Remove publication_year and interpolation meta (#5848)

* Remove publication_year and interpolation meta

* Add type to _COMMON_META and _COMMON_SWAG_META to prevent error from mypy check

* Remove test to check interpolation and publication_year meta
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

torchvision/models/quantization/mobilenetv2.py

@@ -7,7 +7,7 @@ from torch.ao.quantization import QuantStub, DeQuantStub
 from torchvision.models.mobilenetv2 import InvertedResidual, MobileNetV2, MobileNet_V2_Weights
 
 from ...ops.misc import Conv2dNormActivation
-from ...transforms._presets import ImageClassification, InterpolationMode
+from ...transforms._presets import ImageClassification
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
@@ -71,12 +71,10 @@ class MobileNet_V2_QuantizedWeights(WeightsEnum):
         meta={
             "task": "image_classification",
             "architecture": "MobileNetV2",
-            "publication_year": 2018,
             "num_params": 3504872,
             "size": (224, 224),
             "min_size": (1, 1),
             "categories": _IMAGENET_CATEGORIES,
-            "interpolation": InterpolationMode.BILINEAR,
             "backend": "qnnpack",
             "quantization": "Quantization Aware Training",
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2",

torchvision/models/quantization/mobilenetv3.py

@@ -6,7 +6,7 @@ from torch import nn, Tensor
 from torch.ao.quantization import QuantStub, DeQuantStub
 
 from ...ops.misc import Conv2dNormActivation, SqueezeExcitation
-from ...transforms._presets import ImageClassification, InterpolationMode
+from ...transforms._presets import ImageClassification
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
@@ -161,12 +161,10 @@ class MobileNet_V3_Large_QuantizedWeights(WeightsEnum):
         meta={
             "task": "image_classification",
             "architecture": "MobileNetV3",
-            "publication_year": 2019,
             "num_params": 5483032,
             "size": (224, 224),
             "min_size": (1, 1),
             "categories": _IMAGENET_CATEGORIES,
-            "interpolation": InterpolationMode.BILINEAR,
             "backend": "qnnpack",
             "quantization": "Quantization Aware Training",
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv3",

torchvision/models/quantization/resnet.py

@@ -13,7 +13,7 @@ from torchvision.models.resnet import (
     ResNeXt101_32X8D_Weights,
 )
 
-from ...transforms._presets import ImageClassification, InterpolationMode
+from ...transforms._presets import ImageClassification
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
@@ -151,7 +151,6 @@ _COMMON_META = {
     "size": (224, 224),
     "min_size": (1, 1),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
     "backend": "fbgemm",
     "quantization": "Post Training Quantization",
     "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
@@ -165,7 +164,6 @@ class ResNet18_QuantizedWeights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 11689512,
             "unquantized": ResNet18_Weights.IMAGENET1K_V1,
             "acc@1": 69.494,
@@ -182,7 +180,6 @@ class ResNet50_QuantizedWeights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 25557032,
             "unquantized": ResNet50_Weights.IMAGENET1K_V1,
             "acc@1": 75.920,
@@ -195,7 +192,6 @@ class ResNet50_QuantizedWeights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 25557032,
             "unquantized": ResNet50_Weights.IMAGENET1K_V2,
             "acc@1": 80.282,
@@ -212,7 +208,6 @@ class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
-            "publication_year": 2016,
             "num_params": 88791336,
             "unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V1,
             "acc@1": 78.986,
@@ -225,7 +220,6 @@ class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
-            "publication_year": 2016,
             "num_params": 88791336,
             "unquantized": ResNeXt101_32X8D_Weights.IMAGENET1K_V2,
             "acc@1": 82.574,

torchvision/models/quantization/shufflenetv2.py

@@ -6,7 +6,7 @@ import torch.nn as nn
 from torch import Tensor
 from torchvision.models import shufflenetv2
 
-from ...transforms._presets import ImageClassification, InterpolationMode
+from ...transforms._presets import ImageClassification
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
@@ -104,11 +104,9 @@ def _shufflenetv2(
 _COMMON_META = {
     "task": "image_classification",
     "architecture": "ShuffleNetV2",
-    "publication_year": 2018,
     "size": (224, 224),
     "min_size": (1, 1),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
     "backend": "fbgemm",
     "quantization": "Post Training Quantization",
     "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",

torchvision/models/regnet.py

@@ -405,20 +405,16 @@ def _regnet(
 _COMMON_META = {
     "task": "image_classification",
     "architecture": "RegNet",
-    "publication_year": 2020,
     "size": (224, 224),
     "min_size": (1, 1),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
 _COMMON_SWAG_META = {
     **_COMMON_META,
-    "publication_year": 2022,
     "size": (384, 384),
     "recipe": "https://github.com/facebookresearch/SWAG",
     "license": "https://github.com/facebookresearch/SWAG/blob/main/LICENSE",
-    "interpolation": InterpolationMode.BICUBIC,
 }
 
 

torchvision/models/resnet.py

@@ -5,7 +5,7 @@ import torch
 import torch.nn as nn
 from torch import Tensor
 
-from ..transforms._presets import ImageClassification, InterpolationMode
+from ..transforms._presets import ImageClassification
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -306,7 +306,6 @@ _COMMON_META = {
     "size": (224, 224),
     "min_size": (1, 1),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
 
@@ -317,7 +316,6 @@ class ResNet18_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 11689512,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
             "acc@1": 69.758,
@@ -334,7 +332,6 @@ class ResNet34_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 21797672,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
             "acc@1": 73.314,
@@ -351,7 +348,6 @@ class ResNet50_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 25557032,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
             "acc@1": 76.130,
@@ -364,7 +360,6 @@ class ResNet50_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 25557032,
             "recipe": "https://github.com/pytorch/vision/issues/3995#issuecomment-1013906621",
             "acc@1": 80.858,
@@ -381,7 +376,6 @@ class ResNet101_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 44549160,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
             "acc@1": 77.374,
@@ -394,7 +388,6 @@ class ResNet101_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 44549160,
             "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe",
             "acc@1": 81.886,
@@ -411,7 +404,6 @@ class ResNet152_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 60192808,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet",
             "acc@1": 78.312,
@@ -424,7 +416,6 @@ class ResNet152_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
-            "publication_year": 2015,
             "num_params": 60192808,
             "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe",
             "acc@1": 82.284,
@@ -441,7 +432,6 @@ class ResNeXt50_32X4D_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
-            "publication_year": 2016,
             "num_params": 25028904,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext",
             "acc@1": 77.618,
@@ -454,7 +444,6 @@ class ResNeXt50_32X4D_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
-            "publication_year": 2016,
             "num_params": 25028904,
             "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe",
             "acc@1": 81.198,
@@ -471,7 +460,6 @@ class ResNeXt101_32X8D_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
-            "publication_year": 2016,
             "num_params": 88791336,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext",
             "acc@1": 79.312,
@@ -484,7 +472,6 @@ class ResNeXt101_32X8D_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
-            "publication_year": 2016,
             "num_params": 88791336,
             "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres",
             "acc@1": 82.834,
@@ -501,7 +488,6 @@ class Wide_ResNet50_2_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "WideResNet",
-            "publication_year": 2016,
             "num_params": 68883240,
             "recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439",
             "acc@1": 78.468,
@@ -514,7 +500,6 @@ class Wide_ResNet50_2_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "WideResNet",
-            "publication_year": 2016,
             "num_params": 68883240,
             "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres",
             "acc@1": 81.602,
@@ -531,7 +516,6 @@ class Wide_ResNet101_2_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "WideResNet",
-            "publication_year": 2016,
             "num_params": 126886696,
             "recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439",
             "acc@1": 78.848,
@@ -544,7 +528,6 @@ class Wide_ResNet101_2_Weights(WeightsEnum):
         meta={
             **_COMMON_META,
             "architecture": "WideResNet",
-            "publication_year": 2016,
             "num_params": 126886696,
             "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe",
             "acc@1": 82.510,

torchvision/models/segmentation/deeplabv3.py

@@ -5,7 +5,7 @@ import torch
 from torch import nn
 from torch.nn import functional as F
 
-from ...transforms._presets import SemanticSegmentation, InterpolationMode
+from ...transforms._presets import SemanticSegmentation
 from .._api import WeightsEnum, Weights
 from .._meta import _VOC_CATEGORIES
 from .._utils import IntermediateLayerGetter, handle_legacy_interface, _ovewrite_value_param
@@ -131,9 +131,7 @@ def _deeplabv3_resnet(
 _COMMON_META = {
     "task": "image_semantic_segmentation",
     "architecture": "DeepLabV3",
-    "publication_year": 2017,
     "categories": _VOC_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
 

torchvision/models/segmentation/fcn.py

@@ -3,7 +3,7 @@ from typing import Any, Optional
 
 from torch import nn
 
-from ...transforms._presets import SemanticSegmentation, InterpolationMode
+from ...transforms._presets import SemanticSegmentation
 from .._api import WeightsEnum, Weights
 from .._meta import _VOC_CATEGORIES
 from .._utils import IntermediateLayerGetter, handle_legacy_interface, _ovewrite_value_param
@@ -50,9 +50,7 @@ class FCNHead(nn.Sequential):
 _COMMON_META = {
     "task": "image_semantic_segmentation",
     "architecture": "FCN",
-    "publication_year": 2014,
     "categories": _VOC_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
 

torchvision/models/segmentation/lraspp.py

@@ -5,7 +5,7 @@ from typing import Any, Dict, Optional
 from torch import nn, Tensor
 from torch.nn import functional as F
 
-from ...transforms._presets import SemanticSegmentation, InterpolationMode
+from ...transforms._presets import SemanticSegmentation
 from ...utils import _log_api_usage_once
 from .._api import WeightsEnum, Weights
 from .._meta import _VOC_CATEGORIES
@@ -100,10 +100,8 @@ class LRASPP_MobileNet_V3_Large_Weights(WeightsEnum):
         meta={
             "task": "image_semantic_segmentation",
             "architecture": "LRASPP",
-            "publication_year": 2019,
             "num_params": 3221538,
             "categories": _VOC_CATEGORIES,
-            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/segmentation#lraspp_mobilenet_v3_large",
             "mIoU": 57.9,
             "acc": 91.2,

torchvision/models/shufflenetv2.py

@@ -5,7 +5,7 @@ import torch
 import torch.nn as nn
 from torch import Tensor
 
-from ..transforms._presets import ImageClassification, InterpolationMode
+from ..transforms._presets import ImageClassification
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -186,11 +186,9 @@ def _shufflenetv2(
 _COMMON_META = {
     "task": "image_classification",
     "architecture": "ShuffleNetV2",
-    "publication_year": 2018,
     "size": (224, 224),
     "min_size": (1, 1),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
     "recipe": "https://github.com/barrh/Shufflenet-v2-Pytorch/tree/v0.1.0",
 }
 

torchvision/models/squeezenet.py

@@ -5,7 +5,7 @@ import torch
 import torch.nn as nn
 import torch.nn.init as init
 
-from ..transforms._presets import ImageClassification, InterpolationMode
+from ..transforms._presets import ImageClassification
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -117,10 +117,8 @@ def _squeezenet(
 _COMMON_META = {
     "task": "image_classification",
     "architecture": "SqueezeNet",
-    "publication_year": 2016,
     "size": (224, 224),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
     "recipe": "https://github.com/pytorch/vision/pull/49#issuecomment-277560717",
 }
 

torchvision/models/vgg.py

@@ -4,7 +4,7 @@ from typing import Union, List, Dict, Any, Optional, cast
 import torch
 import torch.nn as nn
 
-from ..transforms._presets import ImageClassification, InterpolationMode
+from ..transforms._presets import ImageClassification
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -109,11 +109,9 @@ def _vgg(cfg: str, batch_norm: bool, weights: Optional[WeightsEnum], progress: b
 _COMMON_META = {
     "task": "image_classification",
     "architecture": "VGG",
-    "publication_year": 2014,
     "size": (224, 224),
     "min_size": (32, 32),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
     "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
 }
 

torchvision/models/video/resnet.py

@@ -4,7 +4,7 @@ from typing import Tuple, Optional, Callable, List, Sequence, Type, Any, Union
 import torch.nn as nn
 from torch import Tensor
 
-from ...transforms._presets import VideoClassification, InterpolationMode
+from ...transforms._presets import VideoClassification
 from ...utils import _log_api_usage_once
 from .._api import WeightsEnum, Weights
 from .._meta import _KINETICS400_CATEGORIES
@@ -310,11 +310,9 @@ def _video_resnet(
 
 _COMMON_META = {
     "task": "video_classification",
-    "publication_year": 2017,
     "size": (112, 112),
     "min_size": (1, 1),
     "categories": _KINETICS400_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
     "recipe": "https://github.com/pytorch/vision/tree/main/references/video_classification",
 }
 

torchvision/models/vision_transformer.py

 import math
 from collections import OrderedDict
 from functools import partial
-from typing import Any, Callable, List, NamedTuple, Optional, Sequence
+from typing import Any, Callable, List, NamedTuple, Optional, Sequence, Dict
 
 import torch
 import torch.nn as nn
@@ -318,20 +318,16 @@ def _vision_transformer(
     return model
 
 
-_COMMON_META = {
+_COMMON_META: Dict[str, Any] = {
     "task": "image_classification",
     "architecture": "ViT",
-    "publication_year": 2020,
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
-_COMMON_SWAG_META = {
+_COMMON_SWAG_META: Dict[str, Any] = {
     **_COMMON_META,
-    "publication_year": 2022,
     "recipe": "https://github.com/facebookresearch/SWAG",
     "license": "https://github.com/facebookresearch/SWAG/blob/main/LICENSE",
-    "interpolation": InterpolationMode.BICUBIC,
 }