pytorch · pmeier · Feb 7, 2023 · Feb 6, 2023 · Feb 7, 2023 · Feb 7, 2023
diff --git a/docs/source/models.rst b/docs/source/models.rst
@@ -70,16 +70,10 @@ Migrating to the new API is very straightforward. The following method calls bet
     # Using pretrained weights:
     resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)
     resnet50(weights="IMAGENET1K_V1")
-    resnet50(pretrained=True)  # deprecated
-    resnet50(True)  # deprecated
 
     # Using no weights:
     resnet50(weights=None)
     resnet50()
-    resnet50(pretrained=False)  # deprecated
-    resnet50(False)  # deprecated
-
-Note that the ``pretrained`` parameter is now deprecated, using it will emit warnings and will be removed on v0.15.
 
 Using the pre-trained models
 ----------------------------

diff --git a/test/test_functional_tensor.py b/test/test_functional_tensor.py
@@ -2,7 +2,6 @@
 import itertools
 import math
 import os
-import re
 from functools import partial
 from typing import Sequence
 
@@ -144,20 +143,6 @@ def test_rotate_batch(self, device, dt):
         center = (20, 22)
         _test_fn_on_batch(batch_tensors, F.rotate, angle=32, interpolation=NEAREST, expand=True, center=center)
 
-    def test_rotate_interpolation_type(self):
-        tensor, _ = _create_data(26, 26)
-        # assert changed type warning
-        with pytest.warns(
-            UserWarning,
-            match=re.escape(
-                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
-                "Please use InterpolationMode enum."
-            ),
-        ):
-            res1 = F.rotate(tensor, 45, interpolation=2)
-            res2 = F.rotate(tensor, 45, interpolation=BILINEAR)
-            assert_equal(res1, res2)
-
 
 class TestAffine:
 
@@ -364,22 +349,6 @@ def test_batches(self, device, dt):
 
         _test_fn_on_batch(batch_tensors, F.affine, angle=-43, translate=[-3, 4], scale=1.2, shear=[4.0, 5.0])
 
-    @pytest.mark.parametrize("device", cpu_and_gpu())
-    def test_warnings(self, device):
-        tensor, pil_img = _create_data(26, 26, device=device)
-
-        # assert changed type warning
-        with pytest.warns(
-            UserWarning,
-            match=re.escape(
-                "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
-                "Please use InterpolationMode enum."
-            ),
-        ):
-            res1 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=2)
-            res2 = F.affine(tensor, 45, translate=[0, 0], scale=1.0, shear=[0.0, 0.0], interpolation=BILINEAR)
-            assert_equal(res1, res2)
-
 
 def _get_data_dims_and_points_for_perspective():
     # Ideally we would parametrize independently over data dims and points, but
@@ -478,23 +447,6 @@ def test_perspective_batch(device, dims_and_points, dt):
     )
 
 
-def test_perspective_interpolation_warning():
-    # assert changed type warning
-    spoints = [[0, 0], [33, 0], [33, 25], [0, 25]]
-    epoints = [[3, 2], [32, 3], [30, 24], [2, 25]]
-    tensor = torch.randint(0, 256, (3, 26, 26))
-    with pytest.warns(
-        UserWarning,
-        match=re.escape(
-            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
-            "Please use InterpolationMode enum."
-        ),
-    ):
-        res1 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=2)
-        res2 = F.perspective(tensor, startpoints=spoints, endpoints=epoints, interpolation=BILINEAR)
-        assert_equal(res1, res2)
-
-
 @pytest.mark.parametrize("device", cpu_and_gpu())
 @pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
 @pytest.mark.parametrize(
@@ -568,19 +520,6 @@ def test_resize_asserts(device):
 
     tensor, pil_img = _create_data(26, 36, device=device)
 
-    # assert changed type warning
-    with pytest.warns(
-        UserWarning,
-        match=re.escape(
-            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
-            "Please use InterpolationMode enum."
-        ),
-    ):
-        res1 = F.resize(tensor, size=32, interpolation=2)
-
-    res2 = F.resize(tensor, size=32, interpolation=BILINEAR)
-    assert_equal(res1, res2)
-
     for img in (tensor, pil_img):
         exp_msg = "max_size should only be passed if size specifies the length of the smaller edge"
         with pytest.raises(ValueError, match=exp_msg):

diff --git a/test/test_prototype_transforms_consistency.py b/test/test_prototype_transforms_consistency.py
@@ -87,12 +87,6 @@ def __init__(
             ArgsKwargs((32, 29)),
             ArgsKwargs((31, 28), interpolation=prototype_transforms.InterpolationMode.NEAREST),
             ArgsKwargs((33, 26), interpolation=prototype_transforms.InterpolationMode.BICUBIC),
-            # FIXME: these are currently failing, since the new transform only supports the enum. The int input is
-            #  already deprecated and scheduled to be removed in 0.15. Should we support ints on the prototype
-            #  transform? I guess it depends if we roll out before 0.15 or not.
-            # ArgsKwargs((30, 27), interpolation=0),
-            # ArgsKwargs((35, 29), interpolation=2),
-            # ArgsKwargs((34, 25), interpolation=3),
             NotScriptableArgsKwargs(31, max_size=32),
             ArgsKwargs([31], max_size=32),
             NotScriptableArgsKwargs(30, max_size=100),

diff --git a/test/test_transforms.py b/test/test_transforms.py
@@ -1872,17 +1872,6 @@ def test_random_rotation():
     # Checking if RandomRotation can be printed as string
     t.__repr__()
 
-    # assert changed type warning
-    with pytest.warns(
-        UserWarning,
-        match=re.escape(
-            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
-            "Please use InterpolationMode enum."
-        ),
-    ):
-        t = transforms.RandomRotation((-10, 10), interpolation=2)
-        assert t.interpolation == transforms.InterpolationMode.BILINEAR
-
 
 def test_random_rotation_error():
     # assert fill being either a Sequence or a Number
@@ -2212,17 +2201,6 @@ def test_random_affine():
     t = transforms.RandomAffine(10, interpolation=transforms.InterpolationMode.BILINEAR)
     assert "bilinear" in t.__repr__()
 
-    # assert changed type warning
-    with pytest.warns(
-        UserWarning,
-        match=re.escape(
-            "Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
-            "Please use InterpolationMode enum."
-        ),
-    ):
-        t = transforms.RandomAffine(10, interpolation=2)
-        assert t.interpolation == transforms.InterpolationMode.BILINEAR
-
 
 def test_elastic_transformation():
     with pytest.raises(TypeError, match=r"alpha should be float or a sequence of floats"):

diff --git a/torchvision/datasets/utils.py b/torchvision/datasets/utils.py
@@ -48,19 +48,6 @@ def _urlretrieve(url: str, filename: str, chunk_size: int = 1024 * 32) -> None:
         _save_response_content(iter(lambda: response.read(chunk_size), b""), filename, length=response.length)
 
 
-def gen_bar_updater() -> Callable[[int, int, int], None]:
-    warnings.warn("The function `gen_bar_update` is deprecated since 0.13 and will be removed in 0.15.")
-    pbar = tqdm(total=None)
-
-    def bar_update(count, block_size, total_size):
-        if pbar.total is None and total_size:
-            pbar.total = total_size
-        progress_bytes = count * block_size
-        pbar.update(progress_bytes - pbar.n)
-
-    return bar_update
-
-
 def calculate_md5(fpath: str, chunk_size: int = 1024 * 1024) -> str:
     # Setting the `usedforsecurity` flag does not change anything about the functionality, but indicates that we are
     # not using the MD5 checksum for cryptography. This enables its usage in restricted environments like FIPS. Without

diff --git a/torchvision/models/alexnet.py b/torchvision/models/alexnet.py
@@ -117,14 +117,3 @@ def alexnet(*, weights: Optional[AlexNet_Weights] = None, progress: bool = True,
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
-
-
-# The dictionary below is internal implementation detail and will be removed in v0.15
-from ._utils import _ModelURLs
-
-
-model_urls = _ModelURLs(
-    {
-        "alexnet": AlexNet_Weights.IMAGENET1K_V1.url,
-    }
-)
diff --git a/torchvision/models/densenet.py b/torchvision/models/densenet.py
@@ -446,16 +446,3 @@ 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)
-
-
-# The dictionary below is internal implementation detail and will be removed in v0.15
-from ._utils import _ModelURLs
-
-model_urls = _ModelURLs(
-    {
-        "densenet121": DenseNet121_Weights.IMAGENET1K_V1.url,
-        "densenet169": DenseNet169_Weights.IMAGENET1K_V1.url,
-        "densenet201": DenseNet201_Weights.IMAGENET1K_V1.url,
-        "densenet161": DenseNet161_Weights.IMAGENET1K_V1.url,
-    }
-)
diff --git a/torchvision/models/detection/faster_rcnn.py b/torchvision/models/detection/faster_rcnn.py
@@ -841,16 +841,3 @@ def fasterrcnn_mobilenet_v3_large_fpn(
         trainable_backbone_layers=trainable_backbone_layers,
         **kwargs,
     )
-
-
-# The dictionary below is internal implementation detail and will be removed in v0.15
-from .._utils import _ModelURLs
-
-
-model_urls = _ModelURLs(
-    {
-        "fasterrcnn_resnet50_fpn_coco": FasterRCNN_ResNet50_FPN_Weights.COCO_V1.url,
-        "fasterrcnn_mobilenet_v3_large_320_fpn_coco": FasterRCNN_MobileNet_V3_Large_320_FPN_Weights.COCO_V1.url,
-        "fasterrcnn_mobilenet_v3_large_fpn_coco": FasterRCNN_MobileNet_V3_Large_FPN_Weights.COCO_V1.url,
-    }
-)
diff --git a/torchvision/models/detection/fcos.py b/torchvision/models/detection/fcos.py
@@ -769,14 +769,3 @@ def fcos_resnet50_fpn(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
-
-
-# The dictionary below is internal implementation detail and will be removed in v0.15
-from .._utils import _ModelURLs
-
-
-model_urls = _ModelURLs(
-    {
-        "fcos_resnet50_fpn_coco": FCOS_ResNet50_FPN_Weights.COCO_V1.url,
-    }
-)
diff --git a/torchvision/models/detection/keypoint_rcnn.py b/torchvision/models/detection/keypoint_rcnn.py
@@ -470,16 +470,3 @@ def keypointrcnn_resnet50_fpn(
             overwrite_eps(model, 0.0)
 
     return model
-
-
-# The dictionary below is internal implementation detail and will be removed in v0.15
-from .._utils import _ModelURLs
-
-
-model_urls = _ModelURLs(
-    {
-        # legacy model for BC reasons, see https://github.com/pytorch/vision/issues/1606
-        "keypointrcnn_resnet50_fpn_coco_legacy": KeypointRCNN_ResNet50_FPN_Weights.COCO_LEGACY.url,
-        "keypointrcnn_resnet50_fpn_coco": KeypointRCNN_ResNet50_FPN_Weights.COCO_V1.url,
-    }
-)
diff --git a/torchvision/models/detection/mask_rcnn.py b/torchvision/models/detection/mask_rcnn.py
@@ -585,14 +585,3 @@ def maskrcnn_resnet50_fpn_v2(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
-
-
-# The dictionary below is internal implementation detail and will be removed in v0.15
-from .._utils import _ModelURLs
-
-
-model_urls = _ModelURLs(
-    {
-        "maskrcnn_resnet50_fpn_coco": MaskRCNN_ResNet50_FPN_Weights.COCO_V1.url,
-    }
-)
diff --git a/torchvision/models/detection/retinanet.py b/torchvision/models/detection/retinanet.py
@@ -897,14 +897,3 @@ def retinanet_resnet50_fpn_v2(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
-
-
-# The dictionary below is internal implementation detail and will be removed in v0.15
-from .._utils import _ModelURLs
-
-
-model_urls = _ModelURLs(
-    {
-        "retinanet_resnet50_fpn_coco": RetinaNet_ResNet50_FPN_Weights.COCO_V1.url,
-    }
-)
diff --git a/torchvision/models/detection/ssd.py b/torchvision/models/detection/ssd.py
@@ -680,25 +680,3 @@ def ssd300_vgg16(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
-
-
-# The dictionary below is internal implementation detail and will be removed in v0.15
-from .._utils import _ModelURLs
-
-
-model_urls = _ModelURLs(
-    {
-        "ssd300_vgg16_coco": SSD300_VGG16_Weights.COCO_V1.url,
-    }
-)
-
-
-backbone_urls = _ModelURLs(
-    {
-        # We port the features of a VGG16 backbone trained by amdegroot because unlike the one on TorchVision, it uses
-        # the same input standardization method as the paper.
-        # Ref: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
-        # Only the `features` weights have proper values, those on the `classifier` module are filled with nans.
-        "vgg16_features": VGG16_Weights.IMAGENET1K_FEATURES.url,
-    }
-)
diff --git a/torchvision/models/detection/ssdlite.py b/torchvision/models/detection/ssdlite.py
@@ -329,14 +329,3 @@ def ssdlite320_mobilenet_v3_large(
         model.load_state_dict(weights.get_state_dict(progress=progress))
 
     return model
-
-
-# The dictionary below is internal implementation detail and will be removed in v0.15
-from .._utils import _ModelURLs
-
-
-model_urls = _ModelURLs(
-    {
-        "ssdlite320_mobilenet_v3_large_coco": SSDLite320_MobileNet_V3_Large_Weights.COCO_V1.url,
-    }
-)
diff --git a/torchvision/models/efficientnet.py b/torchvision/models/efficientnet.py
@@ -1,6 +1,5 @@
 import copy
 import math
-import warnings
 from dataclasses import dataclass
 from functools import partial
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
@@ -239,7 +238,6 @@ def __init__(
         num_classes: int = 1000,
         norm_layer: Optional[Callable[..., nn.Module]] = None,
         last_channel: Optional[int] = None,
-        **kwargs: Any,
     ) -> None:
         """
         EfficientNet V1 and V2 main class
@@ -263,16 +261,6 @@ def __init__(
         ):
             raise TypeError("The inverted_residual_setting should be List[MBConvConfig]")
 
-        if "block" in kwargs:
-            warnings.warn(
-                "The parameter 'block' is deprecated since 0.13 and will be removed 0.15. "
-                "Please pass this information on 'MBConvConfig.block' instead."
-            )
-            if kwargs["block"] is not None:
-                for s in inverted_residual_setting:
-                    if isinstance(s, MBConvConfig):
-                        s.block = kwargs["block"]
-
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
 
@@ -1141,21 +1129,3 @@ def efficientnet_v2_l(
         norm_layer=partial(nn.BatchNorm2d, eps=1e-03),
         **kwargs,
     )
-
-
-# The dictionary below is internal implementation detail and will be removed in v0.15
-from ._utils import _ModelURLs
-
-
-model_urls = _ModelURLs(
-    {
-        "efficientnet_b0": EfficientNet_B0_Weights.IMAGENET1K_V1.url,
-        "efficientnet_b1": EfficientNet_B1_Weights.IMAGENET1K_V1.url,
-        "efficientnet_b2": EfficientNet_B2_Weights.IMAGENET1K_V1.url,
-        "efficientnet_b3": EfficientNet_B3_Weights.IMAGENET1K_V1.url,
-        "efficientnet_b4": EfficientNet_B4_Weights.IMAGENET1K_V1.url,
-        "efficientnet_b5": EfficientNet_B5_Weights.IMAGENET1K_V1.url,
-        "efficientnet_b6": EfficientNet_B6_Weights.IMAGENET1K_V1.url,
-        "efficientnet_b7": EfficientNet_B7_Weights.IMAGENET1K_V1.url,
-    }
-)