ResNet basic modifications (multi-target + regression)

username: mflova
cooked: <p>Hi!<br>
I have been studying Machine Learning for such a long time and I decided to start with Deep Learning models. I am trying to implement a regression problem (2 targets) from an BW processed image dataset that I have created.<br>
Since I am using the ResNet architecture, I have tried to make some changes to the model, but I still have so many doubts regarding some modifications:</p>
<p>Regarding the input, I have make several tests with different channels and it is working properly<br>
However, about the outputs, I have two main concerns:</p>
<ul>
<li>First one is, how can I modify the resnet architecture in order to train a multi-target ResNet?</li>
<li>Second one is about regression problem. I have deleted the softmax layer at the end. However, I cannot test wether it works properly or not, since I couldn’t implement the multi-target ResNet. Will it work if the previous modification (multi-target) is done?</li>
</ul>
<p>ResNet Model used is exactly the same found here:</p><aside class="quote quote-modified" data-post="1" data-topic="82492">
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    <img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/f/97f17d/48.png" class="avatar">
    <a href="https://discuss.pytorch.org/t/change-input-shape-dimensions-for-resnet-model/82492">Change input shape dimensions for ResNet model</a> <a class="badge-category__wrapper " href="/c/vision/5"><span data-category-id="5" data-drop-close="true" class="badge-category " title="Topics related to either pytorch/vision or vision research related topics"><span class="badge-category__name">vision</span></span></a>
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  <blockquote>
    Hi there, 
I want to feed my  3,320,320 pictures  in an existing ResNet model. The model actually  expects input of size 3,32,32 . 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? 
import torch
import torch.nn as nn
import torch.nn.functional as F
from py…
  </blockquote>
</aside>

<pre><code class="lang-auto">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>Thank you!</p>

username: ptrblck
cooked: <p>I assume multi-target refers to a multi-class classification, i.e. each sample corresponds to one target only.<br>
If that’s the case, you could create the last linear layer with <code>out_features=nb_classes</code>, such that each sample will yield the logits for all classes.<br>
For the criterion you could use <code>nn.CrossEntropyLoss</code>, which expects the model output to be raw logits in the shape <code>[batch_size, nb_classes]</code> (which should be the case using the mentioned modification) and a target in the shape <code>[batch_size]</code>, containing the class indices in the range <code>[0, nb_classes-1]</code>.</p>
<p>If you are working on a regression task, you could also modify the last linear layer and set <code>out_features=regression_features</code>, where <code>regression_features</code> would refer to the number of features your model should predict.<br>
You could add an additional activation function, but it depends on your use case.<br>
<code>nn.MSELoss</code> could be used as the criterion (also <code>nn.L1Loss</code> etc.). The target should have the same shape as the model output in this case.</p>