src/training/train_svm.py

# src/training/train_svm.py

import os
import json
import argparse

import torch
from torch.utils.data import DataLoader
from torchvision import transforms, datasets

import numpy as np
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score
import joblib


def get_transforms():
    return transforms.Compose([
        transforms.Resize((64, 64)),
        transforms.Grayscale(num_output_channels=1),
        transforms.ToTensor(),  # (1, 64, 64) in [0, 1]
    ])


def build_datasets(data_root: str):
    tx = get_transforms()

    train_ds = datasets.OxfordIIITPet(
        root=data_root,
        split="trainval",
        target_types="category",
        transform=tx,
        download=True,
    )

    test_ds = datasets.OxfordIIITPet(
        root=data_root,
        split="test",
        target_types="category",
        transform=tx,
        download=True,
    )

    return train_ds, test_ds


def dataset_to_numpy(dataset):
    """
    Convert a torchvision dataset to (X, y) numpy arrays.
    X: (N, 4096) flattened grayscale pixels
    y: (N,) integer labels
    """
    loader = DataLoader(dataset, batch_size=64, shuffle=False, num_workers=2)

    xs = []
    ys = []
    for images, targets in loader:
        # images: (B, 1, 64, 64)
        b = images.shape[0]
        images = images.view(b, -1)  # (B, 4096)
        xs.append(images.numpy())
        ys.append(targets.numpy())

    X = np.concatenate(xs, axis=0)
    y = np.concatenate(ys, axis=0)
    return X, y


def ensure_labels_json(train_ds, labels_path: str):
    os.makedirs(os.path.dirname(labels_path), exist_ok=True)

    if os.path.exists(labels_path):
        with open(labels_path, "r") as f:
            labels = json.load(f)
        # sanity: if it already exists, just return
        return labels

    # OxfordIIITPet: category targets are indices into .categories
    id_to_name = {i: name for i, name in enumerate(train_ds.categories)}

    with open(labels_path, "w") as f:
        json.dump(id_to_name, f, indent=2)

    return id_to_name


def train_svm(
    data_root: str = "data/oxford-iiit-pet",
    ckpt_path: str = "checkpoints/svm_model.joblib",
    labels_path: str = "configs/labels.json",
):
    os.makedirs(os.path.dirname(ckpt_path), exist_ok=True)

    print(f"[+] Loading datasets from {data_root} ...")
    train_ds, test_ds = build_datasets(data_root)

    print("[+] Building labels.json (if missing) ...")
    labels = ensure_labels_json(train_ds, labels_path)
    num_classes = len(labels)
    print(f"[+] Num classes (from labels.json): {num_classes}")

    print("[+] Converting train dataset to numpy features ...")
    X_train, y_train = dataset_to_numpy(train_ds)
    print(f"    X_train shape: {X_train.shape}, y_train shape: {y_train.shape}")

    print("[+] Converting test dataset to numpy features ...")
    X_test, y_test = dataset_to_numpy(test_ds)
    print(f"    X_test shape: {X_test.shape}, y_test shape: {y_test.shape}")

    print("[+] Training Linear SVM on raw pixels ...")
    svm = LinearSVC(
        C=1.0,
        penalty="l2",
        loss="squared_hinge",
        max_iter=2000,
        # dual=True (default) is fine when n_samples > n_features,
        # which is the case here.
    )

    svm.fit(X_train, y_train)

    print("[+] Evaluating on train and test ...")
    y_pred_train = svm.predict(X_train)
    y_pred_test = svm.predict(X_test)

    train_acc = accuracy_score(y_train, y_pred_train)
    test_acc = accuracy_score(y_test, y_pred_test)

    print(f"    Train accuracy: {train_acc:.4f}")
    print(f"    Test accuracy : {test_acc:.4f}")

    print(f"[+] Saving SVM model to {ckpt_path} ...")
    joblib.dump(
        {
            "model": svm,
            "labels_path": labels_path,
            "train_acc": float(train_acc),
            "test_acc": float(test_acc),
        },
        ckpt_path,
    )

    print("[+] Done.")


def parse_args():
    parser = argparse.ArgumentParser(description="Train Linear SVM on raw pixel features.")

    parser.add_argument(
        "--data-root",
        type=str,
        default="data/oxford-iiit-pet",
        help="Root directory for Oxford-IIIT Pet dataset.",
    )
    parser.add_argument(
        "--ckpt-path",
        type=str,
        default="checkpoints/svm_model.joblib",
        help="Where to save the trained SVM model.",
    )
    parser.add_argument(
        "--labels-path",
        type=str,
        default="configs/labels.json",
        help="Path to labels mapping JSON.",
    )

    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()
    train_svm(
        data_root=args.data_root,
        ckpt_path=args.ckpt_path,
        labels_path=args.labels_path,
    )