Multiweight DenseNet prototype models (pytorch#4680)

* Densenet121 added

* All densenet prototypes added

* fixing flake8 errors

* fixing argument type

Author: jdsgomes

torchvision/prototype/models/__init__.py

@@ -1,5 +1,6 @@
 from .alexnet import *
 from .resnet import *
+from .densenet import *
 from .vgg import *
 from . import detection
 from . import quantization

torchvision/prototype/models/densenet.py

@@ -0,0 +1,156 @@
+import re
+import warnings
+from functools import partial
+from typing import Any, Optional, Tuple
+
+import torch.nn as nn
+
+from ...models.densenet import DenseNet
+from ..transforms.presets import ImageNetEval
+from ._api import Weights, WeightEntry
+from ._meta import _IMAGENET_CATEGORIES
+
+
+__all__ = [
+    "DenseNet",
+    "DenseNet121Weights",
+    "DenseNet161Weights",
+    "DenseNet169Weights",
+    "DenseNet201Weights",
+    "densenet121",
+    "densenet161",
+    "densenet169",
+    "densenet201",
+]
+
+
+def _load_state_dict(model: nn.Module, weights: Weights, progress: bool) -> None:
+    # '.'s are no longer allowed in module names, but previous _DenseLayer
+    # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
+    # They are also in the checkpoints in model_urls. This pattern is used
+    # to find such keys.
+    pattern = re.compile(
+        r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
+    )
+
+    state_dict = weights.state_dict(progress=progress)
+    for key in list(state_dict.keys()):
+        res = pattern.match(key)
+        if res:
+            new_key = res.group(1) + res.group(2)
+            state_dict[new_key] = state_dict[key]
+            del state_dict[key]
+    model.load_state_dict(state_dict)
+
+
+def _densenet(
+    growth_rate: int,
+    block_config: Tuple[int, int, int, int],
+    num_init_features: int,
+    weights: Optional[Weights],
+    progress: bool,
+    **kwargs: Any,
+) -> DenseNet:
+    if weights is not None:
+        kwargs["num_classes"] = len(weights.meta["categories"])
+
+    model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
+
+    if weights is not None:
+        _load_state_dict(model=model, weights=weights, progress=progress)
+
+    return model
+
+
+_common_meta = {
+    "size": (224, 224),
+    "categories": _IMAGENET_CATEGORIES,
+}
+
+
+class DenseNet121Weights(Weights):
+    ImageNet1K_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "",
+            "acc@1": 74.434,
+            "acc@5": 91.972,
+        },
+    )
+
+
+class DenseNet161Weights(Weights):
+    ImageNet1K_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/densenet161-8d451a50.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "",
+            "acc@1": 77.138,
+            "acc@5": 93.560,
+        },
+    )
+
+
+class DenseNet169Weights(Weights):
+    ImageNet1K_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/densenet169-b2777c0a.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "",
+            "acc@1": 75.600,
+            "acc@5": 92.806,
+        },
+    )
+
+
+class DenseNet201Weights(Weights):
+    ImageNet1K_RefV1 = WeightEntry(
+        url="https://download.pytorch.org/models/densenet201-c1103571.pth",
+        transforms=partial(ImageNetEval, crop_size=224),
+        meta={
+            **_common_meta,
+            "recipe": "",
+            "acc@1": 76.896,
+            "acc@5": 93.370,
+        },
+    )
+
+
+def densenet121(weights: Optional[DenseNet121Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = DenseNet121Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+    weights = DenseNet121Weights.verify(weights)
+
+    return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs)
+
+
+def densenet161(weights: Optional[DenseNet161Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = DenseNet161Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+    weights = DenseNet161Weights.verify(weights)
+
+    return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs)
+
+
+def densenet169(weights: Optional[DenseNet169Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = DenseNet169Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+    weights = DenseNet169Weights.verify(weights)
+
+    return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs)
+
+
+def densenet201(weights: Optional[DenseNet201Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
+    if "pretrained" in kwargs:
+        warnings.warn("The argument pretrained is deprecated, please use weights instead.")
+        weights = DenseNet201Weights.ImageNet1K_RefV1 if kwargs.pop("pretrained") else None
+    weights = DenseNet201Weights.verify(weights)
+
+    return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)