====================================================================
- check parameters
vim conf/generate.ymln_bg: 1
n_font: 1
ch_h: 32
n_pic: 20
sample_size: 10
min_len: 4
max_len: 6
bg_dir: data/bgs
font_dir: data/chn_fonts
target_dir: data/ocr_dataset_train_20_10_val
chn_map_path: data/chn_corpus/word_freq.jsonput your font files eg xyz.ttf to [font dir] attribute put your background files eg xyz.jpg to [bg dir] attribute
- example generated file
train with pytorch crnn model
- edit training configuration
vim conf/train.yml- start the training
python train.py
- example output TODO
infer with trained model
- edit infer configuration
vim conf/infer.yml- start infering
python infer.py
- example output
DEBUG:root:crnn inited
CRNN(
(cnn): Sequential(
(conv0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu0): ReLU(inplace)
(pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu1): ReLU(inplace)
(pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(conv2): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(batchnorm2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu2): ReLU(inplace)
(conv3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu3): ReLU(inplace)
(pooling2): MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)
(conv4): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(batchnorm4): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu4): ReLU(inplace)
(conv5): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(relu5): ReLU(inplace)
(pooling3): MaxPool2d(kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)
(conv6): Conv2d(512, 512, kernel_size=(2, 2), stride=(1, 1))
(batchnorm6): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu6): ReLU(inplace)
)
(rnn): Sequential(
(0): BidirectionalLSTM(
(rnn): LSTM(512, 512, bidirectional=True)
(embedding): Linear(in_features=1024, out_features=512, bias=True)
)
(1): BidirectionalLSTM(
(rnn): LSTM(512, 512, bidirectional=True)
(embedding): Linear(in_features=1024, out_features=11, bias=True)
)
)
)
INFO:root:img to infer example.jpg
INFO:root:raw output -------的------那那----人人----人人人--------个 real output 的那人人个-actual image
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