| |
| import math |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import torch.utils.checkpoint as cp |
| from mmcv.cnn import build_conv_layer, build_norm_layer |
|
|
| from mmseg.registry import MODELS |
| from ..utils import ResLayer |
| from .resnet import Bottleneck as _Bottleneck |
| from .resnet import ResNetV1d |
|
|
|
|
| class RSoftmax(nn.Module): |
| """Radix Softmax module in ``SplitAttentionConv2d``. |
| |
| Args: |
| radix (int): Radix of input. |
| groups (int): Groups of input. |
| """ |
|
|
| def __init__(self, radix, groups): |
| super().__init__() |
| self.radix = radix |
| self.groups = groups |
|
|
| def forward(self, x): |
| batch = x.size(0) |
| if self.radix > 1: |
| x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2) |
| x = F.softmax(x, dim=1) |
| x = x.reshape(batch, -1) |
| else: |
| x = torch.sigmoid(x) |
| return x |
|
|
|
|
| class SplitAttentionConv2d(nn.Module): |
| """Split-Attention Conv2d in ResNeSt. |
| |
| Args: |
| in_channels (int): Same as nn.Conv2d. |
| out_channels (int): Same as nn.Conv2d. |
| kernel_size (int | tuple[int]): Same as nn.Conv2d. |
| stride (int | tuple[int]): Same as nn.Conv2d. |
| padding (int | tuple[int]): Same as nn.Conv2d. |
| dilation (int | tuple[int]): Same as nn.Conv2d. |
| groups (int): Same as nn.Conv2d. |
| radix (int): Radix of SpltAtConv2d. Default: 2 |
| reduction_factor (int): Reduction factor of inter_channels. Default: 4. |
| conv_cfg (dict): Config dict for convolution layer. Default: None, |
| which means using conv2d. |
| norm_cfg (dict): Config dict for normalization layer. Default: None. |
| dcn (dict): Config dict for DCN. Default: None. |
| """ |
|
|
| def __init__(self, |
| in_channels, |
| channels, |
| kernel_size, |
| stride=1, |
| padding=0, |
| dilation=1, |
| groups=1, |
| radix=2, |
| reduction_factor=4, |
| conv_cfg=None, |
| norm_cfg=dict(type='BN'), |
| dcn=None): |
| super().__init__() |
| inter_channels = max(in_channels * radix // reduction_factor, 32) |
| self.radix = radix |
| self.groups = groups |
| self.channels = channels |
| self.with_dcn = dcn is not None |
| self.dcn = dcn |
| fallback_on_stride = False |
| if self.with_dcn: |
| fallback_on_stride = self.dcn.pop('fallback_on_stride', False) |
| if self.with_dcn and not fallback_on_stride: |
| assert conv_cfg is None, 'conv_cfg must be None for DCN' |
| conv_cfg = dcn |
| self.conv = build_conv_layer( |
| conv_cfg, |
| in_channels, |
| channels * radix, |
| kernel_size, |
| stride=stride, |
| padding=padding, |
| dilation=dilation, |
| groups=groups * radix, |
| bias=False) |
| self.norm0_name, norm0 = build_norm_layer( |
| norm_cfg, channels * radix, postfix=0) |
| self.add_module(self.norm0_name, norm0) |
| self.relu = nn.ReLU(inplace=True) |
| self.fc1 = build_conv_layer( |
| None, channels, inter_channels, 1, groups=self.groups) |
| self.norm1_name, norm1 = build_norm_layer( |
| norm_cfg, inter_channels, postfix=1) |
| self.add_module(self.norm1_name, norm1) |
| self.fc2 = build_conv_layer( |
| None, inter_channels, channels * radix, 1, groups=self.groups) |
| self.rsoftmax = RSoftmax(radix, groups) |
|
|
| @property |
| def norm0(self): |
| """nn.Module: the normalization layer named "norm0" """ |
| return getattr(self, self.norm0_name) |
|
|
| @property |
| def norm1(self): |
| """nn.Module: the normalization layer named "norm1" """ |
| return getattr(self, self.norm1_name) |
|
|
| def forward(self, x): |
| x = self.conv(x) |
| x = self.norm0(x) |
| x = self.relu(x) |
|
|
| batch, rchannel = x.shape[:2] |
| batch = x.size(0) |
| if self.radix > 1: |
| splits = x.view(batch, self.radix, -1, *x.shape[2:]) |
| gap = splits.sum(dim=1) |
| else: |
| gap = x |
| gap = F.adaptive_avg_pool2d(gap, 1) |
| gap = self.fc1(gap) |
|
|
| gap = self.norm1(gap) |
| gap = self.relu(gap) |
|
|
| atten = self.fc2(gap) |
| atten = self.rsoftmax(atten).view(batch, -1, 1, 1) |
|
|
| if self.radix > 1: |
| attens = atten.view(batch, self.radix, -1, *atten.shape[2:]) |
| out = torch.sum(attens * splits, dim=1) |
| else: |
| out = atten * x |
| return out.contiguous() |
|
|
|
|
| class Bottleneck(_Bottleneck): |
| """Bottleneck block for ResNeSt. |
| |
| Args: |
| inplane (int): Input planes of this block. |
| planes (int): Middle planes of this block. |
| groups (int): Groups of conv2. |
| width_per_group (int): Width per group of conv2. 64x4d indicates |
| ``groups=64, width_per_group=4`` and 32x8d indicates |
| ``groups=32, width_per_group=8``. |
| radix (int): Radix of SpltAtConv2d. Default: 2 |
| reduction_factor (int): Reduction factor of inter_channels in |
| SplitAttentionConv2d. Default: 4. |
| avg_down_stride (bool): Whether to use average pool for stride in |
| Bottleneck. Default: True. |
| kwargs (dict): Key word arguments for base class. |
| """ |
| expansion = 4 |
|
|
| def __init__(self, |
| inplanes, |
| planes, |
| groups=1, |
| base_width=4, |
| base_channels=64, |
| radix=2, |
| reduction_factor=4, |
| avg_down_stride=True, |
| **kwargs): |
| """Bottleneck block for ResNeSt.""" |
| super().__init__(inplanes, planes, **kwargs) |
|
|
| if groups == 1: |
| width = self.planes |
| else: |
| width = math.floor(self.planes * |
| (base_width / base_channels)) * groups |
|
|
| self.avg_down_stride = avg_down_stride and self.conv2_stride > 1 |
|
|
| self.norm1_name, norm1 = build_norm_layer( |
| self.norm_cfg, width, postfix=1) |
| self.norm3_name, norm3 = build_norm_layer( |
| self.norm_cfg, self.planes * self.expansion, postfix=3) |
|
|
| self.conv1 = build_conv_layer( |
| self.conv_cfg, |
| self.inplanes, |
| width, |
| kernel_size=1, |
| stride=self.conv1_stride, |
| bias=False) |
| self.add_module(self.norm1_name, norm1) |
| self.with_modulated_dcn = False |
| self.conv2 = SplitAttentionConv2d( |
| width, |
| width, |
| kernel_size=3, |
| stride=1 if self.avg_down_stride else self.conv2_stride, |
| padding=self.dilation, |
| dilation=self.dilation, |
| groups=groups, |
| radix=radix, |
| reduction_factor=reduction_factor, |
| conv_cfg=self.conv_cfg, |
| norm_cfg=self.norm_cfg, |
| dcn=self.dcn) |
| delattr(self, self.norm2_name) |
|
|
| if self.avg_down_stride: |
| self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1) |
|
|
| self.conv3 = build_conv_layer( |
| self.conv_cfg, |
| width, |
| self.planes * self.expansion, |
| kernel_size=1, |
| bias=False) |
| self.add_module(self.norm3_name, norm3) |
|
|
| def forward(self, x): |
|
|
| def _inner_forward(x): |
| identity = x |
|
|
| out = self.conv1(x) |
| out = self.norm1(out) |
| out = self.relu(out) |
|
|
| if self.with_plugins: |
| out = self.forward_plugin(out, self.after_conv1_plugin_names) |
|
|
| out = self.conv2(out) |
|
|
| if self.avg_down_stride: |
| out = self.avd_layer(out) |
|
|
| if self.with_plugins: |
| out = self.forward_plugin(out, self.after_conv2_plugin_names) |
|
|
| out = self.conv3(out) |
| out = self.norm3(out) |
|
|
| if self.with_plugins: |
| out = self.forward_plugin(out, self.after_conv3_plugin_names) |
|
|
| if self.downsample is not None: |
| identity = self.downsample(x) |
|
|
| out += identity |
|
|
| return out |
|
|
| if self.with_cp and x.requires_grad: |
| out = cp.checkpoint(_inner_forward, x) |
| else: |
| out = _inner_forward(x) |
|
|
| out = self.relu(out) |
|
|
| return out |
|
|
|
|
| @MODELS.register_module() |
| class ResNeSt(ResNetV1d): |
| """ResNeSt backbone. |
| |
| This backbone is the implementation of `ResNeSt: |
| Split-Attention Networks <https://arxiv.org/abs/2004.08955>`_. |
| |
| Args: |
| groups (int): Number of groups of Bottleneck. Default: 1 |
| base_width (int): Base width of Bottleneck. Default: 4 |
| radix (int): Radix of SpltAtConv2d. Default: 2 |
| reduction_factor (int): Reduction factor of inter_channels in |
| SplitAttentionConv2d. Default: 4. |
| avg_down_stride (bool): Whether to use average pool for stride in |
| Bottleneck. Default: True. |
| kwargs (dict): Keyword arguments for ResNet. |
| """ |
|
|
| arch_settings = { |
| 50: (Bottleneck, (3, 4, 6, 3)), |
| 101: (Bottleneck, (3, 4, 23, 3)), |
| 152: (Bottleneck, (3, 8, 36, 3)), |
| 200: (Bottleneck, (3, 24, 36, 3)) |
| } |
|
|
| def __init__(self, |
| groups=1, |
| base_width=4, |
| radix=2, |
| reduction_factor=4, |
| avg_down_stride=True, |
| **kwargs): |
| self.groups = groups |
| self.base_width = base_width |
| self.radix = radix |
| self.reduction_factor = reduction_factor |
| self.avg_down_stride = avg_down_stride |
| super().__init__(**kwargs) |
|
|
| def make_res_layer(self, **kwargs): |
| """Pack all blocks in a stage into a ``ResLayer``.""" |
| return ResLayer( |
| groups=self.groups, |
| base_width=self.base_width, |
| base_channels=self.base_channels, |
| radix=self.radix, |
| reduction_factor=self.reduction_factor, |
| avg_down_stride=self.avg_down_stride, |
| **kwargs) |
|
|
