Spaces:
Sleeping
Sleeping
File size: 4,909 Bytes
52dd1ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
# 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,
)
|