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f9ed2b4 | 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 | #!/usr/bin/env python3
'Fit and evaluate an eight-class real-image-source classifier on frozen CLIP features.'
from __future__ import annotations
import argparse
import csv
import json
import random
import sys
from collections import defaultdict
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
import torchvision.transforms as T
from PIL import Image
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, balanced_accuracy_score, classification_report
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
try:
import clip
except ImportError:
raise ImportError("Please install clip: pip install git+https://github.com/openai/CLIP.git")
PROJECT_ROOT = Path(__file__).resolve().parents[2]
ARTIFACT_ROOT = PROJECT_ROOT / "artifacts"
DEFAULT_SPLIT_DIR = ARTIFACT_ROOT / "metadata" / "fixed_split"
CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073]
CLIP_STD = [0.26862954, 0.26130258, 0.27577711]
csv.field_size_limit(min(sys.maxsize, 2_147_483_647))
class RealSourceDataset(Dataset):
def __init__(self, rows: list[dict[str, str]], label_to_idx: dict[str, int], transform):
self.rows = rows
self.label_to_idx = label_to_idx
self.transform = transform
def __len__(self) -> int:
return len(self.rows)
def __getitem__(self, idx: int):
row = self.rows[idx]
image = Image.open(row["image_path"]).convert("RGB")
return self.transform(image), self.label_to_idx[row["source_dataset"]]
def read_rows(path: Path) -> list[dict[str, str]]:
with path.open("r", newline="", encoding="utf-8") as f:
return list(csv.DictReader(f))
def sample_per_source(rows: list[dict[str, str]], per_source: int | None, seed: int) -> list[dict[str, str]]:
grouped: dict[str, list[dict[str, str]]] = defaultdict(list)
for row in rows:
grouped[row["source_dataset"]].append(row)
rng = random.Random(seed)
selected = []
for source in sorted(grouped):
source_rows = list(grouped[source])
rng.shuffle(source_rows)
if per_source is not None:
source_rows = source_rows[:per_source]
selected.extend(source_rows)
return selected
def make_transform() -> T.Compose:
return T.Compose([
T.Resize(int(224 * 1.15)),
T.CenterCrop(224),
T.ToTensor(),
T.Normalize(CLIP_MEAN, CLIP_STD),
])
@torch.no_grad()
def extract_features(model, loader, device: torch.device) -> tuple[np.ndarray, np.ndarray]:
features = []
labels = []
for images, y in tqdm(loader, desc="Extracting real-source features"):
images = images.to(device)
feat = model.encode_image(images).float()
feat = F.normalize(feat, dim=-1)
features.append(feat.cpu().numpy())
labels.append(y.numpy())
return np.concatenate(features, axis=0), np.concatenate(labels, axis=0)
def main() -> None:
parser = argparse.ArgumentParser(description="training real-source bias control.")
parser.add_argument("--real-train-csv", type=Path, default=DEFAULT_SPLIT_DIR / "real_train_pool.csv")
parser.add_argument("--real-val-csv", type=Path, default=DEFAULT_SPLIT_DIR / "real_validation.csv")
parser.add_argument("--train-per-source", type=int, default=5000)
parser.add_argument("--val-per-source", type=int, default=1000)
parser.add_argument("--batch-size", type=int, default=128)
parser.add_argument("--num-workers", type=int, default=8)
parser.add_argument("--device", type=str, default="cuda")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--output", type=Path, default=ARTIFACT_ROOT / "results" / "training" / "real_source_bias_clip_logreg.json")
args = parser.parse_args()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
device = torch.device(args.device if torch.cuda.is_available() else "cpu")
train_rows = sample_per_source(read_rows(args.real_train_csv), args.train_per_source, args.seed)
val_rows = sample_per_source(read_rows(args.real_val_csv), args.val_per_source, args.seed)
labels = sorted({row["source_dataset"] for row in train_rows + val_rows})
label_to_idx = {label: idx for idx, label in enumerate(labels)}
idx_to_label = {idx: label for label, idx in label_to_idx.items()}
model, _ = clip.load("ViT-L/14", device=device)
model.eval()
transform = make_transform()
train_loader = DataLoader(
RealSourceDataset(train_rows, label_to_idx, transform),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
)
val_loader = DataLoader(
RealSourceDataset(val_rows, label_to_idx, transform),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True,
)
x_train, y_train = extract_features(model, train_loader, device)
x_val, y_val = extract_features(model, val_loader, device)
clf = LogisticRegression(max_iter=1000, C=1.0, solver="lbfgs")
clf.fit(x_train, y_train)
pred = clf.predict(x_val)
report = classification_report(
y_val,
pred,
target_names=[idx_to_label[i] for i in range(len(idx_to_label))],
output_dict=True,
zero_division=0,
)
result = {
"seed": args.seed,
"train_per_source": args.train_per_source,
"val_per_source": args.val_per_source,
"labels": idx_to_label,
"accuracy": float(accuracy_score(y_val, pred)),
"balanced_accuracy": float(balanced_accuracy_score(y_val, pred)),
"classification_report": report,
}
args.output.parent.mkdir(parents=True, exist_ok=True)
with args.output.open("w", encoding="utf-8") as f:
json.dump(result, f, indent=2, ensure_ascii=False)
print(json.dumps(result, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
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