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##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## 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
from .splat import SplAtConv2d
__all__ = ['ResNet', 'Bottleneck']
class DropBlock2D(object):
def __init__(self, *args, **kwargs):
raise NotImplementedError
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool2d(inputs, 1).view(inputs.size(0), -1)
class Bottleneck(nn.Module):
"""ResNet Bottleneck
"""
# pylint: disable=unused-argument
expansion = 4
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
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
class ResNet(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."
"""
# pylint: disable=unused-variable
def __init__(self, block, layers, radix=1, groups=1, bottleneck_width=64,
num_classes=1000, dilated=False, dilation=1,
deep_stem=False, stem_width=64, avg_down=False,
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):
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
super(ResNet, self).__init__()
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.layer1 = self._make_layer(block, 64, layers[0], stride=2, 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.fc = nn.Linear(512 * block.expansion, num_classes)
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, 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)
def forward(self, x):
'''
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)
'''
x = self.avgpool(x)
#x = x.view(x.size(0), -1)
x = torch.flatten(x, 1)
if self.drop:
x = self.drop(x)
x = self.fc(x)
'''
return x
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