Changing the input channels from 3 to 1

Qur3an: <p>Hi,<br>
I am new to Pytorch, I want to train a Resnet18 model using gray_scale images ( number of channel=1). which part of the following code should be modified to accept my gray_scale images.</p>
<pre><code class="lang-auto">'''
--------------------------
Author: Muhammad Alrabeiah
July 2019
'''

import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.utils.model_zoo import load_url as load_state_dict_from_url

# from .utils import load_state_dict_from_url


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
           'wide_resnet50_2', 'wide_resnet101_2']

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',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation &gt; 1:
            raise NotImplementedError("Dilation &gt; 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = 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:
            identity = self.downsample(x)

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

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = 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:
            identity = self.downsample(x)

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

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.output_dim = num_classes
        print('Output layer dim = ' + str(self.output_dim))
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False) 
        self.bn1 = norm_layer(self.inplanes)
        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,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, self.output_dim)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(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.reshape(x.size(0), -1)
        x = self.fc(x)

        return x


def _resnet(arch, block, layers, pretrained, progress, **kwargs):
    model = ResNet(block, layers, **kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls[arch],
                                              progress=progress)
        # print(state_dict.keys())
        # print(model.fc.weight)
        for key in kwargs:
            if key == 'num_classes':
                num_classes = kwargs[key]

        if num_classes != 1000:
            state_dict['fc.weight'] = nn.init.xavier_normal_(model.fc.weight, gain=1)
            state_dict['fc.bias'] = nn.init.constant_(model.fc.bias, val=0)

        model.load_state_dict(state_dict)
    return model


def resnet18_mod(pretrained=False, progress=True, **kwargs):
    """Constructs a ResNet-18 model for camera-beam prediction.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
                   **kwargs)
</code></pre>
<p>Thanks plenty!</p>

Kapil_Rana: <p>You just need to change the first layer.<br>
There are plenty of solutions already discussed here.</p><aside class="quote quote-modified" data-post="1" data-topic="22762">
  <div class="title">
    <div class="quote-controls"></div>
    <img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/alex_ge/48/17912_2.png" class="avatar">
    <a href="https://discuss.pytorch.org/t/modify-resnet-or-vgg-for-single-channel-grayscale/22762">Modify ResNet or VGG for single channel grayscale</a> 
  </div>
  <blockquote>
    Hi, 
I’m working on infrared data which I convert to grayscale 64x64 (although I can use other sizes, but usually my GPU runs out of memory). I used VGG11 but I manually recreated the architecture in order to use it, which is a very cumbersome and error prone task. 
I was wondering if there is an easier way to modify VGG19 or ResNet architectures in a fast and simpler way to use my 64x64 single channel input, and if yes, would that make sense since those models are fine-tuned for 3 channel RGB? 
…
  </blockquote>
</aside>
<aside class="quote" data-post="1" data-topic="48693">
  <div class="title">
    <div class="quote-controls"></div>
    <img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/nishanth_sasankan/48/13219_2.png" class="avatar">
    <a href="https://discuss.pytorch.org/t/grayscale-images-for-resenet-and-deeplabv3/48693">Grayscale images for resenet and deeplabv3</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>
  </div>
  <blockquote>
    How can I modify a resnet or VGG network to use grayscale images. I am loading the network the following way 
m=torchvision.models.segmentation.fcn_resnet50(pretrained=False, progress=True, num_classes=2, aux_loss=None)

Is there some way I can tweak this model after loading it? 
Thanks
  </blockquote>
</aside>


Qur3an: <p>Thank you <a class="mention" href="/u/kapil_rana">@Kapil_Rana</a>  for your reply,<br>
I changed the code to be as the following</p>
<aside class="quote no-group quote-modified" data-username="Qur3an" data-post="1" data-topic="125764">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/qur3an/48/39934_2.png" class="avatar"> Qur3an:</div>
<blockquote>
<pre><code class="lang-auto">        self.conv1 = nn.Conv2d(1, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False) 
</code></pre>
</blockquote>
</aside>
<p>but I got the following error:</p>
<p>RuntimeError: Error(s) in loading state_dict for ResNet:<br>
size mismatch for conv1.weight: copying a param with shape torch.Size([64, 3, 7, 7]) from checkpoint, the shape in current model is torch.Size([64, 1, 7, 7]).</p>
<p>I think it is related to torch.utils.model_zoo.load_url from which I downloaded the pretrained resnet18.</p>
<p>how I can solve this error?</p>

Kapil_Rana: <p>I guess there is typing mistake<br>
what is the value of self.inplanes</p>

Qur3an: <p>The code is working properly when I use RGB images, so I think there is no errors with the code.<br>
sorry, I am still new to Pytorch, I dont know what is self.inplanes for. But it exist in resnet18 code.</p>

scyron: <p>My similar solution to fix this was:</p>
<pre><code class="lang-auto">model = some_Network()
sd = model.state_dict()
sd.update(torch.load(some_Network_model_path))

model.load_state_dict(sd)
model.moduleFeatures.moduleOne = torch.nn.Sequential(
                    torch.nn.Conv2d(in_channels=1, out_channels=32, kernel_size=7, stride=1, padding=3),
                    torch.nn.LeakyReLU(inplace=False, negative_slope=0.1)
                )
</code></pre>
<p>Where I change the first layer of the model from a 3-channel input convolution layer to a 1-channel input convolution layer to handle grayscale images as input</p>