Fixing imports

Author: datumbox

test/test_prototype_models.py

@@ -5,8 +5,8 @@
 import test_models as TM
 import torch
 from common_utils import cpu_and_gpu, needs_cuda
-from torchvision.prototype import models
 from torchvision.models._api import WeightsEnum, Weights
+from torchvision.prototype import models
 from torchvision.prototype.models._utils import handle_legacy_interface
 
 run_if_test_with_prototype = pytest.mark.skipif(

torchvision/_utils.py

@@ -1,5 +1,5 @@
 import enum
-from typing import TypeVar, Type
+from typing import Sequence, TypeVar, Type
 
 T = TypeVar("T", bound=enum.Enum)
 
@@ -18,3 +18,15 @@ def from_str(self: Type[T], member: str) -> T:  # type: ignore[misc]
 
 class StrEnum(enum.Enum, metaclass=StrEnumMeta):
     pass
+
+
+def sequence_to_str(seq: Sequence, separate_last: str = "") -> str:
+    if not seq:
+        return ""
+    if len(seq) == 1:
+        return f"'{seq[0]}'"
+
+    head = "'" + "', '".join([str(item) for item in seq[:-1]]) + "'"
+    tail = f"{'' if separate_last and len(seq) == 2 else ','} {separate_last}'{seq[-1]}'"
+
+    return head + tail

torchvision/models/_utils.py

@@ -1,11 +1,12 @@
 import functools
+import inspect
 import warnings
 from collections import OrderedDict
 from typing import Any, Dict, Optional, TypeVar, Callable, Tuple, Union
 
 from torch import nn
-from torchvision.prototype.utils._internal import kwonly_to_pos_or_kw
 
+from .._utils import sequence_to_str
 from ._api import WeightsEnum
 
 
@@ -88,6 +89,60 @@ def _make_divisible(v: float, divisor: int, min_value: Optional[int] = None) ->
     return new_v
 
 
+D = TypeVar("D")
+
+
+def kwonly_to_pos_or_kw(fn: Callable[..., D]) -> Callable[..., D]:
+    """Decorates a function that uses keyword only parameters to also allow them being passed as positionals.
+
+    For example, consider the use case of changing the signature of ``old_fn`` into the one from ``new_fn``:
+
+    .. code::
+
+        def old_fn(foo, bar, baz=None):
+            ...
+
+        def new_fn(foo, *, bar, baz=None):
+            ...
+
+    Calling ``old_fn("foo", "bar, "baz")`` was valid, but the same call is no longer valid with ``new_fn``. To keep BC
+    and at the same time warn the user of the deprecation, this decorator can be used:
+
+    .. code::
+
+        @kwonly_to_pos_or_kw
+        def new_fn(foo, *, bar, baz=None):
+            ...
+
+        new_fn("foo", "bar, "baz")
+    """
+    params = inspect.signature(fn).parameters
+
+    try:
+        keyword_only_start_idx = next(
+            idx for idx, param in enumerate(params.values()) if param.kind == param.KEYWORD_ONLY
+        )
+    except StopIteration:
+        raise TypeError(f"Found no keyword-only parameter on function '{fn.__name__}'") from None
+
+    keyword_only_params = tuple(inspect.signature(fn).parameters)[keyword_only_start_idx:]
+
+    @functools.wraps(fn)
+    def wrapper(*args: Any, **kwargs: Any) -> D:
+        args, keyword_only_args = args[:keyword_only_start_idx], args[keyword_only_start_idx:]
+        if keyword_only_args:
+            keyword_only_kwargs = dict(zip(keyword_only_params, keyword_only_args))
+            warnings.warn(
+                f"Using {sequence_to_str(tuple(keyword_only_kwargs.keys()), separate_last='and ')} as positional "
+                f"parameter(s) is deprecated. Please use keyword parameter(s) instead."
+            )
+            kwargs.update(keyword_only_kwargs)
+
+        return fn(*args, **kwargs)
+
+    return wrapper
+
+
 W = TypeVar("W", bound=WeightsEnum)
 M = TypeVar("M", bound=nn.Module)
 V = TypeVar("V")

torchvision/models/alexnet.py

@@ -4,13 +4,12 @@
 import torch
 import torch.nn as nn
 
+from ..transforms import ImageClassificationEval, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
 from ._utils import handle_legacy_interface, _ovewrite_named_param
 
-from ..transforms import ImageClassificationEval, InterpolationMode
-
 
 __all__ = ["AlexNet", "AlexNet_Weights", "alexnet"]
 
@@ -94,4 +93,4 @@ def alexnet(*, weights: Optional[AlexNet_Weights] = None, progress: bool = True,
     if weights is not None:
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
-    return model
+    return model

torchvision/models/convnext.py

@@ -7,10 +7,8 @@
 
 from ..ops.misc import Conv2dNormActivation
 from ..ops.stochastic_depth import StochasticDepth
-from ..utils import _log_api_usage_once
-
 from ..transforms import ImageClassificationEval, InterpolationMode
-
+from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
 from ._utils import handle_legacy_interface, _ovewrite_named_param

torchvision/models/densenet.py

@@ -1,6 +1,6 @@
 import re
-from functools import partial
 from collections import OrderedDict
+from functools import partial
 from typing import Any, List, Optional, Tuple
 
 import torch
@@ -11,7 +11,6 @@
 
 from ..transforms import ImageClassificationEval, InterpolationMode
 from ..utils import _log_api_usage_once
-
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
 from ._utils import handle_legacy_interface, _ovewrite_named_param
@@ -277,6 +276,7 @@ def _densenet(
     "recipe": "https://github.com/pytorch/vision/pull/116",
 }
 
+
 class DenseNet121_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
@@ -398,4 +398,4 @@ def densenet201(*, weights: Optional[DenseNet201_Weights] = None, progress: bool
     """
     weights = DenseNet201_Weights.verify(weights)
 
-    return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)
+    return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)

torchvision/models/efficientnet.py

@@ -12,7 +12,6 @@
 from ..ops.misc import Conv2dNormActivation, SqueezeExcitation
 from ..transforms import ImageClassificationEval, InterpolationMode
 from ..utils import _log_api_usage_once
-
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
 from ._utils import handle_legacy_interface, _ovewrite_named_param, _make_divisible

torchvision/models/googlenet.py

@@ -1,16 +1,15 @@
 import warnings
-from functools import partial
 from collections import namedtuple
+from functools import partial
 from typing import Optional, Tuple, List, Callable, Any
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch import Tensor
 
-from ..utils import _log_api_usage_once
 from ..transforms import ImageClassificationEval, InterpolationMode
-
+from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
 from ._utils import handle_legacy_interface, _ovewrite_named_param
@@ -333,4 +332,4 @@ def googlenet(*, weights: Optional[GoogLeNet_Weights] = None, progress: bool = T
                 "auxiliary heads in the pretrained googlenet model are NOT pretrained, so make sure to train them"
             )
 
-    return model
+    return model

torchvision/models/inception.py

@@ -7,10 +7,8 @@
 import torch.nn.functional as F
 from torch import nn, Tensor
 
-from ..utils import _log_api_usage_once
-
-
 from ..transforms import ImageClassificationEval, InterpolationMode
+from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
 from ._utils import handle_legacy_interface, _ovewrite_named_param
@@ -465,4 +463,4 @@ def inception_v3(*, weights: Optional[Inception_V3_Weights] = None, progress: bo
             model.aux_logits = False
             model.AuxLogits = None
 
-    return model
+    return model

torchvision/models/mnasnet.py

@@ -1,5 +1,5 @@
-from functools import partial
 import warnings
+from functools import partial
 from typing import Any, Dict, List, Optional
 
 import torch