{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 🚀 Barrier Reef YOLOX [Training]","metadata":{"papermill":{"duration":0.046451,"end_time":"2021-09-23T19:25:56.908502","exception":false,"start_time":"2021-09-23T19:25:56.862051","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"### Hi kagglers, This is `Training` notebook using `YOLOX`.\n\n\n### Other notebooks in the competition\n- [Barrier Reef YOLOX [Inference]](https://www.kaggle.com/ammarnassanalhajali/barrier-reef-yolox-inference/edit)\n\n\n\n### Please if this kernel is useful, please upvote !!","metadata":{"papermill":{"duration":0.041709,"end_time":"2021-09-23T19:25:56.993059","exception":false,"start_time":"2021-09-23T19:25:56.95135","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"![logo.png](attachment:655ae146-1300-4143-bd34-9d011cacf766.png)","metadata":{},"attachments":{"655ae146-1300-4143-bd34-9d011cacf766.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities.","metadata":{"papermill":{"duration":0.039068,"end_time":"2021-09-23T19:25:57.073963","exception":false,"start_time":"2021-09-23T19:25:57.034895","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"# ☀️ Importing Libraries","metadata":{"papermill":{"duration":0.048741,"end_time":"2021-09-23T19:26:23.979079","exception":false,"start_time":"2021-09-23T19:26:23.930338","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings(\"ignore\")\nimport ast\nimport os\nimport json\nimport pandas as pd\nimport torch\nimport importlib\nimport cv2 \n\nimport shutil\nfrom shutil import copyfile\nfrom tqdm.notebook import tqdm\ntqdm.pandas()\nfrom sklearn.model_selection import GroupKFold\nfrom PIL import Image\nfrom string import Template\nfrom IPython.display import display\nTRAIN_PATH = '/kaggle/input/tensorflow-great-barrier-reef'","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":1.141507,"end_time":"2021-09-23T19:26:25.168692","exception":false,"start_time":"2021-09-23T19:26:24.027185","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-01-02T16:08:49.742409Z","iopub.execute_input":"2022-01-02T16:08:49.742702Z","iopub.status.idle":"2022-01-02T16:08:49.752815Z","shell.execute_reply.started":"2022-01-02T16:08:49.742672Z","shell.execute_reply":"2022-01-02T16:08:49.751981Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Check Torch & CUDA ","metadata":{}},{"cell_type":"code","source":"print(f\"Torch: {torch.__version__}\")\n!nvcc --version","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:08:49.754379Z","iopub.execute_input":"2022-01-02T16:08:49.755304Z","iopub.status.idle":"2022-01-02T16:08:50.430143Z","shell.execute_reply.started":"2022-01-02T16:08:49.755263Z","shell.execute_reply":"2022-01-02T16:08:50.429211Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# ⬇️ Download YOLOX","metadata":{"papermill":{"duration":0.039027,"end_time":"2021-09-23T19:25:57.151955","exception":false,"start_time":"2021-09-23T19:25:57.112928","status":"completed"},"tags":[]}},{"cell_type":"code","source":"!git clone https://github.com/Megvii-BaseDetection/YOLOX -q\n\n%cd YOLOX\n!pip install -U pip && pip install -r requirements.txt\n!pip install -v -e . ","metadata":{"papermill":{"duration":12.425803,"end_time":"2021-09-23T19:26:09.617619","exception":false,"start_time":"2021-09-23T19:25:57.191816","status":"completed"},"tags":[],"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-01-02T16:08:50.590522Z","iopub.execute_input":"2022-01-02T16:08:50.591491Z","iopub.status.idle":"2022-01-02T16:09:35.203434Z","shell.execute_reply.started":"2022-01-02T16:08:50.591437Z","shell.execute_reply":"2022-01-02T16:09:35.202568Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-01-02T16:09:35.207224Z","iopub.execute_input":"2022-01-02T16:09:35.207459Z","iopub.status.idle":"2022-01-02T16:09:45.515588Z","shell.execute_reply.started":"2022-01-02T16:09:35.207432Z","shell.execute_reply":"2022-01-02T16:09:45.514626Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🔨 Functions","metadata":{}},{"cell_type":"code","source":"def get_bbox(annots):\n bboxes = [list(annot.values()) for annot in annots]\n return bboxes\ndef get_path(row):\n row['image_path'] = f'{TRAIN_PATH}/train_images/video_{row.video_id}/{row.video_frame}.jpg'\n return row\ndef load_image(image_path):\n return cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-01-02T16:09:45.51745Z","iopub.execute_input":"2022-01-02T16:09:45.517811Z","iopub.status.idle":"2022-01-02T16:09:45.525885Z","shell.execute_reply.started":"2022-01-02T16:09:45.517769Z","shell.execute_reply":"2022-01-02T16:09:45.524313Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🍮 Loading Data","metadata":{"papermill":{"duration":0.05131,"end_time":"2021-09-23T19:26:25.48976","exception":false,"start_time":"2021-09-23T19:26:25.43845","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Read in the data CSV files\ndf = pd.read_csv(\"/kaggle/input/tensorflow-great-barrier-reef/train.csv\")\ndf.head(5)","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:45.528477Z","iopub.execute_input":"2022-01-02T16:09:45.52876Z","iopub.status.idle":"2022-01-02T16:09:45.595546Z","shell.execute_reply.started":"2022-01-02T16:09:45.528715Z","shell.execute_reply":"2022-01-02T16:09:45.594744Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# BBoxes\n##### 📌 Note \n> We can see there are many images without any BBox. ","metadata":{}},{"cell_type":"code","source":"df[\"NumBBox\"]=df['annotations'].apply(lambda x: str.count(x, 'x'))\ndf.head(5)","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:45.596987Z","iopub.execute_input":"2022-01-02T16:09:45.597809Z","iopub.status.idle":"2022-01-02T16:09:45.629761Z","shell.execute_reply.started":"2022-01-02T16:09:45.597772Z","shell.execute_reply":"2022-01-02T16:09:45.628959Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(df[\"NumBBox\"].unique())","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:45.631043Z","iopub.execute_input":"2022-01-02T16:09:45.631632Z","iopub.status.idle":"2022-01-02T16:09:45.638329Z","shell.execute_reply.started":"2022-01-02T16:09:45.631594Z","shell.execute_reply":"2022-01-02T16:09:45.637306Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train=df[df[\"NumBBox\"]>0]\ndf_train.sample(2)","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:45.639952Z","iopub.execute_input":"2022-01-02T16:09:45.641014Z","iopub.status.idle":"2022-01-02T16:09:45.664248Z","shell.execute_reply.started":"2022-01-02T16:09:45.640978Z","shell.execute_reply":"2022-01-02T16:09:45.663526Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(df_train['NumBBox'].sum())","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:45.665682Z","iopub.execute_input":"2022-01-02T16:09:45.66655Z","iopub.status.idle":"2022-01-02T16:09:45.672456Z","shell.execute_reply.started":"2022-01-02T16:09:45.66651Z","shell.execute_reply":"2022-01-02T16:09:45.671462Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> We have just 4919 images with 11898 BBoxs, we will use them in training.","metadata":{}},{"cell_type":"code","source":"df_train['annotations'] = df_train['annotations'].progress_apply(lambda x: ast.literal_eval(x))\ndf_train['bboxes'] = df_train.annotations.progress_apply(get_bbox)\ndf_train.sample(2)","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:45.674168Z","iopub.execute_input":"2022-01-02T16:09:45.674923Z","iopub.status.idle":"2022-01-02T16:09:46.04128Z","shell.execute_reply.started":"2022-01-02T16:09:45.674884Z","shell.execute_reply":"2022-01-02T16:09:46.040489Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Size of Images\n##### 📌 Note \n> All images have Width=1280 & Height=720 ","metadata":{}},{"cell_type":"code","source":"df_train[\"width\"]=1280\ndf_train[\"height\"]=720\ndf_train.sample(2)","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:46.045104Z","iopub.execute_input":"2022-01-02T16:09:46.045551Z","iopub.status.idle":"2022-01-02T16:09:46.065703Z","shell.execute_reply.started":"2022-01-02T16:09:46.045511Z","shell.execute_reply":"2022-01-02T16:09:46.064983Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Path of Images","metadata":{}},{"cell_type":"code","source":"df_train = df_train.progress_apply(get_path, axis=1)\ndf_train.sample(2)","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:46.067102Z","iopub.execute_input":"2022-01-02T16:09:46.067545Z","iopub.status.idle":"2022-01-02T16:09:49.451172Z","shell.execute_reply.started":"2022-01-02T16:09:46.067507Z","shell.execute_reply":"2022-01-02T16:09:49.450306Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🍚 Splitting Dataset","metadata":{"papermill":{"duration":0.050056,"end_time":"2021-09-23T19:26:26.165618","exception":false,"start_time":"2021-09-23T19:26:26.115562","status":"completed"},"tags":[]}},{"cell_type":"code","source":"n_spl=3\nSelected_Fold=2 #0..2\n\nfrom sklearn.model_selection import GroupKFold\ngkf = GroupKFold(n_splits = n_spl) # num_folds=3 as there are total 3 videos\ndf_train = df_train.reset_index(drop=True)\ndf_train['fold'] = -1\nfor fold, (train_idx, val_idx) in enumerate(gkf.split(df_train, groups = df_train.video_id.tolist())):\n df_train.loc[val_idx, 'fold'] = fold\ndisplay(df_train.fold.value_counts())","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:49.452565Z","iopub.execute_input":"2022-01-02T16:09:49.452862Z","iopub.status.idle":"2022-01-02T16:09:49.468983Z","shell.execute_reply.started":"2022-01-02T16:09:49.452824Z","shell.execute_reply":"2022-01-02T16:09:49.468235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 🍚 Organize Directories\n\nI organized train and val images according to the example below.\n\n```\n/Kaggle/working\n /dataset\n /images\n /train2017\n /val2017\n /annotations\n /YOLOX\n \n \n```","metadata":{"papermill":{"duration":0.053507,"end_time":"2021-09-23T19:26:26.524316","exception":false,"start_time":"2021-09-23T19:26:26.470809","status":"completed"},"tags":[]}},{"cell_type":"code","source":"Work_Dir = '/kaggle/working/' \nDataSet_Path = 'dataset/images'\n\nos.makedirs(f'{Work_Dir}{DataSet_Path}/train2017', exist_ok=True)\nos.makedirs(f'{Work_Dir}{DataSet_Path}/val2017', exist_ok=True)\nos.makedirs(f'{Work_Dir}{DataSet_Path}/annotations', exist_ok=True)","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:49.470114Z","iopub.execute_input":"2022-01-02T16:09:49.470771Z","iopub.status.idle":"2022-01-02T16:09:49.476532Z","shell.execute_reply.started":"2022-01-02T16:09:49.47072Z","shell.execute_reply":"2022-01-02T16:09:49.475866Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in tqdm(range(len(df_train))):\n row = df_train.loc[i]\n if row.fold != Selected_Fold:\n copyfile(f'{row.image_path}', f'{Work_Dir}{DataSet_Path}/train2017/{row.image_id}.jpg')\n else:\n copyfile(f'{row.image_path}', f'{Work_Dir}{DataSet_Path}/val2017/{row.image_id}.jpg') ","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:09:49.477818Z","iopub.execute_input":"2022-01-02T16:09:49.478245Z","iopub.status.idle":"2022-01-02T16:10:26.420797Z","shell.execute_reply.started":"2022-01-02T16:09:49.478206Z","shell.execute_reply":"2022-01-02T16:10:26.419934Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f'Number of training files: {len(os.listdir(f\"{Work_Dir}{DataSet_Path}/train2017/\"))}')\nprint(f'Number of validation files: {len(os.listdir(f\"{Work_Dir}{DataSet_Path}/val2017/\"))}')","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:10:26.422297Z","iopub.execute_input":"2022-01-02T16:10:26.423198Z","iopub.status.idle":"2022-01-02T16:10:26.43371Z","shell.execute_reply.started":"2022-01-02T16:10:26.423153Z","shell.execute_reply":"2022-01-02T16:10:26.432905Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🍜 Creating COCO Annitation","metadata":{}},{"cell_type":"code","source":"annotion_id = 0\n\ndef save_annot_json(json_annotation, filename):\n with open(filename, 'w') as f:\n output_json = json.dumps(json_annotation)\n f.write(output_json)","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:10:26.435579Z","iopub.execute_input":"2022-01-02T16:10:26.436075Z","iopub.status.idle":"2022-01-02T16:10:34.444991Z","shell.execute_reply.started":"2022-01-02T16:10:26.436029Z","shell.execute_reply":"2022-01-02T16:10:34.444167Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def dataset2coco(df, dest_path):\n \n global annotion_id\n \n annotations_json = {\n \"info\": [],\n \"licenses\": [],\n \"categories\": [],\n \"images\": [],\n \"annotations\": []\n }\n \n info = {\n \"year\": \"2021\",\n \"version\": \"1\",\n \"description\": \"COTS dataset - COCO format\",\n \"contributor\": \"\",\n \"url\": \"https://kaggle.com\",\n \"date_created\": \"2021-11-30T15:01:26+00:00\"\n }\n annotations_json[\"info\"].append(info)\n \n lic = {\n \"id\": 1,\n \"url\": \"\",\n \"name\": \"Unknown\"\n }\n annotations_json[\"licenses\"].append(lic)\n\n classes = {\"id\": 0, \"name\": \"starfish\", \"supercategory\": \"none\"}\n\n annotations_json[\"categories\"].append(classes)\n\n \n for ann_row in df.itertuples():\n \n images = {\n \"id\": ann_row[0],\n \"license\": 1,\n \"file_name\": ann_row.image_id + '.jpg',\n \"height\": ann_row.height,\n \"width\": ann_row.width,\n \"date_captured\": \"2021-11-30T15:01:26+00:00\"\n }\n \n annotations_json[\"images\"].append(images)\n \n bbox_list = ann_row.bboxes\n \n for bbox in bbox_list:\n b_width = bbox[2]\n b_height = bbox[3]\n \n # some boxes in COTS are outside the image height and width\n if (bbox[0] + bbox[2] > 1280):\n b_width = 1280 - bbox[0] \n if (bbox[1] + bbox[3] > 720):\n b_height = 720 - bbox[1] \n \n image_annotations = {\n \"id\": annotion_id,\n \"image_id\": ann_row[0],\n \"category_id\": 0,\n \"bbox\": [bbox[0], bbox[1], b_width, b_height],\n \"area\": bbox[2] * bbox[3],\n \"segmentation\": [],\n \"iscrowd\": 0\n }\n \n annotion_id += 1\n annotations_json[\"annotations\"].append(image_annotations)\n \n \n print(f\"Dataset COTS annotation to COCO json format completed! Files: {len(df)}\")\n return annotations_json","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:10:34.446334Z","iopub.execute_input":"2022-01-02T16:10:34.446561Z","iopub.status.idle":"2022-01-02T16:10:41.540867Z","shell.execute_reply.started":"2022-01-02T16:10:34.446536Z","shell.execute_reply":"2022-01-02T16:10:41.539785Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n# Convert COTS dataset to JSON COCO\ntrain_annot_json = dataset2coco(df_train[df_train.fold != Selected_Fold], f\"{Work_Dir}{DataSet_Path}/train2017/\")\nval_annot_json = dataset2coco(df_train[df_train.fold == Selected_Fold], f\"{Work_Dir}{DataSet_Path}/val2017/\")\n\n# Save converted annotations\nsave_annot_json(train_annot_json, f\"{Work_Dir}{DataSet_Path}/annotations/train.json\")\nsave_annot_json(val_annot_json, f\"{Work_Dir}{DataSet_Path}/annotations/valid.json\")\n","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:10:41.543682Z","iopub.execute_input":"2022-01-02T16:10:41.544361Z","iopub.status.idle":"2022-01-02T16:10:41.653445Z","shell.execute_reply.started":"2022-01-02T16:10:41.544319Z","shell.execute_reply":"2022-01-02T16:10:41.652688Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def draw_yolox_predictions(img, bboxes, scores, bbclasses, confthre, classes_dict):\n alpha=0.5\n for i in range(len(bboxes)):\n box = bboxes[i]\n cls_id = int(bbclasses[i])\n score = scores[i]\n if score < confthre:\n continue\n x0 = int(box[0])\n y0 = int(box[1])\n x1 =int(box[0])+ int(box[2])\n y1 = int(box[1])+ int(box[3])\n\n cv2.rectangle(img, (x0, y0), (x1, y1), (255, 0, 255), 1)\n cv2.putText(img, '{}:{:.1f}%'.format(classes_dict[cls_id], score * 100), (x0, y0 - 1), cv2.FONT_HERSHEY_SIMPLEX, 0.7,(255,0,255), thickness = 2)\n \n return img","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:10:41.654856Z","iopub.execute_input":"2022-01-02T16:10:41.655271Z","iopub.status.idle":"2022-01-02T16:10:41.663649Z","shell.execute_reply.started":"2022-01-02T16:10:41.655233Z","shell.execute_reply":"2022-01-02T16:10:41.66286Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 🌈 Visualizing BBoxes","metadata":{}},{"cell_type":"code","source":"COCO_CLASSES = (\n \"starfish\",\n)\n\nscores =[]\nconfthre=0.5\nbbclasses=[]\nbboxes=[]\n\nid=1270\n\nimageid=train_annot_json[\"images\"][id][\"id\"]\nfile_name=train_annot_json[\"images\"][id][\"file_name\"]\nheight=train_annot_json[\"images\"][id][\"height\"]\nwidth=train_annot_json[\"images\"][id][\"width\"]\n#img=load_image(\"/kaggle/working/dataset/images/train_cot/\"+file_name)\nimg=load_image(f'{Work_Dir}{DataSet_Path}/train2017/{file_name}')\n\n\nfor i in train_annot_json[\"annotations\"]:\n if i[\"image_id\"]==imageid :\n bboxes.append(i[\"bbox\"])\n scores.append(1)\n bbclasses.append(0)\n \nout_image = draw_yolox_predictions(img, bboxes, scores, bbclasses, confthre, COCO_CLASSES)\ndisplay(Image.fromarray(out_image))","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:10:41.66508Z","iopub.execute_input":"2022-01-02T16:10:41.665365Z","iopub.status.idle":"2022-01-02T16:10:42.172477Z","shell.execute_reply.started":"2022-01-02T16:10:41.665319Z","shell.execute_reply":"2022-01-02T16:10:42.171805Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Select a Model\n### Standard Models.\n","metadata":{"papermill":{"duration":1.19241,"end_time":"2021-09-23T19:32:47.895537","exception":false,"start_time":"2021-09-23T19:32:46.703127","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"![01.JPG](attachment:50a58e3e-1971-4495-87f7-182916019b52.JPG)","metadata":{"papermill":{"duration":0.892457,"end_time":"2021-09-23T19:32:49.666829","exception":false,"start_time":"2021-09-23T19:32:48.774372","status":"completed"},"tags":[]},"attachments":{"50a58e3e-1971-4495-87f7-182916019b52.JPG":{"image/jpeg":"/9j/4AAQSkZJRgABAQEAYABgAAD/4RDiRXhpZgAATU0AKgAAAAgABAE7AAIAAAAIAAAISodpAAQAAAABAAAIUpydAAEAAAAQAAAQyuocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFRPU0hJQkEAAAWQAwACAAAAFAAAEKCQBAACAAAAFAAAELSSkQACAAAAAzcwAACSkgACAAAAAzcwAADqHAAHAAAIDAAACJQAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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"}}},{"cell_type":"markdown","source":" We will select YOLOX-L Model","metadata":{}},{"cell_type":"markdown","source":"# YOLOX-L Experiment Configuration File \nTraining parameters could be set up in experiment config files.","metadata":{}},{"cell_type":"code","source":"config_file_template = '''\n\n#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) Megvii, Inc. and its affiliates.\n\nimport os\n\nfrom yolox.exp import Exp as MyExp\n\n\nclass Exp(MyExp):\n def __init__(self):\n super(Exp, self).__init__()\n self.depth = 1.33\n self.width = 1.25\n self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(\".\")[0]\n \n # Define yourself dataset path\n self.data_dir = \"/kaggle/working/dataset/images\"\n self.train_ann = \"train.json\"\n self.val_ann = \"valid.json\"\n\n self.num_classes = 1\n\n self.max_epoch = $max_epoch\n self.data_num_workers = 2\n self.eval_interval = 1\n \n self.mosaic_prob = 1.0\n self.mixup_prob = 1.0\n self.hsv_prob = 1.0\n self.flip_prob = 0.5\n self.no_aug_epochs = 2\n \n self.input_size = (960, 960)\n self.mosaic_scale = (0.5, 1.5)\n self.random_size = (10, 20)\n self.test_size = (960, 960)\n'''","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:10:42.173844Z","iopub.execute_input":"2022-01-02T16:10:42.174225Z","iopub.status.idle":"2022-01-02T16:10:42.179237Z","shell.execute_reply.started":"2022-01-02T16:10:42.174189Z","shell.execute_reply":"2022-01-02T16:10:42.178502Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### I will train the model for 30 Epochs.","metadata":{}},{"cell_type":"code","source":"PIPELINE_CONFIG_PATH='cots_config.py'\npipeline = Template(config_file_template).substitute(max_epoch = 15)\nwith open(PIPELINE_CONFIG_PATH, 'w') as f:\n f.write(pipeline)","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:10:42.180514Z","iopub.execute_input":"2022-01-02T16:10:42.181079Z","iopub.status.idle":"2022-01-02T16:10:42.196913Z","shell.execute_reply.started":"2022-01-02T16:10:42.181037Z","shell.execute_reply":"2022-01-02T16:10:42.195774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# ./yolox/data/datasets/voc_classes.py\n\nvoc_cls = '''\nVOC_CLASSES = (\n \"starfish\",\n)\n'''\nwith open('./yolox/data/datasets/voc_classes.py', 'w') as f:\n f.write(voc_cls)\n\n# ./yolox/data/datasets/coco_classes.py\n\ncoco_cls = '''\nCOCO_CLASSES = (\n \"starfish\",\n)\n'''\nwith open('./yolox/data/datasets/coco_classes.py', 'w') as f:\n f.write(coco_cls)\n\n# check if everything is ok \n!more ./yolox/data/datasets/coco_classes.py","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:10:42.198587Z","iopub.execute_input":"2022-01-02T16:10:42.199004Z","iopub.status.idle":"2022-01-02T16:10:42.881471Z","shell.execute_reply.started":"2022-01-02T16:10:42.198968Z","shell.execute_reply":"2022-01-02T16:10:42.880587Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Getting Weights\nWe selected YOLOLX_x","metadata":{}},{"cell_type":"code","source":"#%cp -r /kaggle/input/yolox-models /kaggle/working/\n\nsh = 'wget https://github.com/Megvii-BaseDetection/storage/releases/download/0.0.1/yolox_x.pth'\nMODEL_FILE = 'yolox_x.pth'\n\n#MODEL_FILE ='/kaggle/input/yolox-models/best_v3_yolox_s.pth.pth'\n\nwith open('script.sh', 'w') as file:\n file.write(sh)\n\n!bash script.sh","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-01-02T16:10:42.884256Z","iopub.execute_input":"2022-01-02T16:10:42.884722Z","iopub.status.idle":"2022-01-02T16:12:12.837654Z","shell.execute_reply.started":"2022-01-02T16:10:42.884678Z","shell.execute_reply":"2022-01-02T16:12:12.836807Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Training The Model","metadata":{}},{"cell_type":"code","source":"!cp ./tools/train.py ./","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:12:12.840813Z","iopub.execute_input":"2022-01-02T16:12:12.84104Z","iopub.status.idle":"2022-01-02T16:12:13.506338Z","shell.execute_reply.started":"2022-01-02T16:12:12.841014Z","shell.execute_reply":"2022-01-02T16:12:13.505171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(os.getcwd())","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:12:13.508962Z","iopub.execute_input":"2022-01-02T16:12:13.509443Z","iopub.status.idle":"2022-01-02T16:12:13.516318Z","shell.execute_reply.started":"2022-01-02T16:12:13.509401Z","shell.execute_reply":"2022-01-02T16:12:13.515396Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!python train.py \\\n -f cots_config.py \\\n -d 1 \\\n -b 4 \\\n --fp16 \\\n -o \\\n -c {MODEL_FILE}","metadata":{"execution":{"iopub.status.busy":"2022-01-02T16:12:13.517905Z","iopub.execute_input":"2022-01-02T16:12:13.518271Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"path = \"/kaggle/working/dataset\"\nshutil.rmtree(path)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Please if this kernel is useful, please upvote !!","metadata":{"papermill":{"duration":7.483164,"end_time":"2021-09-24T02:30:39.722316","exception":false,"start_time":"2021-09-24T02:30:32.239152","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"![download.jpg](attachment:07de9c65-7c16-40e7-a821-d5354296394c.jpg)","metadata":{"papermill":{"duration":7.275248,"end_time":"2021-09-24T02:30:54.177776","exception":false,"start_time":"2021-09-24T02:30:46.902528","status":"completed"},"tags":[]},"attachments":{"07de9c65-7c16-40e7-a821-d5354296394c.jpg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"# 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