Issue when using Keras NetVLAD with functional api #1
Description
Hi,
First of all, thanks for this repository.
I'm trying to stack your NetVLAD implementation in keras on the top of a ResNet50, but i'm having some issue doing it.
That's my code:
# instantiating the resnet50
resnet = ResNet50(weights='imagenet', include_top=False, pooling=False,
input_shape=input_shape)
# deleting the default Input layer
resnet.layers.pop(0)
input_q = Input(shape=(224, 224, 3))
# stacking the resnet on a new input (seems useless but i need this to having multiple inputs)
resnet_q = resnet(input_q)
# permuting the tensor from resnet last layer shape which is (None, 7,7,2048) to (None, 2048, 7,7)
transpose = Permute((3,1,2), input_shape=(7,7,2048))(resnet_p)
# reshaping from (2048, 7,7) to (2048, 7*7), in the form (max_samples, feature_size) like requested from NetVLAD
reshape = Reshape((2048, 7*7))(transpose)
# stacking netvlad
netvlad = NetVLAD(feature_size=7*7, max_samples=2048, cluster_size=64, output_dim=1024)(reshape)But i'm getting this error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-20-7cb139e75b78> in <module>
19 transpose = Permute((3,1,2), input_shape=(7,7,2048))(resnet_p)
20 reshape = Reshape((2048, 7*7))(transpose)
---> 21 netvlad = NetVLAD(feature_size=7*7, max_samples=2048, cluster_size=64, output_dim=1024)(reshape)
~/Documenti/netvlad/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in symbolic_fn_wrapper(*args, **kwargs)
73 if _SYMBOLIC_SCOPE.value:
74 with get_graph().as_default():
---> 75 return func(*args, **kwargs)
76 else:
77 return func(*args, **kwargs)
~/Documenti/netvlad/venv/lib/python3.6/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
461 'You can build it manually via: '
462 '`layer.build(batch_input_shape)`')
--> 463 self.build(unpack_singleton(input_shapes))
464 self.built = True
465
~/Documenti/netvlad/loupe_keras.py in build(self, input_shape)
81 self.cluster_weights = self.add_weight(name='kernel_W1',
82 shape=(self.feature_size, self.cluster_size),
---> 83 initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)),
84 trainable=True)
85 self.cluster_biases = self.add_weight(name='kernel_B1',
~/Documenti/netvlad/venv/lib/python3.6/site-packages/keras/engine/base_layer.py in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint)
280 dtype=dtype,
281 name=name,
--> 282 constraint=constraint)
283 if regularizer is not None:
284 with K.name_scope('weight_regularizer'):
~/Documenti/netvlad/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in variable(value, dtype, name, constraint)
618 """
619 v = tf_keras_backend.variable(
--> 620 value, dtype=dtype, name=name, constraint=constraint)
621 if hasattr(value, 'tocoo'):
622 v._keras_shape = value.tocoo().shape
~/Documenti/netvlad/venv/lib/python3.6/site-packages/tensorflow_core/python/keras/backend.py in variable(value, dtype, name, constraint)
809 dtype=dtypes_module.as_dtype(dtype),
810 name=name,
--> 811 constraint=constraint)
812 if isinstance(value, np.ndarray):
813 v._keras_shape = value.shape
~/Documenti/netvlad/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py in __call__(cls, *args, **kwargs)
258 return cls._variable_v1_call(*args, **kwargs)
259 elif cls is Variable:
--> 260 return cls._variable_v2_call(*args, **kwargs)
261 else:
262 return super(VariableMetaclass, cls).__call__(*args, **kwargs)
~/Documenti/netvlad/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py in _variable_v2_call(cls, initial_value, trainable, validate_shape, caching_device, name, variable_def, dtype, import_scope, constraint, synchronization, aggregation, shape)
252 synchronization=synchronization,
253 aggregation=aggregation,
--> 254 shape=shape)
255
256 def __call__(cls, *args, **kwargs):
~/Documenti/netvlad/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py in <lambda>(**kws)
233 shape=None):
234 """Call on Variable class. Useful to force the signature."""
--> 235 previous_getter = lambda **kws: default_variable_creator_v2(None, **kws)
236 for _, getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access
237 previous_getter = _make_getter(getter, previous_getter)
~/Documenti/netvlad/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/variable_scope.py in default_variable_creator_v2(next_creator, **kwargs)
2554 synchronization=synchronization,
2555 aggregation=aggregation,
-> 2556 shape=shape)
2557
2558
~/Documenti/netvlad/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/variables.py in __call__(cls, *args, **kwargs)
260 return cls._variable_v2_call(*args, **kwargs)
261 else:
--> 262 return super(VariableMetaclass, cls).__call__(*args, **kwargs)
263
264
~/Documenti/netvlad/venv/lib/python3.6/site-ackages/tensorflow_core/python/ops/resource_variable_ops.py in __init__(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, variable_def, import_scope, constraint, distribute_strategy, synchronization, aggregation, shape)
1404 aggregation=aggregation,
1405 shape=shape,
-> 1406 distribute_strategy=distribute_strategy)
1407
1408 def _init_from_args(self,
~/Documenti/netvlad/venv/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py in _init_from_args(self, initial_value, trainable, collections, caching_device, name, dtype, constraint, synchronization, aggregation, distribute_strategy, shape)
1487 if isinstance(initial_value, ops.Tensor) and hasattr(
1488 initial_value, "graph") and initial_value.graph.building_function:
-> 1489 raise ValueError("Tensor-typed variable initializers must either be "
1490 "wrapped in an init_scope or callable "
1491 "(e.g., `tf.Variable(lambda : "
ValueError: Tensor-typed variable initializers must either be wrapped in an init_scope or callable (e.g., `tf.Variable(lambda : tf.truncated_normal([10, 40]))`) when building functions. Please file a feature request if this restriction inconveniences you.
Do you have any idea what could be the issue here? I really can't figure out, i looked at your code and i did some variation trying to spot the bug but nothing changed.
Thank you!