\nThroughout this notebook, I will be using PyTorch to train a series of models for the Cassava Leaf competition. You can learn more about this competition here, [link]. An initial version of this notebook showcased a different approach to the competition; however, I felt the previous version fell short of my desired outcome, leading me to revise it. My current objective is to achieve a commendable score, adhering to the limitations imposed by Kaggle's GPU resources.
","metadata":{}},{"cell_type":"markdown","source":"# \n\n* [1-Libraries and Data load](#section-one)\n* [2-Config](#section-two)\n* [3-Preprocessing](#section-three)\n * [3.1-Target distribution](#subsection-three-one)\n * [3.2-Data splitting](#subsection-three-two)\n* [4-Transfer learning with Timm and Pytorch Ligthning](#section-four)\n * [4.1-Dataset and Lightning Datamodule](#subsection-four-one)\n * [4.2-Batch testing](#subsection-four-two)\n * [4.3-Images checking](#subsection-four-three)\n * [4.4-Lightning Module](#subsection-four-four)\n * [4.5-Training](#subsection-four-five)\n* [5-Predictions and Evaluation](#section-five)\n * [5.1-Inference](#subsection-five-one)\n * [5.2-Leaderboard results](#subsection-five-two)\n* [6-Improvements](#section-six)\n* [7-References](#section-seven)","metadata":{}},{"cell_type":"markdown","source":"\n# \n","metadata":{"id":"655-SPFZuHyy"}},{"cell_type":"code","source":"# Tools\nimport os, gc, sys, yaml, glob, random\nfrom pathlib import Path\nfrom tqdm import tqdm\n\n# Data handling\nimport numpy as np\nimport pandas as pd\n\n# Viz\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Images handling\nfrom skimage import io\n\n# Augmentations\nimport albumentations as A\n\n# Torch\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim import lr_scheduler \nfrom torch.utils.data import Dataset, DataLoader\n\n# PL\nimport pytorch_lightning as pl\nfrom pytorch_lightning.loggers import WandbLogger, CSVLogger\nfrom pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping\n\n# Sklearn\nfrom sklearn import metrics, preprocessing, model_selection\n\n# Models\nimport timm\n\n# Logger\nimport wandb\n\n# Kaggle \nfrom kaggle_secrets import UserSecretsClient\n\n# Warnings\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# Viz style\nplt.style.use('ggplot')\nsns.set_style('darkgrid')\n\n# print(f'Cuda init = {torch.cuda.is_initialized()}')","metadata":{"id":"RESaEKaiuHkJ","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"torch.version.cuda, torch.__version__, pl.__version__, timm.__version__","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"files_path = '../input/cassava-leaf-disease-classification'","metadata":{"id":"32ycZLSjq1Ax","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"path = Path(files_path)\nos.listdir(path)","metadata":{"id":"dX3HI59b2wsc","outputId":"dacd3db1-187a-41a5-c7a7-1f7abbd65c0a","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(path/'train.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train['path'] = f'{path}/train_images/' + train['image_id']","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.sample(5)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.read_csv(path/'sample_submission.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test['path'] = f'{path}/test_images/' + test['image_id']","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n# ","metadata":{}},{"cell_type":"code","source":"class CFG:\n # Sim\n SIM_NUMBER = 1\n \n # Logger\n LOGGER = False\n \n # Debug\n DEBUG = True\n SUBSET = .5 # To avoid unnecessary GPU usage, I'll train the model using a subset of the training data.\n \n # Paths\n DATA_PATH = files_path\n OUTPUT_DIR = \"/kaggle/working/\"\n \n # Basic Configs\n SEED = 42\n TRAIN_BS = 32 \n VALID_BS = TRAIN_BS * 2\n FOLDS = 3\n WORKERS = 2\n \n # Model\n MODEL = 'tf_efficientnet_b3_ns'\n \n # Augmentations\n IMG_SIZE = 224\n TRAIN_TRANS = {\n 'RandomResizedCrop':\n {'width': IMG_SIZE,\n 'height': IMG_SIZE},\n 'Transpose': {'p':.5},\n 'HorizontalFlip':{},\n 'ShiftScaleRotate':{'p':0.2},\n 'CoarseDropout':{'p':0.5},\n }\n \n VALID_TRANS = {\n 'CenterCrop': \n {'width': IMG_SIZE,\n 'height': IMG_SIZE},\n 'Resize':\n {'width': IMG_SIZE,\n 'height': IMG_SIZE}\n }\n \n TEST_TRANS = {\n 'RandomCrop': \n {'height': IMG_SIZE, \n 'width': IMG_SIZE},\n 'HorizontalFlip': {},\n 'Resize':\n {'width': IMG_SIZE,\n 'height': IMG_SIZE}\n }\n \n # Training\n EARLY_STOP = {\n 'monitor': \"val_loss\",\n 'mode': \"min\",\n 'patience': 999,\n 'verbose': 1}\n \n EPOCHS = 10\n TRAINER = {\n 'accelerator': 'auto',\n 'devices': 'auto', \n 'max_epochs': EPOCHS, \n 'min_epochs': EPOCHS, \n 'precision': '16' if torch.cuda.device_count() == 2 else '32',\n 'deterministic': True}\n \n OPTIMIZER = 'Adam'\n OPTIMIZER_PARAMS = {'lr': 3e-4, \n 'weight_decay': 0.0}\n\n SCHEDULER = {\n 'OneCycleLR':\n {'pct_start': 0.1,\n 'max_lr': 0.01,\n 'total_steps': 50,\n 'verbose': False}\n }","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n# \n\n\n## ","metadata":{"id":"WoVjmqTBLhfX"}},{"cell_type":"code","source":"plt.figure(figsize=(10,5))\ng = sns.histplot(data=train['label'], x=train.label, bins=5, color='lightblue')\nmids = [rect.get_x() + rect.get_width() / 2 for rect in g.patches]\ng.set_xticks(mids)\ng.set_xticklabels([0,1,2,3,4])\ng.set_title('Target Distribution');","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\nWe can clearly see that the dataset labels are quite biased towards the class 3.
","metadata":{}},{"cell_type":"code","source":"targets = {\n 0: \"Cassava Bacterial Blight (CBB)\",\n 1: \"Cassava Brown Streak Disease (CBSD)\",\n 2: \"Cassava Green Mottle (CGM)\",\n 3: \"Cassava Mosaic Disease (CMD)\",\n 4: \"Healthy\",\n }","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"CFG.NUM_CLASSES = len(targets)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n## \n\n\nDue to the imbalance of classes, we must employ a proper cross-validation strategy, such as StratifiedKFold, to split the data into stratified folds, thereby maintaining the original imbalance of classes.
","metadata":{"id":"LES1C5tE02NT"}},{"cell_type":"code","source":"def folds (df):\n df = df.copy()\n skf = model_selection.StratifiedKFold(n_splits=CFG.FOLDS, shuffle=True, random_state=CFG.SEED)\n df['kfold'] = -1\n for fold, (_, val_idx) in enumerate(skf.split(X = df, y = df['label'])):\n df.loc[val_idx, 'kfold'] = fold\n return df","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = folds(train)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n# \n\n\nThis section will utilize the timm library, offering a wide range of pre-trained models. Additionally, the framework PyTorch Lightning will be leveraged, as its ability to facilitate experimentation in a remarkably simple manner was found highly beneficial for this type of competition. Finally, the valuable tool Weights & Biases will be employed for comprehensive experiment logging and visualization.
","metadata":{"id":"BnHfGcBlbCr7"}},{"cell_type":"code","source":"if CFG.LOGGER:\n user_secrets = UserSecretsClient() \n wandb_api = user_secrets.get_secret(\"WANDB\") \n wandb.login(key=wandb_api)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"timm.list_models(pretrained=False)\ntimm.list_models('efficientnet*')[:10]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\nHere we can see a list of some efficientnet pretrained models available in the timm library.\n
","metadata":{}},{"cell_type":"markdown","source":"\n## \n\n\nThe PyTorch Lightning DataModule is a reusable class that encapsulates all the steps needed to process data. Together with the LightningModule, they are the two main components of this framework. For further details, please refer to the following link:\n[link] \n
\nImportant note: If you encounter a GPU issue when using the 2 T4s, move all data processing code into the hooks of LightningDataModule or LightningModule, as demonstrated in the code snippet above.
\nTo prevent potential issues and ensure the correct application of image augmentations, It's recommendable checking the content of the dataloader batches. If you intend to utilize two GPUs, avoid using the DataModule class as it will transfer the data to the GPUs, potentially causing your notebook to crash. Therefore, I am utilizing the following function instead (display_random_images).
","metadata":{}},{"cell_type":"code","source":"train_df = train[train.kfold != 0].reset_index(drop=True)\nvalid_df = train[train.kfold == 0].reset_index(drop=True)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset_train = CassavaDataset(\n train_df, \n transforms=A.Compose([getattr(A, trans)(**params) for trans, params in CFG.TRAIN_TRANS.items() ]) if CFG.TRAIN_TRANS else None\n)\n\ndata_loader_train = DataLoader(\n dataset_train,\n batch_size=CFG.TRAIN_BS,\n shuffle=True,\n num_workers=CFG.WORKERS\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"images, labels = next(iter(data_loader_train))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"images.shape, labels.shape, images.dtype, labels.dtype","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dataset_valid = CassavaDataset(\n valid_df, \n transforms=A.Compose([getattr(A, trans)(**params) for trans, params in CFG.TRAIN_TRANS.items() ]) if CFG.TRAIN_TRANS else None\n)\n\ndata_loader_valid = DataLoader(\n dataset_valid,\n batch_size=CFG.VALID_BS,\n shuffle=True,\n num_workers=CFG.WORKERS\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"images, labels = next(iter(data_loader_valid))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"images.shape, labels.shape, images.dtype, labels.dtype","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\nHere we can see that everything works as we specify, we have a batch of 64 images, with a size of 224x224 each, and the labels are 64 as well.
","metadata":{}},{"cell_type":"markdown","source":"\n## ","metadata":{}},{"cell_type":"code","source":"def display_random_images(dataset, n=10, rows=1, seed=None):\n if seed:\n random.seed(seed)\n random_samples_idx = random.sample(range(len(dataset)), k=n)\n \n plt.figure(figsize=(30, 20)) \n cols = int(n/rows)\n \n for i, targ_sample in enumerate(random_samples_idx):\n targ_image, targ_label = dataset[targ_sample][0], dataset[targ_sample][1]\n targ_image = targ_image.permute(1, 2, 0)\n \n plt.subplot(rows, cols, i+1)\n plt.imshow(targ_image)\n plt.axis(False)\n plt.tight_layout(pad=0)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display_random_images(dataset_train, n=10, rows=2, seed=42)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display_random_images(dataset_valid, n=10, rows=2, seed=42)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n## \n\n\nThe LightningModule is one of the two main components of this framework. As you can see, it closely resembles how we define models in PyTorch, but includes additional functionalities. You can read more about it here [link]
","metadata":{"id":"QtSMf0wmVdDu"}},{"cell_type":"code","source":"def accuracy(outputs, labels):\n _, preds = torch.max(outputs, dim=1)\n return torch.tensor(torch.sum(preds == labels).item() / len(preds))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class Model(pl.LightningModule):\n\n def __init__(self, config):\n super().__init__()\n self.save_hyperparameters(config)\n self.model = timm.create_model(self.hparams['MODEL'], pretrained=True)\n self.model.classifier = nn.Linear(self.model.classifier.in_features, CFG.NUM_CLASSES)\n \n self.loss = torch.nn.CrossEntropyLoss()\n \n def training_step(self, batch, batch_idx):\n x, y = batch\n y_hat = self.model(x)\n loss = self.loss(y_hat, y)\n acc = accuracy(y_hat, y)\n self.log('train_loss', loss)\n self.log('train_acc', acc, prog_bar=True)\n return loss\n\n def validation_step(self, batch, batch_idx):\n x, y = batch\n y_hat = self.model(x)\n val_loss = self.loss(y_hat, y)\n val_acc = accuracy(y_hat, y)\n self.log('val_loss', val_loss)\n self.log('val_acc', val_acc, prog_bar=True)\n return {'val_loss':val_loss, 'val_acc':val_acc}\n\n def configure_optimizers(self):\n optimizer = getattr(torch.optim, self.hparams['OPTIMIZER'])(self.parameters(), **self.hparams['OPTIMIZER_PARAMS'])\n if 'SCHEDULER' in self.hparams:\n schedulers = [\n getattr(torch.optim.lr_scheduler, scheduler)(optimizer, **params)\n for scheduler, params in self.hparams['SCHEDULER'].items()\n ]\n return [optimizer], schedulers \n return optimizer\n\n def forward(self, batch):\n return self.model(batch)","metadata":{"id":"R2EROloOZSvw","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n## ","metadata":{}},{"cell_type":"code","source":"for fold in range(CFG.FOLDS):\n print(f'fold n={fold}')\n \n X_train = train[train.kfold != fold].reset_index(drop=True)\n X_valid = train[train.kfold == fold].reset_index(drop=True)\n \n if CFG.SEED:\n pl.seed_everything(CFG.SEED)\n os.environ[\"PYTHONHASHSEED\"] = str(CFG.SEED)\n torch.backends.cudnn.deterministic = False\n \n if CFG.LOGGER:\n logger = WandbLogger(project=\"Cassava\", name=f\"{CFG.MODEL}-{CFG.SIM_NUMBER}-{fold}\")\n \n early_stop_callback = EarlyStopping(**CFG.EARLY_STOP)\n \n checkpoint_callback = ModelCheckpoint(\n save_weights_only=True,\n monitor='val_acc', \n dirpath=CFG.OUTPUT_DIR,\n mode=\"max\",\n filename=f\"{CFG.MODEL}_{'sim_num'}_{CFG.SIM_NUMBER}_{{epoch}}_{{val_acc:.3f}}\",\n save_top_k=1,\n save_last=False,\n verbose=1,\n )\n \n trainer = pl.Trainer(\n logger=[logger if CFG.LOGGER else CSVLogger(save_dir=f\"logs_sim_{CFG.SIM_NUMBER}/\")],\n callbacks=[early_stop_callback, checkpoint_callback],\n **CFG.TRAINER, \n )\n \n dm = DataModule(CFG, X_train, X_valid)\n dm.setup()\n \n config_dict=dict((name, getattr(CFG, name)) for name in dir(CFG) if not name.startswith('__'))\n \n model = Model(config_dict) \n trainer.fit(model, dm)\n\n if CFG.LOGGER:\n logger.experiment.finish()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n## \n\n\nOne can track their experiment runs in a simple and visually appealing manner through Weights and Biases. Below is a screenshot of one experiment. This tool was recently discovered and appears to offer significant utility..
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\n\n\n# \n\n\nA separate notebook is recommended for making predictions. This avoids the need for retraining the model and allows for a sole focus on inference. For this purpose, both the models and the timm library (if used) need to be uploaded to Kaggle. Below, an inference example for this competition is shared.
","metadata":{}},{"cell_type":"code","source":"ckpt_path = './'","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ckpt_path = Path(ckpt_path)\nckpt_path","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"models_path = list(ckpt_path.glob(\"*.ckpt\"))\nmodels_path","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_ds = CassavaDataset(test, transforms=A.Compose([getattr(A, trans)(**params) for trans, params in CFG.TEST_TRANS.items() ]) if CFG.TEST_TRANS else None)\ntest_dataloader = DataLoader(test_ds, batch_size=CFG.VALID_BS, shuffle=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def predict(models_path, dl, device): \n fin_preds = []\n for path in tqdm(models_path):\n model = Model.load_from_checkpoint(path)\n model.eval()\n model.to(device) \n preds = torch.tensor([]).to(device)\n with torch.no_grad():\n for i, l in dl:\n i = i.to(device)\n y_hat = model(i)\n preds = torch.cat([preds, y_hat])\n fin_preds.append(preds)\n fin_preds = torch.stack(fin_preds).mean(axis=0)\n return torch.argmax(fin_preds, axis=1).cpu().numpy()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"preds = predict(models_path, test_dataloader, 'cuda')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df = test[['image_id', 'label']].copy()\ntest_df['label'] = preds","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df.to_csv('submission.csv', index=False)\ntest_df","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n# \n\n> 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can observe the various submissions, ranging from a basic CNN to an ensemble model trained with 3-fold cross-validation using the EfficientNet B3 architecture with 512x512 images. This ensemble model achieved a score of 0.87, demonstrating a significant improvement over the baseline EfficientNet B3 score of 0.82. Finally, the last run using the EfficientNet B3 model with 512x512 images achieved a top 5% ranking. \n
","metadata":{}},{"cell_type":"markdown","source":"\n# \n\n\nThere is room for improvement in the models, and some ideas can be gleaned from the winning solutions. Notably, the importance of ensembles, incorporating additional data, utilizing pseudolabeling, and employing larger architectures has been highlighted. Below, the top 3 solutions are shared.
\n\n> * First place\n> * Seconde place\n> * Third place","metadata":{}},{"cell_type":"markdown","source":"\n# \n\n\nFor reference, the documentation for PyTorch, PyTorch Lightning, and Weights and Biases is provided alongside a discussion offering information about courses and materials for learning deep learning.
\n\n> * Pytorch docs\n> * Pytorch Lightning docs\n> * Weights and Biases docs\n> * Pytorch courses and materials\n\n\nPs, If you know spanish, a good place to learn is the Juan Sensio youtube channel, [link].
","metadata":{}},{"cell_type":"markdown","source":"\nThank you very much for taking the time to read my notebook. Greetings!
","metadata":{}}]}