When trying to apply the GroupNormalization Layer to fully convolutionnal networks with a dynamic input shape (as there are not mathematical constraints for a fixed input shape and keras usually allows most of its layers to have a dynamic input shape) i have found the Layer unable to work. This may be due to the K.reshape call that only allows one dimension (the batch size) to be None,
Would you have a solution in mind for GroupNormalization to work w/ dynamic input shapes ? (Is it even possible to implement it this way, like BN ?)
Regards,
Here is the code I'm trying to launch:
i = Input(shape=(None,None,3))
c = Conv2D(64, (3,3), padding="same")(i)
o = GroupNormalization(axis=3)(c)
m = Model(inputs=i,outputs=o)
And the error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
516 try:
--> 517 str_values = [compat.as_bytes(x) for x in proto_values]
518 except TypeError:
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in <listcomp>(.0)
516 try:
--> 517 str_values = [compat.as_bytes(x) for x in proto_values]
518 except TypeError:
/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/compat.py in as_bytes(bytes_or_text, encoding)
66 raise TypeError('Expected binary or unicode string, got %r' %
---> 67 (bytes_or_text,))
68
TypeError: Expected binary or unicode string, got -1
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
<ipython-input-5-91a4d6819a5e> in <module>()
1 i = Input(shape=(None,None,3))
2 c = Conv2D(64, (3,3), padding="same")(i)
----> 3 o = GroupNormalization(axis=3)(c)
4 m = Model(inputs=i,outputs=o)
/opt/conda/lib/python3.6/site-packages/keras/engine/topology.py in __call__(self, inputs, **kwargs)
617
618 # Actually call the layer, collecting output(s), mask(s), and shape(s).
--> 619 output = self.call(inputs, **kwargs)
620 output_mask = self.compute_mask(inputs, previous_mask)
621
/home/code/keras_shipdetection/layers/group_normalization.py in call(self, inputs, **kwargs)
135 needs_broadcasting = (sorted(reduction_axes) != list(range(ndim))[:-1])
136
--> 137 inputs = K.reshape(inputs, group_shape)
138
139 mean = K.mean(inputs, axis=group_reduction_axes[2:], keepdims=True)
/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in reshape(x, shape)
1896 A tensor.
1897 """
-> 1898 return tf.reshape(x, shape)
1899
1900
/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py in reshape(tensor, shape, name)
6111 if _ctx is None or not _ctx._eager_context.is_eager:
6112 _, _, _op = _op_def_lib._apply_op_helper(
-> 6113 "Reshape", tensor=tensor, shape=shape, name=name)
6114 _result = _op.outputs[:]
6115 _inputs_flat = _op.inputs
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
511 except TypeError as err:
512 if dtype is None:
--> 513 raise err
514 else:
515 raise TypeError(
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
508 dtype=dtype,
509 as_ref=input_arg.is_ref,
--> 510 preferred_dtype=default_dtype)
511 except TypeError as err:
512 if dtype is None:
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx)
1102
1103 if ret is None:
-> 1104 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1105
1106 if ret is NotImplemented:
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
233 as_ref=False):
234 _ = as_ref
--> 235 return constant(v, dtype=dtype, name=name)
236
237
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name, verify_shape)
212 tensor_value.tensor.CopyFrom(
213 tensor_util.make_tensor_proto(
--> 214 value, dtype=dtype, shape=shape, verify_shape=verify_shape))
215 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
216 const_tensor = g.create_op(
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
519 raise TypeError("Failed to convert object of type %s to Tensor. "
520 "Contents: %s. Consider casting elements to a "
--> 521 "supported type." % (type(values), values))
522 tensor_proto.string_val.extend(str_values)
523 return tensor_proto
TypeError: Failed to convert object of type <class 'list'> to Tensor. Contents: [-1, 32, None, None, 2]. Consider casting elements to a supported type.
Hi,
When trying to apply the GroupNormalization Layer to fully convolutionnal networks with a dynamic input shape (as there are not mathematical constraints for a fixed input shape and keras usually allows most of its layers to have a dynamic input shape) i have found the Layer unable to work. This may be due to the K.reshape call that only allows one dimension (the batch size) to be None,
Would you have a solution in mind for GroupNormalization to work w/ dynamic input shapes ? (Is it even possible to implement it this way, like BN ?)
Regards,
Here is the code I'm trying to launch:
And the error:
I am running tensorflow 1.8.0 and keras 2.1.6