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>

test/test_extended_models.py

@@ -81,7 +81,7 @@ def test_naming_conventions(model_fn):
 def test_schema_meta_validation(model_fn):
     classification_fields = ["size", "categories", "acc@1", "acc@5", "min_size"]
     defaults = {
-        "all": ["task", "architecture", "publication_year", "interpolation", "recipe", "num_params"],
+        "all": ["task", "architecture", "recipe", "num_params"],
         "models": classification_fields,
         "detection": ["categories", "map"],
         "quantization": classification_fields + ["backend", "quantization", "unquantized"],

torchvision/models/alexnet.py

@@ -4,7 +4,7 @@ from typing import Any, Optional
 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
@@ -59,12 +59,10 @@ class AlexNet_Weights(WeightsEnum):
         meta={
             "task": "image_classification",
             "architecture": "AlexNet",
-            "publication_year": 2012,
             "num_params": 61100840,
             "size": (224, 224),
             "min_size": (63, 63),
             "categories": _IMAGENET_CATEGORIES,
-            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
             "acc@1": 56.522,
             "acc@5": 79.066,

torchvision/models/convnext.py

@@ -7,7 +7,7 @@ from torch.nn import functional as F
 
 from ..ops.misc import Conv2dNormActivation
 from ..ops.stochastic_depth import StochasticDepth
-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
@@ -206,11 +206,9 @@ def _convnext(
 _COMMON_META = {
     "task": "image_classification",
     "architecture": "ConvNeXt",
-    "publication_year": 2022,
     "size": (224, 224),
     "min_size": (32, 32),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
     "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#convnext",
 }
 

torchvision/models/densenet.py

@@ -9,7 +9,7 @@ import torch.nn.functional as F
 import torch.utils.checkpoint as cp
 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
@@ -268,11 +268,9 @@ def _densenet(
 _COMMON_META = {
     "task": "image_classification",
     "architecture": "DenseNet",
-    "publication_year": 2016,
     "size": (224, 224),
     "min_size": (29, 29),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
     "recipe": "https://github.com/pytorch/vision/pull/116",
 }
 

torchvision/models/detection/faster_rcnn.py

@@ -6,7 +6,7 @@ from torch import nn
 from torchvision.ops import MultiScaleRoIAlign
 
 from ...ops import misc as misc_nn_ops
-from ...transforms._presets import ObjectDetection, InterpolationMode
+from ...transforms._presets import ObjectDetection
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_value_param
@@ -372,9 +372,7 @@ class FastRCNNPredictor(nn.Module):
 _COMMON_META = {
     "task": "image_object_detection",
     "architecture": "FasterRCNN",
-    "publication_year": 2015,
     "categories": _COCO_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
 
@@ -398,7 +396,6 @@ class FasterRCNN_ResNet50_FPN_V2_Weights(WeightsEnum):
         transforms=ObjectDetection,
         meta={
             **_COMMON_META,
-            "publication_year": 2021,
             "num_params": 43712278,
             "recipe": "https://github.com/pytorch/vision/pull/5763",
             "map": 46.7,

torchvision/models/detection/fcos.py

@@ -11,7 +11,7 @@ from ...ops import sigmoid_focal_loss, generalized_box_iou_loss
 from ...ops import boxes as box_ops
 from ...ops import misc as misc_nn_ops
 from ...ops.feature_pyramid_network import LastLevelP6P7
-from ...transforms._presets import ObjectDetection, InterpolationMode
+from ...transforms._presets import ObjectDetection
 from ...utils import _log_api_usage_once
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
@@ -653,10 +653,8 @@ class FCOS_ResNet50_FPN_Weights(WeightsEnum):
         meta={
             "task": "image_object_detection",
             "architecture": "FCOS",
-            "publication_year": 2019,
             "num_params": 32269600,
             "categories": _COCO_CATEGORIES,
-            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#fcos-resnet-50-fpn",
             "map": 39.2,
         },

torchvision/models/detection/keypoint_rcnn.py

@@ -5,7 +5,7 @@ from torch import nn
 from torchvision.ops import MultiScaleRoIAlign
 
 from ...ops import misc as misc_nn_ops
-from ...transforms._presets import ObjectDetection, InterpolationMode
+from ...transforms._presets import ObjectDetection
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_PERSON_CATEGORIES, _COCO_PERSON_KEYPOINT_NAMES
 from .._utils import handle_legacy_interface, _ovewrite_value_param
@@ -310,10 +310,8 @@ class KeypointRCNNPredictor(nn.Module):
 _COMMON_META = {
     "task": "image_object_detection",
     "architecture": "KeypointRCNN",
-    "publication_year": 2017,
     "categories": _COCO_PERSON_CATEGORIES,
     "keypoint_names": _COCO_PERSON_KEYPOINT_NAMES,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
 

torchvision/models/detection/mask_rcnn.py

@@ -5,7 +5,7 @@ from torch import nn
 from torchvision.ops import MultiScaleRoIAlign
 
 from ...ops import misc as misc_nn_ops
-from ...transforms._presets import ObjectDetection, InterpolationMode
+from ...transforms._presets import ObjectDetection
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_value_param
@@ -354,7 +354,6 @@ _COMMON_META = {
     "task": "image_object_detection",
     "architecture": "MaskRCNN",
     "categories": _COCO_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
 
@@ -364,7 +363,6 @@ class MaskRCNN_ResNet50_FPN_Weights(WeightsEnum):
         transforms=ObjectDetection,
         meta={
             **_COMMON_META,
-            "publication_year": 2017,
             "num_params": 44401393,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#mask-r-cnn",
             "map": 37.9,
@@ -380,7 +378,6 @@ class MaskRCNN_ResNet50_FPN_V2_Weights(WeightsEnum):
         transforms=ObjectDetection,
         meta={
             **_COMMON_META,
-            "publication_year": 2021,
             "num_params": 46359409,
             "recipe": "https://github.com/pytorch/vision/pull/5773",
             "map": 47.4,

torchvision/models/detection/retinanet.py

@@ -11,7 +11,7 @@ from ...ops import sigmoid_focal_loss
 from ...ops import boxes as box_ops
 from ...ops import misc as misc_nn_ops
 from ...ops.feature_pyramid_network import LastLevelP6P7
-from ...transforms._presets import ObjectDetection, InterpolationMode
+from ...transforms._presets import ObjectDetection
 from ...utils import _log_api_usage_once
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
@@ -677,7 +677,6 @@ _COMMON_META = {
     "task": "image_object_detection",
     "architecture": "RetinaNet",
     "categories": _COCO_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
 
@@ -687,7 +686,6 @@ class RetinaNet_ResNet50_FPN_Weights(WeightsEnum):
         transforms=ObjectDetection,
         meta={
             **_COMMON_META,
-            "publication_year": 2017,
             "num_params": 34014999,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#retinanet",
             "map": 36.4,
@@ -702,7 +700,6 @@ class RetinaNet_ResNet50_FPN_V2_Weights(WeightsEnum):
         transforms=ObjectDetection,
         meta={
             **_COMMON_META,
-            "publication_year": 2019,
             "num_params": 38198935,
             "recipe": "https://github.com/pytorch/vision/pull/5756",
             "map": 41.5,

torchvision/models/detection/ssd.py

@@ -7,7 +7,7 @@ import torch.nn.functional as F
 from torch import nn, Tensor
 
 from ...ops import boxes as box_ops
-from ...transforms._presets import ObjectDetection, InterpolationMode
+from ...transforms._presets import ObjectDetection
 from ...utils import _log_api_usage_once
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
@@ -32,11 +32,9 @@ class SSD300_VGG16_Weights(WeightsEnum):
         meta={
             "task": "image_object_detection",
             "architecture": "SSD",
-            "publication_year": 2015,
             "num_params": 35641826,
             "size": (300, 300),
             "categories": _COCO_CATEGORIES,
-            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssd300-vgg16",
             "map": 25.1,
         },

torchvision/models/detection/ssdlite.py

@@ -7,7 +7,7 @@ import torch
 from torch import nn, Tensor
 
 from ...ops.misc import Conv2dNormActivation
-from ...transforms._presets import ObjectDetection, InterpolationMode
+from ...transforms._presets import ObjectDetection
 from ...utils import _log_api_usage_once
 from .. import mobilenet
 from .._api import WeightsEnum, Weights
@@ -191,11 +191,9 @@ class SSDLite320_MobileNet_V3_Large_Weights(WeightsEnum):
         meta={
             "task": "image_object_detection",
             "architecture": "SSDLite",
-            "publication_year": 2018,
             "num_params": 3440060,
             "size": (320, 320),
             "categories": _COCO_CATEGORIES,
-            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssdlite320-mobilenetv3-large",
             "map": 21.3,
         },

torchvision/models/efficientnet.py

@@ -439,8 +439,6 @@ _COMMON_META = {
 _COMMON_META_V1 = {
     **_COMMON_META,
     "architecture": "EfficientNet",
-    "publication_year": 2019,
-    "interpolation": InterpolationMode.BICUBIC,
     "min_size": (1, 1),
 }
 
@@ -448,8 +446,6 @@ _COMMON_META_V1 = {
 _COMMON_META_V2 = {
     **_COMMON_META,
     "architecture": "EfficientNetV2",
-    "publication_year": 2021,
-    "interpolation": InterpolationMode.BILINEAR,
     "min_size": (33, 33),
 }
 
@@ -494,7 +490,6 @@ class EfficientNet_B1_Weights(WeightsEnum):
             **_COMMON_META_V1,
             "num_params": 7794184,
             "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-lr-wd-crop-tuning",
-            "interpolation": InterpolationMode.BILINEAR,
             "size": (240, 240),
             "acc@1": 79.838,
             "acc@5": 94.934,

torchvision/models/googlenet.py

@@ -8,7 +8,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 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
@@ -282,12 +282,10 @@ class GoogLeNet_Weights(WeightsEnum):
         meta={
             "task": "image_classification",
             "architecture": "GoogLeNet",
-            "publication_year": 2014,
             "num_params": 6624904,
             "size": (224, 224),
             "min_size": (15, 15),
             "categories": _IMAGENET_CATEGORIES,
-            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#googlenet",
             "acc@1": 69.778,
             "acc@5": 89.530,

torchvision/models/inception.py

@@ -7,7 +7,7 @@ import torch
 import torch.nn.functional as F
 from torch import nn, 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
@@ -414,12 +414,10 @@ class Inception_V3_Weights(WeightsEnum):
         meta={
             "task": "image_classification",
             "architecture": "InceptionV3",
-            "publication_year": 2015,
             "num_params": 27161264,
             "size": (299, 299),
             "min_size": (75, 75),
             "categories": _IMAGENET_CATEGORIES,
-            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#inception-v3",
             "acc@1": 77.294,
             "acc@5": 93.450,

torchvision/models/mnasnet.py

@@ -6,7 +6,7 @@ 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
@@ -214,11 +214,9 @@ class MNASNet(torch.nn.Module):
 _COMMON_META = {
     "task": "image_classification",
     "architecture": "MNASNet",
-    "publication_year": 2018,
     "size": (224, 224),
     "min_size": (1, 1),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
     "recipe": "https://github.com/1e100/mnasnet_trainer",
 }
 

torchvision/models/mobilenetv2.py

@@ -7,7 +7,7 @@ from torch import Tensor
 from torch import nn
 
 from ..ops.misc import Conv2dNormActivation
-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
@@ -197,12 +197,10 @@ class MobileNetV2(nn.Module):
 _COMMON_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,
 }
 
 

torchvision/models/mobilenetv3.py

@@ -6,7 +6,7 @@ import torch
 from torch import nn, Tensor
 
 from ..ops.misc import Conv2dNormActivation, SqueezeExcitation as SElayer
-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
@@ -306,11 +306,9 @@ def _mobilenet_v3(
 _COMMON_META = {
     "task": "image_classification",
     "architecture": "MobileNetV3",
-    "publication_year": 2019,
     "size": (224, 224),
     "min_size": (1, 1),
     "categories": _IMAGENET_CATEGORIES,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
 

torchvision/models/optical_flow/raft.py

@@ -8,7 +8,7 @@ from torch.nn.modules.batchnorm import BatchNorm2d
 from torch.nn.modules.instancenorm import InstanceNorm2d
 from torchvision.ops import Conv2dNormActivation
 
-from ...transforms._presets import OpticalFlow, InterpolationMode
+from ...transforms._presets import OpticalFlow
 from ...utils import _log_api_usage_once
 from .._api import Weights, WeightsEnum
 from .._utils import handle_legacy_interface
@@ -514,8 +514,6 @@ class RAFT(nn.Module):
 _COMMON_META = {
     "task": "optical_flow",
     "architecture": "RAFT",
-    "publication_year": 2020,
-    "interpolation": InterpolationMode.BILINEAR,
 }
 
 

torchvision/models/quantization/googlenet.py

@@ -7,7 +7,7 @@ import torch.nn as nn
 from torch import Tensor
 from torch.nn import functional as F
 
-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
@@ -113,12 +113,10 @@ class GoogLeNet_QuantizedWeights(WeightsEnum):
         meta={
             "task": "image_classification",
             "architecture": "GoogLeNet",
-            "publication_year": 2014,
             "num_params": 6624904,
             "size": (224, 224),
             "min_size": (15, 15),
             "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/quantization/inception.py

@@ -9,7 +9,7 @@ from torch import Tensor
 from torchvision.models import inception as inception_module
 from torchvision.models.inception import InceptionOutputs, Inception_V3_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
@@ -179,12 +179,10 @@ class Inception_V3_QuantizedWeights(WeightsEnum):
         meta={
             "task": "image_classification",
             "architecture": "InceptionV3",
-            "publication_year": 2015,
             "num_params": 27161264,
             "size": (299, 299),
             "min_size": (75, 75),
             "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",