moved_repo_first_update
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rpotter6298
Executable
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"""REFUGE optic disc / cup segmentation utilities."""
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from __future__ import annotations
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import math
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Dict, Iterable, List, Optional, Sequence, Tuple
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import numpy as np
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from PIL import Image
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import torch
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from torch import nn
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from torch.utils.data import DataLoader, Dataset
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from torchvision import transforms
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from classes.refuge_preprocessing import RefugePreprocessing, RefugeSample
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# ---------------------------------------------------------------------------
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# Dataset helpers
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# ---------------------------------------------------------------------------
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def _load_rgb(path: Path) -> Image.Image:
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img = Image.open(path)
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if img.mode != "RGB":
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img = img.convert("RGB")
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return img
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def _load_mask_array(path: Path) -> np.ndarray:
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mask_img = Image.open(path).convert("L")
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mask = np.array(mask_img, dtype=np.float32)
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# REFUGE masks encode disc/cup with different intensities; treat any
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# positive value as disc for coarse localisation.
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mask = np.where(mask > 0, 1.0, 0.0)
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return mask
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@dataclass
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class RefugeSegmentationSample:
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sample: RefugeSample
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image_path: Path
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mask_path: Path
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class RefugeSegmentationDataset(Dataset):
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"""Simple segmentation dataset returning tensors."""
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def __init__(
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self,
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samples: Sequence[RefugeSegmentationSample],
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image_size: int = 512,
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) -> None:
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self.samples = list(samples)
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self.image_size = image_size
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self.to_tensor = transforms.ToTensor()
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def __len__(self) -> int:
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return len(self.samples)
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def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
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rec = self.samples[idx]
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image = _load_rgb(rec.image_path)
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mask_arr = _load_mask_array(rec.mask_path)
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if self.image_size is not None:
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image = image.resize((self.image_size, self.image_size), Image.BILINEAR)
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mask_img = Image.fromarray(mask_arr).resize(
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(self.image_size, self.image_size), Image.NEAREST
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)
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mask_arr = np.array(mask_img, dtype=np.float32)
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image_tensor = self.to_tensor(image)
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mask_tensor = torch.from_numpy(mask_arr).unsqueeze(0) # [1,H,W]
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return {
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"image": image_tensor,
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"mask": mask_tensor,
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"sample_id": rec.sample.sample_id,
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}
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# ---------------------------------------------------------------------------
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# Model definition (lightweight U-Net)
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# ---------------------------------------------------------------------------
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class DoubleConv(nn.Module):
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def __init__(self, in_channels: int, out_channels: int):
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super().__init__()
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self.net = nn.Sequential(
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nn.Conv2d(in_channels, out_channels, 3, padding=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(inplace=True),
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.net(x)
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class UNet(nn.Module):
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def __init__(self, in_channels: int = 3, base_channels: int = 64):
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super().__init__()
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self.enc1 = DoubleConv(in_channels, base_channels)
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self.enc2 = DoubleConv(base_channels, base_channels * 2)
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self.enc3 = DoubleConv(base_channels * 2, base_channels * 4)
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self.enc4 = DoubleConv(base_channels * 4, base_channels * 8)
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self.pool = nn.MaxPool2d(2)
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self.bottleneck = DoubleConv(base_channels * 8, base_channels * 16)
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self.up4 = nn.ConvTranspose2d(base_channels * 16, base_channels * 8, 2, stride=2)
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self.dec4 = DoubleConv(base_channels * 16, base_channels * 8)
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self.up3 = nn.ConvTranspose2d(base_channels * 8, base_channels * 4, 2, stride=2)
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self.dec3 = DoubleConv(base_channels * 8, base_channels * 4)
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self.up2 = nn.ConvTranspose2d(base_channels * 4, base_channels * 2, 2, stride=2)
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self.dec2 = DoubleConv(base_channels * 4, base_channels * 2)
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self.up1 = nn.ConvTranspose2d(base_channels * 2, base_channels, 2, stride=2)
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self.dec1 = DoubleConv(base_channels * 2, base_channels)
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self.out = nn.Conv2d(base_channels, 1, 1)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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e1 = self.enc1(x)
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e2 = self.enc2(self.pool(e1))
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e3 = self.enc3(self.pool(e2))
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e4 = self.enc4(self.pool(e3))
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b = self.bottleneck(self.pool(e4))
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d4 = self.up4(b)
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d4 = torch.cat([d4, e4], dim=1)
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d4 = self.dec4(d4)
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d3 = self.up3(d4)
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d3 = torch.cat([d3, e3], dim=1)
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d3 = self.dec3(d3)
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d2 = self.up2(d3)
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d2 = torch.cat([d2, e2], dim=1)
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d2 = self.dec2(d2)
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d1 = self.up1(d2)
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d1 = torch.cat([d1, e1], dim=1)
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d1 = self.dec1(d1)
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return self.out(d1)
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# ---------------------------------------------------------------------------
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# Segmentation manager
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# ---------------------------------------------------------------------------
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class RefugeSegmentation:
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"""Train and run coarse-to-fine OD/OC segmentation for REFUGE."""
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def __init__(
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self,
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preprocessing: RefugePreprocessing,
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model: Optional[nn.Module] = None,
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) -> None:
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self.preprocessing = preprocessing
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self.model = model or UNet()
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self.train_dataset: Optional[RefugeSegmentationDataset] = None
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self.val_dataset: Optional[RefugeSegmentationDataset] = None
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self.train_loader: Optional[DataLoader] = None
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self.val_loader: Optional[DataLoader] = None
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# ------------------------------------------------------------------
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def build_datasets(
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self,
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image_size: int = 512,
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batch_size: int = 8,
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num_workers: int = 4,
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) -> None:
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manifest = self.preprocessing.build_manifest()
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train_samples: List[RefugeSegmentationSample] = []
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val_samples: List[RefugeSegmentationSample] = []
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for sample in manifest:
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if not sample.mask_path or not sample.mask_path.exists():
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continue
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rec = RefugeSegmentationSample(sample=sample, image_path=sample.image_path, mask_path=sample.mask_path)
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if sample.split == "train":
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train_samples.append(rec)
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elif sample.split in {"val", "validation"}:
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val_samples.append(rec)
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if not val_samples:
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# Fall back to using a subset of training data for validation
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split = max(1, int(0.1 * len(train_samples)))
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val_samples = train_samples[:split]
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train_samples = train_samples[split:]
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self.train_dataset = RefugeSegmentationDataset(train_samples, image_size=image_size)
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self.val_dataset = RefugeSegmentationDataset(val_samples, image_size=image_size)
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self.train_loader = DataLoader(
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self.train_dataset,
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batch_size=batch_size,
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shuffle=True,
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num_workers=num_workers,
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pin_memory=True,
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)
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self.val_loader = DataLoader(
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self.val_dataset,
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batch_size=batch_size,
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shuffle=False,
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num_workers=num_workers,
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pin_memory=True,
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)
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# ------------------------------------------------------------------
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def train(
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self,
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epochs: int = 40,
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lr: float = 1e-3,
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weight_decay: float = 1e-5,
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device: Optional[str] = None,
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checkpoint_dir: Optional[Path] = None,
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) -> Dict[str, float]:
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if self.train_loader is None or self.val_loader is None:
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self.build_datasets()
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device = device or ("cuda" if torch.cuda.is_available() else "cpu")
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self.model.to(device)
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criterion = nn.BCEWithLogitsLoss()
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optimizer = torch.optim.Adam(self.model.parameters(), lr=lr, weight_decay=weight_decay)
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best_dice = 0.0
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history: Dict[str, float] = {}
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for epoch in range(1, epochs + 1):
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print(f"[Seg] Processing epoch {epoch}/{epochs}")
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self.model.train()
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running_loss = 0.0
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for batch in self.train_loader: # type: ignore[arg-type]
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images = batch["image"].to(device)
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masks = batch["mask"].to(device)
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optimizer.zero_grad()
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logits = self.model(images)
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loss = criterion(logits, masks)
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loss.backward()
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optimizer.step()
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running_loss += loss.item() * images.size(0)
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train_loss = running_loss / len(self.train_loader.dataset) # type: ignore[arg-type]
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val_metrics = self.evaluate(device=device)
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history[f"epoch_{epoch}_loss"] = train_loss
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history[f"epoch_{epoch}_dice"] = val_metrics.get("dice", float("nan"))
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if val_metrics.get("dice", 0.0) > best_dice:
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best_dice = val_metrics["dice"]
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if checkpoint_dir is not None:
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checkpoint_dir.mkdir(parents=True, exist_ok=True)
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torch.save(self.model.state_dict(), checkpoint_dir / "refuge_segmentation_best.pt")
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return {"best_dice": best_dice, **history}
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# ------------------------------------------------------------------
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def evaluate(self, split: str = "val", device: Optional[str] = None) -> Dict[str, float]:
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if split != "val":
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raise ValueError("Only validation split supported currently")
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if self.val_loader is None:
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self.build_datasets()
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device = device or ("cuda" if torch.cuda.is_available() else "cpu")
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self.model.to(device)
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self.model.eval()
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dices: List[float] = []
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criterion = nn.BCEWithLogitsLoss()
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losses: List[float] = []
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with torch.no_grad():
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for batch in self.val_loader: # type: ignore[arg-type]
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images = batch["image"].to(device)
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masks = batch["mask"].to(device)
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logits = self.model(images)
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loss = criterion(logits, masks)
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losses.append(loss.item() * images.size(0))
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probs = torch.sigmoid(logits)
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preds = (probs > 0.5).float()
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dice = self._dice_coefficient(preds, masks)
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dices.extend(dice)
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mean_dice = float(np.mean(dices)) if dices else 0.0
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mean_loss = float(np.sum(losses) / len(self.val_loader.dataset)) # type: ignore[arg-type]
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return {"dice": mean_dice, "loss": mean_loss}
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# ------------------------------------------------------------------
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def predict_mask(self, sample: RefugeSample, device: Optional[str] = None) -> torch.Tensor:
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if self.train_dataset is None:
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self.build_datasets()
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device = device or ("cuda" if torch.cuda.is_available() else "cpu")
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self.model.to(device)
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self.model.eval()
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image = _load_rgb(sample.image_path)
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original_size = image.size # (width, height)
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image_resized = image.resize((self.train_dataset.image_size, self.train_dataset.image_size), Image.BILINEAR) # type: ignore[union-attr]
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tensor = transforms.ToTensor()(image_resized).unsqueeze(0).to(device)
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with torch.no_grad():
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logits = self.model(tensor)
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mask_resized = torch.sigmoid(logits)[0, 0]
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mask_np = mask_resized.cpu().numpy()
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mask_np = (mask_np > 0.5).astype(np.float32)
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mask_img = Image.fromarray(mask_np)
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mask_img = mask_img.resize(original_size, Image.NEAREST)
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return torch.from_numpy(np.array(mask_img, dtype=np.float32))
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def infer_disc_geometry(
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self,
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sample: RefugeSample,
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scale: float = 2.5,
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) -> Dict[str, float]:
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if sample.mask_path and sample.mask_path.exists():
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mask = _load_mask_array(sample.mask_path)
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else:
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mask = self.predict_mask(sample).numpy()
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coords = np.argwhere(mask > 0.5)
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if coords.size == 0:
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raise RuntimeError(f"Unable to locate disc for sample {sample.sample_id}")
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ys, xs = coords[:, 0], coords[:, 1]
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centre_x = float(xs.mean())
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centre_y = float(ys.mean())
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width = float(xs.max() - xs.min())
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height = float(ys.max() - ys.min())
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diameter = max(width, height)
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radius = diameter / 2.0
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crop_radius = radius * scale
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return {
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"centre_x": centre_x,
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"centre_y": centre_y,
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"radius": radius,
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"crop_radius": crop_radius,
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"crop_size": crop_radius * 2.0,
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}
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def batch_crops(
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self,
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samples: Iterable[RefugeSample],
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scale: float = 2.5,
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output_dir: Optional[Path] = None,
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size: int = 256,
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) -> Dict[str, Path]:
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output_paths: Dict[str, Path] = {}
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if output_dir is not None:
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output_dir.mkdir(parents=True, exist_ok=True)
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for sample in samples:
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geom = self.infer_disc_geometry(sample, scale=scale)
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image = _load_rgb(sample.image_path)
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cx, cy = geom["centre_x"], geom["centre_y"]
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r = geom["crop_radius"]
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left = max(0.0, cx - r)
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upper = max(0.0, cy - r)
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right = min(image.width, cx + r)
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lower = min(image.height, cy + r)
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crop = image.crop((left, upper, right, lower)).resize((size, size), Image.BILINEAR)
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if output_dir is not None:
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out_path = output_dir / f"{sample.sample_id}_crop.png"
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crop.save(out_path)
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output_paths[sample.sample_id] = out_path
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return output_paths
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# ------------------------------------------------------------------
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@staticmethod
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def _dice_coefficient(preds: torch.Tensor, targets: torch.Tensor) -> List[float]:
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eps = 1e-6
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dices = []
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preds = preds.view(preds.size(0), -1)
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targets = targets.view(targets.size(0), -1)
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for p, t in zip(preds, targets):
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intersection = float((p * t).sum().item())
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union = float(p.sum().item() + t.sum().item())
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dice = (2.0 * intersection + eps) / (union + eps)
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dices.append(dice)
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return dices
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