layers.layers.ModelLayer Class Reference

Inheritance diagram for layers.layers.ModelLayer:

Public Member Functions
def	__init__ (self, model, prefix, input_record, predict_input_record_fields=None, tags=None, kwargs)
def	get_type (self)
def	predict_input_record (self)
def	input_record (self)
def	predict_output_schema (self)
def	predict_output_schema (self, output_schema)
def	output_schema (self)
def	output_schema (self, output_schema)
def	get_parameters (self)
def	get_fp16_compatible_parameters (self)
def	get_memory_usage (self)
def	add_operators (self, net, init_net=None, context=InstantiationContext.TRAINING)
def	add_ops (self, net)
def	add_eval_ops (self, net)
def	add_train_ops (self, net)
def	add_calibration_ops (self, net)

Public Attributes
	name
	model
	kwargs
	request_only
	eval_output_schema
	tags
	params

Detailed Description

Definition at line 82 of file layers.py.

Constructor & Destructor Documentation

__init__()

def layers.layers.ModelLayer.__init__	(		self,
			model,
			prefix,
			input_record,
			predict_input_record_fields = None,
			tags = None,
			kwargs
	)

Base class for model layers. Layer is an abstraction that allows to
provide model description in terms of meta-operators, where each of the
meta-operators can have different implementations for training,
evaluation and prediction, that are instantiated later. As an example
SampledSoftmax can do something related to sampling depending on
supervision during the training and just apply softmax if it's used for
prediction/evaluation.

All inputs/outputs from layers are represented as a record (instance of
schema bounded to blobs) and are accessible through input_record and
output_schema. If Layer needs to have only a subset of inputs/provides
subset of outputs during the inference - it should provide
predict_input_record and predict_output_schema correspondingly (those
records are expected to be a subset of input_record/output_schema).

Each layer is also have list of Tags associated with it, that depends on
current context and arguments. It's possible to use those tags during
the instantiation time.

Definition at line 85 of file layers.py.

Member Function Documentation

get_fp16_compatible_parameters()

def layers.layers.ModelLayer.get_fp16_compatible_parameters

(

self

)

Return a subset of parameters which can be converted to fp16

Definition at line 175 of file layers.py.

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The documentation for this class was generated from the following file:

• caffe2/python/layers/layers.py