{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"collapsed":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        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","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install pretrainedmodels\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from torchvision.models import *\nimport pretrainedmodels\n\nfrom fastai import *\nfrom fastai.vision import *\nfrom fastai.vision.models import *\nfrom fastai.vision.learner import model_meta\nimport fastai\n\n# from utils import *\nimport sys\nimport torch\nfastai.__version__\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lis = os.listdir('../input/cassava-leaf-disease-classification/train_images/')\nsub = pd.read_csv('../input/cassava-leaf-disease-classification/sample_submission.csv')\n\nlen(lis)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"path = \"../input/cassava-leaf-disease-classification/train_images/\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"filenames = os.listdir('../input/cassava-leaf-disease-classification/test_images/')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')\ndf.head()\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# from fastai import get_transforms\n\n# tfms = get_transforms(flip_vert=False,max_zoom=1.0,max_warp=0,do_flip=False,xtra_tfms=[cutout()])\ndata = (ImageList.from_csv(path, csv_name = '../train.csv', suffix='.jpg')\n        .split_by_rand_pct()              \n        .label_from_df()            \n        .add_test_folder(test_folder = '../test_images')              \n#         .transform(tfms, size=400)\n        .databunch(num_workers=0,bs=8)) \n\n# tfms1 = aug_transforms(flip_vert=False,max_zoom=1.0,max_warp=0,do_flip=False)\n# data1 = (ImageList.from_csv(path, csv_name = '../train.csv', suffix='.jpg')\n#         .split_by_rand_pct()              \n#         .label_from_df()            \n#         .add_test_folder(test_folder = '../test_images')              \n#         .transform(tfms1, size=400)\n#         .databunch(num_workers=0,bs=8)) ","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}