python - tensorflow :random_crop 中的类型错误

Question

当我使用random_crop裁剪图像数据集时:

tf.random_crop(X, [batch_size, 24, 24, 3])

它引发一个TypeError:

TypeError: Expected int32, got None of type '_Message' instead.

<小时/>

代码(在Python终端中运行下面的3个代码块可以重现问题):

我想在将图像输入网络之前随机裁剪图像，因此我编写random_crop_and_resize:

def random_crop_and_resize():
    # batch_size = tf.shape(X)[0]
    batch_size, _, _, _ = X.get_shape().as_list()
    return tf.image.resize_images \
        (tf.random_crop(X, [batch_size, 24, 24, 3]), [32, 32])

并将模型函数定义为:

def my_model(X, y, is_training):
    # augmentation: shape of X: [None, 32, 32, 3]
    distorted_img = tf.cond(is_training,
                            random_crop_and_resize, lambda: X)
    # ... feed distorted_img into network

然后定义图表:

tf.reset_default_graph()

X = tf.placeholder(tf.float32, [None, 32, 32, 3])
y = tf.placeholder(tf.int64, [None])
is_training = tf.placeholder(tf.bool)

y_out, regularizer = my_model(X, y, is_training)

但它引发了一个 TypeError: Expected int32, got None of type '_Message' 相反。 哪里出了问题？

<小时/>

更多信息:

环境:

• TensorFlow 1.2 GPU
• Anaconda 的 Python 3.6.3
• Ubuntu 14.04.3 LTS

完整回溯:

--------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-63-67c2d74574b6> in <module>()
     77 is_training = tf.placeholder(tf.bool)
     78 
---> 79 y_out, regularizer = my_model(X, y, is_training)
     80 
     81 # regularization

<ipython-input-63-67c2d74574b6> in my_model(X, y, is_training)
     11     # augmentation: shape of X: [None, 32, 32, 3]
     12     distorted_img = tf.cond(is_training,
---> 13                 random_crop_and_resize, lambda: X)
     14 
     15     regularizer = tf.contrib.layers.l2_regularizer(scale=0.03)

/home/hyh/anaconda3/envs/cs231n/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
    287             'in a future version' if date is None else ('after %s' % date),
    288             instructions)
--> 289       return func(*args, **kwargs)
    290     return tf_decorator.make_decorator(func, new_func, 'deprecated',
    291                                        _add_deprecated_arg_notice_to_docstring(

/home/hyh/anaconda3/envs/cs231n/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py in cond(pred, true_fn, false_fn, strict, name, fn1, fn2)
   1812     context_t = CondContext(pred, pivot_1, branch=1)
   1813     context_t.Enter()
-> 1814     orig_res_t, res_t = context_t.BuildCondBranch(true_fn)
   1815     if orig_res_t is None:
   1816       raise ValueError("true_fn must have a return value.")

/home/hyh/anaconda3/envs/cs231n/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py in BuildCondBranch(self, fn)
   1687   def BuildCondBranch(self, fn):
   1688     """Add the subgraph defined by fn() to the graph."""
-> 1689     original_result = fn()
   1690     if original_result is None:
   1691       return None, None

<ipython-input-63-67c2d74574b6> in random_crop_and_resize()
      5     # batch_size = tf.shape(X)[0]
      6     batch_size, _, _, _ = X.get_shape().as_list()
----> 7     return tf.image.resize_images         (tf.random_crop(X, [batch_size, 24, 24, 3]), [32, 32])
      8 
      9 

/home/hyh/anaconda3/envs/cs231n/lib/python3.6/site-packages/tensorflow/python/ops/random_ops.py in random_crop(value, size, seed, name)
    297   with ops.name_scope(name, "random_crop", [value, size]) as name:
    298     value = ops.convert_to_tensor(value, name="value")
--> 299     size = ops.convert_to_tensor(size, dtype=dtypes.int32, name="size")
    300     shape = array_ops.shape(value)
    301     check = control_flow_ops.Assert(

/home/hyh/anaconda3/envs/cs231n/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, preferred_dtype)
    674       name=name,
    675       preferred_dtype=preferred_dtype,
--> 676       as_ref=False)
    677 
    678 

/home/hyh/anaconda3/envs/cs231n/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
    739 
    740         if ret is None:
--> 741           ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    742 
    743         if ret is NotImplemented:

/home/hyh/anaconda3/envs/cs231n/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
    111                                          as_ref=False):
    112   _ = as_ref
--> 113   return constant(v, dtype=dtype, name=name)
    114 
    115 

/home/hyh/anaconda3/envs/cs231n/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name, verify_shape)
    100   tensor_value = attr_value_pb2.AttrValue()
    101   tensor_value.tensor.CopyFrom(
--> 102       tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
    103   dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
    104   const_tensor = g.create_op(

/home/hyh/anaconda3/envs/cs231n/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
    372       nparray = np.empty(shape, dtype=np_dt)
    373     else:
--> 374       _AssertCompatible(values, dtype)
    375       nparray = np.array(values, dtype=np_dt)
    376       # check to them.

/home/hyh/anaconda3/envs/cs231n/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in _AssertCompatible(values, dtype)
    300     else:
    301       raise TypeError("Expected %s, got %s of type '%s' instead." %
--> 302                       (dtype.name, repr(mismatch), type(mismatch).__name__))
    303 
    304 

TypeError: Expected int32, got None of type '_Message' instead.

Answer 1

问题来自于batch_size。在 batch_size, _, _, _ = X.get_shape().as_list() 中，batch_size 不是整数类型。

使用map_fn()而是为了避免在图像相关操作中计算 batch_size:

tf.map_fn(lambda img: tf.random_crop(img, [24, 24, 3]), X)

<小时/>

引用:

1. TensorFlow image operations for batches