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import os
import numpy as np
from PIL import Image
import cv2
import torch
from torch.utils.data import DataLoader
import albumentations as A
import pytorch_lightning as pl
from transformers import AutoImageProcessor
from datasets import Dataset, DatasetDict
# Checkpoint of the model used in the projec
MODEL_CHECKPOINT = "apple/deeplabv3-mobilevit-xx-small"
# Size of the image used to train the model
IMG_SIZE = [256, 256]
class FluorescentNeuronalDataModule(pl.LightningDataModule):
def __init__(self, batch_size, data_dir, dataset_size=1.0):
super().__init__()
self.data_dir = data_dir
self.batch_size = batch_size
self.image_processor = AutoImageProcessor.from_pretrained(
MODEL_CHECKPOINT, do_reduce_labels=False
)
self.image_resizer = A.Compose(
[
A.Resize(
width=IMG_SIZE[0],
height=IMG_SIZE[1],
interpolation=cv2.INTER_NEAREST,
)
]
)
self.image_augmentator = A.Compose(
[
A.HorizontalFlip(p=0.6),
A.VerticalFlip(p=0.6),
A.RandomBrightnessContrast(p=0.6),
A.RandomGamma(p=0.6),
A.HueSaturationValue(p=0.6),
]
)
# Percentage of the dataset
self.dataset_size = dataset_size
def _create_dataset(self):
images_path = os.path.join(self.data_dir, "all_images", "images")
masks_path = os.path.join(self.data_dir, "all_masks", "masks")
list_images = os.listdir(images_path)
# Determine the size of the dataset
if self.dataset_size < 1.0:
n_images = int(len(list_images) * self.dataset_size)
list_images = list_images[:n_images]
images = []
masks = []
for image_filename in list_images:
image_path = os.path.join(images_path, image_filename)
mask_path = os.path.join(masks_path, image_filename)
image = np.array(Image.open(image_path).convert("RGB"), dtype=np.uint8)
mask = np.array(Image.open(mask_path).convert("L"), dtype=np.uint8)
mask = (mask / 255).astype(np.uint8)
images.append(image)
masks.append(mask)
dataset = Dataset.from_dict({"image": images, "mask": masks})
# Split the dataset into train, val, and test sets
dataset = dataset.train_test_split(test_size=0.1)
train_val = dataset["train"]
test_ds = dataset["test"]
del dataset
train_val = train_val.train_test_split(test_size=0.2)
train_ds = train_val["train"]
valid_ds = train_val["test"]
del train_val
dataset = DatasetDict(
{"train": train_ds, "validation": valid_ds, "test": test_ds}
)
del train_ds, valid_ds, test_ds
return dataset
def _transform_train_data(self, batch):
# Preprocess the images
images, masks = [], []
for i, m in zip(batch["image"], batch["mask"]):
img = np.asarray(i, dtype=np.uint8)
mask = np.asarray(m, dtype=np.uint8)
# First resize the images and masks
resized_outputs = self.image_resizer(image=img, mask=mask)
images.append(resized_outputs["image"])
masks.append(resized_outputs["mask"])
# Then augment the images
augmented_outputs = self.image_augmentator(
image=resized_outputs["image"], mask=resized_outputs["mask"]
)
images.append(augmented_outputs["image"])
masks.append(augmented_outputs["mask"])
inputs = self.image_processor(
images=images,
return_tensors="pt",
)
inputs["labels"] = torch.tensor(masks, dtype=torch.long)
return inputs
def _transform_data(self, batch):
# Preprocess the images
images, masks = [], []
for i, m in zip(batch["image"], batch["mask"]):
img = np.asarray(i, dtype=np.uint8)
mask = np.asarray(m, dtype=np.uint8)
# Resize the images and masks
resized_outputs = self.image_resizer(image=img, mask=mask)
images.append(resized_outputs["image"])
masks.append(resized_outputs["mask"])
inputs = self.image_processor(
images=images,
return_tensors="pt",
)
inputs["labels"] = inputs["labels"] = torch.tensor(masks, dtype=torch.long)
return inputs
def setup(self, stage=None):
dataset = self._create_dataset()
train_ds = dataset["train"]
valid_ds = dataset["validation"]
test_ds = dataset["test"]
if stage is None or stage == "fit":
self.train_ds = train_ds.with_transform(self._transform_train_data)
self.valid_ds = valid_ds.with_transform(self._transform_data)
if stage is None or stage == "test" or stage == "predict":
self.test_ds = test_ds.with_transform(self._transform_data)
def train_dataloader(self):
return DataLoader(self.train_ds, batch_size=self.batch_size, shuffle=True)
def val_dataloader(self):
return DataLoader(self.valid_ds, batch_size=self.batch_size)
def test_dataloader(self):
return DataLoader(self.test_ds, batch_size=self.batch_size)
def predict_dataloader(self):
return DataLoader(self.test_ds, batch_size=self.batch_size)
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