allow train lut from scratch

src/train.py

@@ -46,6 +46,8 @@ class TrainOptions:
         parser.add_argument('--prefetch_factor', '-p', type=int, default=16, help="Prefetch factor of dataloader workers.")
         parser.add_argument('--save_predictions', action='store_true', default=False, help='Save model predictions to exp_dir/val/dataset_name')
         parser.add_argument('--device', default='cuda', help='Device of the model')
+        parser.add_argument('--quantization_bits', '-q', type=int, default=4, help="Used when model is LUT. Number of 4DLUT buckets defined as 2**bits. Value is in range [1, 8].")  
+        
         self.parser = parser
 
     def parse_args(self):         
@@ -101,7 +103,10 @@ if __name__ == "__main__":
         model = LoadCheckpoint(config.model_path)
         config.model = model.__class__.__name__
     else:
-        model = AVAILABLE_MODELS[config.model]( hidden_dim = config.hidden_dim, scale = config.scale)
+        if 'net' in config.model.lower():
+            model = AVAILABLE_MODELS[config.model]( hidden_dim = config.hidden_dim, scale = config.scale)
+        if 'lut' in config.model.lower():
+            model = AVAILABLE_MODELS[config.model]( quantization_interval = 2**(8-config.quantization_bits), scale = config.scale)
     model = model.to(torch.device(config.device))
     optimizer = AdamWScheduleFree(model.parameters())
     print(optimizer)