Make spatial transform inverse compatible for multiple passes

[liamchalcroft]

Using SpacingD as a pre-processing transform in a MONAILabel app, and the Deepedit click transforms often require multiple iterations. The default inverse transform runs into 2 errors when trying to inverse the SpacingD transform for the second time:

1. 'TypeError: Cannot interpret 'torch.float64' as a data type' from 'get_numpy_dtype_from_string' in 'monai/utils/type_conversion.py' . I am able to fix this locally by changing the function to remove any numpy/torch prefix: 'dtype=str(dtype).split('.')[-1]'
2. ' KeyError: 'src_affine' ' from 'monai/transforms/spatial/array.py", line 226, in inverse: kw_args["dst_affine"] = kw_args.pop("src_affine")' . I am able to fix this locally by adding this line before the inverse fn return: 'kw_args["src_affine"] = kw_args["dst_affine"]' in order to restore the src_affine.

[wyli]

thanks for reporting these, if you plan to contribute,
for 1, please add tests to

MONAI/tests/test_get_equivalent_dtype.py

Lines 45 to 50 in 06defb7

	@parameterized.expand([["float", np.float64], ["float32", np.float32], ["float64", np.float64]])
	def test_from_string(self, dtype_str, expected_np):
	expected_pt = get_equivalent_dtype(expected_np, torch.Tensor)
	# numpy
	dtype = get_numpy_dtype_from_string(dtype_str)
	self.assertEqual(dtype, expected_np)

for 2, please change it to kw_args["src_affine"] = kw_wargs.get("dst_affine") to allow for empty src_affine before calling the inverse fn