python - Estimator.predict() TypeError: 期望任何非张量类型，取而代之的是张量
问题描述
我尝试使用 VGG 神经网络从视频中通过唇读来预测单词，tensorflow.compat.v1.version但我无法从估计器中获得预测。该模型是定制的，我在培训或评估方面没有任何问题。
这是代码：
# Create the classifier
print("Creating classifier from {}".format(checkpoint_path))
classifier = tf.estimator.Estimator(
model_fn=vgg_model_fn,
params=params,
model_dir=checkpoint_path,
)
print("Computing predictions")
predictions = classifier.predict(
input_fn=tf.estimator.inputs.numpy_input_fn(
{"x": video},
batch_size=1,
shuffle=False,
)
)
# Print predictions
predictions = list(predictions)
predicted_class = predictions["classes"]
top_k_classes = (-predictions["probabilities"]).argsort()[:int(k)]
线路predictions = list(predictions)抛出TypeError: Expected any non-tensor type, got a tensor instead.，我找不到任何替代方案。
next(generator)也不行。
完整的错误日志：
INFO:tensorflow:Calling model_fn.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_util.py in _AssertCompatible(values, dtype)
329 try:
--> 330 fn(values)
331 except ValueError as e:
15 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_util.py in _check_not_tensor(values)
281 def _check_not_tensor(values):
--> 282 _ = [_check_failed(v) for v in nest.flatten(values)
283 if isinstance(v, ops.Tensor)]
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_util.py in <listcomp>(.0)
282 _ = [_check_failed(v) for v in nest.flatten(values)
--> 283 if isinstance(v, ops.Tensor)]
284 # pylint: enable=invalid-name
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_util.py in _check_failed(v)
249 # it is safe to use here.
--> 250 raise ValueError(v)
251
ValueError: Tensor("fifo_queue_DequeueUpTo:1", shape=(?, 64, 64, 29), dtype=float64, device=/device:CPU:0)
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-21-f9ab355166d2> in <module>()
78
79 print("######################\n\n")
---> 80 predictions = list(predictions)
81
82 predicted_class = predictions["classes"]
/usr/local/lib/python3.7/dist-packages/tensorflow_estimator/python/estimator/estimator.py in predict(self, input_fn, predict_keys, hooks, checkpoint_path, yield_single_examples)
611 input_fn, ModeKeys.PREDICT)
612 estimator_spec = self._call_model_fn(features, None, ModeKeys.PREDICT,
--> 613 self.config)
614
615 # Call to warm_start has to be after model_fn is called.
/usr/local/lib/python3.7/dist-packages/tensorflow_estimator/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
1161
1162 logging.info('Calling model_fn.')
-> 1163 model_fn_results = self._model_fn(features=features, **kwargs)
1164 logging.info('Done calling model_fn.')
1165
<ipython-input-7-9d657a95e832> in vgg_model_fn(features, labels, mode, params)
12 """
13 # Useful variables
---> 14 tf.dtypes.cast(features, tf.float64)
15 num_classes = params["num_classes"]
16 if (mode == tf.estimator.ModeKeys.TRAIN):
/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
199 """Call target, and fall back on dispatchers if there is a TypeError."""
200 try:
--> 201 return target(*args, **kwargs)
202 except (TypeError, ValueError):
203 # Note: convert_to_eager_tensor currently raises a ValueError, not a
/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/math_ops.py in cast(x, dtype, name)
962 # allows some conversions that cast() can't do, e.g. casting numbers to
963 # strings.
--> 964 x = ops.convert_to_tensor(x, name="x")
965 if x.dtype.base_dtype != base_type:
966 x = gen_math_ops.cast(x, base_type, name=name)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/profiler/trace.py in wrapped(*args, **kwargs)
161 with Trace(trace_name, **trace_kwargs):
162 return func(*args, **kwargs)
--> 163 return func(*args, **kwargs)
164
165 return wrapped
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1538
1539 if ret is None:
-> 1540 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1541
1542 if ret is NotImplemented:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
337 as_ref=False):
338 _ = as_ref
--> 339 return constant(v, dtype=dtype, name=name)
340
341
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name)
263 """
264 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 265 allow_broadcast=True)
266
267
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
281 tensor_util.make_tensor_proto(
282 value, dtype=dtype, shape=shape, verify_shape=verify_shape,
--> 283 allow_broadcast=allow_broadcast))
284 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
285 attrs = {"value": tensor_value, "dtype": dtype_value}
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
455 nparray = np.empty(shape, dtype=np_dt)
456 else:
--> 457 _AssertCompatible(values, dtype)
458 nparray = np.array(values, dtype=np_dt)
459 # check to them.
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_util.py in _AssertCompatible(values, dtype)
332 [mismatch] = e.args
333 if dtype is None:
--> 334 raise TypeError("Expected any non-tensor type, got a tensor instead.")
335 else:
336 raise TypeError("Expected %s, got %s of type '%s' instead." %
TypeError: Expected any non-tensor type, got a tensor instead.
有人遇到过这种问题吗？
解决方案
我改为：
predictions = classifier.predict(
input_fn=tf.estimator.inputs.numpy_input_fn(
x=video,
y=None,
shuffle=False
)
)
不是我有一个非常不同类型的错误。
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: tensor_name = bn_conv1/beta; expected dtype double does not equal original dtype float
tensor_name = bn_conv1/gamma; expected dtype double does not equal original dtype float
...
File "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py", line 1990, in __init__
self._traceback = tf_stack.extract_stack()
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py in restore(self, sess, save_path)
1332 # We add a more reasonable error message here to help users (b/110263146)
1333 raise _wrap_restore_error_with_msg(
-> 1334 err, "a mismatch between the current graph and the graph")
1335
1336 @staticmethod
InvalidArgumentError: Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:
2 root error(s) found.
(0) Invalid argument: tensor_name = bn_conv1/beta; expected dtype double does not equal original dtype float
tensor_name = bn_conv1/gamma; expected dtype double does not equal original dtype float
...
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