RandomCrop 导致 INVALID_ARGUMENT：所需的可广播形状

我正在使用 Keras 训练神经网络，并尝试使用 RandomCrop 层。我正在使用动态大小的数据集（不同的分辨率），但我发现它目前不是此问题的原因。

当我运行 model.fit() 时，不久之后，我收到上述错误|| |。我能够获得模型的摘要，因此那里不存在一些不匹配。 INVALID_ARGUMENT: required broadcastable shapes 当我删除该层时，我的模型工作正常，但我需要它来减少输入的大小（因此使用

）。 RandomCrop 完整回溯+张量流状态

如何重现

2022-03-23 13:27:28.772937: W tensorflow/core/framework/op_kernel.cc:1733] INVALID_ARGUMENT: required broadcastable shapes

Traceback (most recent call last):
  File "c:\Users\samue\Desktop\rcrop\main.py", line 37, in <module>
    conv_model.fit(
  File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:

Detected at node 'mean_squared_error/SquaredDifference' defined at (most recent call last):
    File "C:\Program Files\Python310\lib\threading.py", line 966, in _bootstrap
      self._bootstrap_inner()
    File "C:\Program Files\Python310\lib\threading.py", line 1009, in _bootstrap_inner
      self.run()
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 1000, in run_step
      outputs = model.train_step(data)
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 860, in train_step
      loss = self.compute_loss(x, y, y_pred, sample_weight)
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 918, in compute_loss
      return self.compiled_loss(
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\compile_utils.py", line 201, in __call__
      loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 141, in __call__
      losses = call_fn(y_true, y_pred)
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 245, in call
      return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 1329, in mean_squared_error
      return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)
Node: 'mean_squared_error/SquaredDifference'
Detected at node 'mean_squared_error/SquaredDifference' defined at (most recent call last):
    File "C:\Program Files\Python310\lib\threading.py", line 966, in _bootstrap
      self._bootstrap_inner()
    File "C:\Program Files\Python310\lib\threading.py", line 1009, in _bootstrap_inner
      self.run()
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 1000, in run_step
      outputs = model.train_step(data)
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 860, in train_step
      loss = self.compute_loss(x, y, y_pred, sample_weight)
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 918, in compute_loss
      return self.compiled_loss(
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\engine\compile_utils.py", line 201, in __call__
      loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 141, in __call__
      losses = call_fn(y_true, y_pred)
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 245, in call
      return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "C:\Users\samue\AppData\Roaming\Python\Python310\site-packages\keras\losses.py", line 1329, in mean_squared_error
      return backend.mean(tf.math.squared_difference(y_pred, y_true), axis=-1)
Node: 'mean_squared_error/SquaredDifference'
2 root error(s) found.
  (0) INVALID_ARGUMENT:  required broadcastable shapes
         [[{{node mean_squared_error/SquaredDifference}}]]
         [[div_no_nan/ReadVariableOp/_84]]
  (1) INVALID_ARGUMENT:  required broadcastable shapes
         [[{{node mean_squared_error/SquaredDifference}}]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_1308]

我创建了一个最小的可重现示例，只有两个分辨率为

的图像都保存为 [10, 10] 和rgb色彩空间。 .png 运行main.py 加载这些图像并尝试开始训练（因错误而失败）。

当我排除

层时，它工作得很好。 RandomCrop 文件夹结构

main.py

/main_folder
--main.py
--/data
   --001.png
   --002.png

main.py

import cv2, os
import keras
import tensorflow as tf
from keras import layers


strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
    input_layer = keras.Input(shape=(None, None, 3))
    cropped = layers.RandomCrop(32, 32)(input_layer)
    out = layers.Conv2D(3, (3, 3), activation='sigmoid', padding='same')(cropped)

    conv_model = keras.Model(input_layer, out)
    conv_model.compile(
        optimizer='adam', 
        loss=tf.keras.losses.MeanSquaredError()
    )

conv_model.summary()

path = "data"
data = [cv2.imread(os.path.join(path, f)) / 255 for f in os.listdir(os.path.join(path))]


def data_generator():
    for i in range(len(data)):
        yield data[i], data[i]


dataset = tf.data.Dataset.from_generator(
    data_generator, 
    output_types=(tf.float32, tf.float32), 
    output_shapes=((None, None, 3), (None, None, 3))
).batch(1)

conv_model.fit(
    dataset,
    epochs=1,
    validation_data=dataset
)

错误“INVALID_ARGUMENT：需要可广播的形状”是由如何使用 tf.data.Dataset.from_generator 定义 dataset 引起的。尽管在 output_shapes 中指定了动态形状，但在迭代 data 列表时，它不会将实际批次形状传递给模型。因此， RandomCrop 层无法确定输出形状，并且在计算损失时会发生形状不匹配。

以下是修复代码的方法：

1. 不要在 output_shapes 中指定批次维度： 从 output_shapes 中删除批次维度 (1)，因为它由 .batch(1) 处理。

2. 在生成器中调整图像大小： 由于 RandomCrop 需要一个固定的输入大小，因此在生成器函数中使用 cv2.resize 将每个图像调整为所需的大小 (32x32)。

以下是更新后的代码：

import cv2, os
import keras
import tensorflow as tf
from keras import layers

strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
    input_layer = keras.Input(shape=(32, 32, 3))  # 定义固定大小的输入
    cropped = layers.RandomCrop(32, 32)(input_layer)
    out = layers.Conv2D(3, (3, 3), activation='sigmoid', padding='same')(cropped)

    conv_model = keras.Model(input_layer, out)
    conv_model.compile(
        optimizer='adam',
        loss=tf.keras.losses.MeanSquaredError()
    )

conv_model.summary()

path = "data"
data = [cv2.imread(os.path.join(path, f)) / 255 for f in os.listdir(os.path.join(path))]


def data_generator():
    for img in data:
        # 调整图像大小以匹配 RandomCrop 输入
        resized_img = cv2.resize(img, (32, 32))
        yield resized_img, resized_img


dataset = tf.data.Dataset.from_generator(
    data_generator,
    output_types=(tf.float32, tf.float32),
    output_shapes=((32, 32, 3), (32, 32, 3))  # 定义固定大小的输出形状
).batch(1)

conv_model.fit(
    dataset,
    epochs=1,
    validation_data=dataset
)

通过这些更改， RandomCrop 层将接收到具有固定大小的图像，并且该模型应该可以正确训练而不会出现任何错误。