Hi @albanD , thank you for reaching out.
I want to get these:
grad_weight += cont_loss_weight and grad_bias += cont_loss_bias.
For this I have two ideas in my mind, however i don’t know which one is correct (If you can suggest!).
Idea-1:
# keeping remaining code unchanged
if ctx.needs_input_grad[1]:
grad_weight = torch.nn.grad.conv2d_weight(input, weight.shape, grad_output) #shape = ([128,96,5,5])
grad_feat = torch.autograd.grad(cont_loss, feat) # has to be of shape = ([[batch_size= 100,128, 4, 4])
cont_loss_weight = torch.nn.grad.conv2d_weight(input, weight.shape, grad_feat )
grad_weight += cont_loss_weight
# But not sure how to get `cont_loss_bias` in same manner.
Idea-2:
# keeping remaining code unchanged
if ctx.needs_input_grad[1]:
grad_weight = torch.nn.grad.conv2d_weight(input, weight.shape, grad_output) #shape = ([128,96,5,5])
cont_loss_weight = torch.autograd.grad(outputs= cont_loss, inputs= weight, retain_graph=(True))
grad_weight += cont_loss_weight
if bias is not None and ctx.needs_input_grad[2]:
grad_bias = grad_output.sum((0,2,3)) #shape = ([128])
cont_loss_bias = torch.autograd.grad(outputs= cont_loss, inputs= bias, retain_graph=(True))
grad_bias += cont_loss_bias
Here’s the code :
class Custom_Convolution(torch.autograd.Function):
@staticmethod
def forward(ctx, input, weight, bias, stride, padding): #input(from previous layer)'s shape = ([batch_size=100, 96, 8, 8])
output = torch.nn.functional.conv2d(input, weight, bias, stride, padding)
ctx.save_for_backward(input, weight, bias, output)
return output #output's shape = ([[batch_size= 100,128, 4, 4])
@staticmethod
def backward(ctx, grad_output): # grad_output size = ([batch_size, 128,4,4])
input, weight, bias, output = ctx.saved_tensors #input size = ([batch_size, 96,8,8])
feat = output.clone()#.requires_grad_(True) # output from forward with size = ([batch_size, 128,4,4])
feat = feat.view(feat.shape[0], feat.shape[1], -1) # features size = ([batch_size, 128,16])
i = 0
cont = torch.tensor([0.]).to(dev)
while i in range(0, feat.shape[0]):
for f in range(len(feat[i])):
Zi_unnormalized = feat[i][f]
Zi = torch.nn.functional.normalize(Zi_unnormalized, dim = 0)
# Zj and Zk are tensors made from feat[i][*] and feat[other than i][*]. Zj and Zk varies for each Zi (or f)
Zi_Zk = torch.Tensor([0]).to(dev)
for k in Zk:
k= torch.nn.functional.normalize(k, dim = 0)
zi_zk = ...
Zi_Zk = Zi_Zk.add(zi_zk)
# Similarly computing Zi_Zj
# Li = some algebra of Zi_Zj and Zi_Zk
# number of 'Li' values = feat.shape[0] * feat.shape[1]
cont = cont.add(Li) # 1 value
i+=1
grad_input = grad_weight = grad_bias = None
if ctx.needs_input_grad[0]:
grad_input = torch.nn.grad.conv2d_input(input.shape, weight, grad_output) #shape = ([batch_size,96,8,8])
# If I go with Idea-2
if ctx.needs_input_grad[1]:
grad_weight = torch.nn.grad.conv2d_weight(input, weight.shape, grad_output) #shape = ([128,96,5,5])
cont_loss_weight = torch.autograd.grad(outputs= cont_loss,
inputs= weight, retain_graph=(True))
grad_weight += cont_loss_weight
if bias is not None and ctx.needs_input_grad[2]:
grad_bias = grad_output.sum((0,2,3)) #shape = ([128])
cont_loss_bias = torch.autograd.grad(outputs= cont_loss, inputs= bias, retain_graph=(True))
grad_bias += cont_loss_bias
if bias is not None:
return grad_input, grad_weight, grad_bias, None, None
else:
return grad_input, grad_weight, None, None
It gives:
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
If I put feat = output.clone().requires_grad_(True), it gives :
RuntimeError: One of the differentiated Tensors appears to not have been used in the graph. Set allow_unused=True if this is the desired behavior.
)
but I haven’t seen such implementation or thread regarding this. 
