pythontensorflowkerasdeep-learningocr
InvalidArgumentError: Cannot add tensor to the batch: number of elements does not match. Shapes are: [tensor]: [4], [batch]: [5] [Op:IteratorGetNext]
Task: Keras captcha ocr model training.
Problem: I am trying to print CAPTCHAS from my validation set, but doing so is causing the following error
InvalidArgumentError Traceback (most recent call last)
<ipython-input-6-df1fce607804> in <module>()
1
2 #_, ax = plt.subplots(1, 4, figsize=(10, 5))
----> 3 for batch in validation_dataset.take(1):
4 images = batch["image"]
5 labels = batch["label"]
3 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e, name)
7105 def raise_from_not_ok_status(e, name):
7106 e.message += (" name: " + name if name is not None else "")
-> 7107 raise core._status_to_exception(e) from None # pylint: disable=protected-access
7108
7109
InvalidArgumentError: Cannot add tensor to the batch: number of elements does not match. Shapes are: [tensor]: [4], [batch]: [5] [Op:IteratorGetNext]
The code to print my output, this is what I tried:
#_, ax = plt.subplots(1, 4, figsize=(10, 5))
for batch in validation_dataset.take(1):
images = batch["image"]
labels = batch["label"]
for i in range(batch_size):
img = (images[i] * 255).numpy().astype("uint8")
label = tf.strings.reduce_join(num_to_char(labels[i])).numpy().decode("utf-8")
plt.title(label)
plt.imshow(img[:, :, 0].T, cmap="gray")
plt.show()
For this task, i tried setting batch size 1, but i want to train my model with higher batch size my batch size = 16
# Mapping integers back to original characters
num_to_char = layers.StringLookup(
vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
)
This is the code taken from tensorflow datasets docs to convert data to dataset type in tf Creating Dataset objects
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train]))
train_dataset = (
train_dataset.map(
encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE
)
.batch(batch_size)
.prefetch(buffer_size=tf.data.AUTOTUNE).repeat(10)
)
validation_dataset = tf.data.Dataset.from_tensor_slices((x_valid, y_valid]))
validation_dataset = (
validation_dataset.map(
encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE
)
.batch(batch_size)
.prefetch(buffer_size=tf.data.AUTOTUNE)
)
This code does reading the image and preprocessing the image to bring all the images to uniform shape Function encode single sample
def encode_single_sample(img_path, label):
# 1. Read image
img = tf.io.read_file(img_path)
# 2. Decode and convert to grayscale
img = tf.io.decode_png(img, channels=3)
# 3. Convert to float32 in [0, 1] range
img = tf.image.convert_image_dtype(img, tf.float32)
# 4. Resize to the desired size
img = tf.image.resize(img, [img_height, img_width])
# 5. Transpose the image because we want the time
# dimension to correspond to the width of the image.
img = tf.transpose(img, perm=[1, 0, 2])
# 6. Map the characters in label to numbers
label = char_to_num(tf.strings.unicode_split(label, input_encoding="UTF-8"))
# 7. Return a dict as our model is expecting two inputs
return {"image": img, "label": label}
Edits: Regarding the data: This is a sample from the data. Its similar to the keras ocr example dataset. Although the image sizes vary, although very less variability in the captcha patterns. It maintains 5 length words with 1 or two numbers in between. Words are always in uppercase.
Where am I going wrong?
Solution
Here is a complete running example based on your dataset running in Google Colab:
!pip install unrar
!unrar x /content/captcha_caps.rar
import os
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from collections import Counter
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
data_dir = Path("/content/captcha_caps/")
images = sorted(list(map(str, list(data_dir.glob("*.PNG")))))
labels = [img.split(os.path.sep)[-1].split(".PNG")[0] for img in images]
characters = set(char for label in labels for char in label)
print("Number of images found: ", len(images))
print("Number of labels found: ", len(labels))
print("Number of unique characters: ", len(characters))
print("Characters present: ", characters)
batch_size = 16
img_width = 200
img_height = 50
downsample_factor = 4
max_length = max([len(label) for label in labels])
char_to_num = layers.StringLookup(
vocabulary=list(characters), mask_token=None
)
num_to_char = layers.StringLookup(
vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
)
def split_data(images, labels, train_size=0.9, shuffle=True):
size = len(images)
indices = np.arange(size)
if shuffle:
np.random.shuffle(indices)
train_samples = int(size * train_size)
x_train, y_train = images[indices[:train_samples]], labels[indices[:train_samples]]
x_valid, y_valid = images[indices[train_samples:]], labels[indices[train_samples:]]
return x_train, x_valid, y_train, y_valid
x_train, x_valid, y_train, y_valid = split_data(np.array(images), np.array(labels))
def encode_single_sample(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, [img_height, img_width])
img = tf.transpose(img, perm=[1, 0, 2])
label = char_to_num(tf.strings.unicode_split(label, input_encoding="UTF-8"))
return {"image": img, "label": label}
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = (
train_dataset.map(
encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE
)
.batch(batch_size)
.prefetch(buffer_size=tf.data.AUTOTUNE).repeat(10)
)
validation_dataset = tf.data.Dataset.from_tensor_slices((x_valid, y_valid))
validation_dataset = (
validation_dataset.map(
encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE
)
.batch(batch_size)
.prefetch(buffer_size=tf.data.AUTOTUNE)
)
_, ax = plt.subplots(4, 4, figsize=(10, 5))
for batch in validation_dataset.take(1):
images = batch["image"]
labels = batch["label"]
for i in range(16):
img = (images[i] * 255).numpy().astype("uint8")
label = tf.strings.reduce_join(num_to_char(labels[i])).numpy().decode("utf-8")
ax[i // 4, i % 4].imshow(img[:, :, 0].T, cmap="gray")
ax[i // 4, i % 4].set_title(label)
ax[i // 4, i % 4].axis("off")
plt.show()
Requirement already satisfied: unrar in /usr/local/lib/python3.7/dist-packages (0.4)
UNRAR 5.50 freeware Copyright (c) 1993-2017 Alexander Roshal
Extracting from /content/captcha_caps.rar
Creating captcha caps OK
Extracting captcha caps/24VCZ.PNG OK
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Extracting captcha caps/FF5AZ.png OK
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All OK
Number of images found: 300
Number of labels found: 300
Number of unique characters: 30
Characters present: {'9', 'A', '6', 'Y', '2', 'J', '3', 'F', 'C', 'X', 'D', 'N', 'V', '4', '5', 'K', 'Z', '7', 'P', 'L', 'H', 'T', 'Q', 'E', 'U', '8', 'S', 'G', 'R', 'B'}
