tensorflow - Invalid argument: Input size should match but they differ by 2

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I am trying to train a dl model with tf.keras. I have 67 classes of images inside the image directory like airports, bookstore, casino. And for each classes i have at least 100 images. The data is from mit indoor scene dataset But when I am trying to train the model, I am constantly getting this error.

tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument:  Input size should match (header_size + row_size * abs_height) but they differ by 2
         [[{{node decode_image/DecodeImage}}]]
         [[IteratorGetNext]]
  (1) Invalid argument:  Input size should match (header_size + row_size * abs_height) but they differ by 2
         [[{{node decode_image/DecodeImage}}]]
         [[IteratorGetNext]]
         [[IteratorGetNext/_7]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_1570]

Function call stack:
train_function -> train_function

I tried to resolve the problem by resizing the image with the resizing layer, also included the labels='inferred' and label_mode='categorical' in the image_dataset_from_directory method and included loss='categorical_crossentropy' in the model compile method. Previously labels and label_model were not set and loss was sparse_categorical_crossentropy which i think is not right. so I changed them as described above.But I am still having problems.

There is one question related to this in stackoverflow but the person did not mentioned how he solved the problem just updated that - My suggestion is to check the metadata of the dataset. It helped to fix my problem. But did not mentioned what metadata to look for or what he did to solve the problem.

The code that I am using to train the model -

import os
import PIL
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D, GlobalAveragePooling2D
from tensorflow.keras.layers import Flatten, Dropout, BatchNormalization, Rescaling
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.regularizers import l1, l2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from pathlib import Path
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# define directory paths
PROJECT_PATH = Path.cwd()
DATA_PATH = PROJECT_PATH.joinpath('data', 'Images')

# create a dataset
batch_size = 32
img_height = 180
img_width = 180

train = tf.keras.utils.image_dataset_from_directory(
    DATA_PATH,
    validation_split=0.2,
    subset="training",
    labels="inferred",
    label_mode="categorical",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size
)

valid = tf.keras.utils.image_dataset_from_directory(
    DATA_PATH,
    validation_split=0.2,
    subset="validation",
    labels="inferred",
    label_mode="categorical",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size
)

class_names = train.class_names

for image_batch, label_batch in train.take(1):
    print("\nImage shape:", image_batch.shape)
    print("Label Shape", label_batch.shape)

# resize image
resize_layer = tf.keras.layers.Resizing(img_height, img_width)
train = train.map(lambda x, y: (resize_layer(x), y))
valid = valid.map(lambda x, y: (resize_layer(x), y))

# standardize the data
normalization_layer = tf.keras.layers.Rescaling(1./255)
train = train.map(lambda x, y: (normalization_layer(x), y))
valid = valid.map(lambda x, y: (normalization_layer(x), y))

image_batch, labels_batch = next(iter(train))
first_image = image_batch[0]
print("\nImage (min, max) value:", (np.min(first_image), np.max(first_image)))
print()

# configure the dataset for performance
AUTOTUNE = tf.data.AUTOTUNE

train = train.cache().prefetch(buffer_size=AUTOTUNE)
valid = valid.cache().prefetch(buffer_size=AUTOTUNE)


# create a basic model architecture

num_classes = len(class_names)

# initiate a sequential model
model = Sequential()

# CONV1
model.add(Conv2D(filters=64, kernel_size=3, activation="relu",
          input_shape=(img_height, img_width, 3)))
model.add(BatchNormalization())

# CONV2
model.add(Conv2D(filters=64, kernel_size=3,
          activation="relu"))
model.add(BatchNormalization())

# Pool + Dropout
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))

# CONV3
model.add(Conv2D(filters=128, kernel_size=3,
          activation="relu"))
model.add(BatchNormalization())

# CONV4
model.add(Conv2D(filters=128, kernel_size=3,
          activation="relu"))
model.add(BatchNormalization())

# POOL + Dropout
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))

# FC5
model.add(Flatten())
model.add(Dense(128, activation="relu"))
model.add(Dense(num_classes, activation="softmax"))


# compile the model

model.compile(loss="categorical_crossentropy",
              optimizer="adam", metrics=['accuracy'])

# train the model
epochs = 25
early_stopping_cb = EarlyStopping(patience=10, restore_best_weights=True)

history = model.fit(train, validation_data=valid, epochs=epochs,
                    callbacks=[early_stopping_cb], verbose=2)

result = pd.DataFrame(history.history)
print()
print(result.head())

Note - I just modified the code to make it as simple as possible to reduce the error. The model run for few batches than again got the above error.

Epoch 1/10
732/781 [===========================>..] - ETA: 22s - loss: 3.7882Traceback (most recent call last):
  File ".\02_model1.py", line 139, in <module>
    model.fit(train, epochs=10, validation_data=valid)
  File "C:\Users\BHOLA\anaconda3\lib\site-packages\keras\engine\training.py", line 1184, in fit
    tmp_logs = self.train_function(iterator)
  File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 885, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 917, in _call
    return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
  File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 3039, in __call__
    return graph_function._call_flat(
  File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 1963, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 591, in call
    outputs = execute.execute(
  File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
  (0) Invalid argument:  Input size should match (header_size + row_size * abs_height) but they differ by 2
         [[{{node decode_image/DecodeImage}}]]
         [[IteratorGetNext]]
  (1) Invalid argument:  Input size should match (header_size + row_size * abs_height) but they differ by 2
         [[{{node decode_image/DecodeImage}}]]
         [[IteratorGetNext]]
         [[IteratorGetNext/_2]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_11840]

Function call stack:
train_function -> train_function

Modified code -

# create a dataset
batch_size = 16
img_height = 256
img_width = 256

train = image_dataset_from_directory(
    DATA_PATH,
    validation_split=0.2,
    subset="training",
    labels="inferred",
    label_mode="categorical",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size
)

valid = image_dataset_from_directory(
    DATA_PATH,
    validation_split=0.2,
    subset="validation",
    labels="inferred",
    label_mode="categorical",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size
)

model = tf.keras.applications.Xception(
    weights=None, input_shape=(img_height, img_width, 3), classes=67)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model.fit(train, epochs=10, validation_data=valid)
keras tensorflow deep-learning
2 Answers

I think it might be a corrupted file. It is throwing an exception after a data integrity check in the DecodeBMPv2 function (https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/kernels/image/decode_image_op.cc#L594)

If that's the issue and you want to find out which file(s) are throwing the exception, you can try something like this below on the directory containing the files. Remove/replace any files you find and it should train normally.

import glob

img_paths = glob.glob(os.path.join(<path_to_dataset>,'*/*.*') # assuming you point to the directory containing the label folders.

bad_paths = []

for image_path in img_paths:
    try:
      img_bytes = tf.io.read_file(path)
      decoded_img = tf.decode_image(img_bytes)
    except tf.errors.InvalidArgumentError as e:
      print(f"Found bad path {image_path}...{e}")
      bad_paths.append(image_path)

    print(f"{image_path}: OK")

print("BAD PATHS:")
for bad_path in bad_paths:
    print(f"{bad_path}")
user_50199680

This is in fact a corrupted file problem. However, the underlying issue is far more subtle. Here is an explanation of what is going on and how to circumvent this obstacle. I encountered the very same problem on the very same MIT Indoor Scene Classification dataset. All the images are JPEG files (spoiler alert: well, are they?).

It has been correctly noted that the exception is raised exactly here, in a C++ file related to the tf.io.decode_image() function. It is the decode_image() function where the issue lies, which is called by the tf.keras.utils.image_dataset_from_directory().

On the other hand, tf.keras.preprocessing.image.ImageDataGenerator().flow_from_directory() relies on Pillow under the hood (shown here, which is called from here). This is the reason why adopting the ImageDataGenerator class works.

After closer inspection of the corresponding C++ source file, one can observe that the function is actually called DecodeBmpV2(...), as defined here. This raises the question of why a JPEG image is being treated as a BMP one. The aforementioned function is actually called here, as part of a basic switch statement the aim of which is further direct data conversion according to the determined type. Thus, the piece of code that determines the file type should be subjected to deeper analysis. The file type is determined according to the value of starting bytes (see here). Long story short, a simple comparison of so-called magic bytes that signify file type is performed.

Here is a code extract with the corresponding magic bytes.

static const char kPngMagicBytes[] = "\x89\x50\x4E\x47\x0D\x0A\x1A\x0A";
static const char kGifMagicBytes[] = "\x47\x49\x46\x38";
static const char kBmpMagicBytes[] = "\x42\x4d";
static const char kJpegMagicBytes[] = "\xff\xd8\xff";

After identifying which files raise the exception, I saw that they were supposed to be JPEG files, however, their starting bytes indicated a BMP format instead.

Here is an example of 3 files and their first 10 bytes.

laundromat\laundry_room_area.jpg
    b'ffd8ffe000104a464946'

laundromat\Laundry_Room_Edens1A.jpg
    b'ffd8ffe000104a464946'

laundromat\Laundry_Room_bmp.jpg
    b'424d3800030000000000'

Look at the last one. It even contains the word bmp in the file name. Why is that so? I do not know. The dataset does contain corrupted image files. Someone probably converted the file from BMP to JPEG, yet the tool used did not work correctly. We can just guess the real reason, but that is now irrelevant.

The method by which the file type is determined is different from the one performed by the Pillow package, thus, there is nothing we can do about it. The recommendation is to identify the corrupted files, which is actually easy or to rely on the ImageDataGenerator. However, I would advise against doing so as this class has been marked as deprecated. It is not a bug in code per se, but rather bad data inadvertently introduced into the dataset.

user_20161500
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