open3d.ml.tf.models.BatchNormBlock

class open3d.ml.tf.models.BatchNormBlock(*args, **kwargs)
__init__(in_dim, use_bn, bn_momentum)

Initialize a batch normalization block. If network does not use batch normalization, replace with biases. :param in_dim: dimension input features. :param use_bn: boolean indicating if we use Batch Norm. :param bn_momentum: Batch norm momentum.

add_loss(losses, **kwargs)

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

This method can be used inside a subclassed layer or model’s call function, in which case losses should be a Tensor or list of Tensors.

Example:

```python class MyLayer(tf.keras.layers.Layer):

def call(self, inputs):

self.add_loss(tf.abs(tf.reduce_mean(inputs))) return inputs

```

This method can also be called directly on a Functional Model during construction. In this case, any loss Tensors passed to this Model must be symbolic and be able to be traced back to the model’s Input`s. These losses become part of the model’s topology and are tracked in `get_config.

Example:

`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Activity regularization. model.add_loss(tf.abs(tf.reduce_mean(x))) `

If this is not the case for your loss (if, for example, your loss references a Variable of one of the model’s layers), you can wrap your loss in a zero-argument lambda. These losses are not tracked as part of the model’s topology since they can’t be serialized.

Example:

`python inputs = tf.keras.Input(shape=(10,)) d = tf.keras.layers.Dense(10) x = d(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) # Weight regularization. model.add_loss(lambda: tf.reduce_mean(d.kernel)) `

Parameters

  • losses – Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor.

  • **kwargs –

    Additional keyword arguments for backward compatibility. Accepted values:

    inputs - Deprecated, will be automatically inferred.

add_metric(value, name=None, **kwargs)

Adds metric tensor to the layer.

This method can be used inside the call() method of a subclassed layer or model.

```python class MyMetricLayer(tf.keras.layers.Layer):

def __init__(self):

super(MyMetricLayer, self).__init__(name=’my_metric_layer’) self.mean = metrics_module.Mean(name=’metric_1’)

def call(self, inputs):

self.add_metric(self.mean(x)) self.add_metric(math_ops.reduce_sum(x), name=’metric_2’) return inputs

```

This method can also be called directly on a Functional Model during construction. In this case, any tensor passed to this Model must be symbolic and be able to be traced back to the model’s Input`s. These metrics become part of the model’s topology and are tracked when you save the model via `save().

`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) model.add_metric(math_ops.reduce_sum(x), name='metric_1') `

Note: Calling add_metric() with the result of a metric object on a Functional Model, as shown in the example below, is not supported. This is because we cannot trace the metric result tensor back to the model’s inputs.

`python inputs = tf.keras.Input(shape=(10,)) x = tf.keras.layers.Dense(10)(inputs) outputs = tf.keras.layers.Dense(1)(x) model = tf.keras.Model(inputs, outputs) model.add_metric(tf.keras.metrics.Mean()(x), name='metric_1') `

Parameters

  • value – Metric tensor.

  • name – String metric name.

  • **kwargs – Additional keyword arguments for backward compatibility. Accepted values: aggregation - When the value tensor provided is not the result of calling a keras.Metric instance, it will be aggregated by default using a keras.Metric.Mean.

add_update(updates, inputs=None)

Add update op(s), potentially dependent on layer inputs. (deprecated arguments)

Warning: SOME ARGUMENTS ARE DEPRECATED: (inputs). They will be removed in a future version. Instructions for updating: inputs is now automatically inferred

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing the same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

This call is ignored when eager execution is enabled (in that case, variable updates are run on the fly and thus do not need to be tracked for later execution).

Parameters

  • updates – Update op, or list/tuple of update ops, or zero-arg callable that returns an update op. A zero-arg callable should be passed in order to disable running the updates by setting trainable=False on this Layer, when executing in Eager mode.

  • inputs – Deprecated, will be automatically inferred.

add_variable(*args, **kwargs)

Deprecated, do NOT use! Alias for add_weight. (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.add_weight method instead.

add_weight(name=None, shape=None, dtype=None, initializer=None, regularizer=None, trainable=None, constraint=None, partitioner=None, use_resource=None, synchronization=<VariableSynchronization.AUTO: 0>, aggregation=<VariableAggregation.NONE: 0>, **kwargs)

Adds a new variable to the layer.

Parameters

  • name – Variable name.

  • shape – Variable shape. Defaults to scalar if unspecified.

  • dtype – The type of the variable. Defaults to self.dtype or float32.

  • initializer – Initializer instance (callable).

  • regularizer – Regularizer instance (callable).

  • trainable – Boolean, whether the variable should be part of the layer’s “trainable_variables” (e.g. variables, biases) or “non_trainable_variables” (e.g. BatchNorm mean and variance). Note that trainable cannot be True if synchronization is set to ON_READ.

  • constraint – Constraint instance (callable).

  • partitioner – Partitioner to be passed to the Trackable API.

  • use_resource – Whether to use ResourceVariable.

  • synchronization – Indicates when a distributed a variable will be aggregated. Accepted values are constants defined in the class tf.VariableSynchronization. By default the synchronization is set to AUTO and the current DistributionStrategy chooses when to synchronize. If synchronization is set to ON_READ, trainable must not be set to True.

  • aggregation – Indicates how a distributed variable will be aggregated. Accepted values are constants defined in the class tf.VariableAggregation.

  • **kwargs – Additional keyword arguments. Accepted values are getter, collections, experimental_autocast and caching_device.

Returns

The created variable. Usually either a Variable or ResourceVariable instance. If partitioner is not None, a PartitionedVariable instance is returned.

Raises

  • RuntimeError – If called with partitioned variable regularization and eager execution is enabled.

  • ValueError – When giving unsupported dtype and no initializer or when trainable has been set to True with synchronization set as ON_READ.

apply(inputs, *args, **kwargs)

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.__call__ method instead.

This is an alias of self.__call__.

Parameters

  • inputs – Input tensor(s).

  • *args – additional positional arguments to be passed to self.call.

  • **kwargs – additional keyword arguments to be passed to self.call.

Returns

Output tensor(s).

build(input_shape)

Creates the variables of the layer (optional, for subclass implementers).

This is a method that implementers of subclasses of Layer or Model can override if they need a state-creation step in-between layer instantiation and layer call.

This is typically used to create the weights of Layer subclasses.

Parameters

input_shape – Instance of TensorShape, or list of instances of TensorShape if the layer expects a list of inputs (one instance per input).

call(x, training=False)

This is where the layer’s logic lives.

Note here that call() method in tf.keras is little bit different from keras API. In keras API, you can pass support masking for layers as additional arguments. Whereas tf.keras has compute_mask() method to support masking.

Parameters

  • inputs – Input tensor, or list/tuple of input tensors.

  • **kwargs – Additional keyword arguments. Currently unused.

Returns

A tensor or list/tuple of tensors.

compute_mask(inputs, mask=None)

Computes an output mask tensor.

Parameters

  • inputs – Tensor or list of tensors.

  • mask – Tensor or list of tensors.

Returns

None or a tensor (or list of tensors,

one per output tensor of the layer).

compute_output_shape(input_shape)

Computes the output shape of the layer.

If the layer has not been built, this method will call build on the layer. This assumes that the layer will later be used with inputs that match the input shape provided here.

Parameters

input_shape – Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.

Returns

An input shape tuple.

compute_output_signature(input_signature)

Compute the output tensor signature of the layer based on the inputs.

Unlike a TensorShape object, a TensorSpec object contains both shape and dtype information for a tensor. This method allows layers to provide output dtype information if it is different from the input dtype. For any layer that doesn’t implement this function, the framework will fall back to use compute_output_shape, and will assume that the output dtype matches the input dtype.

Parameters

input_signature – Single TensorSpec or nested structure of TensorSpec objects, describing a candidate input for the layer.

Returns

Single TensorSpec or nested structure of TensorSpec objects, describing

how the layer would transform the provided input.

Raises

TypeError – If input_signature contains a non-TensorSpec object.

count_params()

Count the total number of scalars composing the weights.

Returns

An integer count.

Raises

ValueError – if the layer isn’t yet built (in which case its weights aren’t yet defined).

classmethod from_config(config)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Parameters

config – A Python dictionary, typically the output of get_config.

Returns

A layer instance.

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns

Python dictionary.

get_input_at(node_index)

Retrieves the input tensor(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A tensor (or list of tensors if the layer has multiple inputs).

Raises

RuntimeError – If called in Eager mode.

get_input_mask_at(node_index)

Retrieves the input mask tensor(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A mask tensor (or list of tensors if the layer has multiple inputs).

get_input_shape_at(node_index)

Retrieves the input shape(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A shape tuple (or list of shape tuples if the layer has multiple inputs).

Raises

RuntimeError – If called in Eager mode.

get_losses_for(inputs)

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.losses instead.

Retrieves losses relevant to a specific set of inputs.

Parameters

inputs – Input tensor or list/tuple of input tensors.

Returns

List of loss tensors of the layer that depend on inputs.

get_output_at(node_index)

Retrieves the output tensor(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A tensor (or list of tensors if the layer has multiple outputs).

Raises

RuntimeError – If called in Eager mode.

get_output_mask_at(node_index)

Retrieves the output mask tensor(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A mask tensor (or list of tensors if the layer has multiple outputs).

get_output_shape_at(node_index)

Retrieves the output shape(s) of a layer at a given node.

Parameters

node_index – Integer, index of the node from which to retrieve the attribute. E.g. node_index=0 will correspond to the first time the layer was called.

Returns

A shape tuple (or list of shape tuples if the layer has multiple outputs).

Raises

RuntimeError – If called in Eager mode.

get_updates_for(inputs)

Deprecated, do NOT use! (deprecated)

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Please use layer.updates instead.

Retrieves updates relevant to a specific set of inputs.

Parameters

inputs – Input tensor or list/tuple of input tensors.

Returns

List of update ops of the layer that depend on inputs.

get_weights()

Returns the current weights of the layer.

The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of Numpy arrays, which can in turn be used to load state into similarly parameterized layers.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
Returns

Weights values as a list of numpy arrays.

set_weights(weights)

Sets the weights of the layer, from Numpy arrays.

The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer’s weights must be instantiated before calling this function by calling the layer.

For example, a Dense layer returns a list of two values– per-output weights and the bias value. These can be used to set the weights of another Dense layer:

>>> a = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(1.))
>>> a_out = a(tf.convert_to_tensor([[1., 2., 3.]]))
>>> a.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
>>> b = tf.keras.layers.Dense(1,
...   kernel_initializer=tf.constant_initializer(2.))
>>> b_out = b(tf.convert_to_tensor([[10., 20., 30.]]))
>>> b.get_weights()
[array([[2.],
       [2.],
       [2.]], dtype=float32), array([0.], dtype=float32)]
>>> b.set_weights(a.get_weights())
>>> b.get_weights()
[array([[1.],
       [1.],
       [1.]], dtype=float32), array([0.], dtype=float32)]
Parameters

weights – a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of get_weights).

Raises

ValueError – If the provided weights list does not match the layer’s specifications.

classmethod with_name_scope(method)

Decorator to automatically enter the module name scope.

>>> class MyModule(tf.Module):
...   @tf.Module.with_name_scope
...   def __call__(self, x):
...     if not hasattr(self, 'w'):
...       self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
...     return tf.matmul(x, self.w)

Using the above module would produce `tf.Variable`s and `tf.Tensor`s whose names included the module name:

>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>
Parameters

method – The method to wrap.

Returns

The original method wrapped such that it enters the module’s name scope.

property activity_regularizer

Optional regularizer function for the output of this layer.

property dtype

Dtype used by the weights of the layer, set in the constructor.

property dynamic

Whether the layer is dynamic (eager-only); set in the constructor.

property inbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

property input

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

Returns

Input tensor or list of input tensors.

Raises

  • RuntimeError – If called in Eager mode.

  • AttributeError – If no inbound nodes are found.

property input_mask

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns

Input mask tensor (potentially None) or list of input mask tensors.

Raises

  • AttributeError – if the layer is connected to

  • more than one incoming layers. –

property input_shape

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

Returns

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

Raises

  • AttributeError – if the layer has no defined input_shape.

  • RuntimeError – if called in Eager mode.

property input_spec

InputSpec instance(s) describing the input format for this layer.

When you create a layer subclass, you can set self.input_spec to enable the layer to run input compatibility checks when it is called. Consider a Conv2D layer: it can only be called on a single input tensor of rank 4. As such, you can set, in __init__():

`python self.input_spec = tf.keras.layers.InputSpec(ndim=4) `

Now, if you try to call the layer on an input that isn’t rank 4 (for instance, an input of shape (2,), it will raise a nicely-formatted error:

` ValueError: Input 0 of layer conv2d is incompatible with the layer: expected ndim=4, found ndim=1. Full shape received: [2] `

Input checks that can be specified via input_spec include: - Structure (e.g. a single input, a list of 2 inputs, etc) - Shape - Rank (ndim) - Dtype

For more information, see tf.keras.layers.InputSpec.

Returns

A tf.keras.layers.InputSpec instance, or nested structure thereof.

property losses

List of losses added using the add_loss() API.

Variable regularization tensors are created when this property is accessed, so it is eager safe: accessing losses under a tf.GradientTape will propagate gradients back to the corresponding variables.

Examples:

>>> class MyLayer(tf.keras.layers.Layer):
...   def call(self, inputs):
...     self.add_loss(tf.abs(tf.reduce_mean(inputs)))
...     return inputs
>>> l = MyLayer()
>>> l(np.ones((10, 1)))
>>> l.losses
[1.0]

>>> inputs = tf.keras.Input(shape=(10,))
>>> x = tf.keras.layers.Dense(10)(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Activity regularization.
>>> model.add_loss(tf.abs(tf.reduce_mean(x)))
>>> model.losses
[<tf.Tensor 'Abs:0' shape=() dtype=float32>]

>>> inputs = tf.keras.Input(shape=(10,))
>>> d = tf.keras.layers.Dense(10, kernel_initializer='ones')
>>> x = d(inputs)
>>> outputs = tf.keras.layers.Dense(1)(x)
>>> model = tf.keras.Model(inputs, outputs)
>>> # Weight regularization.
>>> model.add_loss(lambda: tf.reduce_mean(d.kernel))
>>> model.losses
[<tf.Tensor: shape=(), dtype=float32, numpy=1.0>]
Returns

A list of tensors.

property metrics

List of metrics added using the add_metric() API.

Example:

>>> input = tf.keras.layers.Input(shape=(3,))
>>> d = tf.keras.layers.Dense(2)
>>> output = d(input)
>>> d.add_metric(tf.reduce_max(output), name='max')
>>> d.add_metric(tf.reduce_min(output), name='min')
>>> [m.name for m in d.metrics]
['max', 'min']
Returns

A list of tensors.

property name

Name of the layer (string), set in the constructor.

property name_scope

Returns a tf.name_scope instance for this class.

property non_trainable_variables
property non_trainable_weights

List of all non-trainable weights tracked by this layer.

Non-trainable weights are not updated during training. They are expected to be updated manually in call().

Returns

A list of non-trainable variables.

property outbound_nodes

Deprecated, do NOT use! Only for compatibility with external Keras.

property output

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

Returns

Output tensor or list of output tensors.

Raises

  • AttributeError – if the layer is connected to more than one incoming layers.

  • RuntimeError – if called in Eager mode.

property output_mask

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

Returns

Output mask tensor (potentially None) or list of output mask tensors.

Raises

  • AttributeError – if the layer is connected to

  • more than one incoming layers. –

property output_shape

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

Returns

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

Raises

  • AttributeError – if the layer has no defined output shape.

  • RuntimeError – if called in Eager mode.

property stateful
property submodules

Sequence of all sub-modules.

Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

>>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True
Returns

A sequence of all submodules.

property supports_masking

Whether this layer supports computing a mask using compute_mask.

property trainable
property trainable_variables

Sequence of trainable variables owned by this module and its submodules.

Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don’t expect the return value to change.

Returns

A sequence of variables for the current module (sorted by attribute name) followed by variables from all submodules recursively (breadth first).

property trainable_weights

List of all trainable weights tracked by this layer.

Trainable weights are updated via gradient descent during training.

Returns

A list of trainable variables.

property updates

DEPRECATED FUNCTION

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: This property should not be used in TensorFlow 2.0, as updates are applied automatically.

property variables

Returns the list of all layer variables/weights.

Alias of self.weights.

Returns

A list of variables.

property weights

Returns the list of all layer variables/weights.

Returns

A list of variables.