I wish create a Spatial Trasformer Layer in ResNet 18 with pytorch. I’m following “Spatial Trasformer Network tutorial pytorch”: http://pytorch.org/tutorials/intermediate/spatial_transformer_tutorial.html 6
My ResNet with Spatial Trasformer Layer is:
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
all = [‘ResNet’, ‘resnet18’, ‘resnet34’, ‘resnet50’, ‘resnet101’,
‘resnet152’]
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 1’,
‘resnet152’: ‘https://download.pytorch.org/models/resnet152-b121ed2d.pth’,
}
def conv3x3(in_planes, out_planes, stride=1):
“3x3 convolution with padding”
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
    super(BasicBlock, self).__init__()
    self.conv1 = conv3x3(inplanes, planes, stride)
    self.bn1 = nn.BatchNorm2d(planes)
    self.relu = nn.ReLU(inplace=True)
    self.conv2 = conv3x3(planes, planes)
    self.bn2 = nn.BatchNorm2d(planes)
    self.downsample = downsample
    self.stride = stride

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)

    if self.downsample is not None:
        residual = self.downsample(x)

    out += residual
    out = self.relu(out)

    return out

class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
    super(Bottleneck, self).__init__()
    self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(planes)
    self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                           padding=1, bias=False)
    self.bn2 = nn.BatchNorm2d(planes)
    self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
    self.bn3 = nn.BatchNorm2d(planes * 4)
    self.relu = nn.ReLU(inplace=True)
    self.downsample = downsample
    self.stride = stride

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

class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
    self.inplanes = 64
    super(ResNet, self).__init__()
    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                           bias=False)
    self.bn1 = nn.BatchNorm2d(64)
    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)
    self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
    self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
    self.avgpool = nn.AvgPool2d(7, stride=1)
    self.fc = nn.Linear(512 * block.expansion, num_classes)
    
    self.localization = nn.Sequential(
        nn.Conv2d(1, 8, kernel_size=7),
        nn.MaxPool2d(2, stride=2),
        nn.ReLU(True),
        nn.Conv2d(8, 10, kernel_size=5),
        nn.MaxPool2d(2, stride=2),
        nn.ReLU(True)
    )
       # Regressor for the 3 * 2 affine matrix
    self.fc_loc = nn.Sequential(
        nn.Linear(10 * 3 * 3, 32),
        nn.ReLU(True),
        nn.Linear(32, 3 * 2)
    )
    self.fc_loc[2].weight.data.fill_(0)
    self.fc_loc[2].bias.data = torch.FloatTensor([1, 0, 0, 0, 1, 0])

    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, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()

def _make_layer(self, block, planes, blocks, stride=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.BatchNorm2d(planes * block.expansion),
        )

    layers = []
    layers.append(block(self.inplanes, planes, stride, downsample))
    self.inplanes = planes * block.expansion
    for i in range(1, blocks):
        layers.append(block(self.inplanes, planes))

    return nn.Sequential(*layers)

def stn(self, x):
    xs = self.localization(x)
    xs = xs.view(-1, 10 * 3 * 3)
    theta = self.fc_loc(xs)
    theta = theta.view(-1, 2, 3)

    grid = F.affine_grid(theta, x.size())
    x = F.grid_sample(x, grid)

    return x

def forward(self, x):
    
    x = self.conv1(x)
    x = self.bn1(x)
    x = self.stn(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 = self.fc(x)

    return x

def resnet18(pretrained=False, **kwargs):
“”"Constructs a ResNet-18 model.
Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model

def resnet34(pretrained=False, **kwargs):
“”"Constructs a ResNet-34 model.
Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model

def resnet50(pretrained=False, **kwargs):
“”"Constructs a ResNet-50 model.
Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model

def resnet101(pretrained=False, **kwargs):
“”"Constructs a ResNet-101 model.
Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
return model

def resnet152(pretrained=False, **kwargs):
“”"Constructs a ResNet-152 model.
Args:
    pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
return model`` 

Is right this implementation? I want use pre-trained ResNet18