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Improve dataloader robustness with safe normalization and consistent tensor conversion #147

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4522ee2
Update train_dataloaders.py
SarthakJagota Feb 21, 2026
579c780
Add epsilon normalization and enforce float32 in dataloaders
SarthakJagota Feb 22, 2026
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15 changes: 12 additions & 3 deletions ..._the_DeepLense_Pipeline__Saranga_K_Mahanta/Anomaly_Detection/Model_II/test_dataloaders.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,13 +48,16 @@ def __getitem__(self, idx):
img = np.load(img_path, allow_pickle = True)
if class_name == 'axion':
img = img[0]
img = (img - np.min(img))/(np.max(img) - np.min(img))
eps = 1e-8
img_min = np.min(img)
img_max = np.max(img)
img = (img - img_min) / (img_max - img_min + eps)

if self.transform:
aug = self.transform(image=img)
img = aug['image']

img = img.to(torch.float)
img = img.to(torch.float32)
class_id = self.class_map[class_name]
class_id = torch.tensor(class_id)

Expand All @@ -80,4 +83,10 @@ def create_full_test_dataloader(full_test_data_path, test_transforms, batch_size
)

full_test_loader, class_map = create_full_test_dataloader(FULL_TEST_DATA_PATH, test_transforms, BATCH_SIZE)

# ---- normalization sanity check ----
const_img = np.ones((64, 64))

eps = 1e-8
out = (const_img - const_img.min()) / (const_img.max() - const_img.min() + eps)

print("Normalization NaN check:", np.isnan(out).any())
12 changes: 9 additions & 3 deletions ...the_DeepLense_Pipeline__Saranga_K_Mahanta/Anomaly_Detection/Model_II/train_dataloaders.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,14 +33,20 @@ def __len__(self):
def __getitem__(self, idx):
path = self.data[idx]
img = np.load(path, allow_pickle = True)
img = (img - np.min(img))/(np.max(img) - np.min(img))
eps = 1e-8
img_min = np.min(img)
img_max = np.max(img)
img = (img - img_min) / (img_max - img_min + eps)


if self.transform:
aug = self.transform(image = img)
img = aug['image']

img = img.to(torch.float)
if isinstance(img, torch.Tensor):
img = img.to(torch.float32)
else:
img = torch.tensor(img, dtype=torch.float32)

return img

Expand Down Expand Up @@ -96,7 +102,7 @@ def create_dataloaders(train_data_path, test_data_path, train_transforms, test_t
BATCH_SIZE
)
single_batch = next(iter(test_loader))
print(f"Shape of a single batch of data: {single_batch[0].shape}")
print(f"Shape of a single batch of data: {single_batch.shape}")

single_batch_grid = utils.make_grid(single_batch[:16], nrow=8)
plt.figure(figsize = (20,70))
Expand Down