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8df47b0 | 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 | from __future__ import annotations
import argparse
from pathlib import Path
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import models, transforms
import ocular_preliminary_benchmark as base
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Extract ImageNet EfficientNetB3 embeddings in benchmark sample order."
)
parser.add_argument("--data-root", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, default=Path("runs/phase1/features"))
parser.add_argument("--input-size", type=int, default=384)
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--num-workers", type=int, default=0)
parser.add_argument("--device", default="auto")
parser.add_argument("--force", action="store_true")
return parser.parse_args()
def build_model(device: torch.device) -> nn.Module:
weights = models.EfficientNet_B3_Weights.DEFAULT
model = models.efficientnet_b3(weights=weights)
model.classifier = nn.Identity()
model.eval()
return model.to(device)
def main() -> None:
args = parse_args()
args.output_dir.mkdir(parents=True, exist_ok=True)
feature_path = args.output_dir / f"efficientnet_b3_features_{args.input_size}.npy"
label_path = args.output_dir / f"efficientnet_b3_labels_{args.input_size}.npy"
if feature_path.exists() and label_path.exists() and not args.force:
print(f"Feature cache already exists: {feature_path}")
return
device = base.get_device(args.device)
samples = base.discover_samples(args.data_root.expanduser().resolve(), None)
labels = np.asarray([sample.label for sample in samples], dtype=np.int64)
transform = transforms.Compose(
[
transforms.Resize(int(args.input_size * 1.14)),
transforms.CenterCrop(args.input_size),
transforms.ToTensor(),
transforms.Normalize(base.IMAGENET_MEAN, base.IMAGENET_STD),
]
)
dataset = base.FundusDataset(samples, transform)
loader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
pin_memory=device.type == "cuda",
persistent_workers=args.num_workers > 0,
)
model = build_model(device)
features: list[np.ndarray] = []
with torch.no_grad():
for batch_index, (images, _) in enumerate(loader, start=1):
images = images.to(device, non_blocking=device.type == "cuda")
embedding = model(images)
if device.type == "mps" and hasattr(torch, "mps"):
torch.mps.synchronize()
features.append(embedding.detach().cpu().numpy().astype(np.float32))
if batch_index <= 3 or batch_index % 25 == 0:
print(
f"extracted {min(batch_index * args.batch_size, len(samples))}/{len(samples)}",
flush=True,
)
matrix = np.concatenate(features, axis=0)
np.save(feature_path, matrix)
np.save(label_path, labels)
print(f"saved {feature_path} shape={matrix.shape}")
print(f"saved {label_path} shape={labels.shape}")
if __name__ == "__main__":
main()
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