Skip to content

GitLab

Unverified Commit bac678c8 authored by Philip Meier's avatar Philip Meier Committed by GitHub
Browse files

remove functionality scheduled for 0.15 after deprecation (#7176)

parent a05d8179
Hide whitespace changes
Inline Side-by-side
Showing with 0 additions and 321 deletions
test/test_functional_tensor.py
View file @ bac678c8
......@@ -2,7 +2,6 @@ import colorsys
import itertools
import math
import os
import re
from functools import partial
from typing import Sequence
......@@ -144,20 +143,6 @@ class TestRotate:
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 @@ class TestAffine:
_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):
......
test/test_prototype_transforms_consistency.py
View file @ bac678c8
......@@ -87,12 +87,6 @@ CONSISTENCY_CONFIGS = [
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),
......
test/test_transforms.py
View file @ bac678c8
......@@ -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"):
......
torchvision/datasets/utils.py
View file @ bac678c8
......@@ -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
......
torchvision/models/alexnet.py
View file @ bac678c8
......@@ -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,
}
)
torchvision/models/densenet.py
View file @ bac678c8
......@@ -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,
}
)
torchvision/models/detection/faster_rcnn.py
View file @ bac678c8
......@@ -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,
}
)
torchvision/models/detection/fcos.py
View file @ bac678c8
......@@ -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,
}
)
torchvision/models/detection/keypoint_rcnn.py
View file @ bac678c8
......@@ -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,
}
)
torchvision/models/detection/mask_rcnn.py
View file @ bac678c8
......@@ -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,
}
)
torchvision/models/detection/retinanet.py
View file @ bac678c8
......@@ -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,
}
)
torchvision/models/detection/ssd.py
View file @ bac678c8
......@@ -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,
}
)
torchvision/models/detection/ssdlite.py
View file @ bac678c8
......@@ -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,
}
)
torchvision/models/efficientnet.py
View file @ bac678c8
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 @@ class EfficientNet(nn.Module):
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 @@ class EfficientNet(nn.Module):
):
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,
}
)
torchvision/models/googlenet.py
View file @ bac678c8
......@@ -343,15 +343,3 @@ def googlenet(*, weights: Optional[GoogLeNet_Weights] = None, progress: bool = T
)
return model
# The dictionary below is internal implementation detail and will be removed in v0.15
from ._utils import _ModelURLs
model_urls = _ModelURLs(
{
# GoogLeNet ported from TensorFlow
"googlenet": GoogLeNet_Weights.IMAGENET1K_V1.url,
}
)
torchvision/models/inception.py
View file @ bac678c8
......@@ -476,15 +476,3 @@ def inception_v3(*, weights: Optional[Inception_V3_Weights] = None, progress: bo
model.AuxLogits = None
return model
# The dictionary below is internal implementation detail and will be removed in v0.15
from ._utils import _ModelURLs
model_urls = _ModelURLs(
{
# Inception v3 ported from TensorFlow
"inception_v3_google": Inception_V3_Weights.IMAGENET1K_V1.url,
}
)
torchvision/models/mobilenetv2.py
View file @ bac678c8
......@@ -258,14 +258,3 @@ 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
model_urls = _ModelURLs(
{
"mobilenet_v2": MobileNet_V2_Weights.IMAGENET1K_V1.url,
}
)
torchvision/models/mobilenetv3.py
View file @ bac678c8
......@@ -421,15 +421,3 @@ def mobilenet_v3_small(
inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_small", **kwargs)
return _mobilenet_v3(inverted_residual_setting, last_channel, weights, progress, **kwargs)
# The dictionary below is internal implementation detail and will be removed in v0.15
from ._utils import _ModelURLs
model_urls = _ModelURLs(
{
"mobilenet_v3_large": MobileNet_V3_Large_Weights.IMAGENET1K_V1.url,
"mobilenet_v3_small": MobileNet_V3_Small_Weights.IMAGENET1K_V1.url,
}
)
torchvision/models/quantization/googlenet.py
View file @ bac678c8
......@@ -208,16 +208,3 @@ def googlenet(
)
return model
# The dictionary below is internal implementation detail and will be removed in v0.15
from .._utils import _ModelURLs
from ..googlenet import model_urls # noqa: F401
quant_model_urls = _ModelURLs(
{
# fp32 GoogLeNet ported from TensorFlow, with weights quantized in PyTorch
"googlenet_fbgemm": GoogLeNet_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
}
)
torchvision/models/quantization/inception.py
View file @ bac678c8
......@@ -271,16 +271,3 @@ def inception_v3(
model.AuxLogits = None
return model
# The dictionary below is internal implementation detail and will be removed in v0.15
from .._utils import _ModelURLs
from ..inception import model_urls # noqa: F401
quant_model_urls = _ModelURLs(
{
# fp32 weights ported from TensorFlow, quantized in PyTorch
"inception_v3_google_fbgemm": Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1.url,
}
)
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment