Adding interpolation in meta for all models and cleaning up unnecessary vars. (#4876)

Author: datumbox

torchvision/prototype/models/detection/faster_rcnn.py

@@ -1,6 +1,8 @@
 import warnings
 from typing import Any, Optional, Union
 
+from torchvision.transforms.functional import InterpolationMode
+
 from ....models.detection.faster_rcnn import (
     _mobilenet_extractor,
     _resnet_fpn_extractor,
@@ -28,7 +30,10 @@
 ]
 
 
-_common_meta = {"categories": _COCO_CATEGORIES}
+_common_meta = {
+    "categories": _COCO_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+}
 
 
 class FasterRCNNResNet50FPNWeights(Weights):

torchvision/prototype/models/detection/mask_rcnn.py

@@ -1,6 +1,8 @@
 import warnings
 from typing import Any, Optional
 
+from torchvision.transforms.functional import InterpolationMode
+
 from ....models.detection.mask_rcnn import (
     _resnet_fpn_extractor,
     _validate_trainable_layers,
@@ -27,6 +29,7 @@ class MaskRCNNResNet50FPNWeights(Weights):
         transforms=CocoEval,
         meta={
             "categories": _COCO_CATEGORIES,
+            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#mask-r-cnn",
             "box_map": 37.9,
             "mask_map": 34.6,

torchvision/prototype/models/detection/retinanet.py

@@ -1,6 +1,8 @@
 import warnings
 from typing import Any, Optional
 
+from torchvision.transforms.functional import InterpolationMode
+
 from ....models.detection.retinanet import (
     _resnet_fpn_extractor,
     _validate_trainable_layers,
@@ -28,6 +30,7 @@ class RetinaNetResNet50FPNWeights(Weights):
         transforms=CocoEval,
         meta={
             "categories": _COCO_CATEGORIES,
+            "interpolation": InterpolationMode.BILINEAR,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#retinanet",
             "map": 36.4,
         },

torchvision/prototype/models/segmentation/deeplabv3.py

@@ -2,6 +2,8 @@
 from functools import partial
 from typing import Any, Optional
 
+from torchvision.transforms.functional import InterpolationMode
+
 from ....models.segmentation.deeplabv3 import DeepLabV3, _deeplabv3_mobilenetv3, _deeplabv3_resnet
 from ...transforms.presets import VocEval
 from .._api import Weights, WeightEntry
@@ -22,7 +24,10 @@
 ]
 
 
-_common_meta = {"categories": _VOC_CATEGORIES}
+_common_meta = {
+    "categories": _VOC_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+}
 
 
 class DeepLabV3ResNet50Weights(Weights):

torchvision/prototype/models/segmentation/fcn.py

@@ -2,6 +2,8 @@
 from functools import partial
 from typing import Any, Optional
 
+from torchvision.transforms.functional import InterpolationMode
+
 from ....models.segmentation.fcn import FCN, _fcn_resnet
 from ...transforms.presets import VocEval
 from .._api import Weights, WeightEntry
@@ -12,7 +14,10 @@
 __all__ = ["FCN", "FCNResNet50Weights", "FCNResNet101Weights", "fcn_resnet50", "fcn_resnet101"]
 
 
-_common_meta = {"categories": _VOC_CATEGORIES}
+_common_meta = {
+    "categories": _VOC_CATEGORIES,
+    "interpolation": InterpolationMode.BILINEAR,
+}
 
 
 class FCNResNet50Weights(Weights):

torchvision/prototype/models/segmentation/lraspp.py

@@ -2,6 +2,8 @@
 from functools import partial
 from typing import Any, Optional
 
+from torchvision.transforms.functional import InterpolationMode
+
 from ....models.segmentation.lraspp import LRASPP, _lraspp_mobilenetv3
 from ...transforms.presets import VocEval
 from .._api import Weights, WeightEntry
@@ -18,6 +20,7 @@ class LRASPPMobileNetV3LargeWeights(Weights):
         transforms=partial(VocEval, resize_size=520),
         meta={
             "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/prototype/models/vgg.py

@@ -31,7 +31,7 @@
 ]
 
 
-def _vgg(arch: str, cfg: str, batch_norm: bool, weights: Optional[Weights], progress: bool, **kwargs: Any) -> VGG:
+def _vgg(cfg: str, batch_norm: bool, weights: Optional[Weights], progress: bool, **kwargs: Any) -> VGG:
     if weights is not None:
         kwargs["num_classes"] = len(weights.meta["categories"])
     model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
@@ -150,7 +150,7 @@ def vgg11(weights: Optional[VGG11Weights] = None, progress: bool = True, **kwarg
         weights = VGG11Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = VGG11Weights.verify(weights)
 
-    return _vgg("vgg11", "A", False, weights, progress, **kwargs)
+    return _vgg("A", False, weights, progress, **kwargs)
 
 
 def vgg11_bn(weights: Optional[VGG11BNWeights] = None, progress: bool = True, **kwargs: Any) -> VGG:
@@ -159,7 +159,7 @@ def vgg11_bn(weights: Optional[VGG11BNWeights] = None, progress: bool = True, **
         weights = VGG11BNWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = VGG11BNWeights.verify(weights)
 
-    return _vgg("vgg11_bn", "A", True, weights, progress, **kwargs)
+    return _vgg("A", True, weights, progress, **kwargs)
 
 
 def vgg13(weights: Optional[VGG13Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
@@ -168,7 +168,7 @@ def vgg13(weights: Optional[VGG13Weights] = None, progress: bool = True, **kwarg
         weights = VGG13Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = VGG13Weights.verify(weights)
 
-    return _vgg("vgg13", "B", False, weights, progress, **kwargs)
+    return _vgg("B", False, weights, progress, **kwargs)
 
 
 def vgg13_bn(weights: Optional[VGG13BNWeights] = None, progress: bool = True, **kwargs: Any) -> VGG:
@@ -177,7 +177,7 @@ def vgg13_bn(weights: Optional[VGG13BNWeights] = None, progress: bool = True, **
         weights = VGG13BNWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = VGG13BNWeights.verify(weights)
 
-    return _vgg("vgg13_bn", "B", True, weights, progress, **kwargs)
+    return _vgg("B", True, weights, progress, **kwargs)
 
 
 def vgg16(weights: Optional[VGG16Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
@@ -186,7 +186,7 @@ def vgg16(weights: Optional[VGG16Weights] = None, progress: bool = True, **kwarg
         weights = VGG16Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = VGG16Weights.verify(weights)
 
-    return _vgg("vgg16", "D", False, weights, progress, **kwargs)
+    return _vgg("D", False, weights, progress, **kwargs)
 
 
 def vgg16_bn(weights: Optional[VGG16BNWeights] = None, progress: bool = True, **kwargs: Any) -> VGG:
@@ -195,7 +195,7 @@ def vgg16_bn(weights: Optional[VGG16BNWeights] = None, progress: bool = True, **
         weights = VGG16BNWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = VGG16BNWeights.verify(weights)
 
-    return _vgg("vgg16_bn", "D", True, weights, progress, **kwargs)
+    return _vgg("D", True, weights, progress, **kwargs)
 
 
 def vgg19(weights: Optional[VGG19Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
@@ -204,7 +204,7 @@ def vgg19(weights: Optional[VGG19Weights] = None, progress: bool = True, **kwarg
         weights = VGG19Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = VGG19Weights.verify(weights)
 
-    return _vgg("vgg19", "E", False, weights, progress, **kwargs)
+    return _vgg("E", False, weights, progress, **kwargs)
 
 
 def vgg19_bn(weights: Optional[VGG19BNWeights] = None, progress: bool = True, **kwargs: Any) -> VGG:
@@ -213,4 +213,4 @@ def vgg19_bn(weights: Optional[VGG19BNWeights] = None, progress: bool = True, **
         weights = VGG19BNWeights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
     weights = VGG19BNWeights.verify(weights)
 
-    return _vgg("vgg19_bn", "E", True, weights, progress, **kwargs)
+    return _vgg("E", True, weights, progress, **kwargs)