Questions about numpy compatible versions

[Aure712]

I got an error while running the Deep learning-based approach step: "TypeError: argument 1 must be numpy.ndarray, not Numpy.ndarray", doubting the compatibility of numpy versions if possible, I used numpy version 2.0.2. Would you like to ask if this is the compatibility of numpy version? If so, which version of numpy should I use?

[Sichao25]

In general, I recommend using NumPy <2.0. We haven’t updated to the latest version to maintain compatibility with other popular tools in this field. About your specific issue, would you mind sharing the error traceback so I can pinpoint where the issue occurs?

[Aure712]

Thanks for your reply!

This is the code I used:

st.cs.stardist(adata, equalize=2.0, out_layer='stardist_labels')

fig, ax = st.pl.imshow(adata, 'stain', save_show_or_return='return')
st.pl.imshow(adata, 'stardist_labels', labels=True, alpha=0.5, ax=ax)

Here is the relevant running information that occurs:

|-----> <select> stain layer in AnnData Object
|-----> Equalizing image with CLAHE.
|-----> Running StarDist with model 2D_versatile_fluo.
Found model '2D_versatile_fluo' for 'StarDist2D'.
2024-11-20 19:46:02.936824: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:152] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)
Loading network weights from 'weights_best.h5'.
Loading thresholds from 'thresholds.json'.
Using default values: prob_thresh=0.479071, nms_thresh=0.3.
/public4/software/conda_env/spateo/lib/python3.12/site-packages/csbdeep/models/base_model.py:316: UserWarning:

skipping normalization step after prediction because number of input and output channels differ.

/public4/software/conda_env/spateo/lib/python3.12/site-packages/keras/src/models/functional.py:225: UserWarning:

The structure of `inputs` doesn't match the expected structure: ['input']. Received: the structure of inputs=*

100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 110/110 [04:02<00:00,  2.20s/it]
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[8], line 1
----> 1 st.cs.stardist(adata, equalize=2.0, out_layer='stardist_labels')
      3 fig, ax = st.pl.imshow(adata, 'stain', save_show_or_return='return')
      4 st.pl.imshow(adata, 'stardist_labels', labels=True, alpha=0.5, ax=ax)

File /public4/software/conda_env/spateo/lib/python3.12/site-packages/spateo/segmentation/external/stardist.py:188, in stardist(adata, model, tilesize, min_overlap, context, normalizer, equalize, sanitize, layer, out_layer, **kwargs)
    186 if not min_overlap:
    187     n_tiles = (math.ceil(img.shape[0] / tilesize), math.ceil(img.shape[1] / tilesize)) if tilesize > 0 else (1, 1)
--> 188     labels = _stardist(img, model, n_tiles=n_tiles, normalizer=normalizer, **kwargs)
    189 else:
    190     labels = _stardist_big(
    191         img,
    192         model,
   (...)
    197         normalizer=normalizer,
    198     )

File /public4/software/conda_env/spateo/lib/python3.12/site-packages/spateo/segmentation/external/stardist.py:54, in _stardist(img, model, **kwargs)
     51     model = StarDist2D.from_pretrained(model)
     53 lm.main_debug(f"Running StarDist with kwargs {kwargs}")
---> 54 labels, _ = model.predict_instances(img, **kwargs)
     55 return labels

File /public4/software/conda_env/spateo/lib/python3.12/site-packages/stardist/models/base.py:788, in StarDistBase.predict_instances(self, *args, **kwargs)
    775 @functools.wraps(_predict_instances_generator)
    776 def predict_instances(self, *args, **kwargs):
    777     # the reason why the actual computation happens as a generator function
   (...)
    785 
    786     # return last "yield"ed value of generator
    787     r = None
--> 788     for r in self._predict_instances_generator(*args, **kwargs):
    789         pass
    790     return r

File /public4/software/conda_env/spateo/lib/python3.12/site-packages/stardist/models/base.py:758, in StarDistBase._predict_instances_generator(self, img, axes, normalizer, sparse, prob_thresh, nms_thresh, scale, n_tiles, show_tile_progress, verbose, return_labels, predict_kwargs, nms_kwargs, overlap_label, return_predict)
    755     prob_class = None
    757 yield 'nms'  # indicate that non-maximum suppression is starting
--> 758 res_instances = self._instances_from_prediction(_shape_inst, prob, dist,
    759                                                 points=points,
    760                                                 prob_class=prob_class,
    761                                                 prob_thresh=prob_thresh,
    762                                                 nms_thresh=nms_thresh,
    763                                                 scale=(None if scale is None else dict(zip(_axes,scale))),
    764                                                 return_labels=return_labels,
    765                                                 overlap_label=overlap_label,
    766                                                 **nms_kwargs)
    768 # last "yield" is the actual output that would have been "return"ed if this was a regular function
    769 if return_predict:

File /public4/software/conda_env/spateo/lib/python3.12/site-packages/stardist/models/model2d.py:526, in StarDist2D._instances_from_prediction(self, img_shape, prob, dist, points, prob_class, prob_thresh, nms_thresh, overlap_label, return_labels, scale, **nms_kwargs)
    524 # sparse prediction
    525 if points is not None:
--> 526     points, probi, disti, indsi = non_maximum_suppression_sparse(dist, prob, points, nms_thresh=nms_thresh, **nms_kwargs)
    527     if prob_class is not None:
    528         prob_class = prob_class[indsi]

File /public4/software/conda_env/spateo/lib/python3.12/site-packages/stardist/nms.py:177, in non_maximum_suppression_sparse(dist, prob, points, b, nms_thresh, use_bbox, use_kdtree, verbose)
    174     print("non-maximum suppression...")
    175     t = time()
--> 177 inds = non_maximum_suppression_inds(disti, pointsi, scores=probi, thresh=nms_thresh, use_kdtree = use_kdtree, verbose=verbose)
    179 if verbose:
    180     print("keeping %s/%s polyhedra" % (np.count_nonzero(inds), len(inds)))

File /public4/software/conda_env/spateo/lib/python3.12/site-packages/stardist/nms.py:220, in non_maximum_suppression_inds(dist, points, scores, thresh, use_bbox, use_kdtree, verbose)
    217 def _prep(x, dtype):
    218     return np.ascontiguousarray(x.astype(dtype, copy=False))
--> 220 inds = c_non_max_suppression_inds(_prep(dist,  np.float32),
    221                                   _prep(points, np.float32),
    222                                   int(use_kdtree),
    223                                   int(use_bbox),
    224                                   int(verbose),
    225                                   np.float32(thresh))
    227 return inds

TypeError: argument 1 must be numpy.ndarray, not numpy.ndarray

[Sichao25]

Seems to be related to the stardist package. I’m wondering if downgrading resolved your issue?