tensorflow - 无效参数 : Input size should match but they differ by 2

Question

我正在尝试使用 tf.keras 训练 dl 模型。我的图像目录中有 67 类图像，例如机场、书店、赌场。对于每个类，我至少有 100 张图像。数据来自mit indoor scene但是当我尝试训练模型时，我不断收到此错误。

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

我尝试通过使用调整大小图层调整图像大小来解决该问题，还包括 labels='inferred'和label_mode='categorical'在 image_dataset_from_directory方法并包含loss='categorical_crossentropy'在模型编译方法中。以前标签和 label_model 没有设置，损失是稀疏_分类_交叉熵，我认为这是不正确的。所以我按照上面的描述更改了它们。但我仍然遇到问题。

stackoverflow中有一个与此相关的问题但该人没有提及他如何解决问题，只是更新了 - 我的建议是检查数据集的元数据。它帮助解决了我的问题。但没有提到要寻找哪些元数据或者他做了什么来解决问题。

我用来训练模型的代码 -

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())

注意-我只是修改了代码，使其尽可能简单以减少错误。模型运行几个批处理后再次出现上述错误。

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

修改代码-

# 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)

Answer 1

我认为这可能是一个损坏的文件。在 DecodeBMPv2 函数 ( https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/kernels/image/decode_image_op.cc#L594 )

中进行数据完整性检查后引发异常

如果这就是问题所在，并且您想找出哪些文件引发了异常，您可以在包含这些文件的目录上尝试如下操作。删除/替换您找到的任何文件，它应该可以正常训练。

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.io.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}")