# Self define layer not autograd

I tried to customize a layer to automatically adjust the brightness and contrast by a linear layer to determine the coefficient, but could not backpropagation, my layer is:

``````class MyLinear(torch.autograd.Function):
def __init__(self, in_units, units):
super().__init__()
self.weight = nn.Parameter(torch.randn(in_units, units))
self.bias = nn.Parameter(torch.randn(units,))
self.reset_parameters()

def reset_parameters(self):
stdv = 1. / math.sqrt(self.weight.size(0))
self.weight.data.uniform_(0, stdv)

def forward(self, x1, x2):
x1 = torch.matmul(x1, self.weight.data) + self.bias.data
print(x1 ** 2)
x1 = nn.functional.relu(x1)
print(x1 ** 2)
print('-' * 50)
img_new = torch.zeros((x2.shape[0], 3, size, size))
for i in range(0, x2.shape[0]):
for j in range(0, x2.shape[1]):
if j == 0:
coef1 = round((x1[0][0] * x1[0][0]).item(), 8)
coef2 = round((x1[0][1] * x1[0][1]).item(), 8)

elif j == 1:
coef1 = round((x1[0][2] * x1[0][2]).item(), 8)
coef2 = round((x1[0][3] * x1[0][3]).item(), 8)

elif j == 2:
coef1 = round((x1[0][4] * x1[0][4]).item(), 8)
coef2 = round((x1[0][5] * x1[0][5]).item(), 8)
# print(coef1, coef2)
img = x2[i, j, :, :]
img = img.reshape(-1, size, size)

if j == 0:
z = img.reshape(1, size, size).cuda()
else:
z = torch.cat((z, img), 0)
z = z.reshape(1, 3, size, size)
img_new[i] = z.clone().detach()

return img_new.cuda()
``````

And my model is:

``````module = torchvision.models.efficientnet_b4(pretrained=True)
class bsEfficientnetb4(nn.Module):
def __init__(self):
super(bsEfficientnetb4, self).__init__()
self.brightcontrast = MyLinear(1, 6)
self.eff = module.features
self.avgpool = module.avgpool
self.classifier = module.classifier

def forward(self, x1, x2):
x = self.brightcontrast(x1, x2)
x = self.eff(x)
x = self.avgpool(x)
x = x.reshape(-1, 1792)
x = self.classifier(x)
return x
``````

My input x1 is:

``````x1 = torch.tensor([[1.,]], requires_grad=True).cuda()
``````

And x2 is image, I’d like to get your suggestions for modifications, thanks!

Doing things like calling .item() or calling .detach() will break the graph. You’ll want to avoid doing those things during your forward if you’d like to backprop through that part of the computation.