我的理解是Keras的Resizing图层可以接受不同大小的输入图像，但我一直无法让它工作。

import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras import layers
dsTrain = tfds.load('CatsVsDogs', split='train[:10%]', as_supervised=True).batch(128).prefetch(tf.data.AUTOTUNE)

model = tf.keras.models.Sequential()
model.add(layers.InputLayer(input_shape=(None, None, 3)))
model.add(layers.Resizing(200, 200, crop_to_aspect_ratio=True)) # PROBLEM CAUSED HERE. See https://www.tensorflow.org/tutorials/images/data_augmentation
model.add(layers.Rescaling(1./255))
model.add(layers.Conv2D(8, (3, 3), activation='relu', kernel_initializer='random_normal', padding='same'))
model.add(layers.MaxPooling2D((3,3)))
model.add(layers.Dense(2))

model.compile(optimizer='rmsprop',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])
model.fit(dsTrain,epochs=10,batch_size=128) 

# Result:
# Cannot batch tensors with different shapes in component 0. First element had shape [262,350,3] and element 1 had shape [409,336,3].

有人知道这个层的设置吗？显然我可以在将图像传递到模型之前调整数据集中图像的大小（这是我以前一直在做的），但我很想了解这个层的正确用法。

编辑1

这是我的尝试（在我的回复中作为笔记本链接），以制作一个将预处理集成到模型中的版本

import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow import keras
from tensorflow.keras import layers
dsTrain = tfds.load('CatsVsDogs', split='train[:10%]', as_supervised=True).batch(128).prefetch(tf.data.AUTOTUNE)
IMAGE_SIZE = 100

# https://keras.io/guides/preprocessing_layers/
# https://blog.tensorflow.org/2021/11/an-introduction-to-keras-preprocessing.html 

preprocessing_model = tf.keras.models.Sequential()
preprocessing_model.add(layers.InputLayer(input_shape=(None, None, 3)))
preprocessing_model.add(layers.Resizing(IMAGE_SIZE, IMAGE_SIZE, crop_to_aspect_ratio=True))
preprocessing_model.add(layers.Rescaling(1./255))

training_model = tf.keras.models.Sequential()
training_model.add(layers.InputLayer(input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)))
training_model.add(layers.Conv2D(8, (3, 3), activation='relu', kernel_initializer='random_normal', padding='same'))
training_model.add(layers.MaxPooling2D((5,5)))
training_model.add(layers.Flatten())
training_model.add(layers.Dense(2))

inputs = keras.Input(shape=preprocessing_model.input_shape)
outputs = training_model(preprocessing_model(inputs))
model = tf.keras.Model(inputs, outputs)

model.summary()
model.compile(optimizer='rmsprop',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])
model.fit(dsTrain,epochs=3,batch_size=128)

这会产生以下错误：

WARNING:tensorflow:Model was constructed with shape (None, None, None, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, None, None, 3), dtype=tf.float32, name='input_117'), name='input_117', description="created by layer 'input_117'"), but it was called on an input with incompatible shape (None, None, None, None, 3).
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-82-6e99f023839a> in <module>
     22 
     23 inputs = keras.Input(shape=preprocessing_model.input_shape)
---> 24 outputs = training_model(preprocessing_model(inputs))
     25 model = tf.keras.Model(inputs, outputs)
     26 

1 frames
/usr/local/lib/python3.7/dist-packages/keras/preprocessing/image.py in smart_resize(x, size, interpolation)
    110     if img.shape.rank < 3 or img.shape.rank > 4:
    111       raise ValueError(
--> 112           'Expected an image array with shape `(height, width, channels)`, '
    113           'or `(batch_size, height, width, channels)`, but '
    114           f'got input with incorrect rank, of shape {img.shape}.')

ValueError: Exception encountered when calling layer "resizing_72" (type Resizing).

Expected an image array with shape `(height, width, channels)`, or `(batch_size, height, width, channels)`, but got input with incorrect rank, of shape (None, None, None, None, 3).

Call arguments received:
  • inputs=tf.Tensor(shape=(None, None, None, None, 3), dtype=float32)

编辑2

我现在已经把预处理层作为模型的一部分，但是它们仍然不接受不同大小的图像。下面的程序失败了，并指出了错误，但是如果我把所有的图像调整到统一的尺寸，它就可以工作。Notebook version

import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow import keras
from tensorflow.keras import layers
IMAGE_SIZE = 100
dsTrain = tfds.load('CatsVsDogs', split='train[:10%]', as_supervised=True).batch(128).prefetch(tf.data.AUTOTUNE)

preprocessing_model = tf.keras.models.Sequential()
preprocessing_model.add(layers.InputLayer(input_shape=(None, None, 3)))
preprocessing_model.add(layers.Resizing(IMAGE_SIZE, IMAGE_SIZE, crop_to_aspect_ratio=True))
preprocessing_model.add(layers.Rescaling(1./255))

training_model = tf.keras.models.Sequential()
training_model.add(layers.InputLayer(input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3)))
training_model.add(layers.Conv2D(8, (3, 3), activation='relu', kernel_initializer='random_normal', padding='same'))
training_model.add(layers.MaxPooling2D((5,5)))
training_model.add(layers.Flatten())
training_model.add(layers.Dense(2))

inputs = keras.Input(shape=preprocessing_model.input_shape[1:]) # [1:] Adds a dimension for a batching
outputs = training_model(preprocessing_model(inputs))
model = tf.keras.Model(inputs, outputs)

model.summary()
model.compile(optimizer='rmsprop',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])
model.fit(dsTrain,epochs=3,batch_size=128) 

# error from model.fit: Cannot batch tensors with different shapes in component 0. First element had shape [262,350,3] and element 1 had shape [409,336,3]

最终编辑+解决方案

好吧，Souresh Mirzaei非常耐心地试图向我解释的信息最终在我离开的时候被我理解了，我得到了我的解决方案。对于任何关注这个问题的人来说，答案是，虽然训练数据不能有不同的大小（我猜是因为它破坏了批处理）一个在开始时有一个调整大小层的训练模型将接受不同大小的图像作为输入

import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt
import random

IMAGE_SIZE = 100
dsTrain = tfds.load('CatsVsDogs', split='train[:10%]', as_supervised=True)
dsPredict = dsTrain.take(77)
def preprocess(image, label):
    image = tf.image.resize(image, (100,100))
    return image, label
dsTrain = dsTrain.map(preprocess).shuffle(1024).batch(128).prefetch(tf.data.AUTOTUNE)

preprocessing_model = tf.keras.models.Sequential()
preprocessing_model.add(layers.Resizing(IMAGE_SIZE, IMAGE_SIZE, crop_to_aspect_ratio=True, input_shape=(None, None, 3))) # redundant
preprocessing_model.add(layers.Rescaling(1./255))

training_model = tf.keras.models.Sequential()
training_model.add(layers.Conv2D(8, (3, 3), activation='relu', kernel_initializer='random_normal', padding='same'))
training_model.add(layers.MaxPooling2D((5,5)))
training_model.add(layers.Flatten())
training_model.add(layers.Dense(2))

inputs = keras.Input(shape=(None, None, None, 3)) # [1:] Remove the dimension added by batching
inputs = keras.Input(shape=preprocessing_model.input_shape[1:]) # [1:] Remove the dimension added by batching
outputs = training_model(preprocessing_model(inputs))
model = tf.keras.Model(inputs, outputs)

model.summary()
model.compile(optimizer='rmsprop',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])
model.fit(dsTrain,epochs=1,batch_size=128) 

# Test an image
for image, labelId in dsPredict.skip(random.randint(0, 50)).take(1):
    dsImage = tf.keras.preprocessing.image.img_to_array(image)
    dsImage = np.expand_dims(dsImage, axis = 0)
    prediction = model.predict(dsImage)
    plt.imshow(image)
    plt.title(f"{prediction[0][0]}, {prediction[0][1]}")
    plt.show()

notebook of the above