def replace_pattern(graph: Graph, match: [str, Node]):
pow = match['inv']
mul = match['mul']
const = match['const']
name = mul.soft_get('name', mul.id)
devidend_port = mul.in_port(0).get_source() if mul.in_port(
1).get_source().node.id == pow.id else mul.in_port(1).get_source()
divider_port = pow.in_port(0).get_source() if pow.in_port(
1).get_source().node.id == const.id else pow.in_port(
1).get_source()
div = Div(graph, {'name': name + '/div'}).create_node()
mul.out_port(0).get_connection().set_source(div.out_port(0))
devidend_port.connect(div.in_port(0))
divider_port.connect(div.in_port(1))
def floor_div_replacement(floor_div: Node):
graph = floor_div.graph
name = floor_div.soft_get('name', floor_div.id)
div = Div(graph, {'name': name + '/Div'}).create_node()
floor = Floor(graph, {'name': name}).create_node()
div.out_port(0).connect(floor.in_port(0))
div.in_port(0).connect(floor_div.in_port(0).get_source())
div.in_port(1).connect(floor_div.in_port(1).get_source())
floor_div.out_port(0).get_connection().set_source(floor.out_port(0))
graph.remove_node(floor_div.id)
rename_node(floor, name)
def replace_op(self, graph: Graph, node: Node):
"""
Replace Softsign according to formula feature/(abs(feature)+1)
"""
abs_node = Abs(graph, {'name': "abs_" + node.id}).create_node()
abs_node.in_port(0).connect(node.in_port(0).get_source())
add_node = create_op_node_with_second_input(graph, Add, np.ones(
[1]), {"name": node.id + "_plus_1"})
add_node.in_port(0).connect(abs_node.out_port(0))
div_node = Div(graph, {"name": "div_" + node.id}).create_node()
div_node.in_port(0).connect(node.in_port(0).get_source())
div_node.in_port(1).connect(add_node.out_port(0))
return [div_node.id]
def test_value_propagation(self, a_shape, a_value, b_shape, b_value,
elem_type):
graph = build_graph(nodes_attrs=graph_nodes_attrs,
edges=graph_edges,
update_attributes={
'A': {
'shape': int64_array(a_shape),
'value': a_value.astype(elem_type)
},
'A_data': {
'shape': int64_array(a_shape),
'value': a_value.astype(elem_type)
},
'B': {
'shape': int64_array(b_shape),
'value': b_value.astype(elem_type)
},
'B_data': {
'shape': int64_array(b_shape),
'value': b_value.astype(elem_type)
},
})
node = Node(graph, 'div')
node['infer'] = Div(graph, node.attrs()).create_node().infer
node.infer(node)
node_data = node.out_port(0).get_destination().data.get_value()
def func_for_ref():
if np.issubdtype(elem_type, np.integer):
return lambda a, b: a // b
else:
return lambda a, b: a / b
ref_data = func_for_ref()(a_value, b_value)
node_data_shape = node_data.shape
ref_data_shape = ref_data.shape
msg = "Value propagation for 'div' node is not correct."
self.assertTrue(
node_data_shape == ref_data_shape
and np.all(node_data == ref_data), msg)
def extract(cls, node):
Div.update_node_stat(
node, {'data_type': tf_dtype_extractor(node.pb.attr["T"].type)})
return cls.enabled
def replace_resize(graph: Graph, resize: Node):
log.debug("Converting of ONNX Resize-11 to Interpolate-4 "
"is triggered for node {}.".format(
resize.soft_get('name', resize.id)))
input_shape = resize.in_port(0).data.get_shape()
input_rank = len(input_shape)
resize_name = resize.soft_get('name', resize.id)
if input_rank not in {4, 5}:
log.warning(
'The input shape is not 4D or 5D for op with name {}'.format(
resize_name))
return
num_of_inputs = len([
port for port in resize.in_ports().values() if not port.disconnected()
])
assert num_of_inputs in {3, 4}, \
"Number of inputs of ONNXResize (with name {}) should be equal to 3 or 4".format(resize_name)
assert resize.soft_get('coordinate_transformation_mode') != 'tf_crop_and_resize', \
'Mode tf_crop_and_resize is not supported for op {} with name {}'.format(resize.op, resize_name)
layout = graph.graph['layout']
if input_rank == 4:
begin_dim = get_height_dim(layout, input_rank)
end_dim = get_width_dim(layout, input_rank) + 1
else:
begin_dim = get_depth_dim(layout, input_rank)
end_dim = get_width_dim(layout, input_rank) + 1
sizes_ss = create_op_with_const_inputs(
graph, StridedSlice, {
1: int64_array([begin_dim]),
2: int64_array([end_dim]),
3: int64_array([1])
}, {
'name': resize_name + '/StridedSlice_sizes',
'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])
})
scales_ss = create_op_with_const_inputs(
graph, StridedSlice, {
1: int64_array([begin_dim]),
2: int64_array([end_dim]),
3: int64_array([1])
}, {
'name': resize_name + '/StridedSlice_scales',
'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])
})
axes_node = Const(
graph, {
'name': resize_name + '/axis',
'value': int64_array(np.arange(begin_dim, end_dim))
}).create_node()
shape_calculation_mode = 'scales' if num_of_inputs == 3 else 'sizes'
interpolate_node = Interpolate(
graph, {
'version': 'opset4',
'mode': convert_mode(resize.mode),
'coordinate_transformation_mode':
resize.coordinate_transformation_mode,
'cube_coeff': resize.cube_coeff,
'nearest_mode': resize.nearest_mode,
'pads_begin': int64_array([0]),
'pads_end': int64_array([0]),
'antialias': 0,
'shape_calculation_mode': shape_calculation_mode,
'in_ports_count': 4
}).create_node()
axes_node.out_port(0).connect(interpolate_node.in_port(3))
shape_of = Shape(graph, {'name': resize_name + '/ShapeOf'}).create_node()
add_node = create_op_with_const_inputs(graph, Add,
{1: float_array([1.0e-5])},
{'name': resize_name + '/Add'})
input_data_type = data_type_str_to_np(graph.graph['cmd_params'].data_type)
if num_of_inputs == 3:
cast_shape_to_float = Cast(graph, {
'dst_type': input_data_type
}).create_node()
mul_node = Mul(graph, {'name': resize_name + '/Mul'}).create_node()
shape_of.out_port(0).connect(cast_shape_to_float.in_port(0))
cast_shape_to_float.out_port(0).connect(mul_node.in_port(0))
cast_add_result_to_int = Cast(graph, {
'dst_type': np.int64
}).create_node()
floor_node = Floor(graph, {
'name': resize_name + '/Floor'
}).create_node()
mul_node.out_port(0).connect(add_node.in_port(0))
add_node.out_port(0).connect(floor_node.in_port(0))
floor_node.out_port(0).connect(cast_add_result_to_int.in_port(0))
cast_add_result_to_int.out_port(0).connect(sizes_ss.in_port(0))
sizes_ss.out_port(0).connect(interpolate_node.in_port(1))
scales_ss.out_port(0).connect(interpolate_node.in_port(2))
connection_of_resize_input = resize.in_port(0).get_connection()
connection_of_resize_input.set_destination(interpolate_node.in_port(0))
connection_of_scales = resize.in_port(2).get_connection()
connection_of_scales.set_destination(scales_ss.in_port(0))
connection_of_resize_input.get_source().connect(shape_of.in_port(0))
connection_of_scales.get_source().connect(mul_node.in_port(1))
else:
cast_shape_to_float = Cast(graph, {
'dst_type': input_data_type
}).create_node()
cast_sizes_to_float = Cast(graph, {
'dst_type': input_data_type
}).create_node()
div_node = Div(graph, {'name': resize_name + '/Div'}).create_node()
cast_sizes_to_float.out_port(0).connect(div_node.in_port(0))
cast_shape_to_float.out_port(0).connect(div_node.in_port(1))
shape_of.out_port(0).connect(cast_shape_to_float.in_port(0))
div_node.out_port(0).connect(add_node.in_port(0))
add_node.out_port(0).connect(scales_ss.in_port(0))
scales_ss.out_port(0).connect(interpolate_node.in_port(2))
sizes_ss.out_port(0).connect(interpolate_node.in_port(1))
connection_of_resize_input = resize.in_port(0).get_connection()
connection_of_resize_input.set_destination(interpolate_node.in_port(0))
connection_of_sizes = resize.in_port(3).get_connection()
connection_of_sizes.set_destination(sizes_ss.in_port(0))
connection_of_resize_input.get_source().connect(shape_of.in_port(0))
connection_of_sizes.get_source().connect(
cast_sizes_to_float.in_port(0))
rename_nodes([(resize, resize_name + '/delete'),
(interpolate_node, resize_name)])
resize.out_port(0).get_connection().set_source(
interpolate_node.out_port(0))
def replace_sub_graph(self, graph: Graph, match: Dict[str, Node]):
node = match['op']
name = node.name
min_port_tuple = (node.in_port(1).get_source().node,
node.in_port(1).get_source().idx)
max_port_tuple = (node.in_port(2).get_source().node,
node.in_port(2).get_source().idx)
node.in_port(1).disconnect()
node.in_port(2).disconnect()
# make sure min < max
min_less_max = Less(graph, {
'name': name + '/if_min_less_max'
}).create_node([min_port_tuple, max_port_tuple])
minimum = Select(graph, {
'name': name + '/minimum'
}).create_node([min_less_max, min_port_tuple, max_port_tuple])
maximum = Select(graph, {
'name': name + '/maximum'
}).create_node([min_less_max, max_port_tuple, min_port_tuple])
# to create zero of limits data type, we multiply it by integer zero
zero = create_op_node_with_second_input(graph,
Mul,
int64_array(0),
{'name': name + '/zero'},
input_node=minimum)
# if 0 < min < max: min_adj = 0 and max_adj = max - min
min_greater_zero = Greater(graph, {
'name': name + '/if_minimum_greater_zero'
}).create_node([minimum, zero])
max_minus_min = Sub(graph, {
'name': name + '/max_minus_min'
}).create_node([maximum, minimum])
minimum = Select(graph, {
'name': name + '/first_adj_min'
}).create_node([min_greater_zero, zero, minimum])
maximum = Select(graph, {
'name': name + '/first_adj_max'
}).create_node([min_greater_zero, max_minus_min, maximum])
# if min < max < 0: min_adj = min - max and max_adj = 0
max_less_zero = Less(graph, {
'name': name + '/if_max_less_zero'
}).create_node([maximum, zero])
min_minus_max = Sub(graph, {
'name': name + '/min_minus_max'
}).create_node([minimum, maximum])
minimum = Select(graph, {
'name': name + '/second_adj_min'
}).create_node([max_less_zero, min_minus_max, minimum])
maximum = Select(graph, {
'name': name + '/second_adj_max'
}).create_node([max_less_zero, zero, maximum])
# scale = (max - min) / (2 ^ num_bits - 1),
float_range = Sub(graph, {
'name': name + '/float_range'
}).create_node([maximum, minimum])
quant_min_value, quant_max_value = int(
node.narrow_range), 2**node.num_bits - 1
int_range = Const(
graph,
dict(name=name + '/int_range',
value=quant_max_value - quant_min_value)).create_node()
scale = Div(graph, {
'name': name + '/scale'
}).create_node([float_range, int_range])
# min_adj = scale * round(min / scale)
descaled_min = Div(graph, {
'name': name + '/descaled_min'
}).create_node([minimum, scale])
rounded_descaled_min = Round(graph, {
'name': name + '/rounded_descaled_min'
}).create_node([descaled_min])
min_adj = Mul(graph, {
'name': name + '/min_adj'
}).create_node([scale, rounded_descaled_min])
# max_adj = max + min_adj - min.
adjustment = Sub(graph, {
'name': name + '/limits_adjustment'
}).create_node([min_adj, minimum])
max_adj = Add(graph, {
'name': name + '/max_adj'
}).create_node([maximum, adjustment])
# FakeQuantize operation has 5 inputs instead of 3 inputs in TensorFlow
node.add_input_port(3, skip_if_exist=True)
node.add_input_port(4, skip_if_exist=True)
node.in_port(1).connect(min_adj.out_port(0))
node.in_port(2).connect(max_adj.out_port(0))
node.in_port(3).connect(min_adj.out_port(0))
node.in_port(4).connect(max_adj.out_port(0))
FakeQuantize.update_node_stat(node, {'levels': node['levels']})
def find_and_replace_pattern(self, graph: Graph):
for embedding_segments_mean in graph.get_op_nodes(
op='EmbeddingSegmentsMean'):
embedding_segments_mean_name = embedding_segments_mean.soft_get(
'name', embedding_segments_mean.id)
embedding_table_input = embedding_segments_mean.in_port(0)
segment_ids_input = embedding_segments_mean.in_port(2)
num_segments_input = embedding_segments_mean.in_port(3)
# TODO: support EmbeddingSegmentsMean with specified weights vector.
# now this case has not appeared in models so far so EmbeddingSegmentsOperation fusion
# transformations do not handle it either
if embedding_segments_mean.is_in_port_connected(5):
return
# 1. compute indices membership matrix, i.e. which indices belong to some object
# the shape of this matrix is [num_segments, num_indices]
non_norm_range_1_to_num_segments = create_op_with_const_inputs(
graph, Range, {
0: int64_array(0),
2: int64_array(1)
}, {
'name':
embedding_segments_mean_name + '/Range1ToNumSegments',
'output_type': np.int64
})
num_segments_input.get_connection().add_destination(
non_norm_range_1_to_num_segments.in_port(1))
range_1_to_num_segments = ConvertLike(graph, {
'name':
embedding_segments_mean_name + '/Range1ToNumSegmentsNorm'
}).create_node()
range_1_to_num_segments.in_port(0).connect(
non_norm_range_1_to_num_segments.out_port(0))
num_segments_input.get_connection().add_destination(
range_1_to_num_segments.in_port(1))
unsqueeze_range_1_to_num_segments = create_op_with_const_inputs(
graph, Unsqueeze, {1: int64_array(1)}, {
'name':
embedding_segments_mean_name +
'/Range1ToNumSegmentsUnsqueeze'
})
unsqueeze_range_1_to_num_segments.in_port(0).connect(
range_1_to_num_segments.out_port(0))
unsqueeze_segment_ids = create_op_with_const_inputs(
graph, Unsqueeze, {1: int64_array(0)}, {
'name':
embedding_segments_mean_name + '/SegmentIdsUnsqueeze'
})
segment_ids_input.get_connection().add_destination(
unsqueeze_segment_ids.in_port(0))
boolean_membership_matrix = Equal(graph, {
'name':
embedding_segments_mean_name + '/BooleanMembershipMatrix'
}).create_node()
boolean_membership_matrix.in_port(0).connect(
unsqueeze_range_1_to_num_segments.out_port(0))
boolean_membership_matrix.in_port(1).connect(
unsqueeze_segment_ids.out_port(0))
shape_of_membership_matrix = Shape(graph, {
'name':
embedding_segments_mean_name + '/ShapeOfMembershipMatrix'
}).create_node([boolean_membership_matrix])
one_scalar_constant = Const(
graph, {
'name': embedding_segments_mean_name + '/OneScalar',
'value': int64_array([1])
}).create_node()
one_constant = Broadcast(graph, {
'name':
embedding_segments_mean_name + '/One'
}).create_node([one_scalar_constant, shape_of_membership_matrix])
zero_constant = Const(
graph, {
'name': embedding_segments_mean_name + '/Zero',
'value': int64_array(0)
}).create_node()
membership_matrix = Select(
graph, {
'name': embedding_segments_mean_name + '/MembershipMatrix',
'auto_broadcast': 'numpy'
}).create_node(
[boolean_membership_matrix, one_constant, zero_constant])
# 2. compute a number of indices belong to each object from the batch
# it computes the normalization coefficients
num_indices_per_object = create_op_with_const_inputs(
graph, ReduceSum, {1: int64_array(1)}, {
'name':
embedding_segments_mean_name + '/NumIndicesPerObject'
})
num_indices_per_object.in_port(0).connect(
membership_matrix.out_port(0))
# 3. replace zero coefficient (zero number of indices belong to an object) with one
# because for such object the single default embedding vector is used
where_zero_number = Equal(graph, {
'name':
embedding_segments_mean_name + '/WhereZeroIndicesNumber'
}).create_node([num_indices_per_object, zero_constant])
normalized_num_indices_per_object = Select(
graph, {
'name':
embedding_segments_mean_name + '/NormNumIndicesPerObject',
'auto_broadcast': 'numpy'
}).create_node([
where_zero_number, one_scalar_constant,
num_indices_per_object
])
# 4. cast normalized_num_indices_per_object to the same type as embedding vector table
norm_coefficients = ConvertLike(
graph, {
'name': embedding_segments_mean_name + '/NormCoefficients'
}).create_node()
norm_coefficients.in_port(0).connect(
normalized_num_indices_per_object.out_port(0))
embedding_table_input.get_connection().add_destination(
norm_coefficients.in_port(1))
# 5. replace EmbeddingSegmentMean with EmbeddingSegmentSum
embedding_segments_sum = EmbeddingSegmentsSum(
graph, {
'name':
embedding_segments_mean_name + '/EmbeddingSegmentsSum'
}).create_node()
for in_port in embedding_segments_mean.in_ports():
if embedding_segments_mean.is_in_port_connected(in_port):
embedding_segments_mean.in_port(
in_port).get_connection().set_destination(
embedding_segments_sum.in_port(in_port))
# 6. normalize EmbeddingSegmentSum results by computed coefficients
result_node = Div(graph, {
'name': embedding_segments_mean_name + '/Div'
}).create_node([embedding_segments_sum, norm_coefficients])
embedding_segments_mean.out_port(0).get_connection().set_source(
result_node.out_port(0))
rename_nodes([(embedding_segments_mean,
embedding_segments_mean_name + '/AbandonedName'),
(result_node, embedding_segments_mean_name)])
graph.remove_nodes_from([embedding_segments_mean.id])
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()
layout = graph.graph['layout']
height_dim = get_height_dim(layout, 4)
width_dim = get_width_dim(layout, 4)
ss = create_op_with_const_inputs(
graph, StridedSlice, {
1: int64_array([height_dim]),
2: int64_array([width_dim + 1]),
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([height_dim, width_dim])}, {
'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)])
def extract(cls, node):
Div.update_node_stat(node)
return cls.enabled
def extract(cls, node: Node):
axis = onnx_attr(node, 'axis', 'i', default=None)
Div.update_node_stat(node, {'axis': axis})
return cls.enabled
def replace_sub_graph(self, graph: Graph, match: Dict[str, Node]):
node = match['op']
name = node.name
# Zero Point Nudging : Scale counting
f_min = node.in_port(1).get_source()
node.in_port(1).disconnect()
f_max = node.in_port(2).get_source()
node.in_port(2).disconnect()
f_diff = Sub(graph, {'name': name + '/float_range'}).create_node()
f_max.connect(f_diff.in_port(0))
f_min.connect(f_diff.in_port(1))
quant_min_value = int(node.narrow_range)
quant_max_value = 2 ** node.num_bits - 1
i_diff = Const(graph, dict(name=name + '/int_range', value=quant_max_value - quant_min_value)).create_node()
scale = Div(graph, {'name': name + '/scale'}).create_node()
f_diff.out_port(0).connect(scale.in_port(0))
i_diff.out_port(0).connect(scale.in_port(1))
# Zero Point Nudging : ZP from min counting
descaled_min = Div(graph, {'name': name + '/descaled_min'}).create_node()
f_min.connect(descaled_min.in_port(0))
scale.out_port(0).connect(descaled_min.in_port(1))
zero_point_from_min = Sub(graph, {'name': name + '/zero_point_from_min'}).create_node()
quant_min = Const(graph, {'value': quant_min_value, 'name': name + '/quant_min'}).create_node()
quant_min.out_port(0).connect(zero_point_from_min.in_port(0))
descaled_min.out_port(0).connect(zero_point_from_min.in_port(1))
# Zero Point Nudging : Nudged Zero Point counting
zp_lesser_q_mi = Less(graph, {'name': name + '/zero_point_from_min_less_quant_min'}).create_node()
zero_point_from_min.out_port(0).connect(zp_lesser_q_mi.in_port(0))
quant_min.out_port(0).connect(zp_lesser_q_mi.in_port(1))
zp_greater_q_ma = Greater(graph, {'name': name + '/zero_point_from_min_greater_quant_max'}).create_node()
zero_point_from_min.out_port(0).connect(zp_greater_q_ma.in_port(0))
quant_max = Const(graph, {'value': quant_max_value, 'name': name + '/quant_max'}).create_node()
quant_max.out_port(0).connect(zp_greater_q_ma.in_port(1))
rounded_zero_point_from_min = Round(graph, {'name': name + '/zero_point_from_min_rounding'}).create_node()
zero_point_from_min.out_port(0).connect(rounded_zero_point_from_min.in_port(0))
nudged_zero_point = Select(graph, {'name': name + '/nudging_zp_1_select_less_condition'}).create_node()
greater_condition = Select(graph, {'name': name + '/nudging_zp_2_select_greater_condition'}).create_node()
greater_condition.in_port(0).connect(zp_greater_q_ma.out_port(0))
greater_condition.in_port(1).connect(quant_max.out_port(0))
greater_condition.in_port(2).connect(rounded_zero_point_from_min.out_port(0))
nudged_zero_point.in_port(0).connect(zp_lesser_q_mi.out_port(0))
nudged_zero_point.in_port(1).connect(quant_max.out_port(0))
nudged_zero_point.in_port(2).connect(greater_condition.out_port(0))
nudged_i_min = Sub(graph, {'name': name + '/nudged_i_min'}).create_node()
quant_min.out_port(0).connect(nudged_i_min.in_port(0))
nudged_zero_point.out_port(0).connect(nudged_i_min.in_port(1))
nudged_i_max = Sub(graph, {'name': name + '/nudged_i_max'}).create_node()
quant_max.out_port(0).connect(nudged_i_max.in_port(0))
nudged_zero_point.out_port(0).connect(nudged_i_max.in_port(1))
nudged_min = Mul(graph, {'name': name + '/nudged_min'}).create_node()
nudged_i_min.out_port(0).connect(nudged_min.in_port(0))
scale.out_port(0).connect(nudged_min.in_port(1))
nudged_max = Mul(graph, {'name': name + '/nudged_max'}).create_node()
nudged_i_max.out_port(0).connect(nudged_max.in_port(0))
scale.out_port(0).connect(nudged_max.in_port(1))
nudged_min.out_port(0).connect(node.in_port(1))
nudged_max.out_port(0).connect(node.in_port(2))
# FakeQuantize operation has 5 inputs instead of 3 inputs in TensorFlow
node.add_input_port(3, skip_if_exist=True)
node.add_input_port(4, skip_if_exist=True)
node.in_port(3).connect(nudged_min.out_port(0))
node.in_port(4).connect(nudged_max.out_port(0))
FakeQuantize.update_node_stat(node, {'levels': node['levels']})
def dequantize_data(fake_quantize: Node, dst_type: type, quantized_type: type) -> Node:
graph = fake_quantize.graph
quantized_data = fake_quantize.in_port(0).get_source().node
name = fake_quantize.soft_get('name', fake_quantize.id)
assert quantized_data.soft_get('type') == 'Convert' and quantized_data.dst_type == quantized_type, \
'Weights aren`t compressed as expected for node {}'.format(fake_quantize.soft_get('name', fake_quantize.id))
dequantizing_cast = Cast(graph, dict(
name=quantized_data.name + "/to_{}".format(np_data_type_to_destination_type(dst_type)),
dst_type=dst_type, stop_value_propagation=True)).create_node()
fake_quantize.in_port(0).get_connection().set_destination(dequantizing_cast.in_port(0))
# limits of dequantize
in_low = fake_quantize.in_port(1).get_source()
in_high = fake_quantize.in_port(2).get_source()
out_low = fake_quantize.in_port(3).get_source()
out_high = fake_quantize.in_port(4).get_source()
# scale calculation
output_range = Sub(graph, {'name': name + '/output_range'}).create_node()
output_range.in_port(0).connect(out_high)
output_range.in_port(1).connect(out_low)
input_range = Sub(graph, {'name': name + '/input_range'}).create_node()
input_range.in_port(0).connect(in_high)
input_range.in_port(1).connect(in_low)
scale = Div(graph, {'name': name + '/scale'}).create_node()
scale.in_port(0).connect(output_range.out_port(0))
scale.in_port(1).connect(input_range.out_port(0))
# shift calculation
descaled_output_low = Div(graph, {'name': name + '/descaled_output_low'}).create_node()
descaled_output_low.in_port(0).connect(out_low)
descaled_output_low.in_port(1).connect(scale.out_port(0))
shift = Sub(graph, {'name': name + '/zero_point'}).create_node()
shift.in_port(0).connect(in_low)
shift.in_port(1).connect(descaled_output_low.out_port(0))
# DeQuantize(x) == Mul(Sub(x, zero_point), scale)
sub_zp = Sub(graph, {'name': name + '/minus_zp'}).create_node()
sub_zp.in_port(0).connect(dequantizing_cast.out_port(0))
sub_zp.in_port(1).connect(shift.out_port(0))
mul_scale = Mul(graph, {'name': name + '/mulpiply_by_scale'}).create_node()
mul_scale.in_port(0).connect(sub_zp.out_port(0))
mul_scale.in_port(1).connect(scale.out_port(0))
fake_quantize.out_port(0).get_connection().set_source(mul_scale.out_port(0))
graph.remove_nodes_from([fake_quantize.id, fake_quantize.out_node(0)])
def extract(node):
Div.update_node_stat(node)
return __class__.enabled
