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Unverified Commit bac678c8 authored by Philip Meier's avatar Philip Meier Committed by GitHub
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remove functionality scheduled for 0.15 after deprecation (#7176)

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torchvision/models/quantization/mobilenetv2.py
View file @ bac678c8
......@@ -152,15 +152,3 @@ def mobilenet_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
from ..mobilenetv2 import model_urls # noqa: F401
quant_model_urls = _ModelURLs(
{
"mobilenet_v2_qnnpack": MobileNet_V2_QuantizedWeights.IMAGENET1K_QNNPACK_V1.url,
}
)
torchvision/models/quantization/mobilenetv3.py
View file @ bac678c8
......@@ -235,15 +235,3 @@ def mobilenet_v3_large(
inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs)
return _mobilenet_v3_model(inverted_residual_setting, last_channel, weights, progress, quantize, **kwargs)
# The dictionary below is internal implementation detail and will be removed in v0.15
from .._utils import _ModelURLs
from ..mobilenetv3 import model_urls # noqa: F401
quant_model_urls = _ModelURLs(
{
"mobilenet_v3_large_qnnpack": MobileNet_V3_Large_QuantizedWeights.IMAGENET1K_QNNPACK_V1.url,
}
)
torchvision/models/quantization/resnet.py
View file @ bac678c8
......@@ -482,17 +482,3 @@ def resnext101_64x4d(
_ovewrite_named_param(kwargs, "groups", 64)
_ovewrite_named_param(kwargs, "width_per_group", 4)
return _resnet(QuantizableBottleneck, [3, 4, 23, 3], weights, progress, quantize, **kwargs)
# The dictionary below is internal implementation detail and will be removed in v0.15
from .._utils import _ModelURLs
from ..resnet import model_urls # noqa: F401
quant_model_urls = _ModelURLs(
{
"resnet18_fbgemm": ResNet18_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
"resnet50_fbgemm": ResNet50_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
"resnext101_32x8d_fbgemm": ResNeXt101_32X8D_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
}
)
torchvision/models/quantization/shufflenetv2.py
View file @ bac678c8
......@@ -425,16 +425,3 @@ def shufflenet_v2_x2_0(
return _shufflenetv2(
[4, 8, 4], [24, 244, 488, 976, 2048], weights=weights, progress=progress, quantize=quantize, **kwargs
)
# The dictionary below is internal implementation detail and will be removed in v0.15
from .._utils import _ModelURLs
from ..shufflenetv2 import model_urls # noqa: F401
quant_model_urls = _ModelURLs(
{
"shufflenetv2_x0.5_fbgemm": ShuffleNet_V2_X0_5_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
"shufflenetv2_x1.0_fbgemm": ShuffleNet_V2_X1_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
}
)
torchvision/models/regnet.py
View file @ bac678c8
......@@ -1569,27 +1569,3 @@ def regnet_x_32gf(*, weights: Optional[RegNet_X_32GF_Weights] = None, progress:
params = BlockParams.from_init_params(depth=23, w_0=320, w_a=69.86, w_m=2.0, group_width=168, **kwargs)
return _regnet(params, weights, progress, **kwargs)
# The dictionary below is internal implementation detail and will be removed in v0.15
from ._utils import _ModelURLs
model_urls = _ModelURLs(
{
"regnet_y_400mf": RegNet_Y_400MF_Weights.IMAGENET1K_V1.url,
"regnet_y_800mf": RegNet_Y_800MF_Weights.IMAGENET1K_V1.url,
"regnet_y_1_6gf": RegNet_Y_1_6GF_Weights.IMAGENET1K_V1.url,
"regnet_y_3_2gf": RegNet_Y_3_2GF_Weights.IMAGENET1K_V1.url,
"regnet_y_8gf": RegNet_Y_8GF_Weights.IMAGENET1K_V1.url,
"regnet_y_16gf": RegNet_Y_16GF_Weights.IMAGENET1K_V1.url,
"regnet_y_32gf": RegNet_Y_32GF_Weights.IMAGENET1K_V1.url,
"regnet_x_400mf": RegNet_X_400MF_Weights.IMAGENET1K_V1.url,
"regnet_x_800mf": RegNet_X_800MF_Weights.IMAGENET1K_V1.url,
"regnet_x_1_6gf": RegNet_X_1_6GF_Weights.IMAGENET1K_V1.url,
"regnet_x_3_2gf": RegNet_X_3_2GF_Weights.IMAGENET1K_V1.url,
"regnet_x_8gf": RegNet_X_8GF_Weights.IMAGENET1K_V1.url,
"regnet_x_16gf": RegNet_X_16GF_Weights.IMAGENET1K_V1.url,
"regnet_x_32gf": RegNet_X_32GF_Weights.IMAGENET1K_V1.url,
}
)
torchvision/models/resnet.py
View file @ bac678c8
......@@ -983,22 +983,3 @@ def wide_resnet101_2(
_ovewrite_named_param(kwargs, "width_per_group", 64 * 2)
return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs)
# The dictionary below is internal implementation detail and will be removed in v0.15
from ._utils import _ModelURLs
model_urls = _ModelURLs(
{
"resnet18": ResNet18_Weights.IMAGENET1K_V1.url,
"resnet34": ResNet34_Weights.IMAGENET1K_V1.url,
"resnet50": ResNet50_Weights.IMAGENET1K_V1.url,
"resnet101": ResNet101_Weights.IMAGENET1K_V1.url,
"resnet152": ResNet152_Weights.IMAGENET1K_V1.url,
"resnext50_32x4d": ResNeXt50_32X4D_Weights.IMAGENET1K_V1.url,
"resnext101_32x8d": ResNeXt101_32X8D_Weights.IMAGENET1K_V1.url,
"wide_resnet50_2": Wide_ResNet50_2_Weights.IMAGENET1K_V1.url,
"wide_resnet101_2": Wide_ResNet101_2_Weights.IMAGENET1K_V1.url,
}
)
torchvision/models/segmentation/deeplabv3.py
View file @ bac678c8
......@@ -388,16 +388,3 @@ def deeplabv3_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(
{
"deeplabv3_resnet50_coco": DeepLabV3_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1.url,
"deeplabv3_resnet101_coco": DeepLabV3_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1.url,
"deeplabv3_mobilenet_v3_large_coco": DeepLabV3_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1.url,
}
)
torchvision/models/segmentation/fcn.py
View file @ bac678c8
......@@ -230,15 +230,3 @@ def fcn_resnet101(
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(
{
"fcn_resnet50_coco": FCN_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1.url,
"fcn_resnet101_coco": FCN_ResNet101_Weights.COCO_WITH_VOC_LABELS_V1.url,
}
)
torchvision/models/segmentation/lraspp.py
View file @ bac678c8
......@@ -176,14 +176,3 @@ def lraspp_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(
{
"lraspp_mobilenet_v3_large_coco": LRASPP_MobileNet_V3_Large_Weights.COCO_WITH_VOC_LABELS_V1.url,
}
)
torchvision/models/shufflenetv2.py
View file @ bac678c8
......@@ -406,17 +406,3 @@ def shufflenet_v2_x2_0(
weights = ShuffleNet_V2_X2_0_Weights.verify(weights)
return _shufflenetv2(weights, progress, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)
# The dictionary below is internal implementation detail and will be removed in v0.15
from ._utils import _ModelURLs
model_urls = _ModelURLs(
{
"shufflenetv2_x0.5": ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1.url,
"shufflenetv2_x1.0": ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1.url,
"shufflenetv2_x1.5": None,
"shufflenetv2_x2.0": None,
}
)
torchvision/models/squeezenet.py
View file @ bac678c8
......@@ -221,15 +221,3 @@ def squeezenet1_1(
"""
weights = SqueezeNet1_1_Weights.verify(weights)
return _squeezenet("1_1", weights, progress, **kwargs)
# The dictionary below is internal implementation detail and will be removed in v0.15
from ._utils import _ModelURLs
model_urls = _ModelURLs(
{
"squeezenet1_0": SqueezeNet1_0_Weights.IMAGENET1K_V1.url,
"squeezenet1_1": SqueezeNet1_1_Weights.IMAGENET1K_V1.url,
}
)
torchvision/models/vgg.py
View file @ bac678c8
......@@ -509,21 +509,3 @@ def vgg19_bn(*, weights: Optional[VGG19_BN_Weights] = None, progress: bool = Tru
weights = VGG19_BN_Weights.verify(weights)
return _vgg("E", True, weights, progress, **kwargs)
# The dictionary below is internal implementation detail and will be removed in v0.15
from ._utils import _ModelURLs
model_urls = _ModelURLs(
{
"vgg11": VGG11_Weights.IMAGENET1K_V1.url,
"vgg13": VGG13_Weights.IMAGENET1K_V1.url,
"vgg16": VGG16_Weights.IMAGENET1K_V1.url,
"vgg19": VGG19_Weights.IMAGENET1K_V1.url,
"vgg11_bn": VGG11_BN_Weights.IMAGENET1K_V1.url,
"vgg13_bn": VGG13_BN_Weights.IMAGENET1K_V1.url,
"vgg16_bn": VGG16_BN_Weights.IMAGENET1K_V1.url,
"vgg19_bn": VGG19_BN_Weights.IMAGENET1K_V1.url,
}
)
torchvision/models/vision_transformer.py
View file @ bac678c8
......@@ -862,17 +862,3 @@ def interpolate_embeddings(
model_state = model_state_copy
return model_state
# The dictionary below is internal implementation detail and will be removed in v0.15
from ._utils import _ModelURLs
model_urls = _ModelURLs(
{
"vit_b_16": ViT_B_16_Weights.IMAGENET1K_V1.url,
"vit_b_32": ViT_B_32_Weights.IMAGENET1K_V1.url,
"vit_l_16": ViT_L_16_Weights.IMAGENET1K_V1.url,
"vit_l_32": ViT_L_32_Weights.IMAGENET1K_V1.url,
}
)
torchvision/transforms/functional.py
View file @ bac678c8
......@@ -421,8 +421,6 @@ def resize(
Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
``InterpolationMode.NEAREST_EXACT``, ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are
supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
max_size (int, optional): The maximum allowed for the longer edge of
the resized image: if the longer edge of the image is greater
than ``max_size`` after being resized according to ``size``, then
......@@ -441,13 +439,6 @@ def resize(
"""
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(resize)
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(interpolation, InterpolationMode):
raise TypeError("Argument interpolation should be a InterpolationMode")
......@@ -623,8 +614,6 @@ def resized_crop(
Default is ``InterpolationMode.BILINEAR``. If input is Tensor, only ``InterpolationMode.NEAREST``,
``InterpolationMode.NEAREST_EXACT``, ``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are
supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
antialias (bool, optional): antialias flag. If ``img`` is PIL Image, the flag is ignored and anti-alias
is always used. If ``img`` is Tensor, the flag is False by default and can be set to True for
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` modes.
......@@ -707,8 +696,6 @@ def perspective(
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
fill (sequence or number, optional): Pixel fill value for the area outside the transformed
image. If given a number, the value is used for all bands respectively.
......@@ -724,14 +711,6 @@ def perspective(
coeffs = _get_perspective_coeffs(startpoints, endpoints)
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(interpolation, InterpolationMode):
raise TypeError("Argument interpolation should be a InterpolationMode")
......@@ -1067,8 +1046,6 @@ def rotate(
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
expand (bool, optional): Optional expansion flag.
If true, expands the output image to make it large enough to hold the entire rotated image.
If false or omitted, make the output image the same size as the input image.
......@@ -1090,14 +1067,6 @@ def rotate(
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(rotate)
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(angle, (int, float)):
raise TypeError("Argument angle should be int or float")
......@@ -1148,8 +1117,6 @@ def affine(
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
fill (sequence or number, optional): Pixel fill value for the area outside the transformed
image. If given a number, the value is used for all bands respectively.
......@@ -1165,14 +1132,6 @@ def affine(
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(affine)
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
if not isinstance(angle, (int, float)):
raise TypeError("Argument angle should be int or float")
......
torchvision/transforms/transforms.py
View file @ bac678c8
......@@ -298,8 +298,6 @@ class Resize(torch.nn.Module):
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
max_size (int, optional): The maximum allowed for the longer edge of
the resized image: if the longer edge of the image is greater
than ``max_size`` after being resized according to ``size``, then
......@@ -324,14 +322,6 @@ class Resize(torch.nn.Module):
self.size = size
self.max_size = max_size
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.interpolation = interpolation
self.antialias = antialias
......@@ -752,8 +742,6 @@ class RandomPerspective(torch.nn.Module):
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
fill (sequence or number): Pixel fill value for the area outside the transformed
image. Default is ``0``. If given a number, the value is used for all bands respectively.
"""
......@@ -763,14 +751,6 @@ class RandomPerspective(torch.nn.Module):
_log_api_usage_once(self)
self.p = p
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.interpolation = interpolation
self.distortion_scale = distortion_scale
......@@ -867,8 +847,6 @@ class RandomResizedCrop(torch.nn.Module):
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.NEAREST_EXACT``,
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
antialias (bool, optional): antialias flag. If ``img`` is PIL Image, the flag is ignored and anti-alias
is always used. If ``img`` is Tensor, the flag is False by default and can be set to True for
``InterpolationMode.BILINEAR`` and ``InterpolationMode.BICUBIC`` modes.
......@@ -894,14 +872,6 @@ class RandomResizedCrop(torch.nn.Module):
if (scale[0] > scale[1]) or (ratio[0] > ratio[1]):
warnings.warn("Scale and ratio should be of kind (min, max)")
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.interpolation = interpolation
self.antialias = antialias
self.scale = scale
......@@ -1288,8 +1258,6 @@ class RandomRotation(torch.nn.Module):
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
expand (bool, optional): Optional expansion flag.
If true, expands the output to make it large enough to hold the entire rotated image.
If false or omitted, make the output image the same size as the input image.
......@@ -1307,14 +1275,6 @@ class RandomRotation(torch.nn.Module):
super().__init__()
_log_api_usage_once(self)
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
if center is not None:
......@@ -1398,8 +1358,6 @@ class RandomAffine(torch.nn.Module):
interpolation (InterpolationMode): Desired interpolation enum defined by
:class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``.
If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
For backward compatibility integer values (e.g. ``PIL.Image[.Resampling].NEAREST``) are still accepted,
but deprecated since 0.13 and will be removed in 0.15. Please use InterpolationMode enum.
fill (sequence or number): Pixel fill value for the area outside the transformed
image. Default is ``0``. If given a number, the value is used for all bands respectively.
center (sequence, optional): Optional center of rotation, (x, y). Origin is the upper left corner.
......@@ -1422,14 +1380,6 @@ class RandomAffine(torch.nn.Module):
super().__init__()
_log_api_usage_once(self)
# Backward compatibility with integer value
if isinstance(interpolation, int):
warnings.warn(
"Argument 'interpolation' of type int is deprecated since 0.13 and will be removed in 0.15. "
"Please use InterpolationMode enum."
)
interpolation = _interpolation_modes_from_int(interpolation)
self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
if translate is not None:
......
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