Merge pull request #76 from BloodAxe/develop

0.5.2 Release

Author: BloodAxe

pytorch_toolbelt/__init__.py

@@ -1,3 +1,3 @@
 from __future__ import absolute_import
 
-__version__ = "0.5.1"
+__version__ = "0.5.2"

pytorch_toolbelt/inference/ensembling.py

@@ -13,18 +13,22 @@ class ApplySoftmaxTo(nn.Module):
     dim: int
 
     def __init__(
-        self, model: nn.Module, output_key: Union[str, Iterable[str]] = "logits", dim: int = 1, temperature: float = 1
+        self,
+        model: nn.Module,
+        output_key: Union[str, int, Iterable[str]] = "logits",
+        dim: int = 1,
+        temperature: float = 1,
     ):
         """
         Apply softmax activation on given output(s) of the model
         :param model: Model to wrap
-        :param output_key: string or list of strings, indicating to what outputs softmax activation should be applied.
+        :param output_key: string, index or list of strings, indicating to what outputs softmax activation should be applied.
         :param dim: Tensor dimension for softmax activation
         :param temperature: Temperature scaling coefficient. Values > 1 will make logits sharper.
         """
         super().__init__()
         # By converting to set, we prevent double-activation by passing output_key=["logits", "logits"]
-        output_key = tuple(set(output_key)) if isinstance(output_key, Iterable) else tuple([output_key])
+        output_key = (output_key,) if isinstance(output_key, (str, int)) else tuple(set(output_key))
         self.output_keys = output_key
         self.model = model
         self.dim = dim
@@ -41,16 +45,16 @@ class ApplySigmoidTo(nn.Module):
     output_keys: Tuple
     temperature: float
 
-    def __init__(self, model: nn.Module, output_key: Union[str, Iterable[str]] = "logits", temperature=1):
+    def __init__(self, model: nn.Module, output_key: Union[str, int, Iterable[str]] = "logits", temperature=1):
         """
         Apply sigmoid activation on given output(s) of the model
         :param model: Model to wrap
-        :param output_key: string or list of strings, indicating to what outputs sigmoid activation should be applied.
+        :param output_key: string index, or list of strings, indicating to what outputs sigmoid activation should be applied.
         :param temperature: Temperature scaling coefficient. Values > 1 will make logits sharper.
         """
         super().__init__()
         # By converting to set, we prevent double-activation by passing output_key=["logits", "logits"]
-        output_key = tuple(set(output_key)) if isinstance(output_key, Iterable) else tuple([output_key])
+        output_key = (output_key,) if isinstance(output_key, (str, int)) else tuple(set(output_key))
         self.output_keys = output_key
         self.model = model
         self.temperature = temperature

pytorch_toolbelt/utils/fs.py

@@ -14,8 +14,10 @@
     "auto_file",
     "change_extension",
     "find_images_in_dir",
+    "find_images_in_dir_recursive",
     "find_in_dir",
     "find_in_dir_glob",
+    "find_in_dir_with_ext",
     "find_subdirectories_in_dir",
     "has_ext",
     "has_image_ext",
@@ -64,7 +66,15 @@ def find_in_dir_with_ext(dirname: str, extensions: Union[str, List[str]]) -> Lis
 
 
 def find_images_in_dir(dirname: str) -> List[str]:
-    return [fname for fname in find_in_dir(dirname) if has_image_ext(fname)]
+    return [fname for fname in find_in_dir(dirname) if has_image_ext(fname) and os.path.isfile(fname)]
+
+
+def find_images_in_dir_recursive(dirname: str) -> List[str]:
+    return [
+        fname
+        for fname in glob.glob(os.path.join(dirname, "**"), recursive=True)
+        if has_image_ext(fname) and os.path.isfile(fname)
+    ]
 
 
 def find_in_dir_glob(dirname: str, recursive=False):
@@ -76,13 +86,17 @@ def id_from_fname(fname: str) -> str:
     return os.path.splitext(os.path.basename(fname))[0]
 
 
-def change_extension(fname: Union[str, Path], new_ext: str) -> str:
-    if type(fname) == str:
+def change_extension(fname: Union[str, Path], new_ext: str) -> Union[str, Path]:
+    if isinstance(fname, str):
         return os.path.splitext(fname)[0] + new_ext
-    else:
+    elif isinstance(fname, Path):
         if new_ext[0] != ".":
             new_ext = "." + new_ext
         return fname.with_suffix(new_ext)
+    else:
+        raise RuntimeError(
+            f"Received input argument `fname` for unsupported type {type(fname)}. Argument must be string or Path."
+        )
 
 
 def auto_file(filename: str, where: str = ".") -> str:

pytorch_toolbelt/utils/torch_utils.py

@@ -34,6 +34,7 @@
     "to_tensor",
     "transfer_weights",
     "move_to_device_non_blocking",
+    "describe_outputs",
 ]
 
 
@@ -102,8 +103,11 @@ def count_parameters(
     parameters = {"total": total, "trainable": trainable}
 
     for key in keys:
-        if hasattr(model, key) and model.__getattr__(key) is not None:
-            parameters[key] = int(sum(p.numel() for p in model.__getattr__(key).parameters()))
+        try:
+            if hasattr(model, key) and model.__getattr__(key) is not None:
+                parameters[key] = int(sum(p.numel() for p in model.__getattr__(key).parameters()))
+        except AttributeError:
+            pass
 
     if human_friendly:
         for key in parameters.keys():
@@ -289,3 +293,27 @@ def move_to_device_non_blocking(x: Tensor, device: torch.device) -> Tensor:
 
 
 resize_as = resize_like
+
+
+def describe_outputs(outputs: Union[Tensor, Dict[str, Tensor], Iterable[Tensor]]) -> Union[List[Dict], Dict[str, Any]]:
+    """
+    Describe outputs and return shape, mean & std for each tensor in list or dict (Supports nested tensors)
+
+    Args:
+        outputs: Input (Usually model outputs)
+    Returns:
+        Same structure but each item represents tensor shape, mean & std
+    """
+    if torch.is_tensor(outputs):
+        desc = dict(size=tuple(outputs.size()), mean=outputs.mean().item(), std=outputs.std().item())
+    elif isinstance(outputs, collections.Mapping):
+        desc = {}
+        for key, value in outputs.items():
+            desc[key] = describe_outputs(value)
+    elif isinstance(outputs, collections.Iterable):
+        desc = []
+        for index, output in enumerate(outputs):
+            desc.append(describe_outputs(output))
+    else:
+        raise NotImplementedError
+    return desc