qurator-spk/eynollah
1
0
Fork 0
mirror of https://github.com/qurator-spk/eynollah.git synced 2026-05-31 01:59:27 +02:00
Code Issues Projects Releases Wiki Activity

models.cnn_rnn_ocr_model: add inference option, drop model name

Browse source
This commit is contained in:
Robert Sachunsky 2026-05-28 17:32:02 +02:00
parent c4a7eec5b3
commit faef1967f8
1 changed files with 17 additions and 17 deletions
Download patch file Download diff file Expand all files Collapse all files

34
src/eynollah/training/models.py
View file

@ -422,11 +422,11 @@ def machine_based_reading_order_model(n_classes,input_height=224,input_width=224
return model
def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_seq=None):
input_img = Input(shape=(image_height, image_width, 3), name="image")
def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_len=None, inference=False):
inputs = Input(shape=(image_height, image_width, 3), name="image")
labels = Input(name="label", shape=(None,))
x = Conv2D(64,kernel_size=(3,3),padding="same")(input_img)
x = Conv2D(64,kernel_size=(3,3),padding="same")(inputs)
x = BatchNormalization(name="bn1")(x)
x = Activation("relu", name="relu1")(x)
x = Conv2D(64,kernel_size=(3,3),padding="same")(x)
@ -458,44 +458,44 @@ def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_s
x = Activation("relu", name="relu8")(x)
x2d = MaxPooling2D(pool_size=(1,2),strides=(1,2))(x)
x4d = MaxPooling2D(pool_size=(1,2),strides=(1,2))(x2d)
new_shape = (x.shape[1]*x.shape[2], x.shape[3])
new_shape2 = (x2d.shape[1]*x2d.shape[2], x2d.shape[3])
new_shape4 = (x4d.shape[1]*x4d.shape[2], x4d.shape[3])
x = Reshape(new_shape, name="reshape")(x)
x2d = Reshape(new_shape2, name="reshape2")(x2d)
x4d = Reshape(new_shape4, name="reshape4")(x4d)
xrnnorg = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x)
xrnn2d = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x2d)
xrnn4d = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x4d)
xrnn2d = Reshape((1, xrnn2d.shape[1], xrnn2d.shape[2]), name="reshape6")(xrnn2d)
xrnn4d = Reshape((1, xrnn4d.shape[1], xrnn4d.shape[2]), name="reshape8")(xrnn4d)
xrnn2dup = UpSampling2D(size=(1, 2), interpolation="nearest")(xrnn2d)
xrnn4dup = UpSampling2D(size=(1, 4), interpolation="nearest")(xrnn4d)
xrnn2dup = Reshape((xrnn2dup.shape[2], xrnn2dup.shape[3]), name="reshape10")(xrnn2dup)
xrnn4dup = Reshape((xrnn4dup.shape[2], xrnn4dup.shape[3]), name="reshape12")(xrnn4dup)
addition = Add()([xrnnorg, xrnn2dup, xrnn4dup])
addition_rnn = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(addition)
out = Conv1D(max_seq, 1, data_format="channels_first")(addition_rnn)
out = Conv1D(max_len, 1, data_format="channels_first")(addition_rnn)
out = BatchNormalization(name="bn9")(out)
out = Activation("relu", name="relu9")(out)
#out = Conv1D(n_classes, 1, activation='relu', data_format="channels_last")(out)
out = Dense(n_classes, activation="softmax", name="dense2")(out)
# Add CTC layer for calculating CTC loss at each step.
output = CTCLayer(name="ctc_loss")(labels, out)
model = Model(inputs=(input_img, labels), outputs=output, name="handwriting_recognizer")
if inference:
return Model(inputs, out)
# Add CTC layer for calculating CTC loss at each step.
out = CTCLayer(name="ctc_loss")(labels, out)
return Model((inputs, labels), out)
return model