ImageGenerator 和 Model.predict 的问题答案

【问题标题】：Problem with ImageGenerator and Model.predictImageGenerator 和 Model.predict 的问题
【发布时间】：2020-05-17 19:22:01
【问题描述】：

我需要 3 张图像作为 CNN 的输入，我使用 ImageGenerator 和 flow_from_dataframe 对其进行预处理：

idg = ImageDataGenerator(rescale = 1./255)

A_gen = idg.flow_from_dataframe(df,directory = path,x_col = 'A',y_col = 'class',target_size = (IMG_HEIGHT,IMG_WIDTH),
                       class_mode = 'binary',seed=1,batch_size=batch_size)
B_gen = idg.flow_from_dataframe(df,directory = path,x_col = 'taste1',y_col = 'class',target_size = (IMG_HEIGHT,IMG_WIDTH),
                       class_mode = 'binary',seed=1,batch_size=batch_size)
C_gen = idg.flow_from_dataframe(df,directory = path,x_col = 'taste2',y_col = 'class',target_size = (IMG_HEIGHT,IMG_WIDTH),
                       class_mode = 'binary',seed=1,batch_size=batch_size)

然后，我将所有 3 个生成器合二为一，使用：

    def combine(A,B,C):
        while True:
            X1i = A.next()
            X2i = B.next()
            X3i = C.next()
            yield [X1i[0], X2i[0],X3i[0]], X1i[1]

inputgenerator = combine(A_gen,B_gen,C_gen)

我的 CNN 的开头是这样的：

def simple_cnn():
    pic_input1 = Input(shape=(IMG_HEIGHT, IMG_WIDTH, 3))
    pic_input2 = Input(shape=(IMG_HEIGHT, IMG_WIDTH, 3))
    pic_input3 = Input(shape=(IMG_HEIGHT, IMG_WIDTH, 3))


    cnn1 = BatchNormalization()(pic_input1)
    cnn2 = BatchNormalization()(pic_input2)
    cnn3 = BatchNormalization()(pic_input3)
... (rest is not relevant I guess)

然后，我使用以下方法拟合我的模型：

model.fit(inputgenerator,steps_per_epoch=len(df) / batch_size, epochs=4)

到这里为止，一切都完美无缺。（我知道，我需要使用验证集等，但首先我想确保我知道如何处理多个生成器）

但是，当我想进行预测时，我的测试生成器是：

idg2 = ImageDataGenerator(rescale = 1./255)

D_gen = idg2.flow_from_dataframe(df2,directory = path,x_col = 'D',y_col = 'None',target_size = (IMG_HEIGHT,IMG_WIDTH),
                       class_mode = None,seed=1,batch_size=1)
E_gen = idg2.flow_from_dataframe(df2,directory = path,x_col = 'E',y_col = 'None',target_size = (IMG_HEIGHT,IMG_WIDTH),
                       class_mode = None,seed=1,batch_size=1)
F_gen = idg2.flow_from_dataframe(df2,directory = path,x_col = 'F',y_col = 'None',target_size = (IMG_HEIGHT,IMG_WIDTH),
                       class_mode = None,seed=1,batch_size=1)
testgenerator = combine_test(D_gen,E_gen,F_gen)
pred = model.predict(testgenerator)

def combine_test(A,B,C):
    while True:
        X1i = A.next()
        X2i = B.next()
        X3i = C.next()
        yield [X1i[0], X2i[0],X3i[0]]

我收到以下错误：

Traceback (most recent call last):

  File "/home/maeul/Documents/ETHZ/2ndSemester/IntroToMachineLearning/Task4/Task4.py", line 228, in <module>
    pred = model.predict(testgenerator)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py", line 1013, in predict
    use_multiprocessing=use_multiprocessing)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 498, in predict
    workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 426, in _model_iteration
    use_multiprocessing=use_multiprocessing)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 706, in _process_inputs
    use_multiprocessing=use_multiprocessing)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/data_adapter.py", line 767, in __init__
    dataset = standardize_function(dataset)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 684, in standardize_function
    return dataset.map(map_fn, num_parallel_calls=dataset_ops.AUTOTUNE)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 1591, in map
    self, map_func, num_parallel_calls, preserve_cardinality=True)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 3926, in __init__
    use_legacy_function=use_legacy_function)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 3147, in __init__
    self._function = wrapper_fn._get_concrete_function_internal()

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 2395, in _get_concrete_function_internal
    *args, **kwargs)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 2389, in _get_concrete_function_internal_garbage_collected
    graph_function, _, _ = self._maybe_define_function(args, kwargs)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 2703, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 2593, in _create_graph_function
    capture_by_value=self._capture_by_value),

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/framework/func_graph.py", line 978, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 3140, in wrapper_fn
    ret = _wrapper_helper(*args)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 3082, in _wrapper_helper
    ret = autograph.tf_convert(func, ag_ctx)(*nested_args)

  File "/home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py", line 237, in wrapper
    raise e.ag_error_metadata.to_exception(e)

ValueError: in converted code:

    /home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py:677 map_fn
        batch_size=None)
    /home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py:2410 _standardize_tensors
        exception_prefix='input')
    /home/maeul/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_utils.py:573 standardize_input_data
        'with shape ' + str(data_shape))

    ValueError: Error when checking input: expected input_10 to have 4 dimensions, but got array with shape (None, None, None)

我猜这与单个生成器的批量大小有关，但我不知道如何通过在生成的每个图像中添加一个微不足道的维度来“欺骗”model.predict...

提前感谢您的帮助！

【问题讨论】：

标签： image keras generator predict

【解决方案1】：

我终于找到了答案：因为生成器中没有标签

D_gen = idg2.flow_from_dataframe(df2,directory = path,x_col = 'D',y_col = 'None',target_size = (IMG_HEIGHT,IMG_WIDTH),
                       class_mode = None,seed=1,batch_size=1)
E_gen = idg2.flow_from_dataframe(df2,directory = path,x_col = 'E',y_col = 'None',target_size = (IMG_HEIGHT,IMG_WIDTH),
                       class_mode = None,seed=1,batch_size=1)
F_gen = idg2.flow_from_dataframe(df2,directory = path,x_col = 'F',y_col = 'None',target_size = (IMG_HEIGHT,IMG_WIDTH),
                       class_mode = None,seed=1,batch_size=1)

这意味着生成器生成的是一个列表而不是一个表，所以当我在 combine_test 中写 Xi[0] 时，我实际上调用了列表的第一个元素而不是整个列表（技术术语可能是错误的，但是我如何用我的基本知识得到它），所以我需要将 Xi[0] 简单地替换为 Xi。

要修复这个错误，修改“combine_test”函数如下：

def combine_test(A,B,C):
    while True:
        X1i = A.next()
        X2i = B.next()
        X3i = C.next()
        yield [X1i, X2i, X3i]

【讨论】：