Audio Dataset

main.py

@@ -82,47 +82,9 @@ def run_lightning_loop(config_obj):
             # Save the last state & all parameters
             trainer.save_checkpoint(str(logger.log_dir / 'weights.ckpt'))
             model.save_to_disk(logger.log_dir)
+            # trainer.run_evaluation(test_mode=True)
 
-        # Evaluate It
-        if config_obj.main.eval:
-            with torch.no_grad():
-                model.eval()
-                if torch.cuda.is_available():
-                    model.cuda()
-                outputs = []
-                from tqdm import tqdm
-                for idx, batch in enumerate(tqdm(model.val_dataloader()[0])):
-                    batch_x, label = batch
-                    batch_x = batch_x.to(device='cuda' if model.on_gpu else 'cpu')
-                    label = label.to(device='cuda' if model.on_gpu else 'cpu')
-                    outputs.append(
-                        model.validation_step((batch_x, label), idx, 1)
-                    )
-                    model.validation_epoch_end([outputs])
 
-                # trainer.test()
-                outpath = Path(config_obj.train.outpath)
-                model_type = config_obj.model.type
-                parameters = logger.name
-                version = f'version_{logger.version}'
-                inference_out = f'{parameters}_test_out.csv'
-
-                from main_inference import prepare_dataloader
-                import variables as V
-                test_dataloader = prepare_dataloader(config_obj)
-
-                with (outpath / model_type / parameters / version / inference_out).open(mode='w') as outfile:
-                    outfile.write(f'file_name,prediction\n')
-
-                    from tqdm import tqdm
-                    for batch in tqdm(test_dataloader, total=len(test_dataloader)):
-                        batch_x, file_names = batch
-                        batch_x = batch_x.to(device='cuda' if model.on_gpu else 'cpu')
-                        y = model(batch_x).main_out
-                        predictions = (y >= 0.5).int()
-                        for prediction, file_name in zip(predictions, file_names):
-                            prediction_text = 'clear' if prediction == V.CLEAR else 'mask'
-                            outfile.write(f'{file_name},{prediction_text}\n')
     return model