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ec94e2e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | #!/usr/bin/env python3
"""
MILK10k dataset utilities shared by training scripts.
Keep dataframe construction and torch Dataset classes here; training scripts
should build transforms/loaders and own model/training logic.
"""
from __future__ import annotations
import os
import random
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from PIL import Image, ImageFile
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset
ImageFile.LOAD_TRUNCATED_IMAGES = True
REQUIRED_DATA_FILES = (
"MILK10k_Training_GroundTruth.csv",
"MILK10k_Training_Metadata.csv",
"MILK10k_Training_Input",
)
LABEL_COLUMNS = [
"AKIEC",
"BCC",
"BEN_OTH",
"BKL",
"DF",
"INF",
"MAL_OTH",
"MEL",
"NV",
"SCCKA",
"VASC",
]
class Milk10kDataset(Dataset):
def __init__(self, df: pd.DataFrame, label_to_idx: dict[str, int], transform=None) -> None:
self.paths = df["path"].tolist()
self.labels = [label_to_idx[label] for label in df["label"].tolist()]
self.transform = transform
def __len__(self) -> int:
return len(self.paths)
def __getitem__(self, idx: int) -> tuple[torch.Tensor, int]:
with Image.open(self.paths[idx]) as img:
img = img.convert("RGB")
if self.transform is not None:
img = self.transform(img)
return img, self.labels[idx]
class PairedMilk10kDataset(Dataset):
def __init__(self, df: pd.DataFrame, label_to_idx: dict[str, int], transform=None) -> None:
self.clinical_paths = df["clinical_path"].tolist()
self.dermoscopic_paths = df["dermoscopic_path"].tolist()
self.labels = [label_to_idx[label] for label in df["label"].tolist()]
self.transform = transform
def __len__(self) -> int:
return len(self.labels)
def _load_image(self, path: str) -> torch.Tensor:
with Image.open(path) as img:
img = img.convert("RGB")
if self.transform is not None:
img = self.transform(img)
return img
def __getitem__(self, idx: int) -> tuple[torch.Tensor, int]:
clinical = self._load_image(self.clinical_paths[idx])
dermoscopic = self._load_image(self.dermoscopic_paths[idx])
return torch.stack([clinical, dermoscopic], dim=0), self.labels[idx]
def set_seed(seed: int) -> None:
os.environ["PYTHONHASHSEED"] = str(seed)
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = True
def normalize_image_type(image_type: str) -> str:
if image_type == "clinical: close-up":
return "clinical_close_up"
return image_type.replace(" ", "_").replace(":", "").replace("-", "_")
def has_milk10k_files(path: Path) -> bool:
return all((path / name).exists() for name in REQUIRED_DATA_FILES)
def resolve_data_dir(data_dir: Path | None) -> Path:
if data_dir is not None:
data_dir = data_dir.expanduser().resolve()
if not has_milk10k_files(data_dir):
expected = ", ".join(REQUIRED_DATA_FILES)
raise FileNotFoundError(f"--data-dir={data_dir} does not contain required MILK10k files: {expected}")
return data_dir
candidates = [Path.cwd()]
kaggle_input = Path("/kaggle/input")
if kaggle_input.exists():
candidates.extend(path.parent for path in kaggle_input.rglob("MILK10k_Training_GroundTruth.csv"))
seen = set()
for candidate in candidates:
candidate = candidate.resolve()
if candidate in seen:
continue
seen.add(candidate)
if has_milk10k_files(candidate):
return candidate
expected = ", ".join(REQUIRED_DATA_FILES)
raise FileNotFoundError(
f"Could not auto-detect MILK10k data dir. Pass --data-dir PATH containing: {expected}"
)
def load_dataframe(data_dir: Path, image_type: str) -> pd.DataFrame:
input_dir = data_dir / "MILK10k_Training_Input"
gt = pd.read_csv(data_dir / "MILK10k_Training_GroundTruth.csv")
meta = pd.read_csv(data_dir / "MILK10k_Training_Metadata.csv")
gt["label"] = gt[LABEL_COLUMNS].idxmax(axis=1)
df = meta.merge(gt[["lesion_id", "label"]], on="lesion_id", how="inner")
df["image_type_norm"] = df["image_type"].map(normalize_image_type)
if image_type != "all":
df = df[df["image_type_norm"] == image_type].copy()
df["path"] = df.apply(lambda r: input_dir / r["lesion_id"] / f"{r['isic_id']}.jpg", axis=1)
df = df[df["path"].map(lambda p: p.exists())].copy()
df["path"] = df["path"].map(str)
if df.empty:
raise ValueError(f"No images found for image_type={image_type!r} under {input_dir}")
return df[["path", "label", "lesion_id", "isic_id", "image_type_norm"]]
def to_paired_lesion_dataframe(df: pd.DataFrame) -> pd.DataFrame:
clinical = (
df[df["image_type_norm"] == "clinical_close_up"][["lesion_id", "path"]]
.rename(columns={"path": "clinical_path"})
.drop_duplicates("lesion_id")
)
dermoscopic = (
df[df["image_type_norm"] == "dermoscopic"][["lesion_id", "path"]]
.rename(columns={"path": "dermoscopic_path"})
.drop_duplicates("lesion_id")
)
labels = df[["lesion_id", "label"]].drop_duplicates("lesion_id")
paired = labels.merge(clinical, on="lesion_id", how="inner").merge(dermoscopic, on="lesion_id", how="inner")
if paired.empty:
raise ValueError("No paired clinical/dermoscopic lesions found.")
return paired[["lesion_id", "label", "clinical_path", "dermoscopic_path"]]
def lesion_level_train_val_split(
df: pd.DataFrame,
val_size: float,
seed: int,
) -> tuple[pd.DataFrame, pd.DataFrame]:
lesion_df = df[["lesion_id", "label"]].drop_duplicates("lesion_id")
train_lesions, val_lesions = train_test_split(
lesion_df,
test_size=val_size,
stratify=lesion_df["label"],
random_state=seed,
)
train_df = df[df["lesion_id"].isin(train_lesions["lesion_id"])].copy()
val_df = df[df["lesion_id"].isin(val_lesions["lesion_id"])].copy()
return train_df, val_df
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