Making protected params of MobileNetV3 public

torchvision/models/detection/ssdlite.py

@@ -95,11 +95,9 @@ def __init__(self, in_channels: List[int], num_anchors: List[int], norm_layer: C
 
 
 class SSDLiteFeatureExtractorMobileNet(nn.Module):
-    def __init__(self, backbone: nn.Module, c4_pos: int, norm_layer: Callable[..., nn.Module], **kwargs: Any):
+    def __init__(self, backbone: nn.Module, c4_pos: int, norm_layer: Callable[..., nn.Module], width_mult: float = 1.0,
+                 min_depth: int = 16, **kwargs: Any):
         super().__init__()
-        # non-public config parameters
-        min_depth = kwargs.pop('_min_depth', 16)
-        width_mult = kwargs.pop('_width_mult', 1.0)
 
         assert not backbone[c4_pos].use_res_connect
         self.features = nn.Sequential(
@@ -197,7 +195,7 @@ def ssdlite320_mobilenet_v3_large(pretrained: bool = False, progress: bool = Tru
         norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.03)
 
     backbone = _mobilenet_extractor("mobilenet_v3_large", progress, pretrained_backbone, trainable_backbone_layers,
-                                    norm_layer, _reduced_tail=reduce_tail, _width_mult=1.0)
+                                    norm_layer, reduced_tail=reduce_tail, **kwargs)
 
     size = (320, 320)
     anchor_generator = DefaultBoxGenerator([[2, 3] for _ in range(6)], min_ratio=0.2, max_ratio=0.95)

torchvision/models/mobilenetv3.py

@@ -106,7 +106,8 @@ def __init__(
             last_channel: int,
             num_classes: int = 1000,
             block: Optional[Callable[..., nn.Module]] = None,
-            norm_layer: Optional[Callable[..., nn.Module]] = None
+            norm_layer: Optional[Callable[..., nn.Module]] = None,
+            **kwargs: Any
     ) -> None:
         """
         MobileNet V3 main class
@@ -184,11 +185,10 @@ def forward(self, x: Tensor) -> Tensor:
         return self._forward_impl(x)
 
 
-def _mobilenet_v3_conf(arch: str, params: Dict[str, Any]):
-    # non-public config parameters
-    reduce_divider = 2 if params.pop('_reduced_tail', False) else 1
-    dilation = 2 if params.pop('_dilated', False) else 1
-    width_mult = params.pop('_width_mult', 1.0)
+def _mobilenet_v3_conf(arch: str, width_mult: float = 1.0, reduced_tail: bool = False, dilated: bool = False,
+                       **kwargs: Any):
+    reduce_divider = 2 if reduced_tail else 1
+    dilation = 2 if dilated else 1
 
     bneck_conf = partial(InvertedResidualConfig, width_mult=width_mult)
     adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_mult=width_mult)
@@ -260,7 +260,7 @@ def mobilenet_v3_large(pretrained: bool = False, progress: bool = True, **kwargs
         progress (bool): If True, displays a progress bar of the download to stderr
     """
     arch = "mobilenet_v3_large"
-    inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, kwargs)
+    inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs)
     return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, **kwargs)
 
 
@@ -274,5 +274,5 @@ def mobilenet_v3_small(pretrained: bool = False, progress: bool = True, **kwargs
         progress (bool): If True, displays a progress bar of the download to stderr
     """
     arch = "mobilenet_v3_small"
-    inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, kwargs)
+    inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs)
     return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, **kwargs)

torchvision/models/quantization/mobilenetv3.py

@@ -127,5 +127,5 @@ def mobilenet_v3_large(pretrained=False, progress=True, quantize=False, **kwargs
      quantize (bool): If True, returns a quantized model, else returns a float model
     """
     arch = "mobilenet_v3_large"
-    inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, kwargs)
+    inverted_residual_setting, last_channel = _mobilenet_v3_conf(arch, **kwargs)
     return _mobilenet_v3_model(arch, inverted_residual_setting, last_channel, pretrained, progress, quantize, **kwargs)

torchvision/models/segmentation/segmentation.py

@@ -32,7 +32,7 @@ def _segm_model(name, backbone_name, num_classes, aux, pretrained_backbone=True)
         aux_layer = 'layer3'
         aux_inplanes = 1024
     elif 'mobilenet_v3' in backbone_name:
-        backbone = mobilenetv3.__dict__[backbone_name](pretrained=pretrained_backbone, _dilated=True).features
+        backbone = mobilenetv3.__dict__[backbone_name](pretrained=pretrained_backbone, dilated=True).features
 
         # Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks.
         # The first and last blocks are always included because they are the C0 (conv1) and Cn.
@@ -87,7 +87,7 @@ def _load_weights(model, arch_type, backbone, progress):
 
 
 def _segm_lraspp_mobilenetv3(backbone_name, num_classes, pretrained_backbone=True):
-    backbone = mobilenetv3.__dict__[backbone_name](pretrained=pretrained_backbone, _dilated=True).features
+    backbone = mobilenetv3.__dict__[backbone_name](pretrained=pretrained_backbone, dilated=True).features
 
     # Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks.
     # The first and last blocks are always included because they are the C0 (conv1) and Cn.