can not run on windows !

[BackT0TheFuture]

hi,there did you test the code on windows? thx!

[novioleo]

no~i just run it on ubuntu 16.04. can you show me your problem.?

[BackT0TheFuture]

thanks for your reply. I just tested the code on centos, It does not work either. please refer to attached image for details.

[novioleo]

my runtime environment is anaconda3 4.2.0,it means the vision of python is 3.5.2. and the mxnet which i built from sourcew with the warpctc plugin. the first error,you can replace it with data = [];label = []

the second error maybe not occur.

[BackT0TheFuture]

I tried as you said , the second error is still there .

[novioleo]

can you show me the script you run this train?

[BackT0TheFuture]

sure, the code is roughly same to your commit, just modification as aboved.

`from future import print_function

import argparse import logging import os

import mxnet as mx import numpy as np from PIL import Image

from crnn import crnn

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

@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)]

class OCRIter(mx.io.DataIter): def init(self, batch_size, classes, data_shape, num_label, init_states,dataset_lst): super(OCRIter, self).init() self.batch_size = batch_size self.data_shape = data_shape 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.classes = classes self.dataset_lst_file = open(dataset_lst) self.provide_data = [('data', (batch_size, 1, data_shape[1], data_shape[0]))] + init_states self.provide_label = [('label', (self.batch_size, num_label))]

def __iter__(self):
    init_state_names = [x[0] for x in self.init_states]
    data = []
    label = []
    cnt = 0
    for m_line in self.dataset_lst_file:
        img_path,img_label = m_line.strip().split('\t')
        cnt += 1
        img = Image.open(img_path).convert('L').resize(self.data_shape)
        img = np.array(img).reshape((1, data_shape[1], data_shape[0]))
        data.append(img)
        plate_str = img_label
        ret = np.zeros(self.num_label, int)
        for number in range(len(plate_str)):
            ret[number] = self.classes.index(plate_str[number]) + 1
        label.append(ret)
        if cnt % self.batch_size == 0:
            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 =[]
            label=[]
            #data.clear()
            #label.clear()
            yield SimpleBatch(data_names, data_all, label_names, label_all)
            continue

def reset(self):
    if self.dataset_lst_file.seekable():
        self.dataset_lst_file.seek(0)

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 remove_blank(l): ret = [] for i in range(len(l)): if l[i] == 0: break

    ret.append(l[i])
return ret

def Accuracy(label, pred): global BATCH_SIZE global SEQ_LENGTH hit = 0. total = 0. for i in range(BATCH_SIZE): l = remove_blank(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': parser = argparse.ArgumentParser() parser.add_argument('--name',required=True,help='the model name') parser.add_argument('--charset',required=True,help='the charset file') parser.add_argument('--train_lst',required=True,help='the csv which contains all train list') parser.add_argument('--val_lst',required=True,help='the csv which contains all val list') parser.add_argument('--batch_size',type=int,default=64,help='train/val batch size,default is 64') parser.add_argument('--seq_len',type=int,default=17,help='the sequence length effected by image width') parser.add_argument('--num_label',type=int,default=9,help='output label length,must less than seq_len') parser.add_argument('--imgH',type=int,default=32,help='image height,must divided by 16') parser.add_argument('--imgW',type=int,default=200,help='image width') parser.add_argument('--num_hidden',type=int,default=256,help='num of parameters in lstm hidden layer') parser.add_argument('--num_lstm',type=int,default=2,help='num of lstm layers') parser.add_argument('--gpu',action='store_true',help='enable train with gpu(0)') parser.add_argument('--from_epoch',type=int,help='continue train from specific epoch file') parser.add_argument('--learning_rate',type=float,help='the learning rate of adam') opt = parser.parse_args() model_name = opt.name log_file_name = model_name+'.log' log_file = open(log_file_name, 'w') log_file.close() logging.basicConfig() logger = logging.getLogger() logger.setLevel(logging.INFO) fh = logging.FileHandler(log_file_name) logger.addHandler(fh) model_dir_path = os.path.join(os.getcwd(), 'model') if not os.path.exists(model_dir_path): os.mkdir(model_dir_path) prefix = os.path.join(os.getcwd(), 'model', model_name)

BATCH_SIZE = opt.batch_size
SEQ_LENGTH = opt.seq_len

num_hidden = opt.num_hidden
num_lstm_layer = opt.num_lstm

num_label = opt.num_label
data_shape = (opt.imgW, opt.imgH)
with open(opt.charset) as to_read: classes = list(to_read.read().strip())
num_classes = len(classes) + 1

def sym_gen(seq_len):
    return crnn(num_lstm_layer, BATCH_SIZE, seq_len,
                num_hidden=num_hidden, num_classes=num_classes,
                num_label=num_label, dropout=0.3)

init_c = [('l%d_init_c' % l, (BATCH_SIZE, num_hidden)) for l in range(num_lstm_layer * 2)]
init_h = [('l%d_init_h' % l, (BATCH_SIZE, num_hidden)) for l in range(num_lstm_layer * 2)]
init_states = init_c + init_h

data_train = OCRIter(BATCH_SIZE, classes, data_shape, num_label, init_states,opt.train_lst)
data_val = OCRIter(BATCH_SIZE, classes, data_shape, num_label, init_states,opt.val_lst)

ctx = mx.gpu(0) if opt.gpu else mx.cpu(0)
data_names = ['data', 'l0_init_c', 'l1_init_c', 'l2_init_c', 'l3_init_c', 'l0_init_h', 'l1_init_h', 'l2_init_h',
                    'l3_init_h']
label_names = ['label', ]
if opt.from_epoch is None:
    symbol = sym_gen(SEQ_LENGTH)
    model = mx.module.Module(
        symbol=symbol,
        data_names=data_names,
        label_names=label_names,
        context=ctx
    )
else:
    model = mx.module.Module.load(
        prefix,
        opt.from_epoch,
        data_names=data_names,
        label_names=label_names,
        context=ctx,
    )

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

logger.info('begin fit')

model.fit(
    train_data=data_train,
    eval_data=data_val,
    eval_metric=mx.metric.np(Accuracy),
    batch_end_callback=mx.callback.Speedometer(BATCH_SIZE, 100),
    epoch_end_callback=mx.callback.do_checkpoint(prefix, 1),
    optimizer='adam',
    optimizer_params={'learning_rate': opt.learning_rate},
    initializer=mx.init.Xavier(factor_type="in", magnitude=2.34),
    num_epoch=100,
    begin_epoch=opt.from_epoch if opt.from_epoch else 0
)
model.save_params(model_name)

`

[novioleo]

i not mean the source code,the run script is the arguments for main function. looks like this: --name chinese2 --charset chinesechars.txt --train_lst ../../data/train.csv --val_lst ../../data/val.csv --seq_len 51 --num_label 25 --gpu --learning_rate 0.0001 --from_epoch 20

[BackT0TheFuture]

my bad, I just noticed that learning rate has no default value ! now I run script with arguments as follows

python train.py --name numl --charset ./eng.txt --train_lst ./data/train.csv --val_lst ./data/val.csv --gpu --learning_rate 0.0001

error occurred after just one epoch

INFO:root:begin fit INFO:root:Epoch[0] Train-Accuracy=0.000000 INFO:root:Epoch[0] Time cost=13.511 INFO:root:Saved checkpoint to "/home/crnn.mxnet/model/numl-0001.params" INFO:root:Epoch[0] Validation-Accuracy=0.000000 Traceback (most recent call last): File "train.py", line 243, in begin_epoch=opt.from_epoch if opt.from_epoch else 0 File "/usr/lib/python2.7/site-packages/mxnet-0.11.1-py2.7.egg/mxnet/module/base_module.py", line 482, in fit next_data_batch = next(data_iter) StopIteration

[novioleo]

can you debug it step by step? i don't know what happens to it neither.

[BackT0TheFuture]

finally，I found the answer about StopIteration, now it works . I found some new problems. let's say after 100 epochs, both train and validation accuracy can be about 0.96-0.98, then I predict on test images, many result have repeated letter. for example truth ----- predict about aabbout some soome what wwhat and repeated letters are mostly at the beginning of the word. how come ? thanks!

[novioleo]

sorry,i meets the same error too,and i'm working on change the network structure. you can optimize it by yourself now,i'll add the new structure to this project which i tested .

[BackT0TheFuture]

I see, I'll have a try. thanks!