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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 | |