Hi! I’m trying to implement transfer learning on binary classification using ResNet model. I have tried the solution showed in  this discussion 1 but I’m stuck in this error:
RuntimeError: running_mean should contain 128 elements not 64

This is the way I define and call the pretrained model:
class ResnetPretrained(models.resnet.ResNet):
    def __init__(self, block, layers, num_classes=2):
        self.inplanes = 128
        super(ResnetPretrained, self).__init__(block, layers)
        self.conv1 = nn.Conv2d(1, 128, kernel_size=(7, 7), bias=False)

model = ResnetPretrained(models.resnet.Bottleneck, [3, 8, 36, 3]).to(device)

My input is 128x128, 1 channel and I’m normalizing the datasets with its own mean and std parameters.
This line calls the error:
for epoch in range(self.num_epochs):
    i = 0
    for images, labels in self.train_set_loader:
        images = images.to(device)
        labels = labels.to(device))
        outputs = model(images)       <----------- this line calls the error
        loss = criterion(outputs, labels.long())  

The error reference points to this batch_norm nn.functional function
I don’t understand what this error means, where should I look to solve it?
Thanks!