Random Nearest captions followed by an error

[SundareshPrasanna]

on running generate.story(z, './images/ex2.jpg') which is image of a flower, i get random nearest neighbor captions followed by an error as below:

generate.story(z, './images/ex2.jpg') NEAREST-CAPTIONS: b'A black and white cityscape shows lots of people , mainly a tall , smiling man in suit and tie , who is paying attention to a woman standing beside a second smiling man in glasses and headset , who is also holding a microphone and notepad .' b'A doll , wearing clothing and a knit hat with ears , and a teddy bear looking up and standing side by side on a wooden bridge with wooden fences on both sides and some dead leaves , with trees with green leaves and bushes in background .' b'Visible through a windshield : in the distance , sidewalks , lined with snow , utility poles , retail outposts , and a few approaching vehicles , in the foreground , a crosswalk with a turning truck at one side and two large vehicles directly past it .' b'A cloudy sky rests over gently rolling hills with melting snow and lots of green , while in the foreground rests a big mound of snow and a person , hurling through the air on a skateboard , hunched over , their arms out like a bird .' b'A woman with elbows and arms folded on table , smiling for camera , with a cloth with a tray with plate on rice covered with sour cream , by an upside down coffee cup by spoon on a saucer , inside room with lattice over window .'

Traceback (most recent call last): File "", line 1, in File "C:\Users\Dell\Documents\Neural_storyteller\neural-storyteller-master\generate.py", line 59, in story print('') File "C:\Users\Dell\Documents\Neural_storyteller\neural-storyteller-master\skipthoughts.py", line 84, in encode X = preprocess(X) File "C:\Users\Dell\Documents\Neural_storyteller\neural-storyteller-master\skipthoughts.py", line 149, in preprocess sents = sent_detector.tokenize(t) File "C:\Users\Dell\Anaconda3\envs\tensorflow-gpu\lib\site-packages\nltk\tokenize\punkt.py", line 1237, in tokenize return list(self.sentences_from_text(text, realign_boundaries)) File "C:\Users\Dell\Anaconda3\envs\tensorflow-gpu\lib\site-packages\nltk\tokenize\punkt.py", line 1285, in sentences_from_text return [text[s:e] for s, e in self.span_tokenize(text, realign_boundaries)] File "C:\Users\Dell\Anaconda3\envs\tensorflow-gpu\lib\site-packages\nltk\tokenize\punkt.py", line 1276, in span_tokenize return [(sl.start, sl.stop) for sl in slices] File "C:\Users\Dell\Anaconda3\envs\tensorflow-gpu\lib\site-packages\nltk\tokenize\punkt.py", line 1276, in return [(sl.start, sl.stop) for sl in slices] File "C:\Users\Dell\Anaconda3\envs\tensorflow-gpu\lib\site-packages\nltk\tokenize\punkt.py", line 1316, in _realign_boundaries for sl1, sl2 in _pair_iter(slices): File "C:\Users\Dell\Anaconda3\envs\tensorflow-gpu\lib\site-packages\nltk\tokenize\punkt.py", line 312, in _pair_iter prev = next(it) File "C:\Users\Dell\Anaconda3\envs\tensorflow-gpu\lib\site-packages\nltk\tokenize\punkt.py", line 1289, in _slices_from_text for match in self._lang_vars.period_context_re().finditer(text): TypeError: cannot use a string pattern on a bytes-like object

[HappyCoderMan]

You may need to add a t.decode('utf-8') like this:

sent_detector = nltk.data.load('tokenizers/punkt/english.pickle')
for t in text:
    sents = sent_detector.tokenize(t.decode('utf-8'))

[SundareshPrasanna]

Thanks @ErikOmberg that solved that issue.. but i am faced with a new error in search.py

Traceback (most recent call last): File "", line 1, in File "C:\Users\Dell\Documents\Neural_storyteller\neural-storyteller-master\generate.py", line 67, in story passage = decoder.run_sampler(z['dec'], shift, beam_width=bw) File "C:\Users\Dell\Documents\Neural_storyteller\neural-storyteller-master\decoder.py", line 63, in run_sampler use_unk=use_unk) File "C:\Users\Dell\Documents\Neural_storyteller\neural-storyteller-master\search.py", line 72, in gen_sample new_hyp_samples.append(hyp_samples[ti]+[wi]) TypeError: list indices must be integers or slices, not numpy.float64

on trying to print ti values, i get the following: [ 7.65000000e-03 2.50000000e-04 1.45000000e-03 1.70000000e-03 6.45000000e-03 5.85000000e-03 5.16500000e-02 7.80000000e-03 2.60000000e-03 2.65000000e-03 2.70500000e-02 2.15000000e-03 6.00000000e-03 1.94000000e-02 6.10000000e-03 6.49500000e-02 6.42500000e-02 1.79650000e-01 1.56650000e-01 1.04500000e-02 4.28400000e-01 5.38000000e-02 9.15000000e-03 1.93400000e-01 3.92000000e-02 1.71300000e-01 7.10000000e-03 1.25900000e-01 8.68500000e-02 3.80000000e-03 1.88450000e-01 9.03500000e-02 1.37500000e-02 7.23500000e-02 1.84850000e-01 1.14850000e-01 3.13450000e-01 9.05000000e-03 1.20050000e-01 2.30500000e-01 3.24000000e-02 8.71800000e-01 4.88000000e-02 3.75000000e-02 1.22250000e-01 1.01950000e-01 4.62350000e-01 2.75200000e-01 1.11000000e-02 4.95000000e-03] so the ti values are indeed float64 values and cannot be used to index the hyp_sample variable. Any idea how i can correct this? am i missing something?

[HappyCoderMan]

I think I had to add this f2 thing that casts. I'm not entirely sure it's "correct", so don't entirely trust it.

        f2 = numpy.vectorize(numpy.int)
        trans_indices = f2(ranks_flat / voc_size)
        word_indices = ranks_flat % voc_size
        costs = cand_flat[ranks_flat]

        new_hyp_samples = []
        new_hyp_scores = numpy.zeros(k-dead_k).astype('float32')
        new_hyp_states = []

        for idx, [ti, wi] in enumerate(zip(trans_indices, word_indices)):
            new_hyp_samples.append(hyp_samples[ti]+[wi])

[SundareshPrasanna]

Thanks @ErikOmberg that solved the error. But the code now gives out almost similar output irrespective of the image i feed in

for example: if i feed image ex3 (which i think is about french fries & ketchup) in the images folder, i get,

generate.story(z, './images/ex3.jpg') NEAREST-CAPTIONS: b'Person snowboarding down a hill .' b'some zebras dirt plants and bushes' b'People are crossing the street in NYC' b'Black bear is hiding behind tall leaves' b'two people on a field playing lacrosse' OUTPUT: I had a feeling it was never going to be a waste of time , but I could n't keep quiet until she looked at me as I closed my eyes and watched her lean back against him as he pulled out his cell phone . The only thing that mattered to me was that the two of them were just a few minutes away . We were only a few short minutes after the charity event , and I had no idea what to do . By the time I opened the door , I thought I was going to walk through the streets of New York City . She was dressed in a nice , white dress with a gun pointed at the collar of her shirt , and I could see no trace of it . In fact , my mom 's screams and screams filled her ears . My God , he said .

I also get the same nearest caption and output when i feed it a tennis image,

generate.story(z, './images/tennis.jpg')

NEAREST-CAPTIONS: b'Person snowboarding down a hill .' b'some zebras dirt plants and bushes' b'People are crossing the street in NYC' b'Black bear is hiding behind tall leaves' b'two people on a field playing lacrosse' OUTPUT: I had a feeling it was never going to be a waste of time , but I could n't keep quiet until she looked at me as I closed my eyes and watched her lean back against him as he pulled out his cell phone . The only thing that mattered to me was that the two of them were just a few minutes away . We were only a few short minutes after the charity event , and I had no idea what to do . By the time I opened the door , I thought I was going to walk through the streets of New York City . She was dressed in a nice , white dress with a gun pointed at the collar of her shirt , and I could see no trace of it . In fact , my mom 's screams and screams filled her ears . My God , he said .

[HappyCoderMan]

Yes, that's what I observe too. Please let me know if you find how to get quality captions.