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I've been running the codebase with one of the latest torch versions (18.1) with only 3 small compatibility problems coming up, per my usage.
dpc/main.py, lines 215 and 267:
target_flattened raises 2 errors:
It is not automatically placed on cuda when created, which leads to an error when criterion() is called. => Solution: explicitly place on cuda before the call to criterion()
It is a boolean tensor, and argmax() now raises an error with boolean tensors. => Solution: convert to int before calling argmax().
utils/utils.py, line 53:
.view(-1) in correct_k = correct[:k].view(-1).float().sum(0) raises a runtime error (message: view size is not compatible with input tensor's size and stride...) => Solution: either use .reshape(-1) or make a call to .contiguous() before using .view(-1).
The bugs are quite minor with easy solutions that shouldn't cause a problem for previous torch versions. I am happy to make a pull request if helpful!
Thanks! :)
The text was updated successfully, but these errors were encountered:
I've been running the codebase with one of the latest torch versions (18.1) with only 3 small compatibility problems coming up, per my usage.
dpc/main.py
, lines 215 and 267:target_flattened
raises 2 errors:cuda
when created, which leads to an error whencriterion()
is called.=> Solution: explicitly place on cuda before the call to
criterion()
boolean
tensor, andargmax()
now raises an error with boolean tensors.=> Solution: convert to
int
before callingargmax()
.utils/utils.py
, line 53:.view(-1)
incorrect_k = correct[:k].view(-1).float().sum(0)
raises a runtime error (message: view size is not compatible with input tensor's size and stride...)=> Solution: either use
.reshape(-1)
or make a call to.contiguous()
before using.view(-1)
.The bugs are quite minor with easy solutions that shouldn't cause a problem for previous torch versions. I am happy to make a pull request if helpful!
Thanks! :)
The text was updated successfully, but these errors were encountered: