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download from google drive or upload file by yourself\n\n!pip install --upgrade gdown\n\n!gdown --fuzzy '1jXEzNJr7iYJzhzl7695_0scTqJB0_5L7' --output retina-dataset.zip\n\n!unzip -q retina-dataset.zip","metadata":{"id":"yv09J78pHBwZ","executionInfo":{"status":"ok","timestamp":1732681718666,"user_tz":480,"elapsed":29511,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"a956b7a9-bf86-4269-fde8-8b5337aa6b65"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torch\n\nimport torch.nn as nn\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\nimport cv2\n\nimport os\n\nfrom glob import glob\n\nfrom tqdm.auto import tqdm\n\nfrom sklearn.model_selection import train_test_split\n\nimport pandas as pd","metadata":{"id":"rfo9NM6DG3FX","executionInfo":{"status":"ok","timestamp":1732681735877,"user_tz":480,"elapsed":6071,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Define Parameters\n\nNUM_CLASS = 5\n\nIMG_SIZE = 224\n\nBS = 64\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\ndevice","metadata":{"id":"mKmZtVNKG5nZ","executionInfo":{"status":"ok","timestamp":1732681737755,"user_tz":480,"elapsed":188,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"5cb60ad5-80ce-42b0-8939-7aa4543d5579"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### Data Visualization","metadata":{"id":"BHRgSYG8H6hQ"}},{"cell_type":"code","source":"df = pd.read_csv(\"kaggle_retina/train.csv\")\n\ndf.head()","metadata":{"id":"N2swXMcoW4CW","executionInfo":{"status":"ok","timestamp":1732681739889,"user_tz":480,"elapsed":179,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"5a8fa67a-1774-40c2-cec7-32b703bce316"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"paths = glob('kaggle_retina/train/*.png')\n\npaths_test = sorted(glob('kaggle_retina/test/*.png'))\n\nlen(paths), len(paths_test)","metadata":{"id":"so0Up92MH9US","executionInfo":{"status":"ok","timestamp":1732681741894,"user_tz":480,"elapsed":136,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"3501fe92-5d2a-4164-ff35-58e7090b042f"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"idx = np.random.randint(0, len(df))\n\nrow = df.iloc[idx]\n\nfilename = row[\"id_code\"]\n\npath = f\"kaggle_retina/train/{filename}.png\"\n\nimg = cv2.imread(path)[:,:,::-1]\n\nplt.imshow(img)\n\nprint(path)\n\nprint(f'label: {row[\"diagnosis\"]}')","metadata":{"id":"0EE1Y6puIDSG","executionInfo":{"status":"ok","timestamp":1732681747231,"user_tz":480,"elapsed":916,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"7663db40-fd7e-4b37-e614-1e0661557ccd"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"all_cls = df['diagnosis'].tolist()\n\nprint(all_cls[:3])","metadata":{"id":"p9d7TljgIki7","executionInfo":{"status":"ok","timestamp":1732681749465,"user_tz":480,"elapsed":186,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"a7899c12-9717-4f1e-eca8-6d778ba2c020"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Class distribution\n\ncls, count = np.unique(all_cls, return_counts=True)\n\nplt.bar(cls, count)","metadata":{"id":"Hpyh1JPjKJnr","executionInfo":{"status":"ok","timestamp":1732681751274,"user_tz":480,"elapsed":407,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"c1640c4f-18db-46e6-b6e2-0543b7faf37a"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### Dataset, DataLoader","metadata":{"id":"Fs4ABS3_H1g-"}},{"cell_type":"code","source":"import imgaug.augmenters as iaa\n\nimport imgaug as ia\n\n\n\nclass RetinaDataset(torch.utils.data.Dataset):\n\n    def __init__(self, df, aug, img_size=224):\n\n        self.df = df\n\n        self.transform = iaa.Sequential([\n\n            iaa.Fliplr(0.5), # 50% horizontal flip\n\n            iaa.Flipud(0.5), # 50% vertical flip\n\n            iaa.Affine(\n\n                rotate=(-45, 45),\n\n                shear=(-16,16),\n\n                scale={\"x\": (0.8, 1.2), \"y\": (0.8, 1.2)}\n\n            ),\n\n        ])\n\n        self.aug = aug\n\n        self.img_size = img_size\n\n\n\n    def __len__(self):\n\n        return len(self.df)\n\n\n\n    def __getitem__(self, idx):\n\n        row = self.df.iloc[idx]\n\n        filename = row[\"id_code\"]\n\n        path = f\"kaggle_retina/train/{filename}.png\"\n\n        img = cv2.imread(path)[:,:,::-1]\n\n        img = cv2.resize(img, (self.img_size, self.img_size))\n\n        if self.aug:\n\n            img = self.transform.augment_image(image=img)\n\n        img = img / 255.\n\n        label = row[\"diagnosis\"]\n\n\n\n        img = torch.tensor(img, dtype=torch.float).permute(2, 0, 1)\n\n        label = torch.tensor(label, dtype=torch.long)\n\n\n\n        return img, label","metadata":{"id":"ctb1Fc762aFP","executionInfo":{"status":"ok","timestamp":1732681753749,"user_tz":480,"elapsed":661,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df = pd.read_csv(\"kaggle_retina/train.csv\")\n\ntrain_df, val_df = train_test_split(df,\n\n                                    test_size=0.2,\n\n                                    random_state=5566)","metadata":{"id":"qA9FwazT2-ZP","executionInfo":{"status":"ok","timestamp":1732681755936,"user_tz":480,"elapsed":139,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_ds = RetinaDataset(train_df, aug=True, img_size=IMG_SIZE)\n\nval_ds = RetinaDataset(val_df, aug=False, img_size=IMG_SIZE)","metadata":{"id":"fReUxW4K3Rzx","executionInfo":{"status":"ok","timestamp":1732681758923,"user_tz":480,"elapsed":134,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"idx = np.random.randint(0, len(train_ds))\n\nimg, label = train_ds[idx]\n\nprint(idx, label)\n\nplt.imshow(img.permute(1, 2, 0))\n\nplt.show()","metadata":{"id":"8GR7ag-64ogY","executionInfo":{"status":"ok","timestamp":1732681761083,"user_tz":480,"elapsed":651,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"be2a2733-1ae7-4785-f91a-102232399006"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_loader = torch.utils.data.DataLoader(train_ds, BS, shuffle=True)\n\nval_loader = torch.utils.data.DataLoader(val_ds, BS)","metadata":{"id":"YHZ-47eW9BYM","executionInfo":{"status":"ok","timestamp":1732681763855,"user_tz":480,"elapsed":155,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### [TODO] Model","metadata":{"id":"SQCQwXL_8YI4"}},{"cell_type":"code","source":"from torchvision import models\n\n\n\n# Define a ResNet model\n\nclass RetinaClassifier(nn.Module):\n\n    def __init__(self, num_classes=5):\n\n        super(RetinaClassifier, self).__init__()\n\n        # Load a pre-trained ResNet model\n\n        self.model = models.resnet18(pretrained=True)  # ResNet18 for simplicity\n\n        # Modify the fully connected layer for 5 output classes\n\n        self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)\n\n\n\n    def forward(self, x):\n\n        return self.model(x)\n\n\n\n# Initialize the model\n\nmodel = RetinaClassifier(num_classes=NUM_CLASS).to(device)","metadata":{"id":"m4N4qWwE6s_e","executionInfo":{"status":"ok","timestamp":1732682380178,"user_tz":480,"elapsed":6771,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"1eaa3b32-9439-4df7-d127-dfecbc001cd1"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### Training","metadata":{"id":"VrgX3Wksckar"}},{"cell_type":"code","source":"loss_fn = nn.CrossEntropyLoss()\n\noptimizer = torch.optim.Adam(model.parameters())","metadata":{"id":"B9Rz3QqA8423","executionInfo":{"status":"ok","timestamp":1732682403289,"user_tz":480,"elapsed":210,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def train(dataloader, model, loss_fn, optimizer):\n\n    size = len(dataloader.dataset) # number of samples\n\n    num_batches = len(dataloader) # batches per epoch\n\n\n\n    model.train() # to training mode.\n\n    epoch_loss, epoch_correct = 0, 0\n\n    for batch_i, (x, y) in enumerate(tqdm(dataloader, leave=False)):\n\n        x, y = x.to(device), y.to(device) # move data to GPU\n\n\n\n        # zero the parameter gradients\n\n        optimizer.zero_grad()\n\n\n\n        # Compute prediction loss\n\n        pred = model(x)\n\n        loss = loss_fn(pred, y)\n\n\n\n        # Optimization by gradients\n\n        loss.backward() # backpropagation to compute gradients\n\n        optimizer.step() # update model params\n\n\n\n        # write to logs\n\n        epoch_loss += loss.item() # tensor -> python value\n\n        # (N, Class)\n\n        epoch_correct += (pred.argmax(dim=1) == y).sum().item()\n\n\n\n    # return avg loss of epoch, acc of epoch\n\n    return epoch_loss/num_batches, epoch_correct/size\n\n\n\n\n\ndef test(dataloader, model, loss_fn):\n\n    size = len(dataloader.dataset) # number of samples\n\n    num_batches = len(dataloader) # batches per epoch\n\n\n\n    model.eval() # model to test mode.\n\n    epoch_loss, epoch_correct = 0, 0\n\n\n\n    # No gradient for test data\n\n    with torch.no_grad():\n\n        for batch_i, (x, y) in enumerate(dataloader):\n\n            x, y = x.to(device), y.to(device)\n\n\n\n            # Compute prediction loss\n\n            pred = model(x)\n\n            loss = loss_fn(pred, y)\n\n\n\n            # write to logs\n\n            epoch_loss += loss.item()\n\n            epoch_correct += (pred.argmax(1) == y).sum().item()\n\n\n\n    return epoch_loss/num_batches, epoch_correct/size","metadata":{"id":"QLQHfpZG854w","executionInfo":{"status":"ok","timestamp":1732682406070,"user_tz":480,"elapsed":158,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"EPOCHS = 100\n\nlogs = {\n\n    'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []\n\n}\n\n# Earlystopping\n\npatience = 5\n\ncounter = 0\n\nbest_loss = np.inf\n\n\n\nfor epoch in tqdm(range(EPOCHS)):\n\n    train_loss, train_acc = train(train_loader, model, loss_fn, optimizer)\n\n    val_loss, val_acc = test(val_loader, model, loss_fn)\n\n\n\n    print(f'EPOCH: {epoch:04d} \\\n\n    train_loss: {train_loss:.4f}, train_acc: {train_acc:.3f} \\\n\n    val_loss: {val_loss:.4f}, val_acc: {val_acc:.3f} ')\n\n\n\n    logs['train_loss'].append(train_loss)\n\n    logs['train_acc'].append(train_acc)\n\n    logs['val_loss'].append(val_loss)\n\n    logs['val_acc'].append(val_acc)\n\n\n\n    torch.save(model.state_dict(), \"last.pth\")\n\n    # chcek improvement\n\n    if val_loss < best_loss:\n\n        counter = 0\n\n        best_loss = val_loss\n\n        torch.save(model.state_dict(), \"best.pth\")\n\n    else:\n\n        counter += 1\n\n    if counter >= patience:\n\n        print(\"Earlystop!\")\n\n        break","metadata":{"id":"erk9TbDA89ac","executionInfo":{"status":"ok","timestamp":1732682750878,"user_tz":480,"elapsed":342885,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"b8fc34a6-2ade-4a41-b1b0-c9598152f6b0"},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"#### Kaggle Submission\n\n\n\nhttps://www.kaggle.com/competitions/diabetic-retinopathy-classification-3/overview","metadata":{"id":"GciqGz0G8SIo"}},{"cell_type":"code","source":"model.load_state_dict(torch.load('best.pth'))\n\n_ = model.eval().to(device)","metadata":{"id":"V1zohpNw1KMj","executionInfo":{"status":"ok","timestamp":1732682827313,"user_tz":480,"elapsed":141,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}},"outputId":"bde087be-ee88-4978-d7b4-6499268d7b04"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"class TestDataset(torch.utils.data.Dataset):\n\n    def __init__(self, df, img_size=224):\n\n        self.df = df\n\n        self.img_size = img_size\n\n\n\n    def __len__(self):\n\n        return len(self.df)\n\n\n\n    def __getitem__(self, idx):\n\n        row = self.df.iloc[idx]\n\n        filename = row[\"id_code\"]\n\n        path = f\"kaggle_retina/test/{filename}.png\"\n\n        img = cv2.imread(path)[:,:,::-1]\n\n        img = cv2.resize(img, (self.img_size, self.img_size))\n\n        img = img / 255.\n\n        img = torch.tensor(img, dtype=torch.float).permute(2, 0, 1)\n\n        return img\n\n\n\ndf = pd.read_csv('kaggle_retina/submission.csv')\n\ntest_ds = TestDataset(df, IMG_SIZE)\n\ntest_loader = torch.utils.data.DataLoader(test_ds, BS)","metadata":{"id":"0WVPXIVSAsiI","executionInfo":{"status":"ok","timestamp":1732681783706,"user_tz":480,"elapsed":139,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Inference\n\ny_pred = []\n\nwith torch.no_grad():\n\n    for x in test_loader:\n\n        x = x.to(device)\n\n        pred = model(x)\n\n        y_pred.append(pred.argmax(dim=1))\n\n\n\ny_pred = torch.cat(y_pred, dim=0).cpu().numpy()","metadata":{"id":"RZvItAhmBM4f","executionInfo":{"status":"ok","timestamp":1732682839338,"user_tz":480,"elapsed":7927,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df['diagnosis'] = y_pred\n\ndf.to_csv('submission.csv', index=None)","metadata":{"id":"R8MkUXbQBuwe","executionInfo":{"status":"ok","timestamp":1732682842419,"user_tz":480,"elapsed":141,"user":{"displayName":"Spencer Chang","userId":"13891955898120448041"}}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"id":"H93wnfpCCjZ0"},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"id":"pzDTUdClCLC-"},"outputs":[],"execution_count":null}]}