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resnet.py
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import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None,dilation=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.GroupNorm(32, planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,dilation=dilation,
padding=dilation, bias=False)
self.bn2 = nn.GroupNorm(32, planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.GroupNorm(32, planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.dilation = dilation
gn_init(self.bn1)
gn_init(self.bn2)
gn_init(self.bn3, zero_init=True)
def forward(self, x):
residual = 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:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
def conv2d_init(m):
assert 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))
def gn_init(m, zero_init=False):
assert isinstance(m, nn.GroupNorm)
m.weight.data.fill_(0. if zero_init else 1.)
m.bias.data.zero_()
class ResNetMODTRI3(nn.Module):
def __init__(self, block, layers, output_stride, pretrained=True):
self.inplanes = 64
super(ResNetMODTRI3, self).__init__()
blocks = [1, 2, 4]
if output_stride == 16:
strides = [1, 2, 2, 1]
dilations = [1, 1, 1, 2]
elif output_stride == 8:
strides = [1, 2, 1, 1]
dilations = [1, 1, 2, 4]
else:
strides = [1, 2, 2, 2]
dilations = [1, 1, 1, 1]
self.conv0 = nn.Sequential(nn.Conv2d(6, 32, kernel_size=3, stride=1, padding=1,
bias=True),nn.PReLU(32))
self.conv1a = nn.Sequential(nn.Conv2d(32, 32, kernel_size=3, stride=2, padding=1,
bias=True),nn.PReLU(32),nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),nn.PReLU(64))
self.conv2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,
bias=True))
self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1])
self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2])
# self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3])
self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3])
if pretrained:
self._load_pretrained_model()
def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.GroupNorm(32, planes * block.expansion),
)
m = downsample[1]
assert isinstance(m, nn.GroupNorm)
gn_init(m)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, dilation = dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes,dilation=dilation))
return nn.Sequential(*layers)
def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.GroupNorm(32,planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation,
downsample=downsample))
self.inplanes = planes * block.expansion
for i in range(1, len(blocks)):
layers.append(block(self.inplanes, planes, stride=1,
dilation=blocks[i]*dilation))
return nn.Sequential(*layers)
def forward(self, x,return_feat=False,return_feat_aux=False):
conv_out = [x]
x0=self.conv0(x)
conv_out.append(x0)
x = self.conv1a(x0)
conv_out.append(x)
x_ = self.conv2(x)
x = self.layer1(x_)
conv_out.append(x)
x = self.layer2(x)
conv_out.append(x)
x = self.layer3(x)
conv_out.append(x)
if return_feat_aux:
return conv_out,x_
x = self.layer4(x)
conv_out.append(x)
if return_feat:
return conv_out,x_
return x
def ResNet50_MODTRI(os,pretrained=False):
model = ResNetMODTRI3(Bottleneck, [3, 4, 6, 3], os, pretrained=pretrained)
return model