models/OCR_network.py

import torch
from .networks import *


class BidirectionalLSTM(nn.Module):

    def __init__(self, nIn, nHidden, nOut):
        super(BidirectionalLSTM, self).__init__()

        self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
        self.embedding = nn.Linear(nHidden * 2, nOut)


    def forward(self, input):
        recurrent, _ = self.rnn(input)
        T, b, h = recurrent.size()
        t_rec = recurrent.view(T * b, h)

        output = self.embedding(t_rec)  # [T * b, nOut]
        output = output.view(T, b, -1)

        return output


class CRNN(nn.Module):

    def __init__(self, args, leakyRelu=False):
        super(CRNN, self).__init__()
        self.args = args
        self.name = 'OCR'
        self.add_noise = False
        self.noise_fac = torch.distributions.Normal(loc=torch.tensor([0.]), scale=torch.tensor([0.2]))
        #assert opt.imgH % 16 == 0, 'imgH has to be a multiple of 16'

        ks = [3, 3, 3, 3, 3, 3, 2]
        ps = [1, 1, 1, 1, 1, 1, 0]
        ss = [1, 1, 1, 1, 1, 1, 1]
        nm = [64, 128, 256, 256, 512, 512, 512]

        cnn = nn.Sequential()
        nh = 256
        dealwith_lossnone=False # whether to replace all nan/inf in gradients to zero

        def convRelu(i, batchNormalization=False):
            nIn = 1 if i == 0 else nm[i - 1]
            nOut = nm[i]
            cnn.add_module('conv{0}'.format(i),
                           nn.Conv2d(nIn, nOut, ks[i], ss[i], ps[i]))
            if batchNormalization:
                cnn.add_module('batchnorm{0}'.format(i), nn.BatchNorm2d(nOut))
            if leakyRelu:
                cnn.add_module('relu{0}'.format(i),
                               nn.LeakyReLU(0.2, inplace=True))
            else:
                cnn.add_module('relu{0}'.format(i), nn.ReLU(True))

        convRelu(0)
        cnn.add_module('pooling{0}'.format(0), nn.MaxPool2d(2, 2))  # 64x16x64
        convRelu(1)
        cnn.add_module('pooling{0}'.format(1), nn.MaxPool2d(2, 2))  # 128x8x32
        convRelu(2, True)
        convRelu(3)
        cnn.add_module('pooling{0}'.format(2),
                       nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 256x4x16
        convRelu(4, True)
        if self.args.resolution==63:
            cnn.add_module('pooling{0}'.format(3),
                           nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 256x4x16
        convRelu(5)
        cnn.add_module('pooling{0}'.format(4),
                       nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 512x2x16
        convRelu(6, True)  # 512x1x16

        self.cnn = cnn
        self.use_rnn = False
        if self.use_rnn:
            self.rnn = nn.Sequential(
                BidirectionalLSTM(512, nh, nh),
                BidirectionalLSTM(nh, nh, ))
        else:
            self.linear = nn.Linear(512, self.args.vocab_size)

        # replace all nan/inf in gradients to zero
        if dealwith_lossnone:
            self.register_backward_hook(self.backward_hook)

        self.device = torch.device('cuda:{}'.format(0)) 
        self.init = 'N02'
        # Initialize weights
        
        self = init_weights(self, self.init)

    def forward(self, input):
        # conv features
        if self.add_noise:
            input = input + self.noise_fac.sample(input.size()).squeeze(-1).to(self.args.device)
        conv = self.cnn(input)
        b, c, h, w = conv.size()
        if h!=1:
            print('a')
        assert h == 1, "the height of conv must be 1"
        conv = conv.squeeze(2)
        conv = conv.permute(2, 0, 1)  # [w, b, c]

        if self.use_rnn:
            # rnn features
            output = self.rnn(conv)
        else:
            output = self.linear(conv)
        return output

    def backward_hook(self, module, grad_input, grad_output):
        for g in grad_input:
            g[g != g] = 0  # replace all nan/inf in gradients to zero


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.
    """

    def __init__(self, alphabet, ignore_case=False):
        self._ignore_case = ignore_case
        if self._ignore_case:
            alphabet = alphabet.lower()
        self.alphabet = alphabet + '-'  # for `-1` index

        self.dict = {}
        for i, char in enumerate(alphabet):
            # NOTE: 0 is reserved for 'blank' required by wrap_ctc
            self.dict[char] = i + 1

    def encode(self, text):
        """Support batch or single str.
        Args:
            text (str or list of str): texts to convert.
        Returns:
            torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts.
            torch.IntTensor [n]: length of each text.
        """
        length = []
        result = []
        results = []
        for item in text:
            if isinstance(item, bytes): item = item.decode('utf-8', 'strict')
            length.append(len(item))
            for char in item:
                index = self.dict[char]
                result.append(index)
            results.append(result)
            result = []

        return torch.nn.utils.rnn.pad_sequence([torch.LongTensor(text) for text in results], batch_first=True), torch.IntTensor(length), None

    def decode(self, t, length, raw=False):
        """Decode encoded texts back into strs.
        Args:
            torch.IntTensor [length_0 + length_1 + ... length_{n - 1}]: encoded texts.
            torch.IntTensor [n]: length of each text.
        Raises:
            AssertionError: when the texts and its length does not match.
        Returns:
            text (str or list of str): texts to convert.
        """
        if length.numel() == 1:
            length = length[0]
            assert t.numel() == length, "text with length: {} does not match declared length: {}".format(t.numel(),
                                                                                                         length)
            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