example/warpctc/toy_ctc.py

# pylint: disable=C0111,too-many-arguments,too-many-instance-attributes,too-many-locals,redefined-outer-name,fixme
# pylint: disable=superfluous-parens, no-member, invalid-name
from __future__ import print_function
import sys
sys.path.insert(0, "../../python")
import numpy as np
import mxnet as mx
import random
from lstm import lstm_unroll

class SimpleBatch(object):
    def __init__(self, data_names, data, label_names, label):
        self.data = data
        self.label = label
        self.data_names = data_names
        self.label_names = label_names

        self.pad = 0
        self.index = None # TODO: what is index?

    @property
    def provide_data(self):
        return [(n, x.shape) for n, x in zip(self.data_names, self.data)]

    @property
    def provide_label(self):
        return [(n, x.shape) for n, x in zip(self.label_names, self.label)]

def gen_feature(n):
    ret = np.zeros(10)
    ret[n] = 1
    return ret

def gen_rand():
    num = random.randint(0, 9999)
    buf = str(num)
    while len(buf) < 4:
        buf = "0" + buf
    ret = []
    for i in range(80):
        c = int(buf[i // 20])
        ret.append(gen_feature(c))
    return buf, ret

def get_label(buf):
    ret = np.zeros(4)
    for i in range(4):
        ret[i] = 1 + int(buf[i])
    return ret

class DataIter(mx.io.DataIter):
    def __init__(self, count, batch_size, num_label, init_states):
        super(DataIter, self).__init__()
        self.batch_size = batch_size
        self.count = count
        self.num_label = num_label
        self.init_states = init_states
        self.init_state_arrays = [mx.nd.zeros(x[1]) for x in init_states]
        self.provide_data = [('data', (batch_size, 80, 10))] + init_states
        self.provide_label = [('label', (self.batch_size, 4))]

    def __iter__(self):
        init_state_names = [x[0] for x in self.init_states]
        for k in range(self.count):
            data = []
            label = []
            for i in range(self.batch_size):
                num, img = gen_rand()
                data.append(img)
                label.append(get_label(num))
                
            data_all = [mx.nd.array(data)] + self.init_state_arrays
            label_all = [mx.nd.array(label)]
            data_names = ['data'] + init_state_names
            label_names = ['label']
            
            
            data_batch = SimpleBatch(data_names, data_all, label_names, label_all)
            yield data_batch

    def reset(self):
        pass

BATCH_SIZE = 32
SEQ_LENGTH = 80

def ctc_label(p):
    ret = []
    p1 = [0] + p
    for i in range(len(p)):
        c1 = p1[i]
        c2 = p1[i+1]
        if c2 == 0 or c2 == c1:
            continue
        ret.append(c2)
    return ret
        

def Accuracy(label, pred):
    global BATCH_SIZE
    global SEQ_LENGTH
    hit = 0.
    total = 0.
    for i in range(BATCH_SIZE):
        l = label[i]
        p = []
        for k in range(SEQ_LENGTH):
            p.append(np.argmax(pred[k * BATCH_SIZE + i]))
        p = ctc_label(p)
        if len(p) == len(l):
            match = True
            for k in range(len(p)):
                if p[k] != int(l[k]):
                    match = False
                    break
            if match:
                hit += 1.0
        total += 1.0
    return hit / total

if __name__ == '__main__':
    num_hidden = 100
    num_lstm_layer = 1

    num_epoch = 10
    learning_rate = 0.001
    momentum = 0.9
    num_label = 4

    contexts = [mx.context.gpu(0)]

    def sym_gen(seq_len):
        return lstm_unroll(num_lstm_layer, seq_len,
                           num_hidden=num_hidden,
                           num_label = num_label)

    init_c = [('l%d_init_c'%l, (BATCH_SIZE, num_hidden)) for l in range(num_lstm_layer)]
    init_h = [('l%d_init_h'%l, (BATCH_SIZE, num_hidden)) for l in range(num_lstm_layer)]
    init_states = init_c + init_h

    data_train = DataIter(100000, BATCH_SIZE, num_label, init_states)
    data_val = DataIter(1000, BATCH_SIZE, num_label, init_states)

    symbol = sym_gen(SEQ_LENGTH)

    model = mx.model.FeedForward(ctx=contexts,
                                 symbol=symbol,
                                 num_epoch=num_epoch,
                                 learning_rate=learning_rate,
                                 momentum=momentum,
                                 wd=0.00001,
                                 initializer=mx.init.Xavier(factor_type="in", magnitude=2.34))

    import logging
    head = '%(asctime)-15s %(message)s'
    logging.basicConfig(level=logging.DEBUG, format=head)
    
    print('begin fit')

    model.fit(X=data_train, eval_data=data_val,
              eval_metric = mx.metric.np(Accuracy),
              batch_end_callback=mx.callback.Speedometer(BATCH_SIZE, 50),)

    model.save("ocr")