Hi
I am pretty new to this forum and pyTorch. Basically I am trying to use EfficientNet as a classifier to detect emotion in FER2013 database. Basically I have needs to modify the input (need it to be 1 channel , 48x48 for image size)  and the output (need to be 7). Here is my modification
class Classifier(nn.Net):
def init(self,n_classes):
    super(Classifier, self).__init__()
    
    self.effnet                  =  EfficientNet.from_pretrained('efficientnet-b0')
    self.effnet._fc.out_features = 7
    self.effnet._conv_stem = Conv2dStaticSamePadding(1,32,kernel_size=(3,3), stride=(2,2),
                                                      image_size=(48),bias=False)

def forward(self, input):
    x = self.effnet(input)
    return x

model = Classifier()
I am trying to test out the pretrained network model(ImageNet) so that means I will need to train the input and last FC layer as I replaced them
Read about how to freeze layers, I am thinking of omething along this line
for child in model.children():
     ct += 1
     if ct < x and ct >1:
           for param in child.parameters():
                 param.requires_grad = False

However is this the right method? Don no see the requires_grad param anywhere?

Regards