[BUG] indexing with Booleans leads to crash

[v923z]

As mentioned in https://github.com/v923z/micropython-ulab/issues/671, the following code leads to a crash:

filtered_boxes = np_boxes[np_boxes[:, 4] > 0.0]

@kwagyeman

[v923z]

Wouldn't it be better to use https://numpy.org/doc/stable/reference/generated/numpy.select.html for this? My reasoning is that first, that would lead to a cleaner implementation, second, it would be easier to customise the firmware. Excluding a function/method is more meaningful than excluding a sub-feature of a feature.

[kwagyeman]

Sounds fine. Just need a way to do it.

[kwagyeman]

Question, does where() work?

def yolov5_vectorized(model, input, output):
    out = output[0]
    ib, ih, iw, ic = model.input_shape[0]
    ob, ow, oc = model.output_shape[0]
    if ob != 1:
        raise ValueError("Expected model output batch to be 1!")
    if oc < 6:
        raise ValueError("Expected model output channels to be >= 6")

    # Extract relevant output slices
    scores = out[0, :, 4]
    coords = out[0, :, :4]

    # Filter indices where score > 0.5
    valid_indices = np.where(scores > 0.5)[0]

    # Compute bounding box coordinates
    cx = coords[valid_indices, 0]
    cy = coords[valid_indices, 1]
    cw = coords[valid_indices, 2] * 0.5
    ch = coords[valid_indices, 3] * 0.5

    xmin = (cx - cw) * iw
    ymin = (cy - ch) * ih
    xmax = (cx + cw) * iw
    ymax = (cy + ch) * ih

    # Compute label index
    labels = out[0, valid_indices, 5:]
    label_index = np.argmax(labels, axis=1)

    # Create bounding boxes
    nms = NMS(iw, ih, input[0].roi)
    for i in range(len(valid_indices)):
        nms.add_bounding_box(xmin[i], ymin[i], xmax[i], ymax[i], scores[valid_indices[i]], label_index[i])

    boxes = nms.get_bounding_boxes()
    return boxes

I could use it to accomplish my goal of being able to select indices. ChatGPT wrote the code above, though, and it might have made a mistake regarding how to select columns.

[v923z]

Yes, where should work, if you enabled it in ulab.h.

[kwagyeman]

Having issues with this stuff still:

a = np.array(range(36)).reshape((6, 6))

i = np.nonzero(np.asarray(a[:, 4] > 15))

print(a[i])

Doesn't work. Not sure why. I get a IndexError: indices must be integers, slices, or Boolean lists.

And then this says:

a = np.array(range(36)).reshape((6, 6))

i = np.where(a[:, 4] > 15, 1, 0)

print(i)

NotImplementedError: operation is implemented for 1D Boolean arrays only which it mentions in the docs. Could this restriction be removed? Need this to process tensor outputs.

It's not clear how to move forward without writing a for loop. Trying to stay vectorized.

[v923z]

NotImplementedError: operation is implemented for 1D Boolean arrays only which it mentions in the docs. Could this restriction be removed? Need this to process tensor outputs.

It's not trivial (and this is why I didn't implement it in the first place), but I'll try to find a way.

[kwagyeman]

@v923z - Any updates on this? Happy to pay for this to get done sooner.

[v923z]

The last 2-3 months were a bit hectic for me, but I'll try to devote some time to it. Sorry for the delay!

[v923z]

I'm wondering, whether we're trying to fix something that's already correct:

a = np.array(range(36)).reshape((6, 6))

i = np.where(a[:, 4] > 15, 1, 0)

print(i)

NotImplementedError: operation is implemented for 1D Boolean arrays only which it mentions in the docs. Could this restriction be removed? Need this to process tensor outputs.

It's not clear how to move forward without writing a for loop. Trying to stay vectorized.

Here is my output:

>>> import ulab
>>> ulab.__version__
'6.5.4-2D-c'

>>> a = np.arange(36).reshape((6,6))
>>> a
array([[0, 1, 2, 3, 4, 5],
       [6, 7, 8, 9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35]], dtype=int16)
>>> a[:, 4]
array([4, 10, 16, 22, 28, 34], dtype=int16)
>>> a[:, 4] > 15
array([False, False, True, True, True, True], dtype=bool)
>>> np.where(a[:, 4] > 15, 1, 0)
array([0, 0, 1, 1, 1, 1], dtype=uint8)

Is this not what you need? I've just looked at the implementation of where, and that shouldn't throw the error that you mention.

In fact, that particular exception is raised at one location only, when you try to get a slice via a higher-dimensional tensor: https://github.com/v923z/micropython-ulab/blob/c0b3262be49de3162c9c0a7082bcd2d52907012e/code/ndarray.c#L1317-L1319

[kwagyeman]

Looks like it's working now.

a = np.array(range(36)).reshape((6, 6))

i = np.nonzero(np.asarray(a[:, 4] > 15))

print(a[i])

Doesn't work still.

[kwagyeman]

Looks like it fails on the a[i] part:

from ulab import numpy as np

a = np.array(range(36)).reshape((6, 6))

t = np.asarray(a[:, 4])
print(t)
t = t > 15
print(t)
i = np.nonzero(t)
print(i)

i = np.nonzero(np.asarray(a[:, 4] > 15))

print(a[i])

[v923z]

Oh, I see. The problem is actually with i:

>>> i = np.nonzero(np.asarray(a[:, 4] > 15))
>>> i
(array([2, 3, 4, 5], dtype=uint16),)

As a workaround, could you try with i[0]? I'll try to figure out, why nonzero returns a tuple.

[kwagyeman]

? Not sure what you mean, I'm trying to slice into the array a using i to extract the matching rows.

[v923z]

Yes, I get that, but i is actually a tuple, so that's why the slicing doesn't work. That's implemented for 1D arrays only.

>>> i
(array([2, 3, 4, 5], dtype=uint16),)

[kwagyeman]

Yeah, this is the operation I'd like to have so that I can use lab to vectorize non-max-suppression code. Per the post above...

[v923z]

I think there might be two issues here: one is with i. np.asarray(a[:, 4] > 15) is clearly a 1D array

>>> np.asarray(a[:, 4] > 15)
array([False, False, True, True, True, True], dtype=bool)

The problem occurs, when this is passed to nonzero.

Then the second issue is that you want to use 2D Booleans for indexing/slicing.

[v923z]

As for nonzero, the method works in the same way in numpy:

>>> from numpy import *
>>> x = arange(5)
>>> x
array([0, 1, 2, 3, 4])
>>> nonzero(x)
(array([1, 2, 3, 4]),)

[v923z]

It seems to me that numpy simply ignores the second, empty, member of the tuple:

>>> a = arange(36).reshape((6, 6))
>>> a
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35]])
>>> i = nonzero(asarray(a[:, 4] > 15))
>>> i
(array([2, 3, 4, 5]),)
>>> a[i]
array([[12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35]])

So, if we caught that here https://github.com/v923z/micropython-ulab/blob/c0b3262be49de3162c9c0a7082bcd2d52907012e/code/ndarray.c#L1317-L1319 then we'd be done, right?

[kwagyeman]

Not sure if I'm following. The trace above is what I want to happen. However, a[i] doesn't work like above. You get IndexError: indices must be integers, slices, or Boolean lists.

EDIT: So, you are saying all you need to do is make a[i] support a tuple and grab the first element of it?

[v923z]

EDIT: So, you are saying all you need to do is make a[i] support a tuple and grab the first element of it?

numpy works, because, while i is the same as in ulab, they simply drop the second element in the tuple. So, if we catch that particular case in the code, then the behaviour would be same on both platforms.

In the interim, you could simply use i[0]. That should work everywhere. But I'll fix this tomorrow.

[kwagyeman]

Great!

[v923z]

I'm trying to understand what exactly you need, and it seems to me that https://github.com/v923z/micropython-ulab/issues/661 could do, so I'm wondering, whether I should clean that one up, and then we would kill two birds with one stone.

[kwagyeman]

I just need to be able to do your Numpy example above. To select rows from a 2D array using a 1D array of row indices.

[v923z]

Can you check out https://github.com/v923z/micropython-ulab/tree/take and see if it works for you?

[kwagyeman]

Yeah, that should work. I'll have a list of row indexes. You may also wish to implement take_along_axis since it's just a wrapper around take.

[v923z]

take_along_axis also allows for broadcasting, so it's a bit more than a wrapper.

[kwagyeman]

Okay, take is sufficient.

[v923z]

OK, thanks for the feedback! I'll write up the documentation and merge the code.

[v923z]

Fixed through https://github.com/v923z/micropython-ulab/pull/688.