Thank you again for the reply. I still got the same size
torch.Size([1, 512, 10, 10])
Here is my model.
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 ResNet(nn.Module):
    def __init__(self, block, layers, num_classes, grayscale):
        self.inplanes = 64
        super (ResNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 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)
        
        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, (2. / n) **.5)
            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 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.view(x.size(0), -1)
        x = self.fc(x)
        
        return x
    
def resnet34(num_classes):
    model = ResNet(block = BasicBlock, layers = [3, 4, 6, 3],
                     num_classes = classes, grayscale = 1 )
    return model

And here is following
#instantiating model
classes=10
model = resnet34(classes)

#setting loss function
criterion = nn.CrossEntropyLoss()

#setting optimizer
optimizer = optim.SGD(model.parameters(), lr = 0.001, momentum = 0.9)

Here is feature extraction part.
model.eval()

print(model)

num_ftrs = model.fc.in_features

#extracting features v1 stripping layer before average pool
feature_extractor = torch.nn.Sequential(*list(model.children())[:-1])

#testing the feature extractor output
ftr1 = feature_extractor(torch.randn(1,1,512,512))

print(ftr1.size())



And i got the same
torch.Size([1, 512, 10, 10])

I am very thankful for making time for me.