UnboundLocalError: local variable 'label' referenced before assignment

[sandipmgiri]

Description

Following 2 tests are failing with error UnboundLocalError: local variable 'label' referenced before assignment

1. tests/python/unittest/test_image.py::TestImage::test_det_augmenters <- /mxnet/tests/python/unittest/test_image.py FAILED
2. tests/python/unittest/test_image.py::TestImage::test_image_detiter <- /mxnet/tests/python/unittest/test_image.py FAILED

Environment info (Required)

ubuntu:16.04 (x86) Python 2.7.12 numpy 1.15.0

What to do:
1. Download the diagnosis script from https://raw.githubusercontent.com/apache/incubator-mxnet/master/tools/diagnose.py
2. Run the script using `python diagnose.py` and paste its output here.
----------Python Info----------
('Version      :', '2.7.12')
('Compiler     :', 'GCC 5.4.0 20160609')
('Build        :', ('default', 'Dec  4 2017 14:50:18'))
('Arch         :', ('64bit', 'ELF'))
------------Pip Info-----------
('Version      :', '18.0')
('Directory    :', '/usr/local/lib/python2.7/dist-packages/pip')
----------MXNet Info-----------
/usr/lib/python2.7/dist-packages/scipy/stats/morestats.py:16: DeprecationWarning: Importing from numpy.testing.decorators is deprecated, import from numpy.testing instead.
  from numpy.testing.decorators import setastest
('Version      :', '1.3.0')
('Directory    :', '/mxnet/python/mxnet')
Hashtag not found. Not installed from pre-built package.
----------System Info----------
('Platform     :', 'Linux-4.4.0-116-generic-x86_64-with-Ubuntu-16.04-xenial')
('system       :', 'Linux')
('node         :', '445fd99b7e00')
('release      :', '4.4.0-116-generic')
('version      :', '#140-Ubuntu SMP Mon Feb 12 21:23:04 UTC 2018')
----------Hardware Info----------
('machine      :', 'x86_64')
('processor    :', 'x86_64')
Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                4
On-line CPU(s) list:   0-3
Thread(s) per core:    1
Core(s) per socket:    1
Socket(s):             4
NUMA node(s):          1
Vendor ID:             GenuineIntel
CPU family:            6
Model:                 42
Model name:            Intel Xeon E312xx (Sandy Bridge)
Stepping:              1
CPU MHz:               2399.970
BogoMIPS:              4799.94
Virtualization:        VT-x
Hypervisor vendor:     KVM
Virtualization type:   full
L1d cache:             32K
L1i cache:             32K
L2 cache:              4096K
NUMA node0 CPU(s):     0-3
Flags:                 fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl eagerfpu pni pclmulqdq vmx ssse3 cx16 pcid sse4_1 sse4_2 x2apic popcnt tsc_deadline_timer aes xsave avx hypervisor lahf_lm retpoline kaiser vnmi ept xsaveopt
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0235 sec, LOAD: 1.4152 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0135 sec, LOAD: 2.6120 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0221 sec, LOAD: 1.5329 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.1229 sec, LOAD: 1.3467 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.3364 sec, LOAD: 1.0559 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 1.0087 sec, LOAD: 1.1898 sec.

Package used (Python/R/Scala/Julia): (I'm using Python...)

Build info (Required if built from source)

Compiler (gcc/clang/mingw/visual studio): gcc MXNet commit hash: (Paste the output of git rev-parse HEAD here.) 564e01acdf460535d4ab7340db39b0d10028b453

Build config: (Paste the content of config.mk, or the build command.)

git clone --recursive https://github.com/apache/incubator-mxnet.git mxnet
cd mxnet
make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_PROFILER=1
rm -r build
cd python
sudo pip install --upgrade pip
sudo pip install -e .
cd ..
python -m pytest -v tests/python/unittest

(Paste the complete error message, including stack trace.) $ python -m pytest -v tests/python/unittest/test_image.py::TestImage::test_image_detiter

========================================================================= test session starts ==========================================================================
platform linux2 -- Python 2.7.12, pytest-3.7.0, py-1.5.4, pluggy-0.7.1 -- /usr/bin/python
cachedir: .pytest_cache
rootdir: /mxnet, inifile:
collected 1 item

tests/python/unittest/test_image.py::TestImage::test_image_detiter FAILED                                                                                        [100%]

=============================================================================== FAILURES ===============================================================================
_____________________________________________________________________ TestImage.test_image_detiter _____________________________________________________________________

self = <test_image.TestImage testMethod=test_image_detiter>

    def test_image_detiter(self):
        im_list = [_generate_objects() + [x] for x in TestImage.IMAGES]
>       det_iter = mx.image.ImageDetIter(2, (3, 300, 300), imglist=im_list, path_root='')

tests/python/unittest/test_image.py:189:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
python/mxnet/image/detection.py:682: in __init__
    label_shape = self._estimate_label_shape()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <mxnet.image.detection.ImageDetIter object at 0x7fdebefab290>

    def _estimate_label_shape(self):
        """Helper function to estimate label shape"""
        max_count = 0
        self.reset()
        try:
            while True:
                label, _ = self.next_sample()
                label = self._parse_label(label)
                max_count = max(max_count, label.shape[0])
        except StopIteration:
            pass
        self.reset()
>       return (max_count, label.shape[1])
E       UnboundLocalError: local variable 'label' referenced before assignment

python/mxnet/image/detection.py:708: UnboundLocalError
======================================================================= 1 failed in 2.54 seconds ==============================================

$ python -m pytest -v tests/python/unittest/test_image.py::TestImage::test_det_augmenters

========================================================================= test session starts ==========================================================================
platform linux2 -- Python 2.7.12, pytest-3.7.0, py-1.5.4, pluggy-0.7.1 -- /usr/bin/python
cachedir: .pytest_cache
rootdir: /mxnet, inifile:
collected 1 item

tests/python/unittest/test_image.py::TestImage::test_det_augmenters FAILED                                                                                       [100%]

=============================================================================== FAILURES ===============================================================================
____________________________________________________________________ TestImage.test_det_augmenters _____________________________________________________________________

self = <test_image.TestImage testMethod=test_det_augmenters>

    def test_det_augmenters(self):
        # only test if all augmenters will work
        # TODO(Joshua Zhang): verify the augmenter outputs
        im_list = [_generate_objects() + [x] for x in TestImage.IMAGES]
        det_iter = mx.image.ImageDetIter(2, (3, 300, 300), imglist=im_list, path_root='',
            resize=640, rand_crop=1, rand_pad=1, rand_gray=0.1, rand_mirror=True, mean=True,
            std=np.array([1.1, 1.03, 1.05]), brightness=0.1, contrast=0.1, saturation=0.1,
            pca_noise=0.1, hue=0.1, inter_method=10, min_object_covered=0.5,
            aspect_ratio_range=(0.2, 5), area_range=(0.1, 4.0), min_eject_coverage=0.5,
>           max_attempts=50)

tests/python/unittest/test_image.py:220:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
python/mxnet/image/detection.py:682: in __init__
    label_shape = self._estimate_label_shape()
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <mxnet.image.detection.ImageDetIter object at 0x7f6c9d664290>

    def _estimate_label_shape(self):
        """Helper function to estimate label shape"""
        max_count = 0
        self.reset()
        try:
            while True:
                label, _ = self.next_sample()
                label = self._parse_label(label)
                max_count = max(max_count, label.shape[0])
        except StopIteration:
            pass
        self.reset()
>       return (max_count, label.shape[1])
E       UnboundLocalError: local variable 'label' referenced before assignment

python/mxnet/image/detection.py:708: UnboundLocalError
======================================================================= 1 failed in 1.93 seconds ===========

Steps to reproduce

(Paste the commands you ran that produced the error.)

1. python -m pytest -v tests/python/unittest/test_image.py::TestImage::test_image_detiter
2. python -m pytest -v tests/python/unittest/test_image.py::TestImage::test_det_augmenters

What have you tried to solve it?

Tried installing different numpy version, but still these 2 tests are failing.

Looks like configuration/dependency issue. Any inputs would be appreciated. Thanks!

[Roshrini]

@anirudh2290 Can you please add label: Python, test

[srochel]

@mxnet-label-bot: ["Good First Issue"]

[srochel]

@mxnet-label-bot [Good First Issue]

[sayakpaul]

Hi,

Any help on this? I am also running into the same issue.

[leezu]

Seems that StopIteration is raised directly, ie the iterator is empty.