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# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_plugin_layer
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding."""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
def conv1x1(in_planes, out_planes):
"""1x1 convolution with padding."""
return nn.Conv2d(
in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
use_conv1x1=False,
plugins=None):
super(BasicBlock, self).__init__()
if use_conv1x1:
self.conv1 = conv1x1(inplanes, planes)
self.conv2 = conv3x3(planes, planes * self.expansion, stride)
else:
self.conv1 = conv3x3(inplanes, planes, stride)
self.conv2 = conv3x3(planes, planes * self.expansion)
self.with_plugins = False
if plugins:
if isinstance(plugins, dict):
plugins = [plugins]
self.with_plugins = True
# collect plugins for conv1/conv2/
self.before_conv1_plugin = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'before_conv1'
]
self.after_conv1_plugin = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_conv1'
]
self.after_conv2_plugin = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_conv2'
]
self.after_shortcut_plugin = [
plugin['cfg'] for plugin in plugins
if plugin['position'] == 'after_shortcut'
]
self.planes = planes
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.bn2 = nn.BatchNorm2d(planes * self.expansion)
self.downsample = downsample
self.stride = stride
if self.with_plugins:
self.before_conv1_plugin_names = self.make_block_plugins(
inplanes, self.before_conv1_plugin)
self.after_conv1_plugin_names = self.make_block_plugins(
planes, self.after_conv1_plugin)
self.after_conv2_plugin_names = self.make_block_plugins(
planes, self.after_conv2_plugin)
self.after_shortcut_plugin_names = self.make_block_plugins(
planes, self.after_shortcut_plugin)
def make_block_plugins(self, in_channels, plugins):
"""make plugins for block.
Args:
in_channels (int): Input channels of plugin.
plugins (list[dict]): List of plugins cfg to build.
Returns:
list[str]: List of the names of plugin.
"""
assert isinstance(plugins, list)
plugin_names = []
for plugin in plugins:
plugin = plugin.copy()
name, layer = build_plugin_layer(
plugin,
in_channels=in_channels,
out_channels=in_channels,
postfix=plugin.pop('postfix', ''))
assert not hasattr(self, name), f'duplicate plugin {name}'
self.add_module(name, layer)
plugin_names.append(name)
return plugin_names
def forward_plugin(self, x, plugin_names):
out = x
for name in plugin_names:
out = getattr(self, name)(x)
return out
def forward(self, x):
if self.with_plugins:
x = self.forward_plugin(x, self.before_conv1_plugin_names)
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv1_plugin_names)
out = self.conv2(out)
out = self.bn2(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_conv2_plugin_names)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
if self.with_plugins:
out = self.forward_plugin(out, self.after_shortcut_plugin_names)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=False):
super().__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, 3, stride, 1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(
planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
if downsample:
self.downsample = nn.Sequential(
nn.Conv2d(
inplanes, planes * self.expansion, 1, stride, bias=False),
nn.BatchNorm2d(planes * self.expansion),
)
else:
self.downsample = nn.Sequential()
def forward(self, x):
residual = self.downsample(x)
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
out += residual
out = self.relu(out)
return out
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