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import math |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.utils.checkpoint as cp |
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from mmcv.cnn import build_conv_layer, build_norm_layer |
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from ..builder import BACKBONES |
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from ..utils import ResLayer |
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from .resnet import Bottleneck as _Bottleneck |
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from .resnet import ResNetV1d |
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class RSoftmax(nn.Module): |
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"""Radix Softmax module in ``SplitAttentionConv2d``. |
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Args: |
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radix (int): Radix of input. |
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groups (int): Groups of input. |
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""" |
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def __init__(self, radix, groups): |
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super().__init__() |
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self.radix = radix |
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self.groups = groups |
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def forward(self, x): |
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batch = x.size(0) |
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if self.radix > 1: |
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x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2) |
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x = F.softmax(x, dim=1) |
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x = x.reshape(batch, -1) |
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else: |
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x = torch.sigmoid(x) |
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return x |
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class SplitAttentionConv2d(nn.Module): |
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"""Split-Attention Conv2d in ResNeSt. |
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Args: |
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in_channels (int): Same as nn.Conv2d. |
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out_channels (int): Same as nn.Conv2d. |
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kernel_size (int | tuple[int]): Same as nn.Conv2d. |
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stride (int | tuple[int]): Same as nn.Conv2d. |
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padding (int | tuple[int]): Same as nn.Conv2d. |
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dilation (int | tuple[int]): Same as nn.Conv2d. |
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groups (int): Same as nn.Conv2d. |
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radix (int): Radix of SpltAtConv2d. Default: 2 |
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reduction_factor (int): Reduction factor of inter_channels. Default: 4. |
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conv_cfg (dict): Config dict for convolution layer. Default: None, |
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which means using conv2d. |
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norm_cfg (dict): Config dict for normalization layer. Default: None. |
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dcn (dict): Config dict for DCN. Default: None. |
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""" |
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def __init__(self, |
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in_channels, |
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channels, |
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kernel_size, |
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stride=1, |
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padding=0, |
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dilation=1, |
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groups=1, |
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radix=2, |
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reduction_factor=4, |
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conv_cfg=None, |
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norm_cfg=dict(type='BN'), |
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dcn=None): |
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super(SplitAttentionConv2d, self).__init__() |
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inter_channels = max(in_channels * radix // reduction_factor, 32) |
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self.radix = radix |
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self.groups = groups |
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self.channels = channels |
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self.with_dcn = dcn is not None |
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self.dcn = dcn |
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fallback_on_stride = False |
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if self.with_dcn: |
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fallback_on_stride = self.dcn.pop('fallback_on_stride', False) |
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if self.with_dcn and not fallback_on_stride: |
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assert conv_cfg is None, 'conv_cfg must be None for DCN' |
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conv_cfg = dcn |
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self.conv = build_conv_layer( |
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conv_cfg, |
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in_channels, |
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channels * radix, |
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kernel_size, |
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stride=stride, |
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padding=padding, |
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dilation=dilation, |
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groups=groups * radix, |
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bias=False) |
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self.norm0_name, norm0 = build_norm_layer( |
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norm_cfg, channels * radix, postfix=0) |
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self.add_module(self.norm0_name, norm0) |
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self.relu = nn.ReLU(inplace=True) |
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self.fc1 = build_conv_layer( |
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None, channels, inter_channels, 1, groups=self.groups) |
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self.norm1_name, norm1 = build_norm_layer( |
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norm_cfg, inter_channels, postfix=1) |
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self.add_module(self.norm1_name, norm1) |
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self.fc2 = build_conv_layer( |
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None, inter_channels, channels * radix, 1, groups=self.groups) |
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self.rsoftmax = RSoftmax(radix, groups) |
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@property |
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def norm0(self): |
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"""nn.Module: the normalization layer named "norm0" """ |
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return getattr(self, self.norm0_name) |
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@property |
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def norm1(self): |
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"""nn.Module: the normalization layer named "norm1" """ |
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return getattr(self, self.norm1_name) |
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def forward(self, x): |
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x = self.conv(x) |
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x = self.norm0(x) |
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x = self.relu(x) |
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batch, rchannel = x.shape[:2] |
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batch = x.size(0) |
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if self.radix > 1: |
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splits = x.view(batch, self.radix, -1, *x.shape[2:]) |
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gap = splits.sum(dim=1) |
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else: |
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gap = x |
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gap = F.adaptive_avg_pool2d(gap, 1) |
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gap = self.fc1(gap) |
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gap = self.norm1(gap) |
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gap = self.relu(gap) |
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atten = self.fc2(gap) |
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atten = self.rsoftmax(atten).view(batch, -1, 1, 1) |
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if self.radix > 1: |
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attens = atten.view(batch, self.radix, -1, *atten.shape[2:]) |
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out = torch.sum(attens * splits, dim=1) |
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else: |
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out = atten * x |
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return out.contiguous() |
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class Bottleneck(_Bottleneck): |
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"""Bottleneck block for ResNeSt. |
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Args: |
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inplane (int): Input planes of this block. |
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planes (int): Middle planes of this block. |
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groups (int): Groups of conv2. |
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width_per_group (int): Width per group of conv2. 64x4d indicates |
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``groups=64, width_per_group=4`` and 32x8d indicates |
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``groups=32, width_per_group=8``. |
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radix (int): Radix of SpltAtConv2d. Default: 2 |
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reduction_factor (int): Reduction factor of inter_channels in |
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SplitAttentionConv2d. Default: 4. |
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avg_down_stride (bool): Whether to use average pool for stride in |
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Bottleneck. Default: True. |
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kwargs (dict): Key word arguments for base class. |
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""" |
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expansion = 4 |
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def __init__(self, |
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inplanes, |
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planes, |
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groups=1, |
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base_width=4, |
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base_channels=64, |
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radix=2, |
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reduction_factor=4, |
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avg_down_stride=True, |
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**kwargs): |
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"""Bottleneck block for ResNeSt.""" |
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super(Bottleneck, self).__init__(inplanes, planes, **kwargs) |
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if groups == 1: |
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width = self.planes |
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else: |
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width = math.floor(self.planes * |
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(base_width / base_channels)) * groups |
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self.avg_down_stride = avg_down_stride and self.conv2_stride > 1 |
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self.norm1_name, norm1 = build_norm_layer( |
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self.norm_cfg, width, postfix=1) |
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self.norm3_name, norm3 = build_norm_layer( |
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self.norm_cfg, self.planes * self.expansion, postfix=3) |
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self.conv1 = build_conv_layer( |
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self.conv_cfg, |
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self.inplanes, |
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width, |
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kernel_size=1, |
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stride=self.conv1_stride, |
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bias=False) |
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self.add_module(self.norm1_name, norm1) |
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self.with_modulated_dcn = False |
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self.conv2 = SplitAttentionConv2d( |
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width, |
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width, |
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kernel_size=3, |
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stride=1 if self.avg_down_stride else self.conv2_stride, |
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padding=self.dilation, |
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dilation=self.dilation, |
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groups=groups, |
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radix=radix, |
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reduction_factor=reduction_factor, |
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conv_cfg=self.conv_cfg, |
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norm_cfg=self.norm_cfg, |
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dcn=self.dcn) |
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delattr(self, self.norm2_name) |
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if self.avg_down_stride: |
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self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1) |
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self.conv3 = build_conv_layer( |
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self.conv_cfg, |
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width, |
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self.planes * self.expansion, |
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kernel_size=1, |
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bias=False) |
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self.add_module(self.norm3_name, norm3) |
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def forward(self, x): |
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def _inner_forward(x): |
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identity = x |
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out = self.conv1(x) |
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out = self.norm1(out) |
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out = self.relu(out) |
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if self.with_plugins: |
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out = self.forward_plugin(out, self.after_conv1_plugin_names) |
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out = self.conv2(out) |
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if self.avg_down_stride: |
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out = self.avd_layer(out) |
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if self.with_plugins: |
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out = self.forward_plugin(out, self.after_conv2_plugin_names) |
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out = self.conv3(out) |
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out = self.norm3(out) |
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if self.with_plugins: |
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out = self.forward_plugin(out, self.after_conv3_plugin_names) |
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if self.downsample is not None: |
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identity = self.downsample(x) |
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out += identity |
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return out |
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if self.with_cp and x.requires_grad: |
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out = cp.checkpoint(_inner_forward, x) |
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else: |
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out = _inner_forward(x) |
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out = self.relu(out) |
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return out |
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@BACKBONES.register_module() |
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class ResNeSt(ResNetV1d): |
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"""ResNeSt backbone. |
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Args: |
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groups (int): Number of groups of Bottleneck. Default: 1 |
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base_width (int): Base width of Bottleneck. Default: 4 |
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radix (int): Radix of SpltAtConv2d. Default: 2 |
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reduction_factor (int): Reduction factor of inter_channels in |
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SplitAttentionConv2d. Default: 4. |
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avg_down_stride (bool): Whether to use average pool for stride in |
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Bottleneck. Default: True. |
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kwargs (dict): Keyword arguments for ResNet. |
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""" |
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arch_settings = { |
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50: (Bottleneck, (3, 4, 6, 3)), |
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101: (Bottleneck, (3, 4, 23, 3)), |
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152: (Bottleneck, (3, 8, 36, 3)), |
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200: (Bottleneck, (3, 24, 36, 3)) |
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} |
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def __init__(self, |
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groups=1, |
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base_width=4, |
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radix=2, |
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reduction_factor=4, |
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avg_down_stride=True, |
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**kwargs): |
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self.groups = groups |
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self.base_width = base_width |
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self.radix = radix |
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self.reduction_factor = reduction_factor |
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self.avg_down_stride = avg_down_stride |
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super(ResNeSt, self).__init__(**kwargs) |
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def make_res_layer(self, **kwargs): |
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"""Pack all blocks in a stage into a ``ResLayer``.""" |
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return ResLayer( |
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groups=self.groups, |
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base_width=self.base_width, |
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base_channels=self.base_channels, |
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radix=self.radix, |
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reduction_factor=self.reduction_factor, |
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avg_down_stride=self.avg_down_stride, |
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**kwargs) |
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