Adding multiweight support to SSD. (#4881)

torchvision/prototype/models/detection/__init__.py

@@ -2,4 +2,5 @@ from .faster_rcnn import *
 from .keypoint_rcnn import *
 from .mask_rcnn import *
 from .retinanet import *
+from .ssd import *
 from .ssdlite import *

torchvision/prototype/models/detection/ssd.py

+import warnings
+from typing import Any, Optional
+
+from torchvision.transforms.functional import InterpolationMode
+
+from ....models.detection.ssd import (
+    _validate_trainable_layers,
+    _vgg_extractor,
+    DefaultBoxGenerator,
+    SSD,
+)
+from ...transforms.presets import CocoEval
+from .._api import Weights, WeightEntry
+from .._meta import _COCO_CATEGORIES
+from ..vgg import VGG16Weights, vgg16
+
+
+__all__ = [
+    "SSD300VGG16Weights",
+    "ssd300_vgg16",
+]
+
+
+class SSD300VGG16Weights(Weights):
+    Coco_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/ssd300_vgg16_coco-b556d3b4.pth",
+        transforms=CocoEval,
+        meta={
+            "size": (300, 300),
+            "categories": _COCO_CATEGORIES,
+            "interpolation": InterpolationMode.BILINEAR,
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/detection#ssd300-vgg16",
+            "map": 25.1,
+        },
+    )
+
+
+def ssd300_vgg16(
+    weights: Optional[SSD300VGG16Weights] = None,
+    weights_backbone: Optional[VGG16Weights] = None,
+    progress: bool = True,
+    num_classes: int = 91,
+    trainable_backbone_layers: Optional[int] = None,
+    **kwargs: Any,
+) -> SSD:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = SSD300VGG16Weights.Coco_RefV1 if kwargs.pop("pretrained") else None
+    weights = SSD300VGG16Weights.verify(weights)
+    if "pretrained_backbone" in kwargs:
+        warnings.warn("The argument pretrained_backbone is deprecated, please use weights_backbone instead.")
+        weights_backbone = VGG16Weights.ImageNet1K_Features if kwargs.pop("pretrained_backbone") else None
+    weights_backbone = VGG16Weights.verify(weights_backbone)
+
+    if "size" in kwargs:
+        warnings.warn("The size of the model is already fixed; ignoring the argument.")
+
+    if weights is not None:
+        weights_backbone = None
+        num_classes = len(weights.meta["categories"])
+
+    trainable_backbone_layers = _validate_trainable_layers(
+        weights is not None or weights_backbone is not None, trainable_backbone_layers, 5, 4
+    )
+
+    # Use custom backbones more appropriate for SSD
+    backbone = vgg16(weights=weights_backbone, progress=progress)
+    backbone = _vgg_extractor(backbone, False, trainable_backbone_layers)
+    anchor_generator = DefaultBoxGenerator(
+        [[2], [2, 3], [2, 3], [2, 3], [2], [2]],
+        scales=[0.07, 0.15, 0.33, 0.51, 0.69, 0.87, 1.05],
+        steps=[8, 16, 32, 64, 100, 300],
+    )
+
+    defaults = {
+        # Rescale the input in a way compatible to the backbone
+        "image_mean": [0.48235, 0.45882, 0.40784],
+        "image_std": [1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0],  # undo the 0-1 scaling of toTensor
+    }
+    kwargs: Any = {**defaults, **kwargs}
+    model = SSD(backbone, anchor_generator, (300, 300), num_classes, **kwargs)
+
+    if weights is not None:
+        model.load_state_dict(weights.state_dict(progress=progress))
+
+    return model

torchvision/prototype/models/vgg.py

@@ -106,6 +106,23 @@ class VGG16Weights(Weights):
             "acc@5": 90.382,
         },
     )
+    # 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. Only the `features` weights have proper values, those on the
+    # `classifier` module are filled with nans.
+    ImageNet1K_Features = WeightEntry(
+        url="https://download.pytorch.org/models/vgg16_features-amdegroot-88682ab5.pth",
+        transforms=partial(
+            ImageNetEval, crop_size=224, mean=(0.48235, 0.45882, 0.40784), std=(1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0)
+        ),
+        meta={
+            "size": (224, 224),
+            "categories": None,
+            "interpolation": InterpolationMode.BILINEAR,
+            "recipe": "https://github.com/amdegroot/ssd.pytorch#training-ssd",
+            "acc@1": float("nan"),
+            "acc@5": float("nan"),
+        },
+    )
 
 
 class VGG16BNWeights(Weights):