This template is used for the headword line of Ukrainian nouns. Term4 = torch.pow(mod_q1m,2) + torch.The following documentation is located at Template:uk-noun/documentation. I’ve made slight changes in the code, trying to solve the issue. Exploiting the debug there are not strange things, such as zero divisions and also the final value of the loss is correct and coherent.Ĭould some recursive operations ( onion_mult() and onion_mult2D() functions) be the real problem? There is a way to convert a stack of images in quaternions and to apply the operations more efficiently? I haven’t looked through your entire code and while this solves the gradient error ( a.grad will show values), the gradient will contain all NaNs, so check if you are e.g. a list and create a tensor via torch.stack(): values = Instead of recreating the values tensor, try to append the outputs of onion_quality to e.g. data attribute to manipulate tensors, as it’s deprecated and can yield unwanted side effects. Unrelated to this particular issue, but also don’t use the. Which is not attached to any computation graph (and thus also not to the input to the criterion). You are manually creating a new tensor with requires_grad=True inside your module: values = torch.zeros((bs, dim3, stepx, stepy), device=vice, requires_grad=True) The problem is that this loss does not update the weights of the network during the training loop. Index_map = torch.sqrt(torch.sum(values**2, dim=1)) O = onions_quality(labels, outputs, self.Q_block_size, vice) Values = torch.zeros((bs, dim3, stepx, stepy), device=vice, requires_grad=True) Outputs = torch.cat((outputs, diff), dim=1) Labels = torch.cat((labels, diff), dim=1) If(math.ceil(math.log2(dim3)) - math.log2(dim3) != 0):Įxp_difference = 2 ** (torch.ceil(torch.log2(dim3))) - dim3ĭiff = torch.zeros((bs, exp_difference, dim1, dim2), device=vice, requires_grad=True).type(torch.int16) Labels = reference.type(torch.int16).to(vice) Outputs = fused.type(torch.int16).to(vice) Padding = torch.nn.ReflectionPad2d((0, est1, 0, est2)) #qv = torch.zeros((batch_size, dim3), device=device, requires_grad=True) Q = torch.zeros((batch_size, 1, 1, dim3), device=device, requires_grad=True) Temp = om_numpy(np.asarray(temp)).to(device) Ris3 = onion_mult2D(torch.cat((torch.unsqueeze(a,1), -a), dim=1), d)ĭef onions_quality(im1, im2, size, device): Ris2 = onion_mult(d, torch.cat((torch.unsqueeze(b, 1), -b), dim=1)) The code to reproduce the error is: import torchī = torch.cat((torch.unsqueeze(b, 1), -b), dim=1)ĭ = torch.cat((torch.unsqueeze(d, 1), -d), dim=1) I’m analyzing the code but, even if there are many things that I’m not understanding, I don’t find the real cause of the problem.Ĭould someone help me in this desperate effort? backward() function it returns to me a NoneType. Strangely it compiles, but when I try to call the. I have tried to implement a “complicated” (for me…) loss function (a porting from MATLAB repo).
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