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Fix: Prevent NaN errors and optimize tensor loading in LensingDataset #142

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Fix: Prevent NaN errors and optimize tensor loading in LensingDataset #142

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28 changes: 21 additions & 7 deletions Grid_based_strong_lensing_for_unsupervised_super_resolution_Anirudh_Shankar/data.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import os
import torch
import numpy as np

class LensingDataset(torch.utils.data.Dataset):
def __init__(self, directory, classes, num_samples):
"""
Expand All @@ -14,11 +15,12 @@ def __init__(self, directory, classes, num_samples):
self.directory = directory
self.classes = classes
self.num_samples = num_samples

def __len__(self):
"""
:return: Returns the length of the dataset
"""
return self.num_samples*len(self.classes)
return self.num_samples * len(self.classes)

def __getitem__(self, index):
"""
Expand All @@ -27,8 +29,20 @@ def __getitem__(self, index):
:param index: Index in the dataset to look for
:return: LR image, min-max normalized
"""
selected_class = self.classes[index//self.num_samples]
class_index = index%self.num_samples
image = torch.tensor(np.array([np.load(self.directory+selected_class+'/sim_%d.npy'%(class_index))]))
image = (image - torch.min(image))/(torch.max(image)-torch.min(image))
return image
# Determine class and specific image index
selected_class = self.classes[index // self.num_samples]
class_index = index % self.num_samples

# Safely construct the file path using f-strings and os.path.join
file_path = os.path.join(self.directory, selected_class, f'sim_{class_index}.npy')

# Load efficiently and add the channel dimension (1, H, W)
np_img = np.load(file_path)
image = torch.from_numpy(np_img).float().unsqueeze(0)

# Safe min-max normalization (adding 1e-8 prevents division by zero)
img_min = torch.min(image)
img_max = torch.max(image)
image = (image - img_min) / (img_max - img_min + 1e-8)

return image