{"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":"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        #print(os.path.join(dirname, filename))\n\n# You can write up to 20GB 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-12-16T18:11:20.475882Z","iopub.execute_input":"2022-12-16T18:11:20.476351Z","iopub.status.idle":"2022-12-16T18:11:20.49138Z","shell.execute_reply.started":"2022-12-16T18:11:20.476309Z","shell.execute_reply":"2022-12-16T18:11:20.489141Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# install fastkaggle if not available\ntry: import fastkaggle\nexcept ModuleNotFoundError:\n    !pip install -Uq fastkaggle\n\nfrom fastkaggle import *\nfrom fastai.vision.all import *\n\n!pip install fastai --upgrade\nfrom pathlib import Path\n\nfrom fastai.metrics import error_rate\n\n!pip install -Uqq fastbook\nimport fastbook\nfastbook.setup_book()\n\n\n#import fastai.vision.data.ImageDataLoaders\n\nfrom fastbook import *\n\n# install fastkaggle if not available\ntry: import fastkaggle\nexcept ModuleNotFoundError:\n    !pip install -Uq fastkaggle\n\nfrom fastkaggle import *\n\nimport pandas as pd\n\nfrom fastai.data.all import *\nfrom fastai.vision.all import *\n\nfrom collections import defaultdict\nimport pandas as pd\nimport numpy as np\n\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:22.231179Z","iopub.execute_input":"2022-12-16T18:11:22.231651Z","iopub.status.idle":"2022-12-16T18:12:11.339849Z","shell.execute_reply.started":"2022-12-16T18:11:22.231574Z","shell.execute_reply":"2022-12-16T18:12:11.338535Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"comp = '/kaggle/input/rsna-breast-cancer-detection-roi/bc_768_roi'\n\npath = setup_comp(comp, install='fastai \"timm>=0.6.2.dev0\"')","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:11.345702Z","iopub.execute_input":"2022-12-16T18:12:11.348225Z","iopub.status.idle":"2022-12-16T18:12:26.694579Z","shell.execute_reply.started":"2022-12-16T18:12:11.348176Z","shell.execute_reply":"2022-12-16T18:12:26.693499Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#train_csv_path = '/kaggle/input/rsna-breast-cancer-detection/train.csv'","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:08.71421Z","iopub.status.idle":"2022-12-16T18:11:08.71606Z","shell.execute_reply.started":"2022-12-16T18:11:08.71579Z","shell.execute_reply":"2022-12-16T18:11:08.715817Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trn_path = path/'train'\nfiles = get_image_files(trn_path)\n#pat = r'(.+)_\\d+.jpg$'","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:26.697345Z","iopub.execute_input":"2022-12-16T18:12:26.700877Z","iopub.status.idle":"2022-12-16T18:12:48.619604Z","shell.execute_reply.started":"2022-12-16T18:12:26.700836Z","shell.execute_reply":"2022-12-16T18:12:48.618472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train = pd.read_csv('/kaggle/input/rsna-breast-cancer-detection/train.csv')\ndf_test = pd.read_csv('/kaggle/input/rsna-breast-cancer-detection/test.csv')","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:48.625883Z","iopub.execute_input":"2022-12-16T18:12:48.628274Z","iopub.status.idle":"2022-12-16T18:12:48.761457Z","shell.execute_reply.started":"2022-12-16T18:12:48.628233Z","shell.execute_reply":"2022-12-16T18:12:48.760423Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from fastai.vision.all import *\nset_seed(42)\n\npath.ls()","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:48.762974Z","iopub.execute_input":"2022-12-16T18:12:48.763662Z","iopub.status.idle":"2022-12-16T18:12:48.779574Z","shell.execute_reply.started":"2022-12-16T18:12:48.763598Z","shell.execute_reply":"2022-12-16T18:12:48.778679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* https://eagerai.github.io/fastai/reference/get_image_files.html\n* https://rdrr.io/cran/fastai/man/get_image_files.html\n* https://kurianbenoy.com/2021-08-07-get_image_files/","metadata":{}},{"cell_type":"code","source":"df_train.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.120327Z","iopub.status.idle":"2022-12-16T18:11:12.122805Z","shell.execute_reply.started":"2022-12-16T18:11:12.122517Z","shell.execute_reply":"2022-12-16T18:11:12.122544Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.126905Z","iopub.status.idle":"2022-12-16T18:11:12.129318Z","shell.execute_reply.started":"2022-12-16T18:11:12.129039Z","shell.execute_reply":"2022-12-16T18:11:12.129065Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"img = PILImage.create(files[5])\nprint(img.size)\nimg.to_thumb(128)","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:48.780974Z","iopub.execute_input":"2022-12-16T18:12:48.781602Z","iopub.status.idle":"2022-12-16T18:12:48.817994Z","shell.execute_reply.started":"2022-12-16T18:12:48.781564Z","shell.execute_reply":"2022-12-16T18:12:48.816099Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train['img_path'] = df_train.apply(\n    lambda i: os.path.join(\n        f\"{trn_path}\", str(i['patient_id']) + \"_\" + str(i['image_id']) + '.png'\n    ), axis=1\n)","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:48.821954Z","iopub.execute_input":"2022-12-16T18:12:48.824599Z","iopub.status.idle":"2022-12-16T18:12:50.1894Z","shell.execute_reply.started":"2022-12-16T18:12:48.82456Z","shell.execute_reply":"2022-12-16T18:12:50.188337Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.head()","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:50.190954Z","iopub.execute_input":"2022-12-16T18:12:50.191638Z","iopub.status.idle":"2022-12-16T18:12:50.219414Z","shell.execute_reply.started":"2022-12-16T18:12:50.191579Z","shell.execute_reply":"2022-12-16T18:12:50.218397Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"row = df_train.loc[df_train['image_id'] == 154581138]\nprint(row)","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.147198Z","iopub.status.idle":"2022-12-16T18:11:12.147978Z","shell.execute_reply.started":"2022-12-16T18:11:12.147714Z","shell.execute_reply":"2022-12-16T18:11:12.147739Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.drop(df_train.index[4394], inplace=True)\ndf_train.drop(df_train.index[47296], inplace=True)\ndf_train.drop(df_train.index[13573], inplace=True)\ndf_train.drop(df_train.index[52743], inplace=True)\ndf_train.drop(df_train.index[40008], inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.149362Z","iopub.status.idle":"2022-12-16T18:11:12.150164Z","shell.execute_reply.started":"2022-12-16T18:11:12.149878Z","shell.execute_reply":"2022-12-16T18:11:12.149904Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'14771_1380005429'","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.151793Z","iopub.status.idle":"2022-12-16T18:11:12.152554Z","shell.execute_reply.started":"2022-12-16T18:11:12.152295Z","shell.execute_reply":"2022-12-16T18:11:12.15232Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.160607Z","iopub.status.idle":"2022-12-16T18:11:12.161382Z","shell.execute_reply.started":"2022-12-16T18:11:12.161113Z","shell.execute_reply":"2022-12-16T18:11:12.161138Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#def label_func(path):\n #   df_train.image_id[df_train['img_path']==str(path)].values","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.162779Z","iopub.status.idle":"2022-12-16T18:11:12.163537Z","shell.execute_reply.started":"2022-12-16T18:11:12.163277Z","shell.execute_reply":"2022-12-16T18:11:12.163302Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#def label_func(path):\n#    df_train.cancer[df_train['img_path']==str(path)].values ","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.164933Z","iopub.status.idle":"2022-12-16T18:11:12.165711Z","shell.execute_reply.started":"2022-12-16T18:11:12.165434Z","shell.execute_reply":"2022-12-16T18:11:12.165458Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def label_func(path):\n    # df_train.cancer[df_train['img_path']==str(path)].value\n    return df_train.cancer[df_train['img_path']==str(path)].item()","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:50.223656Z","iopub.execute_input":"2022-12-16T18:12:50.226103Z","iopub.status.idle":"2022-12-16T18:12:50.233353Z","shell.execute_reply.started":"2022-12-16T18:12:50.226064Z","shell.execute_reply":"2022-12-16T18:12:50.232246Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"label_func(files[0])\n#type(files[0].stem)","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.169219Z","iopub.status.idle":"2022-12-16T18:11:12.16998Z","shell.execute_reply.started":"2022-12-16T18:11:12.169721Z","shell.execute_reply":"2022-12-16T18:11:12.169746Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#test = files[0]\n#print(re.match(r'(?<=_).*',files[0].stem)#.group())\n#print(test.stem)\n","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.173871Z","iopub.status.idle":"2022-12-16T18:11:12.174654Z","shell.execute_reply.started":"2022-12-16T18:11:12.174379Z","shell.execute_reply":"2022-12-16T18:11:12.174404Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"item_tfms = RandomResizedCrop(64, min_scale=0.75, ratio=(1.,1.))\nbatch_tfms = [*aug_transforms(size=64, max_warp=0), Normalize.from_stats(*imagenet_stats)]","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:50.241077Z","iopub.execute_input":"2022-12-16T18:12:50.243497Z","iopub.status.idle":"2022-12-16T18:12:54.43991Z","shell.execute_reply.started":"2022-12-16T18:12:50.24346Z","shell.execute_reply":"2022-12-16T18:12:54.43882Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"bs=64","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:54.444962Z","iopub.execute_input":"2022-12-16T18:12:54.447378Z","iopub.status.idle":"2022-12-16T18:12:54.45372Z","shell.execute_reply.started":"2022-12-16T18:12:54.447338Z","shell.execute_reply":"2022-12-16T18:12:54.452768Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dblock = DataBlock(blocks    = (ImageBlock, CategoryBlock),\n                   get_items = get_image_files,\n                   #get_y     = lambda x: df_train.loc[x, \"cancer\"],  # Use a lambda function to get the label from the \"cancer\" DataFrame\n                   get_y     = label_func,\n                   splitter  = RandomSplitter(),\n                   item_tfms = Resize(64))\n\ndls = dblock.dataloaders(path/\"train\")\ndls.show_batch()","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:12:54.458183Z","iopub.execute_input":"2022-12-16T18:12:54.45954Z","iopub.status.idle":"2022-12-16T18:17:53.254951Z","shell.execute_reply.started":"2022-12-16T18:12:54.459505Z","shell.execute_reply":"2022-12-16T18:17:53.254005Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dls = dblock.dataloaders(path/\"train\")\ndls.show_batch()","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:11:12.188724Z","iopub.status.idle":"2022-12-16T18:11:12.189468Z","shell.execute_reply.started":"2022-12-16T18:11:12.189219Z","shell.execute_reply":"2022-12-16T18:11:12.189243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Creating the learner and training for a single epoch¶\n","metadata":{}},{"cell_type":"code","source":"learn = vision_learner(dls, resnet18, metrics=error_rate, pretrained=True)","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:24:06.035014Z","iopub.execute_input":"2022-12-16T18:24:06.035475Z","iopub.status.idle":"2022-12-16T18:24:07.317376Z","shell.execute_reply.started":"2022-12-16T18:24:06.035436Z","shell.execute_reply":"2022-12-16T18:24:07.316226Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"learn.lr_find(suggest_funcs=(slide, valley))","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:24:20.456784Z","iopub.execute_input":"2022-12-16T18:24:20.457218Z","iopub.status.idle":"2022-12-16T18:26:20.26993Z","shell.execute_reply.started":"2022-12-16T18:24:20.457181Z","shell.execute_reply":"2022-12-16T18:26:20.26884Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"learn.fit(1, lr=0.03)","metadata":{"execution":{"iopub.status.busy":"2022-12-16T18:31:53.697901Z","iopub.execute_input":"2022-12-16T18:31:53.698368Z","iopub.status.idle":"2022-12-16T18:47:50.194338Z","shell.execute_reply.started":"2022-12-16T18:31:53.698329Z","shell.execute_reply":"2022-12-16T18:47:50.193065Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:278305bc-1c86-4ed3-a09f-bd0927de5f92.png)![image.png](attachment:9010696f-99fd-4daf-acef-717c5baf1964.png)","metadata":{},"attachments":{"278305bc-1c86-4ed3-a09f-bd0927de5f92.png":{"image/png":"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"},"9010696f-99fd-4daf-acef-717c5baf1964.png":{"image/png":"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"}}},{"cell_type":"code","source":"interp 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