def __init__(self):
self.sequence = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
ConverterSequenceNode('tile', ['Tile']),
NonConsumableConverterSequenceNode('multiples', ['?'])
])
self.sequence.set_inputs('tile', ['input', 'multiples'])
self.sequence.set_outputs(['tile'])
# sequence input->shape->stridedslice->pack->tile
self.sequence_pack = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
NonConsumableConverterSequenceNode('shape', ['Shape']),
NonConsumableConverterSequenceNode('stridedslice', ['StridedSlice']),
NonConsumableConverterSequenceNode('const_3', ['Const']),
NonConsumableConverterSequenceNode('const_4', ['Const']),
NonConsumableConverterSequenceNode('const_5', ['Const']),
ConverterSequenceNode('tile', ['Tile']),
NonConsumableConverterSequenceNode('tile_multiples_pack', ['Pack']),
NonConsumableConverterSequenceNode('const_1', ['Const']),
NonConsumableConverterSequenceNode('const_2', ['Const']),
NonConsumableConverterSequenceNode('tile_input', ['?'])
])
self.sequence_pack.set_inputs('shape', ['input'])
self.sequence_pack.set_inputs('stridedslice', ['shape', 'const_3', 'const_4', 'const_5'])
self.sequence_pack.set_inputs('tile_multiples_pack', ['stridedslice', 'const_1', 'const_2'])
self.sequence_pack.set_inputs('tile', ['tile_input', 'tile_multiples_pack'])
self.sequence_pack.set_outputs(['tile'])
class Relu6LayerResolver(ReluMinMaxLayerResolver, object):
class Descriptor(ReluMinMaxLayerResolver.Descriptor):
def __init__(self, name, nodes):
super(Relu6LayerResolver.Descriptor, self).__init__('Relu6',
name,
nodes,
min_clamp=0,
max_clamp=6)
def __init__(self):
self.sequence = GraphSequence(
[ConverterSequenceNode('root', ['Relu6'])])
self.sequence.set_outputs(['root'])
def resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
if len(matches) == 0:
return []
potential_descriptors = []
for match in matches:
relu6_op = match['root']
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
Relu6LayerResolver.Descriptor(str(relu6_op.name),
consumed_nodes))
return potential_descriptors
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
]
class EluLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, layer_type, name, nodes):
super(EluLayerResolver.Descriptor,
self).__init__(layer_type, name, nodes)
@property
def output_names(self):
return [str(self.child_ops[0].outputs[0].name)]
def is_output_op(self, op):
return op in self.child_ops
def get_output_names_for(self, input_tensors):
return self.output_names
def __init__(self):
self.sequence = GraphSequence([ConverterSequenceNode('root', ['Elu'])])
self.sequence.set_outputs(['root'])
def resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
if len(matches) == 0:
return []
potential_descriptors = []
for match in matches:
elu_op = match['root']
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
EluLayerResolver.Descriptor('ELU', str(elu_op.name),
consumed_nodes))
return potential_descriptors
class EltWiseUnaryLayerResolver(LayerResolver, object):
__metaclass__ = ABCMeta
def __init__(self, layer_type, op_type, descriptor_class):
super(EltWiseUnaryLayerResolver, 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]),
NonConsumableConverterSequenceNode('input1', ['?']),
])
self.sequence.set_inputs('root', ['input1'])
self.sequence.set_outputs(['root'])
def resolve_layer(self, graph_matcher, graph_helper):
descriptors = []
non_const_input_sequences = [self.sequence]
for sequence in non_const_input_sequences:
for match in graph_matcher.match_sequence(sequence):
eltwise_op = match['root']
descriptor = self._descriptor_class(self._layer_type,
str(eltwise_op.name),
match.consumed_nodes)
descriptors.append(descriptor)
return descriptors
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):
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):
self.sequence = GraphSequence([
ConverterSequenceNode('root', ['FakeQuantWithMinMaxVars']),
ConverterSequenceNode('min', ['?']),
ConverterSequenceNode('max', ['?']),
NonConsumableConverterSequenceNode('input', ['?'])
])
self.sequence.set_inputs('root', ['input', 'min', 'max'])
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):
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):
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'])
class ConcatLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes, axis, output_names=None):
super(ConcatLayerResolver.Descriptor, self).__init__('Concatenation', name, nodes,
output_names=output_names)
self.axis = axis
def __init__(self):
self.sequence = GraphSequence([ConverterSequenceNode('root', ['Concat', 'ConcatV2'])])
self.sequence.set_outputs(['root'])
def resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
if len(matches) == 0:
return []
descriptors = []
for match in matches:
concat_op = match['root']
consumed_nodes = match.consumed_nodes
concat_descriptor = ConcatLayerResolver.Descriptor(str(concat_op.name), consumed_nodes,
None, [concat_op.outputs[0].name])
non_const_inputs = [tensor for tensor in concat_op.inputs if tensor.op.type != 'Const']
const_ops = [tensor.op for tensor in concat_op.inputs if tensor.op.type == 'Const']
axis_tensor = None
if len(non_const_inputs) < 2 or len(const_ops) > 1:
for i in range(0, len(const_ops) - 1):
const_value = graph_helper.evaluate_tensor_output(const_ops[i].outputs[0])
const_shape = graph_helper.get_op_output_shape(const_ops[i].outputs[0])
descriptors.append(ConstantLayerResolver.Descriptor(str(const_ops[i]),
[const_ops[i]],
const_value,
const_shape,
concat_descriptor))
# Make the assumption that the axis is always the last constant
axis_tensor = const_ops[-1]
max_shape = 0
for t in non_const_inputs:
shape = graph_helper.get_op_output_shape(t.op)
if len(shape) > max_shape:
max_shape = len(shape)
if not axis_tensor:
axis_tensor = GraphHelper.filter_single_op_by_type([t.op for t in concat_op.inputs], 'Const')
axis = int(graph_helper.evaluate_tensor_output(axis_tensor.outputs[0]))
if axis < 0:
axis += max_shape
concat_descriptor.axis = axis
descriptors.append(concat_descriptor)
return descriptors
def __init__(self, layer_type, op_type, descriptor_class):
super(EltWiseUnaryLayerResolver, 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]),
NonConsumableConverterSequenceNode('input1', ['?']),
])
self.sequence.set_inputs('root', ['input1'])
self.sequence.set_outputs(['root'])
class InstanceNormRMSLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, operations, shape):
super(InstanceNormRMSLayerResolver.Descriptor,
self).__init__('InstanceNormRMS', name, operations)
self.shape = shape
# SNPE runtime algo is y = x * WEIGHT / rms + BIAS
# While L2 Normalization is y = x / rms
# That requires WEIGHT = 1.0 and BIAS = 0.0 to mimic L2 Norm in SNPE
# Shape of weights/biases should be same as the last dimension of input.
self.weights = np.ones(shape[-1])
self.biases = np.zeros(shape[-1])
def __init__(self):
# Graph topology of tf.math.l2_normalize
self.sequence = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
ConverterSequenceNode('a', ['Square']),
ConverterSequenceNode('weights', ['Const', 'Identity']),
ConverterSequenceNode('b', ['Sum']),
ConverterSequenceNode('epsilon', ['Const', 'Identity']),
ConverterSequenceNode('c', ['Maximum']),
ConverterSequenceNode('d', ['Rsqrt']),
ConverterSequenceNode('e', ['Mul'])
])
self.sequence.set_inputs('a', ['input'])
self.sequence.set_inputs('b', ['a', 'weights'])
self.sequence.set_inputs('c', ['b', 'epsilon'])
self.sequence.set_inputs('d', ['c'])
self.sequence.set_inputs('e', ['d', 'input'])
self.sequence.set_outputs(['e'])
# For now, elementwise resolver cannot work with epsilon node.
# Will meet error "ElementWise resolver must implement broadcast method.".
def is_final_resolution(self):
return True
def resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
potential_descriptors = []
for match in matches:
bn_op = match['SquaredDifference']
input_op = match['input']
shape = graph_helper.get_op_output_shape(input_op)
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
InstanceNormRMSLayerResolver.Descriptor(str(bn_op.name),
consumed_nodes,
shape=shape))
return potential_descriptors
def __init__(self):
super(DepthwiseConvolutionLayerResolver, self).__init__()
self.graph_sequence_with_bias = GraphSequence([
ConverterSequenceNode('conv', ['DepthwiseConv2dNative']),
ConverterSequenceNode('bias', ['BiasAdd', 'Add']),
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, 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):
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'])
class ArgMaxLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes, axis, output_names=None):
super(ArgMaxLayerResolver.Descriptor,
self).__init__('ArgMax',
name,
nodes,
output_names=output_names)
self.axis = axis
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 resolve_layer(self, graph_matcher, graph_helper):
descriptors = []
for match in graph_matcher.match_sequence(self.sequence):
argmax_op = match['root']
input_op = match['input']
axis_op = match['axis']
input_shape = graph_helper.get_op_output_shape(input_op)
input_rank = len(input_shape)
axis = int(graph_helper.evaluate_tensor_output(axis_op.outputs[0]))
if axis < 0:
axis += input_rank
if axis < 0 or axis >= input_rank:
raise ConverterError(
code_to_message.get_error_message(
'ERROR_TF_ARGMAX_INVALID_AXIS')(axis, input_rank))
consumed_nodes = match.consumed_nodes
argmax_descriptor = ArgMaxLayerResolver.Descriptor(
str(argmax_op.name),
consumed_nodes,
axis,
output_names=[str(argmax_op.outputs[0].name)])
descriptors.extend([argmax_descriptor])
return descriptors
class ImageProjectiveTransformLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self,
name,
operations,
interpolation_mode,
output_names=None):
super(ImageProjectiveTransformLayerResolver.Descriptor,
self).__init__('ImageProjectiveTransform',
name,
operations,
output_names=output_names)
self.interpolation_mode = interpolation_mode
def __init__(self):
self.sequence = GraphSequence(
[ConverterSequenceNode('root', ['ImageProjectiveTransform'])])
self.sequence.set_outputs(['root'])
def resolve_layer(self, graph_matcher, graph_helper):
potential_descriptors = []
matches = graph_matcher.match_sequence(self.sequence)
for match in matches:
image_proj_transform = match['root']
output_op_nodes_names = [str(image_proj_transform.outputs[0].name)]
consumed_nodes = match.consumed_nodes
interpolation = str(
image_proj_transform.get_attr('interpolation').decode('utf-8'))
if interpolation == "BILINEAR":
interpolation_mode = 0
elif interpolation == "NEAREST":
interpolation_mode = 1
else:
raise ConverterError(
code_to_message.get_error_message(
'ERROR_TF_RESOLVE_IMAGE_TRANSFORM_INTERPOLATION'))
potential_descriptors.append(
ImageProjectiveTransformLayerResolver.Descriptor(
str(image_proj_transform.name),
consumed_nodes,
interpolation_mode,
output_names=output_op_nodes_names))
return potential_descriptors
class BatchNormWithGlobalNormLayerResolver(BatchNormLayerResolver):
class Descriptor(BatchNormLayerResolver.Descriptor):
pass
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('root', ['BatchNormWithGlobalNormalization'])
])
self.sequence.set_outputs(['root'])
def resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
if len(matches) == 0:
return []
potential_descriptors = []
for match in matches:
bn_op = match['root']
parameter_tensors = self._const_inputs(graph_helper, bn_op)
if len(parameter_tensors) < 4:
raise ConverterError(
code_to_message.get_error_message(
'ERROR_TF_BATCHNORM_GLOBALNORMALIZATION_INPUT'))
epsilon = bn_op.get_attr('variance_epsilon')
mean = parameter_tensors[0]
variance = parameter_tensors[1]
beta = parameter_tensors[2]
scale = parameter_tensors[3]
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
BatchNormWithGlobalNormLayerResolver.Descriptor(
str(bn_op.name),
consumed_nodes,
bn_mul_op=bn_op,
mean=mean,
variance=variance,
epsilon=epsilon,
scale=scale,
beta=beta))
return potential_descriptors
@classmethod
def _const_inputs(cls, graph_helper, bn_op):
return [
graph_helper.evaluate_tensor_output(tensor)
for tensor in bn_op.inputs if tensor.op.type == 'Const'
]
def __init__(self):
sequence_keras = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
ConverterSequenceNode('root', ['Relu']),
ConverterSequenceNode('min', ['Minimum']),
ConverterSequenceNode('min_cast', ['Cast']),
ConverterSequenceNode('min_const', ['Const']),
ConverterSequenceNode('max', ['Maximum']),
ConverterSequenceNode('max_const', ['Const'])
])
sequence_keras.set_inputs('root', ['input'])
sequence_keras.set_inputs('min_cast', ['min_const'])
sequence_keras.set_inputs('min', ['root', 'min_cast'])
sequence_keras.set_inputs('max', ['min', 'max_const'])
sequence_keras.set_outputs(['max'])
self.sequences = [sequence_keras]
class FusedBatchNormNormLayerResolver(BatchNormLayerResolver):
class Descriptor(BatchNormLayerResolver.Descriptor):
pass
def __init__(self):
self.sequence = GraphSequence(
[ConverterSequenceNode('root', ['FusedBatchNorm'])])
self.sequence.set_outputs(['root'])
def resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
potential_descriptors = []
for match in matches:
bn_op = match['root']
parameter_tensors = self._get_parameter_tensors(
graph_helper, bn_op)
if len(parameter_tensors) < 4:
raise ConverterError(
code_to_message.get_error_message(
'ERROR_TF_BATCHNORM_GLOBALNORMALIZATION_INPUT'))
epsilon = bn_op.get_attr('epsilon')
scale = parameter_tensors[0]
beta = parameter_tensors[1]
mean = parameter_tensors[2]
variance = parameter_tensors[3]
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
FusedBatchNormNormLayerResolver.Descriptor(str(bn_op.name),
consumed_nodes,
bn_mul_op=bn_op,
mean=mean,
variance=variance,
epsilon=epsilon,
scale=scale,
beta=beta))
return potential_descriptors
@classmethod
def _get_parameter_tensors(cls, graph_helper, bn_op):
parameter_tensors = [
t for t in bn_op.inputs if t.op.type in ['Const', 'Identity']
]
tensors_outputs = graph_helper.evaluate_tensors_output(
parameter_tensors)
return [tensors_outputs[t] for t in parameter_tensors]
def __init__(self):
# Graph topology of tf.math.l2_normalize
self.sequence = GraphSequence([
NonConsumableConverterSequenceNode('input', ['?']),
ConverterSequenceNode('a', ['Square']),
ConverterSequenceNode('weights', ['Const', 'Identity']),
ConverterSequenceNode('b', ['Sum']),
ConverterSequenceNode('epsilon', ['Const', 'Identity']),
ConverterSequenceNode('c', ['Maximum']),
ConverterSequenceNode('d', ['Rsqrt']),
ConverterSequenceNode('e', ['Mul'])
])
self.sequence.set_inputs('a', ['input'])
self.sequence.set_inputs('b', ['a', 'weights'])
self.sequence.set_inputs('c', ['b', 'epsilon'])
self.sequence.set_inputs('d', ['c'])
self.sequence.set_inputs('e', ['d', 'input'])
self.sequence.set_outputs(['e'])
class SliceLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes, axis, split_sizes, split_count):
super(SliceLayerResolver.Descriptor, self).__init__('Slice', name, nodes)
self.axis = axis
self.split_sizes = split_sizes
self.split_count = split_count
@property
def output_names(self):
return [str(t.name) for t in self.child_ops[-1].outputs]
def __init__(self):
self.sequence = GraphSequence([ConverterSequenceNode('root', ['Split', 'SplitV'])])
self.sequence.set_outputs(['root'])
def resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
if len(matches) == 0:
return []
potential_descriptors = []
for match in matches:
split_op = match['root']
split_axis, split_sizes = self.get_split_axis_and_sizes(graph_helper, split_op)
split_count = int(split_op.get_attr('num_split'))
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
SliceLayerResolver.Descriptor(str(split_op.name), consumed_nodes,
split_axis,
split_sizes,
split_count))
return potential_descriptors
@classmethod
def get_split_axis_and_sizes(cls, graph_helper, split_op):
try:
_, split_sizes, split_axis = GraphHelper.get_op_input_tensors(split_op, ('?', 'Const', 'Const'))
split_sizes = list(graph_helper.evaluate_tensor_output(split_sizes))
except TensorNotFoundError:
split_axis, _ = GraphHelper.get_op_input_tensors(split_op, ('Const', '?'))
split_sizes = []
split_axis = int(graph_helper.evaluate_tensor_output(split_axis))
return split_axis, split_sizes
def __init__(self):
super(DilatedConvolutionLayerResolver, self).__init__()
self.graph_sequence = GraphSequence([
ConverterSequenceNode('space_to_batch', ['SpaceToBatchND']),
NonConsumableConverterSequenceNode('inputs', ['?']),
ConverterSequenceNode('dilation_sizes', ['?']),
ConverterSequenceNode('paddings', ['?']),
ConverterSequenceNode('conv_op', ['Conv2D']),
ConverterSequenceNode('kernel', ['?']),
ConverterSequenceNode('batch_to_space', ['BatchToSpaceND']),
ConverterSequenceNode('block_shape_out', ['?']),
ConverterSequenceNode('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('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'])
class TanhLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
pass
def __init__(self):
self.sequence = GraphSequence(
[ConverterSequenceNode('root', ['Tanh'])])
self.sequence.set_outputs(['root'])
def resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
if len(matches) == 0:
return []
potential_descriptors = []
for match in matches:
tanh_op = match['root']
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
TanhLayerResolver.Descriptor('Tanh', str(tanh_op.name),
consumed_nodes))
return potential_descriptors
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.sequence_with_reflect_padding = GraphSequence([
ConverterSequenceNode('mirror_pad', ['MirrorPad']),
ConverterSequenceNode('paddings', ['Const']),
NonConsumableConverterSequenceNode('input', ['?']),
])
self.sequence_with_reflect_padding.set_inputs('mirror_pad',
['input', 'paddings'])
self.sequence_with_reflect_padding.set_outputs(['mirror_pad'])
self.sequences = [
self.sequence_with_zero_padding, self.sequence_with_const_padding,
self.sequence_with_reflect_padding
]
class PixelShuffleLayerResolver(LayerResolver, object):
TF_ATTRIBUTE_BLOCK_SIZE = 'block_size'
class Descriptor(LayerDescriptor):
def __init__(self, layer_type, name, nodes, upscale_factor):
super(PixelShuffleLayerResolver.Descriptor, self).__init__(layer_type, name, nodes)
self.upscale_factor = upscale_factor
@property
def output_names(self):
return [str(self.child_ops[0].outputs[0].name)]
def is_output_op(self, op):
return op in self.child_ops
def get_output_names_for(self, input_tensors):
return self.output_names
def __init__(self):
self.sequence = GraphSequence([ConverterSequenceNode('root', ['DepthToSpace'])])
self.sequence.set_outputs(['root'])
def resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
# Nothing matched
if len(matches) == 0:
return []
potential_descriptors = []
for match in matches:
depth_to_space_op = match['root']
upscale_factor = depth_to_space_op.get_attr(self.TF_ATTRIBUTE_BLOCK_SIZE)
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
PixelShuffleLayerResolver.Descriptor('PixelShuffle', str(depth_to_space_op.name), consumed_nodes, upscale_factor))
return potential_descriptors
class PoolingLayerResolver(LayerResolver, object):
__metaclass__ = ABCMeta
class Descriptor(LayerDescriptor):
def __init__(self, layer_type, name, operations, pooling_type, strides,
padding, kernel_dims):
super(PoolingLayerResolver.Descriptor,
self).__init__(layer_type, name, operations)
self.pooling_type = pooling_type
self.strides = strides
self.padding = padding
self.kernel_dims = kernel_dims
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 resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
if len(matches) == 0:
return []
potential_descriptors = []
for match in matches:
pooling_op = match['root']
kernel_dims = pooling_op.get_attr('ksize')
strides = pooling_op.get_attr('strides')
padding = pooling_op.get_attr('padding')
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
self._descriptor_type(self._layer_type, str(pooling_op.name),
consumed_nodes, self._polling_type,
strides, padding, kernel_dims))
return potential_descriptors
