def __init__( self, ops, threshold, l1_fraction=0, regularizer_decorator: Type[generic_regularizers.OpRegularizer] = None, decorator_parameters=None, force_group=None, regularizer_blacklist=None): """Creates a GroupLassoActivationRegularizer object. Args: ops: A list of tf.Operation. An OpRegularizer will be created for all the ops in `ops`, and recursively for all ops they depend on via data dependency. Typically `ops` would contain a single tf.Operation, which is the output of the network. threshold: A float scalar, will be used as a 'threshold' for all regularizer instances created by this class. l1_fraction: Relative weight of L1 in L1 + L2 regularization. regularizer_decorator: A class of OpRegularizer decorator to use. decorator_parameters: A dictionary of parameters to pass to the decorator factory. To be used only with decorators that requires parameters, otherwise use None. force_group: List of regex for ops that should be force-grouped. Each regex corresponds to a separate group. Use '|' operator to specify multiple patterns in a single regex. See op_regularizer_manager for more detail. regularizer_blacklist: List of regex for ops that should not be regularized. See op_regularizer_manager for more detail. """ conv2d_handler = conv2d_source_op_handler.Conv2DSourceOpHandler( threshold, l1_fraction) conv2d_transpose_handler = ( conv2d_transpose_source_op_handler.Conv2DTransposeSourceOpHandler( threshold, l1_fraction)) matmul_handler = matmul_source_op_handler.MatMulSourceOpHandler( threshold, l1_fraction) if regularizer_decorator: conv2d_handler = op_handler_decorator.OpHandlerDecorator( conv2d_handler, regularizer_decorator, decorator_parameters) conv2d_transpose_handler = op_handler_decorator.OpHandlerDecorator( conv2d_transpose_handler, regularizer_decorator, decorator_parameters) matmul_handler = op_handler_decorator.OpHandlerDecorator( matmul_handler, regularizer_decorator, decorator_parameters) op_handler_dict = op_handlers.get_group_lasso_op_handler_dict() op_handler_dict.update({ 'Conv2D': conv2d_handler, 'Conv2DBackpropInput': conv2d_transpose_handler, 'MatMul': matmul_handler, }) self._manager = orm.OpRegularizerManager( ops, op_handler_dict, force_group=force_group, regularizer_blacklist=regularizer_blacklist) self._calculator = cost_calculator.CostCalculator( self._manager, resource_function.activation_count_function)
def __init__(self, output_boundary: List[tf.Operation], threshold, l1_fraction=0.0, regularizer_decorator: Optional[Type[ generic_regularizers.OpRegularizer]] = None, decorator_parameters=None, input_boundary: Optional[List[tf.Operation]] = None, force_group: Optional[List[Text]] = None, regularizer_blacklist: Optional[List[Text]] = None): """Creates a GroupLassoModelSizeRegularizer object. Args: output_boundary: An OpRegularizer will be created for all these operations, and recursively for all ops they depend on via data dependency that does not involve ops from input_boundary. threshold: A float scalar, will be used as a 'threshold' for all regularizer instances created by this class. l1_fraction: A float scalar. The relative weight of L1 in L1 + L2 regularization. regularizer_decorator: A class of OpRegularizer decorator to use. decorator_parameters: A dictionary of parameters to pass to the decorator factory. To be used only with decorators that requires parameters, otherwise use None. input_boundary: A list of ops that represent the input boundary of the subgraph being regularized (input boundary is not regularized). force_group: List of regex for ops that should be force-grouped. Each regex corresponds to a separate group. Use '|' operator to specify multiple patterns in a single regex. See op_regularizer_manager for more detail. regularizer_blacklist: List of regex for ops that should not be regularized. See op_regularizer_manager for more detail. """ custom_handlers = { 'Conv2D': conv_handler.ConvSourceOpHandler(threshold, l1_fraction), 'Conv3D': conv_handler.ConvSourceOpHandler(threshold, l1_fraction), 'Conv2DBackpropInput': conv2d_transpose_handler.Conv2DTransposeSourceOpHandler( threshold, l1_fraction), 'MatMul': matmul_handler.MatMulSourceOpHandler(threshold, l1_fraction) } if regularizer_decorator: for key in custom_handlers: custom_handlers[key] = op_handler_decorator.OpHandlerDecorator( custom_handlers[key], regularizer_decorator, decorator_parameters) op_handler_dict = op_handlers.get_group_lasso_op_handler_dict() op_handler_dict.update(custom_handlers) self._manager = orm.OpRegularizerManager( output_boundary, op_handler_dict, input_boundary=input_boundary, force_group=force_group, regularizer_blacklist=regularizer_blacklist) self._calculator = cost_calculator.CostCalculator( self._manager, resource_function.model_size_function)
def __init__( self, output_boundary: List[tf.Operation], gamma_threshold, hardware, batch_size=1, regularizer_decorator: Type[generic_regularizers.OpRegularizer] = None, decorator_parameters=None, input_boundary: List[tf.Operation] = None, force_group=None, regularizer_blacklist=None) -> None: """Creates a GammaLatencyRegularizer object. Latency cost and regularization loss is calculated for a specified hardware platform. Args: output_boundary: An OpRegularizer will be created for all these operations, and recursively for all ops they depend on via data dependency that does not involve ops from input_boundary. gamma_threshold: A float scalar, will be used as a 'gamma_threshold' for all instances GammaL1Regularizer created by this class. hardware: String name of hardware platform to target. Must be a key from resource_function.PEAK_COMPUTE. batch_size: Integer batch size to calculate cost/loss for. regularizer_decorator: A string, the name of the regularizer decorators to use. Supported decorators are listed in op_regularizer_decorator.SUPPORTED_DECORATORS. decorator_parameters: A dictionary of parameters to pass to the decorator factory. To be used only with decorators that requires parameters, otherwise use None. input_boundary: A list of ops that represent the input boundary of the subgraph being regularized (input boundary is not regularized). force_group: List of regex for ops that should be force-grouped. Each regex corresponds to a separate group. Use '|' operator to specify multiple patterns in a single regex. See op_regularizer_manager for more detail. regularizer_blacklist: List of regex for ops that should not be regularized. See op_regularizer_manager for more detail. """ source_op_handler = batch_norm_source_op_handler.BatchNormSourceOpHandler( gamma_threshold) if regularizer_decorator: source_op_handler = op_handler_decorator.OpHandlerDecorator( source_op_handler, regularizer_decorator, decorator_parameters) op_handler_dict = op_handlers.get_gamma_op_handler_dict() op_handler_dict.update({ 'FusedBatchNorm': source_op_handler, 'FusedBatchNormV2': source_op_handler, 'FusedBatchNormV3': source_op_handler, }) self._manager = orm.OpRegularizerManager( output_boundary, op_handler_dict, input_boundary=input_boundary, force_group=force_group, regularizer_blacklist=regularizer_blacklist) self._calculator = cost_calculator.CostCalculator( self._manager, resource_function.latency_function_factory(hardware, batch_size)) self._hardware = hardware
def __init__(self, output_boundary: List[tf.Operation], regularize_on_mask=True, alive_threshold=0.1, mask_as_alive_vector=True, regularizer_decorator: Type[ generic_regularizers.OpRegularizer] = None, decorator_parameters=None, input_boundary: List[tf.Operation] = None, force_group=None, regularizer_blacklist=None): """Creates a LogisticSigmoidFlopsRegularizer object. Args: output_boundary: An OpRegularizer will be created for all these operations, and recursively for all ops they depend on via data dependency that does not involve ops from input_boundary. regularize_on_mask: Bool. If True uses the binary mask as the regularization vector. Else uses the probability vector. alive_threshold: Float. Threshold below which values are considered dead. This can be used both when mask_as_alive_vector is True and then the threshold is used to binarize the sampled values and when mask_as_alive_vector is False, and then the threshold is on the channel probability. mask_as_alive_vector: Bool. If True use the thresholded sampled mask as the alive vector. Else, use thresholded probabilities from the logits. regularizer_decorator: A class of OpRegularizer decorator to use. decorator_parameters: A dictionary of parameters to pass to the decorator factory. To be used only with decorators that requires parameters, otherwise use None. input_boundary: A list of ops that represent the input boundary of the subgraph being regularized (input boundary is not regularized). force_group: List of regex for ops that should be force-grouped. Each regex corresponds to a separate group. Use '|' operator to specify multiple patterns in a single regex. See op_regularizer_manager for more detail. regularizer_blacklist: List of regex for ops that should not be regularized. See op_regularizer_manager for more detail. """ source_op_handler = ls_handler.LogisticSigmoidSourceOpHandler( regularize_on_mask, alive_threshold, mask_as_alive_vector) if regularizer_decorator: source_op_handler = op_handler_decorator.OpHandlerDecorator( source_op_handler, regularizer_decorator, decorator_parameters) op_handler_dict = op_handlers.get_gamma_op_handler_dict() op_handler_dict.update({ 'LogisticSigmoidGating': source_op_handler, }) self._manager = orm.OpRegularizerManager( output_boundary, op_handler_dict, create_grouping_regularizer=pgr.ProbabilisticGroupingRegularizer, input_boundary=input_boundary, force_group=force_group, regularizer_blacklist=regularizer_blacklist) self._calculator = self.get_calculator()
def __init__(self, ops, gamma_threshold, regularizer_decorator: Type[ generic_regularizers.OpRegularizer] = None, decorator_parameters=None, input_boundary=None, force_group=None, regularizer_blacklist=None): """Creates a GammaModelSizeRegularizer object. Args: ops: A list of tf.Operation. An OpRegularizer will be created for all the ops in `ops`, and recursively for all ops they depend on via data dependency. Typically `ops` would contain a single tf.Operation, which is the output of the network. gamma_threshold: A float scalar, will be used as a 'gamma_threshold' for all instances GammaL1Regularizer created by this class. regularizer_decorator: A string, the name of the regularizer decorators to use. Supported decorators are listed in op_regularizer_decorator.SUPPORTED_DECORATORS. decorator_parameters: A dictionary of parameters to pass to the decorator factory. To be used only with decorators that requires parameters, otherwise use None. input_boundary: A list of ops that represent the input boundary of the subgraph being regularized (input boundary is not regularized). force_group: List of regex for ops that should be force-grouped. Each regex corresponds to a separate group. Use '|' operator to specify multiple patterns in a single regex. See op_regularizer_manager for more detail. regularizer_blacklist: List of regex for ops that should not be regularized. See op_regularizer_manager for more detail. """ source_op_handler = batch_norm_source_op_handler.BatchNormSourceOpHandler( gamma_threshold) if regularizer_decorator: source_op_handler = op_handler_decorator.OpHandlerDecorator( source_op_handler, regularizer_decorator, decorator_parameters) op_handler_dict = op_handlers.get_gamma_op_handler_dict() op_handler_dict.update({ 'FusedBatchNorm': source_op_handler, 'FusedBatchNormV2': source_op_handler, }) self._manager = orm.OpRegularizerManager( ops, op_handler_dict, input_boundary=input_boundary, force_group=force_group, regularizer_blacklist=regularizer_blacklist) self._calculator = cost_calculator.CostCalculator( self._manager, resource_function.model_size_function)
def __init__(self, output_boundary: List[tf.Operation], gamma_threshold, regularizer_decorator: Type[ generic_regularizers.OpRegularizer] = None, decorator_parameters=None, input_boundary: List[tf.Operation] = None, force_group=None, regularizer_blacklist=None): """Creates a GammaActivationRegularizer object. Args: output_boundary: An OpRegularizer will be created for all these operations, and recursively for all ops they depend on via data dependency that does not involve ops from input_boundary. gamma_threshold: A float scalar, will be used as a 'gamma_threshold' for all instances GammaL1Regularizer created by this class. regularizer_decorator: A class of OpRegularizer decorator to use. decorator_parameters: A dictionary of parameters to pass to the decorator factory. To be used only with decorators that requires parameters, otherwise use None. input_boundary: A list of ops that represent the input boundary of the subgraph being regularized (input boundary is not regularized). force_group: List of regex for ops that should be force-grouped. Each regex corresponds to a separate group. Use '|' operator to specify multiple patterns in a single regex. See op_regularizer_manager for more detail. regularizer_blacklist: List of regex for ops that should not be regularized. See op_regularizer_manager for more detail. """ source_op_handler = batch_norm_source_op_handler.BatchNormSourceOpHandler( gamma_threshold) if regularizer_decorator: source_op_handler = op_handler_decorator.OpHandlerDecorator( source_op_handler, regularizer_decorator, decorator_parameters) op_handler_dict = op_handlers.get_gamma_op_handler_dict() op_handler_dict.update({ 'FusedBatchNorm': source_op_handler, 'FusedBatchNormV2': source_op_handler, 'FusedBatchNormV3': source_op_handler, }) self._manager = orm.OpRegularizerManager( output_boundary, op_handler_dict, input_boundary=input_boundary, force_group=force_group, regularizer_blacklist=regularizer_blacklist) self._calculator = cost_calculator.CostCalculator( self._manager, resource_function.activation_count_function)
def testOpHandlerDecorator(self): image = tf.constant(0.0, shape=[1, 17, 19, 3]) kernel = tf.ones([5, 5, 3, 3]) output = tf.nn.conv2d(image, kernel, strides=[1, 1, 1, 1], padding='SAME') decorated_op_handler = op_handler_decorator.OpHandlerDecorator( conv_source_op_handler.ConvSourceOpHandler(1e-3, 0), DummyDecorator) op_slice = orm.OpSlice(output.op, orm.Slice(0, 3)) regularizer = decorated_op_handler.create_regularizer(op_slice) self.assertAllClose(0.5 * np.ones(3), regularizer.regularization_vector) self.assertAllClose(np.ones(3), regularizer.alive_vector)
def __init__(self, output_boundary, threshold, hardware, batch_size=1, l1_fraction=0, regularizer_decorator=None, decorator_parameters=None, input_boundary=None, force_group=None, regularizer_blacklist=None, convert_to_variable=True): """Creates a GroupLassoFlopsRegularizer object. Args: output_boundary: An OpRegularizer will be created for all these operations, and recursively for all ops they depend on via data dependency that does not involve ops from input_boundary. threshold: A float scalar, will be used as a 'threshold' for all regularizer instances created by this class. hardware: String name of hardware platform to target. Must be a key from resource_function.PEAK_COMPUTE. batch_size: Integer batch size to calculate cost/loss for. l1_fraction: Relative weight of L1 in L1 + L2 regularization. regularizer_decorator: A class of OpRegularizer decorator to use. decorator_parameters: A dictionary of parameters to pass to the decorator factory. To be used only with decorators that requires parameters, otherwise use None. input_boundary: A list of ops that represent the input boundary of the subgraph being regularized (input boundary is not regularized). force_group: List of regex for ops that should be force-grouped. Each regex corresponds to a separate group. Use '|' operator to specify multiple patterns in a single regex. See op_regularizer_manager for more detail. regularizer_blacklist: List of regex for ops that should not be regularized. See op_regularizer_manager for more detail. convert_to_variable: If `True` convert to variable in the `GroupLassoBaseOpHandler`. If your graph creates variables outside of `tf.get_variable`, set to `False`. """ conv2d_handler = conv2d_source_op_handler.Conv2DSourceOpHandler( threshold, l1_fraction, convert_to_variable) conv2d_transpose_handler = ( conv2d_transpose_source_op_handler.Conv2DTransposeSourceOpHandler( threshold, l1_fraction, convert_to_variable)) matmul_handler = matmul_source_op_handler.MatMulSourceOpHandler( threshold, l1_fraction, convert_to_variable) if regularizer_decorator: conv2d_handler = op_handler_decorator.OpHandlerDecorator( conv2d_handler, regularizer_decorator, decorator_parameters) conv2d_transpose_handler = op_handler_decorator.OpHandlerDecorator( conv2d_transpose_handler, regularizer_decorator, decorator_parameters) matmul_handler = op_handler_decorator.OpHandlerDecorator( matmul_handler, regularizer_decorator, decorator_parameters) op_handler_dict = op_handlers.get_group_lasso_op_handler_dict() op_handler_dict.update({ 'Conv2D': conv2d_handler, 'Conv2DBackpropInput': conv2d_transpose_handler, 'MatMul': matmul_handler, }) self._manager = orm.OpRegularizerManager( output_boundary, op_handler_dict, input_boundary=input_boundary, force_group=force_group, regularizer_blacklist=regularizer_blacklist) self._calculator = cost_calculator.CostCalculator( self._manager, resource_function.latency_function_factory(hardware, batch_size)) self._hardware = hardware
def __init__(self, ops, gamma_threshold, regularizer_decorator: Type[ generic_regularizers.OpRegularizer] = None, decorator_parameters=None, force_group=None, regularizer_blacklist=None): """Creates a GammaFlopsRegularizer object. Args: ops: A list of tf.Operation. An OpRegularizer will be created for all the ops in `ops`, and recursively for all ops they depend on via data dependency. Typically `ops` would contain a single tf.Operation, which is the output of the network. gamma_threshold: A float scalar, will be used as a 'gamma_threshold' for all instances GammaL1Regularizer created by this class. regularizer_decorator: A class of OpRegularizer decorator to use. decorator_parameters: A dictionary of parameters to pass to the decorator factory. To be used only with decorators that requires parameters, otherwise use None. force_group: List of regex for ops that should be force-grouped. Each regex corresponds to a separate group. Use '|' operator to specify multiple patterns in a single regex. See op_regularizer_manager for more detail. regularizer_blacklist: List of regex for ops that should not be regularized. See op_regularizer_manager for more detail. """ source_op_handler = batch_norm_source_op_handler.BatchNormSourceOpHandler( gamma_threshold) if regularizer_decorator: source_op_handler = op_handler_decorator.OpHandlerDecorator( source_op_handler, regularizer_decorator, decorator_parameters) op_handler_dict = collections.defaultdict( grouping_op_handler.GroupingOpHandler) op_handler_dict.update({ 'FusedBatchNorm': source_op_handler, 'FusedBatchNormV2': source_op_handler, 'Conv2D': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), 'ConcatV2': concat_op_handler.ConcatOpHandler(), 'DepthToSpace': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), 'DepthwiseConv2dNative': depthwise_convolution_op_handler.DepthwiseConvolutionOpHandler(), 'MatMul': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), 'TensorArrayGatherV3': leaf_op_handler.LeafOpHandler(), 'RandomUniform': leaf_op_handler.LeafOpHandler(), 'Reshape': leaf_op_handler.LeafOpHandler(), 'Transpose': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), 'ExpandDims': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), }) self._manager = orm.OpRegularizerManager( ops, op_handler_dict, force_group=force_group, regularizer_blacklist=regularizer_blacklist) self._calculator = cost_calculator.CostCalculator( self._manager, resource_function.flop_function)
def __init__(self, ops, threshold, l1_fraction=0, regularizer_decorator: Type[ generic_regularizers.OpRegularizer] = None, decorator_parameters=None, force_group=None, regularizer_blacklist=None, convert_to_variable=True): """Creates a GroupLassoFlopsRegularizer object. Args: ops: A list of tf.Operation. An OpRegularizer will be created for all the ops in `ops`, and recursively for all ops they depend on via data dependency. Typically `ops` would contain a single tf.Operation, which is the output of the network. threshold: A float scalar, will be used as a 'threshold' for all regularizer instances created by this class. l1_fraction: Relative weight of L1 in L1 + L2 regularization. regularizer_decorator: A class of OpRegularizer decorator to use. decorator_parameters: A dictionary of parameters to pass to the decorator factory. To be used only with decorators that requires parameters, otherwise use None. force_group: List of regex for ops that should be force-grouped. Each regex corresponds to a separate group. Use '|' operator to specify multiple patterns in a single regex. See op_regularizer_manager for more detail. regularizer_blacklist: List of regex for ops that should not be regularized. See op_regularizer_manager for more detail. convert_to_variable: If `True` convert to variable in the `GroupLassoBaseOpHandler`. If you're graph creates variables outside of `tf.get_variable`, set to `False`. """ conv2d_handler = conv2d_source_op_handler.Conv2DSourceOpHandler( threshold, l1_fraction, convert_to_variable) conv2d_transpose_handler = ( conv2d_transpose_source_op_handler.Conv2DTransposeSourceOpHandler( threshold, l1_fraction, convert_to_variable)) matmul_handler = matmul_source_op_handler.MatMulSourceOpHandler( threshold, l1_fraction, convert_to_variable) if regularizer_decorator: conv2d_handler = op_handler_decorator.OpHandlerDecorator( conv2d_handler, regularizer_decorator, decorator_parameters) conv2d_transpose_handler = op_handler_decorator.OpHandlerDecorator( conv2d_transpose_handler, regularizer_decorator, decorator_parameters) matmul_handler = op_handler_decorator.OpHandlerDecorator( matmul_handler, regularizer_decorator, decorator_parameters) op_handler_dict = collections.defaultdict( grouping_op_handler.GroupingOpHandler) op_handler_dict.update({ 'Conv2D': conv2d_handler, 'Conv2DBackpropInput': conv2d_transpose_handler, 'ConcatV2': concat_op_handler.ConcatOpHandler(), 'DepthToSpace': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), 'DepthwiseConv2dNative': depthwise_convolution_op_handler.DepthwiseConvolutionOpHandler(), 'MatMul': matmul_handler, 'RandomUniform': leaf_op_handler.LeafOpHandler(), 'Reshape': leaf_op_handler.LeafOpHandler(), 'Shape': leaf_op_handler.LeafOpHandler(), 'TensorArrayGatherV3': leaf_op_handler.LeafOpHandler(), 'Transpose': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), 'StridedSlice': leaf_op_handler.LeafOpHandler(), }) self._manager = orm.OpRegularizerManager( ops, op_handler_dict, force_group=force_group, regularizer_blacklist=regularizer_blacklist) self._calculator = cost_calculator.CostCalculator( self._manager, resource_function.flop_function)
def __init__(self, ops, gamma_threshold, hardware, batch_size=1, regularizer_decorator: Type[ generic_regularizers.OpRegularizer] = None, decorator_parameters=None, force_group=None, regularizer_blacklist=None) -> None: """Creates a GammaLatencyRegularizer object. Latency cost and regularization loss is calculated for a specified hardware platform. Args: ops: A list of tf.Operation. An OpRegularizer will be created for all the ops in `ops`, and recursively for all ops they depend on via data dependency. Typically `ops` would contain a single tf.Operation, which is the output of the network. gamma_threshold: A float scalar, will be used as a 'gamma_threshold' for all instances GammaL1Regularizer created by this class. hardware: String name of hardware platform to target. Must be a key from resource_function.PEAK_COMPUTE. batch_size: Integer batch size to calculate cost/loss for. regularizer_decorator: A string, the name of the regularizer decorators to use. Supported decorators are listed in op_regularizer_decorator.SUPPORTED_DECORATORS. decorator_parameters: A dictionary of parameters to pass to the decorator factory. To be used only with decorators that requires parameters, otherwise use None. force_group: List of regex for ops that should be force-grouped. Each regex corresponds to a separate group. Use '|' operator to specify multiple patterns in a single regex. See op_regularizer_manager for more detail. regularizer_blacklist: List of regex for ops that should not be regularized. See op_regularizer_manager for more detail. """ source_op_handler = batch_norm_source_op_handler.BatchNormSourceOpHandler( gamma_threshold) if regularizer_decorator: source_op_handler = op_handler_decorator.OpHandlerDecorator( source_op_handler, regularizer_decorator, decorator_parameters) op_handler_dict = collections.defaultdict( grouping_op_handler.GroupingOpHandler) op_handler_dict.update({ 'FusedBatchNorm': source_op_handler, 'FusedBatchNormV2': source_op_handler, 'Conv2D': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), 'ConcatV2': concat_op_handler.ConcatOpHandler(), 'DepthToSpace': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), 'DepthwiseConv2dNative': depthwise_convolution_op_handler.DepthwiseConvolutionOpHandler(), 'MatMul': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), 'TensorArrayGatherV3': leaf_op_handler.LeafOpHandler(), 'Transpose': output_non_passthrough_op_handler.OutputNonPassthroughOpHandler(), }) self._manager = orm.OpRegularizerManager( ops, op_handler_dict, force_group=force_group, regularizer_blacklist=regularizer_blacklist) self._calculator = cost_calculator.CostCalculator( self._manager, resource_function.latency_function_factory(hardware, batch_size)) self._hardware = hardware