mltk.core.TflitePooling2dLayer¶

class TflitePooling2dLayer[source]¶

AVERAGE_POOL_2D or MAX_POOL_2D operation TfliteLayer

Properties

`activation`	Fused activation
`index`	Index of this layer in the model
`input_data`	Input tensor data
`input_tensor`	Input tensor data
`inputs`	List of layer input tensor(s)
`metadata`	Additional key/value data to associated with layer
`model`	Reference to associated TfliteModel
`n_inputs`	Return the number of inputs
`n_outputs`	Return the number of outputs
`name`	op<index>-<OpCodeStr>
`opcode`	OpCode numeric value
`opcode_str`	OpCode as a string
`options`	Layer-specific options/config
`output_data`	Output tensor data
`output_tensor`	Output tensor data
`outputs`	List of layer output tensor(s)
`padding`	Kernel padding
`params`	Calculated layer parameters
`pool_size`	Kernel size as height x width
`strides`	Kernel stride as height x width

Methods

`__init__`
`from_flatbuffer`	Instantiate a TfliteLayer from then given TfliteModel flatbuffer operation
`get_input_data`	Get layer input tensor as np.ndarray
`get_input_tensor`	Get layer input tensor as TfliteTensor
`get_output_data`	Layer output tensor as np.ndarray
`get_output_tensor`	Layer output tensor as TfliteTensor

__init__(fb_operation, **kwargs)[source]¶

Parameters:: fb_operation (OperatorT) –

property options: TflitePool2DLayerOptions¶

Layer-specific options/config

Return type:: TflitePool2DLayerOptions

property pool_size: Tuple[int, int]¶

Kernel size as height x width

Return type:: Tuple[int, int]

property strides: Tuple[int, int]¶

Kernel stride as height x width

Return type:: Tuple[int, int]

property padding: str¶

Kernel padding

Return type:: str

property activation: str¶

Fused activation

Return type:: str

property input_tensor: TfliteTensor¶

Input tensor data

Return type:: TfliteTensor

property input_data: ndarray¶

Input tensor data

Return type:: ndarray

property output_tensor: TfliteTensor¶

Output tensor data

Return type:: TfliteTensor

property output_data: ndarray¶

Output tensor data

Return type:: ndarray

property params: TflitePoolParams¶

Calculated layer parameters

Return type:: TflitePoolParams

static from_flatbuffer(index, model, fb_operation)¶

Instantiate a TfliteLayer from then given TfliteModel flatbuffer operation

Return type:

TfliteLayer

Parameters:

index (int) –
model (TfliteModel) –
fb_operation (OperatorT) –

get_input_data(index=0)¶

Get layer input tensor as np.ndarray

Return type:: ndarray

get_input_tensor(index=0)¶

Get layer input tensor as TfliteTensor

Return type:: TfliteTensor

get_output_data(index=0)¶

Layer output tensor as np.ndarray

Return type:: ndarray

get_output_tensor(index=0)¶

Layer output tensor as TfliteTensor

Return type:: TfliteTensor

property index: int¶

Index of this layer in the model

Return type:: int

property inputs: List[TfliteTensor]¶

List of layer input tensor(s)

Return type:: List[TfliteTensor]

property metadata: Dict[str, object]¶

Additional key/value data to associated with layer

NOTE: This information is generated by the framework/Python scripts: (i.e. The information does NOT come from the .tflite model)

Return type:: Dict[str, object]

property model: TfliteModel¶

Reference to associated TfliteModel

Return type:: TypeVar(TfliteModel)

property n_inputs: int¶

Return the number of inputs

Return type:: int

property n_outputs: int¶

Return the number of outputs

Return type:: int

property name: str¶

op<index>-<OpCodeStr>

Type:: Name of current layer as
Return type:: str

property opcode: BuiltinOperator¶

OpCode numeric value

Return type:: BuiltinOperator

property opcode_str: str¶

OpCode as a string

Return type:: str

property outputs: List[TfliteTensor]¶

List of layer output tensor(s)

Return type:: List[TfliteTensor]