add torch.meshgrid to torch frontend #11560
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ivy-leaves
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PyTorch Frontend
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Hi Ahmed! thanks again for your contribution to ivy! as the tests appear to be failing, i'll add a few comments that hopefully help:) |
| @@ -35,6 +35,11 @@ def roll(input, shifts, dims=None): | |||
| return ivy.roll(input, shifts, axis=dims) | |||
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| @to_ivy_arrays_and_back | |||
| def meshgrid(*tensors, indexing=None): | |||
| return ivy.meshgrid(*tensors, indexing=indexing) | |||
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torch's meshgrid returns a tuple so cast the output to a tuple instead of a list, (this also fixes the type issues and output length issues)
| test_flags, | ||
| ): | ||
| dtypes, tensors = dtypes_and_tensors | ||
| if isinstance(dtypes, list): |
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the if else statement here is actually not needed as the output is always a list, also no need to cast the output to a numpy array, it is already one, no need to cast the type either, just keep it as kwargs = { f"tensor{i}": tensor for i, tensor in enumerate(tensors) }
| else: | ||
| args = {"tensor": np.array(tensors, dtype=dtypes)} | ||
| kwargs['indexing'] = indexing | ||
| test_flags.num_positional_args = len(tensors) + 1 |
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there is no need for the +1 in line 174, len(tensor) should be enough
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Hopefully, everything will run with no errors after these little tweaks, and we should be good to merge! |
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thank you so much, Yasser, I will try these changes asap in a different PR, this branch got some problems for me |
No worries! see you there! |
close #11227