[Feature] Support MobileNetV2 backbone (#86)

* [Feature] Support MobileNetV2 backbone

* Fixed import

* Fixed test

* Fixed test

* Fixed dilate

* upload model

* update table

* update table

* update bibtex

* update MMCV requirement

configs/mobilenet_v2/README.md

@@ -0,0 +1,32 @@
+# MobileNetV2: Inverted Residuals and Linear Bottlenecks
+
+## Introduction
+
+```
+@inproceedings{sandler2018mobilenetv2,
+  title={Mobilenetv2: Inverted residuals and linear bottlenecks},
+  author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={4510--4520},
+  year={2018}
+}
+```
+
+
+## Results and models
+
+### Cityscapes
+|   Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) |                                                                                                                                                                                                              download                                                                                                                                                                                                              |
+|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| FCN        | M-V2-D8  | 512x1024  |   80000 |      3.4 | 14.2           | 61.54 | -             | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json)                                         |
+| PSPNet     | M-V2-D8  | 512x1024  |   80000 |      3.6  | 11.2           | 70.23 | -             | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json)                             |
+| DeepLabV3  | M-V2-D8  | 512x1024  |   80000 |      3.9 | 8.4            | 73.84 | -             | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json)                 |
+| DeepLabV3+ | M-V2-D8  | 512x1024  |   80000 |      5.1 | 8.4            | 75.20 | -             | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json) |
+
+### ADE20k
+|   Method   | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU  | mIoU(ms+flip) |                                                                                                                                                                                                      download                                                                                                                                                                                                      |
+|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| FCN        | M-V2-D8  | 512x512   |  160000 |      6.5 | 64.4           | 19.71 | -             | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json)                                         |
+| PSPNet     | M-V2-D8  | 512x512   |  160000 |      6.5 | 57.7           | 29.68 | -             | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json)                             |
+| DeepLabV3  | M-V2-D8  | 512x512   |  160000 |      6.8 | 39.9           | 34.08 | -             | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json)                 |
+| DeepLabV3+ | M-V2-D8  | 512x512   |  160000 |      8.2 | 43.1           | 34.02 | -             | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json) |

configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py

@@ -0,0 +1,12 @@
+_base_ = '../deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py'
+model = dict(
+    pretrained='mmcls://mobilenet_v2',
+    backbone=dict(
+        _delete_=True,
+        type='MobileNetV2',
+        widen_factor=1.,
+        strides=(1, 2, 2, 1, 1, 1, 1),
+        dilations=(1, 1, 1, 2, 2, 4, 4),
+        out_indices=(1, 2, 4, 6)),
+    decode_head=dict(in_channels=320),
+    auxiliary_head=dict(in_channels=96))

configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py

@@ -0,0 +1,12 @@
+_base_ = '../deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py'
+model = dict(
+    pretrained='mmcls://mobilenet_v2',
+    backbone=dict(
+        _delete_=True,
+        type='MobileNetV2',
+        widen_factor=1.,
+        strides=(1, 2, 2, 1, 1, 1, 1),
+        dilations=(1, 1, 1, 2, 2, 4, 4),
+        out_indices=(1, 2, 4, 6)),
+    decode_head=dict(in_channels=320),
+    auxiliary_head=dict(in_channels=96))

configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py

@@ -0,0 +1,12 @@
+_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py'
+model = dict(
+    pretrained='mmcls://mobilenet_v2',
+    backbone=dict(
+        _delete_=True,
+        type='MobileNetV2',
+        widen_factor=1.,
+        strides=(1, 2, 2, 1, 1, 1, 1),
+        dilations=(1, 1, 1, 2, 2, 4, 4),
+        out_indices=(1, 2, 4, 6)),
+    decode_head=dict(in_channels=320, c1_in_channels=24),
+    auxiliary_head=dict(in_channels=96))

configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py

@@ -0,0 +1,12 @@
+_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py'
+model = dict(
+    pretrained='mmcls://mobilenet_v2',
+    backbone=dict(
+        _delete_=True,
+        type='MobileNetV2',
+        widen_factor=1.,
+        strides=(1, 2, 2, 1, 1, 1, 1),
+        dilations=(1, 1, 1, 2, 2, 4, 4),
+        out_indices=(1, 2, 4, 6)),
+    decode_head=dict(in_channels=320, c1_in_channels=24),
+    auxiliary_head=dict(in_channels=96))

configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py

@@ -0,0 +1,12 @@
+_base_ = '../fcn/fcn_r101-d8_512x1024_80k_cityscapes.py'
+model = dict(
+    pretrained='mmcls://mobilenet_v2',
+    backbone=dict(
+        _delete_=True,
+        type='MobileNetV2',
+        widen_factor=1.,
+        strides=(1, 2, 2, 1, 1, 1, 1),
+        dilations=(1, 1, 1, 2, 2, 4, 4),
+        out_indices=(1, 2, 4, 6)),
+    decode_head=dict(in_channels=320),
+    auxiliary_head=dict(in_channels=96))

configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py

@@ -0,0 +1,12 @@
+_base_ = '../fcn/fcn_r101-d8_512x512_160k_ade20k.py'
+model = dict(
+    pretrained='mmcls://mobilenet_v2',
+    backbone=dict(
+        _delete_=True,
+        type='MobileNetV2',
+        widen_factor=1.,
+        strides=(1, 2, 2, 1, 1, 1, 1),
+        dilations=(1, 1, 1, 2, 2, 4, 4),
+        out_indices=(1, 2, 4, 6)),
+    decode_head=dict(in_channels=320),
+    auxiliary_head=dict(in_channels=96))

configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py

@@ -0,0 +1,12 @@
+_base_ = '../pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py'
+model = dict(
+    pretrained='mmcls://mobilenet_v2',
+    backbone=dict(
+        _delete_=True,
+        type='MobileNetV2',
+        widen_factor=1.,
+        strides=(1, 2, 2, 1, 1, 1, 1),
+        dilations=(1, 1, 1, 2, 2, 4, 4),
+        out_indices=(1, 2, 4, 6)),
+    decode_head=dict(in_channels=320),
+    auxiliary_head=dict(in_channels=96))

configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py

@@ -0,0 +1,12 @@
+_base_ = '../pspnet/pspnet_r101-d8_512x512_160k_ade20k.py'
+model = dict(
+    pretrained='mmcls://mobilenet_v2',
+    backbone=dict(
+        _delete_=True,
+        type='MobileNetV2',
+        widen_factor=1.,
+        strides=(1, 2, 2, 1, 1, 1, 1),
+        dilations=(1, 1, 1, 2, 2, 4, 4),
+        out_indices=(1, 2, 4, 6)),
+    decode_head=dict(in_channels=320),
+    auxiliary_head=dict(in_channels=96))

mmseg/__init__.py

@@ -2,8 +2,8 @@ import mmcv
 
 from .version import __version__, version_info
 
-MMCV_MIN = '1.0.5'
+MMCV_MIN = '1.1.2'
-MMCV_MAX = '1.1.1'
+MMCV_MAX = '1.2.0'
 
 
 def digit_version(version_str):

mmseg/models/backbones/__init__.py

@@ -1,10 +1,11 @@
 from .fast_scnn import FastSCNN
 from .hrnet import HRNet
+from .mobilenet_v2 import MobileNetV2
 from .resnest import ResNeSt
 from .resnet import ResNet, ResNetV1c, ResNetV1d
 from .resnext import ResNeXt
 
 __all__ = [
     'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN',
-    'ResNeSt'
+    'ResNeSt', 'MobileNetV2'
 ]

mmseg/models/backbones/mobilenet_v2.py

@@ -0,0 +1,270 @@
+import logging
+
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import ConvModule, constant_init, kaiming_init
+from mmcv.runner import load_checkpoint
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from ..builder import BACKBONES
+from ..utils import make_divisible
+
+
+class InvertedResidual(nn.Module):
+    """InvertedResidual block for MobileNetV2.
+
+    Args:
+        in_channels (int): The input channels of the InvertedResidual block.
+        out_channels (int): The output channels of the InvertedResidual block.
+        stride (int): Stride of the middle (first) 3x3 convolution.
+        expand_ratio (int): Adjusts number of channels of the hidden layer
+            in InvertedResidual by this amount.
+        dilation (int): Dilation rate of depthwise conv. Default: 1
+        conv_cfg (dict): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU6').
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+
+    Returns:
+        Tensor: The output tensor
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 expand_ratio,
+                 dilation=1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU6'),
+                 with_cp=False):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2], f'stride must in [1, 2]. ' \
+            f'But received {stride}.'
+        self.with_cp = with_cp
+        self.use_res_connect = self.stride == 1 and in_channels == out_channels
+        hidden_dim = int(round(in_channels * expand_ratio))
+
+        layers = []
+        if expand_ratio != 1:
+            layers.append(
+                ConvModule(
+                    in_channels=in_channels,
+                    out_channels=hidden_dim,
+                    kernel_size=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+        layers.extend([
+            ConvModule(
+                in_channels=hidden_dim,
+                out_channels=hidden_dim,
+                kernel_size=3,
+                stride=stride,
+                padding=dilation,
+                dilation=dilation,
+                groups=hidden_dim,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg),
+            ConvModule(
+                in_channels=hidden_dim,
+                out_channels=out_channels,
+                kernel_size=1,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=None)
+        ])
+        self.conv = nn.Sequential(*layers)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            if self.use_res_connect:
+                return x + self.conv(x)
+            else:
+                return self.conv(x)
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        return out
+
+
+@BACKBONES.register_module()
+class MobileNetV2(nn.Module):
+    """MobileNetV2 backbone.
+
+    Args:
+        widen_factor (float): Width multiplier, multiply number of
+            channels in each layer by this amount. Default: 1.0.
+        strides (Sequence[int], optional): Strides of the first block of each
+            layer. If not specified, default config in ``arch_setting`` will
+            be used.
+        dilations (Sequence[int]): Dilation of each layer.
+        out_indices (None or Sequence[int]): Output from which stages.
+            Default: (7, ).
+        frozen_stages (int): Stages to be frozen (all param fixed).
+            Default: -1, which means not freezing any parameters.
+        conv_cfg (dict): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU6').
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+    """
+
+    # Parameters to build layers. 3 parameters are needed to construct a
+    # layer, from left to right: expand_ratio, channel, num_blocks.
+    arch_settings = [[1, 16, 1], [6, 24, 2], [6, 32, 3], [6, 64, 4],
+                     [6, 96, 3], [6, 160, 3], [6, 320, 1]]
+
+    def __init__(self,
+                 widen_factor=1.,
+                 strides=(1, 2, 2, 2, 1, 2, 1),
+                 dilations=(1, 1, 1, 1, 1, 1, 1),
+                 out_indices=(1, 2, 4, 6),
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU6'),
+                 norm_eval=False,
+                 with_cp=False):
+        super(MobileNetV2, self).__init__()
+        self.widen_factor = widen_factor
+        self.strides = strides
+        self.dilations = dilations
+        assert len(strides) == len(dilations) == len(self.arch_settings)
+        self.out_indices = out_indices
+        for index in out_indices:
+            if index not in range(0, 7):
+                raise ValueError('the item in out_indices must in '
+                                 f'range(0, 8). But received {index}')
+
+        if frozen_stages not in range(-1, 7):
+            raise ValueError('frozen_stages must be in range(-1, 7). '
+                             f'But received {frozen_stages}')
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+        self.norm_eval = norm_eval
+        self.with_cp = with_cp
+
+        self.in_channels = make_divisible(32 * widen_factor, 8)
+
+        self.conv1 = ConvModule(
+            in_channels=3,
+            out_channels=self.in_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            act_cfg=self.act_cfg)
+
+        self.layers = []
+
+        for i, layer_cfg in enumerate(self.arch_settings):
+            expand_ratio, channel, num_blocks = layer_cfg
+            stride = self.strides[i]
+            dilation = self.dilations[i]
+            out_channels = make_divisible(channel * widen_factor, 8)
+            inverted_res_layer = self.make_layer(
+                out_channels=out_channels,
+                num_blocks=num_blocks,
+                stride=stride,
+                dilation=dilation,
+                expand_ratio=expand_ratio)
+            layer_name = f'layer{i + 1}'
+            self.add_module(layer_name, inverted_res_layer)
+            self.layers.append(layer_name)
+
+    def make_layer(self, out_channels, num_blocks, stride, dilation,
+                   expand_ratio):
+        """Stack InvertedResidual blocks to build a layer for MobileNetV2.
+
+        Args:
+            out_channels (int): out_channels of block.
+            num_blocks (int): Number of blocks.
+            stride (int): Stride of the first block.
+            dilation (int): Dilation of the first block.
+            expand_ratio (int): Expand the number of channels of the
+                hidden layer in InvertedResidual by this ratio.
+        """
+        layers = []
+        for i in range(num_blocks):
+            layers.append(
+                InvertedResidual(
+                    self.in_channels,
+                    out_channels,
+                    stride if i == 0 else 1,
+                    expand_ratio=expand_ratio,
+                    dilation=dilation if i == 0 else 1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    act_cfg=self.act_cfg,
+                    with_cp=self.with_cp))
+            self.in_channels = out_channels
+
+        return nn.Sequential(*layers)
+
+    def init_weights(self, pretrained=None):
+        if isinstance(pretrained, str):
+            logger = logging.getLogger()
+            load_checkpoint(self, pretrained, strict=False, logger=logger)
+        elif pretrained is None:
+            for m in self.modules():
+                if isinstance(m, nn.Conv2d):
+                    kaiming_init(m)
+                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
+                    constant_init(m, 1)
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    def forward(self, x):
+        x = self.conv1(x)
+
+        outs = []
+        for i, layer_name in enumerate(self.layers):
+            layer = getattr(self, layer_name)
+            x = layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+
+        if len(outs) == 1:
+            return outs[0]
+        else:
+            return tuple(outs)
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            for param in self.conv1.parameters():
+                param.requires_grad = False
+        for i in range(1, self.frozen_stages + 1):
+            layer = getattr(self, f'layer{i}')
+            layer.eval()
+            for param in layer.parameters():
+                param.requires_grad = False
+
+    def train(self, mode=True):
+        super(MobileNetV2, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                if isinstance(m, _BatchNorm):
+                    m.eval()

mmseg/models/utils/__init__.py

@@ -1,4 +1,5 @@
+from .make_divisible import make_divisible
 from .res_layer import ResLayer
 from .self_attention_block import SelfAttentionBlock
 
-__all__ = ['ResLayer', 'SelfAttentionBlock']
+__all__ = ['ResLayer', 'SelfAttentionBlock', 'make_divisible']

mmseg/models/utils/make_divisible.py

@@ -0,0 +1,24 @@
+def make_divisible(value, divisor, min_value=None, min_ratio=0.9):
+    """Make divisible function.
+
+    This function rounds the channel number down to the nearest value that can
+    be divisible by the divisor.
+
+    Args:
+        value (int): The original channel number.
+        divisor (int): The divisor to fully divide the channel number.
+        min_value (int, optional): The minimum value of the output channel.
+            Default: None, means that the minimum value equal to the divisor.
+        min_ratio (float, optional): The minimum ratio of the rounded channel
+            number to the original channel number. Default: 0.9.
+    Returns:
+        int: The modified output channel number
+    """
+
+    if min_value is None:
+        min_value = divisor
+    new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than (1-min_ratio).
+    if new_value < min_ratio * value:
+        new_value += divisor
+    return new_value

tests/test_models/test_forward.py

@@ -157,6 +157,11 @@ def test_sem_fpn_forward():
     _test_encoder_decoder_forward('sem_fpn/fpn_r50_512x1024_80k_cityscapes.py')
 
 
+def test_mobilenet_v2_forward():
+    _test_encoder_decoder_forward(
+        'mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py')
+
+
 def get_world_size(process_group):
 
     return 1