Tensorflow / Keras：如何使用自定义图像文件.FITS图像训练CNN

如何解决Tensorflow / Keras：如何使用自定义图像文件.FITS图像训练CNN

我目前正在尝试使用.FITS图像文件来训练CNN（EfficientNet），但是我认为这也适用于其他图像类型。这种图像需要打开库 astropy 才能打开，在我的情况下，为了访问图像数据，我只需键入：

from astropy.io import fits
path = "path/to/file.fits"
hdul = fits.open(path)
image = hdul[1].data

此变量image将具有类型numpy.ndarray。我首先尝试使用keras的image_dataset_from_directory，但正如预期的那样，它没有成功。然后，我在此处https://www.tensorflow.org/tutorials/load_data/images#using_tfdata_for_finer_control处签出了tf.data。我尝试创建类似的管道，直到decode_img函数都解决了。由于我没有处理jpeg，因此尝试了一种变通方法，以便得到：

data_dir = home/astro/train
class_names = np.array(sorted([item.name for item in data_dir.glob('*')]))
# class_names = ["stars","galaxies"]

def get_label(file_path):
    parts = tf.strings.split(file_path,os.path.sep)
    one_hot = parts[-2] == class_names
    return tf.argmax(one_hot)

def decode_img(img):
    hdul = fits.open(img)
    data = hdul[1].data
    data = data.reshape((data.shape[0],data.shape[1],1))
    data = np.pad(data,[(0,0),(0,2)],'constant') # padding to create 3 channels
    img = tf.convert_to_tensor(data,np.float32)
    return tf.image.resize(img,[img_height,img_width])

def process_path(file_path):
    label = get_label(file_path)
    img = decode_img(file_path)
    return img,label

它实际上工作得很好，以某种方式打印process_path时，我得到两个张量，一个张量用于图像，一个张量用于标签，并具有所需的正确形状和值。

问题：

在学习完本教程之后，请按照以下步骤操作：

AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.map(process_path,num_parallel_calls=AUTOTUNE)
val_ds = val_ds.map(process_path,num_parallel_calls=AUTOTUNE)

我收到以下错误：

TypeError                                 Traceback (most recent call last)
 in 
      1 AUTOTUNE = tf.data.experimental.AUTOTUNE
      2 
----> 3 train_ds = train_ds.map(process_path,num_parallel_calls=AUTOTUNE)
      4 val_ds = val_ds.map(process_path,num_parallel_calls=AUTOTUNE)

~/anaconda3/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py in map(self,map_func,num_parallel_calls,deterministic)
   1700           num_parallel_calls,1701           deterministic,-> 1702           preserve_cardinality=True)
   1703 
   1704   def flat_map(self,map_func):

~/anaconda3/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py in __init__(self,input_dataset,deterministic,use_inter_op_parallelism,preserve_cardinality,use_legacy_function)
   4082         self._transformation_name(),4083         dataset=input_dataset,-> 4084         use_legacy_function=use_legacy_function)
   4085     if deterministic is None:
   4086       self._deterministic = "default"

~/anaconda3/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py in __init__(self,func,transformation_name,dataset,input_classes,input_shapes,input_types,input_structure,add_to_graph,use_legacy_function,defun_kwargs)
   3369       with tracking.resource_tracker_scope(resource_tracker):
   3370         # TODO(b/141462134): Switch to using garbage collection.
-> 3371         self._function = wrapper_fn.get_concrete_function()
   3372         if add_to_graph:
   3373           self._function.add_to_graph(ops.get_default_graph())

~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in get_concrete_function(self,*args,**kwargs)
   2937     """
   2938     graph_function = self._get_concrete_function_garbage_collected(
-> 2939         *args,**kwargs)
   2940     graph_function._garbage_collector.release()  # pylint: disable=protected-access
   2941     return graph_function

~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_garbage_collected(self,**kwargs)
   2904       args,kwargs = None,None
   2905     with self._lock:
-> 2906       graph_function,args,kwargs = self._maybe_define_function(args,kwargs)
   2907       seen_names = set()
   2908       captured = object_identity.ObjectIdentitySet(

~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self,kwargs)
   3211 
   3212       self._function_cache.missed.add(call_context_key)
-> 3213       graph_function = self._create_graph_function(args,kwargs)
   3214       self._function_cache.primary[cache_key] = graph_function
   3215       return graph_function,kwargs

~/anaconda3/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self,kwargs,override_flat_arg_shapes)
   3073             arg_names=arg_names,3074             override_flat_arg_shapes=override_flat_arg_shapes,-> 3075             capture_by_value=self._capture_by_value),3076         self._function_attributes,3077         function_spec=self.function_spec,~/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in func_graph_from_py_func(name,python_func,signature,func_graph,autograph,autograph_options,add_control_dependencies,arg_names,op_return_value,collections,capture_by_value,override_flat_arg_shapes)
    984         _,original_func = tf_decorator.unwrap(python_func)
    985 
--> 986       func_outputs = python_func(*func_args,**func_kwargs)
    987 
    988       # invariant: `func_outputs` contains only Tensors,CompositeTensors,~/anaconda3/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py in wrapper_fn(*args)
   3362           attributes=defun_kwargs)
   3363       def wrapper_fn(*args):  # pylint: disable=missing-docstring
-> 3364         ret = _wrapper_helper(*args)
   3365         ret = structure.to_tensor_list(self._output_structure,ret)
   3366         return [ops.convert_to_tensor(t) for t in ret]

~/anaconda3/lib/python3.7/site-packages/tensorflow/python/data/ops/dataset_ops.py in _wrapper_helper(*args)
   3297         nested_args = (nested_args,)
   3298 
-> 3299       ret = autograph.tf_convert(func,ag_ctx)(*nested_args)
   3300       # If `func` returns a list of tensors,`nest.flatten()` and
   3301       # `ops.convert_to_tensor()` would conspire to attempt to stack

~/anaconda3/lib/python3.7/site-packages/tensorflow/python/autograph/impl/api.py in wrapper(*args,**kwargs)
    256       except Exception as e:  # pylint:disable=broad-except
    257         if hasattr(e,'ag_error_metadata'):
--> 258           raise e.ag_error_metadata.to_exception(e)
    259         else:
    260           raise

TypeError: in user code:

    :17 process_path  *
        img = decode_img(file_path)
    :7 decode_img  *
        hdul = fits.open(img)
    /home/marcostidball/anaconda3/lib/python3.7/site-packages/astropy/io/fits/hdu/hdulist.py:154 fitsopen  *
        if not name:
    /home/marcostidball/anaconda3/lib/python3.7/site-packages/tensorflow/python/autograph/operators/logical.py:29 not_
        return _tf_not(a)
    /home/marcostidball/anaconda3/lib/python3.7/site-packages/tensorflow/python/autograph/operators/logical.py:35 _tf_not
        return gen_math_ops.logical_not(a)
    /home/marcostidball/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/gen_math_ops.py:5481 logical_not
        "LogicalNot",x=x,name=name)
    /home/marcostidball/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:493 _apply_op_helper
        (prefix,dtypes.as_dtype(input_arg.type).name))

    TypeError: Input 'x' of 'LogicalNot' Op has type string that does not match expected type of bool.

有人知道解决此问题的方法吗？我搜寻了直接使用numpy数组训练CNN的方法，例如在进行张量转换之前得到的数组，并找到了一些使用带有独立keras的MNIST的示例。不过，我想应用常规的数据增强和批处理训练，并且我不确定是否可以通过遵循我所见的方法来实现。

非常感谢！

解决方法

我遇到了同样的问题。我认为的问题是（但我不确定），当您制作train_ds时，它说的是“这种情况将在我们实际需要下一批时发生”。 astropy.io.fits会在调用时运行，因此会在给定的文件名不存在（或占位符）时抱怨。

我想出的解决方案是编写代码以加载适合文件而不使用astropy。您可以从此GitHub Repo获取它。可能是您喜欢的越野车，但对我来说可以打开适合的图像（我还没有弄清楚桌子）。