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resnet_adv.py
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import torch.nn as nn
import torch.nn.functional as F
import torch
from torch.nn.modules.batchnorm import BatchNorm2d
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=kernel_size//2, bias=False)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
avg_out = torch.mean(x, dim=1, keepdim=True)
max_out, _ = torch.max(x, dim=1, keepdim=True)
x = torch.cat([avg_out, max_out], dim=1)
x = self.conv1(x)
return self.sigmoid(x)
class RPS_net(nn.Module):
def __init__(self, num_class):
super(RPS_net, self).__init__()
self.init(num_class)
def init(self, num_class):
"""Initialize all parameters"""
self.num_class = num_class
self.sa = SpatialAttention()
self.bn = BatchNorm2d(1)
self.sigmoid = nn.Sigmoid()
self.out_dim = 170
self.conv0 = nn.Conv2d(3,21, kernel_size=3, stride=1, padding=1, bias=True)
self.bn0 = nn.BatchNorm2d(21)
self.conv1_1 = nn.Sequential(nn.Conv2d(21, 21, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(21),nn.ReLU(), nn.Conv2d(21, 21, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(21))
self.conv1_2 = nn.Sequential(nn.Conv2d(21, 21, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(21),nn.ReLU(), nn.Conv2d(21, 21, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(21))
self.conv1_x = nn.Sequential(nn.Conv2d(21,42, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(42))
self.conv2_1 = nn.Sequential(nn.Conv2d(21, 42, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(42),nn.ReLU(), nn.Conv2d(42, 42, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(42))
self.conv2_2 = nn.Sequential(nn.Conv2d(42, 42, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(42),nn.ReLU(), nn.Conv2d(42, 42, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(42))
self.conv2_x = nn.Sequential(nn.Conv2d(42,85, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(85))
self.conv3_1 = nn.Sequential(nn.Conv2d(42, 85, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(85),nn.ReLU(), nn.Conv2d(85, 85, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(85))
self.conv3_2 = nn.Sequential(nn.Conv2d(85, 85, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(85),nn.ReLU(), nn.Conv2d(85, 85, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(85))
self.conv3_x = nn.Sequential(nn.Conv2d(85,170, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(170))
self.conv4_1 = nn.Sequential(nn.Conv2d(85, 170, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(170),nn.ReLU(), nn.Conv2d(170, 170, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(170))
self.conv4_2 = nn.Sequential(nn.Conv2d(170, 170, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(170),nn.ReLU(), nn.Conv2d(170, 170, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(170))
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.prelu = nn.PReLU()
self.relu = nn.ReLU()
self.fc = nn.Linear(self.out_dim, self.num_class, bias=False)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
mb = x.size()[0]
x = self.conv0(x)
sa_w = self.sa(x)
sa_w = self.bn(sa_w)
sa_w = self.sigmoid(sa_w)
x = sa_w * x
x = self.bn0(x)
x = self.relu(x)
y = self.conv1_1(x)
x = y + x
x = self.relu(x)
y = self.conv1_2(x)
x = y + x
x = self.relu(x)
y = self.conv2_1(x)
x = y + self.conv1_x(x)
x = self.relu(x)
y = self.conv2_2(x)
x = y + x
x = self.relu(x)
x = self.pool(x)
y = self.conv3_1(x)
x = y + self.conv2_x(x)
x = self.relu(x)
y = self.conv3_2(x)
x = y + x
x = self.relu(x)
x = self.pool(x)
y = self.conv4_1(x)
x = y + self.conv3_x(x)
x = self.relu(x)
y = self.conv4_2(x)
x = y + x
x = self.relu(x)
x = F.avg_pool2d(x.clamp(min=1e-6).pow(3), (x.size(-2), x.size(-1))).pow(1./3)
x = x.view(-1, self.out_dim)
x1 = self.fc(x)
x2 = self.fc(x)
return x2, x1, sa_w
class RPS_net_mlp(nn.Module):
def __init__(self):
super(RPS_net_mlp, self).__init__()
self.init()
def init(self):
"""Initialize all parameters"""
self.mlp1 = nn.Linear(784, 400)
self.mlp2 = nn.Linear(400, 400)
self.relu = nn.ReLU()
self.fc = nn.Linear(400, 10, bias=False)
self.sigmoid = nn.Sigmoid()
self.sa = SpatialAttention()
self.conv0 = nn.Conv2d(1,3, kernel_size=3, stride=1, padding=1,
bias=False)
self.conv1 = nn.Conv2d(3,1, kernel_size=1, stride=1, padding=0,
bias=False)
self.cuda()
def forward(self, x):
y = self.conv0(x)
sa_w = self.sa(y)
x = sa_w * x
# x = self.conv1(x)
x = x.view(-1, 784)
y = self.mlp1(x)
y = F.relu(y)
y = self.mlp2(y)
y = F.relu(y)
x1 = self.fc(y)
x2 = self.fc(y)
return x2, x1, sa_w
class RPS_net_18(nn.Module):
def __init__(self, num_class):
super(RPS_net_18, self).__init__()
self.init(num_class)
def init(self, num_class):
"""Initialize all parameters"""
self.num_class = num_class
self.out_dim = 512
self.conv0 = nn.Sequential(nn.Conv2d(3,64, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(64))
self.conv1_1 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(64),nn.ReLU(), nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(64))
self.conv1_2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(64),nn.ReLU(), nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(64))
self.conv1_x = nn.Sequential(nn.Conv2d(64,128, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(128))
self.conv2_1 = nn.Sequential(nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(128),nn.ReLU(), nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(128))
self.conv2_2 = nn.Sequential(nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(128),nn.ReLU(), nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(128))
self.conv2_x = nn.Sequential(nn.Conv2d(128,256, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(256))
self.conv3_1 = nn.Sequential(nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(256),nn.ReLU(), nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(256))
self.conv3_2 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(256),nn.ReLU(), nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(256))
self.conv3_x = nn.Sequential(nn.Conv2d(256,512, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(512))
self.conv4_1 = nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(512),nn.ReLU(), nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(512))
self.conv4_2 = nn.Sequential(nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(512),nn.ReLU(), nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),nn.BatchNorm2d(512))
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.prelu = nn.PReLU()
self.relu = nn.ReLU()
self.fc = nn.Linear(self.out_dim, 100, bias=False)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
mb = x.size()[0]
x = self.conv0(x)
y = self.conv1_1(x)
x = y + x
x = self.relu(x)
y = self.conv1_2(x)
x = y + x
x = self.relu(x)
y = self.conv2_1(x)
x = y + self.conv1_x(x)
x = self.relu(x)
y = self.conv2_2(x)
x = y + x
x = self.relu(x)
x = self.pool(x)
y = self.conv3_1(x)
x = y + self.conv2_x(x)
x = self.relu(x)
y = self.conv3_2(x)
x = y + x
x = self.relu(x)
x = self.pool(x)
y = self.conv4_1(x)
x = y + self.conv3_x(x)
x = self.relu(x)
y = self.conv4_2(x)
x = y + x
x = self.relu(x)
x = F.avg_pool2d(x.clamp(min=1e-6).pow(3), (x.size(-2), x.size(-1))).pow(1./3)
x = x.view(-1, self.out_dim)
x1 = self.fc(x)
x2 = self.fc(x)
return x2, x1
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.model_zoo import load_url as load_state_dict_from_url
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
__constants__ = ['downsample']
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
__constants__ = ['downsample']
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = 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)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv0 = nn.Conv2d(3,3, kernel_size=3, stride=1, padding=1, bias=True)
self.sa = SpatialAttention()
self.bn = BatchNorm2d(1)
self.sigmoid = nn.Sigmoid()
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=False)
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])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, 100)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv0(x)
sa_w = self.sa(x)
sa_w = self.bn(sa_w)
sa_w = self.sigmoid(sa_w)
x = sa_w * 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 = torch.flatten(x, 1)
x1 = self.fc(x)
x2 = self.fc(x)
return x1, x2, sa_w
def _resnet(arch, block, layers, pretrained, progress, **kwargs):
model = ResNet(block, layers, **kwargs)
return model
def resnet18(pretrained=False, progress=True, **kwargs):
r"""ResNet-18 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
**kwargs)
def resnet34(pretrained=False, progress=True, **kwargs):
r"""ResNet-34 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
**kwargs)
def resnet50(pretrained=False, progress=True, **kwargs):
r"""ResNet-50 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
**kwargs)
def resnet101(pretrained=False, progress=True, **kwargs):
r"""ResNet-101 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
**kwargs)
def resnet152(pretrained=False, progress=True, **kwargs):
r"""ResNet-152 model from
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
**kwargs)
def resnext50_32x4d(pretrained=False, progress=True, **kwargs):
r"""ResNeXt-50 32x4d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 4
return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
pretrained, progress, **kwargs)
def resnext101_32x8d(pretrained=False, progress=True, **kwargs):
r"""ResNeXt-101 32x8d model from
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['groups'] = 32
kwargs['width_per_group'] = 8
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
pretrained, progress, **kwargs)
def wide_resnet50_2(pretrained=False, progress=True, **kwargs):
r"""Wide ResNet-50-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
pretrained, progress, **kwargs)
def wide_resnet101_2(pretrained=False, progress=True, **kwargs):
r"""Wide ResNet-101-2 model from
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
"""
kwargs['width_per_group'] = 64 * 2
return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
pretrained, progress, **kwargs)