Parameter Adjustmens and Ensemble Model Implementation

utils/config.py

@@ -7,8 +7,7 @@ from argparse import Namespace, ArgumentParser
 from collections import defaultdict
 from configparser import ConfigParser
 from pathlib import Path
-
-from ml_lib.utils.model_io import ModelParameters
+import hashlib
 
 
 def is_jsonable(x):
@@ -22,6 +21,30 @@ def is_jsonable(x):
 
 class Config(ConfigParser, ABC):
 
+    @property
+    def name(self):
+        short_name = "".join(c for c in self.model.type if c.isupper())
+        return f'{short_name}_{self.fingerprint}'
+
+    @property
+    def version(self):
+        return f'version_{self.main.seed}'
+
+    @property
+    def exp_path(self):
+        return Path(self.train.outpath) / self.model.type / self.name
+
+    @property
+    def fingerprint(self):
+        h = hashlib.md5()
+        params = deepcopy(self.as_dict)
+        del params['model']['type']
+        del params['data']['worker']
+        del params['main']
+        h.update(str(params).encode())
+        fingerprint = h.hexdigest()
+        return fingerprint
+
     @property
     def _model_weight_init(self):
         mod = __import__('torch.nn.init', fromlist=[self.model.weight_init])
@@ -33,8 +56,8 @@ class Config(ConfigParser, ABC):
         This is function is supposed to return a dict, which holds a mapping from string model names to model classes
 
         Example:
-        from models.binary_classifier import BinaryClassifier
-        return dict(BinaryClassifier=BinaryClassifier,
+        from models.binary_classifier import ConvClassifier
+        return dict(ConvClassifier=ConvClassifier,
                     )
         :return:
         """
@@ -46,8 +69,7 @@ class Config(ConfigParser, ABC):
         try:
             return self._model_map[self.model.type]
         except KeyError:
-            raise KeyError(rf'The model alias you provided ("{self.get("model", "type")}") does not exist! \n'
-                           f'Try one of these:\n{list(self._model_map.keys())}')
+            raise KeyError(rf'The model alias you provided ("{self.get("model", "type")}") does not exist! Try one of these: {list(self._model_map.keys())}')
 
     # TODO: Do this programmatically; This did not work:
     # Initialize Default Sections as Property
@@ -83,6 +105,7 @@ class Config(ConfigParser, ABC):
         params.update(self.train.__dict__)
         assert all(key not in list(params.keys()) for key in self.data.__dict__)
         params.update(self.data.__dict__)
+        params.update(exp_path=str(self.exp_path), exp_fingerprint=str(self.fingerprint))
         return params
 
     @property
@@ -134,7 +157,6 @@ class Config(ConfigParser, ABC):
         new_config.read_dict(sorted_dict)
         return new_config
 
-
     def build_model(self):
         return self.model_class(self.model_paramters)
 

utils/logging.py

@@ -25,7 +25,7 @@ class Logger(LightningLoggerBase, ABC):
 
     @property
     def name(self):
-        return self.config.model.type
+        return self.config.name
 
     @property
     def project_name(self):
@@ -37,7 +37,11 @@ class Logger(LightningLoggerBase, ABC):
 
     @property
     def outpath(self):
-        raise NotImplementedError
+        return Path(self.config.train.outpath) / self.config.model.type
+
+    @property
+    def exp_path(self):
+        return Path(self.outpath) / self.name
 
     def __init__(self, config: Config):
         """
@@ -58,10 +62,12 @@ class Logger(LightningLoggerBase, ABC):
         self._testtube_kwargs = dict(save_dir=self.outpath, version=self.version, name=self.name)
         self._neptune_kwargs = dict(offline_mode=self.debug,
                                     api_key=self.config.project.neptune_key,
+                                    experiment_name=self.name,
                                     project_name=self.project_name,
                                     upload_source_files=list())
         self.neptunelogger = NeptuneLogger(**self._neptune_kwargs)
         self.testtubelogger = TestTubeLogger(**self._testtube_kwargs)
+        self.log_config_as_ini()
 
     def log_hyperparams(self, params):
         self.neptunelogger.log_hyperparams(params)
@@ -80,6 +86,10 @@ class Logger(LightningLoggerBase, ABC):
     def log_config_as_ini(self):
         self.config.write(self.log_dir / 'config.ini')
 
+    def log_text(self, name, text, step_nb=0, **kwargs):
+        # TODO Implement Offline variant.
+        self.neptunelogger.log_text(name, text, step_nb)
+
     def log_metric(self, metric_name, metric_value, **kwargs):
         self.testtubelogger.log_metrics(dict(metric_name=metric_value))
         self.neptunelogger.log_metric(metric_name, metric_value, **kwargs)
@@ -97,7 +107,6 @@ class Logger(LightningLoggerBase, ABC):
     def finalize(self, status):
         self.testtubelogger.finalize(status)
         self.neptunelogger.finalize(status)
-        self.log_config_as_ini()
 
     def __enter__(self):
         return self

utils/transforms.py

@@ -0,0 +1,8 @@
+from torchvision.transforms import ToTensor as TorchvisionToTensor
+
+
+class ToTensor(TorchvisionToTensor):
+
+    def __call__(self, pic):
+        tensor = super(ToTensor, self).__call__(pic).float()
+        return tensor