diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml deleted file mode 100644 index e478022..0000000 --- a/.github/FUNDING.yml +++ /dev/null @@ -1,12 +0,0 @@ -# These are supported funding model platforms - -github: aladdinpersson # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] -patreon: #aladdinpersson # Replace with a single Patreon username -open_collective: # Replace with a single Open Collective username -ko_fi: # Replace with a single Ko-fi username -tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel -community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry -liberapay: # Replace with a single Liberapay username -issuehunt: # Replace with a single IssueHunt username -otechie: # Replace with a single Otechie username -custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] diff --git a/ML/Kaggles/Dog vs Cat Competition/Logistic Regression on EfficientNet Features.ipynb b/ML/Kaggles/Dog vs Cat Competition/Logistic Regression on EfficientNet Features.ipynb new file mode 100644 index 0000000..be2602f --- /dev/null +++ b/ML/Kaggles/Dog vs Cat Competition/Logistic Regression on EfficientNet Features.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "51c78b68", + "metadata": {}, + "outputs": [], + "source": [ + "import sklearn\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import log_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4421a043", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data shape: (25000, 2560), labels shape: (25000,)\n" + ] + }, + { + "data": { + "text/plain": [ + "LogisticRegression(max_iter=2000)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = np.load(f'data_features/X_train_b7.npy')\n", + "y = np.load(f'data_features/y_train_b7.npy')\n", + "\n", + "# Split data and train classifier\n", + "print(f\"Training data shape: {X.shape}, labels shape: {y.shape}\")\n", + "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.001, random_state=1337)\n", + "clf = LogisticRegression(max_iter=2000)\n", + "clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d5cfc5b0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "On validation set:\n", + "Accuracy: 1.0\n", + "LOG LOSS: 7.980845755748817e-05 \n", + "%--------------------------------------------------%\n", + "Getting predictions for test set\n", + "Done getting predictions!\n" + ] + } + ], + "source": [ + "# Check on validation\n", + "val_preds= clf.predict_proba(X_val)[:,1]\n", + "print(f\"On validation set:\")\n", + "print(f\"Accuracy: {clf.score(X_val, y_val)}\")\n", + "print(f\"LOG LOSS: {log_loss(y_val, val_preds)} \")\n", + "print(\"%--------------------------------------------------%\")\n", + "\n", + "# Get predictions on test set\n", + "print(\"Getting predictions for test set\")\n", + "X_test = np.load(f'data_features/X_test_b7.npy')\n", + "X_test_preds = clf.predict_proba(X_test)[:,1]\n", + "df = pd.DataFrame({'id': np.arange(1, 12501), 'label': np.clip(X_test_preds, 0.005, 0.995)})\n", + "df.to_csv(f\"submissions/mysubmission.csv\", index=False)\n", + "print(\"Done getting predictions!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9cce7af", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ML/Kaggles/Dog vs Cat Competition/competition.txt b/ML/Kaggles/Dog vs Cat Competition/competition.txt new file mode 100644 index 0000000..3189fd2 --- /dev/null +++ b/ML/Kaggles/Dog vs Cat Competition/competition.txt @@ -0,0 +1 @@ +https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition \ No newline at end of file diff --git a/ML/Kaggles/Dog vs Cat Competition/config.py b/ML/Kaggles/Dog vs Cat Competition/config.py new file mode 100644 index 0000000..de2e6be --- /dev/null +++ b/ML/Kaggles/Dog vs Cat Competition/config.py @@ -0,0 +1,26 @@ +import torch +import albumentations as A +from albumentations.pytorch import ToTensorV2 + +DEVICE = "cuda" if torch.cuda.is_available() else "cpu" +NUM_WORKERS = 4 +BATCH_SIZE = 20 +PIN_MEMORY = True +LOAD_MODEL = True +SAVE_MODEL = True +CHECKPOINT_FILE = "b7.pth.tar" +WEIGHT_DECAY = 1e-4 +LEARNING_RATE = 1e-4 +NUM_EPOCHS = 1 + +basic_transform = A.Compose( + [ + A.Resize(height=448, width=448), + A.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], + max_pixel_value=255.0, + ), + ToTensorV2(), + ] +) diff --git a/ML/Kaggles/Dog vs Cat Competition/dataset.py b/ML/Kaggles/Dog vs Cat Competition/dataset.py new file mode 100644 index 0000000..b7e6ccb --- /dev/null +++ b/ML/Kaggles/Dog vs Cat Competition/dataset.py @@ -0,0 +1,32 @@ +import os +import re +import numpy as np +from torch.utils.data import Dataset +from PIL import Image + + +class CatDog(Dataset): + def __init__(self, root, transform=None): + self.images = os.listdir(root) + self.images.sort(key=lambda x: int(re.findall(r"\d+", x)[0])) + self.root = root + self.transform = transform + + def __len__(self): + return len(self.images) + + def __getitem__(self, index): + file = self.images[index] + img = np.array(Image.open(os.path.join(self.root, file))) + + if self.transform is not None: + img = self.transform(image=img)["image"] + + if "dog" in file: + label = 1 + elif "cat" in file: + label = 0 + else: + label = -1 + + return img, label diff --git a/ML/Kaggles/Dog vs Cat Competition/submissions/mysubmission.csv b/ML/Kaggles/Dog vs Cat Competition/submissions/mysubmission.csv new file mode 100644 index 0000000..36196db --- /dev/null +++ b/ML/Kaggles/Dog vs Cat Competition/submissions/mysubmission.csv @@ -0,0 +1,12501 @@ +id,label +1,0.995 +2,0.995 +3,0.995 +4,0.995 +5,0.005 +6,0.005 +7,0.005 +8,0.005 +9,0.005 +10,0.005 +11,0.005 +12,0.995 +13,0.005 +14,0.005 +15,0.005 +16,0.005 +17,0.995 +18,0.995 +19,0.005 +20,0.005 +21,0.995 +22,0.005 +23,0.995 +24,0.995 +25,0.005 +26,0.995 +27,0.995 +28,0.005 +29,0.005 +30,0.995 +31,0.995 +32,0.18221635005898879 +33,0.995 +34,0.005 +35,0.005 +36,0.005 +37,0.005 +38,0.005 +39,0.995 +40,0.005 +41,0.995 +42,0.995 +43,0.995 +44,0.995 +45,0.005 +46,0.995 +47,0.005 +48,0.995 +49,0.995 +50,0.005 +51,0.005 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save_feature_vectors(model, loader, output_size=(1, 1), file="trainb7"): + model.eval() + images, labels = [], [] + + for idx, (x, y) in enumerate(tqdm(loader)): + x = x.to(config.DEVICE) + + with torch.no_grad(): + features = model.extract_features(x) + features = F.adaptive_avg_pool2d(features, output_size=output_size) + images.append(features.reshape(x.shape[0], -1).detach().cpu().numpy()) + labels.append(y.numpy()) + + np.save(f"data_features/X_{file}.npy", np.concatenate(images, axis=0)) + np.save(f"data_features/y_{file}.npy", np.concatenate(labels, axis=0)) + model.train() + + +def train_one_epoch(loader, model, loss_fn, optimizer, scaler): + loop = tqdm(loader) + + for batch_idx, (data, targets) in enumerate(loop): + data = data.to(config.DEVICE) + targets = targets.to(config.DEVICE).unsqueeze(1).float() + + with torch.cuda.amp.autocast(): + scores = model(data) + loss = loss_fn(scores, targets) + + optimizer.zero_grad() + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + loop.set_postfix(loss=loss.item()) + + +def main(): + model = EfficientNet.from_pretrained("efficientnet-b7") + model._fc = nn.Linear(2560, 1) + train_dataset = CatDog(root="data/train/", transform=config.basic_transform) + test_dataset = CatDog(root="data/test/", transform=config.basic_transform) + train_loader = DataLoader( + train_dataset, + shuffle=True, + batch_size=config.BATCH_SIZE, + num_workers=config.NUM_WORKERS, + pin_memory=True, + ) + test_loader = DataLoader( + test_dataset, + shuffle=False, + batch_size=config.BATCH_SIZE, + num_workers=config.NUM_WORKERS, + ) + model = model.to(config.DEVICE) + + scaler = torch.cuda.amp.GradScaler() + loss_fn = nn.BCEWithLogitsLoss() + optimizer = optim.Adam( + model.parameters(), lr=config.LEARNING_RATE, weight_decay=config.WEIGHT_DECAY + ) + + if config.LOAD_MODEL and config.CHECKPOINT_FILE in os.listdir(): + load_checkpoint(torch.load(config.CHECKPOINT_FILE), model) + + for epoch in range(config.NUM_EPOCHS): + train_one_epoch(train_loader, model, loss_fn, optimizer, scaler) + check_accuracy(train_loader, model, loss_fn) + + if config.SAVE_MODEL: + checkpoint = {"state_dict": model.state_dict(), "optimizer": optimizer.state_dict()} + save_checkpoint(checkpoint, filename=config.CHECKPOINT_FILE) + + save_feature_vectors(model, train_loader, output_size=(1, 1), file="train_b7") + save_feature_vectors(model, test_loader, output_size=(1, 1), file="test_b7") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ML/Kaggles/Dog vs Cat Competition/utils.py b/ML/Kaggles/Dog vs Cat Competition/utils.py new file mode 100644 index 0000000..2ae750f --- /dev/null +++ b/ML/Kaggles/Dog vs Cat Competition/utils.py @@ -0,0 +1,192 @@ +import torch +import os +import pandas as pd +import numpy as np +import albumentations as A +from albumentations.pytorch import ToTensorV2 +import config +from tqdm import tqdm +from dataset import CatDog +from torch.utils.data import DataLoader +from sklearn.metrics import log_loss + + +def check_accuracy( + loader, model, loss_fn, input_shape=None, toggle_eval=True, print_accuracy=True +): + """ + Check accuracy of model on data from loader + """ + if toggle_eval: + model.eval() + device = next(model.parameters()).device + num_correct = 0 + num_samples = 0 + + y_preds = [] + y_true = [] + + with torch.no_grad(): + for x, y in loader: + x = x.to(device=device) + y = y.to(device=device) + if input_shape: + x = x.reshape(x.shape[0], *input_shape) + scores = model(x) + predictions = torch.sigmoid(scores) > 0.5 + y_preds.append(torch.clip(torch.sigmoid(scores), 0.005, 0.995).cpu().numpy()) + y_true.append(y.cpu().numpy()) + num_correct += (predictions.squeeze(1) == y).sum() + num_samples += predictions.size(0) + + accuracy = num_correct / num_samples + + if toggle_eval: + model.train() + + if print_accuracy: + print(f"Accuracy: {accuracy * 100:.2f}%") + print(log_loss(np.concatenate(y_true, axis=0), np.concatenate(y_preds, axis=0))) + + return accuracy + + +def save_checkpoint(state, filename="my_checkpoint.pth.tar"): + print("=> Saving checkpoint") + torch.save(state, filename) + + +def load_checkpoint(checkpoint, model): + print("=> Loading checkpoint") + model.load_state_dict(checkpoint["state_dict"]) + + +def create_submission(model, model_name, files_dir): + my_transforms = { + "base": A.Compose( + [ + A.Resize(height=240, width=240), + A.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], + max_pixel_value=255.0, + ), + ToTensorV2(), + ] + ), + "horizontal_flip": A.Compose( + [ + A.Resize(height=240, width=240), + A.HorizontalFlip(p=1.0), + A.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], + max_pixel_value=255.0, + ), + ToTensorV2(), + ] + ), + "vertical_flip": A.Compose( + [ + A.Resize(height=240, width=240), + A.VerticalFlip(p=1.0), + A.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], + max_pixel_value=255.0, + ), + ToTensorV2(), + ] + ), + "coloring": A.Compose( + [ + A.Resize(height=240, width=240), + A.ColorJitter(p=1.0), + A.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], + max_pixel_value=255.0, + ), + ToTensorV2(), + ] + ), + "rotate": A.Compose( + [ + A.Resize(height=240, width=240), + A.Rotate(p=1.0, limit=45), + A.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], + max_pixel_value=255.0, + ), + ToTensorV2(), + ] + ), + "shear": A.Compose( + [ + A.Resize(height=240, width=240), + A.IAAAffine(p=1.0), + A.Normalize( + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225], + max_pixel_value=255.0, + ), + ToTensorV2(), + ] + ), + } + + for t in ["base", "horizontal_flip", "vertical_flip", "coloring", "rotate", "shear"]: + predictions = [] + labels = [] + all_files = [] + test_dataset = MyDataset(root=files_dir, transform=my_transforms[t]) + test_loader = DataLoader( + test_dataset, batch_size=32, num_workers=4, shuffle=False, pin_memory=True + ) + model.eval() + + for idx, (x, y, filenames) in enumerate(tqdm(test_loader)): + x = x.to(config.DEVICE) + with torch.no_grad(): + outputs = ( + torch.clip(torch.sigmoid(model(x)), 0.005, 0.995).squeeze(1).cpu().numpy() + ) + predictions.append(outputs) + labels += y.numpy().tolist() + all_files += filenames + + df = pd.DataFrame( + { + "id": np.arange( + 1, + (len(predictions) - 1) * predictions[0].shape[0] + + predictions[-1].shape[0] + + 1, + ), + "label": np.concatenate(predictions, axis=0), + } + ) + df.to_csv(f"predictions_test/submission_{model_name}_{t}.csv", index=False) + + model.train() + print(f"Created submission file for model {model_name} and transform {t}") + + +def blending_ensemble_data(): + pred_csvs = [] + root_dir = "predictions_validation/" + + for file in os.listdir(root_dir): + if "label" not in file: + df = pd.read_csv(root_dir + "/" + file) + pred_csvs.append(df) + else: + label_csv = pd.read_csv(root_dir + "/" + file) + + all_preds = pd.concat(pred_csvs, axis=1) + print(all_preds) + + 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/dev/null +++ b/ML/Pytorch/Basics/Imbalanced_classes/main.py @@ -0,0 +1,54 @@ +import torch +import torchvision.datasets as datasets +import os +from torch.utils.data import WeightedRandomSampler, DataLoader +import torchvision.transforms as transforms +import torch.nn as nn + +# Methods for dealing with imbalanced datasets: +# 1. Oversampling +# 2. Class weighting + +def get_loader(root_dir, batch_size): + my_transforms = transforms.Compose( + [ + transforms.Resize((224, 224)), + transforms.ToTensor(), + ] + ) + + dataset = datasets.ImageFolder(root=root_dir, transform=my_transforms) + class_weights = [] + for root, subdir, files in os.walk(root_dir): + if len(files) > 0: + class_weights.append(1/len(files)) + + sample_weights = [0] * len(dataset) + + for idx, (data, label) in enumerate(dataset): + class_weight = class_weights[label] + sample_weights[idx] = class_weight + + sampler = WeightedRandomSampler(sample_weights, num_samples= + len(sample_weights), replacement=True) + + loader = DataLoader(dataset, batch_size=batch_size, sampler=sampler) + return loader + + +def main(): + loader = get_loader(root_dir="dataset", batch_size=8) + + num_retrievers = 0 + num_elkhounds = 0 + for epoch in range(10): + for data, labels in loader: + num_retrievers += torch.sum(labels==0) + num_elkhounds += torch.sum(labels==1) + + print(num_retrievers) + print(num_elkhounds) + +if __name__ == "__main__": + main() + diff --git a/ML/Pytorch/others/default_setups/image_classification/augmentations.py b/ML/Pytorch/others/default_setups/CV - Image Classification/augmentations.py similarity index 100% rename from ML/Pytorch/others/default_setups/image_classification/augmentations.py rename to ML/Pytorch/others/default_setups/CV - Image Classification/augmentations.py diff --git a/ML/Pytorch/others/default_setups/image_classification/config.py b/ML/Pytorch/others/default_setups/CV - Image Classification/config.py similarity index 100% rename from ML/Pytorch/others/default_setups/image_classification/config.py rename to ML/Pytorch/others/default_setups/CV - Image Classification/config.py diff --git a/ML/Pytorch/others/default_setups/image_classification/dataset.py b/ML/Pytorch/others/default_setups/CV - Image Classification/dataset.py similarity index 100% rename from ML/Pytorch/others/default_setups/image_classification/dataset.py rename to ML/Pytorch/others/default_setups/CV - Image Classification/dataset.py diff --git a/ML/Pytorch/others/default_setups/image_classification/model.py b/ML/Pytorch/others/default_setups/CV - Image Classification/model.py similarity index 100% rename from ML/Pytorch/others/default_setups/image_classification/model.py rename to ML/Pytorch/others/default_setups/CV - Image Classification/model.py diff --git a/ML/Pytorch/others/default_setups/image_classification/train.py b/ML/Pytorch/others/default_setups/CV - Image Classification/train.py similarity index 100% rename from ML/Pytorch/others/default_setups/image_classification/train.py rename to ML/Pytorch/others/default_setups/CV - Image Classification/train.py diff --git a/ML/Pytorch/others/default_setups/image_classification/utils.py b/ML/Pytorch/others/default_setups/CV - Image Classification/utils.py similarity index 100% rename from ML/Pytorch/others/default_setups/image_classification/utils.py rename to ML/Pytorch/others/default_setups/CV - Image Classification/utils.py