{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"# import json\n# import pandas as pd\n\n# def convert_to_df(json_filepath, filename):\n#     with open(json_filepath) as json_file:\n#         data = json.load(json_file)\n        \n    \n#     train_annotations = pd.DataFrame(data['annotations'])\n#     train_images = pd.DataFrame(data['images'])\n#     train_categories = pd.DataFrame(data['categories'])\n\n#     categories_dict = pd.Series(train_categories.name.values,index=train_categories.id).to_dict() # for mapping\n#     train_images.columns = ['file_name', 'image_id'] # for merging\n    \n#     df = pd.merge(train_annotations, train_images, on='image_id', how='left')\n    \n#     df['file_name'] = df['file_name'].apply(lambda x: '/kaggle/input/til2020/train/train/' + x)\n    \n#     df['xmin'] = df['bbox'].apply(lambda x: int(x[0]))\n#     df['ymin'] = df['bbox'].apply(lambda x: int(x[1]))\n\n#     df['xmax'] = df['bbox'].apply(lambda x: int(x[0] +  x[2]))\n#     df['ymax'] = df['bbox'].apply(lambda x: int(x[1] + x[3]))\n#     df['class'] = df['category_id'].apply(lambda x: categories_dict[x])\n#     df = df.iloc[:, 6:]\n#     df.to_csv(filename, index=False)\n    \n#     return df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# convert_to_df('/kaggle/input/til2020/train.json', 'train_df.csv').head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# def convert_to_df(json_filepath, filename):\n#     with open(json_filepath) as json_file:\n#         data = json.load(json_file)\n        \n    \n#     train_annotations = pd.DataFrame(data['annotations'])\n#     train_images = pd.DataFrame(data['images'])\n#     train_categories = pd.DataFrame(data['categories'])\n\n#     categories_dict = pd.Series(train_categories.name.values,index=train_categories.id).to_dict() # for mapping\n#     train_images.columns = ['file_name', 'image_id'] # for merging\n    \n#     df = pd.merge(train_annotations, train_images, on='image_id', how='left')\n    \n#     df['file_name'] = df['file_name'].apply(lambda x: '/kaggle/input/til2020/val/val/' + x)\n    \n#     df['xmin'] = df['bbox'].apply(lambda x: int(x[0]))\n#     df['ymin'] = df['bbox'].apply(lambda x: int(x[1]))\n\n#     df['xmax'] = df['bbox'].apply(lambda x: int(x[0] +  x[2]))\n#     df['ymax'] = df['bbox'].apply(lambda x: int(x[1] + x[3]))\n    \n#     df['class'] = df['category_id'].apply(lambda x: categories_dict[x])\n#     df = df.iloc[:, 6:]\n#     df.to_csv(filename, index=False)\n    \n#     return df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# convert_to_df('/kaggle/input/til2020/val.json', 'val_df.csv').head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# data['categories']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# !git clone https://github.com/xuannianz/EfficientDet.git","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# import os\n\n# os.chdir('/kaggle/input/efficientdet-til/EfficientDet')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!cp -r /kaggle/input/efficientdet-til/EfficientDet /kaggle/working","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nos.chdir('/kaggle/working/EfficientDet')\n\n!ls","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install numpy --user","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install . --user","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!python setup.py build_ext --inplace","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# !pip install -r requirements.txt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install progressbar2 ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!python train.py --snapshot imagenet --phi 0 --gpu 0 --weighted-bifpn --epochs 10 --random-transform --compute-val-loss --batch-size 2 --steps 4113 csv /kaggle/input/til-df/train_df.csv /kaggle/input/til-df/class.csv --val /kaggle/input/til-df/val_df.csv","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow\ntensorflow.test.is_gpu_available()\nprint(tensorflow.__version__)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}