def make_interpolate_reshape_able(self, interpolate: Node, concat: Node):
assert interpolate.soft_get('type') == 'Interpolate'
assert concat.soft_get('type') == 'Concat'
interp_axes = Interpolate.get_axes(interpolate)
concat_axis = self.get_concat_axis(concat)
if concat_axis is None or interp_axes is None \
or np.any(interp_axes < 0) or concat_axis < 0 \
or concat_axis in interp_axes:
# checks that interpolate axes and concat axis are valid and do not intersect
return
non_interp_concat_srcs = self.get_non_interpolate_concat_sources(
concat)
if not len(non_interp_concat_srcs):
# there is no Concat input to take input from
return
graph = interpolate.graph
src = non_interp_concat_srcs[0]
shape = Shape(graph, {
'name': src.node.soft_get('name', src.node.id) + '/Shape'
}).create_node()
shape.in_port(0).connect(src)
gather = create_op_with_const_inputs(
graph,
Gather, {
1: np.array(interp_axes, dtype=np.int32),
2: int64_array(0)
}, {'name': shape.name + '/Gathered'},
input_node=shape)
interpolate.in_port(1).get_connection().set_source(gather.out_port(0))
def find_and_replace_pattern(self, graph: Graph):
for roll_node in graph.get_op_nodes(op='Roll'):
if not roll_node.in_port(2).disconnected():
return
node_name = roll_node.soft_get('name', roll_node.id)
# reshape to 1d tensor
reshape_to_1d = create_op_node_with_second_input(
graph, Reshape, int64_array([-1]),
{'name': node_name + '/reshape'})
roll_node.in_port(0).get_connection().insert_node(reshape_to_1d)
# add zero const as axes input to roll
const_zero = Const(graph, {
'value': int64_array([0]),
'name': node_name + '/axes'
}).create_node()
const_zero.out_port(0).connect(roll_node.in_port(2))
# reshape to original shape
shape_of = Shape(graph, {
'name': node_name + '/shape_of'
}).create_node()
reshape_to_1d.in_port(0).get_connection().add_destination(
shape_of.in_port(0))
reshape_to_orig_shape = Reshape(graph, {}).create_node()
rename_nodes([(roll_node, node_name + '/roll'),
(reshape_to_orig_shape, node_name)])
shape_of.out_port(0).connect(reshape_to_orig_shape.in_port(1))
roll_node.out_port(0).get_connection().insert_node(
reshape_to_orig_shape)
def extract(cls, node: Node):
Shape.update_node_stat(
node, {
'output_type':
tf_dtype_extractor(node.pb.attr['out_type'].type, np.int32)
})
return cls.enabled
def replace_sub_graph(self, graph: Graph, match: dict):
div_sqrt = match['op']
div_sqrt_name = div_sqrt.soft_get('name', div_sqrt.id)
shape_node = Shape(graph,
dict(name=div_sqrt_name + '/Shape')).create_node()
data_out_port = div_sqrt.in_port(0).get_source()
shape_node.in_port(0).connect(data_out_port)
shape_values_node = node_to_get_shape_value_of_indices(
shape_node=shape_node, indices=[-1])
pow_node = AttributedPower(
graph, dict(name=div_sqrt_name + '/Sqrt',
power=mo_array(0.5))).create_node()
# Due to specification, Power must have inputs with the same data type.
convert_pow_input = Cast(
graph,
dict(dst_type=np.float32,
name=shape_values_node.name +
'/ConvertToFP32')).create_node()
div_node = Div(graph, dict(name="Div")).create_node()
shape_values_node.out_port(0).connect(convert_pow_input.in_port(0))
convert_pow_input.out_port(0).connect(pow_node.in_port(0))
div_sqrt.in_port(0).get_connection().set_destination(
div_node.in_port(0))
div_node.in_port(1).connect(pow_node.out_port(0))
div_sqrt.out_port(0).get_connection().set_source(div_node.out_port(0))
rename_nodes([(div_sqrt, div_sqrt_name + '/ShouldBeDeleted'),
(div_node, div_sqrt_name)])
def replace_sub_graph(self, graph: Graph, match: dict):
node = match['mxreshape']
input_index = 0
reshape_index = 0
shape_node = Shape(graph, dict(name=node.id +
'/ShapeMXReshape')).create_node()
shape_node.in_port(0).connect(node.in_port(0).get_source())
output_dims_nodes = []
for d in node.dim:
if reshape_index < len(node.dim):
input_index, reshape_index, output_dims_nodes = self.resolve(
input_index, reshape_index, node.dim, shape_node,
output_dims_nodes)
concat_node = Concat(
shape_node.graph,
dict(name=shape_node.id + '/ConcatMXReshape_',
axis=0,
in_ports_count=len(output_dims_nodes))).create_node()
for in_port_index, dim_node in enumerate(output_dims_nodes):
concat_node.in_port(in_port_index).connect(dim_node.out_port(0))
reshape_node = Reshape(graph,
dict(name=node.id + '/Reshape_')).create_node()
reshape_node.in_port(1).connect(concat_node.out_port(0))
node.in_port(0).get_connection().set_destination(
reshape_node.in_port(0))
node.out_port(0).get_connection().set_source(reshape_node.out_port(0))
def squeeze_initial_states(graph: Graph, match: dict):
"""
Squeeze input initial states of recurrent node to 2-D shape.
"""
hidden_init_port = 5
cell_init_port = 6
rnn_layer = match['rnn_layer']
# Add input ports to rnn_layer
rnn_layer.add_sequence_of_ports(type='in', rng=range(7))
rnn_layer_name = rnn_layer.soft_get('name', rnn_layer.id)
assert hidden_init_port in rnn_layer.in_nodes()
hidden_size = rnn_layer.hidden_size
shape = Shape(graph, dict(name=rnn_layer_name + '/ShapeOf')).create_node()
rnn_layer.in_port(0).get_source().connect(shape.in_port(0))
batch = node_to_get_shape_value_of_indices(shape, int64_array([rnn_layer.batch_dim]))
new_dim = create_op_node_with_second_input(graph, Concat, second_input_value=int64_array([hidden_size]),
op_attrs=dict(name=rnn_layer_name + '/HiddenStateResizeDim',
in_ports_count=2, axis=0), input_node=batch)
reshape_h = Reshape(graph, dict(name=rnn_layer_name + '/HiddenStateResize', override_output_shape=True)).create_node()
new_dim.out_port(0).connect(reshape_h.in_port(1))
rnn_layer.in_port(hidden_init_port).get_connection().insert_node(reshape_h)
if rnn_layer.op == 'LSTM':
assert cell_init_port in rnn_layer.in_nodes()
reshape_c = Reshape(graph, dict(name=rnn_layer_name + '/CellStateResize', override_output_shape=True)).create_node()
new_dim.out_port(0).connect(reshape_c.in_port(1))
rnn_layer.in_port(cell_init_port).get_connection().insert_node(reshape_c)
def make_interpolate_reshapeable(interpolate, concat):
assert interpolate.soft_get('type') == 'Interpolate'
assert concat.soft_get('type') == 'Concat'
output_shape = interpolate.out_port(0).data.get_shape()
interp_axes = [get_canonical_axis_index(output_shape, axis) for axis in Interpolate.get_axes(interpolate)]
concat_axis = get_canonical_axis_index(output_shape, concat.axis)
if concat_axis in interp_axes:
return
concat_srcs = [port.get_source() for port in concat.in_ports().values() if not port.disconnected()]
non_interp_concat_srcs = [src for src in concat_srcs if src.node.soft_get('type') != 'Interpolate']
if len(non_interp_concat_srcs) == 0:
return
graph = interpolate.graph
src = non_interp_concat_srcs[0]
shape = Shape(graph, {'name': src.node.soft_get('name', src.node.id) + '/Shape'}).create_node()
shape.in_port(0).connect(src)
gather = create_op_with_const_inputs(graph, Gather,
{1: np.array(interp_axes, dtype=np.int32), 2: int64_array(0)},
{'name': shape.name + '/Gathered'}, shape)
interpolate.in_port(1).get_connection().set_source(gather.out_port(0))
def replace_sub_graph(self, graph: Graph, match: dict):
node = match['flatten']
name = node.soft_get('name', node.id)
assert node.has_valid(
'axis'
), 'Flatten {} should have `axis` attribute extracted, but it\'s not'.format(
name)
axis = node.axis
reshape_node = Reshape(graph, {
'name': node.id + '/Reshape'
}).create_node()
if axis == 0:
dim = Const(
graph, {
'value': int64_array([1, -1]),
'name': reshape_node.name + '/shape'
}).create_node()
elif axis == 1:
dim = Const(
graph, {
'value': int64_array([0, -1]),
'name': reshape_node.name + '/shape'
}).create_node()
else:
shape = Shape(graph, {'name': name + '/input_shape'}).create_node()
idxs = list(range(axis)) if axis > 0 else list(range(axis, 0))
axis_shape_portion = node_to_get_shape_value_of_indices(
shape, idxs)
first_dims = create_op_node_with_second_input(
graph, ReduceProd, int64_array([0]), {
'name': name + '/first_dims',
'keep_dims': True
})
second_dims = Const(graph, {
'value': int64_array([-1]),
'name': name + '/second_dims'
}).create_node()
node.in_port(0).get_source().connect(shape.in_port(0))
axis_shape_portion.out_port(0).connect(first_dims.in_port(0))
order_of_dims = [first_dims, second_dims
] if axis > 0 else [second_dims, first_dims]
dim = new_shape_node_from_shape_nodes(order_of_dims)
reshape_node.in_port(1).connect(dim.out_port(0))
node.out_port(0).get_connection().set_source(reshape_node.out_port(0))
node.in_port(0).get_connection().set_destination(
reshape_node.in_port(0))
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 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 replace_pattern(self, graph: Graph, match: dict):
if not self.is_applicable(match):
return
unsqueeze_node = match['unsqueeze']
unsqueeze_name = unsqueeze_node.soft_get('name', unsqueeze_node.id)
second_input_of_unsqueeze = unsqueeze_node.in_port(
1).get_connection().get_source().node
d_idx = int(second_input_of_unsqueeze.value)
axis = d_idx - 1
shape_node = Shape(graph,
dict(name=unsqueeze_name + '/Shape')).create_node()
axis_len_node = node_to_get_shape_value_of_indices(shape_node, [axis])
second_input_of_tile = match['tile'].in_port(
1).get_connection().get_source().node
scale = int64_array([second_input_of_tile.value[d_idx]])
float_scale = float32_array([second_input_of_tile.value[d_idx]])
mul_node = create_op_with_const_inputs(
graph, Mul, {1: scale}, {'name': unsqueeze_name + '/Mul'})
axis_len_node.out_port(0).connect(mul_node.in_port(0))
interp_node = create_op_with_const_inputs(
graph, Interpolate, {
2: float_scale,
3: int64_array([axis])
}, {
'mode': 'nearest',
'antialias': 0,
'pads_begin': int64_array([0]),
'pads_end': int64_array([0]),
'coordinate_transformation_mode': 'half_pixel',
'nearest_mode': 'round_prefer_floor',
'cube_coeff': -0.75,
'version': 'opset4',
'shape_calculation_mode': 'scales',
'in_ports_count': 4,
'maybe_part_of_sequence': True
})
mul_node.out_port(0).connect(interp_node.in_port(1))
reshape_node = match['reshape']
reshape_node.out_port(0).get_connection().set_source(
interp_node.out_port(0))
reshape_name = reshape_node.soft_get('name', reshape_node.id)
rename_nodes([(reshape_node, reshape_name + '/delete'),
(interp_node, reshape_name)])
unsqueeze_connection = unsqueeze_node.in_port(0).get_connection()
unsqueeze_connection.set_destination(interp_node.in_port(0))
unsqueeze_connection.get_source().connect(shape_node.in_port(0))
def replace_interpolate_pattern(graph: Graph, match: dict):
split = match['split']
scale = float32_array([get_split_scale(split)])
axis = int(split.in_port(1).get_connection().get_source().node.value)
split_node_name = split.name
axis_node = Const(graph, {'name': split_node_name + '/axis', 'value': int64_array([axis])}).create_node()
shape_node = Shape(graph, dict(name=split_node_name + '/Shape')).create_node()
scales_node = Const(graph, dict(name=split_node_name + '/scales', value=scale)).create_node()
mul_node = Mul(graph, dict(name=split_node_name + '/Mul')).create_node()
scales_node.out_port(0).connect(mul_node.in_port(1))
strided_slice_node = create_op_with_const_inputs(graph,
StridedSlice,
{1: int64_array([axis]), 2: int64_array([axis + 1])},
{
'name': split_node_name + '/StridedSlice',
'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])
})
shape_node.out_port(0).connect(strided_slice_node.in_port(0))
cast_shape_to_float = Cast(graph, {'dst_type': np.float32}).create_node()
strided_slice_node.out_port(0).connect(cast_shape_to_float.in_port(0))
cast_shape_to_float.out_port(0).connect(mul_node.in_port(0))
interp_node = Interpolate(graph,
dict(name=split_node_name + '/Interpolate',
mode='nearest',
antialias=0, pads_begin=int64_array([0]), pads_end=int64_array([0]),
coordinate_transformation_mode='half_pixel', nearest_mode='round_prefer_floor',
cube_coeff=-0.75, version='opset4', shape_calculation_mode='scales',
in_ports_count=4, maybe_part_of_sequence=True)).create_node()
floor_node = Floor(graph, {'name': split_node_name + '/Floor'}).create_node()
cast_mul_result_to_int = Cast(graph, {'dst_type': np.int64}).create_node()
mul_node.out_port(0).connect(floor_node.in_port(0))
floor_node.out_port(0).connect(cast_mul_result_to_int.in_port(0))
cast_mul_result_to_int.out_port(0).connect(interp_node.in_port(1))
scales_node.out_port(0).connect(interp_node.in_port(2))
axis_node.out_port(0).connect(interp_node.in_port(3))
match['concat'].out_port(0).get_connection().set_source(interp_node.out_port(0))
split_connection = split.in_port(0).get_connection()
split_connection.set_destination(interp_node.in_port(0))
split_connection.get_source().connect(shape_node.in_port(0))
def replace(node: Node, const: Node):
graph = node.graph
shape = const.shape
const_name = const.soft_get('name', const.id)
non_one_dims = np.argwhere(shape != 1).flatten()
one_dims = np.argwhere(shape == 1).flatten()
if not (non_one_dims.size == 1 and 5 < np.prod(shape) < 500):
# (5;500) range is deduced to affect less models
return
value = const.value
if not np.array_equal(np.arange(0, np.prod(shape), 1).reshape(shape), value):
return
positive_idx = non_one_dims.item(0)
negative_idx = positive_idx - len(shape)
node_name = node.soft_get('name', node.id)
gather = create_op_with_const_inputs(graph, Gather, {1: int64_array(negative_idx), 2: int64_array(0)},
{'name': node_name + '/BroadcastingDim'})
gather_for_const = create_op_with_const_inputs(graph, Gather, {1: int64_array(negative_idx), 2: int64_array(0)},
{'name': const_name + '/BroadcastingDim'})
shapeof_node = Shape(graph, {'name': const_name + '/ShapeOf'}).create_node()
shapeof_node.out_port(0).connect(gather_for_const.in_port(0))
equal_node = create_op_with_const_inputs(graph, Equal, {1: int64_array(1)}, {'name': node_name + '/ConstOne'})
gather.out_port(0).connect(equal_node.in_port(0))
select_node = Select(graph, {'name': node_name + '/Select',
'auto_broadcast': 'numpy'}).create_node([equal_node, gather_for_const, gather])
const.out_port(0).connect(shapeof_node.in_port(0))
range_node = create_op_with_const_inputs(graph, Range,
{0: mo_array(0, dtype=value.dtype),
2: mo_array(1, dtype=value.dtype)},
{'name': const_name + '/Range', 'dtype': value.dtype})
select_node.out_port(0).connect(range_node.in_port(1))
node.in_port(1).get_connection().add_destination(gather.in_port(0))
node.in_port(0).get_connection().set_source(range_node.out_port(0))
if one_dims.size:
unsqueeze = create_op_node_with_second_input(graph, Unsqueeze, one_dims,
{'name': const_name + '/KeepShape'})
range_node.out_port(0).get_connection().insert_node(unsqueeze)
rename_nodes([(const, const_name + '/ToBeDeleted'), (unsqueeze, const_name)])
else:
rename_nodes([(const, const_name + '/ToBeDeleted'), (range_node, const_name)])
def find_and_replace_pattern(self, graph: Graph):
for node in graph.get_op_nodes(op='ATen', operator='embedding_bag'):
assert node.soft_get('mode') == 0, 'ATen::embedding_bag has unsupported mode, only "sum" ' \
'mode is supported for node {}.'.format(node.id)
node_name = node.soft_get('name', node.id)
rename_node(node, node_name + '/TBR')
is_packed = False
if len(node.in_ports()) < 3 or node.in_port(2).disconnected():
is_packed = True
embedding_bag = EmbeddingBagPackedSum(graph, {'name': node_name}).create_node()
else:
embedding_bag = EmbeddingBagOffsetsSum(graph, {'name': node_name}).create_node()
node.in_port(2).get_connection().set_destination(embedding_bag.in_port(2))
rename_node(embedding_bag, node_name)
node.in_port(0).get_connection().set_destination(embedding_bag.in_port(0))
node.in_port(1).get_connection().set_destination(embedding_bag.in_port(1))
node.out_port(0).get_connection().set_source(embedding_bag.out_port(0))
if len(node.in_ports()) == 4 and not node.in_port(3).disconnected():
if is_packed:
node.in_port(3).get_connection().set_destination(embedding_bag.in_port(2))
else:
# connect per_sample_weights
node.in_port(3).get_connection().set_destination(embedding_bag.in_port(4))
weights_shape_node = Shape(graph, {'name': node_name + '/WeightsShape'}).create_node()
weights_rank_node = Rank(graph, {'name': node_name + '/WeightsRank'}).create_node()
last_dim_node = get_canonical_axis_index_node(weights_rank_node, -1)
weights_last_dim = get_shape_values_by_indices_node(weights_shape_node, last_dim_node)
weights_first_dim = node_to_get_shape_value_of_indices(weights_shape_node, [0])
zero_col_node = create_op_with_const_inputs(graph, Broadcast, {0: int64_array([0])},
{'name': node_name + '/Broadcast'})
zero_col_node.in_port(1).connect(weights_last_dim.out_port(0))
default_embeddings_node = create_op_with_const_inputs(graph, Unsqueeze, {1: int64_array(0)},
{'name': node_name + '/Unsqueeze'})
default_embeddings_node.in_port(0).connect(zero_col_node.out_port(0))
# expand embedding table with zeros
weights_concat = Concat(graph, {'axis': 0, 'in_ports_count': 2,
'name': node_name + '/Concat'}).create_node()
embedding_bag.in_port(0).get_connection().set_destination(weights_concat.in_port(0))
weights_concat.in_port(0).get_connection().add_destination(weights_shape_node.in_port(0))
weights_concat.in_port(0).get_connection().add_destination(weights_rank_node.in_port(0))
weights_concat.in_port(1).connect(default_embeddings_node.out_port(0))
weights_concat.out_port(0).connect(embedding_bag.in_port(0))
# point default index to expanded part of embedding table
weights_first_dim.out_port(0).connect(embedding_bag.in_port(3))
def resolve_minus2(self, shape_node, input_index, reshape_index, dims):
rank_node = Shape(
shape_node.graph,
dict(name=shape_node.id +
'/RankShapeMXReshapeMinus2')).create_node()
rank_node.in_port(0).connect(shape_node.out_port(0))
shape_values_node = get_shape_values_by_range_idxs(shape=shape_node,
rank=rank_node,
begin=input_index,
end=-1,
include_begin=True,
include_end=True)
input_index = None
reshape_index = reshape_index + 1
return input_index, reshape_index, dims, shape_values_node
def replace_sub_graph(self, graph: Graph, match: dict):
mxreshape = match['op']
if not mxreshape.reverse:
return
shape_node = Shape(graph, dict(name=mxreshape.id + '/Shape')).create_node()
forward_reverse_unsqueeze_node = create_op_node_with_second_input(graph, Unsqueeze, int64_array([0]),
dict(name=str(mxreshape.id) + '/ForwardUnsqueeze'))
forward_reverse_node = Reverse(graph, dict(name=mxreshape.id + '/ForwardReverse', axis=1)).create_node()
forward_reverse_squeeze_node = create_op_node_with_second_input(graph, Squeeze, int64_array([0]),
dict(name=str(mxreshape.id) + '/ForwardSqueeze'))
reshape_node = Reshape(graph, dict(name=mxreshape.id + '/Reshape')).create_node()
shape_node.in_port(0).connect(mxreshape.in_port(0).get_source())
mxreshape.in_port(0).get_connection().set_destination(reshape_node.in_port(0))
forward_reverse_unsqueeze_node.in_port(0).connect(shape_node.out_port(0))
forward_reverse_node.in_port(0).connect(forward_reverse_unsqueeze_node.out_port(0))
forward_reverse_squeeze_node.in_port(0).connect(forward_reverse_node.out_port(0))
reshape_node.in_port(1).connect(forward_reverse_squeeze_node.out_port(0))
reshape_shape_node = create_op_node_with_second_input(graph, Reshape, int64_array(np.flip(mxreshape.dim, 0)),
dict(name=str(mxreshape.id) + '/ReshapeShape'))
if np.sum(np.in1d([-2, -3, -4], mxreshape.dim), axis=0):
reshape_shape_node = MXReshape(graph, dict(name=mxreshape.id + '/Reshape',
dim=int64_array(np.flip(mxreshape.dim, 0)))).create_node()
reshape_shape_node.in_port(0).connect(reshape_node.out_port(0))
backward_shape_node = Shape(graph, dict(name=mxreshape.id + '/BackwardShape')).create_node()
backward_reverse_unsqueeze_node = create_op_node_with_second_input(graph, Unsqueeze, int64_array([0]),
dict(name=str(mxreshape.id) + '/BackwardUnsqueeze'))
backward_reverse_node = Reverse(graph, dict(name=mxreshape.id + '/BackwardReverse', axis=1)).create_node()
backward_reverse_squeeze_node = create_op_node_with_second_input(graph, Squeeze, int64_array([0]),
dict(name=str(mxreshape.id) + '/BackwardSqueeze'))
backward_reshape_node = Reshape(graph, dict(name=mxreshape.id + '/BackwardReshape')).create_node()
backward_shape_node.in_port(0).connect(reshape_shape_node.out_port(0))
backward_reverse_unsqueeze_node.in_port(0).connect(backward_shape_node.out_port(0))
backward_reverse_node.in_port(0).connect(backward_reverse_unsqueeze_node.out_port(0))
backward_reverse_squeeze_node.in_port(0).connect(backward_reverse_node.out_port(0))
backward_reshape_node.in_port(0).connect(reshape_shape_node.out_port(0))
backward_reshape_node.in_port(1).connect(backward_reverse_squeeze_node.out_port(0))
mxreshape.out_port(0).get_connection().set_source(backward_reshape_node.out_port(0))
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 make_interpolate_reshapeable(interpolate):
assert interpolate.soft_get('type') == 'Interpolate'
axes = Interpolate.get_axes(interpolate)
input_shape = interpolate.in_port(0).data.get_shape()
output_shape = interpolate.out_port(0).data.get_shape()
if not np.all(np.remainder(output_shape, input_shape) == 0) and \
not np.all(np.remainder(input_shape, output_shape) == 0):
return
graph = interpolate.graph
name = interpolate.soft_get('name', interpolate.id)
shape = Shape(graph, {'name': name + '/ShapeOf'}).create_node()
shape.in_port(0).connect(interpolate.in_port(0).get_source())
gather = create_op_with_const_inputs(graph, Gather, {1: np.array(axes, dtype=np.int32), 2: int64_array(0)},
{'name': shape.name + '/Gathered'}, shape)
multipliers = output_shape[axes] / input_shape[axes]
mul = create_op_node_with_second_input(graph, Mul, multipliers, {'name': gather.name + '/Multiplied'}, gather)
interpolate.in_port(1).get_connection().set_source(mul.out_port(0))
def replace_sub_graph(self, graph: Graph, match: dict):
tf_slice_node = match['op']
slice_name = tf_slice_node.soft_get('name', tf_slice_node.id)
slice_node = Slice(graph).create_node()
rename_nodes([(tf_slice_node, slice_name + '/to_be_removed'),
(slice_node, slice_name)])
ends_node = Add(graph, {'name': slice_name + '/ends'}).create_node()
# reconnect input, begin, and size from TFSlice to the subgraph with Slice
tf_slice_node.in_port(0).get_connection().set_destination(
slice_node.in_port(0))
tf_slice_node.in_port(1).get_connection().set_destination(
slice_node.in_port(1))
tf_slice_node.in_port(2).get_connection().set_destination(
ends_node.in_port(0))
slice_node.in_port(1).get_connection().add_destination(
ends_node.in_port(1))
max_ends = Shape(graph, {
'name': slice_name + '/ShapeOf'
}).create_node()
slice_node.in_port(0).get_connection().add_destination(
max_ends.in_port(0))
# check if size[i] == -1, will be applied elementwisely: len(size) = len(begin) = input_rank
where_max_ends_is_needed = create_op_with_const_inputs(
graph, Equal, {0: int64_array(-1)},
{'name': slice_name + '/where_max_ends_is_needed'})
ends_node.in_port(0).get_connection().add_destination(
where_max_ends_is_needed.in_port(1))
# select requires equal dtypes, need to convert ends to I64
ends_casted_to_i64 = Cast(graph, {
'name': slice_name + '/CastToI64',
'dst_type': np.int64
}).create_node([ends_node])
# if size[i] == 1 then take max_ends values
correct_ends = Select(graph, {
'name': slice_name + '/chosen_ends'
}).create_node(
[where_max_ends_is_needed, max_ends, ends_casted_to_i64])
correct_ends.out_port(0).connect(slice_node.in_port(2))
tf_slice_node.out_port(0).get_connection().set_source(
slice_node.out_port(0))
def replace_sub_graph(self, graph: Graph, match: dict):
node = match['flatten']
name = node.soft_get('name', node.id)
assert node.has_valid('axis'), 'Flatten {} has no mandatory `axis` attribute'.format(name)
assert node.has_valid('end_axis'), 'Flatten {} has no mandatory `end_axis` attribute'.format(name)
axis = node.axis
end_axis = node.end_axis
if end_axis == -1 and axis >= 0:
begin_dims = Const(graph, {'value': int64_array([0] * axis)}).create_node()
middle_dim = Const(graph, {'value': int64_array([-1])}).create_node()
end_dims = Const(graph, {'value': int64_array([])}).create_node()
else:
rank = Rank(graph, {'name': name + '/input_rank'}).create_node()
node.in_port(0).get_source().connect(rank.in_port(0))
shape = Shape(graph, {'name': name + '/input_shape'}).create_node()
node.in_port(0).get_source().connect(shape.in_port(0))
begin_dims = get_shape_values_by_range_idxs(
shape=shape, rank=rank, begin=0, end=axis)
middle_dims = get_shape_values_by_range_idxs(
shape=shape, rank=rank, begin=axis, end=end_axis, include_end=True)
end_dims = get_shape_values_by_range_idxs(
shape=shape, rank=rank, begin=end_axis, end=-1, include_begin=False, include_end=True)
middle_dim = create_op_node_with_second_input(graph, ReduceProd, int64_array([0]), {'keep_dims': True})
middle_dims.out_port(0).connect(middle_dim.in_port(0))
dim = new_shape_node_from_shape_nodes([begin_dims, middle_dim, end_dims])
original_name = node.soft_get('name')
abandoned_name = original_name + '/ShouldBeDeleted'
reshape_node = Reshape(graph, {}).create_node()
# Keep node with the same name to avoid confuse with renaming
rename_nodes([(node, abandoned_name), (reshape_node, original_name)])
reshape_node.in_port(1).connect(dim.out_port(0))
node.out_port(0).get_connection().set_source(reshape_node.out_port(0))
node.in_port(0).get_connection().set_destination(reshape_node.in_port(0))
def replace_op(self, graph: Graph, node: Node):
node_name = node.soft_get('name', node.id)
assert node.has_valid(
'axis'
), 'The node "{}" does not have mandatory attribute "axis"'.format(
node_name)
flatten_node = FlattenONNX(graph, {
'name': node_name + '/FlattenONNX_',
'axis': node.axis
}).create_node()
shape_node = Shape(graph, {
'name': node_name + '/ShapeOf_'
}).create_node()
logsoftmax_node = LogSoftmax(graph, {
'name': node_name + '/LogSoftmax_',
'axis': 1
}).create_node()
reshape_node = Reshape(graph, {}).create_node()
rename_nodes([(node, node_name + '/delete'),
(reshape_node, node_name)])
shape_node.out_port(0).connect(reshape_node.in_port(1))
logsoftmax_node.out_port(0).connect(reshape_node.in_port(0))
flatten_node.out_port(0).connect(logsoftmax_node.in_port(0))
source = node.in_port(0).get_source()
flatten_node.in_port(0).connect(source)
shape_node.in_port(0).connect(source)
return [reshape_node.id]
def replace_pattern(self, graph: Graph, match: dict):
matmul = match['matmul']
reshape = match['reshape']
other_input_port_idx = 0 if match['matmul'].in_port(
0).get_source().node.id == match['other_input'].id else 1
shape_source = match['matmul'].in_port(
other_input_port_idx).get_source()
initial_reshape_pattern = reshape.in_port(1).data.get_value()
if len(initial_reshape_pattern) != 2:
return
reshape_is_A_input = matmul.in_port(
0).get_source().node.id == reshape.id
if reshape_is_A_input:
idx = -1 if matmul.transpose_b else -2
else:
idx = -2 if matmul.transpose_a else -1
idx = get_canonical_axis_index(initial_reshape_pattern, idx)
shape_name = shape_source.node.soft_get('name', shape_source.node.id)
shape = Shape(graph, {'name': shape_name + '/Shape'}).create_node()
shape.in_port(0).connect(shape_source)
C = node_to_get_shape_value_of_indices(shape, [idx])
N = Const(graph, {
'name': shape_name + '/MinusOne',
'value': int64_array([-1])
}).create_node()
if len(initial_reshape_pattern) == 2:
if reshape_is_A_input:
reshape_pattern = [C, N] if matmul.transpose_a else [N, C]
else:
reshape_pattern = [N, C] if matmul.transpose_b else [C, N]
new_reshape_pattern = new_shape_node_from_shape_nodes(
reshape_pattern)
reshape.in_port(1).get_connection().set_source(
new_reshape_pattern.out_port(0))
else:
return
def get_shape_and_rank_nodes_by_port(port: Port,
return_as_a_scalar: bool = True):
"""
The function returns nodes producing shape and rank of the data from the desired port in order to use those
operations on the middle/back phase
:param port: Port object that specifies node output port
:param return_as_a_scalar: boolean flag to return 1D or 0D rank
:return: shape and rank nodes
"""
input_node_name = port.node.soft_get('name', port.node.id)
graph = port.node.graph
shape = Shape(graph, dict(name=input_node_name + '/ShapeOf')).create_node()
rank_1_d = Shape(graph,
dict(name=input_node_name + '/1dRankOf')).create_node()
rank_1_d.in_port(0).connect(shape.out_port(0))
shape.in_port(0).connect(port)
if not return_as_a_scalar:
return shape, rank_1_d
rank = create_op_node_with_second_input(
graph, Squeeze, int64_array([0]),
{'name': input_node_name + '/0dRankOf'}, rank_1_d)
return shape, rank
def replace_sub_graph(self, graph: Graph, match: dict):
node = match['op']
name = node.soft_get('name', node.id)
assert node.has_valid('output_type'), \
'Rank node should have `output_type` attribute, but it`s not for node {}'.format(name)
shape_of = Shape(graph, {
'name': name + '/shape_of',
'output_type': node.output_type
}).create_node()
rank_1d = Shape(graph, {
'name': name + '/rank_of',
'output_type': node.output_type
}).create_node()
rank_0d = create_op_node_with_second_input(
graph, Squeeze, int64_array(0), {'name': name + '/0d_rank_of'},
rank_1d)
shape_of.out_port(0).connect(rank_1d.in_port(0))
node.out_port(0).get_connection().set_source(rank_0d.out_port(0))
node.in_port(0).get_connection().set_destination(shape_of.in_port(0))
rename_nodes([(node, name + '/ToBeDeleted'), (rank_0d, name)])
def replace_pattern(graph: Graph, match: Dict[str, Node]):
node = match['op']
name = node.soft_get('name', node.id)
input_shape = node.in_port(0).data.get_shape()
second_input_shape = node.in_port(1).data.get_shape()
begin_mask = np.zeros(len(input_shape), dtype=np.int64)
end_mask = np.zeros(len(input_shape), dtype=np.int64)
for i in node.axes:
end_mask[i] = np.int64(1)
new_axis_mask = np.zeros(len(input_shape), dtype=np.int64)
shrink_axis_mask = np.zeros(len(input_shape), dtype=np.int64)
ellipsis_mask = np.zeros(len(input_shape), dtype=np.int64)
ss = create_op_with_const_inputs(graph, StridedSlice,
port_value_dict={1: np.zeros(len(input_shape), dtype=np.int64)},
op_attrs={'name': 'StridedSlice', 'begin_mask': begin_mask,
'end_mask': end_mask, 'new_axis_mask': new_axis_mask,
'shrink_axis_mask': shrink_axis_mask,
'ellipsis_mask': ellipsis_mask})
if input_shape.size == second_input_shape.size:
end = Shape(graph, dict(name=name + '/End')).create_node()
end.in_port(0).connect(node.in_port(1).get_source())
ss.in_port(2).connect(end.out_port(0))
else:
shape_like, rank_like = get_shape_and_rank_nodes_by_port(node.in_port(1).get_source())
end_first_part = get_shape_values_by_range_idxs(shape_like, rank_like, 0, node.axes[-1], include_end=True)
if input_shape.size - 1 == node.axes[-1]:
ss.in_port(2).connect(end_first_part.out_port(0))
else:
shape, rank = get_shape_and_rank_nodes_by_port(node.in_port(0).get_source())
end_second_part = get_shape_values_by_range_idxs(shape, rank, node.axes[-1], -1, include_begin=False,
include_end=True)
end = new_shape_node_from_shape_nodes([end_first_part, end_second_part])
ss.in_port(2).connect(end.out_port(0))
node.in_port(0).get_connection().set_destination(ss.in_port(0))
node.in_port(1).disconnect()
node.out_port(0).get_connection().set_source(ss.out_port(0))
rename_nodes([(node, name + '/ShouldBeDeleted'), (ss, name)])
def find_and_replace_pattern(self, graph: Graph):
for node in graph.get_op_nodes(op='SpaceToBatch') + graph.get_op_nodes(
op='BatchToSpace'):
node.add_input_port(3, skip_if_exist=True)
# convert TF representation of the pads/crops as [N, 2] to IE representation: [N] and [N]
transposed_pads = create_op_with_const_inputs(
graph, Transpose, {1: int64_array([1, 0])})
node.in_port(2).get_connection().set_destination(
transposed_pads.in_port(0))
split_pads = create_op_with_const_inputs(graph, Split,
{1: int64_array(0)},
{'num_splits': 2})
transposed_pads.out_port(0).connect(split_pads.in_port(0))
for port_ind in range(2):
node.in_port(port_ind + 2).connect(
split_pads.out_port(port_ind))
node.in_port(port_ind + 2).get_connection().insert_node(
create_op_with_const_inputs(graph, Squeeze,
{1: int64_array([0])}))
# add zeros/ones to related inputs to align it with data input
in0_rank = Rank(graph, {
'name': node.name + '/rank_0'
}).create_node()
in1_shape = Shape(graph, {
'name': node.name + '/rank_1'
}).create_node()
diff_size = Sub(graph, {
'name': node.name + '/sub_0'
}).create_node()
diff = Sub(graph, {'name': node.name + '/sub_1'}).create_node()
const_begin = Const(graph, {
'value': int64_array([1])
}).create_node()
const_pad_val = Const(graph, {
'value': int64_array(1)
}).create_node()
block_shape = Pad(graph, {
'name': node.name + '/aligned_block_shape',
'mode': 'constant'
}).create_node()
# in case of SpaceToBatch begin = pads_begin, end = pads_end
# in case of BatchToSpace begin = crops_begin, end = crops_end
new_begin_name = '/aligned_pads_begin'
new_end_name = '/aligned_pads_end'
if node.type == 'BatchToSpace':
new_begin_name = '/aligned_crops_begin'
new_end_name = '/aligned_crops_end'
begin = Pad(graph, {
'name': node.name + new_begin_name,
'mode': 'constant'
}).create_node()
end = Pad(graph, {
'name': node.name + new_end_name,
'mode': 'constant'
}).create_node()
in0_rank_1d = create_op_node_with_second_input(
graph, Unsqueeze, int64_array([0]),
{'name': node.name + '/1d_rank_of_0'}, in0_rank)
node.in_port(0).get_source().connect(in0_rank.in_port(0))
node.in_port(1).get_source().connect(in1_shape.in_port(0))
in0_rank_1d.out_port(0).connect(diff_size.in_port(0))
in1_shape.out_port(0).connect(diff_size.in_port(1))
diff_size.out_port(0).connect(diff.in_port(0))
const_begin.out_port(0).connect(diff.in_port(1))
const_pad_val.out_port(0).connect(block_shape.in_port(3))
inputs_array = [block_shape, begin, end]
for idx, input_to_node in enumerate(inputs_array):
name_of_input_to_node = input_to_node.name
node.in_port(idx + 1).get_connection().set_destination(
input_to_node.in_port(0))
const_begin.out_port(0).connect(input_to_node.in_port(1))
diff.out_port(0).connect(input_to_node.in_port(2))
input_to_node.out_port(0).connect(node.in_port(idx + 1))
convert = Cast(graph, {
'name': name_of_input_to_node + '/i64',
'dst_type': np.int64
}).create_node()
input_to_node.in_port(0).get_connection().insert_node(convert)
def replace_sub_graph(self, graph: Graph, match: dict):
node = match['op']
if 1 not in node.in_ports() or node.in_port(1).disconnected():
if node.has_valid('factor') and not node.has_valid('width') and not node.has_valid('height'):
factor = Const(graph, {'value': np.array(node.factor)}).create_node()
shape = Shape(graph, {'name': node.name + '/shape'}).create_node()
begin = Const(graph, {'value': np.array([2])}).create_node()
end = Const(graph, {'value': np.array([4])}).create_node()
stride = Const(graph, {'value': np.array([1])}).create_node()
ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': np.array([1]),
'end_mask': np.array([0]), 'new_axis_mask': np.array([0]),
'shrink_axis_mask': np.array([0]),
'ellipsis_mask': np.array([0])}).create_node()
mul = Mul(graph, {'name': node.name + '/factor_mul_'}).create_node()
source = node.in_port(0).get_connection().get_source()
source.connect(shape.in_port(0))
shape.out_port(0).connect(ss.in_port(0))
begin.out_port(0).connect(ss.in_port(1))
end.out_port(0).connect(ss.in_port(2))
stride.out_port(0).connect(ss.in_port(3))
ss.out_port(0).connect(mul.in_port(0))
factor.out_port(0).connect(mul.in_port(1))
node.add_input_port(1, skip_if_exist=True)
assert node.in_port(1).disconnected()
mul.out_port(0).connect(node.in_port(1))
else:
shape = Shape(graph, {'name': node.name + '/shape'}).create_node()
begin = Const(graph, {'value': np.array([2])}).create_node()
end = Const(graph, {'value': np.array([4])}).create_node()
stride = Const(graph, {'value': np.array([1])}).create_node()
ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': np.array([1]),
'end_mask': np.array([0]), 'new_axis_mask': np.array([0]),
'shrink_axis_mask': np.array([0]),
'ellipsis_mask': np.array([0])}).create_node()
source = node.in_port(0).get_connection().get_source()
source.connect(shape.in_port(0))
shape.out_port(0).connect(ss.in_port(0))
begin.out_port(0).connect(ss.in_port(1))
end.out_port(0).connect(ss.in_port(2))
stride.out_port(0).connect(ss.in_port(3))
pads_value = node.pads_begin + node.pads_end
pads_const = Const(graph, {'value': np.array(pads_value)}).create_node()
add = Add(graph, {'name': node.name + '/pad_add'}).create_node()
ss.out_port(0).connect(add.in_port(0))
add.in_port(1).connect(pads_const.out_port(0))
if node.soft_get('shrink_factor') != 1 and node.soft_get('zoom_factor') == 1:
shrink_factor = node.shrink_factor
if shrink_factor < 1:
log.error('Shrink factor should be positive in node {}'.format(node.id))
return None
const = Const(graph, {'name': node.name + '/pre_shrink_sub_const',
'value': np.array(-1)}).create_node()
sub = Add(graph, {'name': node.name + '/pre_shrink_sub'}).create_node()
add.out_port(0).connect(sub.in_port(0))
sub.in_port(1).connect(const.out_port(0))
const = Const(graph, {'value': np.array(1 / shrink_factor),
'name': node.name + 'shrink_factor_div_const'}).create_node()
div = Mul(graph, {'name': node.name + 'shrink_factor_div'}).create_node()
sub.out_port(0).connect(div.in_port(0))
div.in_port(1).connect(const.out_port(0))
const = Const(graph, {'name': node.name + '/shrink_factor_add_one_const', 'value': np.array(1)
}).create_node()
add = Add(graph, {'name': node.name + '/shrink_factor_add_one'}).create_node()
div.out_port(0).connect(add.in_port(0))
const.out_port(0).connect(add.in_port(1))
node.add_input_port(1, skip_if_exist=True)
assert node.in_port(1).disconnected()
add.out_port(0).connect(node.in_port(1))
elif node.soft_get('shrink_factor') == 1 and node.soft_get('zoom_factor') != 1:
zoom_factor = node.zoom_factor
if zoom_factor < 1:
log.error('Zoom factor should be positive in node {}'.format(node.id))
return None
node['debug_message'] = 'Interpolate layer replacer may be wrong, please, try to update it in the' \
' file (openvino/tools/mo/front/InterpolateNormalizer.py at the line {}).' \
''.format(inspect.currentframe().f_lineno) + refer_to_faq_msg(100)
# Reshape methods can be different in some cases
# Commented out section represents reshape that used in deeplab-caffe
# Uncomment the following lines, if your model was trained with deeplab-caffe
# or have the same reshape method
# const = Const(graph, {'value': np.array(-1),
# 'name': node.name + 'zoom_factor_deeplab-caffe_sub_const'}).create_node()
# sub = Add(graph, {'name': node.name + 'zoom_factor_deeplab-caffe_sub'}).create_node()
# add.out_port(0).connect(sub.in_port(0))
# const.out_port(0).connect(sub.in_port(1))
#
# const = Const(graph, {'value': np.array(zoom_factor - 1),
# 'name': node.name + 'zoom_factor_deeplab-caffe_mul_const'}).create_node()
# mul = Mul(graph, {'name': node.name + 'zoom_factor_deeplab-caffe_mul'}).create_node()
# sub.out_port(0).connect(mul.in_port(0))
# const.out_port(0).connect(mul.in_port(1))
#
# sum = Add(graph, {'name': node.name + 'zoom_factor_deeplab-caffe_sum'}).create_node()
# add.out_port(0).connect(sum.in_port(0))
# mul.out_port(0).connect(sum.in_port(1))
#
# node.add_input_port(1, skip_if_exist=True)
# assert node.in_port(1).disconnected()
# sum.out_port(0).connect(node.in_port(1))
# Comment out the following lines if you use the reshape method from previous section
const = Const(graph, {'value': np.array(zoom_factor),
'name': node.name + '/zoom_factor_mul_const'}).create_node()
mul = Mul(graph, {'name': node.name + '/zoom_factor_mul'}).create_node()
add.out_port(0).connect(mul.in_port(0))
const.out_port(0).connect(mul.in_port(1))
node.add_input_port(1, skip_if_exist=True)
assert node.in_port(1).disconnected()
mul.out_port(0).connect(node.in_port(1))
elif node.soft_get('width') != 0 and node.soft_get('height') != 0:
const = Const(graph, {'value': np.array([node.height, node.width])}).create_node()
node.add_input_port(1, skip_if_exist=True)
assert node.in_port(1).disconnected()
const.out_port(0).connect(node.in_port(1))
elif node.soft_get('shrink_factor') != 1 and node.soft_get('zoom_factor') != 1:
shrink_factor = node.shrink_factor
zoom_factor = node.zoom_factor
if shrink_factor < 1:
log.error('Shrink factor should be positive in node {}'.format(node.id))
return None
if zoom_factor < 1:
log.error('Zoom factor should be positive in node {}'.format(node.id))
return None
const = Const(graph, {'value': np.array(-1)}).create_node()
sub = Add(graph, {'name': node.name + '/shrink_zoom_factor_sub'}).create_node()
add.out_port(0).connect(sub.in_port(0))
const.out_port(0).connect(sub.in_port(1))
const = Const(graph, {'value': np.array(1 / (shrink_factor + 1))}).create_node()
div = Mul(graph, {'name': node.name + '/shrink_factor_div'}).create_node()
sub.out_port(0).connect(div.in_port(0))
const.out_port(0).connect(div.in_port(1))
const = Const(graph, {'value': np.array(-1),
'name': node.name + 'shrink_zoom_factor_sum_const'}).create_node()
sum = Add(graph, {'name': node.name + '/shrink_zoom_factor_sum'}).create_node()
div.out_port(0).connect(sum.in_port(0))
const.out_port(0).connect(sum.in_port(1))
const = Const(graph, {'value': np.array(zoom_factor - 1)}).create_node()
mul = Mul(graph, {'name': node.name + '/zoom_factor_mul'}).create_node()
sum.out_port(0).connect(mul.in_port(0))
const.out_port(0).connect(mul.in_port(1))
sum = Add(graph, {'name': node.name + '/final_shrink_zoom_factor_sum'}).create_node()
div.out_port(0).connect(sum.in_port(0))
mul.out_port(0).connect(sum.in_port(1))
node.add_input_port(1, skip_if_exist=True)
assert node.in_port(1).disconnected()
sum.out_port(0).connect(node.in_port(1))
else:
if node.soft_get('fw') == 'caffe':
shape = Shape(graph, {'name': node.name + '/shape'}).create_node()
begin = Const(graph, {'value': np.array([2])}).create_node()
end = Const(graph, {'value': np.array([4])}).create_node()
stride = Const(graph, {'value': np.array([1])}).create_node()
ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': np.array([1]),
'end_mask': np.array([0]), 'new_axis_mask': np.array([0]),
'shrink_axis_mask': np.array([0]),
'ellipsis_mask': np.array([0])}).create_node()
source = node.in_port(1).get_connection().get_source()
node.in_port(1).disconnect()
source.connect(shape.in_port(0))
shape.out_port(0).connect(ss.in_port(0))
begin.out_port(0).connect(ss.in_port(1))
end.out_port(0).connect(ss.in_port(2))
stride.out_port(0).connect(ss.in_port(3))
ss.out_port(0).connect(node.in_port(1))
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}
def replace_sub_graph(self, graph: Graph, match: dict):
node = match['op']
name = node.soft_get('name', node.id)
axis = node.axis
input_shape_node = Shape(graph, {
'name': name + '/ShapeOf'
}).create_node()
range_node = create_op_with_const_inputs(graph, Range, {
0: mo_array(node.start),
2: mo_array(node.step)
}, {'name': name + '/Range'})
node.in_port(0).get_connection().set_destination(
input_shape_node.in_port(0))
if axis is not None:
'''
Replace arange_like op to subgraph:
Shape - Gather - Range
'''
gather_node = create_op_with_const_inputs(graph, Gather, {
1: int64_array([axis]),
2: int64_array(0)
}, {'name': name + '/Gather'})
input_shape_node.out_port(0).connect(gather_node.in_port(0))
gather_node.out_port(0).connect(range_node.in_port(1))
node.out_port(0).get_connection().set_source(
range_node.out_port(0))
rename_nodes([(node, name + '/ShouldBeDeleted'),
(range_node, name)])
else:
r'''
Replace arange_like op to subgraph:
|
ShapeOf ----------- |
| |
ReduceProd |
| |
Range |
| |
Reshape ----------- |
|
'''
flattened_shape_node = create_op_with_const_inputs(
graph, ReduceProd, {1: int64_array([0])}, {
'name': input_shape_node.name + '/ReduceProd',
'keep_dims': True
})
reshape_backward_node = Reshape(graph, {
'name': name + '/Reshape_backward'
}).create_node()
input_shape_node.out_port(0).connect(
flattened_shape_node.in_port(0))
flattened_shape_node.out_port(0).connect(range_node.in_port(1))
range_node.out_port(0).connect(reshape_backward_node.in_port(0))
input_shape_node.out_port(0).connect(
reshape_backward_node.in_port(1))
node.out_port(0).get_connection().set_source(
reshape_backward_node.out_port(0))
rename_nodes([(node, name + '/ShouldBeDeleted'),
(reshape_backward_node, name)])
if node.repeat != 1:
r"""
First, we generate the correct stop value for Range like new_stop_value = stop_value // repeat + 1.
Then repeats each value of the interval using Tile. After that we can get a longer interval
so we reduce it with Slice.
Sub-graph after Range node will be look like
Range - Reshape([-1, 1]) - Tile([1, repeat]) - Reshape(-1) - Slice
"""
if node.repeat < 1:
raise Error(
"Unexpected value {} of the attribute 'repeat' for the node {}"
.format(node.repeat, name))
div_node = create_op_with_const_inputs(
graph, Div, {1: int64_array([node.repeat])},
{'name': name + '/Divide'})
add_node = create_op_with_const_inputs(
graph, Add, {1: int64_array([1])},
{'name': div_node.name + '/Add'})
cast_node = Cast(graph, {
'name': name + '/ConvertToI64',
'dst_type': np.int64
}).create_node()
cast_node.out_port(0).connect(div_node.in_port(0))
div_node.out_port(0).connect(add_node.in_port(0))
range_node.in_port(1).get_connection().set_destination(
cast_node.in_port(0))
add_node.out_port(0).connect(range_node.in_port(1))
tile_forward_reshape = create_op_with_const_inputs(
graph, Reshape, {1: int64_array([-1, 1])},
{'name': range_node.name + '/ForwardReshape'})
tile = create_op_with_const_inputs(
graph, Tile, {1: int64_array([1, node.repeat])},
{'name': tile_forward_reshape.name + '/Tile'})
tile_backward_reshape = create_op_with_const_inputs(
graph, Reshape, {1: int64_array([-1])},
{'name': tile.name + '/BackwardReshape'})
slice_node = create_op_with_const_inputs(
graph, Slice, {
1: int64_array([0]),
3: int64_array([0]),
4: int64_array([1])
}, {'name': tile_backward_reshape.name + '/Slice'})
tile_forward_reshape.out_port(0).connect(tile.in_port(0))
tile.out_port(0).connect(tile_backward_reshape.in_port(0))
tile_backward_reshape.out_port(0).connect(slice_node.in_port(0))
slice_node.in_port(2).connect(div_node.in_port(0).get_source())
range_node.out_port(0).get_connection().set_source(
slice_node.out_port(0))
range_node.out_port(0).connect(tile_forward_reshape.in_port(0))
if axis is not None:
rename_nodes([(range_node, name + '/Range'),
(slice_node, name)])
# MXNet arange_like op has no stop attribute and the result tensor always matches the input shape, so
# we have to correct the stop value for the Range node if step != 1 or start != 0
if node.step != 1:
# If step attribute is not integer, we will generate an interval with a larger size and then reduce it
# using Slice
true_elements_count_port = range_node.in_port(1).get_source()
mul_value = np.ceil(node.step) if node.step > 0 else np.floor(
node.step)
stop_value = create_op_with_const_inputs(
graph,
Mul,
port_value_dict={1: mo_array(np.ceil(mul_value))},
op_attrs={'name': range_node.name + '/Stop'})
range_node.in_port(1).get_connection().insert_node(stop_value)
slice_range_values = create_op_with_const_inputs(
graph, Slice, {
1: int64_array([0]),
3: int64_array([0]),
4: int64_array([1])
}, {'name': range_node.name + '/Slice'})
slice_range_values.in_port(2).connect(true_elements_count_port)
range_node.out_port(0).get_connection().insert_node(
slice_range_values)
if axis is not None and node.repeat == 1:
rename_nodes([(range_node, name + '/Range'),
(slice_range_values, name)])
if node.start != 0:
correct_stop_value = create_op_with_const_inputs(
graph,
Add,
port_value_dict={1: mo_array(node.start)},
op_attrs={'name': range_node.name + '/Correct_Stop'})
range_node.in_port(1).get_connection().insert_node(
correct_stop_value)
# Range node supports only scalar inputs
squeeze_node = create_op_with_const_inputs(
graph,
Squeeze,
port_value_dict={1: int64_array(0)},
op_attrs={"name": range_node.name + '/Stop/Squeeze'})
range_node.in_port(1).get_connection().insert_node(squeeze_node)
def replace_tf_resize(graph: Graph, resize: Node, interpolation_mode: str):
resize_name = resize.soft_get('name', resize.id)
log.debug(
"Converting of {} to Interpolate-4 is triggered for node {}.".format(
resize.op, resize_name))
num_of_inputs = len([
port for port in resize.in_ports().values() if not port.disconnected()
])
assert num_of_inputs == 2, \
"Number of inputs of {} (with name {}) should be equal to 2".format(resize.op, resize_name)
attrs_msg = "If half_pixel_centers attribute of the node {} with op {} is True, " \
"the attribute align_corners must be False"
assert not resize.half_pixel_centers or (resize.half_pixel_centers and not resize.align_corners), \
attrs_msg.format(resize_name, resize.op)
shape = Shape(graph, {'name': resize_name + '/shapeof'}).create_node()
ss = create_op_with_const_inputs(graph, StridedSlice, {
1: int64_array([1]),
2: int64_array([3]),
3: int64_array([1])
}, {
'name': resize_name + '/StridedSlice',
'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])
})
div_node = Div(graph, {'name': resize_name + '/Div'}).create_node()
shape_to_float = Cast(graph, dict(dst_type=np.float32)).create_node()
size_to_float = Cast(graph, dict(dst_type=np.float32)).create_node()
size_to_float.out_port(0).connect(div_node.in_port(0))
shape_to_float.out_port(0).connect(div_node.in_port(1))
ss.out_port(0).connect(shape_to_float.in_port(0))
shape.out_port(0).connect(ss.in_port(0))
align_corners = resize.align_corners
half_pixel_centers = resize.half_pixel_centers
nearest_mode = 'floor' if interpolation_mode == 'nearest' else 'round_prefer_floor'
if align_corners:
coordinate_transformation_mode = 'align_corners'
if interpolation_mode == 'nearest':
nearest_mode = 'round_prefer_ceil'
elif half_pixel_centers:
coordinate_transformation_mode = 'tf_half_pixel_for_nn' if interpolation_mode == 'nearest' else 'half_pixel'
else:
coordinate_transformation_mode = 'asymmetric'
interpolate4 = create_op_with_const_inputs(
graph, Interpolate, {3: int64_array([1, 2])}, {
'name': resize_name + '/interpolate_4',
'mode': interpolation_mode,
'antialias': False,
'coordinate_transformation_mode': coordinate_transformation_mode,
'pads_begin': int64_array([0]),
'pads_end': int64_array([0]),
'nearest_mode': nearest_mode,
'cube_coeff': -0.75,
'shape_calculation_mode': 'sizes',
'version': 'opset4',
'in_ports_count': 4,
})
resize_input_connection = resize.in_port(0).get_connection()
resize_input_connection.set_destination(interpolate4.in_port(0))
resize_input_connection.get_source().connect(shape.in_port(0))
div_node.out_port(0).connect(interpolate4.in_port(2))
sizes_connection = resize.in_port(1).get_connection()
sizes_connection.set_destination(interpolate4.in_port(1))
sizes_connection.get_source().connect(size_to_float.in_port(0))
resize.out_port(0).get_connection().set_source(interpolate4.out_port(0))
rename_nodes([(resize, resize_name + '/delete'),
(interpolate4, resize_name)])
