def replace_op(self, graph: Graph, node: Node):
node_name = node.soft_get('name', node.id)
# broadcast default value to required shape
broadcast_node = Broadcast(graph, {
'name': node_name + '/Broadcast_'
}).create_node()
node.in_port(1).get_connection().set_destination(
broadcast_node.in_port(1))
if not node.in_port(3).disconnected():
node.in_port(3).get_connection().set_destination(
broadcast_node.in_port(0))
else:
broadcast_node.in_port(0).connect(
Const(
graph, {
'name': broadcast_node.name + '/FillValue_',
'value': np.float32(0)
}).create_node().out_port(0))
# update broadcasted tensor with required values at required locations
scatternd_node = ScatterNDUpdate(
graph, {
'name': node_name + '/ScatterNDUpdate_'
}).create_node()
scatternd_node.in_port(0).connect(broadcast_node.out_port(0))
node.in_port(0).get_connection().set_destination(
scatternd_node.in_port(1))
node.in_port(2).get_connection().set_destination(
scatternd_node.in_port(2))
rename_nodes([(node, node_name + "/AbandonedName"),
(scatternd_node, node_name)])
return [scatternd_node.id]
def append_variances(priors_scale_node: Node, variance: list):
graph = priors_scale_node.graph
name = priors_scale_node.name
sp_shape = Shape(graph, {'name': name + '/shape'}).create_node()
priors_scale_node.out_port(0).connect(sp_shape.in_port(0))
begin = Const(graph, {'value': int64_array([-2])}).create_node()
end = Const(graph, {'value': int64_array([-1])}).create_node()
stride = Const(graph, {'value': int64_array([1])}).create_node()
shape_part_for_tiling = StridedSlice(graph, {'name': name + '/get_-2_dim', '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()
sp_shape.out_port(0).connect(shape_part_for_tiling.in_port(0))
begin.out_port(0).connect(shape_part_for_tiling.in_port(1))
end.out_port(0).connect(shape_part_for_tiling.in_port(2))
stride.out_port(0).connect(shape_part_for_tiling.in_port(3))
shape_concat = create_op_node_with_second_input(graph, Concat, int64_array([4]),
{'name': name + '/shape_for_tiling', 'in_ports_count': 2,
'axis': int64_array(0)},
shape_part_for_tiling)
variance = Const(graph, {'name': name + '/variance', 'value': float32_array(variance)}).create_node()
tile = Broadcast(graph, {'name': name + '/variance_tile'}).create_node()
variance.out_port(0).connect(tile.in_port(0))
shape_concat.out_port(0).connect(tile.in_port(1))
reshape_dim = Const(graph, {'value': int64_array([-1, 4])}).create_node()
sp_reshape = Reshape(graph, {'name': name + '/reshape'}).create_node()
sp_reshape.in_port(0).connect(priors_scale_node.out_port(0))
sp_reshape.in_port(1).connect(reshape_dim.out_port(0))
concat = Concat(graph,
{'name': name + '/priors_concat', 'axis': int64_array(0), 'in_ports_count': 2}).create_node()
sp_reshape.out_port(0).connect(concat.in_port(0))
tile.out_port(0).connect(concat.in_port(1))
output_dims = Const(graph, {'value': int64_array([1, 2, -1])}).create_node()
output_node = Reshape(graph, {'name': name + '/3D_priors_wth_variances'}).create_node()
concat.out_port(0).connect(output_node.in_port(0))
output_dims.out_port(0).connect(output_node.in_port(1))
return output_node
def find_and_replace_pattern(self, graph: Graph):
for const_of_shape_node in graph.get_op_nodes(op='ConstantOfShape'):
broadcast_node = Broadcast(graph, {'name': const_of_shape_node.name + '/Broadcast'}).create_node()
const_of_shape_node.in_port(0).get_connection().set_destination(broadcast_node.in_port(1))
broadcast_node.in_port(0).connect(Const(graph, {'name': broadcast_node.name + '/FillValue',
'value': const_of_shape_node.fill_value}
).create_node().out_port(0))
const_of_shape_node.out_port(0).get_connection().set_source(broadcast_node.out_port(0))
def find_and_replace_pattern(self, graph: Graph):
for fill_node in graph.get_op_nodes(op='Fill'):
name = fill_node.soft_get('name', fill_node.id)
broadcast_node = Broadcast(graph, {'name': name + '/Broadcast'}).create_node()
fill_node.in_port(0).get_connection().set_destination(broadcast_node.in_port(1))
fill_node.in_port(1).get_connection().set_destination(broadcast_node.in_port(0))
fill_node.out_port(0).get_connection().set_source(broadcast_node.out_port(0))
for fill_node in graph.get_op_nodes(op='ConstantFill'):
name = fill_node.soft_get('name', fill_node.id)
assert fill_node.has_valid('fill_value')
assert fill_node.has_and_set('input_as_shape')
const = Const(graph, {'value': mo_array(fill_node.fill_value), 'name': name + '/value'}).create_node()
broadcast_node = Broadcast(graph, {'name': name + '/Broadcast'}).create_node()
fill_node.in_port(0).get_connection().set_destination(broadcast_node.in_port(1))
const.out_port(0).connect(broadcast_node.in_port(0))
fill_node.out_port(0).get_connection().set_source(broadcast_node.out_port(0))
def find_and_replace_pattern(self, graph: Graph):
reverse_nodes = graph.get_op_nodes(op='Reverse')
for reverse in reverse_nodes:
reverse_name = reverse.soft_get('name', reverse.id)
assert reverse.in_port(1).disconnected()
assert reverse.has_valid('axis')
in_shape_rank = len(reverse.in_port(0).data.get_shape())
# 1. Add new dimension as batch for rank = 1 to have batch != seq_axis
if in_shape_rank == 1:
unsq_node = create_op_node_with_second_input(graph, Unsqueeze, int64_array([0]),
{'name': reverse_name+"/Unsqueeze"})
reverse.in_port(0).get_source().connect(unsq_node.in_port(0))
new_in = unsq_node.out_port(0)
batch_axis = 0
seq_axis = 1
else:
new_in = reverse.in_port(0).get_source()
seq_axis = reverse['axis']
batch_axis = 0 if seq_axis != 0 else 1
# 2. For ReverseSequence 1-port input is seq_lengths => create this input node as
# shape[seq_axis] broadcasted to shape[batch_axis]
# in ---> ShapeOf ----> Gather(seq_axis) ----> Broadcast----->
# | |
# | -------> Gather(batch_axis)----------|
shape_node = Shape(graph, {'name': reverse_name + "/Shape"}).create_node()
new_in.connect(shape_node.in_port(0))
seq_axis_node = node_to_get_shape_value_of_indices(shape_node, [seq_axis])
batch_node = node_to_get_shape_value_of_indices(shape_node, [batch_axis])
broadcast_node = Broadcast(graph, {'name': reverse_name + "/Broadcast"}).create_node()
broadcast_node.in_port(0).connect(seq_axis_node.out_port(0))
broadcast_node.in_port(1).connect(batch_node.out_port(0))
# 3. Create new ReverseSequence node and reconnect all inputs/outputs to it
rename_node(reverse, reverse_name + '/to_delete')
reverse_sequence = ReverseSequence(graph, {'name': reverse_name, 'seq_axis': seq_axis,
'batch_axis': batch_axis}).create_node()
reverse_sequence.in_port(0).connect(new_in)
reverse_sequence.in_port(1).connect(broadcast_node.out_port(0))
# 4. remove added dimension for rank = 1
if in_shape_rank == 1:
rename_node(reverse_sequence, reverse_name + '/ReverseSequence')
squeeze_node = create_op_node_with_second_input(graph, Squeeze, int64_array([0]),
{'name': reverse_name})
squeeze_node.in_port(0).connect(reverse_sequence.out_port(0))
reverse.out_port(0).get_connection().set_source(squeeze_node.out_port(0))
else:
reverse.out_port(0).get_connection().set_source(reverse_sequence.out_port(0))
# 5. Delete old Reverse node
graph.remove_nodes_from([reverse.id for reverse in reverse_nodes])
def find_and_replace_pattern(self, graph: Graph):
for tf_scatter_nd in graph.get_op_nodes(op='TFScatterND'):
if not tf_scatter_nd.is_in_port_connected(0) or not tf_scatter_nd.is_in_port_connected(1) \
or not tf_scatter_nd.is_in_port_connected(2):
continue
name = tf_scatter_nd.soft_get('name', tf_scatter_nd.soft_get('id'))
indices_port = tf_scatter_nd.in_port(0).get_source()
updates_port = tf_scatter_nd.in_port(1).get_source()
shape_port = tf_scatter_nd.in_port(2).get_source()
# need get type of const type
zero_const = Const(graph, {
'value': int64_array(0.0),
'name': name + '/zero_const'
}).create_node()
# Convert zero value to type of updates node
convert_to_type = ConvertLike(graph, {
'name': name + '/convert_like'
}).create_node()
convert_to_type.in_port(0).connect(zero_const.out_port(0))
convert_to_type.in_port(1).connect(updates_port)
broad_cast_node = Broadcast(graph, {
'name': name + '/broadcast'
}).create_node()
broad_cast_node.in_port(0).connect(convert_to_type.out_port(0))
broad_cast_node.in_port(1).connect(shape_port)
scatter_nd_node = ScatterNDUpdate(graph, {
'name': name + '/replaced'
}).create_node()
scatter_nd_node.in_port(0).connect(broad_cast_node.out_port(0))
scatter_nd_node.in_port(1).connect(indices_port)
scatter_nd_node.in_port(2).connect(updates_port)
rename_nodes([(tf_scatter_nd, name + '/TBD'),
(scatter_nd_node, name)])
tf_scatter_nd.out_port(0).get_connection().set_source(
scatter_nd_node.out_port(0))
tf_scatter_nd.in_port(0).disconnect()
tf_scatter_nd.in_port(1).disconnect()
tf_scatter_nd.in_port(2).disconnect()
def generate_sub_graph(self, graph: Graph, match: SubgraphMatch):
reshape_classes_node = create_op_node_with_second_input(graph, Reshape, int64_array([0, -1]),
dict(name='do_reshape_classes'),
match.single_input_node(1)[0])
initial_priors_node = match.single_input_node(2)[0]
priors_name = initial_priors_node.soft_get('name', initial_priors_node.id)
# model calculates identical prior boxes for each batch, so we take first slice of them
begin = Const(graph, {'value': mo_array([0, 0, 0], dtype=np.int32)}).create_node()
end = Const(graph, {'value': mo_array([1, 0, 0], dtype=np.int32)}).create_node()
stride = Const(graph, {'value': mo_array([1, 1, 1], dtype=np.int32)}).create_node()
priors_node = StridedSlice(graph, {'name': priors_name + '/0_batch_slice',
'begin_mask': int64_array([1, 1, 1]),
'end_mask': int64_array([1, 0, 0]),
'new_axis_mask': int64_array([0]),
'shrink_axis_mask': int64_array([0]),
'ellipsis_mask': int64_array([0])}).create_node()
initial_priors_node.out_port(0).connect(priors_node.in_port(0))
begin.out_port(0).connect(priors_node.in_port(1))
end.out_port(0).connect(priors_node.in_port(2))
stride.out_port(0).connect(priors_node.in_port(3))
placeholders = graph.get_op_nodes(type='Parameter')
assert len(placeholders) == 1, "{} replacer requires model to have one Placeholder, but current model has " \
"{} placeholders".format(self.replacement_id, len(placeholders))
placeholder = placeholders[0]
# scale prior boxes to the [0, 1] interval
node_with_scales_for_prior_boxes = self.placeholder_scales(placeholder)
priors_scale_node = Mul(graph, {'name': 'scale_priors'}).create_node()
broadcast = Broadcast(graph, {'name': 'scales_broadcast'}).create_node()
shape_of_priors = Shape(graph, {'name': 'priors_shape'}).create_node()
priors_node.out_port(0).connect(shape_of_priors.in_port(0))
broadcast.in_port(1).connect(shape_of_priors.out_port(0))
broadcast.in_port(0).connect(node_with_scales_for_prior_boxes.out_port(0))
priors_scale_node.in_port(0).connect(priors_node.out_port(0))
priors_scale_node.in_port(1).connect(broadcast.out_port(0))
try:
variance = match.custom_replacement_desc.custom_attributes['variance']
except:
raise Error('There is no variance attribute in {} replacement config file `custom_attributes`'
''.format(self.replacement_id))
priors = self.append_variances(priors_scale_node, variance)
# calculate prior boxes widths and heights
split_node = create_op_with_const_inputs(
graph, VariadicSplit, {1: int64_array(2), 2: int64_array([1, 1, 1, 1])}, {'out_ports_count': 4},
priors_scale_node)
priors_width_node = Sub(graph, dict(name=split_node.name + '/sub_2-0_')
).create_node([(split_node, 2), (split_node, 0)])
priors_height_node = Sub(graph, dict(name=split_node.name + '/sub_3-1_')
).create_node([(split_node, 3), (split_node, 1)])
# concat weights and heights into a single tensor and multiple with the box coordinates regression values
# WA with 3 Concats instead of 1 for keeping model reshapable
# concat_width_height_node = Concat(graph, {'name': 'concat_priors_width_height', 'axis': -1,
# 'in_ports_count': 4}).create_node(
# [priors_width_node, priors_height_node, priors_width_node, priors_height_node])
concat_1 = Concat(graph, {'name': 'concat_width_height',
'axis': -1, 'in_ports_count': 2}).create_node([priors_width_node, priors_height_node])
concat_2 = Concat(graph, {'name': 'concat_width_height_width',
'axis': -1, 'in_ports_count': 2}).create_node([concat_1, priors_width_node])
concat_width_height_node = Concat(graph, {'name': 'concat_priors_width_height', 'axis': -1, 'in_ports_count': 2}
).create_node([concat_2, priors_height_node])
applied_width_height_regressions_node = Mul(graph, {'name': 'final_regressions'}).create_node(
[concat_width_height_node, match.single_input_node(0)[0]])
# reshape to 2D tensor as Inference Engine Detection Output layer expects
reshape_regression_node = create_op_node_with_second_input(graph, Reshape, int64_array([0, -1]),
dict(name='reshape_regression'),
applied_width_height_regressions_node)
detection_output_op = DetectionOutput(graph, match.custom_replacement_desc.custom_attributes)
# get nms from the original network
iou_threshold = None
nms_nodes = graph.get_op_nodes(op='NonMaxSuppression')
if len(nms_nodes) > 0:
# it is highly unlikely that for different classes NMS has different
# moreover DetectionOutput accepts only scalar values for iou_threshold (nms_threshold)
iou_threshold = nms_nodes[0].in_node(3).value
if iou_threshold is None:
raise Error('During {} `iou_threshold` was not retrieved from RetinaNet graph'.format(self.replacement_id))
detection_output_node = detection_output_op.create_node(
[reshape_regression_node, reshape_classes_node, priors],
dict(name=detection_output_op.attrs['type'], nms_threshold=iou_threshold, clip_after_nms=1, normalized=1,
variance_encoded_in_target=0, background_label_id=1000))
# As outputs are replaced with a postprocessing node, outgoing tensor names are no longer
# correspond to original tensors and should be removed from output->Result edges
out_nodes = []
for out in range(match.outputs_count()):
out_nodes.append(match.output_node(out)[0])
clear_tensor_names_info(out_nodes)
return {'detection_output_node': detection_output_node}
