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]
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
class FillLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes, shape, scalar):
super(FillLayerResolver.Descriptor, self).__init__('Fill', name, nodes)
self.shape = shape
self.scalar = scalar
def __init__(self):
self.sequence = GraphSequence([ConverterSequenceNode('root', ['Fill'])])
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:
fill_op = match['root']
consumed_nodes = match.consumed_nodes
shape_tensor, scalar_tensor = GraphHelper.get_op_input_tensors(fill_op, ('?', 'Const'))
shape = graph_helper.evaluate_tensor_output(shape_tensor).tolist()
while len(shape) > 3:
shape = shape[1:]
while len(shape) < 3:
shape = [1] + shape
scalar = graph_helper.evaluate_tensor_output(scalar_tensor)
d = FillLayerResolver.Descriptor(str(fill_op.name), consumed_nodes, shape, scalar)
descriptors.append(d)
return descriptors
class ReluLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, layer_type, name, nodes):
super(ReluLayerResolver.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', ['Relu'])])
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:
relu_op = match['root']
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
ReluLayerResolver.Descriptor('RELU', str(relu_op.name),
consumed_nodes))
return potential_descriptors
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]
class LrnLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, operations, window_size, alpha, beta, bias):
super(LrnLayerResolver.Descriptor,
self).__init__('LRN', name, operations)
self.window_size = window_size
self.alpha = alpha
self.beta = beta
self.bias = bias
def __init__(self):
self.sequence = GraphSequence([ConverterSequenceNode('root', ['LRN'])])
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:
lrn_op = match['root']
window_size = 1 + lrn_op.get_attr('depth_radius') * 2
alpha = lrn_op.get_attr('alpha')
beta = lrn_op.get_attr('beta')
bias = lrn_op.get_attr('bias')
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
LrnLayerResolver.Descriptor(str(lrn_op.name), consumed_nodes,
window_size, alpha, beta, bias))
return potential_descriptors
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]
class CropLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes, offset, size, output_names=None):
super(CropLayerResolver.Descriptor, self).__init__('Crop', name, nodes, output_names=output_names)
self.offset = offset
self.size = size
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 resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
descriptors = []
for match in matches:
slice_op = match['root']
input_shape = graph_helper.get_op_output_shape(match['input'])
offset = graph_helper.evaluate_tensor_output(match['offsets'].outputs[0])
size = graph_helper.evaluate_tensor_output(match['size'].outputs[0])
for index in range(0, len(size)):
if size[index] == -1:
size[index] = input_shape[index] - offset[index]
consumed_nodes = match.consumed_nodes
descriptors.append(
CropLayerResolver.Descriptor(str(slice_op.name), consumed_nodes, offset, size))
return descriptors
class DilatedConvolutionLayerResolver(ConvolutionLayerResolver, object):
class Descriptor(ConvolutionLayerResolver.Descriptor):
pass
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 resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.graph_sequence)
if len(matches) == 0:
return []
descriptors = []
for match in matches:
conv_op = match['conv_op']
strides = conv_op.get_attr(self.TF_ATTRIBUTE_STRIDES)
padding = conv_op.get_attr(self.TF_ATTRIBUTE_PADDING)
weights = self.get_weights(graph_helper, conv_op)
consumed_nodes = match.consumed_nodes
output_op_nodes_names = [str(match[node.identifier].outputs[0].name) for node in
self.graph_sequence.output_nodes]
try:
batch_to_space_op = match['batch_to_space']
conv_output_ops = graph_helper.get_op_outputs(batch_to_space_op)
bias_op = GraphHelper.filter_single_op_by_type(conv_output_ops, 'BiasAdd')
biases = self.get_biases(graph_helper, conv_op, bias_op)
consumed_nodes.append(bias_op)
output_op_nodes_names = [str(bias_op.outputs[0].name)]
except OperationNotFoundError:
bias_op = None
biases = np.zeros(weights.shape[-1], dtype=np.float32)
dilation_sizes = match['dilation_sizes']
dilation_sizes = graph_helper.evaluate_tensor_output(dilation_sizes.outputs[0])
if np.shape(dilation_sizes) != (2,):
raise ConverterError(code_to_message.get_message('ERROR_TF_CONV_RESOLVE_DILATION')(conv_op.name))
d = ConvolutionLayerResolver.Descriptor(str(conv_op.name), consumed_nodes,
conv_op, bias_op, strides, padding, weights, biases,
output_names=output_op_nodes_names)
d.dilationY = int(dilation_sizes[0])
d.dilationX = int(dilation_sizes[1])
d.input_ops = [match['space_to_batch']]
descriptors.append(d)
return descriptors
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):
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):
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'])
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
class ReductionLayerResolver(LayerResolver, object):
__metaclass__ = ABCMeta
class Descriptor(LayerDescriptor):
def __init__(self, layer_type, name, nodes, axes, keep_dims, output_names=None):
super(ReductionLayerResolver.Descriptor, self).__init__(layer_type, name, nodes, output_names=output_names)
self.axes = axes
self.keep_dims = keep_dims
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 resolve_layer(self, graph_matcher, graph_helper):
descriptors = []
for match in graph_matcher.match_sequence(self.sequence):
reduction_op = match['root']
input_op = match['input']
reduction_indices_op = match['reduction_indices']
axes = graph_helper.evaluate_tensor_output(reduction_indices_op.outputs[0])
keep_dims = bool(reduction_op.get_attr('keep_dims'))
input_shape = graph_helper.get_op_output_shape(input_op)
input_rank = len(input_shape)
axes = [axes] if np.isscalar(axes) else axes.tolist()
for i in range(len(axes)):
axes[i] = int(axes[i])
if axes[i] < 0:
axes[i] += input_rank
reduction_descriptor = self._descriptor_class(self._layer_type, str(reduction_op.name),
match.consumed_nodes, axes, keep_dims,
output_names=[str(reduction_op.outputs[0].name)])
descriptors.extend([reduction_descriptor])
return 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_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'
]
class PReLuLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, operations, coefficients, output_names):
super(PReLuLayerResolver.Descriptor, self).__init__('PReLU', name, operations,
output_names=output_names)
self.coefficients = coefficients
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 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:
coefficients = match['alphas']
add_op = match['f']
if coefficients.type not in ['Identity', 'Const']:
raise ConverterError(code_to_message.get_message('ERROR_TF_RESOLVE_PRELU_COEFF'))
output_op_nodes_names = [str(match[node.identifier].outputs[0].name) for node in self.sequence.output_nodes]
consumed_nodes = match.consumed_nodes
potential_descriptors.append(
PReLuLayerResolver.Descriptor(str(add_op.name), consumed_nodes,
graph_helper.evaluate_tensor_output(coefficients.outputs[0]),
output_names=output_op_nodes_names))
return potential_descriptors
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_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]
class ConcatLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes, axis):
super(ConcatLayerResolver.Descriptor,
self).__init__('Concatenation', name, nodes)
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']
non_const_inputs = [
tensor for tensor in concat_op.inputs
if tensor.op.type != 'Const'
]
if len(non_const_inputs) < 2:
continue
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)
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
consumed_nodes = match.consumed_nodes
descriptors.append(
ConcatLayerResolver.Descriptor(str(concat_op.name),
consumed_nodes, axis))
return descriptors
class GenericBatchNormLayerResolver(BatchNormLayerResolver):
class Descriptor(BatchNormLayerResolver.Descriptor):
pass
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 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:
biases_op = match['biases']
weights_op = match['weights']
biases_op = graph_helper.evaluate_tensor_output(
biases_op.outputs[0])
weights_op = graph_helper.evaluate_tensor_output(
weights_op.outputs[0])
consumed_nodes = match.consumed_nodes
output_op_nodes_names = [
str(match[node.identifier].outputs[0].name)
for node in self.sequence.output_nodes
]
bn_op = match['a']
potential_descriptors.append(
GenericBatchNormLayerResolver.Descriptor(
str(bn_op.name),
consumed_nodes,
bn_mul_op=bn_op,
pre_calculated=True,
weights=weights_op,
biases=biases_op,
output_names=output_op_nodes_names))
return potential_descriptors
class AddNLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes):
super(AddNLayerResolver.Descriptor, self).__init__('ElementWiseSumN', name, nodes)
def __init__(self):
self.sequence = GraphSequence([ConverterSequenceNode('root', ['AddN'])])
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:
add_op = match['root']
descriptors.append(AddNLayerResolver.Descriptor(str(add_op.name), match.consumed_nodes))
return descriptors
class DepthwiseConvolutionLayerResolver(ConvolutionLayerResolver, object):
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 resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.graph_sequence)
matches += graph_matcher.match_sequence(self.graph_sequence_with_bias)
descriptors = []
for match in matches:
self._resolve_from_match(descriptors, graph_helper, match)
return descriptors
def _resolve_from_match(self, descriptors, graph_helper, match):
conv_op = match['conv']
strides = conv_op.get_attr(self.TF_ATTRIBUTE_STRIDES)
padding = conv_op.get_attr(self.TF_ATTRIBUTE_PADDING)
weights = self.get_weights(graph_helper, conv_op)
weights = np.transpose(weights, [0, 1, 3, 2])
if 'bias' in match:
biases = self.get_biases(graph_helper, conv_op, match['bias'])
else:
biases = np.zeros(np.shape(weights)[-1], dtype=np.float32)
consumed_nodes = match.consumed_nodes
d = ConvolutionLayerResolver.Descriptor(str(conv_op.name),
consumed_nodes, conv_op, None,
strides, padding, weights,
biases)
input_tensor, _ = GraphHelper.get_op_input_tensors(conv_op, ('?', '?'))
d.groups = graph_helper.get_op_output_shape(input_tensor)[-1]
descriptors.append(d)
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):
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]
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
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_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
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
]
class ResizeBilinearLayerResolver(LayerResolver, object):
TF_ATTRIBUTE_ALIGN_CORNERS = 'align_corners'
class Descriptor(LayerDescriptor):
def __init__(self,
name,
nodes,
input_tensor_shape,
align_corners=False):
super(ResizeBilinearLayerResolver.Descriptor,
self).__init__('Resize', name, nodes)
self.align_corners = align_corners
self.input_tensor_shape = input_tensor_shape
self.resize_mode = 0
def __init__(self):
self.sequence = GraphSequence(
[ConverterSequenceNode('root', ['ResizeBilinear'])])
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:
resize_op = match['root']
align_corners_bool = resize_op.get_attr(
self.TF_ATTRIBUTE_ALIGN_CORNERS)
input_tensor, _ = GraphHelper.get_op_input_tensors(
resize_op, ('?', '?'))
input_tensor_shape = graph_helper.get_op_output_shape(input_tensor)
consumed_nodes = match.consumed_nodes
descriptors.append(
ResizeBilinearLayerResolver.Descriptor(str(resize_op.name),
consumed_nodes,
input_tensor_shape,
align_corners_bool))
return descriptors
class ReshapeLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes):
super(ReshapeLayerResolver.Descriptor,
self).__init__('Reshape', name, nodes)
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('root', ['Reshape', 'Squeeze', 'ExpandDims'])
])
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:
reshape_op = match['root']
consumed_nodes = match.consumed_nodes
descriptors.append(
ReshapeLayerResolver.Descriptor(str(reshape_op.name),
consumed_nodes))
return descriptors
class PadLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes, paddings, mode, constant_values, output_names=None):
super(PadLayerResolver.Descriptor, self).__init__('Pad', name, nodes, output_names=output_names)
self.paddings = paddings
self.mode = mode
self.constant_values = constant_values
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 resolve_layer(self, graph_matcher, graph_helper):
descriptors = []
for sequence in self.sequences:
for match in graph_matcher.match_sequence(sequence):
pad_op = match['root']
input_op = match['input']
paddings_op = match['paddings']
paddings_tensor = graph_helper.evaluate_tensor_output(paddings_op.outputs[0])
paddings_shape = graph_helper.get_op_output_shape(paddings_op)
input_rank = len(graph_helper.get_op_output_shape(input_op))
if [input_rank, 2] != paddings_shape:
raise ConverterError(code_to_message.get_message(
'ERROR_TF_PAD_INVALUD_PADDINGS')(str([input_rank, 2]), str(paddings_shape)))
if 'const_values' in match:
const_values_op = match['const_values']
const_values = graph_helper.evaluate_tensor_output(const_values_op.outputs[0])
else:
const_values = 0.0
if not np.isscalar(const_values):
raise ConverterError(code_to_message.get_message('ERROR_TF_PAD_CONSTANT_NOT_SCALAR'))
consumed_nodes = match.consumed_nodes
pad_descriptor = PadLayerResolver.Descriptor(
str(pad_op.name), consumed_nodes, paddings_tensor,
snpe.modeltools.PADDING_CONSTANT, const_values,
output_names=[str(pad_op.outputs[0].name)])
descriptors.extend([pad_descriptor])
return descriptors
class GroupedConvolutionLayerResolver(ConvolutionLayerResolver, object):
class Descriptor(ConvolutionLayerResolver.Descriptor):
pass
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 resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
if len(matches) == 0:
return []
descriptors = []
for match in matches:
conv_op = match['conv_op_1']
strides = conv_op.get_attr(self.TF_ATTRIBUTE_STRIDES)
padding = conv_op.get_attr(self.TF_ATTRIBUTE_PADDING)
weights = match['weights']
consumed_nodes = match.consumed_nodes
output_op_nodes_names = [
str(match[node.identifier].outputs[0].name)
for node in self.sequence.output_nodes
]
try:
concat_op = match['concat_op']
concat_op_output_ops = graph_helper.get_op_outputs(concat_op)
bias_op = GraphHelper.filter_single_op_by_type(
concat_op_output_ops, 'BiasAdd')
# need to consume input of bias
biases = self.get_biases(graph_helper, conv_op, bias_op)
consumed_nodes.append(bias_op)
output_op_nodes_names = [str(bias_op.outputs[0].name)]
except OperationNotFoundError:
bias_op = None
biases = np.zeros(weights.outputs[0].get_shape()[-1],
dtype=np.float32)
weights = graph_helper.evaluate_tensor_output(weights.outputs[0])
descriptor = ConvolutionLayerResolver.Descriptor(
str(conv_op.name),
consumed_nodes,
conv_op,
bias_op,
strides,
padding,
weights,
biases,
output_names=output_op_nodes_names)
descriptor.input_ops = [match['a'], match['b']]
descriptors.append(descriptor)
return descriptors