The issue is addressed in the following questions and none provides a clear solution if any answers at all
- CTC loss Tensorflow, No valid path found
- ctc_loss error "No valid path found."
- CTC Loss bug: no valid path found? OCR difficulties in Tf.keras
- Tensorflow ctc_loss_calculator: No valid path found
Here's the code I run ...
from itertools import groupby
from pathlib import Path
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from tensorflow.keras import Model
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import (LSTM, BatchNormalization, Bidirectional,
Conv2D, Dense, Input, Lambda, Layer,
MaxPool2D)
from tensorflow.keras.layers.experimental.preprocessing import StringLookup
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.sequence import pad_sequences
class CTCLayer(Layer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_fn = tf.keras.backend.ctc_batch_cost
def call(self, y_true, *args, **kwargs):
y_pred = args[0]
batch_len = tf.cast(tf.shape(y_true)[0], dtype='int64')
input_length = tf.cast(tf.shape(y_pred)[1], dtype='int64')
label_length = tf.cast(tf.shape(y_true)[1], dtype='int64')
input_length = input_length * tf.ones(shape=(batch_len, 1), dtype='int64')
label_length = label_length * tf.ones(shape=(batch_len, 1), dtype='int64')
loss = self.loss_fn(y_true, y_pred, input_length, label_length)
self.add_loss(loss)
return y_pred
class TrainingManager:
def __init__(
self, images, labels, batch_size=256, validation_size=0.1, resize=(32, 128)
):
self.images = images
self.labels = labels
self.batch_size = batch_size
self.validation_size = validation_size
self.resize = resize
self.vocabulary = sorted(set(''.join(self.labels)))
self.max_label_length = len(max(self.labels, key=len))
self.char_to_num = StringLookup(
vocabulary=self.vocabulary, num_oov_indices=0, mask_token=None
)
self.num_to_char = StringLookup(
vocabulary=self.char_to_num.get_vocabulary(), mask_token=None, invert=True
)
def process_example(self, img_path, label):
img = tf.io.read_file(img_path)
img = tf.io.decode_png(img, channels=1)
img = tf.image.convert_image_dtype(img, tf.float32)
img = tf.image.resize(img, self.resize)
return {'image': img, 'label': label}
def preview_dataset(self, dataset, n_rows, n_cols, fig_size=(15, 10)):
fig, ax = plt.subplots(n_rows, n_cols, figsize=fig_size)
for batch in dataset.take(1):
images = batch['image']
labels = batch['label']
for i in range(n_rows * n_cols):
img = (images[i] * 255).numpy().astype('uint8')
label = (
tf.strings.reduce_join(self.num_to_char(labels[i] + 1))
.numpy()
.decode('utf-8')
.replace('[UNK]', '')
)
ax[i // n_rows, i % n_cols].imshow(img[:, :, 0], cmap='gray')
ax[i // n_rows, i % n_cols].set_title(label)
ax[i // n_rows, i % n_cols].axis('off')
def decode_predictions(self, predictions):
text_list = []
prediction_indices = np.argmax(predictions, axis=2)
for i in range(prediction_indices.shape[0]):
text = ''
for p, _ in groupby(prediction_indices[i]):
if p != len(self.vocabulary):
text += self.vocabulary[p]
text_list.append(text)
return text_list
def create_dataset(self, x, y):
dataset = tf.data.Dataset.from_tensor_slices((x, y))
return (
dataset.map(
self.process_example, num_parallel_calls=tf.data.experimental.AUTOTUNE
)
.batch(self.batch_size)
.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
)
def split_data(self):
separation_idx = int(len(self.images) * (self.validation_size - 1))
train_images = self.images[:separation_idx]
valid_images = self.images[separation_idx:]
labels = [
self.char_to_num(tf.strings.unicode_split(label, input_encoding='UTF-8'))
for label in self.labels
]
labels = pad_sequences(labels, self.max_label_length, padding='post')
train_labels = labels[:separation_idx]
valid_labels = labels[separation_idx:]
return train_images, valid_images, train_labels, valid_labels
def create_datasets(self):
train_images, valid_images, train_labels, valid_labels = self.split_data()
train_dataset = self.create_dataset(train_images, train_labels)
valid_dataset = self.create_dataset(valid_images, valid_labels)
return train_dataset, valid_dataset
def create_model(self, training=True):
x0 = Input(shape=(32, 128, 1), name='image')
x = Conv2D(32, (3, 3), activation='selu', padding='same')(x0)
x = MaxPool2D(pool_size=(2, 2))(x)
x = Conv2D(64, (3, 3), activation='selu', padding='same')(x)
x = MaxPool2D(pool_size=(2, 2))(x)
x = Conv2D(128, (3, 3), activation='selu', padding='same')(x)
x = Conv2D(128, (3, 3), activation='selu', padding='same')(x)
x = MaxPool2D(pool_size=(2, 1))(x)
x = Conv2D(256, (3, 3), activation='selu', padding='same')(x)
x = BatchNormalization()(x)
x = Conv2D(256, (3, 3), activation='selu', padding='same')(x)
x = BatchNormalization()(x)
x = MaxPool2D(pool_size=(2, 1))(x)
x = Conv2D(64, (2, 2), activation='selu')(x)
x = Lambda(lambda i: tf.squeeze(i, 1))(x)
x = Bidirectional(LSTM(128, return_sequences=True))(x)
x = Bidirectional(LSTM(128, return_sequences=True))(x)
output = Dense(len(self.vocabulary) + 1, activation='softmax', name='dense')(x)
if not training:
return Model(x0, output)
labels = Input(name='label', shape=(None,), dtype='float32')
output = CTCLayer(name='ctc_loss')(labels, output)
return Model(inputs=[x0, labels], outputs=output)
if __name__ == '__main__':
photos, texts = [], []
for line in open('labels.txt'):
photo_path, photo_text = line.split(',')
photos.append((Path('examples') / photo_path).as_posix())
texts.append(photo_text.strip())
manager = TrainingManager(photos, texts, batch_size=32)
optimizer = Adam()
model = manager.create_model()
model.compile(optimizer=optimizer, metrics=[tf.keras.metrics.Accuracy()])
model.summary()
tds, vds = manager.create_datasets()
manager.preview_dataset(tds, 2, 2)
plt.show()
history = model.fit(
tds,
epochs=100,
validation_data=vds,
verbose=1,
callbacks=[EarlyStopping(verbose=1, patience=3, restore_best_weights=True)],
shuffle=True,
)
examples + labels.txt (inside examples folder)
Here's the result:
2021-10-14 05:22:31.610 Python[18731:595755] ApplePersistenceIgnoreState: Existing state will not be touched. New state will be written to /var/folders/hr/61r_7jcx2r3cnklwrr2zwbqw0000gn/T/org.python.python.savedState
2021-10-14 05:22:31.746089: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/100
2021-10-14 05:22:41.480510: W ./tensorflow/core/util/ctc/ctc_loss_calculator.h:499] No valid path found.
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1/1 [==============================] - ETA: 0s - loss: inf - accuracy: 0.0000e+002021-10-14 05:22:44.004554: W ./tensorflow/core/util/ctc/ctc_loss_calculator.h:499] No valid path found.
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1/1 [==============================] - 9s 9s/step - loss: inf - accuracy: 0.0000e+00 - val_loss: inf - val_accuracy: 0.0000e+00
Epoch 2/100
2021-10-14 05:22:44.622235: W ./tensorflow/core/util/ctc/ctc_loss_calculator.h:499] No valid path found.
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1/1 [==============================] - ETA: 0s - loss: inf - accuracy: 0.0000e+002021-10-14 05:22:45.075456: W ./tensorflow/core/util/ctc/ctc_loss_calculator.h:499] No valid path found.
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1/1 [==============================] - 1s 1s/step - loss: inf - accuracy: 0.0000e+00 - val_loss: inf - val_accuracy: 0.0000e+00
Epoch 3/100
2021-10-14 05:22:45.698230: W ./tensorflow/core/util/ctc/ctc_loss_calculator.h:499] No valid path found.
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1/1 [==============================] - ETA: 0s - loss: inf - accuracy: 0.0000e+002021-10-14 05:22:46.127968: W ./tensorflow/core/util/ctc/ctc_loss_calculator.h:499] No valid path found.
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1/1 [==============================] - 1s 1s/step - loss: inf - accuracy: 0.0000e+00 - val_loss: inf - val_accuracy: 0.0000e+00
Restoring model weights from the end of the best epoch.
Epoch 00003: early stopping
Note: The code works perfectly fine for labels that are 10 characters long or shorter. The labels and respective photos in the example above have 1-20 characters each. What exactly do I need to modify, to make it work, given a label of length n?
