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import os
from typing import Optional, Tuple
import pandas as pd
from skimage import io
import numpy as np
import torch
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, Dataset, random_split
from torchvision.transforms import transforms
class FocusDataSet(Dataset):
"""Dataset for z-stacked images of neglected tropical diseaeses."""
def __init__(self, csv_file, root_dir, transform=None, in_memory=True):
"""Initialize focus satck dataset.
Args:
csv_file (string): Path to the csv file with annotations.
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
self.metadata = pd.read_csv(csv_file)
self.in_memory = in_memory
self.col_index_path = self.metadata.columns.get_loc("image_path")
self.col_index_focus = self.metadata.columns.get_loc("focus_value")
self.root_dir = root_dir
self.transform = transform
self.images = []
if self.in_memory:
self.images = np.array(
list(map(self._load_img, self.metadata["image_path"].tolist()))
)
def _load_img(self, img_path):
path = os.path.join(self.root_dir, img_path)
img = io.imread(path)
return img
def __len__(self) -> int:
"""Get the length of the dataset.
Returns:
int: the length
"""
return len(self.metadata)
def __getitem__(self, idx):
"""Get one items from the dataset.
Args:
idx (int) The index of the sample that is to be retrieved
Returns:
Item/Items which is a dictionary containing "image" and "focus_value"
"""
if torch.is_tensor(idx):
idx = idx.tolist()
if self.in_memory:
image = self.images[idx]
else:
image = self._load_img(self.metadata.iloc[idx, self.col_index_path])
if self.transform:
image = self.transform(image)
focus_value = torch.from_numpy(
np.asarray(self.metadata.iloc[idx, self.col_index_focus])
).float()
sample = {"image": image, "focus_value": focus_value}
return sample
class FocusDataModule(LightningDataModule):
"""
LightningDataModule for FocusStack dataset.
"""
def __init__(
self,
data_dir: str = "data/",
csv_file: str = "data/metadata.csv",
train_val_test_split_percentage: Tuple[int, int, int] = (0.75, 0.15, 0.15),
batch_size: int = 64,
num_workers: int = 0,
pin_memory: bool = False,
in_memory: bool = True,
):
super().__init__()
# this line allows to access init params with 'self.hparams' attribute
self.save_hyperparameters(logger=False)
# data transformations
self.transforms = transforms.Compose(
[transforms.ToTensor(), transforms.ConvertImageDtype(torch.float)]
)
self.data_train: Optional[Dataset] = None
self.data_val: Optional[Dataset] = None
self.data_test: Optional[Dataset] = None
self.in_memory = in_memory
def prepare_data(self):
"""This method is not implemented as of yet.
Download data if needed. This method is called only from a single GPU.
Do not use it to assign state (self.x = y).
"""
pass
def setup(self, stage: Optional[str] = None):
"""Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
This method is called by lightning twice for `trainer.fit()` and `trainer.test()`, so be careful if you do a random split!
The `stage` can be used to differentiate whether it's called before trainer.fit()` or `trainer.test()`."""
# load datasets only if they're not loaded already
if not self.data_train and not self.data_val and not self.data_test:
dataset = FocusDataSet(
self.hparams.csv_file,
self.hparams.data_dir,
transform=self.transforms,
in_memory=self.in_memory,
)
train_length = int(
len(dataset) * self.hparams.train_val_test_split_percentage[0]
)
val_length = int(
len(dataset) * self.hparams.train_val_test_split_percentage[1]
)
test_length = len(dataset) - val_length - train_length
self.data_train, self.data_val, self.data_test = random_split(
dataset=dataset,
lengths=(train_length, test_length, val_length),
generator=torch.Generator().manual_seed(42),
)
def train_dataloader(self):
return DataLoader(
dataset=self.data_train,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=True,
)
def val_dataloader(self):
return DataLoader(
dataset=self.data_val,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=False,
)
def test_dataloader(self):
return DataLoader(
dataset=self.data_test,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=False,
)
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