# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Created by: Hang Zhang
# Email: zhanghang0704@gmail.com
# Copyright (c) 2020
#
# LICENSE file in the root directory of this source tree
# +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
"""ResNet variants"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Conv2d, Module, ReLU
from torch.nn.modules.utils import _pair
from easycv.framework.errors import KeyError, NotImplementedError, RuntimeError
from ..registry import BACKBONES
[docs]class SplAtConv2d(Module):
"""Split-Attention Conv2d
"""
[docs] def __init__(self,
in_channels,
channels,
kernel_size,
stride=(1, 1),
padding=(0, 0),
dilation=(1, 1),
groups=1,
bias=True,
radix=2,
reduction_factor=4,
rectify=False,
rectify_avg=False,
norm_layer=None,
dropblock_prob=0.0,
**kwargs):
super(SplAtConv2d, self).__init__()
padding = _pair(padding)
self.rectify = rectify and (padding[0] > 0 or padding[1] > 0)
self.rectify_avg = rectify_avg
inter_channels = max(in_channels * radix // reduction_factor, 32)
self.radix = radix
self.cardinality = groups
self.channels = channels
self.dropblock_prob = dropblock_prob
if self.rectify:
from rfconv import RFConv2d
self.conv = RFConv2d(
in_channels,
channels * radix,
kernel_size,
stride,
padding,
dilation,
groups=groups * radix,
bias=bias,
average_mode=rectify_avg,
**kwargs)
else:
self.conv = Conv2d(
in_channels,
channels * radix,
kernel_size,
stride,
padding,
dilation,
groups=groups * radix,
bias=bias,
**kwargs)
self.use_bn = norm_layer is not None
if self.use_bn:
self.bn0 = norm_layer(channels * radix)
self.relu = ReLU(inplace=True)
self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
if self.use_bn:
self.bn1 = norm_layer(inter_channels)
self.fc2 = Conv2d(
inter_channels, channels * radix, 1, groups=self.cardinality)
if dropblock_prob > 0.0:
self.dropblock = DropBlock2D(dropblock_prob, 3)
self.rsoftmax = rSoftMax(radix, groups)
[docs] def forward(self, x):
x = self.conv(x)
if self.use_bn:
x = self.bn0(x)
if self.dropblock_prob > 0.0:
x = self.dropblock(x)
x = self.relu(x)
batch, rchannel = x.shape[:2]
if self.radix > 1:
if torch.__version__ < '1.5':
splited = torch.split(x, int(rchannel // self.radix), dim=1)
else:
splited = torch.split(x, rchannel // self.radix, dim=1)
gap = sum(splited)
else:
gap = x
gap = F.adaptive_avg_pool2d(gap, 1)
gap = self.fc1(gap)
if self.use_bn:
gap = self.bn1(gap)
gap = self.relu(gap)
atten = self.fc2(gap)
atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
if self.radix > 1:
if torch.__version__ < '1.5':
attens = torch.split(atten, int(rchannel // self.radix), dim=1)
else:
attens = torch.split(atten, rchannel // self.radix, dim=1)
out = sum([att * split for (att, split) in zip(attens, splited)])
else:
out = atten * x
return out.contiguous()
[docs]class rSoftMax(nn.Module):
[docs] def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
[docs] def forward(self, x):
batch = x.size(0)
if self.radix > 1:
x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
x = F.softmax(x, dim=1)
x = x.reshape(batch, -1)
else:
x = torch.sigmoid(x)
return x
[docs]class DropBlock2D(object):
[docs] def __init__(self, *args, **kwargs):
raise NotImplementedError
[docs]class GlobalAvgPool2d(nn.Module):
[docs] def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
[docs] def forward(self, inputs):
return nn.functional.adaptive_avg_pool2d(inputs,
1).view(inputs.size(0), -1)
[docs]class Bottleneck(nn.Module):
"""ResNet Bottleneck
"""
# pylint: disable=unused-argument
expansion = 4
[docs] def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
radix=1,
cardinality=1,
bottleneck_width=64,
avd=False,
avd_first=False,
dilation=1,
is_first=False,
rectified_conv=False,
rectify_avg=False,
norm_layer=None,
dropblock_prob=0.0,
last_gamma=False):
super(Bottleneck, self).__init__()
group_width = int(planes * (bottleneck_width / 64.)) * cardinality
self.conv1 = nn.Conv2d(
inplanes, group_width, kernel_size=1, bias=False)
self.bn1 = norm_layer(group_width)
self.dropblock_prob = dropblock_prob
self.radix = radix
self.avd = avd and (stride > 1 or is_first)
self.avd_first = avd_first
if self.avd:
self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
stride = 1
if dropblock_prob > 0.0:
self.dropblock1 = DropBlock2D(dropblock_prob, 3)
if radix == 1:
self.dropblock2 = DropBlock2D(dropblock_prob, 3)
self.dropblock3 = DropBlock2D(dropblock_prob, 3)
if radix >= 1:
self.conv2 = SplAtConv2d(
group_width,
group_width,
kernel_size=3,
stride=stride,
padding=dilation,
dilation=dilation,
groups=cardinality,
bias=False,
radix=radix,
rectify=rectified_conv,
rectify_avg=rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
elif rectified_conv:
from rfconv import RFConv2d
self.conv2 = RFConv2d(
group_width,
group_width,
kernel_size=3,
stride=stride,
padding=dilation,
dilation=dilation,
groups=cardinality,
bias=False,
average_mode=rectify_avg)
self.bn2 = norm_layer(group_width)
else:
self.conv2 = nn.Conv2d(
group_width,
group_width,
kernel_size=3,
stride=stride,
padding=dilation,
dilation=dilation,
groups=cardinality,
bias=False)
self.bn2 = norm_layer(group_width)
self.conv3 = nn.Conv2d(
group_width, planes * 4, kernel_size=1, bias=False)
self.bn3 = norm_layer(planes * 4)
if last_gamma:
from torch.nn.init import zeros_
zeros_(self.bn3.weight)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.dilation = dilation
self.stride = stride
[docs] def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
if self.dropblock_prob > 0.0:
out = self.dropblock1(out)
out = self.relu(out)
if self.avd and self.avd_first:
out = self.avd_layer(out)
out = self.conv2(out)
if self.radix == 0:
out = self.bn2(out)
if self.dropblock_prob > 0.0:
out = self.dropblock2(out)
out = self.relu(out)
if self.avd and not self.avd_first:
out = self.avd_layer(out)
out = self.conv3(out)
out = self.bn3(out)
if self.dropblock_prob > 0.0:
out = self.dropblock3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
[docs]@BACKBONES.register_module
class ResNeSt(nn.Module):
"""ResNet Variants
Parameters
----------
block : Block
Class for the residual block. Options are BasicBlockV1, BottleneckV1.
layers : list of int
Numbers of layers in each block
classes : int, default 1000
Number of classification classes.
dilated : bool, default False
Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
typically used in Semantic Segmentation.
norm_layer : object
Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
for Synchronized Cross-GPU BachNormalization).
Reference:
- He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
"""
arch_settings = {
50: ((3, 4, 6, 3), 32),
101: ((3, 4, 23, 3), 64),
200: ((3, 24, 36, 3), 64),
269: ((3, 30, 48, 8), 64),
}
# pylint: disable=unused-variable
[docs] def __init__(self,
depth=None,
block=Bottleneck,
layers=[3, 4, 6, 3],
radix=2,
groups=1,
bottleneck_width=64,
num_classes=0,
dilated=False,
dilation=1,
deep_stem=True,
stem_width=32,
avg_down=True,
rectified_conv=False,
rectify_avg=False,
avd=False,
avd_first=False,
final_drop=0.0,
dropblock_prob=0,
last_gamma=False,
norm_layer=nn.BatchNorm2d):
super(ResNeSt, self).__init__()
if depth is not None:
if depth not in self.arch_settings:
raise KeyError('invalid depth {} for resnet'.format(depth))
layers, stem_width = self.arch_settings[depth]
self.cardinality = groups
self.bottleneck_width = bottleneck_width
# ResNet-D params
self.inplanes = stem_width * 2 if deep_stem else 64
self.avg_down = avg_down
self.last_gamma = last_gamma
# ResNeSt params
self.radix = radix
self.avd = avd
self.avd_first = avd_first
self.rectified_conv = rectified_conv
self.rectify_avg = rectify_avg
if rectified_conv:
from rfconv import RFConv2d
conv_layer = RFConv2d
else:
conv_layer = nn.Conv2d
conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}
if deep_stem:
self.conv1 = nn.Sequential(
conv_layer(
3,
stem_width,
kernel_size=3,
stride=2,
padding=1,
bias=False,
**conv_kwargs),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(
stem_width,
stem_width,
kernel_size=3,
stride=1,
padding=1,
bias=False,
**conv_kwargs),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(
stem_width,
stem_width * 2,
kernel_size=3,
stride=1,
padding=1,
bias=False,
**conv_kwargs),
)
else:
self.conv1 = conv_layer(
3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False,
**conv_kwargs)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(
block, 64, layers[0], norm_layer=norm_layer, is_first=False)
self.layer2 = self._make_layer(
block, 128, layers[1], stride=2, norm_layer=norm_layer)
if dilated or dilation == 4:
self.layer3 = self._make_layer(
block,
256,
layers[2],
stride=1,
dilation=2,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
self.layer4 = self._make_layer(
block,
512,
layers[3],
stride=1,
dilation=4,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
elif dilation == 2:
self.layer3 = self._make_layer(
block,
256,
layers[2],
stride=2,
dilation=1,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
self.layer4 = self._make_layer(
block,
512,
layers[3],
stride=1,
dilation=2,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
else:
self.layer3 = self._make_layer(
block,
256,
layers[2],
stride=2,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
self.layer4 = self._make_layer(
block,
512,
layers[3],
stride=2,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
self.avgpool = GlobalAvgPool2d()
self.drop = nn.Dropout(final_drop) if final_drop > 0.0 else None
self.norm_layer = norm_layer
if num_classes > 0:
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
[docs] def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, self.norm_layer):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self,
block,
planes,
blocks,
stride=1,
dilation=1,
norm_layer=None,
dropblock_prob=0.0,
is_first=True):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
down_layers = []
if self.avg_down:
if dilation == 1:
down_layers.append(
nn.AvgPool2d(
kernel_size=stride,
stride=stride,
ceil_mode=True,
count_include_pad=False))
else:
down_layers.append(
nn.AvgPool2d(
kernel_size=1,
stride=1,
ceil_mode=True,
count_include_pad=False))
down_layers.append(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=1,
bias=False))
else:
down_layers.append(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False))
down_layers.append(norm_layer(planes * block.expansion))
downsample = nn.Sequential(*down_layers)
layers = []
if dilation == 1 or dilation == 2:
layers.append(
block(
self.inplanes,
planes,
stride,
downsample=downsample,
radix=self.radix,
cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd,
avd_first=self.avd_first,
dilation=1,
is_first=is_first,
rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
elif dilation == 4:
layers.append(
block(
self.inplanes,
planes,
stride,
downsample=downsample,
radix=self.radix,
cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd,
avd_first=self.avd_first,
dilation=2,
is_first=is_first,
rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
else:
raise RuntimeError('=> unknown dilation size: {}'.format(dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(
self.inplanes,
planes,
radix=self.radix,
cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd,
avd_first=self.avd_first,
dilation=dilation,
rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
return nn.Sequential(*layers)
[docs] def forward(self, x):
outs = []
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
if hasattr(self, 'fc'):
x = self.avgpool(x)
x = torch.flatten(x, 1)
if self.drop:
x = self.drop(x)
bs = x.size(0)
x = x.view(bs, -1)
x = self.fc(x)
outs.append(x)
return outs