Model.py

from keras.layers import*
from keras.models import Model
from Global_parameter import *
K.set_learning_phase(0)

# 全部参考于github上韩文车牌识别项目的CRNN网络结构
# # Loss and train functions, network architecture
def ctc_lambda_func(args):
    y_pred, labels, input_length, label_length = args
    y_pred = y_pred[:, 2:, :]
    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)


def get_Model(training):
    input_shape = (img_w, img_h, img_c)     # (128, 64, 1)

    # Make Networkw
    inputs = Input(name='the_input', shape=input_shape, dtype='float32')  # (None, 128, 64, 1)

    # Convolution layer (VGG)
    inner = Conv2D(64, (3, 3), padding='same', name='conv1', kernel_initializer='he_normal')(inputs)  # (None, 128, 64, 64)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(2, 2), name='max1')(inner)  # (None,64, 32, 64)

    inner = Conv2D(128, (3, 3), padding='same', name='conv2', kernel_initializer='he_normal')(inner)  # (None, 64, 32, 128)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(2, 2), name='max2')(inner)  # (None, 32, 16, 128)

    inner = Conv2D(256, (3, 3), padding='same', name='conv3', kernel_initializer='he_normal')(inner)  # (None, 32, 16, 256)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = Conv2D(256, (3, 3), padding='same', name='conv4', kernel_initializer='he_normal')(inner)  # (None, 32, 16, 256)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(1, 2), name='max3')(inner)  # (None, 32, 8, 256)

    inner = Conv2D(512, (3, 3), padding='same', name='conv5', kernel_initializer='he_normal')(inner)  # (None, 32, 8, 512)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = Conv2D(512, (3, 3), padding='same', name='conv6')(inner)  # (None, 32, 8, 512)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)
    inner = MaxPooling2D(pool_size=(1, 2), name='max4')(inner)  # (None, 32, 4, 512)

    inner = Conv2D(512, (2, 2), padding='same', kernel_initializer='he_normal', name='con7')(inner)  # (None, 32, 4, 512)
    inner = BatchNormalization()(inner)
    inner = Activation('relu')(inner)

    # CNN to RNN
    inner = Reshape(target_shape=((32, 10240)), name='reshape')(inner)  # (None, 32, 2048)
    inner = Dense(64, activation='relu', kernel_initializer='he_normal', name='dense1')(inner)  # (None, 32, 64)

    # RNN layer
    lstm_1 = LSTM(256, return_sequences=True, kernel_initializer='he_normal', name='lstm1')(inner)  # (None, 32, 512)
    lstm_1b = LSTM(256, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='lstm1_b')(inner)
    reversed_lstm_1b = Lambda(lambda inputTensor: K.reverse(inputTensor, axes=1)) (lstm_1b)

    lstm1_merged = add([lstm_1, reversed_lstm_1b])  # (None, 32, 512)
    lstm1_merged = BatchNormalization()(lstm1_merged)
    
    lstm_2 = LSTM(256, return_sequences=True, kernel_initializer='he_normal', name='lstm2')(lstm1_merged)
    lstm_2b = LSTM(256, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='lstm2_b')(lstm1_merged)
    reversed_lstm_2b= Lambda(lambda inputTensor: K.reverse(inputTensor, axes=1)) (lstm_2b)

    lstm2_merged = concatenate([lstm_2, reversed_lstm_2b])  # (None, 32, 1024)
    lstm_merged = BatchNormalization()(lstm2_merged)

    # transforms RNN output to character activations:
    inner = Dense(num_classes, kernel_initializer='he_normal',name='dense2')(lstm2_merged) #(None, 32, 63)
    y_pred = Activation('softmax', name='softmax')(inner)

    labels = Input(name='the_labels', shape=[max_text_len], dtype='float32') # (None ,8)
    input_length = Input(name='input_length', shape=[1], dtype='int64')     # (None, 1)
    label_length = Input(name='label_length', shape=[1], dtype='int64')     # (None, 1)

    # Keras doesn't currently support loss funcs with extra parameters
    # so CTC loss is implemented in a lambda layer
    loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length]) #(None, 1)

    if training:
        return Model(inputs=[inputs, labels, input_length, label_length], outputs=loss_out)
    else:
        return Model(inputs=[inputs], outputs=y_pred)