APTOS_Submissions_format.ipynb

{"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 in \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 \"../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# Any results you write to the current directory are saved as output.","execution_count":50,"outputs":[{"output_type":"stream","text":"/kaggle/input/aptos2019-blindness-detection/sample_submission.csv\n/kaggle/input/aptos2019-blindness-detection/train.csv\n/kaggle/input/aptos2019-blindness-detection/test.csv\n/kaggle/input/aptos2019-blindness-detection/train_images/6630f8675a97.png\n/kaggle/input/aptos2019-blindness-detection/train_images/7fe7309d0b4f.png\n/kaggle/input/aptos2019-blindness-detection/train_images/4d21ce39c905.png\n/kaggle/input/aptos2019-blindness-detection/train_images/150fc7127582.png\n/kaggle/input/aptos2019-blindness-detection/train_images/b842b43cb7fb.png\n/kaggle/input/aptos2019-blindness-detection/train_images/a2d349f567a6.png\n/kaggle/input/aptos2019-blindness-detection/train_images/dc3c0d8ee20b.png\n/kaggle/input/aptos2019-blindness-detection/train_images/16ce555748d8.png\n/kaggle/input/aptos2019-blindness-detection/train_images/c561bcd519e9.png\n/kaggle/input/aptos2019-blindness-detection/train_images/3c28fd846b43.png\n/kaggle/input/aptos2019-blindness-detection/train_images/94372043d55b.png\n/kaggle/input/aptos2019-blindness-detection/train_images/bb45257258cc.png\n/kaggle/input/aptos2019-blindness-detection/train_images/87b1938994b5.png\n/kaggle/input/aptos2019-blindness-detection/train_images/c31651ea04c6.png\n/kaggle/input/aptos2019-blindness-detection/train_images/eae901557a84.png\n/kaggle/input/aptos2019-blindness-detection/train_images/db4ed1e07aa3.png\n/kaggle/input/aptos2019-blindness-detection/train_images/40c24aded50c.png\n/kaggle/input/aptos2019-blindness-detection/train_images/026dcd9af143.png\n/kaggle/input/aptos2019-blindness-detection/train_images/9df31421cdd2.png\n/kaggle/input/aptos2019-blindness-detection/train_images/3232b34cbe99.png\n/kaggle/input/aptos2019-blindness-detection/train_images/c613db1cab27.png\n/kaggle/input/aptos2019-blindness-detection/train_images/cd01f4f83336.png\n/kaggle/input/aptos2019-blindness-detection/train_images/a3132c8828e4.png\n/kaggle/input/aptos2019-blindness-detection/train_images/8a3eb86ae4bd.png\n/kaggle/input/aptos2019-blindness-detection/train_images/ea588d1e5d96.png\n/kaggle/input/aptos2019-blindness-detection/train_images/5a11d21c2828.png\n/kaggle/input/aptos2019-blindness-detection/train_images/2241b7e90782.png\n/kaggle/input/aptos2019-blindness-detection/train_images/29f44aea93a4.png\n/kaggle/input/aptos2019-blindness-detection/train_images/1b8ad0afe9fb.png\n/kaggle/input/aptos2019-blindness-detection/train_images/873dcc0b468f.png\n/kaggle/input/aptos2019-blindness-detection/train_images/6efa36d59ada.png\n/kaggle/input/aptos2019-blindness-detection/train_images/cb0cc98d7e35.png\n/kaggle/input/aptos2019-blindness-detection/train_images/4c5ab774a381.png\n/kaggle/input/aptos2019-blindness-detection/train_images/917f76f360b6.png\n/kaggle/input/aptos2019-blindness-detection/train_images/cd29c88c9e36.png\n/kaggle/input/aptos2019-blindness-detection/train_images/d871895742b1.png\n/kaggle/input/aptos2019-blindness-detection/train_images/58184d6fd087.png\n/kaggle/input/aptos2019-blindness-detection/train_images/887c26fc0e1f.png\n/kaggle/input/aptos2019-blindness-detection/train_images/0c38940e1f80.png\n/kaggle/input/aptos2019-blindness-detection/train_images/b9519abce0c1.png\n/kaggle/input/aptos2019-blindness-detection/train_images/dbb2c63f6f08.png\n/kaggle/input/aptos2019-blindness-detection/train_images/ca7570c5925c.png\n/kaggle/input/aptos2019-blindness-detection/train_images/8c29a76fa08c.png\n/kaggle/input/aptos2019-blindness-detection/train_images/197af0de76e2.png\n/kaggle/input/aptos2019-blindness-detection/train_images/7c90ab025331.png\n/kaggle/input/aptos2019-blindness-detection/train_images/586f5c56081e.png\n/kaggle/input/aptos2019-blindness-detection/train_images/f71aca5a7dc3.png\n/kaggle/input/aptos2019-blindness-detection/train_images/e1418d28d668.png\n/kaggle/input/aptos2019-blindness-detection/train_images/62ecdc90dd42.png\n/kaggle/input/aptos2019-blindness-detection/train_images/10eefba568dd.png\n/kaggle/input/aptos2019-blindness-detection/train_images/8f06ca4642bd.png\n/kaggle/input/aptos2019-blindness-detection/train_images/04a6fc58dabc.png\n/kaggle/input/aptos2019-blindness-detection/train_images/a505981d1cab.png\n/kaggle/input/aptos2019-blindness-detection/train_images/8bdb891661a8.png\n/kaggle/input/aptos2019-blindness-detection/train_images/973b0facfa9b.png\n/kaggle/input/aptos2019-blindness-detection/train_images/79540be95177.png\n/kaggle/input/aptos2019-blindness-detection/train_images/8dfff47b06b7.png\n/kaggle/input/aptos2019-blindness-detection/train_images/2f81ee5f2926.png\n/kaggle/input/aptos2019-blindness-detection/train_images/56e56aa08362.png\n/kaggle/input/aptos2019-blindness-detection/train_images/4982378d72f9.png\n/kaggle/input/aptos2019-blindness-detection/train_images/94f9ecf4b8d2.png\n/kaggle/input/aptos2019-blindness-detection/train_images/5baed382f062.png\n/kaggle/input/aptos2019-blindness-detection/train_images/58059e73d2d4.png\n/kaggle/input/aptos2019-bl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torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nimport torch.nn.functional as F\nimport numpy as np\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport matplotlib.pyplot as plt\nimport time\nimport os\nimport copy\nfrom sklearn.metrics import confusion_matrix\nfrom torch.utils.data import random_split, Dataset\nfrom torch.utils.data.sampler import SubsetRandomSampler\nimport os\nfrom PIL import Image\nimport glob\nimport cv2\nimport gc #garbage collector for gpu memory \nfrom sklearn.model_selection import train_test_split","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"transform = transforms.Compose(\n        [transforms.Resize((320,320)),\n        #transforms.RandomCrop(),\n        #transforms.RandomHorizontalFlip(.33),\n        #transforms.RandomVerticalFlip(.33),\n        transforms.RandomApply([\n        #transforms.ColorJitter(brightness=(1,3), contrast=(1,3), saturation=(1,2)),\n        transforms.RandomAffine(degrees = (-360,360), shear = (-45,45))],\n        p=.5),\n        transforms.ToTensor(),\n        transforms.Normalize([0.460, 0.247, 0.080], [0.249, 0.138, 0.081])\n        ])","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class APTOSDataset(Dataset):\n    \"\"\"Eye images dataset.\"\"\"\n    def __init__(self, csv_file, filetype, transform=None):\n        self.eye_frame = pd.read_csv(csv_file)\n        self.filetype = filetype\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.eye_frame)\n    \n    def __getitem__(self, idx):\n        if self.filetype == 'train':\n            img_name = os.path.join('../input/aptos2019-blindness-detection/train_images',\n                                    self.eye_frame.loc[idx,'id_code'] + '.png')\n\n            image = Image.open(img_name)\n            if self.transform:\n                image = self.transform(image)\n            else:\n                image = transforms.ToTensor()(image)\n\n            return image,self.eye_frame.diagnosis[idx]\n        \n        else:\n            img_name = os.path.join('../input/aptos2019-blindness-detection/test_images',\n                                    self.eye_frame.loc[idx,'id_code'] + '.png')\n            image = Image.open(img_name)\n            if self.transform:\n                image = self.transform(image)\n            else:\n                image = transforms.ToTensor()(image)\n            return image, self.eye_frame.loc[idx,'id_code']\n    ","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_dataset = APTOSDataset(csv_file='../input/aptos2019-blindness-detection/train.csv',filetype='train',transform=transform)\ntest_dataset = APTOSDataset(csv_file='../input/aptos2019-blindness-detection/test.csv', filetype='test',transform=transform)\n\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True, num_workers=4)\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False, num_workers=4)\n\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nmodel = torchvision.models.resnet101(pretrained=False)\nct = 0\nfor child in model.children():\n    ct += 1\n    if ct < 5:\n        for param in child.parameters():\n            param.requires_grad = False\n            \nnum_ftrs = model.fc.in_features\nmodel.fc = nn.Linear(num_ftrs, 5)\nmodel = model.to(device)\nmodel.load_state_dict(torch.load(\"/kaggle/input/training-weights/train_valid_best.pth\"))\n\n\n","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"<All keys matched successfully>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tqdm._tqdm_notebook import tqdm_notebook\ndef compute_predictions(model, model_type, data_loader, device):\n    if model_type == 'train':\n        predictions = []\n        correct_pred, num_examples = 0, 0\n        tqdm_notebook()\n        for i, (inputs, labels) in enumerate(tqdm_notebook(data_loader)):\n            inputs = inputs.to(device)\n            labels = labels.to(device)\n            outputs = model(inputs)\n            _, preds = torch.max(outputs, 1)\n            predictions.append(preds)\n            num_examples += labels.size(0)\n            correct_pred += (preds==labels).sum()\n        return predictions, correct_pred.item()/num_examples*100\n    \n    else:\n        predictions = []\n        img_ids = []\n        tqdm_notebook()\n        for i, (inputs, img_id) in enumerate(tqdm_notebook(data_loader)):\n            inputs = inputs.to(device)\n            outputs = model(inputs)\n            _, preds = torch.max(outputs, 1)\n            predictions.extend(preds)\n            img_ids.extend(img_id)\n            \n        predictions = [pred.item() for pred in predictions]\n        final_predictions = pd.DataFrame(np.array([img_ids, predictions])).transpose()\n        final_predictions.columns = ['id_code', 'diagnosis']\n        final_predictions_df = final_predictions.copy()\n        final_predictions.to_csv(\"submission.csv\",index=False)\n        return final_predictions_df\n            \n            ","execution_count":81,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with torch.set_grad_enabled(False):\n    model.eval()\n    #train_predictions, train_accuracy = compute_predictions(model, 'train', train_loader, device)\n    #print('Train Accuracy: ', train_accuracy)\n    print('Computing Test Predictions')\n    test_predictions = compute_predictions(model, 'test', test_loader, device)\n        \n    \n    ","execution_count":82,"outputs":[{"output_type":"stream","text":"Computing Test Predictions\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d56aa8ae2369430b8bdf2abd4f34af4d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"HBox(children=(IntProgress(value=0, max=121), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"411195e843344361800b2cb808d9f466"}},"metadata":{}},{"output_type":"stream","text":"\n\n\n\n\n\n","name":"stdout"}]}],"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":1}