File size: 2,327 Bytes
dfe70ff | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | # data_loader.py
import os
import pandas as pd
import numpy as np
import torch # Add this import
from torch.utils.data import Dataset, DataLoader
from PIL import Image
from sklearn.model_selection import train_test_split
import torchvision.transforms as transforms
class XRayDataset(Dataset):
def __init__(self, dataframe, image_dir, transform=None):
self.dataframe = dataframe
self.image_dir = image_dir
self.transform = transform
self.classes = [
'pneumonia',
'atelectasis',
'pleural effusion',
'consolidation',
'cardiomegaly',
'edema',
'emphysema',
'tuberculosis'
]
def __len__(self):
return len(self.dataframe)
def __getitem__(self, idx):
img_name = self.dataframe.iloc[idx]['image_name'] + '.png'
img_path = os.path.join(self.image_dir, img_name)
image = Image.open(img_path).convert('RGB')
labels = self.dataframe.iloc[idx][self.classes].values.astype(np.float32)
if self.transform:
image = self.transform(image)
return image, torch.tensor(labels)
# Define transforms
data_transforms = {
'train': transforms.Compose([
transforms.Resize((224, 224)),
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(10),
transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
def prepare_loaders(batch_size=16, test_size=0.2):
df = pd.read_csv('data/xray_illness_classification.csv')
train_df, val_df = train_test_split(df, test_size=test_size, random_state=42)
train_dataset = XRayDataset(train_df, 'data/xray_images', data_transforms['train'])
val_dataset = XRayDataset(val_df, 'data/xray_images', data_transforms['val'])
dataloaders = {
'train': DataLoader(train_dataset, batch_size=batch_size, shuffle=True),
'val': DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
}
return dataloaders
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