utils.py:FunctionNegativeTripletSelector - 'anchor_positive' referenced before assignment when len(label_indices) < 2

Hi,

First off I’d like to say thank you for making this repo, it has been immensely helpful to my research! I believe I have found a bug in utils.py: FunctionNegativeTripletSelector when working with certain datasets. Specifically, the issue occurs on lines 174-175:

if len(triplets) == 0:
            triplets.append([anchor_positive[0], anchor_positive[1], negative_indices[0]])

When the dataset is small, for example a validation set of an already small dataset, it may not be possible for label_indices defined on line 161 to ever be >= 2. This is then checked on line 159-160:

if len(label_indices) < 2:
                continue

If every label_indices causes the continue to run, then len(triplets) will be 0. This triggers the offending code on lines 174-175. However at this point under the conditions, anchor_positive has not yet been defined, leading to a fatal error.

To test this, I created a training and validation split, with the validation set being of the following size:

unique, counts = np.unique(val_dataset.val_labels, return_counts=True)
print("y_val: len", len(unique), "\n", np.asarray((unique, counts)).T, "\n")

y_val: len 28 
 [[ 1  2]
 [ 2  2]
 [ 3  4]
 [ 4  2]
 [ 7  2]
 [ 8  5]
 [11  6]
 [13  3]
 [14  4]
 [15  3]
 [16  4]
 [18  3]
 [19  2]
 [21  2]
 [22  3]
 [23  2]
 [24  2]
 [25  3]
 [26  3]
 [28  5]
 [29  3]
 [31  2]
 [32  2]
 [34  5]
 [37  2]
 [39  2]
 [41  2]
 [44  2]] 

Training occurs as normal, as the offending conditional is never hit:

TRAINING
labels: [ 4  4 34 34 11 11  3  3  2  2 13 13 32 32 16 16 22 22 39 39 29 29 25 25
  8  8 24 24 37 37 41 41 44 44 18 18 21 21 31 31 23 23  1  1 15 15  7  7
 26 26 28 28 14 14 19 19]
label_indices [42 43]
label_indices [8 9]
label_indices [6 7]
label_indices [0 1]
label_indices [46 47]
label_indices [24 25]
label_indices [4 5]
label_indices [10 11]
label_indices [52 53]
label_indices [44 45]
label_indices [14 15]
label_indices [34 35]
label_indices [54 55]
label_indices [36 37]
label_indices [16 17]
label_indices [40 41]
label_indices [26 27]
label_indices [22 23]
label_indices [48 49]
label_indices [50 51]
label_indices [20 21]
label_indices [38 39]
label_indices [12 13]
label_indices [2 3]
label_indices [28 29]
label_indices [18 19]
label_indices [30 31]
label_indices [32 33]
Train: [0/181 (0%)]	Loss: 0.987690	Average nonzero triplets: 28.0

However the validation set falls foul:

VALIDATION
labels: [19 13  4 15 14 31 37 21 11 26  3  1 41 29 34  2  8 23 28 39 44 22 32 18
 24 16 25  7]
label_indices [11]
continue triggered
label_indices [15]
continue triggered
label_indices [10]
continue triggered
label_indices [2]
continue triggered
label_indices [27]
continue triggered
label_indices [16]
continue triggered
label_indices [8]
continue triggered
label_indices [1]
continue triggered
label_indices [4]
continue triggered
label_indices [3]
continue triggered
label_indices [25]
continue triggered
label_indices [23]
continue triggered
label_indices [0]
continue triggered
label_indices [7]
continue triggered
label_indices [21]
continue triggered
label_indices [17]
continue triggered
label_indices [24]
continue triggered
label_indices [26]
continue triggered
label_indices [9]
continue triggered
label_indices [18]
continue triggered
label_indices [13]
continue triggered
label_indices [5]
continue triggered
label_indices [22]
continue triggered
label_indices [14]
continue triggered
label_indices [6]
continue triggered
label_indices [19]
continue triggered
label_indices [12]
continue triggered
label_indices [20]
continue triggered

Traceback (most recent call last):
  File "/trainer.py", line 53, in fit
    val_loss, metrics = test_epoch(val_loader, model, loss_fn, cuda, metrics)
  File "/trainer.py", line 190, in test_epoch
    loss_outputs = loss_fn(*loss_inputs)
  File "/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/losses.py", line 82, in forward
    triplets = self.triplet_selector.get_triplets(embeddings, target)
  File "/utils.py", line 187, in get_triplets
    triplets.append([anchor_positive[0], anchor_positive[1], negative_indices[0]])
UnboundLocalError: local variable 'anchor_positive' referenced before assignment

Note the line numbers above may be different from the repo’s code due to the print statements. To me, it seems like currently the only way around this error would be to increase the size of the dataset, increasing the chance of label_indices at some point being >=2. anchor_positive needs to be defined somewhere before the offending conditional to stop the error, but I’m unsure where or how in order to fix.

[bth5032]

Hi, yeah I ran a test overnight, just returning a constant vector, in my case

if len(triplets) == 0:
            triplets.append([0,0,0])

fixed the crash and the model seems to be training just fine

[bth5032]

Hey no problem, thanks for making the post, definitely saved me some time on this!