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training.models for cnn-rnn-ocr: fix config names for height/width…

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- rename `image_height` → `input_height`
- rename `image_width` → `input_width`
This commit is contained in:
Robert Sachunsky 2026-06-12 14:52:23 +02:00
parent 4181e03bc9
commit 9d2412080f
1 changed files with 11 additions and 11 deletions
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22
src/eynollah/training/models.py
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@ -92,16 +92,16 @@ class CTCDecoder(Layer):
# get top path for all sequences in batch # get top path for all sequences in batch
decoded = decoded[0] decoded = decoded[0]
logits = logits[:, 0] - logits[:, 1] logits = logits[:, 0] - logits[:, 1]
probs = tf.exp(-logits)
# convert to dense # convert to dense
outputs = tf.SparseTensor(decoded.indices, decoded.values, outputs = tf.SparseTensor(decoded.indices, decoded.values,
(n_samples, n_steps)) (n_samples, n_steps))
outputs = tf.sparse.to_dense(sp_input=outputs, default_value=-1) outputs = tf.sparse.to_dense(sp_input=outputs, default_value=n_classes-1)
# # drop non-tokens (-1) and OOV (0) # # drop non-tokens (-1) and OOV (0)
# result = [] # result = []
# for output in outputs: # for output in outputs:
# result.append(tf.gather(output, tf.where(output > 0))) # result.append(tf.gather(output, tf.where(output > 0)))
# outputs = tf.stack(result) # outputs = tf.stack(result)
probs = tf.exp(-logits)
return outputs, probs return outputs, probs
def mlp(x, hidden_units, dropout_rate): def mlp(x, hidden_units, dropout_rate):
@ -468,8 +468,8 @@ def machine_based_reading_order_model(n_classes,input_height=224,input_width=224
return model return model
def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_len=None, inference=False, characters_txt_file=None): def cnn_rnn_ocr_model(input_height=None, input_width=None, n_classes=None, max_len=None, inference=False, characters_txt_file=None):
inputs = Input(shape=(image_height, image_width, 3), name="image") inputs = Input(shape=(input_height, input_width, 3), name="image")
labels = Input(name="label", shape=(None,)) labels = Input(name="label", shape=(None,))
x = Conv2D(64,kernel_size=(3,3),padding="same")(inputs) x = Conv2D(64,kernel_size=(3,3),padding="same")(inputs)
@ -496,10 +496,10 @@ def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_l
x = Activation("relu", name="relu6")(x) x = Activation("relu", name="relu6")(x)
x = MaxPooling2D(pool_size=(2,2),strides=(2,2))(x) x = MaxPooling2D(pool_size=(2,2),strides=(2,2))(x)
x = Conv2D(image_width,kernel_size=(3,3),padding="same")(x) x = Conv2D(input_width,kernel_size=(3,3),padding="same")(x)
x = BatchNormalization(name="bn7")(x) x = BatchNormalization(name="bn7")(x)
x = Activation("relu", name="relu7")(x) x = Activation("relu", name="relu7")(x)
x = Conv2D(image_width,kernel_size=(16,1))(x) x = Conv2D(input_width,kernel_size=(16,1))(x)
x = BatchNormalization(name="bn8")(x) x = BatchNormalization(name="bn8")(x)
x = Activation("relu", name="relu8")(x) x = Activation("relu", name="relu8")(x)
x2d = MaxPooling2D(pool_size=(1,2),strides=(1,2))(x) x2d = MaxPooling2D(pool_size=(1,2),strides=(1,2))(x)
@ -513,9 +513,9 @@ def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_l
x2d = Reshape(new_shape2, name="reshape2")(x2d) x2d = Reshape(new_shape2, name="reshape2")(x2d)
x4d = Reshape(new_shape4, name="reshape4")(x4d) x4d = Reshape(new_shape4, name="reshape4")(x4d)
xrnnorg = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x) xrnnorg = Bidirectional(LSTM(input_width, return_sequences=True, dropout=0.25))(x)
xrnn2d = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x2d) xrnn2d = Bidirectional(LSTM(input_width, return_sequences=True, dropout=0.25))(x2d)
xrnn4d = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(x4d) xrnn4d = Bidirectional(LSTM(input_width, return_sequences=True, dropout=0.25))(x4d)
xrnn2d = Reshape((1, xrnn2d.shape[1], xrnn2d.shape[2]), name="reshape6")(xrnn2d) xrnn2d = Reshape((1, xrnn2d.shape[1], xrnn2d.shape[2]), name="reshape6")(xrnn2d)
xrnn4d = Reshape((1, xrnn4d.shape[1], xrnn4d.shape[2]), name="reshape8")(xrnn4d) xrnn4d = Reshape((1, xrnn4d.shape[1], xrnn4d.shape[2]), name="reshape8")(xrnn4d)
@ -528,7 +528,7 @@ def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_l
addition = Add()([xrnnorg, xrnn2dup, xrnn4dup]) addition = Add()([xrnnorg, xrnn2dup, xrnn4dup])
addition_rnn = Bidirectional(LSTM(image_width, return_sequences=True, dropout=0.25))(addition) addition_rnn = Bidirectional(LSTM(input_width, return_sequences=True, dropout=0.25))(addition)
out = Conv1D(max_len, 1, data_format="channels_first")(addition_rnn) out = Conv1D(max_len, 1, data_format="channels_first")(addition_rnn)
out = BatchNormalization(name="bn9")(out) out = BatchNormalization(name="bn9")(out)
@ -539,7 +539,7 @@ def cnn_rnn_ocr_model(image_height=None, image_width=None, n_classes=None, max_l
if inference: if inference:
# add second path for binarization # add second path for binarization
inputs_bin = Input(shape=(image_height, image_width, 3), name="image_bin") inputs_bin = Input(shape=(input_height, input_width, 3), name="image_bin")
out_bin = Model(inputs, out)(inputs_bin) out_bin = Model(inputs, out)(inputs_bin)
# ensemble raw results # ensemble raw results
out = 0.5 * (out + out_bin) out = 0.5 * (out + out_bin)