How to Extract Probabilities CTC

Terminal_White · March 28, 2018, 8:55pm

How to extract -Top(K-10 )- probabilities and labels from CTC layer , I’m trying to get the final top k-10 -words with the associated probabilities.
The code is based on https://github.com/meijieru/crnn.pytorch/blob/master/demo.py
any hint or help ?
I added this code to extract the top k but I’m getting an error
AssertionError: text with length: 260 does not match declared length: 26

my code
model .eval()
output = model(image)
_, predicted = output.topk(10)
cl = predicted.data[0][0]
print (cl)
predicted = predicted.transpose(1, 0).contiguous().view(-1)
predicted_size = Variable(torch.IntTensor([preds.size(0)])) sim_predicted = converter.decode(predicted.data, predicted_size.data, raw=True)

Thanks

royboy · March 28, 2018, 9:30pm

I think this is an issue with the demo code and not pytorch:

github.com

meijieru/crnn.pytorch/blob/4850b736fc8186b98c864a0c95c032a887e7c537/utils.py#L80


    if raw:
        return ''.join([self.alphabet[i - 1] for i in t])
    else:
        char_list = []
        for i in range(length):
            if t[i] != 0 and (not (i > 0 and t[i - 1] == t[i])):
                char_list.append(self.alphabet[t[i] - 1])
        return ''.join(char_list)
else:
    # batch mode
    assert t.numel() == length.sum(), "texts with length: {} does not match declared length: {}".format(t.numel(), length.sum())
    texts = []
    index = 0
    for i in range(length.numel()):
        l = length[i]
        texts.append(
            self.decode(
                t[index:index + l], torch.IntTensor([l]), raw=raw))
        index += l
    return texts

I found this related issue, hopefully it helps. https://github.com/meijieru/crnn.pytorch/issues/62

Terminal_White · March 30, 2018, 3:57pm

Thank you for pointing that out, still I’m not able to get the top k even after changing the index. Any hint/help to point me into the right direction.
Thanks in advance.

royboy · March 30, 2018, 7:30pm

Can you post the full script that you’re running?

Terminal_White · July 9, 2018, 3:09pm

So Sorry for the late reply… Still, I’m not able to run that demo.py with extract multiple words, 2,3 words, and its associated probabilities.

github.com

meijieru/crnn.pytorch/blob/master/demo.py

import torch
from torch.autograd import Variable
import utils
import dataset
from PIL import Image

import models.crnn as crnn


model_path = './data/crnn.pth'
img_path = './data/demo.png'
alphabet = '0123456789abcdefghijklmnopqrstuvwxyz'

model = crnn.CRNN(32, 1, 37, 256)
if torch.cuda.is_available():
    model = model.cuda()
print('loading pretrained model from %s' % model_path)
model.load_state_dict(torch.load(model_path))

converter = utils.strLabelConverter(alphabet)

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Not sure where to start, from my understanding, CTC has softmax, and score. from where we could extract the sum of probabilities for each word1, word2

CTC code

github.com

meijieru/crnn.pytorch/blob/4850b736fc8186b98c864a0c95c032a887e7c537/utils.py

#!/usr/bin/python
# encoding: utf-8

import torch
import torch.nn as nn
from torch.autograd import Variable
import collections


class strLabelConverter(object):
    """Convert between str and label.

    NOTE:
        Insert `blank` to the alphabet for CTC.

    Args:
        alphabet (str): set of the possible characters.
        ignore_case (bool, default=True): whether or not to ignore all of the case.
    """

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Thanks for your help in advance!