diff --git a/.gitignore b/.gitignore index 0413812..d0f8541 100644 --- a/.gitignore +++ b/.gitignore @@ -155,3 +155,4 @@ node_modules/ /test/yolov7-tracker/runs /test/yolov7-tracker/wandb VoTT-v2-none.zip +VoTT-v2-done.zip diff --git a/yolov7-setup/v2/compute_yolov3_anchors_v2.py b/yolov7-setup/v2/compute_yolov3_anchors_v2.py new file mode 100644 index 0000000..8620452 --- /dev/null +++ b/yolov7-setup/v2/compute_yolov3_anchors_v2.py @@ -0,0 +1,96 @@ +import json +import os, sys +import math +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns; sns.set() # for plot styling +from PIL import Image + +imgsize = int(input("Image size for train and run? ")) +print("=> imgsize =",imgsize) +# imgsize = 256 + +BBDlabeldict = {"runner":0} + +datasetpath = '/Users/noham/Documents/GitHub/Stage-2024/yolov7-setup/v2/datasetv2/' +images_path = datasetpath+"images" +labels_path = datasetpath+"labels" + +filenames = sorted(os.listdir(images_path)) +print("Nb d'images =",len(filenames)) +w,h = [] , [] +for i,file in enumerate(filenames): + #print(file) + try: + # if True: + im = Image.open(images_path+"/"+file) + img_width = im.width + img_height = im.height + #print(img_width,img_height) + fo = open(images_path.replace("images","labels")+"/"+file.replace("jpg","txt"), "r+") + lines = fo.readlines() + for line in lines: + a = np.array(line.split(" ")).astype(float) + w.append(a[3]*img_width) + h.append(a[4]*img_height) + if (a[3]*img_height<0.001) or (a[4]*img_height<0.001): + print("!! ATTENTION : boite trop petite dans le fichier ",file, "=> il faut vérifier et supprimer la ligne dans le fichier label au format txt !!") + except: + pass +w=np.asarray(w)#+0.001*(np.random.random((len(w),)) - 0.5) +h=np.asarray(h)#+0.001*(np.random.random((len(w),)) - 0.5) +print("h => min =",h.min()," max =",h.max()) +print("w => min =",w.min()," max =",h.max()) +print("Nb objets entourés =",len(h)) + +x=[w,h] +x=np.asarray(x) +x=x.transpose() +########################################## K- Means +########################################## + +from sklearn.cluster import KMeans +kmeans3 = KMeans(n_clusters=9) +kmeans3.fit(x) +y_kmeans3 = kmeans3.predict(x) + +########################################## +centers3 = kmeans3.cluster_centers_ + +yolo_anchor_average=[] +for ind in range (9): + yolo_anchor_average.append(np.mean(x[y_kmeans3==ind],axis=0)) + +yolo_anchor_average=np.array(yolo_anchor_average) + +plt.scatter(x[:, 0], x[:, 1], c=y_kmeans3, s=2, cmap='viridis') +plt.scatter(yolo_anchor_average[:, 0], yolo_anchor_average[:, 1], c='red', s=50) +yoloV3anchors = yolo_anchor_average +yoloV3anchors[:, 0] =yolo_anchor_average[:, 0] /1920 *imgsize +yoloV3anchors[:, 1] =yolo_anchor_average[:, 1] /1056 *imgsize +yoloV3anchors = np.rint(yoloV3anchors) +fig, ax = plt.subplots() +for ind in range(9): + rectangle= plt.Rectangle((0.5*imgsize-yoloV3anchors[ind,0]/2,0.5*imgsize-yoloV3anchors[ind,1]/2), yoloV3anchors[ind,0],yoloV3anchors[ind,1] , fc='b',edgecolor='b',fill = None) + ax.add_patch(rectangle) +ax.set_aspect(1.0) +plt.axis([0,imgsize,0,imgsize]) +plt.savefig(datasetpath+"boites.png",dpi=150) +plt.show() + +yoloV3anchors.sort(axis=0) +print("Your custom anchor boxes are {}".format(yoloV3anchors)) + +F = open(datasetpath+"YOLOV_BDD_Anchors_"+str(imgsize)+".txt", "w") +F.write("{}".format(yoloV3anchors)) +F.close() + +print("Anchors box for yaml file :") +s_anchors = yoloV3anchors[yoloV3anchors[:, 0].argsort()] +anchor_lists = [s_anchors[i:i+3].tolist() for i in range(0, len(s_anchors), 3)] +# anchor_lists = [[[14.0, 37.0], [15.0, 48.0], [19.0, 53.0]], [[21.0, 70.0], [24.0, 88.0], [31.0, 112.0]], [[34.0, 136.0], [35.0, 155.0], [76.0, 161.0]]] +p = ['] # P3/8', '] # P4/16', '] # P5/32'] +out = "anchors:" +for l in anchor_lists: + out += '\n' + " - [" + ", ".join([f"{int(i[0])},{int(i[1])}" for i in l]) + p[anchor_lists.index(l)] +print(out) \ No newline at end of file diff --git a/yolov7-setup/v2/datasetv2/YOLOV_BDD_Anchors_720.txt b/yolov7-setup/v2/datasetv2/YOLOV_BDD_Anchors_720.txt new file mode 100644 index 0000000..b811e21 --- /dev/null +++ b/yolov7-setup/v2/datasetv2/YOLOV_BDD_Anchors_720.txt @@ -0,0 +1,9 @@ +[[ 23. 58.] + [ 27. 104.] + [ 38. 106.] + [ 46. 146.] + [ 47. 156.] + [ 68. 192.] + [ 68. 232.] + [134. 273.] + [204. 498.]] \ No newline at end of file diff --git a/yolov7-setup/v2/datasetv2/boites.png b/yolov7-setup/v2/datasetv2/boites.png new file mode 100644 index 0000000..0427f04 Binary files 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a/yolov7-setup/v2/datasetv2/labels/image_000000000141.txt b/yolov7-setup/v2/datasetv2/labels/image_000000000141.txt new file mode 100644 index 0000000..3706c00 --- /dev/null +++ b/yolov7-setup/v2/datasetv2/labels/image_000000000141.txt @@ -0,0 +1,7 @@ +0 0.059944577001482584 0.5459151638669302 0.0810525157524092 0.24496150362318841 +0 0.1947652196071164 0.557227334486166 0.06662921608598962 0.24051486330698288 +0 0.3040895454966642 0.5437947751976284 0.07304311063750928 0.243458703886693 +0 0.36515619208673095 0.5238415060935441 0.052084298554484806 0.24714365118577078 +0 0.5115942364714603 0.527150753458498 0.062986471460341 0.28186244235836627 +0 0.6780195978502594 0.4825453927865612 0.08003613787991105 0.24532176383399207 +0 0.7452945468865827 0.47180963850461133 0.07431430689399555 0.23744750494071146 diff --git a/yolov7-setup/v2/datasetv2/labels/image_000000000142.txt b/yolov7-setup/v2/datasetv2/labels/image_000000000142.txt new file mode 100644 index 0000000..9646c8f --- 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--git a/yolov7-setup/v2/datasetv2/labels/image_000000000424.txt b/yolov7-setup/v2/datasetv2/labels/image_000000000424.txt new file mode 100644 index 0000000..0321c33 --- /dev/null +++ b/yolov7-setup/v2/datasetv2/labels/image_000000000424.txt @@ -0,0 +1,8 @@ +0 0.17796908530042918 0.40614406779661016 0.04215657784930856 0.21961731991525424 +0 0.2551617638292799 0.4389896716101695 0.04703519909394373 0.20321106991525423 +0 0.369440532606104 0.43975602489406784 0.046314303171196954 0.2162109375 +0 0.40849278731521216 0.419678561970339 0.03705442298521698 0.19086003707627117 +0 0.5654022114926085 0.4497599973516949 0.04776540891750119 0.2156547934322034 +0 0.6022982981640439 0.4324682203389831 0.04002742012398665 0.20568723516949153 +0 0.621286547746781 0.3851479740466101 0.03624038805436337 0.18476231461864406 +0 0.6935214368741058 0.38558990995762715 0.06260990402956605 0.1899563029661017 diff --git a/yolov7-setup/v2/datasetv2/labels/image_000000000425.txt 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a/yolov7-setup/v2/datasetv2/labels/image_000000000464.txt b/yolov7-setup/v2/datasetv2/labels/image_000000000464.txt new file mode 100644 index 0000000..05ade1f --- /dev/null +++ b/yolov7-setup/v2/datasetv2/labels/image_000000000464.txt @@ -0,0 +1,4 @@ +0 0.6012830829756796 0.6755644200211864 0.2525333810205055 0.6488711599576271 +0 0.4628524380066762 0.6328174655720339 0.20347147711015737 0.4933825476694915 +0 0.46488100560324275 0.4479161149364407 0.09612318192656176 0.24660023834745765 +0 0.919540936456843 0.6565363479872882 0.16091812708631376 0.6680117849576271 diff --git a/yolov7-setup/v2/datasetv2/labels/image_000000000465.txt b/yolov7-setup/v2/datasetv2/labels/image_000000000465.txt new file mode 100644 index 0000000..575420e --- /dev/null +++ b/yolov7-setup/v2/datasetv2/labels/image_000000000465.txt @@ -0,0 +1,2 @@ +0 0.5028211805555556 0.5857471530720338 0.15393456425846447 0.4264731197033898 +0 0.596754664401526 0.6298066737288136 0.36971342989985695 0.7298331567796611 diff 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a/yolov7-setup/v2/datasetv2/labels/image_000000000480.txt b/yolov7-setup/v2/datasetv2/labels/image_000000000480.txt new file mode 100644 index 0000000..0795c44 --- /dev/null +++ b/yolov7-setup/v2/datasetv2/labels/image_000000000480.txt @@ -0,0 +1,8 @@ +0 0.7861230329041489 0.50556640625 0.04339532665712912 0.10103614936440677 +0 0.6922221477110158 0.5174672272245763 0.06073035884597044 0.10998079978813559 +0 0.6072961373390557 0.5328505693855932 0.08154506437768241 0.12183858580508473 +0 0.5298864449213162 0.5433941340042373 0.10249761564139241 0.15558461334745763 +0 0.45300019372913686 0.5799903998940678 0.08523336909871244 0.1535255561440678 +0 0.39535832439198854 0.6125844147245764 0.08295705174058178 0.19795749470338983 +0 0.3384569101692894 0.6599046610169492 0.11363890379113019 0.24638506355932205 +0 0.2713213817358131 0.7306458554025423 0.19821098593228423 0.34126390360169495 diff --git a/yolov7-setup/v2/datasetv2/labels/image_000000000481.txt 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+/home/noham.rivoirard/yolov7-tracker/data/datasetv2/images/image_000000000534.jpg +/home/noham.rivoirard/yolov7-tracker/data/datasetv2/images/image_000000000535.jpg +/home/noham.rivoirard/yolov7-tracker/data/datasetv2/images/image_000000000536.jpg +/home/noham.rivoirard/yolov7-tracker/data/datasetv2/images/image_000000000537.jpg diff --git a/yolov7-setup/v2/instructions_v2.md b/yolov7-setup/v2/instructions_v2.md new file mode 100644 index 0000000..5299b70 --- /dev/null +++ b/yolov7-setup/v2/instructions_v2.md @@ -0,0 +1,153 @@ +# References +[Yolov7-tracker](https://github.com/JackWoo0831/Yolov7-tracker) + +[VoTT](https://github.com/microsoft/VoTT?tab=readme-ov-file#build-and-run-from-source) [LabelIMG](https://github.com/HumanSignal/labelImg?tab=readme-ov-file#label-studio-is-a-modern-multi-modal-data-annotation-tool) [CVAT](https://github.com/cvat-ai/cvat/?tab=readme-ov-file) + +[https://theses.hal.science/tel-01784521/file/TH2017ChapelMarieNeige.pdf](https://theses.hal.science/tel-01784521/file/TH2017ChapelMarieNeige.pdf) + +[OpenCV exemples](https://github.com/opencv/opencv/tree/4.x/samples/python) + +[calcul de distance camera - objet](https://pyimagesearch.com/2015/01/19/find-distance-camera-objectmarker-using-python-opencv/) + +# Yolov7-tracker +```bash +git clone https://github.com/JackWoo0831/Yolov7-tracker.git +mv Yolov7-tracker yolov7-tracker +cd yolov7-tracker +git checkout v2 # change to v2 branch !! +``` +```bash +conda create -n yolov7 python=3.9 pytorch=1.12 +conda activate yolov7 +pip3 install numpy scipy matplotlib cython pandas cuda-python +``` + +``` +conda env : /home/noham.rivoirard/.conda/envs/yolov7 +``` + + +```bash +pip3 install -r requirements.txt +pip3 install ultralytics==8.0.94 +``` + +# Setup cluster +ssh sas +ssh cinaps +ssh node20 +```bash +nvidia-smi => cuda 11.4 sur node20 +``` + +cf notes d'installation de pytorch pour trouver les bonnes versions : https://pytorch.org/get-started/previous-versions/ +```bash +pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu118 +pip3 install filterpy +``` + +```bash +>>> import torch +>>> torch.cuda.is_available() +True +``` + +# Setup macos +```bash +conda install pytorch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 -c pytorch +``` + +# Config train +- Create [datasetv2.yaml](../test/yolov7-tracker/tracker/config_files/datasetv2.yaml) in tracker/config_files (config file of the dataset): +```yaml +DATASET_ROOT: '/data/datasetv2' +SPLIT: train +CATEGORY_NAMES: + - 'runner' + +CATEGORY_DICT: + 0: 'runner' + +CERTAIN_SEQS: + - + +IGNORE_SEQS: # Seqs you want to ignore + - +``` + +- Export from VoTT as json +- Convert to Yolov using [vott2yolov.py](../yolov7-setup/vott2yolov.py) +- Edit [dataset.yaml](../test/yolov7-tracker/data/datasetv2/dataset.yaml) in data/datasetv2: +```yaml +train: data/datasetv2/liste_images.txt +val: data/datasetv2/liste_images.txt +nc: 1 +names: ['runner'] +``` +- Get box sizes from the dataset using [compute_yolov3_anchors.py](../yolov7-setup/compute_yolov3_anchors.py), result as follow: +``` +Your custom anchor boxes are [[ 13. 34.] + [ 17. 46.] + [ 18. 59.] + [ 21. 79.] + [ 26. 102.] + [ 33. 111.] + [ 34. 135.] + [ 40. 155.] + [ 76. 161.]] +Anchors box for yaml file: +anchors: + - [13,34, 17,46, 18,59] # P3/8 + - [21,79, 26,102, 33,111] # P4/16 + - [34,135, 40,155, 76,161] # P5/32 + ``` + - Replace anchors box in cfg/training/yolov7x_datasetv2.yaml, ex: [yolov7x_datasetv2.yaml](../test/yolov7-tracker/cfg/training/yolov7x_datasetv2.yaml) + + +# Launch train +GPU: +```bash +python3 train.py --dataset dataset1_2024_06_19__ --workers 1 --device 0 --batch-size 4 --data data/dataset1_2024_06_19/datasetyaml --img 1280 720 --cfg cfg/training/yolov7x_dataset1_2024_06_19.yaml --weights '' --name yolov7x-dataset1_2024_06_19 --hyp data/hyp.scratch.custom.yaml +``` + +```bash +python3 train.py --dataset datasetv2_20240722_ --epochs 600 --workers 1 --device 0 --batch-size 4 --data data/datasetv2/dataset.yaml --img 1280 720 --cfg cfg/training/yolov7x_datasetv2.yaml --weights '' --name yolov7x-datasetv2_20240722 --hyp data/hyp.scratch.custom.yaml +``` + + + + +CPU: +```bash +python3 train.py --dataset datasetv2_20240718_ --epochs 20 --workers 1 --device cpu --batch-size 4 --data data/datasetv2/dataset.yaml --img 1280 720 --cfg cfg/training/yolov7x_datasetv2.yaml --weights '' --name yolov7x-datasetv2_20240718 --hyp data/hyp.scratch.custom.yaml +``` + +# Note: +``` +Si erreur : _pickle.UnpicklingError: STACK_GLOBAL requires str +Effacer les fichiers .cache : liste_images.cache par exemple... + +Si erreur np.int => remplacer par des np.int64 + +Si erreur cuda/cpu dans loss.py : +you have to replace the line in the file yolo7/utils/loss.py +"from_which_layer.append((torch.ones(size=(len(b),)) * i)" +to "from_which_layer.append((torch.ones(size=(len(b),)) * i).to('cuda'))", +and add new line "fg_mask_inboxes = fg_mask_inboxes.to(torch.device('cuda'))" +after "fg_mask_inboxes = matching_matrix.sum(0) > 0.0" +so you need to do it 3 times in the file +``` + + + + + + +# Result : +10 iterations on MBP M2 : [logs](test3.log) + +On wandb : https://wandb.ai/noham-/YOLOR/runs/ + +PDF : [pdf](run_datasetv2.pdf) + +Weights : [best.pt](../test/yolov7-tracker/runs/train/yolov7x-datasetv2_202407189/weights/best.pt) \ No newline at end of file diff --git a/yolov7-setup/v2/vott2yolov_v2.py b/yolov7-setup/v2/vott2yolov_v2.py new file mode 100644 index 0000000..7f3cb6f --- /dev/null +++ b/yolov7-setup/v2/vott2yolov_v2.py @@ -0,0 +1,77 @@ +import numpy as np +import json, sys, os, shutil + +## cliquer sur export dans Vott puis : + +# prefix = "../exports_VOTT/CH01-20230614-083436-091514-Zone2/vott-json-export/" +# vottjson_filename = "Poulets-export.json" +# cc = 1000000 + +# /Users/noham/Documents/GitHub/Stage-2024/VoTT-v2/output/vott-json-export/dataset-v2-export.json + +prefix = "/Users/noham/Documents/GitHub/Stage-2024/VoTT-v2/output/" +print('Working folder: ', prefix) +jsonname = 'vott-json-export' +jsonfile = 'dataset-v2-export.json' +json_path = prefix + jsonname + '/' + jsonfile +print('Working json file: ', json_path) +jsonfolder = prefix+jsonname +print('Working json folder: ', jsonfolder) + +outputname = 'datasetv2' +print('Ooutput folder: ', outputname) +output = '/Users/noham/Documents/GitHub/Stage-2024/yolov7-setup/v2/'+outputname +print('Output folder: ', output) +cc = 0 + +label = 'runner' + +for folder in [output, output+"/images",output+"/labels"]: + if not os.path.exists(folder): + os.makedirs(folder) + +with open(json_path, "r") as read_file: + data = json.load(read_file) + +imglist = [] +for a in data["assets"]: + asset = data["assets"][a]["asset"] + regions = data["assets"][a]["regions"] + img_name = asset["name"] + width = asset["size"]["width"] + height = asset["size"]["height"] + # print("\nimage name : ",img_name," width =",width," height =",height) + shutil.copyfile(jsonfolder+"/"+img_name,output+"/images/image_"+str(cc).zfill(12)+".jpg") + imglist.append(output+"/images/image_"+str(cc).zfill(12)+".jpg") + f = open(output+"/labels/image_"+str(cc).zfill(12)+".txt", "w") + # f = open(prefix+"/"+img_name.replace(".jpg",".txt"), "w") + for region in regions: + boundingBox = region["boundingBox"] + points = region["points"] + # print(" tags =",region["tags"]," boundingBox =",boundingBox," points =",points) + # ## Labels : + # D’après la doc on doit avoir : + # Label_ID_1 X_CENTER_NORM Y_CENTER_NORM WIDTH_NORM HEIGHT_NORM + # The label_id is the index number in the classes.names file. The id of the first label will be 0 and an increasing integer after that. Note all the position attributes in the label file are not absolute but normalised. + # X_CENTER_NORM = X_CENTER_ABS/IMAGE_WIDTH centre image + # Y_CENTER_NORM = Y_CENTER_ABS/IMAGE_HEIGHT centre image + # WIDTH_NORM = WIDTH_OF_LABEL_ABS/IMAGE_WIDTH largeur boite + # HEIGHT_NORM = HEIGHT_OF_LABEL_ABS/IMAGE_HEIGHT hauteur boite + # dans un repere dont l'origine est en haut a gauche et X de gauche à droite, Y du haut vers le bas + Label_ID = "0" + X_CENTER_NORM = (boundingBox["left"]+boundingBox["width"]/2)/width + Y_CENTER_NORM = (boundingBox["top"]+boundingBox["height"]/2)/height + WIDTH_NORM = boundingBox["width"]/width + HEIGHT_NORM = boundingBox["height"]/height + f.write(Label_ID+" "+str(X_CENTER_NORM)+" "+str(Y_CENTER_NORM)+" "+str(WIDTH_NORM)+" "+str(HEIGHT_NORM)+"\n" ) # 0 = poulet si l'on a qu'une classe d'objet... + f.close() + cc += 1 + # print(" written file :",prefix+"/"+img_name.replace(".jpg",".txt")) +with open(output+"/liste_images.txt", "w") as file: + for item in imglist: + file.write(item + "\n") +with open(output+"/dataset.yaml", "w") as file: + file.write(f"""train: data/{outputname}/liste_images.txt +val: data/{outputname}/liste_images.txt +nc: 1 +names: ['{label}']""") \ No newline at end of file diff --git a/yolov7-setup/v2/yolov7x_datasetv2.yaml b/yolov7-setup/v2/yolov7x_datasetv2.yaml new file mode 100644 index 0000000..d362d64 --- /dev/null +++ b/yolov7-setup/v2/yolov7x_datasetv2.yaml @@ -0,0 +1,156 @@ +# parameters +nc: 1 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [23,58, 27,104, 38,106] # P3/8 + - [46,146, 47,156, 68,192] # P4/16 + - [68,232, 134,273, 204,498] # P5/32 + +# yolov7 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [40, 3, 1]], # 0 + + [-1, 1, Conv, [80, 3, 2]], # 1-P1/2 + [-1, 1, Conv, [80, 3, 1]], + + [-1, 1, Conv, [160, 3, 2]], # 3-P2/4 + [-1, 1, Conv, [64, 1, 1]], + [-2, 1, Conv, [64, 1, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, Conv, [64, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 13 + + [-1, 1, MP, []], + [-1, 1, Conv, [160, 1, 1]], + [-3, 1, Conv, [160, 1, 1]], + [-1, 1, Conv, [160, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 18-P3/8 + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 28 + + [-1, 1, MP, []], + [-1, 1, Conv, [320, 1, 1]], + [-3, 1, Conv, [320, 1, 1]], + [-1, 1, Conv, [320, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 33-P4/16 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 43 + + [-1, 1, MP, []], + [-1, 1, Conv, [640, 1, 1]], + [-3, 1, Conv, [640, 1, 1]], + [-1, 1, Conv, [640, 3, 2]], + [[-1, -3], 1, Concat, [1]], # 48-P5/32 + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [1280, 1, 1]], # 58 + ] + +# yolov7 head +head: + [[-1, 1, SPPCSPC, [640]], # 59 + + [-1, 1, Conv, [320, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [43, 1, Conv, [320, 1, 1]], # route backbone P4 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 73 + + [-1, 1, Conv, [160, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [28, 1, Conv, [160, 1, 1]], # route backbone P3 + [[-1, -2], 1, Concat, [1]], + + [-1, 1, Conv, [128, 1, 1]], + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, Conv, [128, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [160, 1, 1]], # 87 + + [-1, 1, MP, []], + [-1, 1, Conv, [160, 1, 1]], + [-3, 1, Conv, [160, 1, 1]], + [-1, 1, Conv, [160, 3, 2]], + [[-1, -3, 73], 1, Concat, [1]], + + [-1, 1, Conv, [256, 1, 1]], + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, Conv, [256, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [320, 1, 1]], # 102 + + [-1, 1, MP, []], + [-1, 1, Conv, [320, 1, 1]], + [-3, 1, Conv, [320, 1, 1]], + [-1, 1, Conv, [320, 3, 2]], + [[-1, -3, 59], 1, Concat, [1]], + + [-1, 1, Conv, [512, 1, 1]], + [-2, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, Conv, [512, 3, 1]], + [[-1, -3, -5, -7, -8], 1, Concat, [1]], + [-1, 1, Conv, [640, 1, 1]], # 117 + + [87, 1, Conv, [320, 3, 1]], + [102, 1, Conv, [640, 3, 1]], + [117, 1, Conv, [1280, 3, 1]], + + [[118,119,120], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5) + ]