@@ -66,7 +66,7 @@ class HpmStruct:
             `jsonschema.exceptions.SchemaError` if the schema itselfis invalid
         """
         if self.schema is None:
-            raise AttributeError('schema was not set')
+            raise AttributeError("schema was not set")
         else:
             validate(self.__dict__, self.schema)
 
@@ -89,7 +89,7 @@ class Timer:
         :param device: str
             'cpu'\'cuda'
         """
-        self.on_gpu = (device == 'cuda')
+        self.on_gpu = device == "cuda"
         # On GPU time is measured using cuda.events
         if self.on_gpu:
             self.starter = torch.cuda.Event(enable_timing=True)
@@ -141,8 +141,7 @@ class AverageMeter:
     def average(self):
         if self._sum is None:
             return 0
-        return ((self._sum / self._count).__float__()) if self._sum.dim() < 1 else tuple(
-            (self._sum / self._count).cpu().numpy())
+        return ((self._sum / self._count).__float__()) if self._sum.dim() < 1 else tuple((self._sum / self._count).cpu().numpy())
 
         # return (self._sum / self._count).__float__() if self._sum.dim() < 1 or len(self._sum) == 1 \
         #     else tuple((self._sum / self._count).cpu().numpy())
@@ -186,16 +185,16 @@ def get_param(params, name, default_val=None):
     :param default_val: assumed to be the same type as the value searched in the params
     :return:            the found value, or default if not found
     """
-    if isinstance(params, dict):
+    if isinstance(params, Mapping):
         if name in params:
-            if isinstance(default_val, dict):
+            if isinstance(default_val, Mapping):
                 return {**default_val, **params[name]}
             else:
                 return params[name]
         else:
             return default_val
     elif hasattr(params, name):
-        if isinstance(default_val, dict):
+        if isinstance(default_val, Mapping):
             return {**default_val, **getattr(params, name)}
         else:
             return getattr(params, name)
@@ -239,7 +238,7 @@ def random_seed(is_ddp, device, seed):
     :param device: 'cuda','cpu', 'cuda:<device_number>'
     :param seed: int, random seed to be set
     """
-    rank = 0 if not is_ddp else int(device.split(':')[1])
+    rank = 0 if not is_ddp else int(device.split(":")[1])
     torch.manual_seed(seed + rank)
     np.random.seed(seed + rank)
     random.seed(seed + rank)
@@ -266,22 +265,21 @@ def get_filename_suffix_by_framework(framework: str):
     @param framework: (str)
     @return: (str) the suffix for the specific framework
     """
-    frameworks_dict = \
-        {
-            'TENSORFLOW1': '.pb',
-            'TENSORFLOW2': '.zip',
-            'PYTORCH': '.pth',
-            'ONNX': '.onnx',
-            'TENSORRT': '.pkl',
-            'OPENVINO': '.pkl',
-            'TORCHSCRIPT': '.pth',
-            'TVM': '',
-            'KERAS': '.h5',
-            'TFLITE': '.tflite'
-        }
+    frameworks_dict = {
+        "TENSORFLOW1": ".pb",
+        "TENSORFLOW2": ".zip",
+        "PYTORCH": ".pth",
+        "ONNX": ".onnx",
+        "TENSORRT": ".pkl",
+        "OPENVINO": ".pkl",
+        "TORCHSCRIPT": ".pth",
+        "TVM": "",
+        "KERAS": ".h5",
+        "TFLITE": ".tflite",
+    }
 
     if framework.upper() not in frameworks_dict.keys():
-        raise ValueError(f'Unsupported framework: {framework}')
+        raise ValueError(f"Unsupported framework: {framework}")
 
     return frameworks_dict[framework.upper()]
 
@@ -294,15 +292,15 @@ def check_models_have_same_weights(model_1: torch.nn.Module, model_2: torch.nn.M
     @param model_2: Net to be checked
     @return: True iff the two networks have the same weights
     """
-    model_1, model_2 = model_1.to('cpu'), model_2.to('cpu')
+    model_1, model_2 = model_1.to("cpu"), model_2.to("cpu")
     models_differ = 0
     for key_item_1, key_item_2 in zip(model_1.state_dict().items(), model_2.state_dict().items()):
         if torch.equal(key_item_1[1], key_item_2[1]):
             pass
         else:
             models_differ += 1
-            if (key_item_1[0] == key_item_2[0]):
-                print(f'Layer names match but layers have different weights for layers: {key_item_1[0]}')
+            if key_item_1[0] == key_item_2[0]:
+                print(f"Layer names match but layers have different weights for layers: {key_item_1[0]}")
     if models_differ == 0:
         return True
     else:
@@ -320,7 +318,7 @@ def recursive_override(base: dict, extension: dict):
             base[k] = extension[k]
 
 
-def download_and_unzip_from_url(url, dir='.', unzip=True, delete=True):
+def download_and_unzip_from_url(url, dir=".", unzip=True, delete=True):
     """
     Downloads a zip file from url to dir, and unzips it.
 
@@ -341,14 +339,14 @@ def download_and_unzip_from_url(url, dir='.', unzip=True, delete=True):
         if Path(url).is_file():  # exists in current path
             Path(url).rename(f)  # move to dir
         elif not f.exists():
-            print(f'Downloading {url} to {f}...')
+            print(f"Downloading {url} to {f}...")
             torch.hub.download_url_to_file(url, f, progress=True)  # torch download
-        if unzip and f.suffix in ('.zip', '.gz'):
-            print(f'Unzipping {f}...')
-            if f.suffix == '.zip':
+        if unzip and f.suffix in (".zip", ".gz"):
+            print(f"Unzipping {f}...")
+            if f.suffix == ".zip":
                 ZipFile(f).extractall(path=dir)  # unzip
-            elif f.suffix == '.gz':
-                os.system(f'tar xfz {f} --directory {f.parent}')  # unzip
+            elif f.suffix == ".gz":
+                os.system(f"tar xfz {f} --directory {f.parent}")  # unzip
             if delete:
                 f.unlink()  # remove zip
 
@@ -358,7 +356,7 @@ def download_and_unzip_from_url(url, dir='.', unzip=True, delete=True):
         download_one(u, dir)
 
 
-def download_and_untar_from_url(urls: List[str], dir: Union[str, Path] = '.'):
+def download_and_untar_from_url(urls: List[str], dir: Union[str, Path] = "."):
     """
     Download a file from url and untar.
 
@@ -375,13 +373,13 @@ def download_and_untar_from_url(urls: List[str], dir: Union[str, Path] = '.'):
         if url_path.is_file():
             url_path.rename(filepath)
         elif not filepath.exists():
-            logger.info(f'Downloading {url} to {filepath}...')
+            logger.info(f"Downloading {url} to {filepath}...")
             torch.hub.download_url_to_file(url, str(filepath), progress=True)
 
         modes = {".tar.gz": "r:gz", ".tar": "r:"}
         assert filepath.suffix in modes.keys(), f"{filepath} has {filepath.suffix} suffix which is not supported"
 
-        logger.info(f'Extracting to {dir}...')
+        logger.info(f"Extracting to {dir}...")
         with tarfile.open(filepath, mode=modes[filepath.suffix]) as f:
             f.extractall(dir)
         filepath.unlink()
@@ -418,7 +416,7 @@ def get_orientation_key() -> int:
     """Get the orientation key according to PIL, which is useful to get the image size for instance
     :return: Orientation key according to PIL"""
     for key, value in ExifTags.TAGS.items():
-        if value == 'Orientation':
+        if value == "Orientation":
             return key
 
 
@@ -442,14 +440,14 @@ def exif_size(image: Image) -> Tuple[int, int]:
             elif rotation == 8:
                 image_size = (image_size[1], image_size[0])
     except Exception as ex:
-        print('Caught Exception trying to rotate: ' + str(image) + str(ex))
+        print("Caught Exception trying to rotate: " + str(image) + str(ex))
     width, height = image_size
     return height, width
 
 
 def get_image_size_from_path(img_path: str) -> Tuple[int, int]:
     """Get the image size of an image at a specific path"""
-    with open(img_path, 'rb') as f:
+    with open(img_path, "rb") as f:
         return exif_size(Image.open(f))