Closed matty6409 closed 1 year ago
May I ask if you encountered a problem in installing entension cauchy where i stumped , or it goes smoothly when setting up 'entensions cauchy'
Hello @febmatt008, I could speculate that this is related to the S4_lmax hyperparameter. One must adjust this hyperparameter during each training to the length of your sample, otherwise, the S4_lmax will update during training, which will result in an error when trying to load the model weights due to model size missmatch. Regards
Hey @febmatt008,
I just came across the same error you mentioned. The error can be fixed by adapting the register
method of SSKernelNPLR
as follows.
def register(self, name, tensor, trainable=False, lr=None, wd=None):
"""Utility method: register a tensor as a buffer or trainable parameter"""
if trainable:
self.register_parameter(name, nn.Parameter(tensor.clone()))
else:
self.register_buffer(name, tensor.clone())
optim = {}
if trainable and lr is not None:
optim["lr"] = lr
if trainable and wd is not None:
optim["weight_decay"] = wd
if len(optim) > 0:
setattr(getattr(self, name), "_optim", optim)
Hi @jzenn Hi, can you describe in more detail where this method is placed in the code and how it is used, thanks
Hi, ran into the following issue after running imputer.train and imputer.load_weights for the CSDIS4Imputer. Running imputer.impute results in the following error message. The error is thrown when running CSDIS4.py line 2148 when loading the model after training.
RuntimeError: Error(s) in loading state_dict for CSDI_Custom: While copying the parameter named "diffmodel.residual_layers.0.time_layer.s4_layer.kernel.kernel.B", whose dimensions in the model are torch.Size([1, 64, 32, 2]) and whose dimensions in the checkpoint are torch.Size([1, 64, 32, 2]), an exception occurred : ('unsupported operation: more than one element of the written-to tensor refers to a single memory location. Please clone() the tensor before performing the operation.',).
Would love to know a fix for the above issue. Thanks