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from torch.utils.data import Dataset, DataLoader
from typing import *
from dataclasses import dataclass, field
from PIL import Image
from utils import parse_structure
from glob import glob
from random import shuffle
from torchvision.transforms import v2
import os
import lightning.pytorch as pl
import numpy as np
import torch
import random
class BreakhisDataset(Dataset):
def __init__(self, root_dir: str, image_size: Tuple[int, int], subset: str, aug: dict = None) -> None:
self.root_dir = root_dir
self.image_size = image_size
self.classes = {
'benign' : 0,
'malignant' : 1
}
self.ratio = [0.8, 0.1]
self.subset = subset
self.aug = aug
self.benign_subclasses = ['adenosis', 'fibroadenoma', 'phyllodes_tumor', 'tubular_adenoma']
self.malignant_subclasses = ['ductal_carcinoma', 'lobular_carcinoma', 'mucinous_carcinoma', 'papillary_carcinoma']
self.cls2sublst = {
'benign' : self.benign_subclasses,
'malignant' : self.malignant_subclasses
}
self.factors = ['100X', '200X', '400X', '40X']
self.sample_paths = []
self.sample_labels = []
random.seed(42)
for cate in ['benign', 'malignant']:
for subcls in self.cls2sublst[cate]:
for factor in self.factors:
lst = glob(os.path.join(self.root_dir, f'{cate}/*/{subcls}/*/{factor}/*.png'))
random.shuffle(lst)
sublst = self.get_subset(lst)
self.sample_paths += sublst
self.sample_labels += [self.classes[cate]] * len(sublst)
if self.aug is not None:
self.transforms = [v2.Resize(self.image_size, antialias=True)] + \
[getattr(v2, x)(**self.aug[x]) for x in self.aug] + \
[
v2.ToDtype(torch.float32, scale=True),
v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
else:
self.transforms = [
v2.Resize(self.image_size, antialias=True),
v2.ToDtype(torch.float32, scale=True),
v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
self.transform = v2.Compose(self.transforms)
def get_subset(self, x: list):
if self.subset == 'train':
return x[ : int(self.ratio[0] * len(x))]
elif self.subset == 'valid':
return x[int(self.ratio[0] * len(x)) : int((self.ratio[0] + self.ratio[1]) * len(x))]
elif self.subset == 'test':
return x[int((self.ratio[0] + self.ratio[1]) * len(x)) : ]
else:
return ValueError('Unknown subset')
def __len__(self) -> int:
return len(self.sample_paths)
def __getitem__(self, idx: int) -> Tuple[torch.Tensor, int]:
img_path = self.sample_paths[idx]
label = self.sample_labels[idx]
image = Image.open(img_path).convert("RGB")
image = image.resize(self.image_size)
image = np.array(image)
image = torch.from_numpy(image).permute(2, 0, 1)
image = self.transform(image)
return image, label
@dataclass
class BaseDatasetConfig:
data_source: str = ''
batch_size:int = 32
shuffle:bool = True
num_workers:int = 24
image_size:Tuple[int, int] = (224, 224)
aug: dict = field(default_factory=dict)
class BreakhisDataModule(pl.LightningDataModule):
cfg: BaseDatasetConfig
def __init__(self, cfg: BaseDatasetConfig) -> None:
super().__init__()
self.cfg:BaseDatasetConfig = parse_structure(BaseDatasetConfig, cfg)
self.data_source = self.cfg.data_source
self.img_size = self.cfg.image_size
self.aug = self.cfg.aug
def setup(self, stage=None) -> None:
if stage in [None, "fit"]:
self.train_dataset = BreakhisDataset(self.data_source, self.img_size, 'train', self.aug)
if stage in [None, "fit", "validate"]:
self.val_dataset = BreakhisDataset(self.data_source, self.img_size, 'valid', self.aug)
if stage in [None, "test", "predict"]:
self.test_dataset = BreakhisDataset(self.data_source, self.img_size, 'test', self.aug)
def general_loader(self, dataset, batch_size) -> DataLoader:
return DataLoader(
dataset,
num_workers=self.cfg.num_workers,
batch_size=batch_size
)
def train_dataloader(self) -> DataLoader:
return DataLoader(
self.train_dataset,
num_workers=self.cfg.num_workers,
batch_size=self.cfg.batch_size,
shuffle=self.cfg.shuffle
)
def val_dataloader(self) -> DataLoader:
return DataLoader(
self.val_dataset,
num_workers=self.cfg.num_workers,
batch_size=self.cfg.batch_size
)
def test_dataloader(self) -> DataLoader:
return DataLoader(
self.test_dataset,
num_workers=self.cfg.num_workers,
batch_size=self.cfg.batch_size
) |