def replace_sub_graph(self, graph: Graph, match: dict):
node = match['op']
name = node.soft_get('name', node.id)
assert node.has_valid('axis')
axis = Const(graph, {'name': name + '/axis', 'value': int64_array(node.axis)}).create_node()
gather = Gather(graph, {'name': name}).create_node()
node.in_port(0).get_connection().set_destination(gather.in_port(0))
node.in_port(1).get_connection().set_destination(gather.in_port(1))
axis.out_port(0).connect(gather.in_port(2))
node.out_port(0).get_connection().set_source(gather.out_port(0))
def replace_op(self, graph: Graph, node: Node):
axis = Const(graph, {'value': 0}).create_node()
inputs = [node.in_node(1), # weight
node.in_node(0), # input_ids
axis]
gather = Gather(graph, dict(name=node.name)).create_node(inputs)
return [gather.id]
def reorder_inputs_for_shape_or_slice(op_node: Node, input_port: int,
permute_indices_for_gather: list):
"""
axis and slice permutations are almost the same the only difference is that for slice in general
case permutation depends from slice_rank not from input_rank or output_rank
"""
graph = op_node.graph
data_node = op_node.in_node(input_port)
gather_name = op_node.soft_get('name', op_node.id) + '/ShapeGather'
const = Const(
graph, {
'value': permute_indices_for_gather,
'name': gather_name + '/const',
'need_shape_inference': True
}).create_node_with_data()
axis_const = Const(graph, {
'value': int64_array(0),
'name': gather_name + '/axis'
}).create_node_with_data()
gather = Gather(graph, {
'name': gather_name,
'need_shape_inference': True
}).create_node_with_data([data_node, const, axis_const])
attrs = graph.get_edge_data(data_node.id, op_node.id, key=0).copy()
graph.add_edge(gather.id, op_node.id, **attrs)
graph.remove_edge(data_node.id, op_node.id)
# need to run manually to override output shape value to resolve shape collision for nodes with
# 'correct_data_layout' output port attrs
op_node['need_shape_inference'] = True
def placeholder_scales(self, placeholder: Node):
"""
Helper function to get scales for prior boxes out of input image size:
[1 / im_width, 1 / im_height, 1 / im_width, 1 / im_height]
"""
graph = placeholder.graph
name = placeholder.soft_get('name', placeholder.id)
shape_value = placeholder.soft_get('shape', None)
assert shape_value is not None, \
"[ {} replacer ] Placeholder `{}` should have shape attribute".format(self.replacement_id, name)
assert isinstance(shape_value, np.ndarray), \
"[ {} replacer ] Placeholder `{}` shape attribute should be np.ndarray".format(self.replacement_id, name)
assert shape_value.size == 4, \
"[ {} replacer ] Placeholder `{}` should be 4D. Shape: {}".format(self.replacement_id, name, shape_value)
shape = Shape(graph, {'name': 'input_image_shape'}).create_node()
shape.in_port(0).connect(placeholder.out_port(0))
begin = Const(graph, {'value': int64_array([1])}).create_node()
end = Const(graph, {'value': int64_array([3])}).create_node()
stride = Const(graph, {'value': int64_array([1])}).create_node()
spatial = StridedSlice(graph, {'name': name + '/get_h_w', 'begin_mask': int64_array([1]),
'end_mask': int64_array([1]), 'new_axis_mask': int64_array([0]),
'shrink_axis_mask': int64_array([0]), 'ellipsis_mask': int64_array([0])}).create_node()
spatial.in_port(0).connect(shape.out_port(0))
spatial.in_port(1).connect(begin.out_port(0))
spatial.in_port(2).connect(end.out_port(0))
spatial.in_port(3).connect(stride.out_port(0))
power = Const(graph, {'value': float32_array([-1.])}).create_node()
spatial_scale = Pow(graph, {}).create_node()
spatial_scale.in_port(0).connect(spatial.out_port(0))
spatial_scale.in_port(1).connect(power.out_port(0))
# Power `type_infer` requires inputs to have equal data type
convert_to_fp32 = Cast(graph, {'dst_type': np.float32}).create_node()
spatial_scale.in_port(0).get_connection().insert_node(convert_to_fp32)
order = Const(graph, {'value': int64_array([1, 0])}).create_node()
axis_const = Const(graph, {'value': int64_array(0)}).create_node()
reverse = Gather(graph, {}).create_node()
reverse.in_port(0).connect(spatial_scale.out_port(0))
reverse.in_port(1).connect(order.out_port(0))
axis_const.out_port(0).connect(reverse.in_port(2))
priors_scale_node = Concat(graph, {'axis': 0, 'in_ports_count': 2}).create_node()
priors_scale_node.add_input_port(0, skip_if_exist=True)
priors_scale_node.add_input_port(1, skip_if_exist=True)
priors_scale_node.in_port(0).connect(reverse.out_port(0))
priors_scale_node.in_port(1).connect(reverse.out_port(0))
return priors_scale_node
def get_shape_values_by_indices_node(shape_node: Node,
indices_node: Node) -> Node:
"""
The function returns a node that produces values of the specified indices node of the input node 'shape_node'
:param shape_node: the node of 1D output shape to get elements from
:param indices_node: the node of 1D output shape with the list of element indices to get
:return: node producing required elements of the node
"""
graph = shape_node.graph
axis = Const(graph, {
'value': int64_array(0),
'name': shape_node.name + '/Axis'
}).create_node()
gather_node = Gather(graph, {
'name': shape_node.name + '/Gather'
}).create_node()
shape_node.out_port(0).connect(gather_node.in_port(0))
indices_node.out_port(0).connect(gather_node.in_port(1))
axis.out_port(0).connect(gather_node.in_port(2))
return gather_node
def axis(op_node: Node, port_info: str, input_port: int):
"""
Performs layout change related transformation of the data on the in_port_idx port of op_node.
Translates shape indexes from one layout to another according to inverse permutation
Transformation inserts Gather operation with
permutation as 0-port input data and
actual data to translate as 1-port input indexes of Gather
For example:
NHWC Reduce operation has 0-port input with data of shape [1, 2, 3, 4] and
1-port input with axis indices [0, 1].
After translating such operation to NCHW layout:
0-port input shape = [1, 4, 2, 3]
1-port input axis indices = [0, 2]
"""
graph = op_node.graph
permutation_data_node = get_node_with_permutation(op_node, port_info)
assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
'port_info "{}".'.format(permutation_data_node.id,
op_node.id, port_info)
permutation = permutation_data_node.permutation
if len(permutation.perm) == 0:
return
data_node = op_node.in_node(input_port)
gather_name = op_node.soft_get('name', op_node.id) + '/AxisGather'
const = Const(
graph, {
'value': permutation.inv,
'name': gather_name + '/const',
'need_shape_inference': True
}).create_node_with_data()
axis_const = Const(graph, {
'value': int64_array(0),
'name': gather_name + '/axis'
}).create_node_with_data()
gather = Gather(graph, {
'name': gather_name,
'need_shape_inference': True
}).create_node_with_data([const, data_node, axis_const])
attrs = graph.get_edge_data(data_node.id, op_node.id, key=0).copy()
graph.add_edge(gather.id, op_node.id, **attrs)
graph.remove_edge(data_node.id, op_node.id)
op_node['need_shape_inference'] = True
def replace_pattern(graph: Graph, match: dict):
node = match['op']
if not node.has_port('in', 2) or node.in_port(2).disconnected() or not node.has_and_set('shape_input'):
return
if node.has_valid('layout') and not node.layout.startswith('NC') and graph.graph['layout'] == 'NCHW':
input_shape_rank = len(node.in_port(0).data.get_shape())
permutation = PermuteAttrs.get_nhwc_to_nchw_permutation(input_shape_rank)
data_node = node.in_node(2)
name = node.soft_get('name', node.id) + '/ShapeGather'
const = Const(graph, {'value': permutation.perm, 'name': name + '/Const',
'need_shape_inference': True}).create_node_with_data()
axis_const = Const(graph, {'value': int64_array(0), 'name': name + '/Axis'}).create_node_with_data()
gather = Gather(graph, {'name': name,
'need_shape_inference': True}).create_node_with_data([data_node, const, axis_const])
attrs = graph.get_edge_data(data_node.id, node.id, key=0).copy()
graph.add_edge(gather.id, node.id, **attrs)
graph.remove_edge(data_node.id, node.id)
def extract(cls, node):
Gather.update_node_stat(node,
{'batch_dims': node.pb.attr['batch_dims'].i})
return cls.enabled
def order(op_node: Node, port_info: str, input_port: int):
"""
Performs layout change related transformation of the data on the in_port_idx port of op_node.
Translates ordered shape indexes from one layout to another according to permutation
Transformation inserts two Gather operations
1 Gather reorders data to new layout according to direct permutation:
actual data to translate as 1-port input indexes of Gather and
permutation as 0-port input data
2 Gather translates shape indexes from one layout to another according to inverse permutation
permutation as 0-port input data and
actual data to translate as 1-port input indexes of Gather
For example:
NHWC Transpose operation has 0-port input with data of shape [1, 2, 3, 4] and
1-port input with new order indices [0, 1, 3, 2].
After translating such operation to NCHW layout:
0-port input shape = [1, 4, 2, 3]
1 phase (after first Gather insertion):
1-port input order indices = [0, 2, 1, 3]
2 phase (after second Gather insertion):
1-port input order indices = [0, 3, 2, 1]
"""
graph = op_node.graph
permutation_data_node = get_node_with_permutation(op_node, port_info)
assert permutation_data_node.has_and_set('permutation'), 'Data node "{}" does not have permutation for node {}, ' \
'port_info "{}".'.format(permutation_data_node.id,
op_node.id, port_info)
permutation = permutation_data_node.permutation
if len(permutation.perm) == 0:
return
data_node = op_node.in_node(input_port)
gather_name = op_node.soft_get('name', op_node.id) + '/OrderGather_1'
const = Const(
graph, {
'value': permutation.perm,
'name': gather_name + '/const',
'need_shape_inference': True
}).create_node_with_data()
axis_const = Const(graph, {
'value': int64_array(0),
'name': gather_name + '/axis'
}).create_node_with_data()
gather = Gather(graph, {
'name': gather_name,
'need_shape_inference': True
}).create_node_with_data([data_node, const, axis_const])
gather_1_name = op_node.soft_get('name', op_node.id) + '/OrderGather_2'
const_1 = Const(
graph, {
'value': permutation.inv,
'name': gather_1_name + '/const',
'need_shape_inference': True
}).create_node_with_data()
axis_const_1 = Const(graph, {
'value': int64_array(0),
'name': gather_1_name + '/axis'
}).create_node_with_data()
gather_1 = Gather(graph, {
'name': gather_1_name,
'need_shape_inference': True
}).create_node_with_data([const_1, gather, axis_const_1])
attrs = graph.get_edge_data(data_node.id, op_node.id, key=0).copy()
graph.add_edge(gather_1.id, op_node.id, **attrs)
graph.remove_edge(data_node.id, op_node.id)
op_node['need_shape_inference'] = True
