|
| 1 | +import logging |
| 2 | +from pathlib import Path |
| 3 | +from typing import Any |
| 4 | + |
| 5 | +import pandas as pd |
| 6 | +import torch |
| 7 | +from PIL import Image |
| 8 | +from kornia.enhance import Normalize |
| 9 | +from pytorch_lightning import LightningDataModule |
| 10 | +from torch.utils.data import Dataset |
| 11 | +from torchvision.transforms.functional import to_tensor |
| 12 | +from tqdm import tqdm |
| 13 | + |
| 14 | + |
| 15 | +class GrazPedWriDataset(Dataset): |
| 16 | + # calculated over training split |
| 17 | + IMG_MEAN = 0.3505533917353781 |
| 18 | + IMG_STD = 0.22763733675869177 |
| 19 | + |
| 20 | + RESCALE_HW = (384, 224) |
| 21 | + |
| 22 | + CLASS_LABEL = ['23-M/2.1', '23-M/3.1', '23r-E/2.1', '23r-M/2.1', '23r-M/3.1', '23u-E/7', '23u-M/2.1', 'none'] |
| 23 | + CLASS_IDX = {k: v for v, k in enumerate(CLASS_LABEL)} |
| 24 | + N_CLASSES = len(CLASS_LABEL) |
| 25 | + |
| 26 | + def __init__(self, mode: str, fold: int = 0, number_training_samples: int | str = 'all'): |
| 27 | + super().__init__() |
| 28 | + # load data meta and other information |
| 29 | + self.df_meta = pd.read_csv('data/dataset_cv_splits.csv', index_col='filestem') |
| 30 | + # init ground truth parser considering the data split |
| 31 | + if mode == 'train': |
| 32 | + self.df_meta = self.df_meta[self.df_meta['fold'] != fold] |
| 33 | + elif mode == 'val': |
| 34 | + self.df_meta = self.df_meta[self.df_meta['fold'] == fold] |
| 35 | + else: |
| 36 | + raise ValueError(f'Unknown mode: {mode}') |
| 37 | + self.available_file_names = self.df_meta.index.tolist() |
| 38 | + |
| 39 | + # get subset of training samples |
| 40 | + if mode == 'train' and number_training_samples != 'all': |
| 41 | + raise NotImplementedError('number_training_samples is not implemented for GrazPedWriDataset') |
| 42 | + elif mode != 'train' and number_training_samples != 'all': |
| 43 | + logging.warning(f'number_training_samples is not used for mode {mode}') |
| 44 | + |
| 45 | + # load img into memory |
| 46 | + img_path = Path('data/img_only_front_all_left') |
| 47 | + self.data = dict() |
| 48 | + for file_name in tqdm(self.available_file_names, unit='img', desc=f'Loading data for {mode}'): |
| 49 | + # image |
| 50 | + img = Image.open(img_path.joinpath(file_name).with_suffix('.png')).convert('L') |
| 51 | + img = img.resize(self.RESCALE_HW[::-1], Image.BILINEAR) |
| 52 | + img = to_tensor(img) |
| 53 | + |
| 54 | + # classification ground truth |
| 55 | + class_label: str = self.df_meta.loc[file_name, 'ao_classification'] |
| 56 | + class_label: list[str] = class_label.split(';') |
| 57 | + y = torch.zeros(self.N_CLASSES) |
| 58 | + for c in class_label: |
| 59 | + c = c.strip() |
| 60 | + if c not in self.CLASS_IDX: |
| 61 | + continue |
| 62 | + else: |
| 63 | + y[self.CLASS_IDX[c]] = 1 |
| 64 | + assert y.sum() > 0, f'No valid class found for {file_name} with {class_label}' |
| 65 | + |
| 66 | + self.data[file_name] = { |
| 67 | + 'file_name': file_name, |
| 68 | + 'image': img, |
| 69 | + 'y': y |
| 70 | + |
| 71 | + } |
| 72 | + break |
| 73 | + |
| 74 | + def __len__(self): |
| 75 | + return len(self.available_file_names) |
| 76 | + |
| 77 | + def __getitem__(self, index): |
| 78 | + """ |
| 79 | + get item by index |
| 80 | + :param index: index of item |
| 81 | + :return: dict with keys ['image', 'mask', 'file_name'] |
| 82 | + """ |
| 83 | + file_name = self.available_file_names[index] |
| 84 | + data_dict = self.data[file_name] |
| 85 | + |
| 86 | + return data_dict |
| 87 | + |
| 88 | + |
| 89 | +class GrazPedWriDataModule(LightningDataModule): |
| 90 | + def __init__(self, fold: int = 0, batch_size: int = 32, number_training_samples: int | str = 'all'): |
| 91 | + super().__init__() |
| 92 | + self.n_train = number_training_samples |
| 93 | + self.fold = fold |
| 94 | + self.dl_kwargs = {'batch_size': batch_size, 'num_workers': 4, 'pin_memory': torch.cuda.is_available()} |
| 95 | + self.normalize = Normalize(mean=GrazPedWriDataset.IMG_MEAN, std=GrazPedWriDataset.IMG_STD) |
| 96 | + |
| 97 | + def setup(self, stage: str = None): |
| 98 | + if stage == 'fit' or stage is None: |
| 99 | + self.train_dataset = GrazPedWriDataset('train', self.fold, self.n_train) |
| 100 | + self.val_dataset = GrazPedWriDataset('val', self.fold) |
| 101 | + if stage == 'test' or stage is None: |
| 102 | + self.test_dataset = GrazPedWriDataset('val', self.fold) |
| 103 | + |
| 104 | + def train_dataloader(self): |
| 105 | + return torch.utils.data.DataLoader(self.train_dataset, shuffle=True, drop_last=True, **self.dl_kwargs) |
| 106 | + |
| 107 | + def val_dataloader(self): |
| 108 | + return torch.utils.data.DataLoader(self.val_dataset, **self.dl_kwargs) |
| 109 | + |
| 110 | + def test_dataloader(self): |
| 111 | + return torch.utils.data.DataLoader(self.test_dataset, **self.dl_kwargs) |
| 112 | + |
| 113 | + def on_after_batch_transfer(self, batch: Any, dataloader_idx: int) -> Any: |
| 114 | + batch['image'] = self.normalize(batch['image']) |
| 115 | + return batch |
| 116 | + |
| 117 | + |
| 118 | +if __name__ == '__main__': |
| 119 | + import matplotlib.pyplot as plt |
| 120 | + from torch.utils.data import DataLoader |
| 121 | + |
| 122 | + dataset = GrazPedWriDataset('val', fold=0) |
| 123 | + data = dataset[0] |
| 124 | + print(data['image'].shape) |
| 125 | + print(data['y']) |
| 126 | + plt.figure(data['file_name']) |
| 127 | + plt.imshow(data['image'].squeeze().numpy(), cmap='gray') |
| 128 | + plt.title(dataset.CLASS_LABEL[data['y'].argmax()]) |
| 129 | + plt.show() |
0 commit comments