Merge branch 'dev_mobilenetv2' into 'master'

add mobilenetv2

See merge request open-mmlab/mmclassification!4

mmcls/models/backbones/__init__.py

@@ -1,13 +1,10 @@
+from .mobilenet_v2 import MobileNetV2
 from .resnet import ResNet, ResNetV1d
 from .resnext import ResNeXt
-from .shufflenet_v1 import ShuffleNetv1
+from .shufflenet_v1 import ShuffleNetV1
-from .shufflenet_v2 import ShuffleNetv2
+from .shufflenet_v2 import ShuffleNetV2
 
 __all__ = [
-    'ResNet',
+    'ResNet', 'ResNeXt', 'ResNetV1d', 'ResNetV1d', 'ShuffleNetV1',
-    'ResNeXt',
+    'ShuffleNetV2', 'MobileNetV2'
-    'ResNetV1d',
-    'ResNetV1d',
-    'ShuffleNetv1',
-    'ShuffleNetv2',
 ]

mmcls/models/backbones/mobilenet_v2.py

@@ -0,0 +1,265 @@
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import ConvModule, constant_init, kaiming_init
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmcls.models.utils import make_divisible
+from ..builder import BACKBONES
+from .base_backbone import BaseBackbone
+
+
+class InvertedResidual(nn.Module):
+    """InvertedResidual block for MobileNetV2.
+
+    Args:
+        inplanes (int): The input channels of the InvertedResidual block.
+        planes (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.
+        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,
+                 inplanes,
+                 planes,
+                 stride,
+                 expand_ratio,
+                 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 inplanes == planes
+        hidden_dim = int(round(inplanes * expand_ratio))
+
+        layers = []
+        if expand_ratio != 1:
+            layers.append(
+                ConvModule(
+                    in_channels=inplanes,
+                    out_channels=hidden_dim,
+                    kernel_size=3,
+                    stride=1,
+                    padding=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=1,
+                groups=hidden_dim,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg),
+            ConvModule(
+                in_channels=hidden_dim,
+                out_channels=planes,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                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(BaseBackbone):
+    """MobileNetV2 backbone.
+
+    Args:
+        widen_factor (float): Width multiplier, multiply number of
+            channels in each layer by this amount. Default: 1.0.
+        out_indices (None or Sequence[int]): Output from which stages.
+            Default: None
+        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. 4 parameters are needed to construct a
+    # layer, from left to right: expand_ratio, channel, num_blocks, stride.
+    arch_settings = [[1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2],
+                     [6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2],
+                     [6, 320, 1, 1]]
+
+    def __init__(self,
+                 widen_factor=1.,
+                 out_indices=None,
+                 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.out_indices = out_indices
+        if out_indices is not None:
+            assert max(out_indices) < len(self.arch_settings)
+        self.frozen_stages = frozen_stages
+        assert frozen_stages < len(self.arch_settings)
+        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.inplanes = make_divisible(32 * widen_factor, 8)
+
+        self.conv1 = ConvModule(
+            in_channels=3,
+            out_channels=self.inplanes,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            act_cfg=self.act_cfg)
+
+        self.inverted_res_layers = []
+
+        for i, layer_cfg in enumerate(self.arch_settings):
+            expand_ratio, channel, num_blocks, stride = layer_cfg
+            planes = make_divisible(channel * widen_factor, 8)
+            inverted_res_layer = self.make_layer(
+                planes=planes,
+                num_blocks=num_blocks,
+                stride=stride,
+                expand_ratio=expand_ratio)
+            layer_name = f'layer{i + 1}'
+            self.add_module(layer_name, inverted_res_layer)
+            self.inverted_res_layers.append(layer_name)
+
+        if widen_factor > 1.0:
+            self.out_channel = int(1280 * widen_factor)
+        else:
+            self.out_channel = 1280
+
+        self.conv2 = ConvModule(
+            in_channels=self.inplanes,
+            out_channels=self.out_channel,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            act_cfg=self.act_cfg)
+
+    def make_layer(self, planes, num_blocks, stride, expand_ratio):
+        """ Stack InvertedResidual blocks to build a layer for MobileNetV2.
+
+        Args:
+            planes (int): planes of block.
+            num_blocks (int): number of blocks.
+            stride (int): stride of the first block. Default: 1
+            expand_ratio (int): Expand the number of channels of the
+                hidden layer in InvertedResidual by this ratio. Default: 6.
+        """
+        layers = []
+        for i in range(num_blocks):
+            if i >= 1:
+                stride = 1
+            layers.append(
+                InvertedResidual(
+                    self.inplanes,
+                    planes,
+                    stride,
+                    expand_ratio=expand_ratio,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    act_cfg=self.act_cfg,
+                    with_cp=self.with_cp))
+            self.inplanes = planes
+
+        return nn.Sequential(*layers)
+
+    def init_weights(self, pretrained=None):
+        if 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.inverted_res_layers):
+            inverted_res_layer = getattr(self, layer_name)
+            x = inverted_res_layer(x)
+            if self.out_indices is not None and i in self.out_indices:
+                outs.append(x)
+
+        x = self.conv2(x)
+
+        if self.out_indices is None:
+            return x
+        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()

mmcls/models/backbones/shufflenet_v1.py

@@ -6,6 +6,7 @@ from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,
 from torch.nn.modules.batchnorm import _BatchNorm
 
 from mmcls.models.utils import channel_shuffle, make_divisible
+from ..builder import BACKBONES
 from .base_backbone import BaseBackbone
 
 
@@ -139,8 +140,9 @@ class ShuffleUnit(nn.Module):
         return out
 
 
-class ShuffleNetv1(BaseBackbone):
+@BACKBONES.register_module()
-    """ShuffleNetv1 backbone.
+class ShuffleNetV1(BaseBackbone):
+    """ShuffleNetV1 backbone.
 
     Args:
         groups (int, optional): The number of groups to be used in grouped 1x1
@@ -174,7 +176,7 @@ class ShuffleNetv1(BaseBackbone):
                  act_cfg=dict(type='ReLU'),
                  norm_eval=False,
                  with_cp=False):
-        super(ShuffleNetv1, self).__init__()
+        super(ShuffleNetV1, self).__init__()
         self.stage_blocks = [3, 7, 3]
         self.groups = groups
 
@@ -294,7 +296,7 @@ class ShuffleNetv1(BaseBackbone):
             return tuple(outs)
 
     def train(self, mode=True):
-        super(ShuffleNetv1, self).train(mode)
+        super(ShuffleNetV1, self).train(mode)
         self._freeze_stages()
         if mode and self.norm_eval:
             for m in self.modules():

mmcls/models/backbones/shufflenet_v2.py

@@ -5,6 +5,7 @@ from mmcv.cnn import ConvModule, constant_init, kaiming_init
 from torch.nn.modules.batchnorm import _BatchNorm
 
 from mmcls.models.utils import channel_shuffle
+from ..builder import BACKBONES
 from .base_backbone import BaseBackbone
 
 
@@ -125,8 +126,9 @@ class InvertedResidual(nn.Module):
         return out
 
 
-class ShuffleNetv2(BaseBackbone):
+@BACKBONES.register_module()
-    """ShuffleNetv2 backbone.
+class ShuffleNetV2(BaseBackbone):
+    """ShuffleNetV2 backbone.
 
     Args:
         groups (int): The number of groups to be used in grouped 1x1
@@ -160,7 +162,7 @@ class ShuffleNetv2(BaseBackbone):
                  act_cfg=dict(type='ReLU'),
                  norm_eval=False,
                  with_cp=False):
-        super(ShuffleNetv2, self).__init__()
+        super(ShuffleNetV2, self).__init__()
         self.stage_blocks = [4, 8, 4]
         self.groups = groups
         self.out_indices = out_indices
@@ -273,7 +275,7 @@ class ShuffleNetv2(BaseBackbone):
             return tuple(outs)
 
     def train(self, mode=True):
-        super(ShuffleNetv2, self).train(mode)
+        super(ShuffleNetV2, self).train(mode)
         self._freeze_stages()
         if mode and self.norm_eval:
             for m in self.modules():

tests/test_backbones/test_mobilenet_v2.py

@@ -0,0 +1,255 @@
+import pytest
+import torch
+from torch.nn.modules import GroupNorm
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmcls.models.backbones import MobileNetV2
+from mmcls.models.backbones.mobilenet_v2 import InvertedResidual
+
+
+def is_block(modules):
+    """Check if is ResNet building block."""
+    if isinstance(modules, (InvertedResidual, )):
+        return True
+    return False
+
+
+def is_norm(modules):
+    """Check if is one of the norms."""
+    if isinstance(modules, (GroupNorm, _BatchNorm)):
+        return True
+    return False
+
+
+def check_norm_state(modules, train_state):
+    """Check if norm layer is in correct train state."""
+    for mod in modules:
+        if isinstance(mod, _BatchNorm):
+            if mod.training != train_state:
+                return False
+    return True
+
+
+def test_mobilenetv2_invertedresidual():
+
+    with pytest.raises(AssertionError):
+        # stride must be in [1, 2]
+        InvertedResidual(16, 24, stride=3, expand_ratio=6)
+
+    # Test InvertedResidual with checkpoint forward, stride=1
+    block = InvertedResidual(16, 24, stride=1, expand_ratio=6)
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size((1, 24, 56, 56))
+
+    # Test InvertedResidual with expand_ratio=1
+    block = InvertedResidual(16, 16, stride=1, expand_ratio=1)
+    assert len(block.conv) == 2
+
+    # Test InvertedResidual with use_res_connect
+    block = InvertedResidual(16, 16, stride=1, expand_ratio=6)
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    assert block.use_res_connect is True
+    assert x_out.shape == torch.Size((1, 16, 56, 56))
+
+    # Test InvertedResidual with checkpoint forward, stride=2
+    block = InvertedResidual(16, 24, stride=2, expand_ratio=6)
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size((1, 24, 28, 28))
+
+    # Test InvertedResidual with checkpoint forward
+    block = InvertedResidual(16, 24, stride=1, expand_ratio=6, with_cp=True)
+    assert block.with_cp
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size((1, 24, 56, 56))
+
+    # Test InvertedResidual with act_cfg=dict(type='ReLU')
+    block = InvertedResidual(
+        16, 24, stride=1, expand_ratio=6, act_cfg=dict(type='ReLU'))
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size((1, 24, 56, 56))
+
+
+def test_mobilenetv2_backbone():
+    with pytest.raises(TypeError):
+        # pretrained must be a string path
+        model = MobileNetV2()
+        model.init_weights(pretrained=0)
+
+    with pytest.raises(AssertionError):
+        # frozen_stages must less than 7
+        MobileNetV2(frozen_stages=8)
+
+    with pytest.raises(AssertionError):
+        # the max value in out_indices must less than 7
+        MobileNetV2(out_indices=[8])
+
+    # Test MobileNetV2 with first stage frozen
+    frozen_stages = 1
+    model = MobileNetV2(frozen_stages=frozen_stages)
+    model.init_weights()
+    model.train()
+
+    for mod in model.conv1.modules():
+        for param in mod.parameters():
+            assert param.requires_grad is False
+    for i in range(1, frozen_stages + 1):
+        layer = getattr(model, f'layer{i}')
+        for mod in layer.modules():
+            if isinstance(mod, _BatchNorm):
+                assert mod.training is False
+        for param in layer.parameters():
+            assert param.requires_grad is False
+
+    # Test MobileNetV2 with norm_eval=True
+    model = MobileNetV2(norm_eval=True)
+    model.init_weights()
+    model.train()
+
+    assert check_norm_state(model.modules(), False)
+
+    # Test MobileNetV2 forward with widen_factor=1.0
+    model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7))
+    model.init_weights()
+    model.train()
+
+    assert check_norm_state(model.modules(), True)
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 24, 56, 56))
+    assert feat[2].shape == torch.Size((1, 32, 28, 28))
+    assert feat[3].shape == torch.Size((1, 64, 14, 14))
+    assert feat[4].shape == torch.Size((1, 96, 14, 14))
+    assert feat[5].shape == torch.Size((1, 160, 7, 7))
+    assert feat[6].shape == torch.Size((1, 320, 7, 7))
+
+    # Test MobileNetV2 forward with widen_factor=0.5
+    model = MobileNetV2(widen_factor=0.5, out_indices=range(0, 7))
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 8, 112, 112))
+    assert feat[1].shape == torch.Size((1, 16, 56, 56))
+    assert feat[2].shape == torch.Size((1, 16, 28, 28))
+    assert feat[3].shape == torch.Size((1, 32, 14, 14))
+    assert feat[4].shape == torch.Size((1, 48, 14, 14))
+    assert feat[5].shape == torch.Size((1, 80, 7, 7))
+    assert feat[6].shape == torch.Size((1, 160, 7, 7))
+
+    # Test MobileNetV2 forward with widen_factor=2.0
+    model = MobileNetV2(widen_factor=2.0, out_indices=None)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert feat.shape == torch.Size((1, 2560, 7, 7))
+
+    # Test MobileNetV2 forward with out_indices=None
+    model = MobileNetV2(widen_factor=1.0, out_indices=None)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert feat.shape == torch.Size((1, 1280, 7, 7))
+
+    # Test MobileNetV2 forward with dict(type='ReLU')
+    model = MobileNetV2(
+        widen_factor=1.0, act_cfg=dict(type='ReLU'), out_indices=range(0, 7))
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 24, 56, 56))
+    assert feat[2].shape == torch.Size((1, 32, 28, 28))
+    assert feat[3].shape == torch.Size((1, 64, 14, 14))
+    assert feat[4].shape == torch.Size((1, 96, 14, 14))
+    assert feat[5].shape == torch.Size((1, 160, 7, 7))
+    assert feat[6].shape == torch.Size((1, 320, 7, 7))
+
+    # Test MobileNetV2 with GroupNorm forward
+    model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7))
+    for m in model.modules():
+        if is_norm(m):
+            assert isinstance(m, _BatchNorm)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 24, 56, 56))
+    assert feat[2].shape == torch.Size((1, 32, 28, 28))
+    assert feat[3].shape == torch.Size((1, 64, 14, 14))
+    assert feat[4].shape == torch.Size((1, 96, 14, 14))
+    assert feat[5].shape == torch.Size((1, 160, 7, 7))
+    assert feat[6].shape == torch.Size((1, 320, 7, 7))
+
+    # Test MobileNetV2 with BatchNorm forward
+    model = MobileNetV2(
+        widen_factor=1.0,
+        norm_cfg=dict(type='GN', num_groups=2, requires_grad=True),
+        out_indices=range(0, 7))
+    for m in model.modules():
+        if is_norm(m):
+            assert isinstance(m, GroupNorm)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 24, 56, 56))
+    assert feat[2].shape == torch.Size((1, 32, 28, 28))
+    assert feat[3].shape == torch.Size((1, 64, 14, 14))
+    assert feat[4].shape == torch.Size((1, 96, 14, 14))
+    assert feat[5].shape == torch.Size((1, 160, 7, 7))
+    assert feat[6].shape == torch.Size((1, 320, 7, 7))
+
+    # Test MobileNetV2 with layers 1, 3, 5 out forward
+    model = MobileNetV2(widen_factor=1.0, out_indices=(0, 2, 4))
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 3
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 32, 28, 28))
+    assert feat[2].shape == torch.Size((1, 96, 14, 14))
+
+    # Test MobileNetV2 with checkpoint forward
+    model = MobileNetV2(
+        widen_factor=1.0, with_cp=True, out_indices=range(0, 7))
+    for m in model.modules():
+        if is_block(m):
+            assert m.with_cp
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 24, 56, 56))
+    assert feat[2].shape == torch.Size((1, 32, 28, 28))
+    assert feat[3].shape == torch.Size((1, 64, 14, 14))
+    assert feat[4].shape == torch.Size((1, 96, 14, 14))
+    assert feat[5].shape == torch.Size((1, 160, 7, 7))
+    assert feat[6].shape == torch.Size((1, 320, 7, 7))

tests/test_backbones/test_shufflenet_v1.py

@@ -3,7 +3,7 @@ import torch
 from torch.nn.modules import GroupNorm
 from torch.nn.modules.batchnorm import _BatchNorm
 
-from mmcls.models.backbones import ShuffleNetv1
+from mmcls.models.backbones import ShuffleNetV1
 from mmcls.models.backbones.shufflenet_v1 import ShuffleUnit
 
 
@@ -66,30 +66,30 @@ def test_shufflenetv1_backbone():
 
     with pytest.raises(ValueError):
         # frozen_stages must be in  range(-1, 4)
-        ShuffleNetv1(frozen_stages=10)
+        ShuffleNetV1(frozen_stages=10)
 
     with pytest.raises(ValueError):
         # the item in out_indices must be in  range(0, 4)
-        ShuffleNetv1(out_indices=[5])
+        ShuffleNetV1(out_indices=[5])
 
     with pytest.raises(ValueError):
         # groups must be in  [1, 2, 3, 4, 8]
-        ShuffleNetv1(groups=10)
+        ShuffleNetV1(groups=10)
 
     with pytest.raises(TypeError):
         # pretrained must be str or None
-        model = ShuffleNetv1()
+        model = ShuffleNetV1()
         model.init_weights(pretrained=1)
 
-    # Test ShuffleNetv1 norm state
+    # Test ShuffleNetV1 norm state
-    model = ShuffleNetv1()
+    model = ShuffleNetV1()
     model.init_weights()
     model.train()
     assert check_norm_state(model.modules(), True)
 
-    # Test ShuffleNetv1 with first stage frozen
+    # Test ShuffleNetV1 with first stage frozen
     frozen_stages = 1
-    model = ShuffleNetv1(frozen_stages=frozen_stages)
+    model = ShuffleNetV1(frozen_stages=frozen_stages)
     model.init_weights()
     model.train()
     for param in model.conv1.parameters():
@@ -102,8 +102,8 @@ def test_shufflenetv1_backbone():
         for param in layer.parameters():
             assert param.requires_grad is False
 
-    # Test ShuffleNetv1 forward with groups=1
+    # Test ShuffleNetV1 forward with groups=1
-    model = ShuffleNetv1(groups=1)
+    model = ShuffleNetV1(groups=1)
     model.init_weights()
     model.train()
 
@@ -118,8 +118,8 @@ def test_shufflenetv1_backbone():
     assert feat[1].shape == torch.Size((1, 288, 14, 14))
     assert feat[2].shape == torch.Size((1, 576, 7, 7))
 
-    # Test ShuffleNetv1 forward with groups=2
+    # Test ShuffleNetV1 forward with groups=2
-    model = ShuffleNetv1(groups=2)
+    model = ShuffleNetV1(groups=2)
     model.init_weights()
     model.train()
 
@@ -134,8 +134,8 @@ def test_shufflenetv1_backbone():
     assert feat[1].shape == torch.Size((1, 400, 14, 14))
     assert feat[2].shape == torch.Size((1, 800, 7, 7))
 
-    # Test ShuffleNetv1 forward with groups=3
+    # Test ShuffleNetV1 forward with groups=3
-    model = ShuffleNetv1(groups=3)
+    model = ShuffleNetV1(groups=3)
     model.init_weights()
     model.train()
 
@@ -150,8 +150,8 @@ def test_shufflenetv1_backbone():
     assert feat[1].shape == torch.Size((1, 480, 14, 14))
     assert feat[2].shape == torch.Size((1, 960, 7, 7))
 
-    # Test ShuffleNetv1 forward with groups=4
+    # Test ShuffleNetV1 forward with groups=4
-    model = ShuffleNetv1(groups=4)
+    model = ShuffleNetV1(groups=4)
     model.init_weights()
     model.train()
 
@@ -166,8 +166,8 @@ def test_shufflenetv1_backbone():
     assert feat[1].shape == torch.Size((1, 544, 14, 14))
     assert feat[2].shape == torch.Size((1, 1088, 7, 7))
 
-    # Test ShuffleNetv1 forward with groups=8
+    # Test ShuffleNetV1 forward with groups=8
-    model = ShuffleNetv1(groups=8)
+    model = ShuffleNetV1(groups=8)
     model.init_weights()
     model.train()
 
@@ -182,8 +182,8 @@ def test_shufflenetv1_backbone():
     assert feat[1].shape == torch.Size((1, 768, 14, 14))
     assert feat[2].shape == torch.Size((1, 1536, 7, 7))
 
-    # Test ShuffleNetv1 forward with GroupNorm forward
+    # Test ShuffleNetV1 forward with GroupNorm forward
-    model = ShuffleNetv1(
+    model = ShuffleNetV1(
         groups=3, norm_cfg=dict(type='GN', num_groups=2, requires_grad=True))
     model.init_weights()
     model.train()
@@ -199,8 +199,8 @@ def test_shufflenetv1_backbone():
     assert feat[1].shape == torch.Size((1, 480, 14, 14))
     assert feat[2].shape == torch.Size((1, 960, 7, 7))
 
-    # Test ShuffleNetv1 forward with layers 1, 2 forward
+    # Test ShuffleNetV1 forward with layers 1, 2 forward
-    model = ShuffleNetv1(groups=3, out_indices=(1, 2))
+    model = ShuffleNetV1(groups=3, out_indices=(1, 2))
     model.init_weights()
     model.train()
 
@@ -214,8 +214,8 @@ def test_shufflenetv1_backbone():
     assert feat[0].shape == torch.Size((1, 480, 14, 14))
     assert feat[1].shape == torch.Size((1, 960, 7, 7))
 
-    # Test ShuffleNetv1 forward with layers 2 forward
+    # Test ShuffleNetV1 forward with layers 2 forward
-    model = ShuffleNetv1(groups=3, out_indices=(2, ))
+    model = ShuffleNetV1(groups=3, out_indices=(2, ))
     model.init_weights()
     model.train()
 
@@ -228,14 +228,14 @@ def test_shufflenetv1_backbone():
     assert isinstance(feat, torch.Tensor)
     assert feat.shape == torch.Size((1, 960, 7, 7))
 
-    # Test ShuffleNetv1 forward with checkpoint forward
+    # Test ShuffleNetV1 forward with checkpoint forward
-    model = ShuffleNetv1(groups=3, with_cp=True)
+    model = ShuffleNetV1(groups=3, with_cp=True)
     for m in model.modules():
         if is_block(m):
             assert m.with_cp
 
-    # Test ShuffleNetv1 with norm_eval
+    # Test ShuffleNetV1 with norm_eval
-    model = ShuffleNetv1(norm_eval=True)
+    model = ShuffleNetV1(norm_eval=True)
     model.init_weights()
     model.train()
 

tests/test_backbones/test_shufflenet_v2.py

@@ -3,7 +3,7 @@ import torch
 from torch.nn.modules import GroupNorm
 from torch.nn.modules.batchnorm import _BatchNorm
 
-from mmcls.models.backbones import ShuffleNetv2
+from mmcls.models.backbones import ShuffleNetV2
 from mmcls.models.backbones.shufflenet_v2 import InvertedResidual
 
 
@@ -59,26 +59,26 @@ def test_shufflenetv2_backbone():
 
     with pytest.raises(ValueError):
         # groups must be in 0.5, 1.0, 1.5, 2.0]
-        ShuffleNetv2(widen_factor=3.0)
+        ShuffleNetV2(widen_factor=3.0)
 
     with pytest.raises(AssertionError):
         # frozen_stages must be in [0, 1, 2]
-        ShuffleNetv2(widen_factor=3.0, frozen_stages=3)
+        ShuffleNetV2(widen_factor=3.0, frozen_stages=3)
 
     with pytest.raises(TypeError):
         # pretrained must be str or None
-        model = ShuffleNetv2()
+        model = ShuffleNetV2()
         model.init_weights(pretrained=1)
 
-    # Test ShuffleNetv2 norm state
+    # Test ShuffleNetV2 norm state
-    model = ShuffleNetv2()
+    model = ShuffleNetV2()
     model.init_weights()
     model.train()
     assert check_norm_state(model.modules(), True)
 
-    # Test ShuffleNetv2 with first stage frozen
+    # Test ShuffleNetV2 with first stage frozen
     frozen_stages = 1
-    model = ShuffleNetv2(frozen_stages=frozen_stages)
+    model = ShuffleNetV2(frozen_stages=frozen_stages)
     model.init_weights()
     model.train()
     for param in model.conv1.parameters():
@@ -91,15 +91,15 @@ def test_shufflenetv2_backbone():
         for param in layer.parameters():
             assert param.requires_grad is False
 
-    # Test ShuffleNetv2 with norm_eval
+    # Test ShuffleNetV2 with norm_eval
-    model = ShuffleNetv2(norm_eval=True)
+    model = ShuffleNetV2(norm_eval=True)
     model.init_weights()
     model.train()
 
     assert check_norm_state(model.modules(), False)
 
-    # Test ShuffleNetv2 forward with widen_factor=0.5
+    # Test ShuffleNetV2 forward with widen_factor=0.5
-    model = ShuffleNetv2(widen_factor=0.5)
+    model = ShuffleNetV2(widen_factor=0.5)
     model.init_weights()
     model.train()
 
@@ -114,8 +114,8 @@ def test_shufflenetv2_backbone():
     assert feat[1].shape == torch.Size((1, 96, 14, 14))
     assert feat[2].shape == torch.Size((1, 192, 7, 7))
 
-    # Test ShuffleNetv2 forward with widen_factor=1.0
+    # Test ShuffleNetV2 forward with widen_factor=1.0
-    model = ShuffleNetv2(widen_factor=1.0)
+    model = ShuffleNetV2(widen_factor=1.0)
     model.init_weights()
     model.train()
 
@@ -130,8 +130,8 @@ def test_shufflenetv2_backbone():
     assert feat[1].shape == torch.Size((1, 232, 14, 14))
     assert feat[2].shape == torch.Size((1, 464, 7, 7))
 
-    # Test ShuffleNetv2 forward with widen_factor=1.5
+    # Test ShuffleNetV2 forward with widen_factor=1.5
-    model = ShuffleNetv2(widen_factor=1.5)
+    model = ShuffleNetV2(widen_factor=1.5)
     model.init_weights()
     model.train()
 
@@ -146,8 +146,8 @@ def test_shufflenetv2_backbone():
     assert feat[1].shape == torch.Size((1, 352, 14, 14))
     assert feat[2].shape == torch.Size((1, 704, 7, 7))
 
-    # Test ShuffleNetv2 forward with widen_factor=2.0
+    # Test ShuffleNetV2 forward with widen_factor=2.0
-    model = ShuffleNetv2(widen_factor=2.0)
+    model = ShuffleNetV2(widen_factor=2.0)
     model.init_weights()
     model.train()
 
@@ -162,8 +162,8 @@ def test_shufflenetv2_backbone():
     assert feat[1].shape == torch.Size((1, 488, 14, 14))
     assert feat[2].shape == torch.Size((1, 976, 7, 7))
 
-    # Test ShuffleNetv2 forward with layers 3 forward
+    # Test ShuffleNetV2 forward with layers 3 forward
-    model = ShuffleNetv2(widen_factor=1.0, out_indices=(2, ))
+    model = ShuffleNetV2(widen_factor=1.0, out_indices=(2, ))
     model.init_weights()
     model.train()
 
@@ -176,8 +176,8 @@ def test_shufflenetv2_backbone():
     assert isinstance(feat, torch.Tensor)
     assert feat.shape == torch.Size((1, 464, 7, 7))
 
-    # Test ShuffleNetv2 forward with layers 1 2 forward
+    # Test ShuffleNetV2 forward with layers 1 2 forward
-    model = ShuffleNetv2(widen_factor=1.0, out_indices=(1, 2))
+    model = ShuffleNetV2(widen_factor=1.0, out_indices=(1, 2))
     model.init_weights()
     model.train()
 
@@ -191,8 +191,8 @@ def test_shufflenetv2_backbone():
     assert feat[0].shape == torch.Size((1, 232, 14, 14))
     assert feat[1].shape == torch.Size((1, 464, 7, 7))
 
-    # Test ShuffleNetv2 forward with checkpoint forward
+    # Test ShuffleNetV2 forward with checkpoint forward
-    model = ShuffleNetv2(widen_factor=1.0, with_cp=True)
+    model = ShuffleNetV2(widen_factor=1.0, with_cp=True)
     for m in model.modules():
         if is_block(m):
             assert m.with_cp