inference restored

_paramters.py

@@ -32,11 +32,11 @@ main_arg_parser.add_argument("--data_stretch", type=strtobool, default=True, hel
 
 # Transformation Parameters
 main_arg_parser.add_argument("--data_loudness_ratio", type=float, default=0, help="")  # 0.4
-main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0, help="")  # 0.3
+main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0.3, help="")  # 0.3
 main_arg_parser.add_argument("--data_noise_ratio", type=float, default=0, help="")  # 0.4
 main_arg_parser.add_argument("--data_mask_ratio", type=float, default=0, help="")  # 0.2
-main_arg_parser.add_argument("--data_speed_ratio", type=float, default=0.3, help="")  # 0.3
+main_arg_parser.add_argument("--data_speed_ratio", type=float, default=0, help="")  # 0.3
-main_arg_parser.add_argument("--data_speed_factor", type=float, default=0.7, help="")  # 0.7
+main_arg_parser.add_argument("--data_speed_factor", type=float, default=0, help="")  # 0.7
 
 # Model Parameters
 main_arg_parser.add_argument("--model_type", type=str, default="RCC", help="")

main_inference.py

@@ -27,7 +27,6 @@ from datasets.binar_masks import BinaryMasksDataset
 
 def prepare_dataloader(config_obj):
     mel_transforms = Compose([
-        Speed(0, 0),
         AudioToMel(sr=config_obj.data.sr, n_mels=config_obj.data.n_mels, n_fft=config_obj.data.n_fft,
                    hop_length=config_obj.data.hop_length),
         MelToImage()])
@@ -40,16 +39,15 @@ def prepare_dataloader(config_obj):
         NormalizeLocal(), ToTensor()
     ])
 
-    dataset: Dataset = BinaryMasksDataset(config_obj.data.root, setting='train',
+    dataset: Dataset = BinaryMasksDataset(config_obj.data.root, setting='test',
-                                          mel_transforms=mel_transforms, transforms=aug_transforms
+                                          mel_transforms=mel_transforms, transforms=transforms
                                           )
     # noinspection PyTypeChecker
     return DataLoader(dataset, batch_size=None, num_workers=0, shuffle=False)
 
 
-def restore_logger_and_model(config_obj):
+def restore_logger_and_model(log_dir):
-    logger = Logger(config_obj)
+    model = SavedLightningModels.load_checkpoint(models_root_path=log_dir, n=-2)
-    model = SavedLightningModels.load_checkpoint(models_root_path=logger.log_dir, n=-2)
     model = model.restore()
     if torch.cuda.is_available():
         model.cuda()
@@ -63,23 +61,18 @@ if __name__ == '__main__':
     model_type = 'CC'
     parameters = 'CC_213adb16e46592c5a405abfbd693835e/'
     version = 'version_41'
+    model_path = Path('/home/steffen/projects/inter_challenge_2020/output/CC/CC_fd2020a7ead9d5c80609a7364741f24b/version_40')
     config_filename = 'config.ini'
     inference_out = 'manual_test_out.csv'
 
     config = MConfig()
-    config.read_file((outpath / model_type / parameters / version / config_filename).open('r'))
+    config.read_file((Path(model_path) / config_filename).open('r'))
     test_dataloader = prepare_dataloader(config)
-    p = Plotter(outpath)
-    from matplotlib import pyplot as plt
 
-    d = test_dataloader.dataset[100][0].squeeze()
+    loaded_model = restore_logger_and_model(model_path)
-    plt.imshow(d)
-    p.save_current_figure('100')
-
-    loaded_model = restore_logger_and_model(config)
     loaded_model.eval()
 
-    with (outpath / model_type / parameters / version / inference_out).open(mode='w') as outfile:
+    with (model_path / inference_out).open(mode='w') as outfile:
         outfile.write(f'file_name,prediction\n')
 
         for batch in tqdm(test_dataloader, total=len(test_dataloader)):

util/metric_reader.py

@@ -16,6 +16,8 @@ if __name__ == '__main__':
             continue
         out_file = (model_path / metric_file_name)
         for paramter_configuration in model_path.iterdir():
+            if not model_path.is_dir():
+                continue
             uar_scores = defaultdict(list)
             for metric_file in paramter_configuration.rglob(metric_file_name):
                 with metric_file.open('r') as f:
@@ -37,7 +39,7 @@ if __name__ == '__main__':
                                     metric_dict[header].append(value)
                 for score, func in zip(['mean', 'max', 'median', 'std'], [np.mean, np.max, np.median, np.std]):
                     try:
-                        uar_scores[score].append(func(np.asarray(metric_dict['uar_score'])).round(2))
+                        uar_scores[score].append(round(func(np.asarray(metric_dict['uar_score'])) * 100, 2))
                     except ValueError as e:
                         print(e)
                         pass