title: Change input shape dimensions for ResNet model (pytorch)
body: <p>I want to feed my <strong>3,320,320 pictures</strong> in an existing ResNet model. The model actually <strong>expects input of size 3,32,32</strong>. As I am afraid of loosing information I don't simply want to resize my pictures.
  What is the best way to preprocess my images, so that they are able to run on the ResNet34?
  Should I add additional layers in the forward method of ResNet? If yes, what would be a suitable combination in my case?</p>
  
  <pre><code>import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from pytorch_fitmodule import FitModule
  from torch.autograd import Variable
  import numpy as np
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
  
  
  class BasicBlock(FitModule):
      expansion = 1
  
      def __init__(self, in_planes, planes, stride=1):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3(in_planes, planes, stride)
          self.bn1 = nn.BatchNorm2d(planes)
          self.conv2 = conv3x3(planes, planes)
          self.bn2 = nn.BatchNorm2d(planes)
  
          self.shortcut = nn.Sequential()
          if stride != 1 or in_planes != self.expansion * planes:
              self.shortcut = nn.Sequential(
                  nn.Conv2d(in_planes, self.expansion * planes,
                            kernel_size=1, stride=stride, bias=False),
                  nn.BatchNorm2d(self.expansion * planes)
              )
  
      def forward(self, x):
          out = F.relu(self.bn1(self.conv1(x)))
          out = self.bn2(self.conv2(out))
          out += self.shortcut(x)
          out = F.relu(out)
          return out
  
  
  
  class ResNet(FitModule):
      def __init__(self, block, num_blocks, num_classes=10):
          super(ResNet, self).__init__()
          self.in_planes = 64
  
          self.conv1 = conv3x3(3, 64)
          self.bn1 = nn.BatchNorm2d(64)                                           
          self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)      
          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.linear = nn.Linear(512 * block.expansion, num_classes)
  
      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):    # add additional layers here?                                       
          x = x.float()                                              
          out = F.relu(self.bn1(self.conv1(x).float()).float())      
          out = self.layer1(out)                                      
          out = self.layer2(out)                                     
          out = self.layer3(out)                                      
          out = self.layer4(out)                                      
          out = F.avg_pool2d(out, 4)                                 
          out = out.view(out.size(0), -1)                            
          out = self.linear(out)
          return out
  
  
  
  def ResNet34():
      return ResNet(BasicBlock, [3, 4, 6, 3])
  
  
  </code></pre>
  
  <p>Thanks plenty!</p>
  
  <p>Regards,
  Fabian</p>