requirements

main.py

@@ -50,7 +50,7 @@ def run_lightning_loop(config_obj):
                           show_progress_bar=True,
                           weights_save_path=logger.log_dir,
                           gpus=[0] if torch.cuda.is_available() else None,
-                          check_val_every_n_epoch=5,
+                          check_val_every_n_epoch=10,
                           # num_sanity_val_steps=config_obj.train.num_sanity_val_steps,
                           # row_log_interval=(model.n_train_batches * 0.1),  # TODO: Better Value / Setting
                           # log_save_interval=(model.n_train_batches * 0.2),  # TODO: Better Value / Setting

requirements

@@ -0,0 +1,80 @@
+absl-py==0.9.0
+attrs==19.3.0
+audioread==2.1.8
+bravado==10.6.0
+bravado-core==5.17.0
+cachetools==4.1.0
+certifi==2020.4.5.1
+cffi==1.14.0
+chardet==3.0.4
+click==7.1.1
+cycler==0.10.0
+decorator==4.4.2
+future==0.18.2
+gitdb==4.0.4
+GitPython==3.1.1
+google-auth==1.13.1
+google-auth-oauthlib==0.4.1
+grpcio==1.28.1
+idna==2.9
+imageio==2.8.0
+importlib-metadata==1.6.0
+joblib==0.14.1
+jsonpointer==2.0
+jsonref==0.2
+jsonschema==3.2.0
+kiwisolver==1.2.0
+librosa==0.7.2
+llvmlite==0.31.0
+Markdown==3.2.1
+matplotlib==3.2.1
+monotonic==1.5
+msgpack==1.0.0
+msgpack-python==0.5.6
+natsort==7.0.1
+neptune-client==0.4.109
+numba==0.48.0
+numpy==1.18.2
+oauthlib==3.1.0
+pandas==1.0.3
+Pillow==7.1.1
+protobuf==3.11.3
+psutil==5.7.0
+py3nvml==0.2.6
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycparser==2.20
+PyJWT==1.7.1
+pyparsing==2.4.7
+pyrsistent==0.16.0
+python-dateutil==2.8.1
+pytorch-lightning==0.7.3
+pytz==2019.3
+PyYAML==5.3.1
+requests==2.23.0
+requests-oauthlib==1.3.0
+resampy==0.2.2
+rfc3987==1.3.8
+rsa==4.0
+scikit-learn==0.22.2.post1
+scipy==1.4.1
+simplejson==3.17.0
+six==1.14.0
+smmap==3.0.2
+SoundFile==0.10.3.post1
+strict-rfc3339==0.7
+swagger-spec-validator==2.5.0
+tensorboard==2.2.0
+tensorboard-plugin-wit==1.6.0.post3
+test-tube==0.7.5
+torch==1.4.0
+torchcontrib==0.0.2
+torchvision==0.5.0
+tqdm==4.45.0
+typing-extensions==3.7.4.2
+urllib3==1.25.8
+webcolors==1.11.1
+websocket-client==0.57.0
+Werkzeug==1.0.1
+xmltodict==0.12.0
+zipp==3.1.0

util/metric_reader.py

@@ -0,0 +1,50 @@
+import csv
+from collections import defaultdict
+from pathlib import Path
+import numpy as np
+from util.config import MConfig
+
+
+outpath = Path('..', 'output')
+metric_file_name = 'metrics.csv'
+config_file_name = 'config.ini'
+
+
+if __name__ == '__main__':
+    for model_path in outpath.iterdir():
+        out_file = (model_path / metric_file_name)
+        for paramter_configuration in model_path.iterdir():
+            uar_scores = defaultdict(list)
+            for metric_file in paramter_configuration.rglob(metric_file_name):
+                with metric_file.open('r') as f:
+                    config = MConfig()
+                    with (metric_file.parent / config_file_name).open('r') as c:
+                        config.read_file(c)
+                    for key, val in config.data.__dict__.items():
+                        uar_scores[key].append(val)
+
+                    headers = f.readline().split(',')
+                    metric_dict = defaultdict(list)
+                    for line in f:
+                        values = line.split(',')
+                        for header, value in zip(headers, values):
+                            if value:
+                                try:
+                                    metric_dict[header].append(float(value))
+                                except ValueError:
+                                    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))
+                    except ValueError as e:
+                        print(e)
+                        pass
+            file_existed = out_file.exists()
+            with out_file.open('a+') as f:
+                headers = list(uar_scores.keys())
+
+                writer = csv.DictWriter(f, delimiter=',', lineterminator='\n', fieldnames=headers)
+                if not file_existed:
+                    writer.writeheader()  # file doesn't exist yet, write a header
+                for row_idx in range(len(uar_scores['mean'])):
+                    writer.writerow({key: uar_scores[key][row_idx] for key in headers})