def __init__(self):
self.sequence_with_explicit_order = GraphSequence([
ConverterSequenceNode('root', ['Transpose']),
ConverterSequenceNode('order', ['Const']),
NonConsumableConverterSequenceNode('input', ['?']),
])
self.sequence_with_explicit_order.set_inputs('root', ['input', 'order'])
self.sequence_with_explicit_order.set_outputs(['root'])
self.sequence_with_implicit_order = GraphSequence([
ConverterSequenceNode('root', ['Transpose']),
ConverterSequenceNode('order', ['Sub']),
ConverterSequenceNode('a', ['Sub']),
ConverterSequenceNode('b', ['Const']),
ConverterSequenceNode('c', ['Range']),
ConverterSequenceNode('d', ['Const']),
ConverterSequenceNode('e', ['Const']),
ConverterSequenceNode('f', ['Rank']),
NonConsumableConverterSequenceNode('input', ['?'])
])
self.sequence_with_implicit_order.set_inputs('root', ['input', 'order'])
self.sequence_with_implicit_order.set_inputs('order', ['a', 'c'])
self.sequence_with_implicit_order.set_inputs('a', ['b', 'f'])
self.sequence_with_implicit_order.set_inputs('c', ['d', 'e', 'f'])
self.sequence_with_implicit_order.set_inputs('f', ['input'])
self.sequence_with_implicit_order.set_outputs(['root'])
self.sequences = [self.sequence_with_explicit_order, self.sequence_with_implicit_order]
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('root', ['ArgMax']),
ConverterSequenceNode('axis', ['Const']),
NonConsumableConverterSequenceNode('input', ['?']),
])
self.sequence.set_inputs('root', ['input', 'axis'])
self.sequence.set_outputs(['root'])
def __init__(self):
sequence_scalar_pow = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
ConverterSequenceNode('pow', ['Pow']),
ConverterSequenceNode('const', ['Const'])
])
sequence_scalar_pow.set_inputs('pow', ['input', 'const'])
sequence_scalar_pow.set_outputs(['pow'])
self.sequences = [sequence_scalar_pow]
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('root', ['StridedSlice']),
ConverterSequenceNode('begin', ['Const']),
ConverterSequenceNode('end', ['Const']),
ConverterSequenceNode('strides', ['Const']),
NonConsumableConverterSequenceNode('input', ['?']),
])
self.sequence.set_inputs('root', ['input', 'begin', 'end', 'strides'])
self.sequence.set_outputs(['root'])
def __init__(self):
sequence_resize = GraphSequence([ConverterSequenceNode('root', ['ResizeBilinear'])])
sequence_resize.set_outputs(['root'])
sequence_shape_stridedslice_resize = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
ConverterSequenceNode('shape', ['Shape']),
ConverterSequenceNode('stridedSlice', ['StridedSlice']),
ConverterSequenceNode('mul', ['Mul']),
ConverterSequenceNode('const_stridedSlice_1', ['?']),
ConverterSequenceNode('const_stridedSlice_2', ['?']),
ConverterSequenceNode('const_stridedSlice_3', ['?']),
ConverterSequenceNode('mul_const', ['?']),
ConverterSequenceNode('root', ['ResizeBilinear'])])
sequence_shape_stridedslice_resize.set_inputs('shape', ['input'])
sequence_shape_stridedslice_resize.set_inputs('stridedSlice', ['shape',
'const_stridedSlice_1',
'const_stridedSlice_2',
'const_stridedSlice_3'])
sequence_shape_stridedslice_resize.set_inputs('mul', ['stridedSlice', 'mul_const'])
sequence_shape_stridedslice_resize.set_inputs('root', ['mul', 'input'])
sequence_shape_stridedslice_resize.set_outputs(['root'])
self.sequences = [sequence_resize, sequence_shape_stridedslice_resize]
def __init__(self):
self.sequence = GraphSequence([
NonConsumableConverterSequenceNode('inputs', ['?']),
ConverterSequenceNode('a', ['Mul']),
ConverterSequenceNode('b', ['Add']),
ConverterSequenceNode('weights', ['Const', 'Identity']),
ConverterSequenceNode('biases', ['Const', 'Identity'])
])
self.sequence.set_inputs('a', ['inputs', 'weights'])
self.sequence.set_inputs('b', ['a', 'biases'])
self.sequence.set_outputs(['b'])
def __init__(self):
super(DepthwiseConvolutionLayerResolver, self).__init__()
self.graph_sequence_with_bias = GraphSequence([
ConverterSequenceNode('conv', ['DepthwiseConv2dNative']),
ConverterSequenceNode('bias', ['BiasAdd']),
NonConsumableConverterSequenceNode('other', ['?'])
])
self.graph_sequence_with_bias.set_inputs('bias', ['conv', 'other'])
self.graph_sequence_with_bias.set_outputs(['bias'])
self.graph_sequence = GraphSequence([ConverterSequenceNode('conv', ['DepthwiseConv2dNative'])])
self.graph_sequence.set_outputs(['conv'])
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('matmul_op', ['MatMul']),
ConverterSequenceNode('bias_op', ['BiasAdd', 'Add']), # output
NonConsumableConverterSequenceNode('biases',
['Identity', 'Const']),
NonConsumableConverterSequenceNode('weights',
['Identity', 'Const']),
NonConsumableConverterSequenceNode('inputs', ['?'])
])
self.sequence.set_inputs('matmul_op', ['inputs', 'weights'])
self.sequence.set_inputs('bias_op', ['matmul_op', 'biases'])
self.sequence.set_outputs(['bias_op'])
def __init__(self, layer_type, op_type, descriptor_class):
super(ReductionLayerResolver, self).__init__()
self._layer_type = layer_type
self._op_type = op_type
self._descriptor_class = descriptor_class
self.sequence = GraphSequence([
ConverterSequenceNode('root', [self._op_type]),
ConverterSequenceNode('reduction_indices', ['Const']),
NonConsumableConverterSequenceNode('input', ['?']),
])
self.sequence.set_inputs('root', ['input', 'reduction_indices'])
self.sequence.set_outputs(['root'])
def __init__(self):
sequence_two_dim_softmax = GraphSequence(
[ConverterSequenceNode('root', ['SoftMax'])])
sequence_two_dim_softmax.set_outputs(['root'])
sequence_multi_dim_softmax = GraphSequence([
ConverterSequenceNode('max', ['Max']),
ConverterSequenceNode('max_reduction_indicies', ['Const']),
ConverterSequenceNode('sub', ['Sub']),
ConverterSequenceNode('exp', ['Exp']),
ConverterSequenceNode('sum', ['Sum']),
ConverterSequenceNode('sum_reduction_indicies', ['Const']),
ConverterSequenceNode('root', ['RealDiv']),
NonConsumableConverterSequenceNode('input', ['?'])
])
sequence_multi_dim_softmax.set_inputs(
'max', ['input', 'max_reduction_indicies'])
sequence_multi_dim_softmax.set_inputs('sub', ['input', 'max'])
sequence_multi_dim_softmax.set_inputs('exp', ['sub'])
sequence_multi_dim_softmax.set_inputs(
'sum', ['exp', 'sum_reduction_indicies'])
sequence_multi_dim_softmax.set_inputs('root', ['exp', 'sum'])
sequence_multi_dim_softmax.set_outputs(['root'])
self.sequences = [sequence_two_dim_softmax, sequence_multi_dim_softmax]
def __init__(self):
super(DilatedDepthwiseConvolutionLayerResolver, self).__init__()
self.graph_sequence = GraphSequence([
NonConsumableConverterSequenceNode('space_to_batch', ['SpaceToBatchND']),
NonConsumableConverterSequenceNode('inputs', ['?']),
NonConsumableConverterSequenceNode('dilation_sizes', ['?']),
NonConsumableConverterSequenceNode('paddings', ['?']),
ConverterSequenceNode('conv_op', ['DepthwiseConv2dNative']),
ConverterSequenceNode('kernel', ['?']),
NonConsumableConverterSequenceNode('batch_to_space', ['BatchToSpaceND']), # output
NonConsumableConverterSequenceNode('block_shape_out', ['?']),
NonConsumableConverterSequenceNode('crops', ['?'])
])
self.graph_sequence.set_inputs('space_to_batch', ['inputs', 'dilation_sizes', 'paddings'])
self.graph_sequence.set_inputs('conv_op', ['space_to_batch', 'kernel'])
self.graph_sequence.set_inputs('batch_to_space', ['conv_op', 'block_shape_out', 'crops'])
self.graph_sequence.set_outputs(['batch_to_space'])
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('root', ['Slice']),
NonConsumableConverterSequenceNode('input', ['?']),
NonConsumableConverterSequenceNode('offsets', ['?']),
NonConsumableConverterSequenceNode('size', ['?']),
])
self.sequence.set_inputs('root', ['input', 'offsets', 'size'])
self.sequence.set_outputs(['root'])
def __init__(self, layer_type, descriptor_type, pooling_type, op_type):
super(PoolingLayerResolver, self).__init__()
self._layer_type = layer_type
self._descriptor_type = descriptor_type
self._polling_type = pooling_type
self._op_type = op_type
self.sequence = GraphSequence([ConverterSequenceNode('root', [self._op_type])])
self.sequence.set_outputs(['root'])
def __init__(self):
sequence = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
ConverterSequenceNode('tile', ['Tile']),
NonConsumableConverterSequenceNode('multiples', ['?'])
])
sequence.set_inputs('tile', ['input', 'multiples'])
sequence.set_outputs(['tile'])
self.sequences = [sequence]
def __init__(self):
self.sequence_with_zero_padding = GraphSequence([
ConverterSequenceNode('root', ['Pad', 'PadV2']),
ConverterSequenceNode('paddings', ['Const']),
NonConsumableConverterSequenceNode('input', ['?']),
])
self.sequence_with_zero_padding.set_inputs('root', ['input', 'paddings'])
self.sequence_with_zero_padding.set_outputs(['root'])
self.sequence_with_const_padding = GraphSequence([
ConverterSequenceNode('root', ['Pad', 'PadV2']),
ConverterSequenceNode('paddings', ['Const']),
ConverterSequenceNode('const_values', ['Const']),
NonConsumableConverterSequenceNode('input', ['?']),
])
self.sequence_with_const_padding.set_inputs('root', ['input', 'paddings', 'const_values'])
self.sequence_with_const_padding.set_outputs(['root'])
self.sequences = [self.sequence_with_zero_padding, self.sequence_with_const_padding]
def __init__(self):
sequence_extract_glimpse = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
NonConsumableConverterSequenceNode('size', ['?']),
NonConsumableConverterSequenceNode('offsets', ['?']),
ConverterSequenceNode('extract_glimpse', ['ExtractGlimpse'])
])
sequence_extract_glimpse.set_inputs('extract_glimpse', ['input', 'size', 'offsets'])
sequence_extract_glimpse.set_outputs(['extract_glimpse'])
sequence_extract_glimpse_strided_slice = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
NonConsumableConverterSequenceNode('size', ['?']),
ConverterSequenceNode('offsets', ['StridedSlice']),
ConverterSequenceNode('extract_glimpse', ['ExtractGlimpse'])
])
sequence_extract_glimpse_strided_slice.set_inputs('extract_glimpse',
['input', 'size', 'offsets'])
sequence_extract_glimpse_strided_slice.set_outputs(['extract_glimpse'])
self.sequences = [sequence_extract_glimpse, sequence_extract_glimpse_strided_slice]
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('a', ['Add']),
ConverterSequenceNode('b', ['Rsqrt']),
ConverterSequenceNode('c', ['Mul']),
ConverterSequenceNode('d', ['Mul']),
ConverterSequenceNode('e', ['Sub']),
ConverterSequenceNode('f', ['Add']), # output
NonConsumableConverterSequenceNode('inputs', ['?']),
ConverterSequenceNode('mean', ['?']),
ConverterSequenceNode('beta', ['?']),
ConverterSequenceNode('variance', ['?']),
ConverterSequenceNode('epsilon', ['?'])
])
self.sequence.set_inputs('a', ['variance', 'epsilon'])
self.sequence.set_inputs('b', ['a'])
self.sequence.set_inputs('c', ['b', 'inputs'])
self.sequence.set_inputs('d', ['b', 'mean'])
self.sequence.set_inputs('e', ['d', 'beta'])
self.sequence.set_inputs('f', ['c', 'e'])
self.sequence.set_outputs(['f'])
def __init__(self, layer_type, op_type, descriptor_class):
super(EltWiseLayerResolver, self).__init__()
self._layer_type = layer_type
self._op_type = op_type
self._descriptor_class = descriptor_class
self.sequence = GraphSequence([ConverterSequenceNode('root', [self._op_type])])
self.sequence.set_outputs(['root'])
self.sequence_with_identity = GraphSequence([
ConverterSequenceNode('root', [self._op_type]),
ConverterSequenceNode('identity', ['Identity'])
])
self.sequence_with_identity.set_inputs('identity', ['root'])
self.sequence_with_identity.set_outputs(['identity'])
self.sequence_with_const_input = GraphSequence([
ConverterSequenceNode('root', [self._op_type]),
NonConsumableConverterSequenceNode('const', ['Const', 'Identity']),
NonConsumableConverterSequenceNode('other', ['?'])
])
self.sequence_with_const_input.set_inputs('root', ['const', 'other'])
self.sequence_with_const_input.set_outputs(['root'])
self.sequence_with_const_input_and_identity = GraphSequence([
ConverterSequenceNode('root', [self._op_type]),
ConverterSequenceNode('identity', ['Identity']),
NonConsumableConverterSequenceNode('const', ['Const']),
NonConsumableConverterSequenceNode('other', ['?'])
])
self.sequence_with_const_input_and_identity.set_inputs('root', ['const', 'other'])
self.sequence_with_const_input_and_identity.set_inputs('identity', ['root'])
self.sequence_with_const_input_and_identity.set_outputs(['identity'])
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('a', ['Relu']),
ConverterSequenceNode('b', ['Abs']),
ConverterSequenceNode('c', ['Sub']),
ConverterSequenceNode('d', ['Mul']),
ConverterSequenceNode('e', ['Mul']),
ConverterSequenceNode('f', ['Add']), # output
ConverterSequenceNode('unknown', ['?']),
ConverterSequenceNode('alphas', ['?']),
NonConsumableConverterSequenceNode('inputs', ['?'])
])
self.sequence.set_inputs('a', ['inputs'])
self.sequence.set_inputs('b', ['inputs'])
self.sequence.set_inputs('c', ['inputs', 'b'])
self.sequence.set_inputs('d', ['alphas', 'c'])
self.sequence.set_inputs('e', ['d', 'unknown'])
self.sequence.set_inputs('f', ['a', 'e'])
self.sequence.set_outputs(['f'])
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('reshape_out', ['Reshape']),
ConverterSequenceNode('transpose', ['Transpose']),
ConverterSequenceNode('reshape_in', ['Reshape']),
ConverterSequenceNode('shape_in', ['Const']),
ConverterSequenceNode('order', ['Const']),
ConverterSequenceNode('shape_out', ['Const']),
NonConsumableConverterSequenceNode('input', ['?']),
])
self.sequence.set_inputs('reshape_out', ['shape_out', 'transpose'])
self.sequence.set_inputs('transpose', ['order', 'reshape_in'])
self.sequence.set_inputs('reshape_in', ['shape_in', 'input'])
self.sequence.set_outputs(['reshape_out'])
def __init__(self):
super(GroupedConvolutionLayerResolver, self).__init__()
tree_output_node = ConverterSequenceNode('conv_op', ['Conv2D'])
self.sequence = GraphSequence([
ConverterSequenceNode('a', ['Split']),
ConverterSequenceNode('b', ['Split']),
ConverterRepeatableSequenceTreeNode('repeatable_graph',
tree_output_node,
tree_output_node),
ConverterSequenceNode('concat_op', ['Concat']),
ConverterSequenceNode('weights', ['Identity', 'Const']),
NonConsumableConverterSequenceNode('inputs', ['?']),
NonConsumableConverterSequenceNode('concat_dim', ['Const']),
NonConsumableConverterSequenceNode('split_dim1', ['Const']),
ConverterSequenceNode('split_dim2', ['Const'])
])
self.sequence.set_inputs('a', ['inputs', 'split_dim1'])
self.sequence.set_inputs('b', ['weights', 'split_dim2'])
self.sequence.set_inputs('repeatable_graph', ['a', 'b'])
self.sequence.set_inputs('concat_op',
['repeatable_graph', 'concat_dim'])
self.sequence.set_outputs(['concat_op'])
def __init__(self):
self.sequence = GraphSequence(
[ConverterSequenceNode('root', ['Relu6'])])
self.sequence.set_outputs(['root'])
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('root', ['Reshape', 'Squeeze', 'ExpandDims'])
])
self.sequence.set_outputs(['root'])
# // except copyright notices indicating different years of publication for
# // different portions of the software. This notice does not supersede the
# // application of any third party copyright notice to that third party's
# // code.
# //
# // @@-COPYRIGHT-END-@@
# //=============================================================================
from converters.tensorflow.graph_matcher import (
ConverterSequenceNode,
NonConsumableConverterSequenceNode,
GraphSequence
)
box_decoder_sequence = GraphSequence([
NonConsumableConverterSequenceNode('Postprocessor/Tile', ['Tile']),
ConverterSequenceNode('Postprocessor/Reshape_1', ['Reshape']),
ConverterSequenceNode('Postprocessor/Reshape', ['Reshape']),
ConverterSequenceNode('Postprocessor/Decode/transpose', ['Transpose']),
ConverterSequenceNode('Postprocessor/Decode/div_3', ['RealDiv']),
ConverterSequenceNode('Postprocessor/Decode/div_2', ['RealDiv']),
ConverterSequenceNode('Postprocessor/Decode/get_center_coordinates_and_sizes/transpose', ['Transpose']),
ConverterSequenceNode('Postprocessor/Decode/unstack', ['Unpack']),
ConverterSequenceNode('Postprocessor/Decode/Exp', ['Exp']),
ConverterSequenceNode('Postprocessor/Decode/get_center_coordinates_and_sizes/div_1', ['RealDiv']),
ConverterSequenceNode('Postprocessor/Decode/get_center_coordinates_and_sizes/sub', ['Sub']),
ConverterSequenceNode('Postprocessor/Decode/div_1', ['RealDiv']),
ConverterSequenceNode('Postprocessor/Decode/Exp_1', ['Exp']),
ConverterSequenceNode('Postprocessor/Decode/get_center_coordinates_and_sizes/div', ['RealDiv']),
ConverterSequenceNode('Postprocessor/Decode/get_center_coordinates_and_sizes/unstack', ['Unpack']),
ConverterSequenceNode('Postprocessor/Decode/get_center_coordinates_and_sizes/sub_1', ['Sub']),
ConverterSequenceNode('Postprocessor/Decode/div', ['RealDiv']),
#=============================================================================
#
# Copyright (c) 2015-2017 Qualcomm Technologies, Inc.
# All Rights Reserved.
# Confidential and Proprietary - Qualcomm Technologies, Inc.
#
#=============================================================================
from converters.tensorflow.graph_matcher import (
ConverterSequenceNode,
NonConsumableConverterSequenceNode,
GraphSequence
)
cell_sequence = GraphSequence([
NonConsumableConverterSequenceNode('rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel/read', ['Identity']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/basic_lstm_cell/concat',
['ConcatV2']),
NonConsumableConverterSequenceNode('rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias/read', ['Identity']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/basic_lstm_cell/MatMul',
['MatMul']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/basic_lstm_cell/BiasAdd',
['BiasAdd']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/split', ['Split']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/add', ['Add']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/Tanh', ['Tanh']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/Sigmoid_1', ['Sigmoid']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/Sigmoid', ['Sigmoid']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul_1', ['Mul']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul', ['Mul']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/add_1', ['Add']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/Sigmoid_2', ['Sigmoid']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/Tanh_1', ['Tanh']),
def __init__(self):
super(GroupedConvolutionLayerResolver, self).__init__()
# grouped convolution with split
tree_output_node = ConverterSequenceNode('conv_op', ['Conv2D'])
self.sequence = GraphSequence([
ConverterSequenceNode('a', ['Split']),
ConverterSequenceNode('b', ['Split']),
ConverterRepeatableSequenceTreeNode('repeatable_graph', tree_output_node, tree_output_node),
ConverterSequenceNode('concat_op', ['Concat']),
ConverterSequenceNode('weights', ['Identity', 'Const']),
NonConsumableConverterSequenceNode('inputs', ['?']),
NonConsumableConverterSequenceNode('concat_dim', ['Const']),
NonConsumableConverterSequenceNode('split_dim1', ['Const']),
ConverterSequenceNode('split_dim2', ['Const'])
])
self.sequence.set_inputs('a', ['inputs', 'split_dim1'])
self.sequence.set_inputs('b', ['weights', 'split_dim2'])
self.sequence.set_inputs('repeatable_graph', ['a', 'b'])
self.sequence.set_inputs('concat_op', ['repeatable_graph', 'concat_dim'])
self.sequence.set_outputs(['concat_op'])
# grouped convolution with strided slice
repeatable_sequence = GraphSequence([
ConverterSequenceNode('ss', ['StridedSlice']),
ConverterSequenceNode('ss_begin', ['Const']),
ConverterSequenceNode('ss_end', ['Const']),
ConverterSequenceNode('ss_strides', ['Const']),
ConverterSequenceNode('conv', ['Conv2D']),
ConverterSequenceNode('bias', ['BiasAdd']),
ConverterSequenceNode('weights', ['Identity', 'Const']),
ConverterSequenceNode('biases', ['Identity', 'Const'])
])
repeatable_sequence.set_inputs('ss', ['ss_begin', 'ss_end', 'ss_strides'])
repeatable_sequence.set_inputs('conv', ['ss', 'weights'])
repeatable_sequence.set_inputs('bias', ['biases', 'conv'])
repeatable_sequence.set_outputs(['bias'])
self.sequence_with_strided_slice = GraphSequence([
ConverterRepeatableSequenceTreeNode('repeatable_graph',
tree_output_node=repeatable_sequence['bias'],
tree_input_node=repeatable_sequence['ss']),
ConverterSequenceNode('concat', ['Concat', 'ConcatV2']),
ConverterSequenceNode('axis', ['Const']),
NonConsumableConverterSequenceNode('input', ['?'])
])
self.sequence_with_strided_slice.set_inputs('repeatable_graph', ['input'])
self.sequence_with_strided_slice.set_inputs('concat', ['repeatable_graph', 'axis'])
self.sequence_with_strided_slice.set_outputs(['concat'])
#=============================================================================
#
# Copyright (c) 2015-2018 Qualcomm Technologies, Inc.
# All Rights Reserved.
# Confidential and Proprietary - Qualcomm Technologies, Inc.
#
#=============================================================================
from converters.tensorflow.graph_matcher import (
ConverterSequenceNode,
NonConsumableConverterSequenceNode,
GraphSequence
)
real_div_sequence = GraphSequence([
ConverterSequenceNode('root', ['RealDiv']),
NonConsumableConverterSequenceNode('a', ['?']),
NonConsumableConverterSequenceNode('b', ['?'])
])
real_div_sequence.set_inputs('root', ['a', 'b'])
real_div_sequence.set_outputs(['root'])
identity_sequence = GraphSequence([
ConverterSequenceNode('root', ['Identity']),
NonConsumableConverterSequenceNode('any', ['?']),
])
identity_sequence.set_inputs('root', ['any'])
identity_sequence.set_outputs(['root'])
placeholder_with_default_sequence = GraphSequence([
ConverterSequenceNode('root', ['PlaceholderWithDefault']),
def __init__(self):
self.sequence = GraphSequence(
[ConverterSequenceNode('root', ['ResizeNearestNeighbor'])])
self.sequence.set_outputs(['root'])
def __init__(self):
sequence_reshape = GraphSequence([ConverterSequenceNode('root', ['Reshape', 'Squeeze', 'ExpandDims'])])
sequence_reshape.set_outputs(['root'])
sequence_flatten = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
ConverterSequenceNode('shape', ['Shape']),
ConverterSequenceNode('slice_1', ['Slice']),
ConverterSequenceNode('const_1', ['Const']),
ConverterSequenceNode('const_2', ['Const']),
ConverterSequenceNode('slice_2', ['Slice']),
ConverterSequenceNode('const_3', ['Const']),
ConverterSequenceNode('const_4', ['Const']),
ConverterSequenceNode('prod', ['Prod']),
ConverterSequenceNode('const_5', ['Const']),
ConverterSequenceNode('expand_dims', ['ExpandDims']),
ConverterSequenceNode('const_6', ['Const']),
ConverterSequenceNode('concat', ['ConcatV2']),
ConverterSequenceNode('const_7', ['Const']),
ConverterSequenceNode('root', ['Reshape']),
])
sequence_flatten.set_inputs('shape', ['input'])
sequence_flatten.set_inputs('slice_1', ['shape', 'const_1', 'const_2'])
sequence_flatten.set_inputs('slice_2', ['shape', 'const_3', 'const_4'])
sequence_flatten.set_inputs('prod', ['slice_2', 'const_5'])
sequence_flatten.set_inputs('expand_dims', ['prod', 'const_6'])
sequence_flatten.set_inputs('concat', ['slice_1', 'expand_dims', 'const_7'])
sequence_flatten.set_inputs('root', ['input', 'concat'])
sequence_flatten.set_outputs(['root'])
self.sequences = [sequence_reshape, sequence_flatten]
def __init__(self):
sequence_prelu = GraphSequence([
ConverterSequenceNode('a', ['Relu']),
ConverterSequenceNode('b', ['Abs']),
ConverterSequenceNode('c', ['Sub']),
ConverterSequenceNode('d', ['Mul']),
ConverterSequenceNode('e', ['Mul']),
ConverterSequenceNode('f', ['Add']), # output
ConverterSequenceNode('unknown', ['?']),
ConverterSequenceNode('alphas', ['?']),
NonConsumableConverterSequenceNode('inputs', ['?'])
])
sequence_prelu.set_inputs('a', ['inputs'])
sequence_prelu.set_inputs('b', ['inputs'])
sequence_prelu.set_inputs('c', ['inputs', 'b'])
sequence_prelu.set_inputs('d', ['alphas', 'c'])
sequence_prelu.set_inputs('e', ['d', 'unknown'])
sequence_prelu.set_inputs('f', ['a', 'e'])
sequence_prelu.set_outputs(['f'])
sequence_prelu_negative_alpha = GraphSequence([
ConverterSequenceNode('a', ['Relu']),
ConverterSequenceNode('b', ['Neg']),
ConverterSequenceNode('c', ['Neg']),
ConverterSequenceNode('d', ['Relu']),
ConverterSequenceNode('e', ['Mul']),
ConverterSequenceNode('f', ['Add']), # output
ConverterSequenceNode('alphas', ['?']),
NonConsumableConverterSequenceNode('inputs', ['?'])
])
sequence_prelu_negative_alpha.set_inputs('a', ['inputs'])
sequence_prelu_negative_alpha.set_inputs('b', ['inputs'])
sequence_prelu_negative_alpha.set_inputs('c', ['alphas'])
sequence_prelu_negative_alpha.set_inputs('d', ['b'])
sequence_prelu_negative_alpha.set_inputs('e', ['d', 'c'])
sequence_prelu_negative_alpha.set_inputs('f', ['a', 'e'])
sequence_prelu_negative_alpha.set_outputs(['f'])
sequence_prelu_negative_relu = GraphSequence([
ConverterSequenceNode('relu_pos', ['Relu']),
ConverterSequenceNode('neg_1', ['Neg']),
ConverterSequenceNode('neg_2', ['Neg']),
ConverterSequenceNode('relu_neg', ['Relu']),
ConverterSequenceNode('mul', ['Mul']),
ConverterSequenceNode('f', ['Add']), # output
ConverterSequenceNode('alphas', ['?']),
NonConsumableConverterSequenceNode('inputs', ['?'])
])
sequence_prelu_negative_relu.set_inputs('relu_pos', ['inputs'])
sequence_prelu_negative_relu.set_inputs('neg_1', ['inputs'])
sequence_prelu_negative_relu.set_inputs('relu_neg', ['neg_1'])
sequence_prelu_negative_relu.set_inputs('neg_2', ['relu_neg'])
sequence_prelu_negative_relu.set_inputs('mul', ['neg_2', 'alphas'])
sequence_prelu_negative_relu.set_inputs('f', ['relu_pos', 'mul'])
sequence_prelu_negative_relu.set_outputs(['f'])
self.sequences = [
sequence_prelu, sequence_prelu_negative_alpha,
sequence_prelu_negative_relu
]