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import torch.nn as nn
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import torch.utils.checkpoint as cp
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from annotator.uniformer.mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer,
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constant_init, kaiming_init)
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from annotator.uniformer.mmcv.runner import load_checkpoint
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from annotator.uniformer.mmcv.utils.parrots_wrapper import _BatchNorm
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from annotator.uniformer.mmseg.utils import get_root_logger
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from ..builder import BACKBONES
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from ..utils import ResLayer
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class BasicBlock(nn.Module):
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"""Basic block for ResNet."""
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expansion = 1
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def __init__(self,
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inplanes,
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planes,
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stride=1,
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dilation=1,
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downsample=None,
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style='pytorch',
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with_cp=False,
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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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plugins=None):
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super(BasicBlock, self).__init__()
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assert dcn is None, 'Not implemented yet.'
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assert plugins is None, 'Not implemented yet.'
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self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
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self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
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self.conv1 = build_conv_layer(
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conv_cfg,
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inplanes,
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planes,
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3,
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stride=stride,
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padding=dilation,
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dilation=dilation,
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bias=False)
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self.add_module(self.norm1_name, norm1)
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self.conv2 = build_conv_layer(
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conv_cfg, planes, planes, 3, padding=1, bias=False)
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self.add_module(self.norm2_name, norm2)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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self.dilation = dilation
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self.with_cp = with_cp
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@property
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def norm1(self):
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"""nn.Module: normalization layer after the first convolution layer"""
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return getattr(self, self.norm1_name)
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@property
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def norm2(self):
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"""nn.Module: normalization layer after the second convolution layer"""
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return getattr(self, self.norm2_name)
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def forward(self, x):
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"""Forward function."""
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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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out = self.conv2(out)
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out = self.norm2(out)
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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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class Bottleneck(nn.Module):
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"""Bottleneck block for ResNet.
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If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is
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"caffe", the stride-two layer is the first 1x1 conv layer.
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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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stride=1,
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dilation=1,
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downsample=None,
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style='pytorch',
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with_cp=False,
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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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plugins=None):
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super(Bottleneck, self).__init__()
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assert style in ['pytorch', 'caffe']
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assert dcn is None or isinstance(dcn, dict)
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assert plugins is None or isinstance(plugins, list)
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if plugins is not None:
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allowed_position = ['after_conv1', 'after_conv2', 'after_conv3']
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assert all(p['position'] in allowed_position for p in plugins)
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self.inplanes = inplanes
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self.planes = planes
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self.stride = stride
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self.dilation = dilation
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self.style = style
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self.with_cp = with_cp
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self.conv_cfg = conv_cfg
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self.norm_cfg = norm_cfg
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self.dcn = dcn
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self.with_dcn = dcn is not None
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self.plugins = plugins
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self.with_plugins = plugins is not None
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if self.with_plugins:
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self.after_conv1_plugins = [
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plugin['cfg'] for plugin in plugins
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if plugin['position'] == 'after_conv1'
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]
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self.after_conv2_plugins = [
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plugin['cfg'] for plugin in plugins
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if plugin['position'] == 'after_conv2'
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]
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self.after_conv3_plugins = [
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plugin['cfg'] for plugin in plugins
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if plugin['position'] == 'after_conv3'
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]
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if self.style == 'pytorch':
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self.conv1_stride = 1
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self.conv2_stride = stride
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else:
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self.conv1_stride = stride
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self.conv2_stride = 1
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self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
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self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
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self.norm3_name, norm3 = build_norm_layer(
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norm_cfg, planes * self.expansion, postfix=3)
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self.conv1 = build_conv_layer(
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conv_cfg,
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inplanes,
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planes,
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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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fallback_on_stride = False
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if self.with_dcn:
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fallback_on_stride = dcn.pop('fallback_on_stride', False)
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if not self.with_dcn or fallback_on_stride:
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self.conv2 = build_conv_layer(
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conv_cfg,
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planes,
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planes,
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kernel_size=3,
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stride=self.conv2_stride,
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padding=dilation,
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dilation=dilation,
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bias=False)
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else:
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assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
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self.conv2 = build_conv_layer(
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dcn,
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planes,
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planes,
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kernel_size=3,
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stride=self.conv2_stride,
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padding=dilation,
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dilation=dilation,
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bias=False)
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self.add_module(self.norm2_name, norm2)
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self.conv3 = build_conv_layer(
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conv_cfg,
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planes,
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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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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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if self.with_plugins:
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self.after_conv1_plugin_names = self.make_block_plugins(
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planes, self.after_conv1_plugins)
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self.after_conv2_plugin_names = self.make_block_plugins(
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planes, self.after_conv2_plugins)
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self.after_conv3_plugin_names = self.make_block_plugins(
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planes * self.expansion, self.after_conv3_plugins)
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def make_block_plugins(self, in_channels, plugins):
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"""make plugins for block.
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Args:
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in_channels (int): Input channels of plugin.
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plugins (list[dict]): List of plugins cfg to build.
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Returns:
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list[str]: List of the names of plugin.
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"""
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assert isinstance(plugins, list)
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plugin_names = []
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for plugin in plugins:
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plugin = plugin.copy()
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name, layer = build_plugin_layer(
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plugin,
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in_channels=in_channels,
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postfix=plugin.pop('postfix', ''))
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assert not hasattr(self, name), f'duplicate plugin {name}'
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self.add_module(name, layer)
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plugin_names.append(name)
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return plugin_names
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def forward_plugin(self, x, plugin_names):
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"""Forward function for plugins."""
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out = x
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for name in plugin_names:
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out = getattr(self, name)(x)
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return out
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@property
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def norm1(self):
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"""nn.Module: normalization layer after the first convolution layer"""
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return getattr(self, self.norm1_name)
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@property
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def norm2(self):
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"""nn.Module: normalization layer after the second convolution layer"""
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return getattr(self, self.norm2_name)
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@property
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def norm3(self):
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"""nn.Module: normalization layer after the third convolution layer"""
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return getattr(self, self.norm3_name)
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def forward(self, x):
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"""Forward function."""
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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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out = self.norm2(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_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 ResNet(nn.Module):
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"""ResNet backbone.
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Args:
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depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
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in_channels (int): Number of input image channels. Default" 3.
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stem_channels (int): Number of stem channels. Default: 64.
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base_channels (int): Number of base channels of res layer. Default: 64.
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num_stages (int): Resnet stages, normally 4.
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strides (Sequence[int]): Strides of the first block of each stage.
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dilations (Sequence[int]): Dilation of each stage.
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out_indices (Sequence[int]): Output from which stages.
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style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
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layer is the 3x3 conv layer, otherwise the stride-two layer is
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the first 1x1 conv layer.
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deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
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avg_down (bool): Use AvgPool instead of stride conv when
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downsampling in the bottleneck.
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frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
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-1 means not freezing any parameters.
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norm_cfg (dict): Dictionary to construct and config norm layer.
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|
norm_eval (bool): Whether to set norm layers to eval mode, namely,
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freeze running stats (mean and var). Note: Effect on Batch Norm
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and its variants only.
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plugins (list[dict]): List of plugins for stages, each dict contains:
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- cfg (dict, required): Cfg dict to build plugin.
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- position (str, required): Position inside block to insert plugin,
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options: 'after_conv1', 'after_conv2', 'after_conv3'.
|
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|
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- stages (tuple[bool], optional): Stages to apply plugin, length
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should be same as 'num_stages'
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multi_grid (Sequence[int]|None): Multi grid dilation rates of last
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stage. Default: None
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contract_dilation (bool): Whether contract first dilation of each layer
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Default: False
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with_cp (bool): Use checkpoint or not. Using checkpoint will save some
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memory while slowing down the training speed.
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|
zero_init_residual (bool): Whether to use zero init for last norm layer
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|
in resblocks to let them behave as identity.
|
|
|
|
Example:
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>>> from annotator.uniformer.mmseg.models import ResNet
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>>> import torch
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>>> self = ResNet(depth=18)
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>>> self.eval()
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>>> inputs = torch.rand(1, 3, 32, 32)
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>>> level_outputs = self.forward(inputs)
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>>> for level_out in level_outputs:
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... print(tuple(level_out.shape))
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(1, 64, 8, 8)
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(1, 128, 4, 4)
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(1, 256, 2, 2)
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(1, 512, 1, 1)
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"""
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|
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arch_settings = {
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18: (BasicBlock, (2, 2, 2, 2)),
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34: (BasicBlock, (3, 4, 6, 3)),
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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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}
|
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|
|
def __init__(self,
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depth,
|
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in_channels=3,
|
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stem_channels=64,
|
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base_channels=64,
|
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num_stages=4,
|
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strides=(1, 2, 2, 2),
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dilations=(1, 1, 1, 1),
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out_indices=(0, 1, 2, 3),
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style='pytorch',
|
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deep_stem=False,
|
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avg_down=False,
|
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frozen_stages=-1,
|
|
conv_cfg=None,
|
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norm_cfg=dict(type='BN', requires_grad=True),
|
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norm_eval=False,
|
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dcn=None,
|
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stage_with_dcn=(False, False, False, False),
|
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plugins=None,
|
|
multi_grid=None,
|
|
contract_dilation=False,
|
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with_cp=False,
|
|
zero_init_residual=True):
|
|
super(ResNet, self).__init__()
|
|
if depth not in self.arch_settings:
|
|
raise KeyError(f'invalid depth {depth} for resnet')
|
|
self.depth = depth
|
|
self.stem_channels = stem_channels
|
|
self.base_channels = base_channels
|
|
self.num_stages = num_stages
|
|
assert num_stages >= 1 and num_stages <= 4
|
|
self.strides = strides
|
|
self.dilations = dilations
|
|
assert len(strides) == len(dilations) == num_stages
|
|
self.out_indices = out_indices
|
|
assert max(out_indices) < num_stages
|
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self.style = style
|
|
self.deep_stem = deep_stem
|
|
self.avg_down = avg_down
|
|
self.frozen_stages = frozen_stages
|
|
self.conv_cfg = conv_cfg
|
|
self.norm_cfg = norm_cfg
|
|
self.with_cp = with_cp
|
|
self.norm_eval = norm_eval
|
|
self.dcn = dcn
|
|
self.stage_with_dcn = stage_with_dcn
|
|
if dcn is not None:
|
|
assert len(stage_with_dcn) == num_stages
|
|
self.plugins = plugins
|
|
self.multi_grid = multi_grid
|
|
self.contract_dilation = contract_dilation
|
|
self.zero_init_residual = zero_init_residual
|
|
self.block, stage_blocks = self.arch_settings[depth]
|
|
self.stage_blocks = stage_blocks[:num_stages]
|
|
self.inplanes = stem_channels
|
|
|
|
self._make_stem_layer(in_channels, stem_channels)
|
|
|
|
self.res_layers = []
|
|
for i, num_blocks in enumerate(self.stage_blocks):
|
|
stride = strides[i]
|
|
dilation = dilations[i]
|
|
dcn = self.dcn if self.stage_with_dcn[i] else None
|
|
if plugins is not None:
|
|
stage_plugins = self.make_stage_plugins(plugins, i)
|
|
else:
|
|
stage_plugins = None
|
|
|
|
stage_multi_grid = multi_grid if i == len(
|
|
self.stage_blocks) - 1 else None
|
|
planes = base_channels * 2**i
|
|
res_layer = self.make_res_layer(
|
|
block=self.block,
|
|
inplanes=self.inplanes,
|
|
planes=planes,
|
|
num_blocks=num_blocks,
|
|
stride=stride,
|
|
dilation=dilation,
|
|
style=self.style,
|
|
avg_down=self.avg_down,
|
|
with_cp=with_cp,
|
|
conv_cfg=conv_cfg,
|
|
norm_cfg=norm_cfg,
|
|
dcn=dcn,
|
|
plugins=stage_plugins,
|
|
multi_grid=stage_multi_grid,
|
|
contract_dilation=contract_dilation)
|
|
self.inplanes = planes * self.block.expansion
|
|
layer_name = f'layer{i+1}'
|
|
self.add_module(layer_name, res_layer)
|
|
self.res_layers.append(layer_name)
|
|
|
|
self._freeze_stages()
|
|
|
|
self.feat_dim = self.block.expansion * base_channels * 2**(
|
|
len(self.stage_blocks) - 1)
|
|
|
|
def make_stage_plugins(self, plugins, stage_idx):
|
|
"""make plugins for ResNet 'stage_idx'th stage .
|
|
|
|
Currently we support to insert 'context_block',
|
|
'empirical_attention_block', 'nonlocal_block' into the backbone like
|
|
ResNet/ResNeXt. They could be inserted after conv1/conv2/conv3 of
|
|
Bottleneck.
|
|
|
|
An example of plugins format could be :
|
|
>>> plugins=[
|
|
... dict(cfg=dict(type='xxx', arg1='xxx'),
|
|
... stages=(False, True, True, True),
|
|
... position='after_conv2'),
|
|
... dict(cfg=dict(type='yyy'),
|
|
... stages=(True, True, True, True),
|
|
... position='after_conv3'),
|
|
... dict(cfg=dict(type='zzz', postfix='1'),
|
|
... stages=(True, True, True, True),
|
|
... position='after_conv3'),
|
|
... dict(cfg=dict(type='zzz', postfix='2'),
|
|
... stages=(True, True, True, True),
|
|
... position='after_conv3')
|
|
... ]
|
|
>>> self = ResNet(depth=18)
|
|
>>> stage_plugins = self.make_stage_plugins(plugins, 0)
|
|
>>> assert len(stage_plugins) == 3
|
|
|
|
Suppose 'stage_idx=0', the structure of blocks in the stage would be:
|
|
conv1-> conv2->conv3->yyy->zzz1->zzz2
|
|
Suppose 'stage_idx=1', the structure of blocks in the stage would be:
|
|
conv1-> conv2->xxx->conv3->yyy->zzz1->zzz2
|
|
|
|
If stages is missing, the plugin would be applied to all stages.
|
|
|
|
Args:
|
|
plugins (list[dict]): List of plugins cfg to build. The postfix is
|
|
required if multiple same type plugins are inserted.
|
|
stage_idx (int): Index of stage to build
|
|
|
|
Returns:
|
|
list[dict]: Plugins for current stage
|
|
"""
|
|
stage_plugins = []
|
|
for plugin in plugins:
|
|
plugin = plugin.copy()
|
|
stages = plugin.pop('stages', None)
|
|
assert stages is None or len(stages) == self.num_stages
|
|
|
|
if stages is None or stages[stage_idx]:
|
|
stage_plugins.append(plugin)
|
|
|
|
return stage_plugins
|
|
|
|
def make_res_layer(self, **kwargs):
|
|
"""Pack all blocks in a stage into a ``ResLayer``."""
|
|
return ResLayer(**kwargs)
|
|
|
|
@property
|
|
def norm1(self):
|
|
"""nn.Module: the normalization layer named "norm1" """
|
|
return getattr(self, self.norm1_name)
|
|
|
|
def _make_stem_layer(self, in_channels, stem_channels):
|
|
"""Make stem layer for ResNet."""
|
|
if self.deep_stem:
|
|
self.stem = nn.Sequential(
|
|
build_conv_layer(
|
|
self.conv_cfg,
|
|
in_channels,
|
|
stem_channels // 2,
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
bias=False),
|
|
build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
|
|
nn.ReLU(inplace=True),
|
|
build_conv_layer(
|
|
self.conv_cfg,
|
|
stem_channels // 2,
|
|
stem_channels // 2,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
bias=False),
|
|
build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
|
|
nn.ReLU(inplace=True),
|
|
build_conv_layer(
|
|
self.conv_cfg,
|
|
stem_channels // 2,
|
|
stem_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
bias=False),
|
|
build_norm_layer(self.norm_cfg, stem_channels)[1],
|
|
nn.ReLU(inplace=True))
|
|
else:
|
|
self.conv1 = build_conv_layer(
|
|
self.conv_cfg,
|
|
in_channels,
|
|
stem_channels,
|
|
kernel_size=7,
|
|
stride=2,
|
|
padding=3,
|
|
bias=False)
|
|
self.norm1_name, norm1 = build_norm_layer(
|
|
self.norm_cfg, stem_channels, postfix=1)
|
|
self.add_module(self.norm1_name, norm1)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
|
|
|
def _freeze_stages(self):
|
|
"""Freeze stages param and norm stats."""
|
|
if self.frozen_stages >= 0:
|
|
if self.deep_stem:
|
|
self.stem.eval()
|
|
for param in self.stem.parameters():
|
|
param.requires_grad = False
|
|
else:
|
|
self.norm1.eval()
|
|
for m in [self.conv1, self.norm1]:
|
|
for param in m.parameters():
|
|
param.requires_grad = False
|
|
|
|
for i in range(1, self.frozen_stages + 1):
|
|
m = getattr(self, f'layer{i}')
|
|
m.eval()
|
|
for param in m.parameters():
|
|
param.requires_grad = False
|
|
|
|
def init_weights(self, pretrained=None):
|
|
"""Initialize the weights in backbone.
|
|
|
|
Args:
|
|
pretrained (str, optional): Path to pre-trained weights.
|
|
Defaults to None.
|
|
"""
|
|
if isinstance(pretrained, str):
|
|
logger = get_root_logger()
|
|
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)
|
|
|
|
if self.dcn is not None:
|
|
for m in self.modules():
|
|
if isinstance(m, Bottleneck) and hasattr(
|
|
m, 'conv2_offset'):
|
|
constant_init(m.conv2_offset, 0)
|
|
|
|
if self.zero_init_residual:
|
|
for m in self.modules():
|
|
if isinstance(m, Bottleneck):
|
|
constant_init(m.norm3, 0)
|
|
elif isinstance(m, BasicBlock):
|
|
constant_init(m.norm2, 0)
|
|
else:
|
|
raise TypeError('pretrained must be a str or None')
|
|
|
|
def forward(self, x):
|
|
"""Forward function."""
|
|
if self.deep_stem:
|
|
x = self.stem(x)
|
|
else:
|
|
x = self.conv1(x)
|
|
x = self.norm1(x)
|
|
x = self.relu(x)
|
|
x = self.maxpool(x)
|
|
outs = []
|
|
for i, layer_name in enumerate(self.res_layers):
|
|
res_layer = getattr(self, layer_name)
|
|
x = res_layer(x)
|
|
if i in self.out_indices:
|
|
outs.append(x)
|
|
return tuple(outs)
|
|
|
|
def train(self, mode=True):
|
|
"""Convert the model into training mode while keep normalization layer
|
|
freezed."""
|
|
super(ResNet, self).train(mode)
|
|
self._freeze_stages()
|
|
if mode and self.norm_eval:
|
|
for m in self.modules():
|
|
|
|
if isinstance(m, _BatchNorm):
|
|
m.eval()
|
|
|
|
|
|
@BACKBONES.register_module()
|
|
class ResNetV1c(ResNet):
|
|
"""ResNetV1c variant described in [1]_.
|
|
|
|
Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv
|
|
in the input stem with three 3x3 convs.
|
|
|
|
References:
|
|
.. [1] https://arxiv.org/pdf/1812.01187.pdf
|
|
"""
|
|
|
|
def __init__(self, **kwargs):
|
|
super(ResNetV1c, self).__init__(
|
|
deep_stem=True, avg_down=False, **kwargs)
|
|
|
|
|
|
@BACKBONES.register_module()
|
|
class ResNetV1d(ResNet):
|
|
"""ResNetV1d variant described in [1]_.
|
|
|
|
Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in
|
|
the input stem with three 3x3 convs. And in the downsampling block, a 2x2
|
|
avg_pool with stride 2 is added before conv, whose stride is changed to 1.
|
|
"""
|
|
|
|
def __init__(self, **kwargs):
|
|
super(ResNetV1d, self).__init__(
|
|
deep_stem=True, avg_down=True, **kwargs)
|
|
|
