Sample_Finetuning_SIIMACR/I2_segmentation/dataset/dataset_siim_acr.py

from torch.utils.data import DataLoader
import PIL
from torch.utils.data import Dataset
import numpy as np
import pandas as pd
from PIL import Image
import torch
import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2


class SIIM_ACR_Dataset(Dataset):
    def __init__(self, csv_path, is_train=True, percentage=0.01):
        data_info = pd.read_csv(csv_path)
        if is_train == True:
            total_len = int(percentage * len(data_info))
            choice_list = np.random.choice(
                range(len(data_info)), size=total_len, replace=False
            )
            self.img_path_list = data_info["image_path"][choice_list].tolist()
        else:
            self.img_path_list = data_info["image_path"].tolist()

        self.img_root = "SIIM-CLS/siim-acr-pneumothorax/png_images/"
        self.seg_root = "SIIM-CLS/siim-acr-pneumothorax/png_masks/"  # We have pre-processed the original SIIM_ACR data, you may change this to fix your data

        if is_train:
            self.aug = A.Compose(
                [
                    A.RandomResizedCrop(
                        width=224,
                        height=224,
                        scale=(0.2, 1.0),
                        always_apply=True,
                        interpolation=Image.BICUBIC,
                    ),
                    A.HorizontalFlip(p=0.5),
                    A.Normalize(
                        mean=[0.485, 0.456, 0.406],
                        std=[0.229, 0.224, 0.225],
                        always_apply=True,
                    ),
                    ToTensorV2(),
                ]
            )
        else:
            self.aug = A.Compose(
                [
                    A.Resize(width=224, height=224, always_apply=True),
                    A.Normalize(
                        mean=[0.485, 0.456, 0.406],
                        std=[0.229, 0.224, 0.225],
                        always_apply=True,
                    ),
                    ToTensorV2(),
                ]
            )

    def __getitem__(self, index):
        img_path = self.img_root + self.img_path_list[index].split("/")[-1]  # + ".png"
        seg_path = (
            self.seg_root + self.img_path_list[index].split("/")[-1]  # + ".png"
        )  # We have pre-processed the original SIIM_ACR data, you may change this to fix your data
        img = np.array(PIL.Image.open(img_path).convert("RGB"))
        seg_map = np.array(PIL.Image.open(seg_path))[:, :, np.newaxis] / 255

        augmented = self.aug(image=img, mask=seg_map)
        img, seg_map = augmented["image"], augmented["mask"]
        seg_map = seg_map.permute(2, 0, 1)

        class_label = np.array([int(torch.sum(seg_map) > 0)])
        return {"image": img, "seg": seg_map, "label": class_label}

    def __len__(self):
        return len(self.img_path_list)


def create_loader_RSNA(
    datasets, samplers, batch_size, num_workers, is_trains, collate_fns
):
    loaders = []
    for dataset, sampler, bs, n_worker, is_train, collate_fn in zip(
        datasets, samplers, batch_size, num_workers, is_trains, collate_fns
    ):
        if is_train:
            shuffle = sampler is None
            drop_last = True
        else:
            shuffle = False
            drop_last = False
        loader = DataLoader(
            dataset,
            batch_size=bs,
            num_workers=n_worker,
            pin_memory=True,
            sampler=sampler,
            shuffle=shuffle,
            collate_fn=collate_fn,
            drop_last=drop_last,
        )
        loaders.append(loader)
    return loaders