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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.weight_norm as weightNorm
from torch.autograd import Variable
import sys
def conv3x3(in_planes, out_planes, stride=1):
return weightNorm(nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True))
class TReLU(nn.Module):
def __init__(self):
super(TReLU, self).__init__()
self.alpha = nn.Parameter(torch.FloatTensor(1), requires_grad=True)
self.alpha.data.fill_(0)
def forward(self, x):
x = F.relu(x - self.alpha) + self.alpha
return x
def cfg(depth):
depth_lst = [18, 34, 50, 101, 152]
assert (depth in depth_lst), "Error : Resnet depth should be either 18, 34, 50, 101, 152"
cf_dict = {
'18': (BasicBlock, [2,2,2,2]),
'34': (BasicBlock, [3,4,6,3]),
'50': (Bottleneck, [3,4,6,3]),
'101':(Bottleneck, [3,4,23,3]),
'152':(Bottleneck, [3,8,36,3]),
}
return cf_dict[str(depth)]
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.conv2 = conv3x3(planes, planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
weightNorm(nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=True)),
)
self.relu_1 = TReLU()
self.relu_2 = TReLU()
def forward(self, x):
out = self.relu_1(self.conv1(x))
out = self.conv2(out)
out += self.shortcut(x)
out = self.relu_2(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = weightNorm(nn.Conv2d(in_planes, planes, kernel_size=1, bias=True))
self.conv2 = weightNorm(nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=True))
self.conv3 = weightNorm(nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=True))
self.relu_1 = TReLU()
self.relu_2 = TReLU()
self.relu_3 = TReLU()
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
weightNorm(nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=True)),
)
def forward(self, x):
out = self.relu_1(self.conv1(x))
out = self.relu_2(self.conv2(out))
out = self.conv3(out)
out += self.shortcut(x)
out = self.relu_3(out)
return out
class ResNet_wobn(nn.Module):
def __init__(self, num_inputs, depth, num_outputs):
super(ResNet_wobn, self).__init__()
self.in_planes = 64
block, num_blocks = cfg(depth)
self.conv1 = conv3x3(num_inputs, 64, 2)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=2)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.fc = nn.Linear(512 * block.expansion, num_outputs)
self.relu_1 = TReLU()
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
x = self.relu_1(self.conv1(x))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = F.avg_pool2d(x, 4)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
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