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
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):
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 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 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 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)
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 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
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
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
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 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 EltWiseLayerResolver(LayerResolver, object):
__metaclass__ = ABCMeta
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]),
NonConsumableConverterSequenceNode('input1', ['?']),
NonConsumableConverterSequenceNode('input2', ['?'])
])
self.sequence.set_inputs('root', ['input1', 'input2'])
self.sequence.set_outputs(['root'])
self.sequence_with_identity = GraphSequence([
ConverterSequenceNode('root', [self._op_type]),
ConverterSequenceNode('identity', ['Identity']),
NonConsumableConverterSequenceNode('input1', ['?']),
NonConsumableConverterSequenceNode('input2', ['?'])
])
self.sequence_with_identity.set_inputs('identity', ['root'])
self.sequence_with_identity.set_inputs('root', ['input1', 'input2'])
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', 'Identity']),
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 resolve_layer(self, graph_matcher, graph_helper):
descriptors = []
non_const_input_sequences = [
self.sequence_with_identity, 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)
const_input_sequences = [
self.sequence_with_const_input_and_identity,
self.sequence_with_const_input
]
for sequence in const_input_sequences:
for match in graph_matcher.match_sequence(sequence):
eltwise_op = match['root']
eltwise_descriptor = self._descriptor_class(
self._layer_type, str(eltwise_op.name),
match.consumed_nodes)
descriptors.append(eltwise_descriptor)
const_op = match['const']
const_consumed_ops = [const_op]
while const_op.type == 'Identity':
const_op = const_op.inputs[0].op
const_consumed_ops.append(const_op)
if const_op.type != 'Const':
continue
const_tensor = graph_helper.evaluate_tensor_output(
const_op.outputs[0])
eltwise_shape = graph_helper.get_op_output_shape(eltwise_op)
# Do not broadcast the constant for Sub and RealDiv ops because they support
# dynamic broadcasting during runtime. This if statement should be removed once
# all runtimes support dynamic broadcasting.
if self._op_type in ['Sub', 'RealDiv']:
eltwise_shape = graph_helper.get_op_output_shape(const_op)
if not eltwise_shape:
eltwise_shape = [1]
else:
if len(eltwise_shape) > 4:
eltwise_shape = eltwise_shape[-4:]
if len(eltwise_shape) > 3:
broadcast_shape = eltwise_shape[1:]
else:
broadcast_shape = eltwise_shape
if list(const_tensor.shape) != broadcast_shape:
const_tensor = self._broadcast_tensor(
const_tensor, broadcast_shape)
const_descriptor = ConstantLayerResolver.Descriptor(
str(const_op.name), const_consumed_ops, const_tensor,
eltwise_shape, eltwise_descriptor)
descriptors.append(const_descriptor)
return descriptors
def _broadcast_tensor(self, tensor, shape):
raise ConverterError(
'ElementWise resolver must implement broadcast method.')
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
class StridedSliceLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self,
name,
nodes,
input_shape,
begin,
end,
strides,
begin_mask,
end_mask,
ellipsis_mask,
new_axis_mask,
shrink_axis_mask,
output_names=None):
super(StridedSliceLayerResolver.Descriptor,
self).__init__('StridedSlice',
name,
nodes,
output_names=output_names)
self.input_shape = input_shape
self.begin = begin
self.end = end
self.strides = strides
self.begin_mask = begin_mask
self.end_mask = end_mask
self.ellipsis_mask = ellipsis_mask
self.new_axis_mask = new_axis_mask
self.shrink_axis_mask = shrink_axis_mask
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 resolve_layer(self, graph_matcher, graph_helper):
descriptors = []
for match in graph_matcher.match_sequence(self.sequence):
strided_slice_op = match['root']
input_op = match['input']
if input_op.type == "Const":
continue
begin_op = match['begin']
end_op = match['end']
strides_op = match['strides']
begin_tensor = graph_helper.evaluate_tensor_output(
begin_op.outputs[0])
end_tensor = graph_helper.evaluate_tensor_output(end_op.outputs[0])
strides_tensor = graph_helper.evaluate_tensor_output(
strides_op.outputs[0])
input_tensor = graph_helper.evaluate_tensor_output(
input_op.outputs[0])
begin_shape = graph_helper.get_op_output_shape(begin_op)
end_shape = graph_helper.get_op_output_shape(end_op)
strides_shape = graph_helper.get_op_output_shape(strides_op)
input_shape = graph_helper.get_op_output_shape(input_op)
if begin_shape != end_shape or begin_shape != strides_shape:
raise ConverterError(
code_to_message.get_message(
'ERROR_TF_STRIDED_SLICE_SHAPE_MISMATCH'))
begin_mask = strided_slice_op.get_attr("begin_mask")
end_mask = strided_slice_op.get_attr("end_mask")
ellipsis_mask = strided_slice_op.get_attr("ellipsis_mask")
new_axis_mask = strided_slice_op.get_attr("new_axis_mask")
shrink_axis_mask = strided_slice_op.get_attr("shrink_axis_mask")
consumed_nodes = match.consumed_nodes
pad_descriptor = StridedSliceLayerResolver.Descriptor(
str(strided_slice_op.name),
consumed_nodes,
input_shape,
begin_tensor,
end_tensor,
strides_tensor,
begin_mask,
end_mask,
ellipsis_mask,
new_axis_mask,
shrink_axis_mask,
output_names=[str(strided_slice_op.outputs[0].name)])
descriptors.extend([pad_descriptor])
return descriptors
NonConsumableConverterSequenceNode('stub_39', ['?']),
NonConsumableConverterSequenceNode('stub_40', ['?']),
NonConsumableConverterSequenceNode('stub_41', ['?']),
NonConsumableConverterSequenceNode('stub_42', ['?']),
NonConsumableConverterSequenceNode('stub_43', ['?']),
NonConsumableConverterSequenceNode('stub_44', ['?']),
NonConsumableConverterSequenceNode('stub_45', ['?']),
NonConsumableConverterSequenceNode('stub_46', ['?']),
NonConsumableConverterSequenceNode('stub_47', ['?']),
NonConsumableConverterSequenceNode('stub_48', ['?']),
NonConsumableConverterSequenceNode('stub_49', ['?']),
NonConsumableConverterSequenceNode('stub_50', ['?']),
NonConsumableConverterSequenceNode('stub_51', ['?']),
NonConsumableConverterSequenceNode('stub_52', ['?']),
])
box_decoder_sequence.set_inputs('Postprocessor/Decode/add_3', ['Postprocessor/Decode/add_1', 'Postprocessor/Decode/div_7'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/mul_3', ['Postprocessor/Decode/div_1', 'Postprocessor/Decode/get_center_coordinates_and_sizes/sub'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/add_2', ['Postprocessor/Decode/add', 'Postprocessor/Decode/div_6'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/div_6', ['Postprocessor/Decode/mul_1', 'stub_49'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/div_3', ['Postprocessor/Decode/unstack', 'stub_41'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/sub_1', ['Postprocessor/Decode/add_1', 'Postprocessor/Decode/div_5'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/sub', ['Postprocessor/Decode/add', 'Postprocessor/Decode/div_4'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/unstack', ['Postprocessor/Decode/transpose'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/stack', ['Postprocessor/Decode/sub', 'Postprocessor/Decode/sub_1', 'Postprocessor/Decode/add_2', 'Postprocessor/Decode/add_3'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/transpose_1', ['Postprocessor/Decode/stack', 'stub_52'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/div_5', ['Postprocessor/Decode/mul', 'stub_50'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/div', ['Postprocessor/Decode/unstack', 'stub_47'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/Exp', ['Postprocessor/Decode/div_3'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/get_center_coordinates_and_sizes/add_1', ['Postprocessor/Decode/get_center_coordinates_and_sizes/unstack', 'Postprocessor/Decode/get_center_coordinates_and_sizes/div_1'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/get_center_coordinates_and_sizes/div', ['Postprocessor/Decode/get_center_coordinates_and_sizes/sub_1', 'stub_46'])
box_decoder_sequence.set_inputs('Postprocessor/Decode/div_2', ['Postprocessor/Decode/unstack', 'stub_42'])
class EltWiseLayerResolver(LayerResolver, object):
__metaclass__ = ABCMeta
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 resolve_layer(self, graph_matcher, graph_helper):
descriptors = []
non_const_input_sequences = [self.sequence_with_identity, self.sequence]
for sequence in non_const_input_sequences:
for match in graph_matcher.match_sequence(sequence):
eltwise_op = match['root']
consumed_nodes = match.consumed_nodes
descriptor = self._descriptor_class(self._layer_type, str(eltwise_op.name), consumed_nodes)
descriptors.append(descriptor)
const_input_sequences = [self.sequence_with_const_input_and_identity, self.sequence_with_const_input]
for sequence in const_input_sequences:
for match in graph_matcher.match_sequence(sequence):
eltwise_op = match['root']
const_op = match['const']
const_tensor = graph_helper.evaluate_tensor_output(const_op.outputs[0])
eltwise_shape = graph_helper.get_op_output_shape(eltwise_op)
eltwise_shape = expand_to_rank(eltwise_shape, 3)
if len(eltwise_shape) > 3:
eltwise_shape = eltwise_shape[-3:]
if list(const_tensor.shape) != eltwise_shape:
const_tensor = self._broadcast_tensor(const_tensor, eltwise_shape)
eltwise_descriptor = self._descriptor_class(self._layer_type, str(eltwise_op.name), match.consumed_nodes)
const_descriptor = ConstantLayerResolver.Descriptor(str(const_op.name), [const_op], const_tensor,
eltwise_shape, eltwise_descriptor)
descriptors.extend([eltwise_descriptor, const_descriptor])
return descriptors
def _broadcast_tensor(self, tensor, shape):
raise ConverterError('ElementWise resolver must implement broadcast method.')
class ChannelShuffleLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes, groups, output_names=None):
super(ChannelShuffleLayerResolver.Descriptor,
self).__init__('ChannelShuffle',
name,
nodes,
output_names=output_names)
self.groups = groups
self.shuffle_type = snpe.modeltools.CHANNEL_SHUFFLE_GROUPED
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 resolve_layer(self, graph_matcher, graph_helper):
descriptors = []
for match in graph_matcher.match_sequence(self.sequence):
input_op = match['input']
reshape_out_op = match['reshape_out']
reshape_in_op = match['reshape_in']
transpose_op = match['transpose']
input_shape = graph_helper.get_op_output_shape(input_op)
reshape_in_shape = graph_helper.get_op_output_shape(reshape_in_op)
transpose_shape = graph_helper.get_op_output_shape(transpose_op)
reshape_out_shape = graph_helper.get_op_output_shape(
reshape_out_op)
if len(reshape_in_shape) < 2:
continue
num_channels = input_shape[-1]
num_groups = reshape_in_shape[-2]
num_channels_prime = num_channels / num_groups
if num_channels % num_groups != 0:
continue
is_channel_shuffle = True
# first reshape must divide the channel dimension to [num_groups, num_channels_prime]
is_channel_shuffle &= reshape_in_shape == input_shape[:-1] + [
num_groups, num_channels_prime
]
# transpose must permute the last two dimensions only
is_channel_shuffle &= transpose_shape == input_shape[:-1] + [
num_channels_prime, num_groups
]
# output shape must be equal to the input shape
is_channel_shuffle &= reshape_out_shape == input_shape
if not is_channel_shuffle:
continue
consumed_nodes = match.consumed_nodes
descriptors.append(
ChannelShuffleLayerResolver.Descriptor(
str(reshape_out_op.name),
consumed_nodes,
num_groups,
output_names=[str(reshape_out_op.outputs[0].name)]))
return descriptors
class ScaledBatchNormLayerResolver(BatchNormLayerResolver):
def __init__(self):
self.sequence = GraphSequence([
ConverterSequenceNode('a', ['Add']),
ConverterSequenceNode('b', ['Rsqrt']),
ConverterSequenceNode('c', ['Mul']),
ConverterSequenceNode('d', ['Mul']),
ConverterSequenceNode('e', ['Mul']),
ConverterSequenceNode('f', ['Sub']),
ConverterSequenceNode('g', ['Add']),
ConverterSequenceNode('scale', ['?']),
NonConsumableConverterSequenceNode('input', ['?']),
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', 'scale'])
self.sequence.set_inputs('d', ['c', 'input'])
self.sequence.set_inputs('e', ['c', 'mean'])
self.sequence.set_inputs('f', ['e', 'beta'])
self.sequence.set_inputs('g', ['d', 'f'])
self.sequence.set_outputs(['g'])
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:
variance_op = match['variance']
epsilon_op = match['epsilon']
if variance_op.type not in ['Identity', 'Const']:
raise ConverterError(
code_to_message.get_message(
'ERROR_TF_BATCHNORM_RESOLVE_VARIANCE'))
variance = graph_helper.evaluate_tensor_output(
variance_op.outputs[0])
if epsilon_op.type not in ['Identity', 'Const']:
raise ConverterError(
code_to_message.get_message(
'ERROR_TF_BATCHNORM_RESOLVE_EPSILON'))
epsilon = graph_helper.evaluate_tensor_output(
epsilon_op.outputs[0])
scale_op = match['scale']
if scale_op.type not in ['Identity', 'Const', 'Fill']:
raise ConverterError(
code_to_message.get_message(
'ERROR_TF_BATCHNORM_RESOLVE_SCALE'))
scale = graph_helper.evaluate_tensor_output(scale_op.outputs[0])
mean_op = match['mean']
if mean_op.type not in ['Identity', 'Const']:
raise ConverterError(
code_to_message.get_message(
'ERROR_TF_BATCHNORM_RESOLVE_MEAN'))
mean = graph_helper.evaluate_tensor_output(mean_op.outputs[0])
beta_op = match['beta']
if beta_op.type not in ['Identity', 'Const']:
raise ConverterError(
code_to_message.get_message(
'ERROR_TF_BATCHNORM_RESOLVE_BETA'))
beta = graph_helper.evaluate_tensor_output(beta_op.outputs[0])
output_op_nodes_names = [
str(match[node.identifier].outputs[0].name)
for node in self.sequence.output_nodes
]
descriptors.append(
BatchNormLayerResolver.Descriptor(
str(match['d'].name),
match.consumed_nodes,
bn_mul_op=match['d'],
mean=mean,
variance=variance,
epsilon=epsilon,
scale=scale,
beta=beta,
output_names=output_op_nodes_names))
return descriptors
# 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']),
NonConsumableConverterSequenceNode('any', ['?']),
])
placeholder_with_default_sequence.set_inputs('root', ['any'])
placeholder_with_default_sequence.set_outputs(['root'])
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 FullyConnectedLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self,
name,
nodes,
matmul_op,
bias_op,
weights,
biases,
output_names=None):
super(FullyConnectedLayerResolver.Descriptor,
self).__init__('FullyConnected',
name,
nodes,
output_names=output_names)
self.matmul_op = matmul_op
self.bias_op = bias_op
self.weights = weights
self.biases = biases
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 resolve_layer(self, graph_matcher, graph_helper):
matches = graph_matcher.match_sequence(self.sequence)
if len(matches) == 0:
return []
descriptors = []
for match in matches:
matmul_op = match['matmul_op']
weights_op = match['weights']
if weights_op.type not in ['Identity', 'Const']:
raise ConverterError(
code_to_message.get_message(
'ERROR_TF_MATMUL_RESOLVE_WEIGHTS')(matmul_op.name))
weights = graph_helper.evaluate_tensor_output(
weights_op.outputs[0])
bias_add_op = match['bias_op']
biases_op = match['biases']
if biases_op.type not in ['Identity', 'Const']:
# do we still need this check ?
raise ConverterError(
code_to_message.get_message('ERROR_TF_MATMUL_RESOLVE_BIAS')
(bias_add_op.name))
biases = graph_helper.evaluate_tensor_output(biases_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
]
descriptors.append(
FullyConnectedLayerResolver.Descriptor(
str(matmul_op.name),
consumed_nodes,
matmul_op,
bias_add_op,
weights,
biases,
output_names=output_op_nodes_names))
return 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 PermuteLayerResolver(LayerResolver, object):
class Descriptor(LayerDescriptor):
def __init__(self, name, nodes, order, output_names=None):
super(PermuteLayerResolver.Descriptor, self).__init__('Permute', name, nodes, output_names=output_names)
self.order = order
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 resolve_layer(self, graph_matcher, graph_helper):
descriptors = []
for sequence in self.sequences:
for match in graph_matcher.match_sequence(sequence):
transpose_op = match['root']
input_op = match['input']
order_op = match['order']
order_tensor = graph_helper.evaluate_tensor_output(order_op.outputs[0])
input_shape = graph_helper.get_op_output_shape(input_op)
order_shape = graph_helper.get_op_output_shape(order_op)
input_rank = len(input_shape)
order_rank = len(order_shape)
try:
assert order_rank == 1
for d in range(input_rank):
assert d in order_tensor
except AssertionError:
raise ConverterError(code_to_message.get_message(
'ERROR_TF_PERMUTE_INVALID_ORDER_TENSOR')(str(order_tensor)))
consumed_nodes = match.consumed_nodes
permute_descriptor = PermuteLayerResolver.Descriptor(
str(transpose_op.name), consumed_nodes, order_tensor,
output_names=[str(transpose_op.outputs[0].name)])
descriptors.extend([permute_descriptor])
return descriptors
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']),
ConverterSequenceNode('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul_2', ['Mul']),
NonConsumableConverterSequenceNode('stub_16', ['?']),
NonConsumableConverterSequenceNode('stub_17', ['?']),
NonConsumableConverterSequenceNode('stub_18', ['?']),
NonConsumableConverterSequenceNode('stub_19', ['?']),
NonConsumableConverterSequenceNode('stub_20', ['?']),
NonConsumableConverterSequenceNode('stub_21', ['?']),
NonConsumableConverterSequenceNode('stub_22', ['?']),
NonConsumableConverterSequenceNode('stub_23', ['?']),
])
cell_sequence.set_inputs('rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel/read', ['stub_16'])
cell_sequence.set_inputs('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/Tanh_1',
['rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/add_1'])
cell_sequence.set_inputs('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/Sigmoid',
['rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/add'])
cell_sequence.set_inputs('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/basic_lstm_cell/concat',
['stub_17', 'stub_18', 'stub_19'])
cell_sequence.set_inputs('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/Tanh',
['rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/split'])
cell_sequence.set_inputs('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/add',
['rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/split', 'stub_22'])
cell_sequence.set_inputs('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/basic_lstm_cell/MatMul',
['rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/basic_lstm_cell/concat',
'rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel/read'])
cell_sequence.set_inputs('rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/mul',
['stub_23', 'rnn/rnn/multi_rnn_cell/cell_0/cell_0/basic_lstm_cell/Sigmoid'])
class DilatedDepthwiseConvolutionLayerResolver(ConvolutionLayerResolver, object):
class Descriptor(ConvolutionLayerResolver.Descriptor):
pass
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 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)
weights = np.transpose(weights, [0, 1, 3, 2])
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(np.shape(weights)[-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))
space_to_batch_op = match['space_to_batch']
paddings_op = match['paddings']
paddings_tensor = graph_helper.evaluate_tensor_output(paddings_op.outputs[0])
input_op = conv_op
batch_to_space_op = match['batch_to_space']
crop_op = match['crops']
crops_tensor = graph_helper.evaluate_tensor_output(crop_op.outputs[0])
output_names = [str(conv_op.outputs[0].name)]
if paddings_tensor.any() and not np.array_equal(paddings_tensor, crops_tensor):
paddings_tensor = np.pad(paddings_tensor, ((1, 1), (0, 0)), 'constant')
pad_descriptor = PadLayerResolver.Descriptor(
str(space_to_batch_op.name),
[match['space_to_batch'], match['dilation_sizes'], match['paddings']],
paddings_tensor,
snpe.modeltools.PADDING_CONSTANT,
0.0,
output_names=[str(space_to_batch_op.outputs[0].name)])
descriptors.append(pad_descriptor)
else:
consumed_nodes.extend([space_to_batch_op, paddings_op, match['dilation_sizes']])
input_op = space_to_batch_op
if crops_tensor.any() and not np.array_equal(paddings_tensor, crops_tensor):
crops_tensor = np.pad(crops_tensor, ((1, 1), (0, 0)), 'constant')
offsets = crops_tensor[:, 0]
size = np.array(graph_helper.get_op_output_shape(match['batch_to_space']), dtype=np.int32)
crop_descriptor = CropLayerResolver.Descriptor(
str(match['batch_to_space'].name),
[match['batch_to_space'], match['block_shape_out'], match['crops']],
offsets,
size,
output_names=[str(match['batch_to_space'].outputs[0].name)])
descriptors.append(crop_descriptor)
else:
consumed_nodes.extend([batch_to_space_op, crop_op, match['block_shape_out']])
output_names = output_op_nodes_names
d = ConvolutionLayerResolver.Descriptor(str(conv_op.name), consumed_nodes,
conv_op, bias_op, strides, padding, weights,
biases,
output_names=output_names)
d.groups = graph_helper.get_op_output_shape(space_to_batch_op)[-1]
d.dilationY = int(dilation_sizes[0])
d.dilationX = int(dilation_sizes[1])
d.input_ops = [input_op]
descriptors.append(d)
return descriptors
class GroupedConvolutionLayerResolver(ConvolutionLayerResolver, object):
class Descriptor(ConvolutionLayerResolver.Descriptor):
pass
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'])
def resolve_layer(self, graph_matcher, graph_helper):
descriptors = []
for match in graph_matcher.match_sequence(self.sequence):
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)
for match in graph_matcher.match_sequence(self.sequence_with_strided_slice):
if not match.consumed_nodes:
continue
input_op = match['input']
concat_op = match['concat']
axis_op = match['axis']
conv_ops = self._get_repeatable_op_by_id(match, 'conv')
weight_ops = self._get_repeatable_op_by_id(match, 'weights')
bias_ops = self._get_repeatable_op_by_id(match, 'biases')
bias_add_ops = self._get_repeatable_op_by_id(match, 'bias')
ss_ops = self._get_repeatable_op_by_id(match, 'ss')
input_shape = graph_helper.get_op_output_shape(input_op)
weight_shapes = [graph_helper.get_op_output_shape(weight_op) for weight_op in weight_ops]
ss_strides = [graph_helper.evaluate_tensor_output(ss_strides_op.outputs[0]).tolist()
for ss_strides_op in self._get_repeatable_op_by_id(match, 'ss_strides')]
ss_begins = [graph_helper.evaluate_tensor_output(ss_begin_op.outputs[0]).tolist()
for ss_begin_op in self._get_repeatable_op_by_id(match, 'ss_begin')]
ss_ends = [graph_helper.evaluate_tensor_output(ss_end_op.outputs[0]).tolist()
for ss_end_op in self._get_repeatable_op_by_id(match, 'ss_end')]
bias_add_shapes = [graph_helper.get_op_output_shape(bias_add_op) for bias_add_op in bias_add_ops]
strides = [conv_op.get_attr(self.TF_ATTRIBUTE_STRIDES) for conv_op in conv_ops]
paddings = [conv_op.get_attr(self.TF_ATTRIBUTE_PADDING) for conv_op in conv_ops]
ss_shapes = [graph_helper.get_op_output_shape(ss_op.outputs[0])
for ss_op in ss_ops]
num_groups = len(conv_ops)
axis = graph_helper.evaluate_tensor_output(axis_op.outputs[0])
is_grouped_convolution = True
is_grouped_convolution &= self._elements_are_same(bias_add_shapes)
is_grouped_convolution &= self._elements_are_same(weight_shapes)
is_grouped_convolution &= self._elements_are_same(strides)
is_grouped_convolution &= self._elements_are_same(paddings)
is_grouped_convolution &= self._elements_are_same(ss_shapes)
is_grouped_convolution &= self._elements_are_same(ss_strides)
is_grouped_convolution &= not self._elements_are_same(ss_begins)
is_grouped_convolution &= not self._elements_are_same(ss_ends)
# stride slices must evenly divide the last dimension of input to number of groups
is_grouped_convolution &= ss_shapes[0][-1] * num_groups == input_shape[-1]
# strides must be all ones at all dimensions
is_grouped_convolution &= ss_strides[0] == [1] * len(ss_strides[0])
# concat must be on the last axis in grouped convolution
is_grouped_convolution &= axis == -1 or axis == (len(bias_add_shapes[0]) - 1)
if not is_grouped_convolution:
continue
weight_tensors = [graph_helper.evaluate_tensor_output(weight_op.outputs[0])
for weight_op in weight_ops]
weights = np.concatenate(weight_tensors, axis=-1)
bias_tensors = [graph_helper.evaluate_tensor_output(bias_op.outputs[0])
for bias_op in bias_ops]
biases = np.concatenate(bias_tensors, axis=-1)
descriptor = ConvolutionLayerResolver.Descriptor(
str(concat_op.name), match.consumed_nodes, conv_ops[0], None,
strides[0], paddings[0], weights, biases,
output_names=[str(concat_op.outputs[0].name)])
descriptor.input_ops = ss_ops
descriptor.output_op = concat_op
descriptors.append(descriptor)
return descriptors
@classmethod
def _get_repeatable_op_by_id(cls, match, name):
ops = []
indexed_id = name + '_{}'
i = 1
while indexed_id.format(i) in match:
ops.append(match[indexed_id.format(i)])
i += 1
return ops
@classmethod
def _elements_are_same(cls, array):
return all([element == array[0] for element in array])