{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        pass\n        #print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os, sys\nimport glob\nfrom pathlib import Path\nfrom shutil import copytree, rmtree\nimport pickle\n\n# Default Path\npath_input = Path(\"/kaggle/input/\")\npath_output = Path(\"/kaggle/working/\")\n\n# Source Data:\npath_data = path_input / \"global-wheat-detection\"\n\n# Source Data Packages\npath_source_code = path_input / \"gwdpipeline01\"\npath_target_code = path_output / \"gwd\"\n\n# Source Dependency Packages\npath_source_dep = path_input / \"gwd-pip-dep\"\npath_target_dep = path_output / \"gwd_dep\"\n\n# =================\n# Data Preparation\n# =================\n\n#######################################\n# Copy from INPUT DATSET to WORKING DIR\n# Fom src.submissions.bengaliai import kaggle_project_submission\n# This does NOT include the /depends folder.\nif Path(path_target_code).exists():\n    rmtree(path_target_code)\n    print(\"Target Code cleaned.\")\ncopytree(src=path_source_code, dst=path_target_code)\n\nif Path(path_target_dep).exists():\n    rmtree(path_target_dep)\n    print(\"Target Dep cleaned.\")\ncopytree(src=path_source_dep, dst=path_target_dep)\n    \nimport os\nfor dirname, _, filenames in os.walk('/kaggle/working'):\n    for filename in filenames:        \n        #print(os.path.join(dirname, filename))\n        pass\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n# =======================\n# Dependency Preparation\n# =======================\n\nclass SimpleObj:\n    \"\"\"\n    This class data structure is required to decompress the entry and bin data.\n    \"\"\"\n\n    def __init__(self, fname, bdata):\n        self.name = fname\n        self.bdata = bdata\n        return\n\n\n\nprint(os.getcwd())\n\ntry:\n    import yacs\nexcept ImportError as e:\n    os.chdir(path_target_dep)\n    # The file that contain all the binary of all possible dependencies.\n    picklefile = 'dill.pkl'\n\n    # unpack pickle\n    installers = []\n    with open(picklefile, 'rb') as pf:\n        installers = pickle.load(pf)\n        #print(installers)\n\n    for index, i in enumerate(installers):\n        #print(f\"Writing Dependencies {index} \")\n        with open(i.name, 'wb') as p:\n            p.write(i.bdata)\n\n    # install\n    os.system('pip install -r requirements.txt --no-index --find-links .')\nprint(\"Finished Installing all dependencies.\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import torch\nimport torchvision\nprint(torch.__version__)\nprint(torchvision.__version__)\nassert torch.cuda.is_available()\nassert torch.cuda.device_count() > 0","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# =======================\n# Running Prediction\n# =======================\nimport os, sys\nos.chdir(path_target_code)\nprint(os.getcwd())\n\nsys.path.append(\"/kaggle/working/gwd/src/modeling/arch/yolov5\")\nsys.path.append(\"/kaggle/working/gwd/src/modeling/arch/yolov5\")\n\nfrom src.submissions.gwd import GWDSubmission\nsubmission = GWDSubmission(input_path_model_weight=\"/kaggle/input/gwd-yolov5-weights/best.pt\",\n                           input_path_test=\"/kaggle/input/global-wheat-detection/test\")\nsubmission.configure()\npath_submission = \"/kaggle/working/gwd/submission_data\"\nPath(path_submission).mkdir(parents=True, exist_ok=True)\nsubmission.eval(path_out=path_submission)\nsubmission.parse(path_out=path_submission)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"os.chdir(path_submission)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from shutil import copyfile\ncopyfile(Path(path_submission) / \"Submission.csv\", path_output / \"submission.csv\")","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}