min_size_per_dim + changes for scalars inputs+outputs support #354
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… keys() items() and values() to our NDict, and tried to highlight more the faulting op in pipeline ops error
* Changed default deepdiff behavior to ignore nans in comparison, * Added keys() items() and values() to our NDict (until now it returned empty iterables for those which is incorrect) * Tried to highlight more the faulting op in pipeline ops error
…le_ids. fixed a typo in samplers.py and added a describe method to NDict
…ing pipeline ops list
added 14 commits
April 3, 2024 16:06
…sions as it makes it easier for the user not requiring to provide it specifically per key name
YoelShoshan
changed the title
YoelShoshan
changed the title
… list of raw floats, solved it
mosheraboh
previously approved these changes
Jul 25, 2024
fuse/data/ops/ops_cast.py
Outdated
| @@ -37,12 +37,15 @@ def to_tensor( | |||
| value: Any, | |||
| dtype: Optional[torch.dtype] = None, | |||
| device: Optional[torch.device] = None, | |||
| ignore_None: bool = False, | |||
| if np.isscalar(pred[0]): | ||
| pred = np.array(pred) | ||
| else: | ||
| pred = np.concatenate(pred) |
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here pred is numpy? necessarily same dims?
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yes, in the else it's numpy, and it not necessarily same dims (it's 1d each)
…ccumulation, and also to make pearson correlation calculation to support the case of empty inputs
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by allowing min_size_per_dim to be longer than the number of dims in a tensor, it makes usage easier.
for example,
min_size_per_dim=(10_000, -1)
would then work for tensors of single dim - e.g. of size (1234, )
and also for tensors of 2(+) dims - e.g. of size (4321, 100)
this is much more nice for the user in several cases.
also - few changes for scalars inputs+outputs