Add MobileNetV3 architecture for Segmentation

docs/source/models.rst

@@ -271,7 +271,8 @@ The models subpackage contains definitions for the following model
 architectures for semantic segmentation:
 
 - `FCN ResNet50, ResNet101 <https://arxiv.org/abs/1411.4038>`_
-- `DeepLabV3 ResNet50, ResNet101 <https://arxiv.org/abs/1706.05587>`_
+- `DeepLabV3 ResNet50, ResNet101, MobileNetV3-Large <https://arxiv.org/abs/1706.05587>`_
+- `LR-ASPP MobileNetV3-Large <https://arxiv.org/abs/1905.02244>`_
 
 As with image classification models, all pre-trained models expect input images normalized in the same way.
 The images have to be loaded in to a range of ``[0, 1]`` and then normalized using
@@ -298,6 +299,8 @@ FCN ResNet50                      60.5           91.4
 FCN ResNet101                     63.7           91.9
 DeepLabV3 ResNet50                66.4           92.4
 DeepLabV3 ResNet101               67.4           92.4
+DeepLabV3 MobileNetV3-Large       60.3           91.2
+LR-ASPP MobileNetV3-Large         57.9           91.2
 ================================  =============  ====================
 
 
@@ -313,6 +316,13 @@ DeepLabV3
 
 .. autofunction:: torchvision.models.segmentation.deeplabv3_resnet50
 .. autofunction:: torchvision.models.segmentation.deeplabv3_resnet101
+.. autofunction:: torchvision.models.segmentation.deeplabv3_mobilenet_v3_large
+
+
+LR-ASPP
+-------
+
+.. autofunction:: torchvision.models.segmentation.lraspp_mobilenet_v3_large
 
 
 Object Detection, Instance Segmentation and Person Keypoint Detection

hubconf.py

@@ -18,4 +18,4 @@
 
 # segmentation
 from torchvision.models.segmentation import fcn_resnet50, fcn_resnet101, \
-    deeplabv3_resnet50, deeplabv3_resnet101
+    deeplabv3_resnet50, deeplabv3_resnet101, deeplabv3_mobilenet_v3_large, lraspp_mobilenet_v3_large

references/segmentation/README.md

@@ -31,3 +31,13 @@ python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --lr 0.
 ```
 python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --lr 0.02 --dataset coco -b 4 --model deeplabv3_resnet101 --aux-loss
 ```
+
+## deeplabv3_mobilenet_v3_large
+```
+python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --dataset coco -b 4 --model deeplabv3_mobilenet_v3_large --aux-loss --wd 0.000001
+```
+
+## lraspp_mobilenet_v3_large
+```
+python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --dataset coco -b 4 --model lraspp_mobilenet_v3_large --wd 0.000001
+```

test/test_models.py

@@ -61,8 +61,10 @@ def get_available_video_models():
     "wide_resnet101_2",
     "deeplabv3_resnet50",
     "deeplabv3_resnet101",
+    "deeplabv3_mobilenet_v3_large",
     "fcn_resnet50",
     "fcn_resnet101",
+    "lraspp_mobilenet_v3_large",
 )
 
 

torchvision/models/detection/backbone_utils.py

@@ -136,9 +136,9 @@ def mobilenet_backbone(
 ):
     backbone = mobilenet.__dict__[backbone_name](pretrained=pretrained, norm_layer=norm_layer).features
 
-    # Gather the indeces of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks.
+    # 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.
-    stage_indices = [0] + [i for i, b in enumerate(backbone) if getattr(b, "is_strided", False)] + [len(backbone) - 1]
+    stage_indices = [0] + [i for i, b in enumerate(backbone) if getattr(b, "_is_cn", False)] + [len(backbone) - 1]
     num_stages = len(stage_indices)
 
     # find the index of the layer from which we wont freeze

torchvision/models/mobilenetv2.py

@@ -38,14 +38,16 @@ def __init__(
         groups: int = 1,
         norm_layer: Optional[Callable[..., nn.Module]] = None,
         activation_layer: Optional[Callable[..., nn.Module]] = None,
+        dilation: int = 1,
     ) -> None:
-        padding = (kernel_size - 1) // 2
+        padding = (kernel_size - 1) // 2 * dilation
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
         if activation_layer is None:
             activation_layer = nn.ReLU6
         super(ConvBNReLU, self).__init__(
-            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups, bias=False),
+            nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, dilation=dilation, groups=groups,
+                      bias=False),
             norm_layer(out_planes),
             activation_layer(inplace=True)
         )
@@ -88,7 +90,7 @@ def __init__(
         ])
         self.conv = nn.Sequential(*layers)
         self.out_channels = oup
-        self.is_strided = stride > 1
+        self._is_cn = stride > 1
 
     def forward(self, x: Tensor) -> Tensor:
         if self.use_res_connect:

torchvision/models/mobilenetv3.py

@@ -38,14 +38,15 @@ def forward(self, input: Tensor) -> Tensor:
 class InvertedResidualConfig:
 
     def __init__(self, input_channels: int, kernel: int, expanded_channels: int, out_channels: int, use_se: bool,
-                 activation: str, stride: int, width_mult: float):
+                 activation: str, stride: int, dilation: int, width_mult: float):
         self.input_channels = self.adjust_channels(input_channels, width_mult)
         self.kernel = kernel
         self.expanded_channels = self.adjust_channels(expanded_channels, width_mult)
         self.out_channels = self.adjust_channels(out_channels, width_mult)
         self.use_se = use_se
         self.use_hs = activation == "HS"
         self.stride = stride
+        self.dilation = dilation
 
     @staticmethod
     def adjust_channels(channels: int, width_mult: float):
@@ -70,9 +71,10 @@ def __init__(self, cnf: InvertedResidualConfig, norm_layer: Callable[..., nn.Mod
                                            norm_layer=norm_layer, activation_layer=activation_layer))
 
         # depthwise
+        stride = 1 if cnf.dilation > 1 else cnf.stride
         layers.append(ConvBNActivation(cnf.expanded_channels, cnf.expanded_channels, kernel_size=cnf.kernel,
-                                       stride=cnf.stride, groups=cnf.expanded_channels, norm_layer=norm_layer,
-                                       activation_layer=activation_layer))
+                                       stride=stride, dilation=cnf.dilation, groups=cnf.expanded_channels,
+                                       norm_layer=norm_layer, activation_layer=activation_layer))
         if cnf.use_se:
             layers.append(SqueezeExcitation(cnf.expanded_channels))
 
@@ -82,7 +84,7 @@ def __init__(self, cnf: InvertedResidualConfig, norm_layer: Callable[..., nn.Mod
 
         self.block = nn.Sequential(*layers)
         self.out_channels = cnf.out_channels
-        self.is_strided = cnf.stride > 1
+        self._is_cn = cnf.stride > 1
 
     def forward(self, input: Tensor) -> Tensor:
         result = self.block(input)
@@ -194,78 +196,74 @@ def _mobilenet_v3(
     return model
 
 
-def mobilenet_v3_large(pretrained: bool = False, progress: bool = True, reduced_tail: bool = False,
-                       **kwargs: Any) -> MobileNetV3:
+def mobilenet_v3_large(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV3:
     """
     Constructs a large MobileNetV3 architecture from
     `"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
-        reduced_tail (bool): If True, reduces the channel counts of all feature layers
-            between C4 and C5 by 2. It is used to reduce the channel redundancy in the
-            backbone for Detection and Segmentation.
     """
+    # non-public config parameters
+    reduce_divider = 2 if kwargs.pop('_reduced_tail', False) else 1
+    dilation = 2 if kwargs.pop('_dilated', False) else 1
     width_mult = 1.0
+
     bneck_conf = partial(InvertedResidualConfig, width_mult=width_mult)
     adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_mult=width_mult)
 
-    reduce_divider = 2 if reduced_tail else 1
-
     inverted_residual_setting = [
-        bneck_conf(16, 3, 16, 16, False, "RE", 1),
-        bneck_conf(16, 3, 64, 24, False, "RE", 2),  # C1
-        bneck_conf(24, 3, 72, 24, False, "RE", 1),
-        bneck_conf(24, 5, 72, 40, True, "RE", 2),  # C2
-        bneck_conf(40, 5, 120, 40, True, "RE", 1),
-        bneck_conf(40, 5, 120, 40, True, "RE", 1),
-        bneck_conf(40, 3, 240, 80, False, "HS", 2),  # C3
-        bneck_conf(80, 3, 200, 80, False, "HS", 1),
-        bneck_conf(80, 3, 184, 80, False, "HS", 1),
-        bneck_conf(80, 3, 184, 80, False, "HS", 1),
-        bneck_conf(80, 3, 480, 112, True, "HS", 1),
-        bneck_conf(112, 3, 672, 112, True, "HS", 1),
-        bneck_conf(112, 5, 672, 160 // reduce_divider, True, "HS", 2),  # C4
-        bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1),
-        bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1),
+        bneck_conf(16, 3, 16, 16, False, "RE", 1, 1),
+        bneck_conf(16, 3, 64, 24, False, "RE", 2, 1),  # C1
+        bneck_conf(24, 3, 72, 24, False, "RE", 1, 1),
+        bneck_conf(24, 5, 72, 40, True, "RE", 2, 1),  # C2
+        bneck_conf(40, 5, 120, 40, True, "RE", 1, 1),
+        bneck_conf(40, 5, 120, 40, True, "RE", 1, 1),
+        bneck_conf(40, 3, 240, 80, False, "HS", 2, 1),  # C3
+        bneck_conf(80, 3, 200, 80, False, "HS", 1, 1),
+        bneck_conf(80, 3, 184, 80, False, "HS", 1, 1),
+        bneck_conf(80, 3, 184, 80, False, "HS", 1, 1),
+        bneck_conf(80, 3, 480, 112, True, "HS", 1, 1),
+        bneck_conf(112, 3, 672, 112, True, "HS", 1, 1),
+        bneck_conf(112, 5, 672, 160 // reduce_divider, True, "HS", 2, dilation),  # C4
+        bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation),
+        bneck_conf(160 // reduce_divider, 5, 960 // reduce_divider, 160 // reduce_divider, True, "HS", 1, dilation),
     ]
     last_channel = adjust_channels(1280 // reduce_divider)  # C5
 
     return _mobilenet_v3("mobilenet_v3_large", inverted_residual_setting, last_channel, pretrained, progress, **kwargs)
 
 
-def mobilenet_v3_small(pretrained: bool = False, progress: bool = True, reduced_tail: bool = False,
-                       **kwargs: Any) -> MobileNetV3:
+def mobilenet_v3_small(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> MobileNetV3:
     """
     Constructs a small MobileNetV3 architecture from
     `"Searching for MobileNetV3" <https://arxiv.org/abs/1905.02244>`_.
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
-        reduced_tail (bool): If True, reduces the channel counts of all feature layers
-            between C4 and C5 by 2. It is used to reduce the channel redundancy in the
-            backbone for Detection and Segmentation.
     """
+    # non-public config parameters
+    reduce_divider = 2 if kwargs.pop('_reduced_tail', False) else 1
+    dilation = 2 if kwargs.pop('_dilated', False) else 1
     width_mult = 1.0
+
     bneck_conf = partial(InvertedResidualConfig, width_mult=width_mult)
     adjust_channels = partial(InvertedResidualConfig.adjust_channels, width_mult=width_mult)
 
-    reduce_divider = 2 if reduced_tail else 1
-
     inverted_residual_setting = [
-        bneck_conf(16, 3, 16, 16, True, "RE", 2),  # C1
-        bneck_conf(16, 3, 72, 24, False, "RE", 2),  # C2
-        bneck_conf(24, 3, 88, 24, False, "RE", 1),
-        bneck_conf(24, 5, 96, 40, True, "HS", 2),  # C3
-        bneck_conf(40, 5, 240, 40, True, "HS", 1),
-        bneck_conf(40, 5, 240, 40, True, "HS", 1),
-        bneck_conf(40, 5, 120, 48, True, "HS", 1),
-        bneck_conf(48, 5, 144, 48, True, "HS", 1),
-        bneck_conf(48, 5, 288, 96 // reduce_divider, True, "HS", 2),  # C4
-        bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1),
-        bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1),
+        bneck_conf(16, 3, 16, 16, True, "RE", 2, 1),  # C1
+        bneck_conf(16, 3, 72, 24, False, "RE", 2, 1),  # C2
+        bneck_conf(24, 3, 88, 24, False, "RE", 1, 1),
+        bneck_conf(24, 5, 96, 40, True, "HS", 2, 1),  # C3
+        bneck_conf(40, 5, 240, 40, True, "HS", 1, 1),
+        bneck_conf(40, 5, 240, 40, True, "HS", 1, 1),
+        bneck_conf(40, 5, 120, 48, True, "HS", 1, 1),
+        bneck_conf(48, 5, 144, 48, True, "HS", 1, 1),
+        bneck_conf(48, 5, 288, 96 // reduce_divider, True, "HS", 2, dilation),  # C4
+        bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation),
+        bneck_conf(96 // reduce_divider, 5, 576 // reduce_divider, 96 // reduce_divider, True, "HS", 1, dilation),
     ]
     last_channel = adjust_channels(1024 // reduce_divider)  # C5
 

torchvision/models/segmentation/__init__.py

@@ -1,3 +1,4 @@
 from .segmentation import *
 from .fcn import *
 from .deeplabv3 import *
+from .lraspp import *

torchvision/models/segmentation/_utils.py

@@ -1,6 +1,5 @@
 from collections import OrderedDict
 
-import torch
 from torch import nn
 from torch.nn import functional as F
 

torchvision/models/segmentation/lraspp.py

@@ -0,0 +1,69 @@
+from collections import OrderedDict
+
+from torch import nn, Tensor
+from torch.nn import functional as F
+from typing import Dict
+
+
+__all__ = ["LRASPP"]
+
+
+class LRASPP(nn.Module):
+    """
+    Implements a Lite R-ASPP Network for semantic segmentation from
+    `"Searching for MobileNetV3"
+    <https://arxiv.org/abs/1905.02244>`_.
+
+    Args:
+        backbone (nn.Module): the network used to compute the features for the model.
+            The backbone should return an OrderedDict[Tensor], with the key being
+            "high" for the high level feature map and "low" for the low level feature map.
+        low_channels (int): the number of channels of the low level features.
+        high_channels (int): the number of channels of the high level features.
+        num_classes (int): number of output classes of the model (including the background).
+        inter_channels (int, optional): the number of channels for intermediate computations.
+    """
+
+    def __init__(self, backbone, low_channels, high_channels, num_classes, inter_channels=128):
+        super().__init__()
+        self.backbone = backbone
+        self.classifier = LRASPPHead(low_channels, high_channels, num_classes, inter_channels)
+
+    def forward(self, input):
+        features = self.backbone(input)
+        out = self.classifier(features)
+        out = F.interpolate(out, size=input.shape[-2:], mode='bilinear', align_corners=False)
+
+        result = OrderedDict()
+        result["out"] = out
+
+        return result
+
+
+class LRASPPHead(nn.Module):
+
+    def __init__(self, low_channels, high_channels, num_classes, inter_channels):
+        super().__init__()
+        self.cbr = nn.Sequential(
+            nn.Conv2d(high_channels, inter_channels, 1, bias=False),
+            nn.BatchNorm2d(inter_channels),
+            nn.ReLU(inplace=True)
+        )
+        self.scale = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1),
+            nn.Conv2d(high_channels, inter_channels, 1, bias=False),
+            nn.Sigmoid(),
+        )
+        self.low_classifier = nn.Conv2d(low_channels, num_classes, 1)
+        self.high_classifier = nn.Conv2d(inter_channels, num_classes, 1)
+
+    def forward(self, input: Dict[str, Tensor]) -> Tensor:
+        low = input["low"]
+        high = input["high"]
+
+        x = self.cbr(high)
+        s = self.scale(high)
+        x = x * s
+        x = F.interpolate(x, size=low.shape[-2:], mode='bilinear', align_corners=False)
+
+        return self.low_classifier(low) + self.high_classifier(x)