detection=num_detection,SortRestart = SortRestart)
+                            tm2 = time.time()
+                            if gp.DebugPrint_flag:
+                                print("main1 time is  ", (tm2 - tm1) * 1000)
+                            # cv2.imshow("AI-ECO", img_show)
+                            # #cv2.imwrite("../data_MPKFNet/RTSP3/" + "{}.jpg".format(time.strftime('%Y%m%d%H%M%S', time.localtime(time.time()))),img_show)
+                            # if cv2.waitKey(1) & 0xFF == ord('r'):
+                            #     break
+                            save_path =os.path.join(self.SavePath,"{}.jpg".format(str(num_detection).zfill(6)))
+                            cv2.imwrite(save_path,img_show)
+                            tm3 = time.time()
+                            if gp.DebugPrint_flag:
+                                print("main2 time is  ", (tm3 - tm2) * 1000)
+                    else:
+                        print("time is out")
+                    if (num_detection >1000000) :
+                        # save_image = save_sigle_image(frame)
+                        # save_image.start()
+                        # num_detection=0
+                        num_detection = 0
+                    num_detection+=1
+                    num_cuntupian+=1
+                else:
+                    break
+
+
+def commanddeal():
+    SavePath = "/data1/Hatcher/model/project/data_MPKFNet/result/"
+    print("starting ",gp.VideoPath)
+    DFS = DetFineVid(gp.VideoPath,SavePath)
+    DFS.StartRec()
+    DFS.conVideo()
+def filedeal(rootPath):  #deal all file in a folder
+    SavePath = "/data3/DEMO/images/"
+    i = 0
+    for video_path in os.listdir(rootPath):
+        if i == 1:
+            print("111")
+        # print("starting deal {}".format(str(video_path)))
+        # VideoPath = os.path.join(rootPath,video_path)
+        # DFS = DetFineVid(VideoPath,SavePath)
+        # DFS.StartRec()
+        # DFS.conVideo()
+        P = multiprocessing.Process(target=func,args=(video_path,SavePath,rootPath))
+        P.start()
+        P.join()
+        #globalInit()
+        i += 1
+def func(video_path,SavePath,rootPath):
+    print("starting deal {}".format(str(video_path)))
+    VideoPath = os.path.join(rootPath, video_path)
+    DFS = DetFineVid(VideoPath, SavePath)
+    DFS.StartRec()
+    DFS.conVideo()
+def globalInit():  #deal all file in a folder
+
+    parser1 = argparse.ArgumentParser(description="CUI")
+    parser1.add_argument("--video_num", default='/data1/Hatcher/model/project/data_MPKFNet/rawdata/PYH-DEMO.mp4',
+                         type=str,
+                         help='Video name.')
+    parser1.add_argument("--GPU", default='6', type=str,
+                         help='GPU.')
+    parser1.add_argument("--thresh", default=0.4, type=float,
+                         help='GPU.')
+    parser1.add_argument("--red", default=0, type=int,
+                         help='red.')
+    parser1.add_argument("--add", default=0, type=str,
+                         help='add.')
+
+    args = parser1.parse_args()
+    # print(args)
+    gp.root = "/home/gyd/output1"
+    # os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+    os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU
+    gp.devices = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    gp.camera_address = args.add
+    gp.VideoPath = args.video_num
+    gp.red_flag = args.red
+    gp.detect_thresh = args.thresh
+
+    # fine_thresh2 = torch.tensor(fine_thresh2).cuda()
+    gp.birdIndex = 99999
+    # birdIndex = torch.tensor(birdIndex).cuda()
+    gp.is_ex_bird = False  # Ä¿±ê¼ì²â±ê־λ£¬Ã¿¸öÒ»Ãë¼ì²âÒ»´ÎÄ¿±ê¼ì²â£¬Èç¹ûÓÐÄñ£¬ÔòÖÃΪTrue£¬ÎªTrueµÄʱºò˵Ã÷£¬ ´¦Àí30sµÄÏ̻߳¹ÔÚ¼ÌÐø£¬Ö±µ½±ê־λ±äΪÁËFalseÔÙ¿ªÆôеÄÏß³Ì
+    gp.Density_thread_flag = False  # ÃܶȹÀ¼ÆÏ̱߳ê־λ£¬ÎªTrue˵Ã÷¿ªÆôÁËÃܶȹÀ¼ÆỊ̈߳¬´¦ÀíÍê750֡Ϊֹ£¬±ê־λ±êΪFalse£¬
+    gp.Detection_thread_flag = False  # Ä¿±ê¼ì²âºÍϸÁ£¶Èʶ±ðÏ̱߳ê־λ£¬ÎªTrue˵Ã÷¿ªÆôÁËÃܶȹÀ¼ÆỊ̈߳¬´¦ÀíÍê750֡Ϊֹ£¬±ê־λ±êΪFalse£¬
+    gp.Vedio_thread_flag = False  # ´æ´¢ÊÓƵÏ̱߳ê־룬´æ´¢30s
+    gp.SaveImage_thread_flag = False  # ±£´æԭͼÏ̱߳ê־룬´æ´¢30ÕÅԭͼ
+    gp.ALL_thread_flag = False  # ËùÓÐÏ̵߳ıê־룬µ¥¶À¿ªÉèÒ»¸öỊ̈߳¬¼à¿ØËùÓÐÏß³ÌÊÇ·ñ¶¼½áÊø£¬Èç¹û¶¼½áÊø£¬ÔòÖÃΪFalse£¬ºóÐøÌí¼ÓÏ̹߳ÜÀí£¬¿ÉÒÔ·ÅÔÚÕâÀïÃæ
+
+    gp.ShowAllNumFlag = False
+
+    gp.DebugPrint_flag = False  # ´òÓ¡±ê־λ
+
+    gp.num_count_old = 0
+    gp.boxes_fine = None
+    gp.labels_fine = None
+
+    gp.showThresh = 25
+    gp.destory_image = False
+    gp.Camera_device = args.video_num  # ÉãÏñÍ·±àºÅ£¬0001
+    gp.image_time = None  # µ±Ç°´¦ÀíµÄ30ÊÓƵµÄ±àºÅ
+    gp.ImgDealQueue = []  # 750Ö¡ÊÓƵ£¬»º´æ¿Õ¼ä
+    gp.img_show = None
+
+    # event = threading.Event()
+    gp.eventDensity = threading.Event()
+    gp.eventDetection = threading.Event()
+    gp.eventSaveimage = threading.Event()
+    gp.max_age = 2
+    gp.min_hits = 3
+    gp.sortiou_threshold = 0.3
+    gp.Finerestartboxes = []
+    gp.IndexMax = 0
+    gp.radioShowX = 2560.0
+    gp.radioShowY = 1440.0
+    gp.radioShowtext = gp.radioShowX
+    gp.SortLabel_Index = None  # ±êÇ©ºÍË÷Òý¶ÔÓ¦¹Øϵ[[4,1],[3,2]]
+    gp.SortLabel_Indexpre = []  # ±êÇ©ºÍË÷ÒýµÄ¶ÔÓ¦¹Øϵ£¬ÓÃÔÚ×·×Ù¹ý³ÌÖÐгöÏÖµÄÄ¿±ê£¬[[label,Index,Count]]
+    # Detection2 = Detection(devices,root)
+    gp.FineCountThresh = 5
+
+    gp.img_show = None
+
+    gp.Fine_thresh = 0.4
+
+    # DetWeight = "/data1/Hatcher/model/Detection/yolov5/runs/train/exp21/weights/best.pt"
+    # FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-PYH-3-20.7/_42522_PYH-3/best.pth"
+    gp.DetWeight = "/data1/Hatcher/model/Detection/yolov5/runs/train/KunMingCuiHu-Fish2/weights/best.pt"
+    gp.FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-DianChiFish-3-3-0.7/_4289_DianChiFish-4/best.pth"
+    # DetWeight = "/data1/Hatcher/model/Detection/yolov5/runs/train/BeijingCuiHu-Bird26/weights/best.pt"
+    # FineWeight = "/data/Hatcher/data/weigth/areaproject/resnet50-cui90_1model0.7/_42111_CUI-94-1/best.pth"
+    # BHG_chineseName = ["kong", '白鹤', '白头鹤','白枕鹤']#["kong","鲫鱼","麦穗鱼","鳑鲏"]
+    gp.BHG_chineseName = ["kong", "鲫鱼", "麦穗鱼", "鳑鲏"]
+
+    # BHG_chineseName = ['Kong', '白额雁', '白骨顶', '苍鹭', '苍鹭', '白秋沙鸭', '白腰草鹬', '斑头雁', '斑嘴鸭', '苍鹭', '赤膀鸭', '赤颈鸭', '苍鹭', '大麻鳽',
+    #                    '豆雁', '反嘴鹬', '凤头鸊鷉', '凤头麦鸡', '凤头潜鸭', '黑鹳', '黑水鸡', '红嘴鸥', '鸿雁', '灰鹤', '灰雁', '绿翅鸭', '绿头鸭', '琵嘴鸭',
+    #                    '普通鸬鹚', '普通秋沙鸭', '翘鼻麻鸭', '鹊鸭', '扇尾沙锥', '小白额雁', '小天鹅', '针尾鸭', '苍鹭', '暗绿绣眼鸟', '八哥', '白腹鹞', '白鹡鸰',
+    #                    '白眼潜鸭', '草鹭', '池鹭', '戴胜', '黑翅长脚鹬', '黑鸢', '红隼', '花脸鸭', '环颈雉', '灰头绿啄木鸟', '灰头麦鸡', '矶鹬', '栗苇鳽',
+    #                    '罗纹鸭', '牛背鹭', '普通翠鸟', '雀鹰', '喜鹊', '苍鹭', '鸳鸯', '棕背伯劳', '红胸秋沙鸭', '普通燕鸥', '小白额燕鸥', '丘鹬', '凤头蜂鹰',
+    #                    '大天鹅', '大鸨', '小田鸡', '山斑鸠', '帆背潜鸭', '日本松雀鹰', '普通鵟', '松雀鹰', '毛脚鵟', '灰斑鸠', '白头鹞', '白尾鹞', '白眉鸭',
+    #                    '白鹈鹕', '红头潜鸭', '苍鹰', '蒙古银鸥', '西方秧鸡', '赤嘴潜鸭', '阔嘴鹬', '雕鸮', '鹌鹑', '鹗', '黄苇鳽', '黑颈鹤', '小䴙䴘', '赤麻鸭']
+    # BHG_chineseName =["kong","绿头鸭","斑嘴鸭","灰雁","鸿雁","kong","小鷿鷈"]
+
+    gp.FineClassNum = 4
+    gp.color_show = (255, 255, 255)
+    gp.color_box = (255, 0, 0)
+    gp.NoneLabel = "鱼"  # "鹤类"
+    gp.fine_thresh1 = 6000
+    gp.fine_thresh2 = 10000
+    gp.fontScale = 1
+
+
+
+    torch.zero_(gp.Species)
+
+
+
+if __name__ == '__main__':
+    multiprocessing.set_start_method("spawn")
+    rootPath = "/data3/DEMO/video/PYH/"
+    filedeal(rootPath)
+    #commanddeal()
+
+
+
+

DetectImageNetFineSplit.py

@@ -0,0 +1,176 @@
+# -- coding: utf-8 --
+# @Author : Calvin
+# @Email : 934055521@qq.com
+# @File : xld.py
+# @use :
+# @Software: PyCharm
+# @Time : 27/5/2022 3:32 PM
+"""
+python3 FinegrainedIdentificationDatasetGenerate.py
+-rp /data/DataManagement/Project/004_kunmingcuihu/0003_20220507_800/CAS_KMCuihuBird/CMB_KMCuihuBird
+-blp /data/DataManagement/Project/004_kunmingcuihu/0003_20220507_800/CAS_KMCuihuBird/CMB_KMCuihuBird/birdList/KMCuihuBird.xlsx
+"""
+import os
+import shutil
+import argparse
+import random
+import pandas as pd
+import numpy as np
+import cv2
+from tqdm import tqdm
+BHG_chineseName = ['bird', 'Anser_albifrons', 'Fulica_atra', 'Egretta_garzetta', 'Platalea_leucorodia', 'Mergellus_albellus', 'Tringa_ochropus', 'Anser_indicus', 'Anas_zonorhyncha', 'Ardea_cinerea', 'Mareca_strepera', 'Mareca_penelope', 'Ardea_alba', 'Botaurus_stellaris', 'Anser_fabalis', 'Recurvirostra_avosetta', 'Podiceps_cristatus', 'Vanellus_vanellus', 'Aythya_fuligula', 'Ciconia_nigra', 'Gallinula_chloropus', 'Chroicocephalus_ridibundus', 'Anser_cygnoides', 'Grus_grus', 'Anser_anser', 'Anas_crecca', 'Anas_platyrhynchos', 'Spatula_clypeata', 'Phalacrocorax_carbo', 'Mergus_merganser', 'Tadorna_tadorna', 'Bucephala_clangula', 'Gallinago_gallinago', 'Anser_erythropus', 'Cygnus_columbianus', 'Anas_acuta', 'Ardea_intermedia', 'Zosterops_simplex', 'Acridotheres_cristatellus', 'Circus_spilonotus', 'Motacilla_alba', 'Aythya_nyroca', 'Ardea_purpurea', 'Ardeola_bacchus', 'Upupa_epops', 'Himantopus_himantopus', 'Milvus_migrans', 'Falco_tinnunculus', 'Sibirionetta_formosa', 'Phasianus_colchicus', 'Picus_canus', 'Vanellus_cinereus', 'Actitis_hypoleucos', 'Ixobrychus_cinnamomeus', 'Mareca_falcata', 'Bubulcus_coromandus', 'Alcedo_atthis', 'Accipiter_nisus', 'Pica_serica', 'Nycticorax_nycticorax', 'Aix_galericulata', 'Lanius_schach', 'Mergus_serrator', 'Sterna_hirundo', 'Sternula_antillarum', 'Scolopax_rusticola', 'Pernis_ptilorhynchus', 'Cygnus_cygnus', 'Otis_tarda', 'Zapornia_pusilla', 'Streptopelia_orientalis', 'Aythya_valisineria', 'Accipiter_gularis', 'Buteo_japonicus', 'Accipiter_virgatus', 'Buteo_lagopus', 'Streptopelia_decaocto', 'Circus_aeruginosus', 'Circus_cyaneus', 'Spatula_querquedula', 'Pelecanus_onocrotalus', 'Aythya_ferina', 'Accipiter_gentilis', 'Larus_cachinnans', 'Rallus_aquaticus', 'Netta_rufina', 'Calidris_falcinellus', 'Bubo_bubo', 'Coturnix_japonica', 'Pandion_haliaetus', 'Ixobrychus_sinensis', 'Grus_nigricollis', 'Tachybaptus_ruficollis', 'Tadorna_ferruginea', 'Leucogeranus_leucogeranus', 'Grus_monacha', 'Antigone_vipio', 'Cyanopica_cyanus', 'Passer_montanus', 'Hirundo_rustica']
+
+
+#####################################
+Number_last = 0
+LabelSpecies = []
+DataNOlineFeed = []
+WrongImages = []
+DataSpeciesClassier = dict()
+#####################################
+
+
+def parse():
+    arg = argparse.ArgumentParser()
+    arg.add_argument('--root_path', '-rp', default='/data/DataManagement/Project/CAS_ProjectBird/CMB_ProjectBird0615/', type=str)
+    arg.add_argument('--division_ratio', '-dr', default='0.7', type=float)
+    arg.add_argument('--bird_list_path', '-blp', default='/data/DataManagement/Project/CAS_ProjectBird/CMB_ProjectBird0615/birdList/ProjectBirdList.xlsx')
+    args = arg.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse()
+    dataset_txt_path = os.path.join(args.root_path, 'DatasetTxt')
+    species_txt_path = f'{args.root_path}/DatasetTxt/Species.txt'
+    dataset_spectrum_path = f'{args.root_path}/OrginalData'
+    dataset_allspectrum_path = f'{args.root_path}/FinalData/data'
+    dataset_allspectrum_path_train = f'{args.root_path}/FinalData/train'
+    dataset_allspectrum_path_test = f'{args.root_path}/FinalData/test'
+    dataset_allspectrum_txt_path = f'{args.root_path}/FinalData/anno/all.txt'
+    if not os.path.exists(dataset_txt_path):
+        os.makedirs(dataset_txt_path)
+    # for data in tqdm(os.listdir(dataset_spectrum_path), ncols=80, ascii=True, desc="OrginalData images path"):
+    # # for data in os.listdir(dataset_spectrum_path):
+    #     for image in os.listdir(os.path.join(dataset_spectrum_path, data)):
+    #         try:
+    #             im = cv2.imread(os.path.join(dataset_spectrum_path, data, image))
+    #         except:
+    #             WrongImages.append(os.path.join(dataset_spectrum_path, data, image))
+    #             os.remove(os.path.join(dataset_spectrum_path, data, image))
+    # tqdm.write('Wrong images are {:}'.format(WrongImages))
+    bird_list = pd.read_excel(args.bird_list_path, usecols=['拉丁名'])
+    bird_list = np.array(bird_list.stack()).tolist()
+    for num, data in tqdm(enumerate(bird_list), ncols=80, ascii=True, desc="writing species.txt"):
+    # for num, data in enumerate(bird_list):
+        with open(species_txt_path, 'a') as f:
+            filename = data
+            file_final_name = data  + ' ' + str(num)
+            f.write(file_final_name)
+            f.write('\n')
+    for speices in tqdm(os.listdir(dataset_spectrum_path), ncols=80, ascii=True, desc="writing species.txt"):
+    # for speices in os.listdir(dataset_spectrum_path):
+        for data in os.listdir(os.path.join(dataset_spectrum_path, speices)):
+            if data.split('___')[0] != speices:
+                os.rename(os.path.join(dataset_spectrum_path, speices, data), os.path.join(dataset_spectrum_path, speices, '{:}___{:}'.format(speices, data)))
+    tqdm.write('The pictures naming is done!')
+
+    with open(species_txt_path, 'r') as file:
+        Data = file.readlines()
+        for line in Data:
+            line = line.strip('\n')
+            species = line.split(' ')[0]
+            Number = line.split(' ')[1]
+            #Judge Number
+            if Number != Number_last:
+                DataSpeciesClassier[species] = Number
+            Number_last = Number
+
+    tqdm.write(str(DataSpeciesClassier))
+    tqdm.write('The species have been obtained')
+
+    # transfer single files to all files
+    if not os.path.exists(dataset_allspectrum_path):
+        os.makedirs(dataset_allspectrum_path)
+    if not os.path.exists(dataset_allspectrum_path_train):
+        os.makedirs(dataset_allspectrum_path_train)
+    if not os.path.exists(dataset_allspectrum_path_test):
+        os.makedirs(dataset_allspectrum_path_test)
+    for data in tqdm(os.listdir(dataset_spectrum_path), ncols=80, ascii=True, desc="transfer single files"):
+    # for data in os.listdir(dataset_spectrum_path):
+        for file in os.listdir(os.path.join(dataset_spectrum_path, data)):
+            shutil.copy(os.path.join(dataset_spectrum_path, data, file), dataset_allspectrum_path)
+    tqdm.write('Transfer single files to all files is done')
+
+    # generate the all.txt
+    if not os.path.exists('/'.join(dataset_allspectrum_txt_path.split('/')[:-1])):
+        os.makedirs('/'.join(dataset_allspectrum_txt_path.split('/')[:-1]))
+    for data in tqdm(os.listdir(dataset_allspectrum_path), ncols=80, ascii=True, desc="transfer single files"):
+    # for data in os.listdir(dataset_allspectrum_path):
+        with open(dataset_allspectrum_txt_path, 'a') as f:
+            file_new_name = data.split('___')[0]
+            # file_new_name = data
+            # file_new_name = '_'.join(file_new_name.split('_')[:-1])
+            num = DataSpeciesClassier[file_new_name]
+            file_final_name = data  + '  ' + num
+            f.write(file_final_name)
+            f.write('\n')
+    tqdm.write('Generate the all.txt is done')
+
+    # Spilit dataset
+    DataPath = dataset_allspectrum_txt_path
+    # delete file_txt
+    if os.path.exists(DataPath.replace(os.path.basename(DataPath), 'train.txt')) :
+        os.remove(DataPath.replace(os.path.basename(DataPath), 'train.txt'))
+    if os.path.exists(DataPath.replace(os.path.basename(DataPath), 'test.txt')):
+        os.remove(DataPath.replace(os.path.basename(DataPath), 'test.txt'))
+
+    with open(DataPath, 'r')as f:
+        Data = f.readlines()
+        LabelSpecies = []
+        for RowData in Data:
+            RowData = RowData.strip()
+            # find picture-label
+            RowLabels = RowData.split('  ')[1]
+            DataNOlineFeed.append(RowData.split('  '))
+            if RowLabels not in LabelSpecies:
+                LabelSpecies.append(RowLabels)
+        print(LabelSpecies)
+    tqdm.write("classes num of Dataset is : {:}".format(len(LabelSpecies)))
+    for Labels in tqdm(LabelSpecies, ncols=80, ascii=True, desc="transfer single files"):
+    # for Labels in LabelSpecies:
+        Num = 0
+        TrainData = []
+        TestData = []
+        TotalData = []
+        for SpeciesData in DataNOlineFeed:
+            if Labels == SpeciesData[1]:
+                Num += 1
+                TotalData.append(SpeciesData)
+
+        TrainNum = int(Num * args.division_ratio)
+        TestNum = Num - TrainNum
+        random.shuffle(TotalData)
+        with open(DataPath.replace(os.path.basename(DataPath), 'train.txt'), 'a') as f:
+            for i in range(TrainNum):
+                f.write(' '.join(TotalData[i]) + '\n')
+                label_classname = "{}.{}".format(str(Labels).zfill(4),BHG_chineseName[int(Labels)])
+                soursepathTrain = os.path.join(dataset_spectrum_path,label_classname)
+                destpathTrain = os.path.join(dataset_allspectrum_path_train,label_classname)
+                if not os.path.exists(destpathTrain):
+                    os.makedirs(destpathTrain)
+                image_name = TotalData[i][0]
+                shutil.copy(os.path.join(soursepathTrain,str(image_name)),os.path.join(destpathTrain,str(image_name)))
+
+        with open(DataPath.replace(os.path.basename(DataPath), 'test.txt'), 'a') as f:
+            for i in range(TrainNum, Num):
+                f.write(' '.join(TotalData[i]) + '\n')
+                label_classname = "{}.{}".format(str(Labels).zfill(4), BHG_chineseName[int(Labels)])
+                soursepathTest = os.path.join(dataset_spectrum_path, label_classname)
+                destpathTest = os.path.join(dataset_allspectrum_path_test, label_classname)
+                if not os.path.exists(destpathTest):
+                    os.makedirs(destpathTest)
+                image_name = TotalData[i][0]
+                shutil.copy(os.path.join(soursepathTest, str(image_name)), os.path.join(destpathTest, str(image_name)))
+
+

DetectionAILAbel.py

@@ -1,30 +1,40 @@
 import  os,argparse,torch
-from System_I import Detection_Y5
+from System_I import Detection_Y5,Fine_grained_T
 from  tqdm import tqdm
 
-
+#os.environ['CUDA_VISIBLE_DEVICES'] = "1"
 
 if __name__ == '__main__':
     parser1 = argparse.ArgumentParser(description="AILabel")
-    parser1.add_argument("--RawPath", default='/data/DataManagement/Project/001_cuihu/0012_20220517_RTSP/imagesAILabel', type=str,
+    parser1.add_argument("--RawPath", default='/data/DataManagement/Project/006_huanghekou/0006_20220621/Raw_images/imagesAILabel', type=str,
                         help='RawPath.')
-    parser1.add_argument("--GPU", default="1", type=str,
+    parser1.add_argument("--GPU", default="4", type=str,
                         help='GPU.')
     parser1.add_argument("--thresh", default=0.4, type=float,
                         help='thresh.')
-    parser1.add_argument("--Weight", default="/data/Hatcher/data/Detection_weigth/BJCHKMCH-Bird-1-4/weights/best.pt", type=str,
-                        help='Weight.')
     args = parser1.parse_args()
-    os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU
-    devices = torch.device('cuda:{}'.format(str(int(args.GPU))) if torch.cuda.is_available() else 'cpu')
+    os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU#"1"
+    try:
+        if "imagesAILabel" not in args.RawPath:
+            exit()
+        else:
+            os.system("sudo chmod 777 -R {}".format(str(args.RawPath).replace("imagesAILabel","")))
+    except:
+        exit()
+    #devices = torch.device('cuda:{}'.format(str(int(args.GPU))) if torch.cuda.is_available() else 'cpu')
+    devices = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
     if not os.path.exists(args.RawPath.replace("imagesAILabel","NoResult")):
         os.makedirs(args.RawPath.replace("imagesAILabel","NoResult"))
-    if not os.path.exists(args.RawPath.replace("imagesAILabel","YesResult")):
-        os.makedirs(args.RawPath.replace("imagesAILabel","YesResult"))
-    if not os.path.exists(args.RawPath.replace("imagesAILabel","LabelXML")):
-        os.makedirs(args.RawPath.replace("imagesAILabel","LabelXML"))
-    Detection = Detection_Y5(device=devices,root=" ", detect_thresh=args.thresh,DetWeight=args.Weight)
+    if not os.path.exists(args.RawPath.replace("imagesAILabel","JPEGImages")):
+        os.makedirs(args.RawPath.replace("imagesAILabel","JPEGImages"))
+    if not os.path.exists(args.RawPath.replace("imagesAILabel","Annotations")):
+        os.makedirs(args.RawPath.replace("imagesAILabel","Annotations"))
+    if not os.path.exists(args.RawPath.replace("imagesAILabel", "BrokenImage")):
+        os.makedirs(args.RawPath.replace("imagesAILabel", "BrokenImage"))
+
+    Detection = Detection_Y5(device=devices,root=" ", detect_thresh=args.thresh)
+    FineGrained = Fine_grained_T(device=devices)
     imagepaths = os.listdir(args.RawPath)
     for imagepath in tqdm(imagepaths):
         imagepath = os.path.join(args.RawPath,imagepath)
-        Detection.AIlabel(imagepath)
+        Detection.AIlabelFine(imagepath,FineGrained)

DetectionFineAILAbel.py

@@ -0,0 +1,44 @@
+import  os,argparse,torch
+from System_I import Detection_Y5,Fine_grained_T
+from  tqdm import tqdm
+
+#os.environ['CUDA_VISIBLE_DEVICES'] = "1"
+
+if __name__ == '__main__':
+    parser1 = argparse.ArgumentParser(description="AILabel")
+    parser1.add_argument("--RawPath", default='/data/DataManagement/Project/temp/OrginalData', type=str,
+                        help='RawPath.')
+    parser1.add_argument("--GPU", default="2", type=str,
+                        help='GPU.')
+    parser1.add_argument("--thresh", default=0.4, type=float,
+                        help='thresh.')
+    args = parser1.parse_args()
+    os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU#"1"
+    try:
+        if "imagesAILabel" not in args.RawPath:
+            exit()
+        else:
+            os.system("sudo chmod 777 -R {}".format(str(args.RawPath).replace("imagesAILabel","")))
+    except:
+        exit()
+
+    #devices = torch.device('cuda:{}'.format(str(int(args.GPU))) if torch.cuda.is_available() else 'cpu')
+    devices = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    Detection = Detection_Y5(device=devices, root=" ", detect_thresh=args.thresh)
+    FineGrained = Fine_grained_T(device=devices)
+    for RawPaths in os.listdir(args.RawPath):
+        RawPath = os.path.join(args.RawPath,RawPaths,"imagesAILabel")
+    if not os.path.exists(RawPath.replace("imagesAILabel","NoResult")):
+        os.makedirs(RawPath.replace("imagesAILabel","NoResult"))
+    if not os.path.exists(RawPath.replace("imagesAILabel","JPEGImages")):
+        os.makedirs(RawPath.replace("imagesAILabel","JPEGImages"))
+    if not os.path.exists(RawPath.replace("imagesAILabel","Annotations")):
+        os.makedirs(RawPath.replace("imagesAILabel","Annotations"))
+    if not os.path.exists(RawPath.replace("imagesAILabel", "BrokenImage")):
+        os.makedirs(RawPath.replace("imagesAILabel", "BrokenImage"))
+
+
+    imagepaths = os.listdir(RawPath)
+    for imagepath in tqdm(imagepaths):
+        imagepath = os.path.join(args.RawPath,imagepath)
+        Detection.AIlabelFine(imagepath,FineGrained)

DetectionFineAILAbel_mmcls.py

@@ -0,0 +1,66 @@
+import  os,argparse,torch
+from System_I import Detection_Y5,Fine_grained_T,Fine_grained_mmclsonnx
+from  tqdm import tqdm
+
+#os.environ['CUDA_VISIBLE_DEVICES'] = "1"
+"""###
+输入：/根目录/imagesAI
+     -------文件夹1
+            ------NoResult 存放无结果图片
+            ------BrokenImage 存放损坏图片
+            ------imagesAILabel 存放待处理图片
+            ------Annotations 存放识别结果xml标签文件
+            ------JPEGImages  备份原图原图
+            ------result      存放可视化结果图片
+     -------文件夹2
+            ------NoResult 存放无结果图片
+            ------BrokenImage 存放损坏图片
+            ------imagesAILabel 存放待处理图片
+            ------Annotations 存放识别结果xml标签文件
+            ------JPEGImages  备份原图原图
+            ------result      存放可视化结果图片
+
+###"""
+
+
+
+if __name__ == '__main__':
+    parser1 = argparse.ArgumentParser(description="AILabel")
+    parser1.add_argument("--RawPath", default='/data5/student/Hatcher/test/imagesAI', type=str,
+                        help='RawPath.')
+    parser1.add_argument("--GPU", default="2", type=str,
+                        help='GPU.')  #本参数在该工程中不起作用，请修改common.py文件种的全局参数
+    parser1.add_argument("--thresh", default=0.4, type=float,
+                        help='thresh.')
+    args = parser1.parse_args()
+    os.environ['CUDA_VISIBLE_DEVICES'] = args.GPU#"1"
+    try:
+        if "imagesAI" not in args.RawPath:
+            exit()
+        else:
+            os.system("sudo chmod 777 -R {}".format(str(args.RawPath).replace("imagesAI","")))
+    except:
+        exit()
+
+    #devices = torch.device('cuda:{}'.format(str(int(args.GPU))) if torch.cuda.is_available() else 'cpu')
+    devices = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    Detection = Detection_Y5(device=devices, root=" ", detect_thresh=args.thresh)
+    FineGrained = Fine_grained_mmclsonnx(device=devices)
+    for RawPaths in os.listdir(args.RawPath):
+        RawPath = os.path.join(args.RawPath,RawPaths,"imagesAILabel")
+        if not os.path.exists(RawPath.replace("imagesAILabel","NoResult")):
+            os.makedirs(RawPath.replace("imagesAILabel","NoResult"))
+        if not os.path.exists(RawPath.replace("imagesAILabel","JPEGImages")):
+            os.makedirs(RawPath.replace("imagesAILabel","JPEGImages"))
+        if not os.path.exists(RawPath.replace("imagesAILabel","Annotations")):
+            os.makedirs(RawPath.replace("imagesAILabel","Annotations"))
+        if not os.path.exists(RawPath.replace("imagesAILabel", "BrokenImage")):
+            os.makedirs(RawPath.replace("imagesAILabel", "BrokenImage"))
+        if not os.path.exists(RawPath.replace("imagesAILabel", "result")):
+            os.makedirs(RawPath.replace("imagesAILabel", "result"))
+
+
+        imagepaths = os.listdir(RawPath)
+        for imagepath in tqdm(imagepaths):
+            imagepath = os.path.join(RawPath,imagepath)
+            Detection.AIlabelFine(imagepath,FineGrained)

DetectionFineAILAbel_zwq.py

@@ -0,0 +1,61 @@
+import os
+import argparse
+import torch
+from System_zwq import Detection_Y5, Fine_grained_T
+from tqdm import tqdm
+
+#os.environ['CUDA_VISIBLE_DEVICES'] = "1"
+
+def oneFolder():
+    RawPath = os.path.join(args.RawPath, "imagesAILabel")
+    os.system("sudo chmod 777 -R {}".format(str(args.RawPath)))
+    if not os.path.exists(RawPath.replace("imagesAILabel", "NoResult")):
+        os.makedirs(RawPath.replace("imagesAILabel", "NoResult"))
+    if not os.path.exists(RawPath.replace("imagesAILabel", "JPEGImages")):
+        os.makedirs(RawPath.replace("imagesAILabel", "JPEGImages"))
+    if not os.path.exists(RawPath.replace("imagesAILabel", "Annotations")):
+        os.makedirs(RawPath.replace("imagesAILabel", "Annotations"))
+    if not os.path.exists(RawPath.replace("imagesAILabel", "BrokenImage")):
+        os.makedirs(RawPath.replace("imagesAILabel", "BrokenImage"))
+
+    imagepaths = os.listdir(RawPath)
+    for imagepath in tqdm(imagepaths, ncols=120, ascii=True, desc=RawPath + " images"):
+        imagepath = os.path.join(RawPath, imagepath)
+        Detection.AIlabelFine(imagepath, FineGrained)
+
+
+def manyFolder():
+    for RawPaths in tqdm(os.listdir(args.RawPath), ncols=120, ascii=True, desc="bird folders"):
+    # for RawPaths in os.listdir(args.RawPath):
+        RawPath = os.path.join(args.RawPath, RawPaths, "imagesAILabel")
+        # os.system("sudo chmod 777 -R {}".format(str(os.path.join(args.RawPath, RawPaths))))
+        if not os.path.exists(RawPath.replace("imagesAILabel", "NoResult")):
+            os.makedirs(RawPath.replace("imagesAILabel", "NoResult"))
+        if not os.path.exists(RawPath.replace("imagesAILabel", "JPEGImages")):
+            os.makedirs(RawPath.replace("imagesAILabel", "JPEGImages"))
+        if not os.path.exists(RawPath.replace("imagesAILabel", "Annotations")):
+            os.makedirs(RawPath.replace("imagesAILabel", "Annotations"))
+        if not os.path.exists(RawPath.replace("imagesAILabel", "BrokenImage")):
+            os.makedirs(RawPath.replace("imagesAILabel", "BrokenImage"))
+
+        imagepaths = os.listdir(RawPath)
+        for imagepath in tqdm(imagepaths, ncols=120, ascii=True, desc=RawPaths + " images"):
+        # for imagepath in tqdm(imagepaths):
+            imagepath = os.path.join(RawPath, imagepath)
+            Detection.AIlabelFine(imagepath, FineGrained)
+
+
+if __name__ == '__main__':
+    parser1 = argparse.ArgumentParser(description="AILabel")
+    parser1.add_argument("RawPath", default='/data5/PartDataset/003 PYH/0014 20230411/', type=str, help='RawPath.')
+    parser1.add_argument("--GPU", default="7", type=str, help='GPU.')
+    parser1.add_argument("--thresh", default=0.4, type=float, help='thresh.')
+    args = parser1.parse_args()
+    # os.environ['CUDA_VISIBLE_DEVICES'] = "7"  #args.GPU  # "1"
+
+    devices = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+    Detection = Detection_Y5(device=devices, root=" ", detect_thresh=args.thresh)
+    FineGrained = Fine_grained_T(device=devices)
+    oneFolder()
+    # manyFolder()
+    

DetectionVideo_Video.py

@@ -27,7 +27,7 @@ from vediotoima import vedio_img
 from imatovideo import images_to_video,images_to_video_big
 from moviepy.editor import *
 import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '3'
+#os.environ['CUDA_VISIBLE_DEVICES'] = '3'
 
 def parse_args():
 

FineNewNameWithindex.py

@@ -0,0 +1,13 @@
+"""
+FInename---->0001.Finename
+
+"""
+import os
+from  tqdm import tqdm
+BHG_chineseName = ['bird', 'Anser_albifrons', 'Fulica_atra', 'Egretta_garzetta', 'Platalea_leucorodia', 'Mergellus_albellus', 'Tringa_ochropus', 'Anser_indicus', 'Anas_zonorhyncha', 'Ardea_cinerea', 'Mareca_strepera', 'Mareca_penelope', 'Ardea_alba', 'Botaurus_stellaris', 'Anser_fabalis', 'Recurvirostra_avosetta', 'Podiceps_cristatus', 'Vanellus_vanellus', 'Aythya_fuligula', 'Ciconia_nigra', 'Gallinula_chloropus', 'Chroicocephalus_ridibundus', 'Anser_cygnoides', 'Grus_grus', 'Anser_anser', 'Anas_crecca', 'Anas_platyrhynchos', 'Spatula_clypeata', 'Phalacrocorax_carbo', 'Mergus_merganser', 'Tadorna_tadorna', 'Bucephala_clangula', 'Gallinago_gallinago', 'Anser_erythropus', 'Cygnus_columbianus', 'Anas_acuta', 'Ardea_intermedia', 'Zosterops_simplex', 'Acridotheres_cristatellus', 'Circus_spilonotus', 'Motacilla_alba', 'Aythya_nyroca', 'Ardea_purpurea', 'Ardeola_bacchus', 'Upupa_epops', 'Himantopus_himantopus', 'Milvus_migrans', 'Falco_tinnunculus', 'Sibirionetta_formosa', 'Phasianus_colchicus', 'Picus_canus', 'Vanellus_cinereus', 'Actitis_hypoleucos', 'Ixobrychus_cinnamomeus', 'Mareca_falcata', 'Bubulcus_coromandus', 'Alcedo_atthis', 'Accipiter_nisus', 'Pica_serica', 'Nycticorax_nycticorax', 'Aix_galericulata', 'Lanius_schach', 'Mergus_serrator', 'Sterna_hirundo', 'Sternula_antillarum', 'Scolopax_rusticola', 'Pernis_ptilorhynchus', 'Cygnus_cygnus', 'Otis_tarda', 'Zapornia_pusilla', 'Streptopelia_orientalis', 'Aythya_valisineria', 'Accipiter_gularis', 'Buteo_japonicus', 'Accipiter_virgatus', 'Buteo_lagopus', 'Streptopelia_decaocto', 'Circus_aeruginosus', 'Circus_cyaneus', 'Spatula_querquedula', 'Pelecanus_onocrotalus', 'Aythya_ferina', 'Accipiter_gentilis', 'Larus_cachinnans', 'Rallus_aquaticus', 'Netta_rufina', 'Calidris_falcinellus', 'Bubo_bubo', 'Coturnix_japonica', 'Pandion_haliaetus', 'Ixobrychus_sinensis', 'Grus_nigricollis', 'Tachybaptus_ruficollis', 'Tadorna_ferruginea', 'Leucogeranus_leucogeranus', 'Grus_monacha', 'Antigone_vipio', 'Cyanopica_cyanus', 'Passer_montanus', 'Hirundo_rustica']
+rootPath = "/data/DataManagement/Project/CAS_ProjectBird/CMB_ProjectBird0615/OrginalData"
+os.system("sudo chmod 777 -R {}".format(rootPath))
+for path in tqdm(os.listdir(rootPath),ncols=80, ascii=True, desc="change name"):
+    source =  os.path.join(rootPath,path)
+    dest = os.path.join(rootPath,"{}.{}".format(str(BHG_chineseName.index(path)).zfill(4),str(path)))
+    os.rename(source,dest)

GYD_RedCamera.py

@@ -36,7 +36,7 @@ def GYD_RedCameraDeal (root,DetectionCalss,Fine_grainedClass):
 
 
 if __name__ == '__main__':
-    os.environ['CUDA_VISIBLE_DEVICES'] = '4'
+    #os.environ['CUDA_VISIBLE_DEVICES'] = '4'
     devices = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
     root = "/data1/Hatcher/ftp"
     Detection1 = Detection_S(devices, root)

NewDetAiLabelFine.spec

@@ -0,0 +1,38 @@
+# -*- mode: python ; coding: utf-8 -*-
+
+
+a = Analysis(
+    ['NewDetAiLabelFine.py'],
+    pathex=[],
+    binaries=[],
+    datas=[],
+    hiddenimports=[],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    noarchive=False,
+    optimize=0,
+)
+pyz = PYZ(a.pure)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    a.binaries,
+    a.datas,
+    [],
+    name='NewDetAiLabelFine',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    runtime_tmpdir=None,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)

PPdet_VOC.py

@@ -0,0 +1,21 @@
+import os
+in_path = "/data/DataManagement/Project/001_cuihu/CAS_CuihuBird/CDB_Cuihubird/ImageSets/Main/test.txt"
+out_path = "/data/DataManagement/Project/001_cuihu/CAS_CuihuBird/CDB_Cuihubird/ImageSets/Main/test1.txt"
+def change(in_path,out_path):
+    new = []
+    f = open(in_path,'r')
+    lines = f.readlines()
+    for line in lines:
+        line = line.strip('\n')
+        line = "./images/{}.jpg".format(line)+" "+"./Annotations/{}.xml".format(line)+"\n"
+        new.append(line)
+    # while(line):
+    #     line = f.readline().replace("\n","")
+    #     line = "./images/{}".format(line) + " " + "./Annotations/{}".format(line) +"\n"
+    #     print(line)
+    #     new.append(line)
+    handle = open(out_path, 'a')
+    handle.writelines(new)
+
+if __name__ == '__main__':
+    change(in_path,out_path)

PanoramicScanSplicing.py

@@ -0,0 +1,26 @@
+import os
+import tqdm
+import cv2
+from PIL import Image
+result_path  = "E:/2021.10.15-wettrain/results/"
+GT_path  = "E:/2021.10.15-wettrain/result_GT/"
+image_path  ="E:/2021.10.15-wettrain/images/"
+image = []
+for j in range(14):
+    img = []
+    for i in range(60):
+        img.append(Image.open("/data/DataManagement/Project/001_cuihu/CAS_CuihuBird/CDB_Cuihubird/images/train/0010700285.jpg").resize((1024,768)))
+    image.append(img)
+def long_pic_paste(image):
+    image_result = Image.new(image[0][0].mode, ( 1024 * 60,14 * 768), 0xffffff)
+    for j in range(14):
+        for i in range(60):
+            print(j," ",i)
+            image_result.paste(image[j][i], (i*1024, j*768))
+    #image_result.resize((65500,8591))
+    image_result.save("./result.jpg")
+
+
+
+if __name__ == '__main__':
+    long_pic_paste(image)

Y5/models/common.py

@@ -1,18 +1,31 @@
-# This file contains modules common to various models
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Common modules
+"""
 
+import json
 import math
+import platform
+import warnings
+from collections import OrderedDict, namedtuple
+from copy import copy
 from pathlib import Path
-import sys
-sys.path.append("..")
+
+import cv2
 import numpy as np
+import pandas as pd
 import requests
 import torch
 import torch.nn as nn
+import yaml
 from PIL import Image
+from torch.cuda import amp
 
-from Y5.utils.datasets import letterbox
-from Y5.utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh
-from Y5.utils.plots import color_list, plot_one_box
+from utils.dataloaders import exif_transpose, letterbox
+from utils.general import (LOGGER, check_requirements, check_suffix, check_version, colorstr, increment_path,
+                           make_divisible, non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh)
+from utils.plots import Annotator, colors, save_one_box
+from utils.torch_utils import copy_attr, time_sync
 
 
 def autopad(k, p=None):  # kernel, padding
@@ -22,15 +35,10 @@ def autopad(k, p=None):  # kernel, padding
     return p
 
 
-def DWConv(c1, c2, k=1, s=1, act=True):
-    # Depthwise convolution
-    return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
-
-
 class Conv(nn.Module):
     # Standard convolution
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Conv, self).__init__()
+        super().__init__()
         self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
         self.bn = nn.BatchNorm2d(c2)
         self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
@@ -38,14 +46,62 @@ class Conv(nn.Module):
     def forward(self, x):
         return self.act(self.bn(self.conv(x)))
 
-    def fuseforward(self, x):
+    def forward_fuse(self, x):
         return self.act(self.conv(x))
 
 
+class DWConv(Conv):
+    # Depth-wise convolution class
+    def __init__(self, c1, c2, k=1, s=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
+        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
+
+
+class DWConvTranspose2d(nn.ConvTranspose2d):
+    # Depth-wise transpose convolution class
+    def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, kernel, stride, padding, padding_out
+        super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
+
+
+class TransformerLayer(nn.Module):
+    # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
+    def __init__(self, c, num_heads):
+        super().__init__()
+        self.q = nn.Linear(c, c, bias=False)
+        self.k = nn.Linear(c, c, bias=False)
+        self.v = nn.Linear(c, c, bias=False)
+        self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
+        self.fc1 = nn.Linear(c, c, bias=False)
+        self.fc2 = nn.Linear(c, c, bias=False)
+
+    def forward(self, x):
+        x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
+        x = self.fc2(self.fc1(x)) + x
+        return x
+
+
+class TransformerBlock(nn.Module):
+    # Vision Transformer https://arxiv.org/abs/2010.11929
+    def __init__(self, c1, c2, num_heads, num_layers):
+        super().__init__()
+        self.conv = None
+        if c1 != c2:
+            self.conv = Conv(c1, c2)
+        self.linear = nn.Linear(c2, c2)  # learnable position embedding
+        self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
+        self.c2 = c2
+
+    def forward(self, x):
+        if self.conv is not None:
+            x = self.conv(x)
+        b, _, w, h = x.shape
+        p = x.flatten(2).permute(2, 0, 1)
+        return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)
+
+
 class Bottleneck(nn.Module):
     # Standard bottleneck
     def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
-        super(Bottleneck, self).__init__()
+        super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, c_, 1, 1)
         self.cv2 = Conv(c_, c2, 3, 1, g=g)
@@ -58,41 +114,86 @@ class Bottleneck(nn.Module):
 class BottleneckCSP(nn.Module):
     # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(BottleneckCSP, self).__init__()
+        super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, c_, 1, 1)
         self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
         self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
         self.cv4 = Conv(2 * c_, c2, 1, 1)
         self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
-        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
+        self.act = nn.SiLU()
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
 
     def forward(self, x):
         y1 = self.cv3(self.m(self.cv1(x)))
         y2 = self.cv2(x)
-        return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
+        return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
+
+
+class CrossConv(nn.Module):
+    # Cross Convolution Downsample
+    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
+        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = Conv(c1, c_, (1, k), (1, s))
+        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
+        self.add = shortcut and c1 == c2
+
+    def forward(self, x):
+        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
 
 
 class C3(nn.Module):
     # CSP Bottleneck with 3 convolutions
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        super(C3, self).__init__()
+        super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, c_, 1, 1)
         self.cv2 = Conv(c1, c_, 1, 1)
-        self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
-        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-        # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
+        self.cv3 = Conv(2 * c_, c2, 1)  # optional act=FReLU(c2)
+        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
 
     def forward(self, x):
-        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
+        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
+
+
+class C3x(C3):
+    # C3 module with cross-convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)
+        self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
+
+
+class C3TR(C3):
+    # C3 module with TransformerBlock()
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)
+        self.m = TransformerBlock(c_, c_, 4, n)
+
+
+class C3SPP(C3):
+    # C3 module with SPP()
+    def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)
+        self.m = SPP(c_, c_, k)
+
+
+class C3Ghost(C3):
+    # C3 module with GhostBottleneck()
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
+        super().__init__(c1, c2, n, shortcut, g, e)
+        c_ = int(c2 * e)  # hidden channels
+        self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
 
 
 class SPP(nn.Module):
-    # Spatial pyramid pooling layer used in YOLOv3-SPP
+    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
     def __init__(self, c1, c2, k=(5, 9, 13)):
-        super(SPP, self).__init__()
+        super().__init__()
         c_ = c1 // 2  # hidden channels
         self.cv1 = Conv(c1, c_, 1, 1)
         self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
@@ -100,21 +201,70 @@ class SPP(nn.Module):
 
     def forward(self, x):
         x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
             return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
 
 
+class SPPF(nn.Module):
+    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
+    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
+        super().__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * 4, c2, 1, 1)
+        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
+
+    def forward(self, x):
+        x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
+            y1 = self.m(x)
+            y2 = self.m(y1)
+            return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
+
+
 class Focus(nn.Module):
     # Focus wh information into c-space
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Focus, self).__init__()
+        super().__init__()
         self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
         # self.contract = Contract(gain=2)
 
     def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
-        return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
+        return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
         # return self.conv(self.contract(x))
 
 
+class GhostConv(nn.Module):
+    # Ghost Convolution https://github.com/huawei-noah/ghostnet
+    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
+        super().__init__()
+        c_ = c2 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, k, s, None, g, act)
+        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
+
+    def forward(self, x):
+        y = self.cv1(x)
+        return torch.cat((y, self.cv2(y)), 1)
+
+
+class GhostBottleneck(nn.Module):
+    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+    def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
+        super().__init__()
+        c_ = c2 // 2
+        self.conv = nn.Sequential(
+            GhostConv(c1, c_, 1, 1),  # pw
+            DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
+            GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
+        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
+                                                                            act=False)) if s == 2 else nn.Identity()
+
+    def forward(self, x):
+        return self.conv(x) + self.shortcut(x)
+
+
 class Contract(nn.Module):
     # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
     def __init__(self, gain=2):
@@ -122,11 +272,11 @@ class Contract(nn.Module):
         self.gain = gain
 
     def forward(self, x):
-        N, C, H, W = x.size()  # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
+        b, c, h, w = x.size()  # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
         s = self.gain
-        x = x.view(N, C, H // s, s, W // s, s)  # x(1,64,40,2,40,2)
+        x = x.view(b, c, h // s, s, w // s, s)  # x(1,64,40,2,40,2)
         x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
-        return x.view(N, C * s * s, H // s, W // s)  # x(1,256,40,40)
+        return x.view(b, c * s * s, h // s, w // s)  # x(1,256,40,40)
 
 
 class Expand(nn.Module):
@@ -136,170 +286,449 @@ class Expand(nn.Module):
         self.gain = gain
 
     def forward(self, x):
-        N, C, H, W = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
+        b, c, h, w = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
         s = self.gain
-        x = x.view(N, s, s, C // s ** 2, H, W)  # x(1,2,2,16,80,80)
+        x = x.view(b, s, s, c // s ** 2, h, w)  # x(1,2,2,16,80,80)
         x = x.permute(0, 3, 4, 1, 5, 2).contiguous()  # x(1,16,80,2,80,2)
-        return x.view(N, C // s ** 2, H * s, W * s)  # x(1,16,160,160)
+        return x.view(b, c // s ** 2, h * s, w * s)  # x(1,16,160,160)
 
 
 class Concat(nn.Module):
     # Concatenate a list of tensors along dimension
     def __init__(self, dimension=1):
-        super(Concat, self).__init__()
+        super().__init__()
         self.d = dimension
 
     def forward(self, x):
         return torch.cat(x, self.d)
 
 
-class NMS(nn.Module):
-    # Non-Maximum Suppression (NMS) module
-    conf = 0.25  # confidence threshold
-    iou = 0.45  # IoU threshold
-    classes = None  # (optional list) filter by class
+class DetectMultiBackend(nn.Module):
+    # YOLOv5 MultiBackend class for python inference on various backends
+    def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False):
+        # Usage:
+        #   PyTorch:              weights = *.pt
+        #   TorchScript:                    *.torchscript
+        #   ONNX Runtime:                   *.onnx
+        #   ONNX OpenCV DNN:                *.onnx with --dnn
+        #   OpenVINO:                       *.xml
+        #   CoreML:                         *.mlmodel
+        #   TensorRT:                       *.engine
+        #   TensorFlow SavedModel:          *_saved_model
+        #   TensorFlow GraphDef:            *.pb
+        #   TensorFlow Lite:                *.tflite
+        #   TensorFlow Edge TPU:            *_edgetpu.tflite
+        from models.experimental import attempt_download, attempt_load  # scoped to avoid circular import
 
-    def __init__(self):
-        super(NMS, self).__init__()
+        super().__init__()
+        w = str(weights[0] if isinstance(weights, list) else weights)
+        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = self.model_type(w)  # get backend
+        w = attempt_download(w)  # download if not local
+        fp16 &= (pt or jit or onnx or engine) and device.type != 'cpu'  # FP16
+        stride, names = 32, [f'class{i}' for i in range(1000)]  # assign defaults
+        if data:  # assign class names (optional)
+            with open(data, errors='ignore') as f:
+                names = yaml.safe_load(f)['names']
 
-    def forward(self, x):
-        return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
+        if pt:  # PyTorch
+            model = attempt_load(weights if isinstance(weights, list) else w, device=device)
+            stride = max(int(model.stride.max()), 32)  # model stride
+            names = model.module.names if hasattr(model, 'module') else model.names  # get class names
+            model.half() if fp16 else model.float()
+            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
+        elif jit:  # TorchScript
+            LOGGER.info(f'Loading {w} for TorchScript inference...')
+            extra_files = {'config.txt': ''}  # model metadata
+            model = torch.jit.load(w, _extra_files=extra_files)
+            model.half() if fp16 else model.float()
+            if extra_files['config.txt']:
+                d = json.loads(extra_files['config.txt'])  # extra_files dict
+                stride, names = int(d['stride']), d['names']
+        elif dnn:  # ONNX OpenCV DNN
+            LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
+            check_requirements(('opencv-python>=4.5.4',))
+            net = cv2.dnn.readNetFromONNX(w)
+        elif onnx:  # ONNX Runtime
+            LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
+            cuda = torch.cuda.is_available()
+            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
+            import onnxruntime
+            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
+            session = onnxruntime.InferenceSession(w, providers=providers)
+            meta = session.get_modelmeta().custom_metadata_map  # metadata
+            if 'stride' in meta:
+                stride, names = int(meta['stride']), eval(meta['names'])
+        elif xml:  # OpenVINO
+            LOGGER.info(f'Loading {w} for OpenVINO inference...')
+            check_requirements(('openvino',))  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+            from openvino.runtime import Core
+            ie = Core()
+            if not Path(w).is_file():  # if not *.xml
+                w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
+            network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
+            executable_network = ie.compile_model(model=network, device_name="CPU")
+            output_layer = next(iter(executable_network.outputs))
+            meta = Path(w).with_suffix('.yaml')
+            if meta.exists():
+                stride, names = self._load_metadata(meta)  # load metadata
+        elif engine:  # TensorRT
+            LOGGER.info(f'Loading {w} for TensorRT inference...')
+            import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
+            check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
+            Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
+            logger = trt.Logger(trt.Logger.INFO)
+            with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
+                model = runtime.deserialize_cuda_engine(f.read())
+            bindings = OrderedDict()
+            fp16 = False  # default updated below
+            for index in range(model.num_bindings):
+                name = model.get_binding_name(index)
+                dtype = trt.nptype(model.get_binding_dtype(index))
+                shape = tuple(model.get_binding_shape(index))
+                data = torch.from_numpy(np.empty(shape, dtype=np.dtype(dtype))).to(device)
+                bindings[name] = Binding(name, dtype, shape, data, int(data.data_ptr()))
+                if model.binding_is_input(index) and dtype == np.float16:
+                    fp16 = True
+            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
+            context = model.create_execution_context()
+            batch_size = bindings['images'].shape[0]
+        elif coreml:  # CoreML
+            LOGGER.info(f'Loading {w} for CoreML inference...')
+            import coremltools as ct
+            model = ct.models.MLModel(w)
+        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
+            if saved_model:  # SavedModel
+                LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
+                import tensorflow as tf
+                keras = False  # assume TF1 saved_model
+                model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
+            elif pb:  # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
+                LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
+                import tensorflow as tf
+
+                def wrap_frozen_graph(gd, inputs, outputs):
+                    x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped
+                    ge = x.graph.as_graph_element
+                    return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
+
+                gd = tf.Graph().as_graph_def()  # graph_def
+                with open(w, 'rb') as f:
+                    gd.ParseFromString(f.read())
+                frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs="Identity:0")
+            elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
+                try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
+                    from tflite_runtime.interpreter import Interpreter, load_delegate
+                except ImportError:
+                    import tensorflow as tf
+                    Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
+                if edgetpu:  # Edge TPU https://coral.ai/software/#edgetpu-runtime
+                    LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
+                    delegate = {
+                        'Linux': 'libedgetpu.so.1',
+                        'Darwin': 'libedgetpu.1.dylib',
+                        'Windows': 'edgetpu.dll'}[platform.system()]
+                    interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
+                else:  # Lite
+                    LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
+                    interpreter = Interpreter(model_path=w)  # load TFLite model
+                interpreter.allocate_tensors()  # allocate
+                input_details = interpreter.get_input_details()  # inputs
+                output_details = interpreter.get_output_details()  # outputs
+            elif tfjs:
+                raise Exception('ERROR: YOLOv5 TF.js inference is not supported')
+        self.__dict__.update(locals())  # assign all variables to self
+
+    def forward(self, im, augment=False, visualize=False, val=False):
+        # YOLOv5 MultiBackend inference
+        b, ch, h, w = im.shape  # batch, channel, height, width
+        if self.pt:  # PyTorch
+            y = self.model(im, augment=augment, visualize=visualize)[0]
+        elif self.jit:  # TorchScript
+            y = self.model(im)[0]
+        elif self.dnn:  # ONNX OpenCV DNN
+            im = im.cpu().numpy()  # torch to numpy
+            self.net.setInput(im)
+            y = self.net.forward()
+        elif self.onnx:  # ONNX Runtime
+            im = im.cpu().numpy()  # torch to numpy
+            y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0]
+        elif self.xml:  # OpenVINO
+            im = im.cpu().numpy()  # FP32
+            y = self.executable_network([im])[self.output_layer]
+        elif self.engine:  # TensorRT
+            assert im.shape == self.bindings['images'].shape, (im.shape, self.bindings['images'].shape)
+            self.binding_addrs['images'] = int(im.data_ptr())
+            self.context.execute_v2(list(self.binding_addrs.values()))
+            y = self.bindings['output'].data
+        elif self.coreml:  # CoreML
+            im = im.permute(0, 2, 3, 1).cpu().numpy()  # torch BCHW to numpy BHWC shape(1,320,192,3)
+            im = Image.fromarray((im[0] * 255).astype('uint8'))
+            # im = im.resize((192, 320), Image.ANTIALIAS)
+            y = self.model.predict({'image': im})  # coordinates are xywh normalized
+            if 'confidence' in y:
+                box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
+                conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
+                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
+            else:
+                k = 'var_' + str(sorted(int(k.replace('var_', '')) for k in y)[-1])  # output key
+                y = y[k]  # output
+        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
+            im = im.permute(0, 2, 3, 1).cpu().numpy()  # torch BCHW to numpy BHWC shape(1,320,192,3)
+            if self.saved_model:  # SavedModel
+                y = (self.model(im, training=False) if self.keras else self.model(im)).numpy()
+            elif self.pb:  # GraphDef
+                y = self.frozen_func(x=self.tf.constant(im)).numpy()
+            else:  # Lite or Edge TPU
+                input, output = self.input_details[0], self.output_details[0]
+                int8 = input['dtype'] == np.uint8  # is TFLite quantized uint8 model
+                if int8:
+                    scale, zero_point = input['quantization']
+                    im = (im / scale + zero_point).astype(np.uint8)  # de-scale
+                self.interpreter.set_tensor(input['index'], im)
+                self.interpreter.invoke()
+                y = self.interpreter.get_tensor(output['index'])
+                if int8:
+                    scale, zero_point = output['quantization']
+                    y = (y.astype(np.float32) - zero_point) * scale  # re-scale
+            y[..., :4] *= [w, h, w, h]  # xywh normalized to pixels
+
+        if isinstance(y, np.ndarray):
+            y = torch.tensor(y, device=self.device)
+        return (y, []) if val else y
+
+    def warmup(self, imgsz=(1, 3, 640, 640)):
+        # Warmup model by running inference once
+        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb
+        if any(warmup_types) and self.device.type != 'cpu':
+            im = torch.zeros(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
+            for _ in range(2 if self.jit else 1):  #
+                self.forward(im)  # warmup
+
+    @staticmethod
+    def model_type(p='path/to/model.pt'):
+        # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
+        from export import export_formats
+        suffixes = list(export_formats().Suffix) + ['.xml']  # export suffixes
+        check_suffix(p, suffixes)  # checks
+        p = Path(p).name  # eliminate trailing separators
+        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, xml2 = (s in p for s in suffixes)
+        xml |= xml2  # *_openvino_model or *.xml
+        tflite &= not edgetpu  # *.tflite
+        return pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs
+
+    @staticmethod
+    def _load_metadata(f='path/to/meta.yaml'):
+        # Load metadata from meta.yaml if it exists
+        with open(f, errors='ignore') as f:
+            d = yaml.safe_load(f)
+        return d['stride'], d['names']  # assign stride, names
 
 
-class autoShape(nn.Module):
-    # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
-    img_size = 640  # inference size (pixels)
+class AutoShape(nn.Module):
+    # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
     conf = 0.25  # NMS confidence threshold
     iou = 0.45  # NMS IoU threshold
-    classes = None  # (optional list) filter by class
+    agnostic = False  # NMS class-agnostic
+    multi_label = False  # NMS multiple labels per box
+    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
+    max_det = 1000  # maximum number of detections per image
+    amp = False  # Automatic Mixed Precision (AMP) inference
 
-    def __init__(self, model):
-        super(autoShape, self).__init__()
+    def __init__(self, model, verbose=True):
+        super().__init__()
+        if verbose:
+            LOGGER.info('Adding AutoShape... ')
+        copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
+        self.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instance
+        self.pt = not self.dmb or model.pt  # PyTorch model
         self.model = model.eval()
 
-    def autoshape(self):
-        print('autoShape already enabled, skipping... ')  # model already converted to model.autoshape()
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        if self.pt:
+            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
+            m.stride = fn(m.stride)
+            m.grid = list(map(fn, m.grid))
+            if isinstance(m.anchor_grid, list):
+                m.anchor_grid = list(map(fn, m.anchor_grid))
         return self
 
+    @torch.no_grad()
     def forward(self, imgs, size=640, augment=False, profile=False):
-        # Inference from various sources. For height=720, width=1280, RGB images example inputs are:
-        #   filename:   imgs = 'data/samples/zidane.jpg'
-        #   URI:             = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
-        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(720,1280,3)
-        #   PIL:             = Image.open('image.jpg')  # HWC x(720,1280,3)
-        #   numpy:           = np.zeros((720,1280,3))  # HWC
-        #   torch:           = torch.zeros(16,3,720,1280)  # BCHW
+        # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
+        #   file:       imgs = 'data/images/zidane.jpg'  # str or PosixPath
+        #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
+        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
+        #   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # HWC x(640,1280,3)
+        #   numpy:           = np.zeros((640,1280,3))  # HWC
+        #   torch:           = torch.zeros(16,3,320,640)  # BCHW (scaled to size=640, 0-1 values)
         #   multiple:        = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...]  # list of images
 
-        p = next(self.model.parameters())  # for device and type
+        t = [time_sync()]
+        p = next(self.model.parameters()) if self.pt else torch.zeros(1, device=self.model.device)  # for device, type
+        autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
         if isinstance(imgs, torch.Tensor):  # torch
+            with amp.autocast(autocast):
                 return self.model(imgs.to(p.device).type_as(p), augment, profile)  # inference
 
         # Pre-process
-        n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs])  # number of images, list of images
+        n, imgs = (len(imgs), list(imgs)) if isinstance(imgs, (list, tuple)) else (1, [imgs])  # number, list of images
         shape0, shape1, files = [], [], []  # image and inference shapes, filenames
         for i, im in enumerate(imgs):
-            if isinstance(im, str):  # filename or uri
-                im, f = Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im), im  # open
-                im.filename = f  # for uri
-            files.append(Path(im.filename).with_suffix('.jpg').name if isinstance(im, Image.Image) else f'image{i}.jpg')
-            im = np.array(im)  # to numpy
+            f = f'image{i}'  # filename
+            if isinstance(im, (str, Path)):  # filename or uri
+                im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
+                im = np.asarray(exif_transpose(im))
+            elif isinstance(im, Image.Image):  # PIL Image
+                im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
+            files.append(Path(f).with_suffix('.jpg').name)
             if im.shape[0] < 5:  # image in CHW
                 im = im.transpose((1, 2, 0))  # reverse dataloader .transpose(2, 0, 1)
-            im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3)  # enforce 3ch input
+            im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3)  # enforce 3ch input
             s = im.shape[:2]  # HWC
             shape0.append(s)  # image shape
             g = (size / max(s))  # gain
             shape1.append([y * g for y in s])
-            imgs[i] = im  # update
-        shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)]  # inference shape
-        x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
-        x = np.stack(x, 0) if n > 1 else x[0][None]  # stack
-        x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
-        x = torch.from_numpy(x).to(p.device).type_as(p) / 255.  # uint8 to fp16/32
+            imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
+        shape1 = [make_divisible(x, self.stride) if self.pt else size for x in np.array(shape1).max(0)]  # inf shape
+        x = [letterbox(im, shape1, auto=False)[0] for im in imgs]  # pad
+        x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
+        x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
+        t.append(time_sync())
 
+        with amp.autocast(autocast):
             # Inference
-        with torch.no_grad():
-            y = self.model(x, augment, profile)[0]  # forward
-        y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)  # NMS
+            y = self.model(x, augment, profile)  # forward
+            t.append(time_sync())
 
             # Post-process
+            y = non_max_suppression(y if self.dmb else y[0],
+                                    self.conf,
+                                    self.iou,
+                                    self.classes,
+                                    self.agnostic,
+                                    self.multi_label,
+                                    max_det=self.max_det)  # NMS
             for i in range(n):
                 scale_coords(shape1, y[i][:, :4], shape0[i])
 
-        return Detections(imgs, y, files, self.names)
+            t.append(time_sync())
+            return Detections(imgs, y, files, t, self.names, x.shape)
 
 
 class Detections:
-    # detections class for YOLOv5 inference results
-    def __init__(self, imgs, pred, files, names=None):
-        super(Detections, self).__init__()
+    # YOLOv5 detections class for inference results
+    def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None):
+        super().__init__()
         d = pred[0].device  # device
-        gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs]  # normalizations
+        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs]  # normalizations
         self.imgs = imgs  # list of images as numpy arrays
         self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
         self.names = names  # class names
         self.files = files  # image filenames
+        self.times = times  # profiling times
         self.xyxy = pred  # xyxy pixels
         self.xywh = [xyxy2xywh(x) for x in pred]  # xywh pixels
         self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)]  # xyxy normalized
         self.xywhn = [x / g for x, g in zip(self.xywh, gn)]  # xywh normalized
-        self.n = len(self.pred)
+        self.n = len(self.pred)  # number of images (batch size)
+        self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3))  # timestamps (ms)
+        self.s = shape  # inference BCHW shape
 
-    def display(self, pprint=False, show=False, save=False, render=False, save_dir=''):
-        colors = color_list()
-        for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
-            str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
-            if pred is not None:
+    def display(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
+        crops = []
+        for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
+            s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
+            if pred.shape[0]:
                 for c in pred[:, -1].unique():
                     n = (pred[:, -1] == c).sum()  # detections per class
-                    str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
-                if show or save or render:
-                    for *box, conf, cls in pred:  # xyxy, confidence, class
+                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
+                if show or save or render or crop:
+                    annotator = Annotator(im, example=str(self.names))
+                    for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
                         label = f'{self.names[int(cls)]} {conf:.2f}'
-                        plot_one_box(box, img, label=label, color=colors[int(cls) % 10])
-            img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img  # from np
+                        if crop:
+                            file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
+                            crops.append({
+                                'box': box,
+                                'conf': conf,
+                                'cls': cls,
+                                'label': label,
+                                'im': save_one_box(box, im, file=file, save=save)})
+                        else:  # all others
+                            annotator.box_label(box, label if labels else '', color=colors(cls))
+                    im = annotator.im
+            else:
+                s += '(no detections)'
+
+            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
             if pprint:
-                print(str.rstrip(', '))
+                print(s.rstrip(', '))
             if show:
-                img.show(self.files[i])  # show
+                im.show(self.files[i])  # show
             if save:
-                f = Path(save_dir) / self.files[i]
-                img.save(f)  # save
-                print(f"{'Saving' * (i == 0)} {f},", end='' if i < self.n - 1 else ' done.\n')
+                f = self.files[i]
+                im.save(save_dir / f)  # save
+                if i == self.n - 1:
+                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
             if render:
-                self.imgs[i] = np.asarray(img)
+                self.imgs[i] = np.asarray(im)
+        if crop:
+            if save:
+                LOGGER.info(f'Saved results to {save_dir}\n')
+            return crops
 
     def print(self):
         self.display(pprint=True)  # print results
+        print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
 
-    def show(self):
-        self.display(show=True)  # show results
+    def show(self, labels=True):
+        self.display(show=True, labels=labels)  # show results
 
-    def save(self, save_dir='results/'):
-        Path(save_dir).mkdir(exist_ok=True)
-        self.display(save=True, save_dir=save_dir)  # save results
+    def save(self, labels=True, save_dir='runs/detect/exp'):
+        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True)  # increment save_dir
+        self.display(save=True, labels=labels, save_dir=save_dir)  # save results
 
-    def render(self):
-        self.display(render=True)  # render results
+    def crop(self, save=True, save_dir='runs/detect/exp'):
+        save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None
+        return self.display(crop=True, save=save, save_dir=save_dir)  # crop results
+
+    def render(self, labels=True):
+        self.display(render=True, labels=labels)  # render results
         return self.imgs
 
-    def __len__(self):
-        return self.n
+    def pandas(self):
+        # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
+        new = copy(self)  # return copy
+        ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name'  # xyxy columns
+        cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name'  # xywh columns
+        for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
+            a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)]  # update
+            setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
+        return new
 
     def tolist(self):
         # return a list of Detections objects, i.e. 'for result in results.tolist():'
-        x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
-        for d in x:
-            for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
-                setattr(d, k, getattr(d, k)[0])  # pop out of list
+        r = range(self.n)  # iterable
+        x = [Detections([self.imgs[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
+        # for d in x:
+        #    for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
+        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
         return x
 
+    def __len__(self):
+        return self.n  # override len(results)
+
+    def __str__(self):
+        self.print()  # override print(results)
+        return ''
+
 
 class Classify(nn.Module):
     # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1):  # ch_in, ch_out, kernel, stride, padding, groups
-        super(Classify, self).__init__()
+        super().__init__()
         self.aap = nn.AdaptiveAvgPool2d(1)  # to x(b,c1,1,1)
         self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g)  # to x(b,c2,1,1)
         self.flat = nn.Flatten()

Y5/models/experimental.py

@@ -1,36 +1,25 @@
-# This file contains experimental modules
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Experimental modules
+"""
+import math
 
 import numpy as np
 import torch
 import torch.nn as nn
 
-
-from Y5.models.common import Conv, DWConv
-from Y5.utils.google_utils import attempt_download
-
-
-class CrossConv(nn.Module):
-    # Cross Convolution Downsample
-    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
-        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
-        super(CrossConv, self).__init__()
-        c_ = int(c2 * e)  # hidden channels
-        self.cv1 = Conv(c1, c_, (1, k), (1, s))
-        self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
-        self.add = shortcut and c1 == c2
-
-    def forward(self, x):
-        return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
+from models.common import Conv
+from utils.downloads import attempt_download
 
 
 class Sum(nn.Module):
     # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
     def __init__(self, n, weight=False):  # n: number of inputs
-        super(Sum, self).__init__()
+        super().__init__()
         self.weight = weight  # apply weights boolean
         self.iter = range(n - 1)  # iter object
         if weight:
-            self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True)  # layer weights
+            self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True)  # layer weights
 
     def forward(self, x):
         y = x[0]  # no weight
@@ -44,92 +33,72 @@ class Sum(nn.Module):
         return y
 
 
-class GhostConv(nn.Module):
-    # Ghost Convolution https://github.com/huawei-noah/ghostnet
-    def __init__(self, c1, c2, k=1, s=1, g=1, act=True):  # ch_in, ch_out, kernel, stride, groups
-        super(GhostConv, self).__init__()
-        c_ = c2 // 2  # hidden channels
-        self.cv1 = Conv(c1, c_, k, s, None, g, act)
-        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
-
-    def forward(self, x):
-        y = self.cv1(x)
-        return torch.cat([y, self.cv2(y)], 1)
-
-
-class GhostBottleneck(nn.Module):
-    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
-    def __init__(self, c1, c2, k=3, s=1):  # ch_in, ch_out, kernel, stride
-        super(GhostBottleneck, self).__init__()
-        c_ = c2 // 2
-        self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1),  # pw
-                                  DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(),  # dw
-                                  GhostConv(c_, c2, 1, 1, act=False))  # pw-linear
-        self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
-                                      Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
-
-    def forward(self, x):
-        return self.conv(x) + self.shortcut(x)
-
-
 class MixConv2d(nn.Module):
-    # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
-    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
-        super(MixConv2d, self).__init__()
-        groups = len(k)
+    # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
+    def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):  # ch_in, ch_out, kernel, stride, ch_strategy
+        super().__init__()
+        n = len(k)  # number of convolutions
         if equal_ch:  # equal c_ per group
-            i = torch.linspace(0, groups - 1E-6, c2).floor()  # c2 indices
-            c_ = [(i == g).sum() for g in range(groups)]  # intermediate channels
+            i = torch.linspace(0, n - 1E-6, c2).floor()  # c2 indices
+            c_ = [(i == g).sum() for g in range(n)]  # intermediate channels
         else:  # equal weight.numel() per group
-            b = [c2] + [0] * groups
-            a = np.eye(groups + 1, groups, k=-1)
+            b = [c2] + [0] * n
+            a = np.eye(n + 1, n, k=-1)
             a -= np.roll(a, 1, axis=1)
             a *= np.array(k) ** 2
             a[0] = 1
             c_ = np.linalg.lstsq(a, b, rcond=None)[0].round()  # solve for equal weight indices, ax = b
 
-        self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
+        self.m = nn.ModuleList([
+            nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
         self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.LeakyReLU(0.1, inplace=True)
+        self.act = nn.SiLU()
 
     def forward(self, x):
-        return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
+        return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
 
 
 class Ensemble(nn.ModuleList):
     # Ensemble of models
     def __init__(self):
-        super(Ensemble, self).__init__()
+        super().__init__()
 
-    def forward(self, x, augment=False):
-        y = []
-        for module in self:
-            y.append(module(x, augment)[0])
+    def forward(self, x, augment=False, profile=False, visualize=False):
+        y = [module(x, augment, profile, visualize)[0] for module in self]
         # y = torch.stack(y).max(0)[0]  # max ensemble
         # y = torch.stack(y).mean(0)  # mean ensemble
         y = torch.cat(y, 1)  # nms ensemble
         return y, None  # inference, train output
 
 
-def attempt_load(weights, map_location=None):
+def attempt_load(weights, device=None, inplace=True, fuse=True):
     # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
+    from models.yolo import Detect, Model
+
     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
-        attempt_download(w)
-        ckpt = torch.load(w, map_location=map_location)  # load
-        model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model
+        ckpt = torch.load(attempt_download(w), map_location='cpu')  # load
+        ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
+        model.append(ckpt.fuse().eval() if fuse else ckpt.eval())  # fused or un-fused model in eval mode
 
     # Compatibility updates
     for m in model.modules():
-        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-            m.inplace = True  # pytorch 1.7.0 compatibility
-        elif type(m) is Conv:
-            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
+        t = type(m)
+        if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
+            m.inplace = inplace  # torch 1.7.0 compatibility
+            if t is Detect and not isinstance(m.anchor_grid, list):
+                delattr(m, 'anchor_grid')
+                setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
+        elif t is Conv:
+            m._non_persistent_buffers_set = set()  # torch 1.6.0 compatibility
+        elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
+            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
 
     if len(model) == 1:
         return model[-1]  # return model
-    else:
-        print('Ensemble created with %s\n' % weights)
-        for k in ['names', 'stride']:
-            setattr(model, k, getattr(model[-1], k))
+    print(f'Ensemble created with {weights}\n')
+    for k in 'names', 'nc', 'yaml':
+        setattr(model, k, getattr(model[0], k))
+    model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride  # max stride
+    assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
     return model  # return ensemble

Y5/models/hub/anchors.yaml

@@ -1,4 +1,5 @@
-# Default YOLOv5 anchors for COCO data
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Default anchors for COCO data
 
 
 # P5 -------------------------------------------------------------------------------------------------------------------

Y5/models/hub/yolov3-spp.yaml

@@ -1,9 +1,9 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
-
-# anchors
 anchors:
   - [10,13, 16,30, 33,23]  # P3/8
   - [30,61, 62,45, 59,119]  # P4/16

Y5/models/hub/yolov3-tiny.yaml

@@ -1,9 +1,9 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
-
-# anchors
 anchors:
   - [10,14, 23,27, 37,58]  # P4/16
   - [81,82, 135,169, 344,319]  # P5/32

Y5/models/hub/yolov3.yaml

@@ -1,9 +1,9 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
-
-# anchors
 anchors:
   - [10,13, 16,30, 33,23]  # P3/8
   - [30,61, 62,45, 59,119]  # P4/16
@@ -28,7 +28,7 @@ backbone:
 # YOLOv3 head
 head:
   [[-1, 1, Bottleneck, [1024, False]],
-   [-1, 1, Conv, [512, [1, 1]]],
+   [-1, 1, Conv, [512, 1, 1]],
    [-1, 1, Conv, [1024, 3, 1]],
    [-1, 1, Conv, [512, 1, 1]],
    [-1, 1, Conv, [1024, 3, 1]],  # 15 (P5/32-large)

Y5/models/hub/yolov5-bifpn.yaml

@@ -0,0 +1,48 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 6, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
+  ]
+
+# YOLOv5 v6.0 BiFPN head
+head:
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 13
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 14, 6], 1, Concat, [1]],  # cat P4 <--- BiFPN change
+   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
+
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]

Y5/models/hub/yolov5-fpn.yaml

@@ -1,42 +1,42 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
-
-# anchors
 anchors:
   - [10,13, 16,30, 33,23]  # P3/8
   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-   [-1, 3, Bottleneck, [128]],
+   [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 9, BottleneckCSP, [256]],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-   [-1, 9, BottleneckCSP, [512]],
+   [-1, 9, C3, [512]],
    [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 1, SPP, [1024, [5, 9, 13]]],
-   [-1, 6, BottleneckCSP, [1024]],  # 9
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 FPN head
+# YOLOv5 v6.0 FPN head
 head:
-  [[-1, 3, BottleneckCSP, [1024, False]],  # 10 (P5/32-large)
+  [[-1, 3, C3, [1024, False]],  # 10 (P5/32-large)
 
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],
    [[-1, 6], 1, Concat, [1]],  # cat backbone P4
    [-1, 1, Conv, [512, 1, 1]],
-   [-1, 3, BottleneckCSP, [512, False]],  # 14 (P4/16-medium)
+   [-1, 3, C3, [512, False]],  # 14 (P4/16-medium)
 
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],
    [[-1, 4], 1, Concat, [1]],  # cat backbone P3
    [-1, 1, Conv, [256, 1, 1]],
-   [-1, 3, BottleneckCSP, [256, False]],  # 18 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 18 (P3/8-small)
 
    [[18, 14, 10], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
   ]

Y5/models/hub/yolov5-p2.yaml

@@ -1,27 +1,27 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
+anchors: 3  # AutoAnchor evolves 3 anchors per P output layer
 
-# anchors
-anchors: 3
-
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 9
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],
@@ -50,5 +50,5 @@ head:
    [[-1, 10], 1, Concat, [1]],  # cat head P5
    [-1, 3, C3, [1024, False]],  # 30 (P5/32-large)
 
-    [ [ 24, 27, 30 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4, P5)
+   [[21, 24, 27, 30], 1, Detect, [nc, anchors]],  # Detect(P2, P3, P4, P5)
   ]

Y5/models/hub/yolov5-p34.yaml

@@ -0,0 +1,41 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 0.33  # model depth multiple
+width_multiple: 0.50  # layer channel multiple
+anchors: 3  # AutoAnchor evolves 3 anchors per P output layer
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [ [ -1, 1, Conv, [ 64, 6, 2, 2 ] ],  # 0-P1/2
+    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+    [ -1, 3, C3, [ 128 ] ],
+    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+    [ -1, 6, C3, [ 256 ] ],
+    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+    [ -1, 9, C3, [ 512 ] ],
+    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
+    [ -1, 3, C3, [ 1024 ] ],
+    [ -1, 1, SPPF, [ 1024, 5 ] ],  # 9
+  ]
+
+# YOLOv5 v6.0 head with (P3, P4) outputs
+head:
+  [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+    [ -1, 3, C3, [ 512, False ] ],  # 13
+
+    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+    [ -1, 3, C3, [ 256, False ] ],  # 17 (P3/8-small)
+
+    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P4
+    [ -1, 3, C3, [ 512, False ] ],  # 20 (P4/16-medium)
+
+    [ [ 17, 20 ], 1, Detect, [ nc, anchors ] ],  # Detect(P3, P4)
+  ]

Y5/models/hub/yolov5-p6.yaml

@@ -1,29 +1,29 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
+anchors: 3  # AutoAnchor evolves 3 anchors per P output layer
 
-# anchors
-anchors: 3
-
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
    [-1, 3, C3, [768]],
    [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 11
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs
 head:
   [[-1, 1, Conv, [768, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],
@@ -50,7 +50,7 @@ head:
 
    [-1, 1, Conv, [768, 3, 2]],
    [[-1, 12], 1, Concat, [1]],  # cat head P6
-    [ -1, 3, C3, [ 1024, False ] ],  # 32 (P5/64-xlarge)
+   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)
 
    [[23, 26, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6)
   ]

Y5/models/hub/yolov5-p7.yaml

@@ -1,19 +1,19 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
+anchors: 3  # AutoAnchor evolves 3 anchors per P output layer
 
-# anchors
-anchors: 3
-
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
@@ -21,11 +21,11 @@ backbone:
    [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
    [-1, 3, C3, [1024]],
    [-1, 1, Conv, [1280, 3, 2]],  # 11-P7/128
-    [ -1, 1, SPP, [ 1280, [ 3, 5 ] ] ],
-    [ -1, 3, C3, [ 1280, False ] ],  # 13
+   [-1, 3, C3, [1280]],
+   [-1, 1, SPPF, [1280, 5]],  # 13
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs
 head:
   [[-1, 1, Conv, [1024, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

Y5/models/hub/yolov5-panet.yaml

@@ -1,48 +1,48 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
-
-# anchors
 anchors:
   - [10,13, 16,30, 33,23]  # P3/8
   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
-   [-1, 3, BottleneckCSP, [128]],
+   [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 9, BottleneckCSP, [256]],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-   [-1, 9, BottleneckCSP, [512]],
+   [-1, 9, C3, [512]],
    [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 1, SPP, [1024, [5, 9, 13]]],
-   [-1, 3, BottleneckCSP, [1024, False]],  # 9
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 PANet head
+# YOLOv5 v6.0 PANet head
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],
    [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-   [-1, 3, BottleneckCSP, [512, False]],  # 13
+   [-1, 3, C3, [512, False]],  # 13
 
    [-1, 1, Conv, [256, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],
    [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-   [-1, 3, BottleneckCSP, [256, False]],  # 17 (P3/8-small)
+   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
 
    [-1, 1, Conv, [256, 3, 2]],
    [[-1, 14], 1, Concat, [1]],  # cat head P4
-   [-1, 3, BottleneckCSP, [512, False]],  # 20 (P4/16-medium)
+   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
 
    [-1, 1, Conv, [512, 3, 2]],
    [[-1, 10], 1, Concat, [1]],  # cat head P5
-   [-1, 3, BottleneckCSP, [1024, False]],  # 23 (P5/32-large)
+   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
 
    [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
   ]

Y5/models/hub/yolov5l6.yaml

@@ -1,33 +1,33 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
-
-# anchors
 anchors:
   - [19,27,  44,40,  38,94]  # P3/8
   - [96,68,  86,152,  180,137]  # P4/16
   - [140,301,  303,264,  238,542]  # P5/32
   - [436,615,  739,380,  925,792]  # P6/64
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
    [-1, 3, C3, [768]],
    [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 11
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [768, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

Y5/models/hub/yolov5m6.yaml

@@ -1,33 +1,33 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 0.67  # model depth multiple
 width_multiple: 0.75  # layer channel multiple
-
-# anchors
 anchors:
   - [19,27,  44,40,  38,94]  # P3/8
   - [96,68,  86,152,  180,137]  # P4/16
   - [140,301,  303,264,  238,542]  # P5/32
   - [436,615,  739,380,  925,792]  # P6/64
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
    [-1, 3, C3, [768]],
    [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 11
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [768, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

Y5/models/hub/yolov5n6.yaml

@@ -0,0 +1,60 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 0.33  # model depth multiple
+width_multiple: 0.25  # layer channel multiple
+anchors:
+  - [19,27,  44,40,  38,94]  # P3/8
+  - [96,68,  86,152,  180,137]  # P4/16
+  - [140,301,  303,264,  238,542]  # P5/32
+  - [436,615,  739,380,  925,792]  # P6/64
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 6, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
+   [-1, 3, C3, [768]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 11
+  ]
+
+# YOLOv5 v6.0 head
+head:
+  [[-1, 1, Conv, [768, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 8], 1, Concat, [1]],  # cat backbone P5
+   [-1, 3, C3, [768, False]],  # 15
+
+   [-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 19
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 23 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 20], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 26 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 16], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [768, False]],  # 29 (P5/32-large)
+
+   [-1, 1, Conv, [768, 3, 2]],
+   [[-1, 12], 1, Concat, [1]],  # cat head P6
+   [-1, 3, C3, [1024, False]],  # 32 (P6/64-xlarge)
+
+   [[23, 26, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5, P6)
+  ]

Y5/models/hub/yolov5s-ghost.yaml

@@ -0,0 +1,48 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 0.33  # model depth multiple
+width_multiple: 0.50  # layer channel multiple
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+   [-1, 1, GhostConv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3Ghost, [128]],
+   [-1, 1, GhostConv, [256, 3, 2]],  # 3-P3/8
+   [-1, 6, C3Ghost, [256]],
+   [-1, 1, GhostConv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3Ghost, [512]],
+   [-1, 1, GhostConv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 3, C3Ghost, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
+  ]
+
+# YOLOv5 v6.0 head
+head:
+  [[-1, 1, GhostConv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3Ghost, [512, False]],  # 13
+
+   [-1, 1, GhostConv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3Ghost, [256, False]],  # 17 (P3/8-small)
+
+   [-1, 1, GhostConv, [256, 3, 2]],
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3Ghost, [512, False]],  # 20 (P4/16-medium)
+
+   [-1, 1, GhostConv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3Ghost, [1024, False]],  # 23 (P5/32-large)
+
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]

Y5/models/hub/yolov5s-transformer.yaml

@@ -0,0 +1,48 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 0.33  # model depth multiple
+width_multiple: 0.50  # layer channel multiple
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 6, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 3, C3TR, [1024]],  # 9 <--- C3TR() Transformer module
+   [-1, 1, SPPF, [1024, 5]],  # 9
+  ]
+
+# YOLOv5 v6.0 head
+head:
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 13
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
+
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]

Y5/models/hub/yolov5s6.yaml

@@ -1,33 +1,33 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 0.33  # model depth multiple
 width_multiple: 0.50  # layer channel multiple
-
-# anchors
 anchors:
   - [19,27,  44,40,  38,94]  # P3/8
   - [96,68,  86,152,  180,137]  # P4/16
   - [140,301,  303,264,  238,542]  # P5/32
   - [436,615,  739,380,  925,792]  # P6/64
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
    [-1, 3, C3, [768]],
    [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 11
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [768, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

Y5/models/hub/yolov5x6.yaml

@@ -1,33 +1,33 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.33  # model depth multiple
 width_multiple: 1.25  # layer channel multiple
-
-# anchors
 anchors:
   - [19,27,  44,40,  38,94]  # P3/8
   - [96,68,  86,152,  180,137]  # P4/16
   - [140,301,  303,264,  238,542]  # P5/32
   - [436,615,  739,380,  925,792]  # P6/64
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-    [ -1, 9, C3, [ 256 ] ],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [768, 3, 2]],  # 7-P5/32
    [-1, 3, C3, [768]],
    [-1, 1, Conv, [1024, 3, 2]],  # 9-P6/64
-    [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
-    [ -1, 3, C3, [ 1024, False ] ],  # 11
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 11
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [768, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

Y5/models/tf.py

@@ -0,0 +1,574 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+TensorFlow, Keras and TFLite versions of YOLOv5
+Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
+
+Usage:
+    $ python models/tf.py --weights yolov5s.pt
+
+Export:
+    $ python path/to/export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
+"""
+
+import argparse
+import sys
+from copy import deepcopy
+from pathlib import Path
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+# ROOT = ROOT.relative_to(Path.cwd())  # relative
+
+import numpy as np
+import tensorflow as tf
+import torch
+import torch.nn as nn
+from tensorflow import keras
+
+from models.common import (C3, SPP, SPPF, Bottleneck, BottleneckCSP, C3x, Concat, Conv, CrossConv, DWConv,
+                           DWConvTranspose2d, Focus, autopad)
+from models.experimental import MixConv2d, attempt_load
+from models.yolo import Detect
+from utils.activations import SiLU
+from utils.general import LOGGER, make_divisible, print_args
+
+
+class TFBN(keras.layers.Layer):
+    # TensorFlow BatchNormalization wrapper
+    def __init__(self, w=None):
+        super().__init__()
+        self.bn = keras.layers.BatchNormalization(
+            beta_initializer=keras.initializers.Constant(w.bias.numpy()),
+            gamma_initializer=keras.initializers.Constant(w.weight.numpy()),
+            moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()),
+            moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()),
+            epsilon=w.eps)
+
+    def call(self, inputs):
+        return self.bn(inputs)
+
+
+class TFPad(keras.layers.Layer):
+    # Pad inputs in spatial dimensions 1 and 2
+    def __init__(self, pad):
+        super().__init__()
+        if isinstance(pad, int):
+            self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
+        else:  # tuple/list
+            self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]])
+
+    def call(self, inputs):
+        return tf.pad(inputs, self.pad, mode='constant', constant_values=0)
+
+
+class TFConv(keras.layers.Layer):
+    # Standard convolution
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
+        # ch_in, ch_out, weights, kernel, stride, padding, groups
+        super().__init__()
+        assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
+        # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
+        # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
+        conv = keras.layers.Conv2D(
+            filters=c2,
+            kernel_size=k,
+            strides=s,
+            padding='SAME' if s == 1 else 'VALID',
+            use_bias=not hasattr(w, 'bn'),
+            kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
+            bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
+        self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
+        self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
+        self.act = activations(w.act) if act else tf.identity
+
+    def call(self, inputs):
+        return self.act(self.bn(self.conv(inputs)))
+
+
+class TFDWConv(keras.layers.Layer):
+    # Depthwise convolution
+    def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
+        # ch_in, ch_out, weights, kernel, stride, padding, groups
+        super().__init__()
+        assert c2 % c1 == 0, f'TFDWConv() output={c2} must be a multiple of input={c1} channels'
+        conv = keras.layers.DepthwiseConv2D(
+            kernel_size=k,
+            depth_multiplier=c2 // c1,
+            strides=s,
+            padding='SAME' if s == 1 else 'VALID',
+            use_bias=not hasattr(w, 'bn'),
+            depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
+            bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
+        self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
+        self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
+        self.act = activations(w.act) if act else tf.identity
+
+    def call(self, inputs):
+        return self.act(self.bn(self.conv(inputs)))
+
+
+class TFDWConvTranspose2d(keras.layers.Layer):
+    # Depthwise ConvTranspose2d
+    def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
+        # ch_in, ch_out, weights, kernel, stride, padding, groups
+        super().__init__()
+        assert c1 == c2, f'TFDWConv() output={c2} must be equal to input={c1} channels'
+        assert k == 4 and p1 == 1, 'TFDWConv() only valid for k=4 and p1=1'
+        weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy()
+        self.c1 = c1
+        self.conv = [
+            keras.layers.Conv2DTranspose(filters=1,
+                                         kernel_size=k,
+                                         strides=s,
+                                         padding='VALID',
+                                         output_padding=p2,
+                                         use_bias=True,
+                                         kernel_initializer=keras.initializers.Constant(weight[..., i:i + 1]),
+                                         bias_initializer=keras.initializers.Constant(bias[i])) for i in range(c1)]
+
+    def call(self, inputs):
+        return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1]
+
+
+class TFFocus(keras.layers.Layer):
+    # Focus wh information into c-space
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
+        # ch_in, ch_out, kernel, stride, padding, groups
+        super().__init__()
+        self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)
+
+    def call(self, inputs):  # x(b,w,h,c) -> y(b,w/2,h/2,4c)
+        # inputs = inputs / 255  # normalize 0-255 to 0-1
+        inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]]
+        return self.conv(tf.concat(inputs, 3))
+
+
+class TFBottleneck(keras.layers.Layer):
+    # Standard bottleneck
+    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):  # ch_in, ch_out, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
+        self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)
+        self.add = shortcut and c1 == c2
+
+    def call(self, inputs):
+        return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
+
+
+class TFCrossConv(keras.layers.Layer):
+    # Cross Convolution
+    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1)
+        self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2)
+        self.add = shortcut and c1 == c2
+
+    def call(self, inputs):
+        return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
+
+
+class TFConv2d(keras.layers.Layer):
+    # Substitution for PyTorch nn.Conv2D
+    def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
+        super().__init__()
+        assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
+        self.conv = keras.layers.Conv2D(filters=c2,
+                                        kernel_size=k,
+                                        strides=s,
+                                        padding='VALID',
+                                        use_bias=bias,
+                                        kernel_initializer=keras.initializers.Constant(
+                                            w.weight.permute(2, 3, 1, 0).numpy()),
+                                        bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None)
+
+    def call(self, inputs):
+        return self.conv(inputs)
+
+
+class TFBottleneckCSP(keras.layers.Layer):
+    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
+        # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
+        self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)
+        self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3)
+        self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4)
+        self.bn = TFBN(w.bn)
+        self.act = lambda x: keras.activations.swish(x)
+        self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
+
+    def call(self, inputs):
+        y1 = self.cv3(self.m(self.cv1(inputs)))
+        y2 = self.cv2(inputs)
+        return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3))))
+
+
+class TFC3(keras.layers.Layer):
+    # CSP Bottleneck with 3 convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
+        # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
+        self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
+        self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
+        self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
+
+    def call(self, inputs):
+        return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
+
+
+class TFC3x(keras.layers.Layer):
+    # 3 module with cross-convolutions
+    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
+        # ch_in, ch_out, number, shortcut, groups, expansion
+        super().__init__()
+        c_ = int(c2 * e)  # hidden channels
+        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
+        self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
+        self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
+        self.m = keras.Sequential([
+            TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)])
+
+    def call(self, inputs):
+        return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
+
+
+class TFSPP(keras.layers.Layer):
+    # Spatial pyramid pooling layer used in YOLOv3-SPP
+    def __init__(self, c1, c2, k=(5, 9, 13), w=None):
+        super().__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
+        self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)
+        self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k]
+
+    def call(self, inputs):
+        x = self.cv1(inputs)
+        return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3))
+
+
+class TFSPPF(keras.layers.Layer):
+    # Spatial pyramid pooling-Fast layer
+    def __init__(self, c1, c2, k=5, w=None):
+        super().__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
+        self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
+        self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME')
+
+    def call(self, inputs):
+        x = self.cv1(inputs)
+        y1 = self.m(x)
+        y2 = self.m(y1)
+        return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3))
+
+
+class TFDetect(keras.layers.Layer):
+    # TF YOLOv5 Detect layer
+    def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):  # detection layer
+        super().__init__()
+        self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
+        self.nc = nc  # number of classes
+        self.no = nc + 5  # number of outputs per anchor
+        self.nl = len(anchors)  # number of detection layers
+        self.na = len(anchors[0]) // 2  # number of anchors
+        self.grid = [tf.zeros(1)] * self.nl  # init grid
+        self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32)
+        self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2])
+        self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]
+        self.training = False  # set to False after building model
+        self.imgsz = imgsz
+        for i in range(self.nl):
+            ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
+            self.grid[i] = self._make_grid(nx, ny)
+
+    def call(self, inputs):
+        z = []  # inference output
+        x = []
+        for i in range(self.nl):
+            x.append(self.m[i](inputs[i]))
+            # x(bs,20,20,255) to x(bs,3,20,20,85)
+            ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
+            x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no])
+
+            if not self.training:  # inference
+                y = tf.sigmoid(x[i])
+                grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5
+                anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4
+                xy = (y[..., 0:2] * 2 + grid) * self.stride[i]  # xy
+                wh = y[..., 2:4] ** 2 * anchor_grid
+                # Normalize xywh to 0-1 to reduce calibration error
+                xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
+                wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
+                y = tf.concat([xy, wh, y[..., 4:]], -1)
+                z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))
+
+        return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1), x)
+
+    @staticmethod
+    def _make_grid(nx=20, ny=20):
+        # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
+        # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
+        xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny))
+        return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32)
+
+
+class TFUpsample(keras.layers.Layer):
+    # TF version of torch.nn.Upsample()
+    def __init__(self, size, scale_factor, mode, w=None):  # warning: all arguments needed including 'w'
+        super().__init__()
+        assert scale_factor == 2, "scale_factor must be 2"
+        self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode)
+        # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
+        # with default arguments: align_corners=False, half_pixel_centers=False
+        # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x,
+        #                                                            size=(x.shape[1] * 2, x.shape[2] * 2))
+
+    def call(self, inputs):
+        return self.upsample(inputs)
+
+
+class TFConcat(keras.layers.Layer):
+    # TF version of torch.concat()
+    def __init__(self, dimension=1, w=None):
+        super().__init__()
+        assert dimension == 1, "convert only NCHW to NHWC concat"
+        self.d = 3
+
+    def call(self, inputs):
+        return tf.concat(inputs, self.d)
+
+
+def parse_model(d, ch, model, imgsz):  # model_dict, input_channels(3)
+    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
+    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
+    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
+    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
+
+    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
+    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
+        m_str = m
+        m = eval(m) if isinstance(m, str) else m  # eval strings
+        for j, a in enumerate(args):
+            try:
+                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
+            except NameError:
+                pass
+
+        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
+        if m in [
+                nn.Conv2d, Conv, DWConv, DWConvTranspose2d, Bottleneck, SPP, SPPF, MixConv2d, Focus, CrossConv,
+                BottleneckCSP, C3, C3x]:
+            c1, c2 = ch[f], args[0]
+            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
+
+            args = [c1, c2, *args[1:]]
+            if m in [BottleneckCSP, C3, C3x]:
+                args.insert(2, n)
+                n = 1
+        elif m is nn.BatchNorm2d:
+            args = [ch[f]]
+        elif m is Concat:
+            c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
+        elif m is Detect:
+            args.append([ch[x + 1] for x in f])
+            if isinstance(args[1], int):  # number of anchors
+                args[1] = [list(range(args[1] * 2))] * len(f)
+            args.append(imgsz)
+        else:
+            c2 = ch[f]
+
+        tf_m = eval('TF' + m_str.replace('nn.', ''))
+        m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \
+            else tf_m(*args, w=model.model[i])  # module
+
+        torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
+        t = str(m)[8:-2].replace('__main__.', '')  # module type
+        np = sum(x.numel() for x in torch_m_.parameters())  # number params
+        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
+        LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10}  {t:<40}{str(args):<30}')  # print
+        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
+        layers.append(m_)
+        ch.append(c2)
+    return keras.Sequential(layers), sorted(save)
+
+
+class TFModel:
+    # TF YOLOv5 model
+    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)):  # model, channels, classes
+        super().__init__()
+        if isinstance(cfg, dict):
+            self.yaml = cfg  # model dict
+        else:  # is *.yaml
+            import yaml  # for torch hub
+            self.yaml_file = Path(cfg).name
+            with open(cfg) as f:
+                self.yaml = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+
+        # Define model
+        if nc and nc != self.yaml['nc']:
+            LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}")
+            self.yaml['nc'] = nc  # override yaml value
+        self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
+
+    def predict(self,
+                inputs,
+                tf_nms=False,
+                agnostic_nms=False,
+                topk_per_class=100,
+                topk_all=100,
+                iou_thres=0.45,
+                conf_thres=0.25):
+        y = []  # outputs
+        x = inputs
+        for m in self.model.layers:
+            if m.f != -1:  # if not from previous layer
+                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
+
+            x = m(x)  # run
+            y.append(x if m.i in self.savelist else None)  # save output
+
+        # Add TensorFlow NMS
+        if tf_nms:
+            boxes = self._xywh2xyxy(x[0][..., :4])
+            probs = x[0][:, :, 4:5]
+            classes = x[0][:, :, 5:]
+            scores = probs * classes
+            if agnostic_nms:
+                nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres)
+            else:
+                boxes = tf.expand_dims(boxes, 2)
+                nms = tf.image.combined_non_max_suppression(boxes,
+                                                            scores,
+                                                            topk_per_class,
+                                                            topk_all,
+                                                            iou_thres,
+                                                            conf_thres,
+                                                            clip_boxes=False)
+            return nms, x[1]
+        return x[0]  # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...]
+        # x = x[0][0]  # [x(1,6300,85), ...] to x(6300,85)
+        # xywh = x[..., :4]  # x(6300,4) boxes
+        # conf = x[..., 4:5]  # x(6300,1) confidences
+        # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1))  # x(6300,1)  classes
+        # return tf.concat([conf, cls, xywh], 1)
+
+    @staticmethod
+    def _xywh2xyxy(xywh):
+        # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+        x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1)
+        return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1)
+
+
+class AgnosticNMS(keras.layers.Layer):
+    # TF Agnostic NMS
+    def call(self, input, topk_all, iou_thres, conf_thres):
+        # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450
+        return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres),
+                         input,
+                         fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32),
+                         name='agnostic_nms')
+
+    @staticmethod
+    def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25):  # agnostic NMS
+        boxes, classes, scores = x
+        class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32)
+        scores_inp = tf.reduce_max(scores, -1)
+        selected_inds = tf.image.non_max_suppression(boxes,
+                                                     scores_inp,
+                                                     max_output_size=topk_all,
+                                                     iou_threshold=iou_thres,
+                                                     score_threshold=conf_thres)
+        selected_boxes = tf.gather(boxes, selected_inds)
+        padded_boxes = tf.pad(selected_boxes,
+                              paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],
+                              mode="CONSTANT",
+                              constant_values=0.0)
+        selected_scores = tf.gather(scores_inp, selected_inds)
+        padded_scores = tf.pad(selected_scores,
+                               paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
+                               mode="CONSTANT",
+                               constant_values=-1.0)
+        selected_classes = tf.gather(class_inds, selected_inds)
+        padded_classes = tf.pad(selected_classes,
+                                paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
+                                mode="CONSTANT",
+                                constant_values=-1.0)
+        valid_detections = tf.shape(selected_inds)[0]
+        return padded_boxes, padded_scores, padded_classes, valid_detections
+
+
+def activations(act=nn.SiLU):
+    # Returns TF activation from input PyTorch activation
+    if isinstance(act, nn.LeakyReLU):
+        return lambda x: keras.activations.relu(x, alpha=0.1)
+    elif isinstance(act, nn.Hardswish):
+        return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667
+    elif isinstance(act, (nn.SiLU, SiLU)):
+        return lambda x: keras.activations.swish(x)
+    else:
+        raise Exception(f'no matching TensorFlow activation found for PyTorch activation {act}')
+
+
+def representative_dataset_gen(dataset, ncalib=100):
+    # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays
+    for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
+        im = np.transpose(img, [1, 2, 0])
+        im = np.expand_dims(im, axis=0).astype(np.float32)
+        im /= 255
+        yield [im]
+        if n >= ncalib:
+            break
+
+
+def run(
+        weights=ROOT / 'yolov5s.pt',  # weights path
+        imgsz=(640, 640),  # inference size h,w
+        batch_size=1,  # batch size
+        dynamic=False,  # dynamic batch size
+):
+    # PyTorch model
+    im = torch.zeros((batch_size, 3, *imgsz))  # BCHW image
+    model = attempt_load(weights, device=torch.device('cpu'), inplace=True, fuse=False)
+    _ = model(im)  # inference
+    model.info()
+
+    # TensorFlow model
+    im = tf.zeros((batch_size, *imgsz, 3))  # BHWC image
+    tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
+    _ = tf_model.predict(im)  # inference
+
+    # Keras model
+    im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
+    keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im))
+    keras_model.summary()
+
+    LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.')
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
+    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
+    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
+    parser.add_argument('--dynamic', action='store_true', help='dynamic batch size')
+    opt = parser.parse_args()
+    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
+    print_args(vars(opt))
+    return opt
+
+
+def main(opt):
+    run(**vars(opt))
+
+
+if __name__ == "__main__":
+    opt = parse_opt()
+    main(opt)

Y5/models/yolo.py

@@ -1,203 +1,256 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+YOLO-specific modules
+
+Usage:
+    $ python path/to/models/yolo.py --cfg yolov5s.yaml
+"""
+
 import argparse
-import logging
+import os
+import platform
 import sys
 from copy import deepcopy
+from pathlib import Path
 
-sys.path.append('./')  # to run '$ python *.py' files in subdirectories
-logger = logging.getLogger(__name__)
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1]  # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+    sys.path.append(str(ROOT))  # add ROOT to PATH
+if platform.system() != 'Windows':
+    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
-from Y5.models.common import *
-from Y5.models.experimental import *
-from Y5.utils.autoanchor import check_anchor_order
-from Y5.utils.general import make_divisible, check_file, set_logging
-from Y5.utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
-    select_device, copy_attr
+from models.common import *
+from models.experimental import *
+from utils.autoanchor import check_anchor_order
+from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
+from utils.plots import feature_visualization
+from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device,
+                               time_sync)
 
 try:
-    import thop  # for FLOPS computation
+    import thop  # for FLOPs computation
 except ImportError:
     thop = None
 
 
 class Detect(nn.Module):
     stride = None  # strides computed during build
-    export = False  # onnx export
+    onnx_dynamic = False  # ONNX export parameter
+    export = False  # export mode
 
-    def __init__(self, nc=80, anchors=(), ch=()):  # detection layer
-        super(Detect, self).__init__()
+    def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
+        super().__init__()
         self.nc = nc  # number of classes
         self.no = nc + 5  # number of outputs per anchor
         self.nl = len(anchors)  # number of detection layers
         self.na = len(anchors[0]) // 2  # number of anchors
         self.grid = [torch.zeros(1)] * self.nl  # init grid
-        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
-        self.register_buffer('anchors', a)  # shape(nl,na,2)
-        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))  # shape(nl,1,na,1,1,2)
+        self.anchor_grid = [torch.zeros(1)] * self.nl  # init anchor grid
+        self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)
         self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
+        self.inplace = inplace  # use in-place ops (e.g. slice assignment)
 
     def forward(self, x):
-        # x = x.copy()  # for profiling
         z = []  # inference output
-        self.training |= self.export
         for i in range(self.nl):
             x[i] = self.m[i](x[i])  # conv
             bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
             x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
 
             if not self.training:  # inference
-                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
+                if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
+                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
 
                 y = x[i].sigmoid()
-                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
+                if self.inplace:
+                    y[..., 0:2] = (y[..., 0:2] * 2 + self.grid[i]) * self.stride[i]  # xy
                     y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
+                else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
+                    xy, wh, conf = y.split((2, 2, self.nc + 1), 4)  # y.tensor_split((2, 4, 5), 4)  # torch 1.8.0
+                    xy = (xy * 2 + self.grid[i]) * self.stride[i]  # xy
+                    wh = (wh * 2) ** 2 * self.anchor_grid[i]  # wh
+                    y = torch.cat((xy, wh, conf), 4)
                 z.append(y.view(bs, -1, self.no))
 
-        return x if self.training else (torch.cat(z, 1), x)
+        return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
 
-    @staticmethod
-    def _make_grid(nx=20, ny=20):
-        yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
-        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
+    def _make_grid(self, nx=20, ny=20, i=0):
+        d = self.anchors[i].device
+        t = self.anchors[i].dtype
+        shape = 1, self.na, ny, nx, 2  # grid shape
+        y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
+        if check_version(torch.__version__, '1.10.0'):  # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
+            yv, xv = torch.meshgrid(y, x, indexing='ij')
+        else:
+            yv, xv = torch.meshgrid(y, x)
+        grid = torch.stack((xv, yv), 2).expand(shape) - 0.5  # add grid offset, i.e. y = 2.0 * x - 0.5
+        anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
+        return grid, anchor_grid
 
 
 class Model(nn.Module):
+    # YOLOv5 model
     def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
-        super(Model, self).__init__()
+        super().__init__()
         if isinstance(cfg, dict):
             self.yaml = cfg  # model dict
         else:  # is *.yaml
             import yaml  # for torch hub
             self.yaml_file = Path(cfg).name
-            with open(cfg) as f:
-                self.yaml = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
+            with open(cfg, encoding='ascii', errors='ignore') as f:
+                self.yaml = yaml.safe_load(f)  # model dict
 
         # Define model
         ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
         if nc and nc != self.yaml['nc']:
-            logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
+            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
             self.yaml['nc'] = nc  # override yaml value
         if anchors:
-            logger.info(f'Overriding model.yaml anchors with anchors={anchors}')
+            LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
             self.yaml['anchors'] = round(anchors)  # override yaml value
         self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
         self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
-        # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
+        self.inplace = self.yaml.get('inplace', True)
 
         # Build strides, anchors
         m = self.model[-1]  # Detect()
         if isinstance(m, Detect):
             s = 256  # 2x min stride
+            m.inplace = self.inplace
             m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))])  # forward
+            check_anchor_order(m)  # must be in pixel-space (not grid-space)
             m.anchors /= m.stride.view(-1, 1, 1)
-            check_anchor_order(m)
             self.stride = m.stride
             self._initialize_biases()  # only run once
-            # print('Strides: %s' % m.stride.tolist())
 
         # Init weights, biases
         initialize_weights(self)
         self.info()
-        logger.info('')
+        LOGGER.info('')
 
-    def forward(self, x, augment=False, profile=False):
+    def forward(self, x, augment=False, profile=False, visualize=False):
         if augment:
+            return self._forward_augment(x)  # augmented inference, None
+        return self._forward_once(x, profile, visualize)  # single-scale inference, train
+
+    def _forward_augment(self, x):
         img_size = x.shape[-2:]  # height, width
         s = [1, 0.83, 0.67]  # scales
         f = [None, 3, None]  # flips (2-ud, 3-lr)
         y = []  # outputs
         for si, fi in zip(s, f):
             xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
-                yi = self.forward_once(xi)[0]  # forward
+            yi = self._forward_once(xi)[0]  # forward
             # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
-                yi[..., :4] /= si  # de-scale
-                if fi == 2:
-                    yi[..., 1] = img_size[0] - yi[..., 1]  # de-flip ud
-                elif fi == 3:
-                    yi[..., 0] = img_size[1] - yi[..., 0]  # de-flip lr
+            yi = self._descale_pred(yi, fi, si, img_size)
             y.append(yi)
+        y = self._clip_augmented(y)  # clip augmented tails
         return torch.cat(y, 1), None  # augmented inference, train
-        else:
-            return self.forward_once(x, profile)  # single-scale inference, train
 
-    def forward_once(self, x, profile=False):
+    def _forward_once(self, x, profile=False, visualize=False):
         y, dt = [], []  # outputs
         for m in self.model:
             if m.f != -1:  # if not from previous layer
                 x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
-
             if profile:
-                o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
-                t = time_synchronized()
-                for _ in range(10):
-                    _ = m(x)
-                dt.append((time_synchronized() - t) * 100)
-                print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type))
-
+                self._profile_one_layer(m, x, dt)
             x = m(x)  # run
             y.append(x if m.i in self.save else None)  # save output
-
-        if profile:
-            print('%.1fms total' % sum(dt))
+            if visualize:
+                feature_visualization(x, m.type, m.i, save_dir=visualize)
         return x
 
+    def _descale_pred(self, p, flips, scale, img_size):
+        # de-scale predictions following augmented inference (inverse operation)
+        if self.inplace:
+            p[..., :4] /= scale  # de-scale
+            if flips == 2:
+                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
+            elif flips == 3:
+                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
+        else:
+            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
+            if flips == 2:
+                y = img_size[0] - y  # de-flip ud
+            elif flips == 3:
+                x = img_size[1] - x  # de-flip lr
+            p = torch.cat((x, y, wh, p[..., 4:]), -1)
+        return p
+
+    def _clip_augmented(self, y):
+        # Clip YOLOv5 augmented inference tails
+        nl = self.model[-1].nl  # number of detection layers (P3-P5)
+        g = sum(4 ** x for x in range(nl))  # grid points
+        e = 1  # exclude layer count
+        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
+        y[0] = y[0][:, :-i]  # large
+        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
+        y[-1] = y[-1][:, i:]  # small
+        return y
+
+    def _profile_one_layer(self, m, x, dt):
+        c = isinstance(m, Detect)  # is final layer, copy input as inplace fix
+        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
+        t = time_sync()
+        for _ in range(10):
+            m(x.copy() if c else x)
+        dt.append((time_sync() - t) * 100)
+        if m == self.model[0]:
+            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
+        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
+        if c:
+            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")
+
     def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
         # https://arxiv.org/abs/1708.02002 section 3.3
         # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
         m = self.model[-1]  # Detect() module
         for mi, s in zip(m.m, m.stride):  # from
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
-            b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum())  # cls
+            b = mi.bias.view(m.na, -1).detach()  # conv.bias(255) to (3,85)
+            b[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
+            b[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum())  # cls
             mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
 
     def _print_biases(self):
         m = self.model[-1]  # Detect() module
         for mi in m.m:  # from
             b = mi.bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
-            print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
+            LOGGER.info(
+                ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
 
     # def _print_weights(self):
     #     for m in self.model.modules():
     #         if type(m) is Bottleneck:
-    #             print('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
+    #             LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2))  # shortcut weights
 
     def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        #print('Fusing layers... ')
+        LOGGER.info('Fusing layers... ')
         for m in self.model.modules():
-            if type(m) is Conv and hasattr(m, 'bn'):
+            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
                 m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                 delattr(m, 'bn')  # remove batchnorm
-                m.forward = m.fuseforward  # update forward
+                m.forward = m.forward_fuse  # update forward
         self.info()
         return self
 
-    def nms(self, mode=True):  # add or remove NMS module
-        present = type(self.model[-1]) is NMS  # last layer is NMS
-        if mode and not present:
-            print('Adding NMS... ')
-            m = NMS()  # module
-            m.f = -1  # from
-            m.i = self.model[-1].i + 1  # index
-            self.model.add_module(name='%s' % m.i, module=m)  # add
-            self.eval()
-        elif not mode and present:
-            print('Removing NMS... ')
-            self.model = self.model[:-1]  # remove
-        return self
-
-    def autoshape(self):  # add autoShape module
-        print('Adding autoShape... ')
-        m = autoShape(self)  # wrap model
-        copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=())  # copy attributes
-        return m
-
     def info(self, verbose=False, img_size=640):  # print model information
         model_info(self, verbose, img_size)
 
+    def _apply(self, fn):
+        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
+        self = super()._apply(fn)
+        m = self.model[-1]  # Detect()
+        if isinstance(m, Detect):
+            m.stride = fn(m.stride)
+            m.grid = list(map(fn, m.grid))
+            if isinstance(m.anchor_grid, list):
+                m.anchor_grid = list(map(fn, m.anchor_grid))
+        return self
+
 
 def parse_model(d, ch):  # model_dict, input_channels(3)
-    logger.info('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
+    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
     anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
     na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
     no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
@@ -208,24 +261,24 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
         for j, a in enumerate(args):
             try:
                 args[j] = eval(a) if isinstance(a, str) else a  # eval strings
-            except:
+            except NameError:
                 pass
 
-        n = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,
-                 C3]:
+        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
+        if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
+                 BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):
             c1, c2 = ch[f], args[0]
             if c2 != no:  # if not output
                 c2 = make_divisible(c2 * gw, 8)
 
             args = [c1, c2, *args[1:]]
-            if m in [BottleneckCSP, C3]:
+            if m in [BottleneckCSP, C3, C3TR, C3Ghost, C3x]:
                 args.insert(2, n)  # number of repeats
                 n = 1
         elif m is nn.BatchNorm2d:
             args = [ch[f]]
         elif m is Concat:
-            c2 = sum([ch[x] for x in f])
+            c2 = sum(ch[x] for x in f)
         elif m is Detect:
             args.append([ch[x] for x in f])
             if isinstance(args[1], int):  # number of anchors
@@ -237,11 +290,11 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
         else:
             c2 = ch[f]
 
-        m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
+        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
         t = str(m)[8:-2].replace('__main__.', '')  # module type
-        np = sum([x.numel() for x in m_.parameters()])  # number params
+        np = sum(x.numel() for x in m_.parameters())  # number params
         m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        logger.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
+        LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
         save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
         layers.append(m_)
         if i == 0:
@@ -253,23 +306,33 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
+    parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--profile', action='store_true', help='profile model speed')
+    parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer')
+    parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
     opt = parser.parse_args()
-    opt.cfg = check_file(opt.cfg)  # check file
-    set_logging()
+    opt.cfg = check_yaml(opt.cfg)  # check YAML
+    print_args(vars(opt))
     device = select_device(opt.device)
 
     # Create model
+    im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
     model = Model(opt.cfg).to(device)
-    model.train()
 
-    # Profile
-    # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
-    # y = model(img, profile=True)
+    # Options
+    if opt.line_profile:  # profile layer by layer
+        _ = model(im, profile=True)
 
-    # Tensorboard
-    # from torch.utils.tensorboard import SummaryWriter
-    # tb_writer = SummaryWriter()
-    # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/")
-    # tb_writer.add_graph(model.model, img)  # add model to tensorboard
-    # tb_writer.add_image('test', img[0], dataformats='CWH')  # add model to tensorboard
+    elif opt.profile:  # profile forward-backward
+        results = profile(input=im, ops=[model], n=3)
+
+    elif opt.test:  # test all models
+        for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
+            try:
+                _ = Model(cfg)
+            except Exception as e:
+                print(f'Error in {cfg}: {e}')
+
+    else:  # report fused model summary
+        model.fuse()

Y5/models/yolov5l.yaml

@@ -1,30 +1,30 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.0  # model depth multiple
 width_multiple: 1.0  # layer channel multiple
-
-# anchors
 anchors:
   - [10,13, 16,30, 33,23]  # P3/8
   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 9, C3, [256]],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 1, SPP, [1024, [5, 9, 13]]],
-   [-1, 3, C3, [1024, False]],  # 9
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

Y5/models/yolov5m.yaml

@@ -1,30 +1,30 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 0.67  # model depth multiple
 width_multiple: 0.75  # layer channel multiple
-
-# anchors
 anchors:
   - [10,13, 16,30, 33,23]  # P3/8
   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 9, C3, [256]],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 1, SPP, [1024, [5, 9, 13]]],
-   [-1, 3, C3, [1024, False]],  # 9
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

Y5/models/yolov5n.yaml

@@ -0,0 +1,48 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
+nc: 80  # number of classes
+depth_multiple: 0.33  # model depth multiple
+width_multiple: 0.25  # layer channel multiple
+anchors:
+  - [10,13, 16,30, 33,23]  # P3/8
+  - [30,61, 62,45, 59,119]  # P4/16
+  - [116,90, 156,198, 373,326]  # P5/32
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
+   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
+   [-1, 3, C3, [128]],
+   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
+   [-1, 6, C3, [256]],
+   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
+   [-1, 9, C3, [512]],
+   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
+  ]
+
+# YOLOv5 v6.0 head
+head:
+  [[-1, 1, Conv, [512, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
+   [-1, 3, C3, [512, False]],  # 13
+
+   [-1, 1, Conv, [256, 1, 1]],
+   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
+   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
+   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
+
+   [-1, 1, Conv, [256, 3, 2]],
+   [[-1, 14], 1, Concat, [1]],  # cat head P4
+   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
+
+   [-1, 1, Conv, [512, 3, 2]],
+   [[-1, 10], 1, Concat, [1]],  # cat head P5
+   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
+
+   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
+  ]

Y5/models/yolov5s.yaml

@@ -1,30 +1,30 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 0.33  # model depth multiple
 width_multiple: 0.50  # layer channel multiple
-
-# anchors
 anchors:
   - [10,13, 16,30, 33,23]  # P3/8
   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 9, C3, [256]],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 1, SPP, [1024, [5, 9, 13]]],
-   [-1, 3, C3, [1024, False]],  # 9
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

Y5/models/yolov5x.yaml

@@ -1,30 +1,30 @@
-# parameters
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 1.33  # model depth multiple
 width_multiple: 1.25  # layer channel multiple
-
-# anchors
 anchors:
   - [10,13, 16,30, 33,23]  # P3/8
   - [30,61, 62,45, 59,119]  # P4/16
   - [116,90, 156,198, 373,326]  # P5/32
 
-# YOLOv5 backbone
+# YOLOv5 v6.0 backbone
 backbone:
   # [from, number, module, args]
-  [[-1, 1, Focus, [64, 3]],  # 0-P1/2
+  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
    [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
    [-1, 3, C3, [128]],
    [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 9, C3, [256]],
+   [-1, 6, C3, [256]],
    [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
    [-1, 9, C3, [512]],
    [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 1, SPP, [1024, [5, 9, 13]]],
-   [-1, 3, C3, [1024, False]],  # 9
+   [-1, 3, C3, [1024]],
+   [-1, 1, SPPF, [1024, 5]],  # 9
   ]
 
-# YOLOv5 head
+# YOLOv5 v6.0 head
 head:
   [[-1, 1, Conv, [512, 1, 1]],
    [-1, 1, nn.Upsample, [None, 2, 'nearest']],

Y5/utils/__init__.py

@@ -0,0 +1,36 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+utils/initialization
+"""
+
+
+def notebook_init(verbose=True):
+    # Check system software and hardware
+    print('Checking setup...')
+
+    import os
+    import shutil
+
+    from utils.general import check_requirements, emojis, is_colab
+    from utils.torch_utils import select_device  # imports
+
+    check_requirements(('psutil', 'IPython'))
+    import psutil
+    from IPython import display  # to display images and clear console output
+
+    if is_colab():
+        shutil.rmtree('/content/sample_data', ignore_errors=True)  # remove colab /sample_data directory
+
+    # System info
+    if verbose:
+        gb = 1 << 30  # bytes to GiB (1024 ** 3)
+        ram = psutil.virtual_memory().total
+        total, used, free = shutil.disk_usage("/")
+        display.clear_output()
+        s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)'
+    else:
+        s = ''
+
+    select_device(newline=False)
+    print(emojis(f'Setup complete ✅ {s}'))
+    return display