diff --git a/src/data/data_load.py b/src/data/data_load.py
index be7a29e..2265593 100644
--- a/src/data/data_load.py
+++ b/src/data/data_load.py
@@ -38,13 +38,13 @@ def color_transform(images, brightness=0.1, contrast=0.05, saturation=0.1, hue=0
     return train_transform_augment(images)
 
 
-def data_loader_from_config(data_config, using_gpu=False):
+def data_loader_from_config(data_config, image_dtype=torch.float32, using_gpu=False):
     data_dir = data_config['train_dir']
     os_helper.is_valid_dir(data_dir, 'Invalid training data directory\nPath is an invalid directory: ' + data_dir)
     image_height, image_width = get_image_height_and_width(data_config)
     batch_size = int(data_config['batch_size'])
     n_workers = int(data_config['workers'])
-    return create_data_loader(data_dir, image_height, image_width, using_gpu=using_gpu,
+    return create_data_loader(data_dir, image_height, image_width, image_dtype=image_dtype, using_gpu=using_gpu,
                               batch_size=batch_size, n_workers=n_workers)
 
 
diff --git a/src/train_gan.py b/src/train_gan.py
index ec5164e..3925fff 100644
--- a/src/train_gan.py
+++ b/src/train_gan.py
@@ -83,9 +83,9 @@ def train(config_file_path: str):
     data_config['image_height'] = str(int(data_config['base_height']) * (2 ** int(data_config['upsample_layers'])))
     data_config['image_width'] = str(int(data_config['base_width']) * (2 ** int(data_config['upsample_layers'])))
 
-    data_loader = data_loader_from_config(data_config, using_gpu=not running_on_cpu)
+    data_loader = data_loader_from_config(data_config, image_dtype=torch_dtype, using_gpu=not running_on_cpu)
     # Eval data loader is done to keep the same label distribution when evaluating
-    eval_data_loader = data_loader_from_config(data_config, using_gpu=not running_on_cpu)
+    eval_data_loader = data_loader_from_config(data_config, image_dtype=torch_dtype, using_gpu=not running_on_cpu)
     logging.info('Data size is ' + str(len(data_loader.dataset)) + ' images')
 
     data_loader, eval_data_loader = accelerator.prepare(data_loader, eval_data_loader)