def add_constant_to_negative_values(node: Node, port_idx: int, added_value: np.array):
"""
This function adds the given values to negative elements of value from the given input port.
:param node: node with corrected values in the input port port_idx
:param port_idx: input port index for negative values
:param added_value: the value to add
:return: None
"""
negative_values_source = node.in_port(port_idx).get_source()
negative_values_node = node.in_port(port_idx).get_source().node
negative_values_node_name = negative_values_node.soft_get('name', negative_values_node.id)
graph = node.graph
less_node = create_op_with_const_inputs(graph, Less,
{1: mo_array(0, dtype=added_value.dtype)},
{'name': negative_values_node_name + '/Less'})
mul_node = create_op_with_const_inputs(graph, Mul, {1: added_value}, {'name': negative_values_node_name + '/Mul'})
node.in_port(port_idx).get_connection().set_destination(less_node.in_port(0))
less_node.out_port(0).connect(mul_node.in_port(0))
add_node = Add(graph, {}).create_node()
mul_node.out_port(0).connect(add_node.in_port(1))
negative_values_source.connect(add_node.in_port(0))
add_node.out_port(0).connect(node.in_port(port_idx))
def replace_pattern(self, graph: Graph, match: dict):
bias_add = match['BiasAdd']
# Replace BiasAdd by Add operation
new_add = Add(graph, {'name': bias_add.id + '/Add'}).create_node()
bias_add.in_port(0).get_connection().set_destination(new_add.in_port(0))
bias_add.in_port(1).get_connection().set_destination(new_add.in_port(1))
bias_add.out_port(0).get_connection().set_source(new_add.out_port(0))
if bias_add.data_format != 'NCHW':
return
input_shape = new_add.in_port(0).data.get_shape()
bias_shape = new_add.in_port(1).data.get_shape()
assert len(bias_shape) == 1
unsqueeze_dims = np.arange(len(input_shape))
channel_dim = get_features_dim('NCHW', len(input_shape))
unsqueeze_dims = np.delete(unsqueeze_dims, channel_dim, 0)
unsqueeze_node = Unsqueeze(graph, {'name': new_add.id + '/BiasUnsqueeze'}).create_node()
unsqueeze_dims_node = Const(graph, {'name': new_add.id + '/Dims',
'value': unsqueeze_dims}).create_node()
# Reconnecting nodes
unsqueeze_node.in_port(1).connect(unsqueeze_dims_node.out_port(0))
unsqueeze_node['override_output_shape'] = True
new_add.in_port(1).get_connection().insert_node(unsqueeze_node)
def replace_sub_graph(self, graph: Graph, match: dict):
op = match['op']
out_port = op.in_port(0).get_source()
if op.soft_get('scale', 1) != 1:
const = Const(graph, {'value': np.array(op.scale)}).create_node()
mul = Mul(graph, {'name': op.name + '/mul_'}).create_node()
const.out_port(0).connect(mul.in_port(1))
out_port.connect(mul.in_port(0))
out_port = mul.out_port(0)
if op.soft_get('shift', 0) != 0:
const = Const(graph, {'value': np.array(op.shift)}).create_node()
add = Add(graph, {'name': op.name + '/add_'}).create_node()
const.out_port(0).connect(add.in_port(1))
out_port.connect(add.in_port(0))
out_port = add.out_port(0)
if op.soft_get('power', 1) != 1:
const = Const(graph, {'value': np.array(op.power)}).create_node()
pow = Pow(graph, {'name': op.name + '/pow_'}).create_node()
const.out_port(0).connect(pow.in_port(1))
out_port.connect(pow.in_port(0))
out_port = pow.out_port(0)
op.out_port(0).get_connection().set_source(out_port)
def create_bias_node(graph: Graph, src_node):
logger.debug('Creating new bias for {}'.format(src_node.name))
destination_ports = []
for dest_port in src_node.out_port(0).get_destinations():
destination_ports.append(dest_port)
# Create Add and constant with zero bias
bias_shape = src_node.out_port(0).data.get_shape()
add_bias_shape = [1] * len(bias_shape)
add_bias_shape[1] = bias_shape[1]
weights = get_weights_for_node(src_node)
bias_dtype = np.float32
if weights and weights.out_port(0).is_data_type_defined():
bias_dtype = weights.out_port(0).get_data_type()
add_bias = Const(
graph, {
'value': np.zeros(add_bias_shape, dtype=bias_dtype),
'shape': add_bias_shape,
'need_shape_inference': True
}).create_node()
add_op = Add(graph, {
'name': src_node.name + '/add_',
'need_shape_inference': True
}).create_node()
# Connect Const to Add node
add_op.in_port(1).connect(add_bias.out_port(0))
# Reconnect src_node -> output to src_node -> Add -> output
src_node.out_port(0).disconnect()
src_node.out_port(0).get_connection().set_destination(add_op.in_port(0))
for destination_port in destination_ports:
add_op.out_port(0).connect(destination_port)
add_bias.out_node(0)['Insert_Convert_operation_after'] = True
def sub_to_add_replacement(sub: Node):
# we execute this transformation for V10 IR later on middle phase despite graph_condition
# so we prevent Sub replacement on shape-calculating sub-graphs
if sub.in_port(0).data.get_value() is not None and sub.in_port(
1).data.get_value() is not None:
return
graph = sub.graph
name = sub.soft_get('name', sub.id)
# keep Add name the same as Sub -- because of mathematical equality of output tensors
rename_node(node=sub, name=name + '/to_be_removed')
# reconnect Sub in(out)puts to Add
add = Add(graph, {'name': name}).create_node()
rename_node(add, name)
sub.in_port(0).get_connection().set_destination(add.in_port(0))
sub.in_port(1).get_connection().set_destination(add.in_port(1))
sub.out_port(0).get_connection().set_source(add.out_port(0))
# restore mathematical equivalence to Sub operation: Sub(A, B) = Add(A, Mul(B, -1))
const_dtype = sub.soft_get('data_type', np.float32)
negate = create_op_with_const_inputs(
graph, Mul, {1: np.array(-1, dtype=const_dtype)},
{'name': name + '/neg_'})
add.in_port(1).get_connection().insert_node(negate)
def calculate_prior_box_value(value: Node, value_to_div: Port,
value_to_add: Port):
"""
:param value: Node with value. Here is supposed the node with op='Split'
:param value_to_div: Output port with values to be divided by 2
:param value_to_add: Output port with values to be added to values from value_to_div port
:return: Sub and Add nodes
The sub-graph can be described by formulas:
min = value[value_to_add] - (value[value_to_div] / 2)
max = value[value_to_add] + (value[value_to_div] / 2)
"""
graph = value.graph
dtype = data_type_str_to_np(graph.graph['cmd_params'].data_type)
_min = Sub(graph, dict(name=value.name + '/Sub')).create_node()
div = create_op_node_with_second_input(graph,
Div,
mo_array([2], dtype=dtype),
op_attrs=dict(name=value.name +
'/Div'))
div.in_port(0).connect(value_to_div)
_min.in_port(0).connect(value_to_add)
_min.in_port(1).connect(div.out_port(0))
_max = Add(graph, dict(name=value.name + '/Add')).create_node()
_max.in_port(0).connect(div.out_port(0))
_max.in_port(1).connect(value_to_add)
return _min, _max
def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
node = match['op']
name = node.soft_get('name', node.id)
# biases normalization
if 2 in node.in_ports() and not node.in_port(2).disconnected():
bias_node = Add(graph, {'name': name + '/Bias_'}).create_node()
node_name = node.name + '/WithoutBiases'
bias_node_name = node.name
rename_nodes([(node, node_name), (bias_node, bias_node_name)])
node.out_port(0).get_connection().set_source(bias_node.out_port(0))
node.in_port(2).get_connection().set_destination(
bias_node.in_port(1))
node.out_port(0).connect(bias_node.in_port(0))
# weights normalization
assert node.has_valid('out-size')
out_size = node['out-size']
reshape_dim = int64_array([-1, out_size])
if node.has_and_set('transpose_weights'):
reshape_dim = int64_array([out_size, -1])
node.insert_op_on_input_port(
in_port_idx=1,
new_op_class=Reshape,
new_op_attrs={'name': name + '/weights_reshape'},
value=reshape_dim)
if node.has_and_set('transpose_weights'):
node.insert_op_on_input_port(
in_port_idx=1,
new_op_class=Transpose,
new_op_attrs={'name': name + '/weights_transpose'},
value=int64_array([1, 0]))
# input normalization for 4D Caffe and MXNet FullyConnected
if graph.graph['fw'] == 'caffe':
node.insert_op_on_input_port(in_port_idx=0,
new_op_class=Reshape,
new_op_attrs={
'name':
name + '/flatten_fc_input',
'special_zero': True
},
value=int64_array([0, -1]))
if graph.graph['fw'] == 'mxnet':
if node.flatten is not False:
node.insert_op_on_input_port(in_port_idx=0,
new_op_class=Reshape,
new_op_attrs={
'name':
name + '/flatten_fc_input',
'special_zero': True
},
value=int64_array([0, -1]))
MatMul.update_node_stat(node, {})
def get_range_node_of_idxs(rank: Node,
begin: int,
end: int,
include_begin: bool = True,
include_end: bool = False) -> Node:
"""
Returns node that produces 1D output of values of range from begin to end (ex)/(in)cluding begin or end point
:param rank: the node of 0D output shape to get rank of tensor from
:param begin: integer value from [-rank; rank - 1]
:param end: integer value from [-rank; +rank]
:param include_begin: boolean flag to include or exclude start point from range output
:param include_end: boolean flag to include or exclude end point from range output
:return: range node producing 1D output
"""
graph = rank.graph
name = rank.soft_get('name', rank.id)
start_idx = get_canonical_axis_index_node(rank, begin)
end_idx = get_canonical_axis_index_node(rank, end)
if not include_begin:
const = Const(graph, {
'value': int64_array(1),
'name': name + '/exclude_begin/value'
}).create_node()
add = Add(graph, {'name': name + '/exclude_begin'}).create_node()
start_idx.out_port(0).connect(add.in_port(0))
const.out_port(0).connect(add.in_port(1))
start_idx = add
if include_end:
const = Const(graph, {
'value': int64_array(1),
'name': name + '/including_end/value'
}).create_node()
add = Add(graph, {'name': name + '/including_end'}).create_node()
end_idx.out_port(0).connect(add.in_port(0))
const.out_port(0).connect(add.in_port(1))
end_idx = add
delta = Const(graph, {
'name': name + '/delta',
'value': int64_array(1)
}).create_node()
range_node = Range(graph, {'name': name + '/range_idxs'}).create_node()
start_idx.out_port(0).connect(range_node.in_port(0))
end_idx.out_port(0).connect(range_node.in_port(1))
delta.out_port(0).connect(range_node.in_port(2))
return range_node
def find_and_replace_pattern(self, graph: Graph):
for node in graph.get_op_nodes(op='LayerNorm'):
node_name = node.soft_get('name', node.id)
if node.output_mean_var is True:
if not node.out_port(1).disconnected() or not node.out_port(2).disconnected():
raise Error("Node {} is supported with only one output".format(node_name))
log.error('LayerNorm node {} with attribute "output_mean_var" = True is not supported.'
'But since the node has one output, the conversion will continue.'.format(node_name),
extra={'is_warning': True})
input_shape = node.in_port(0).data.get_shape()
assert node.has_valid('axis'), 'Incorrect axis value for the node {}'.format(node_name)
axis = node.axis
mvn = create_op_node_with_second_input(graph, MVN, int64_array([axis]),
dict(eps=node.epsilon, name=node_name + '/LayerNorm/MVN_',
across_channels=1, normalize_variance=1, eps_mode='inside_sqrt'))
mul = Mul(graph, {'name': node_name + '/LayerNorm/mul_'}).create_node()
add = Add(graph, {'name': mul.name + '/LayerNorm/add_'}).create_node()
node.in_port(0).get_connection().set_destination(mvn.in_port(0))
node.in_port(1).get_connection().set_destination(mul.in_port(1))
node.in_port(2).get_connection().set_destination(add.in_port(1))
mvn.out_port(0).connect(mul.in_port(0))
mul.out_port(0).connect(add.in_port(0))
node.out_port(0).get_connection().set_source(add.out_port(0))
# MXNet LayerNorm gamma and beta attributes are 1D tensors with shape = [input_shape[axis]]
# We have to unsqueeze values for Mul and Add operations to avoid shapes incompatibility problems
# if axis != -1
canonical_axis = get_canonical_axis_index(input_shape, axis)
unsqueeze_value = []
for idx, val in enumerate(input_shape):
if idx != canonical_axis:
unsqueeze_value.append(idx)
mul_const_unsqueeze = create_op_node_with_second_input(graph, Unsqueeze,
int64_array(unsqueeze_value),
dict(name=mul.name + '/Unsqueeze',
override_output_shape=True))
add_const_unsqueeze = create_op_node_with_second_input(graph, Unsqueeze,
int64_array(unsqueeze_value),
dict(name=add.name + '/Unsqueeze',
override_output_shape=True))
mul.in_port(1).get_connection().insert_node(mul_const_unsqueeze)
add.in_port(1).get_connection().insert_node(add_const_unsqueeze)
rename_nodes([(node, node_name + '/ShouldBeDeleted'), (add, node_name)])
def find_and_replace_pattern(self, graph: Graph):
for node in graph.get_op_nodes(op='Gemm'):
name = node.soft_get('name', node.id)
node_output_port = node.out_port(0)
if node.has_valid('alpha') and not math.isclose(node.alpha, 1):
mul_alpha = create_op_with_const_inputs(
graph, Mul, {1: mo_array(node.alpha)}, {
'name': name + '/Alpha',
'can_be_scaleshift': False
})
node_output_port.get_connection().insert_node(mul_alpha)
node_output_port = mul_alpha.out_port(0)
del node['alpha']
if node.is_in_port_connected(2):
# biases normalization
bias_node = Add(graph, {
'name': name + '/Bias_',
'can_be_scaleshift': False
}).create_node()
without_biases_node_name = name + '/WithoutBiases'
rename_nodes([(node, without_biases_node_name),
(bias_node, name)])
node_output_port.get_connection().set_source(
bias_node.out_port(0))
node.in_port(2).get_connection().set_destination(
bias_node.in_port(1))
node_output_port.connect(bias_node.in_port(0))
if node.has_valid('beta') and not math.isclose(node.beta, 1):
bias_node.insert_op_on_input_port(in_port_idx=1,
new_op_class=Mul,
value=mo_array(
node.beta),
new_op_attrs={
'name':
name + '/Beta',
'can_be_scaleshift':
False
})
del node['beta']
MatMul.update_node_stat(
node, {
'transpose_a': node.has_and_set('transpose_a'),
'transpose_b': node.has_and_set('transpose_b'),
})
def replace_op(self, graph: Graph, node: Node):
node_name = node.soft_get('name', node.id)
const_dtype = np.float32
if node.has_valid('data_type'):
const_dtype = node.data_type
const = Const(graph, {'value': mo_array([1], dtype=const_dtype)}).create_node()
add = Add(graph, {'name': node.name + '/Add_'}).create_node()
log = Log(graph, {'name': node.name + '/Log_'}).create_node()
# Connect nodes: input -> Add -> Log
const.out_port(0).connect(add.in_port(0))
node.in_port(0).get_connection().set_destination(add.in_port(1))
add.out_port(0).connect(log.in_port(0))
rename_nodes([(node, node_name + '/delete'), (log, node_name)])
# The "explicit" version of the return value is: [(out_node.id, 0)])
return [log.id]
def replace_pattern(graph: Graph, match: dict):
node = match['op']
if node.has_port('in', 2) and not node.in_port(
2).disconnected() and not node.has_and_set('shape_input'):
bias_name = node.name
new_node_name = node.name + '/WithoutBiases'
add = Add(graph, dict(name=bias_name)).create_node()
rename_nodes([(node, new_node_name), (add, bias_name)])
node.out_port(0).get_connection().set_source(add.out_port(0))
node.out_port(0).connect(add.in_port(0))
node.in_port(2).get_connection().set_destination(add.in_port(1))
bias = add.in_port(1).get_source().node
if bias.has_valid("type") and bias.type == "Const":
input_shape = add.in_port(0).data.get_shape()
if len(input_shape) > 2:
dims_to_add = len(input_shape) - 2 if graph.graph[
'layout'] == 'NCHW' else 0
if dims_to_add > 0:
reshape = create_op_node_with_second_input(
graph, Reshape,
int64_array([input_shape[1]] + [1] * dims_to_add),
{'name': node.id + '/Dims'})
add.in_port(1).get_connection().set_destination(
reshape.in_port(0))
reshape.out_port(0).connect(add.in_port(1))
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_pattern(self, graph: Graph, match: dict):
quantize = match['quantize']
sum_node = Add(graph, dict()).create_node()
const = Const(graph, {'value': np.array(0.5)}).create_node()
mul_node = Mul(graph, dict()).create_node()
mul_node.in_port(0).connect(sum_node.out_port(0))
mul_node.in_port(1).connect(const.out_port(0))
quantize.in_port(1).get_connection().get_source().connect(
sum_node.in_port(0))
quantize.in_port(2).get_connection().get_source().connect(
sum_node.in_port(1))
quantize.in_port(1).disconnect()
quantize.in_port(2).disconnect()
mul_node.out_port(0).connect(quantize.in_port(1))
mul_node.out_port(0).connect(quantize.in_port(2))
def replace_op(self, graph: Graph, node: Node):
name = node.soft_get('name', node.id)
# create range of axes for MVN based on `start_axis` and rank of input
rank = Rank(graph, {'name': name + '/Rank'}).create_node()
rng = create_op_with_const_inputs(graph, Range, {
0: int64_array(2),
2: int64_array(1)
}, {
'name': name + '/Range',
'output_type': np.int64
})
mvn = MVN(
graph, {
'eps': node.epsilon,
'eps_mode': 'inside_sqrt',
'normalize_variance': 1,
'name': name + '/Ins_Norm/MVN_',
}).create_node()
node.in_port(0).get_connection().set_destination(mvn.in_port(0))
rng.out_port(0).connect(mvn.in_port(1))
mul = Mul(graph, {
'axis': 1,
'name': name + '/Ins_Norm/mul_'
}).create_node()
mvn.out_port(0).connect(mul.in_port(0))
node.in_port(1).get_connection().set_destination(mul.in_port(1))
add = Add(graph, {
'axis': 1,
'name': name + '/Ins_Norm/add_'
}).create_node()
mul.out_port(0).connect(add.in_port(0))
node.in_port(2).get_connection().set_destination(add.in_port(1))
mvn.in_port(0).get_connection().add_destination(rank.in_port(0))
rng.in_port(1).connect(rank.out_port(0))
rename_nodes([(node, name + '/TBD'), (add, name)])
return [add.id]
def replace_sub_graph(self, graph: Graph, match: dict):
# This replacer replace ImageScalar operation to Mul->Add sequence
# Also it check that weights and biases are good
op = match['op']
# Check that weights and biases are not useless
has_bias, has_weights = True, True
if all([x == 1 for x in np.nditer(op.scale)]):
has_weights = False
if all([x == 0 for x in np.nditer(op.bias)]):
has_bias = False
assert len(op.in_ports()) == 1
last_port = op.in_port(0).get_source()
# Create Mul & Add nodes
if has_weights:
mul_weights = Const(graph,
dict(value=op.scale,
shape=op.scale.shape)).create_node()
mul_op = Mul(graph, dict(name=op.id + '/mul_')).create_node()
op.in_port(0).get_connection().set_destination(mul_op.in_port(0))
mul_weights.out_port(0).connect(mul_op.in_port(1))
last_port = mul_op.out_port(0)
if has_bias:
add_bias = Const(graph, dict(value=op.bias,
shape=op.bias.shape)).create_node()
add_op = Add(graph, dict(name=op.id + '/add_')).create_node()
last_port.get_connection().set_destination(add_op.in_port(0))
add_bias.out_port(0).connect(add_op.in_port(1))
last_port = add_op.out_port(0)
op.in_port(0).disconnect()
op.out_port(0).get_connection().set_source(last_port)
def _fused_batch_norm_decomposition(graph: Graph, tinput: Port, toutput: Port, gamma: Port, beta: Port,
mean: np.ndarray, variance: np.ndarray, can_be_fused=True):
"""
This is common function for TF, Caffe and MXNet
It creates Mul->Add->Mul->Add sub graph
"""
batch_norm_name = tinput.get_connection().get_destination().node.name
# Create first Mul & Add operations
mul1_node = Mul(graph, dict(name=batch_norm_name + "/mean", can_be_fused=can_be_fused)).create_node()
add1_node = Add(graph, dict(name=batch_norm_name + "/variance", can_be_fused=can_be_fused)).create_node()
const_mul1_node = Const(graph, dict(name="data_mul_", value=mo_array(mean))).create_node()
const_add1_node = Const(graph, dict(name="data_add_", value=mo_array(variance))).create_node()
# Broadcast const from scalar
# We can broadcast only when const.value is scalar
if gamma.data.get_shape()[0] != gamma.data.get_value().shape[0]:
value = gamma.data.get_value()
value.resize(gamma.data.get_shape()).fill(value[0])
gamma.data.set_value(value)
# Create second Mul & Add
mul2_node = Mul(graph, dict(name=batch_norm_name + "/gamma", can_be_fused=can_be_fused)).create_node()
add2_node = Add(graph, dict(name=batch_norm_name + "/beta", can_be_fused=can_be_fused)).create_node()
# Connect edges Mul1->Add1->Mul2->Add2
tinput.get_connection().set_destination(mul1_node.in_port(0))
mul1_node.in_port(1).get_connection().set_source(const_mul1_node.out_port(0))
add1_node.in_port(0).get_connection().set_source(mul1_node.out_port(0))
add1_node.in_port(1).get_connection().set_source(const_add1_node.out_port(0))
mul2_node.in_port(0).get_connection().set_source(add1_node.out_port(0))
gamma.get_connection().set_destination(mul2_node.in_port(1))
add2_node.in_port(0).get_connection().set_source(mul2_node.out_port(0))
beta.get_connection().set_destination(add2_node.in_port(1))
toutput.get_connection().set_source(add2_node.out_port(0))
def get_canonical_axis_index_node(rank: Node, axis: int) -> Node:
"""
Returns positive axis value
:param rank: the node of 0D output shape to get rank of tensor from
:param axis: integer value from [-rank; rank - 1]
:return: node producing positive integer value of axis
"""
graph = rank.graph
name = rank.soft_get('name', rank.id)
if axis < 0:
axis = Const(graph, {
'name': name + '/negative_axis',
'value': int64_array(axis)
}).create_node()
add = Add(graph, {'name': name + '/positive_axis'}).create_node()
rank.out_port(0).connect(add.in_port(0))
axis.out_port(0).connect(add.in_port(1))
return add
else:
return Const(graph, {
'name': name + '/positive_axis',
'value': int64_array(axis)
}).create_node()
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 replace_pattern(self, graph: Graph, match: Dict[str, Node]):
group_norm_node = match['op']
group_norm_num_input_dims = len(group_norm_node.in_port(0).data.get_shape())
# node computing initial GroupNorm input shape
initial_shape_op_node = Shape(graph, {'name': group_norm_node.name + '/Shape'}).create_node()
initial_shape_op_node.in_port(0).connect(group_norm_node.in_port(0).get_source())
initial_shape_op_node_float = Cast(
graph, {'name': initial_shape_op_node.name + '/to_float',
'dst_type': data_type_str_to_np(graph.graph['cmd_params'].data_type)}).create_node()
initial_shape_op_node.out_port(0).connect(initial_shape_op_node_float.in_port(0))
initial_batch_dim_node = node_to_get_batch_value(initial_shape_op_node_float)
initial_features_dim_node = node_to_get_features_dimension_value(initial_shape_op_node_float)
initial_spatial_dims_node_int = node_to_get_spatial_dimensions_value(initial_shape_op_node)
initial_spatial_dims_node = Cast(
graph, {'name': initial_spatial_dims_node_int.name + '/to_float',
'dst_type': data_type_str_to_np(graph.graph['cmd_params'].data_type)}).create_node()
initial_spatial_dims_node_int.out_port(0).connect(initial_spatial_dims_node.in_port(0))
group_size_node = Const(graph, {'value': int64_array([group_norm_node.num_groups]),
'name': group_norm_node.name + '/GroupSize'}).create_node()
# calculate "features // group_size" value
reciprocal_group_size_node = Const(graph, {'value': np.array([1.0 / group_norm_node.num_groups]),
'name': group_norm_node.name + '/ReciprocalGroupSize'}).create_node()
c_div_g_node = Mul(graph, {}).create_node()
c_div_g_node.in_port(0).connect(initial_features_dim_node.out_port(0))
c_div_g_node.in_port(1).connect(reciprocal_group_size_node.out_port(0))
batch_mul_group_size_node = Mul(graph, {}).create_node()
batch_mul_group_size_node.in_port(0).connect(initial_batch_dim_node.out_port(0))
batch_mul_group_size_node.in_port(1).connect(group_size_node.out_port(0))
# create new node which concatenates several dims to one
new_shape_node_float = new_shape_node_from_shape_nodes([batch_mul_group_size_node, c_div_g_node,
initial_spatial_dims_node])
new_shape_node = Cast(graph,
{'name': new_shape_node_float.name + '/to_int64', 'dst_type': np.int64}).create_node()
new_shape_node_float.out_port(0).connect(new_shape_node.in_port(0))
reshape_for_mvn_node = Reshape(graph, {}).create_node()
group_norm_node.in_port(0).get_connection().set_destination(reshape_for_mvn_node.in_port(0))
reshape_for_mvn_node.in_port(1).connect(new_shape_node.out_port(0))
# Reshape the gamma and beta constants to correct layout from [C] to [1,C], [1,C,1], [1,C,1,1] etc
gamma_beta_shape = np.ones([group_norm_num_input_dims], dtype=np.int64)
gamma_beta_shape[1] = -1
gamma_value = group_norm_node.in_port(1).get_source().data.get_value()
beta_value = group_norm_node.in_port(2).get_source().data.get_value()
assert gamma_value is not None, 'The gamma should be constant'
assert beta_value is not None, 'The beta should be constant'
gamma_value = np.reshape(gamma_value, gamma_beta_shape)
group_norm_node.in_port(1).get_source().data.set_value(gamma_value)
beta_value = np.reshape(beta_value, gamma_beta_shape)
group_norm_node.in_port(2).get_source().data.set_value(beta_value)
# MVN
mvn_node = MVN(graph, {'name': group_norm_node.name + '/MVN',
'normalize_variance': 1,
'eps': group_norm_node.eps,
'eps_mode': 'inside_sqrt'}).create_node()
mvn_node.in_port(0).connect(reshape_for_mvn_node.out_port(0))
# MVN axes
_, rank = get_shape_and_rank_nodes_by_port(mvn_node.in_port(0).get_connection().get_source(),
return_as_a_scalar=True)
rng = create_op_with_const_inputs(graph, Range, {0: int64_array(1), 2: int64_array(1)},
{'name': group_norm_node.name + '/Range', 'output_type': np.int64})
mvn_node.in_port(1).connect(rng.out_port(0))
rng.in_port(1).connect(rank.out_port(0))
# reshape to the initial shape before multiplying with gamma and adding beta
reshape_to_initial_shape_node = Reshape(graph, {}).create_node()
reshape_to_initial_shape_node.in_port(0).connect(mvn_node.out_port(0))
reshape_to_initial_shape_node.in_port(1).connect(initial_shape_op_node.out_port(0))
mul_node = Mul(graph, {'name': mvn_node.name + '/Mul'}).create_node()
mul_node.in_port(0).connect(reshape_to_initial_shape_node.out_port(0))
group_norm_node.in_port(1).get_connection().set_destination(mul_node.in_port(1))
add_node = Add(graph, {'name': mul_node.name + '/Add'}).create_node()
add_node.in_port(0).connect(mul_node.out_port(0))
group_norm_node.in_port(2).get_connection().set_destination(add_node.in_port(1))
group_norm_node.out_port(0).get_connection().set_source(add_node.out_port(0))
def replace_op(self, graph: Graph, node: Node):
input_out_port = node.in_port(0).get_source()
memory_pair_input = unique_id('id')
memory_pair_output = unique_id('id')
# Input -> FullyConnected
fc_layer_after_input_attrs = {
'name': 'input_fullyconnected',
'out-size': node.gifo_x_weights_shape[0],
'transpose_weights': True,
'bias_term': True,
}
fc_layer_after_input = FullyConnected(
graph, fc_layer_after_input_attrs).create_node()
fc_layer_after_input.in_port(0).connect(input_out_port)
input_as_const(fc_layer_after_input, fc_layer_after_input_attrs, 1,
'weights', node.gifo_x_weights)
input_as_const(fc_layer_after_input, fc_layer_after_input_attrs, 2,
'biases', node.gifo_biases)
init_value_prev_lstm_output = create_const_with_batch_from_input(
input_out_port, node.gifo_r_weights_shape[1])
prev_lstm_output = ReadValue(graph, {
'name': 'prev_memory_output',
'variable_id': memory_pair_input
}).create_node()
prev_lstm_output.in_port(0).connect(
init_value_prev_lstm_output.out_port(0))
# *Memory(output) -> FullyConnected
fc_layer_from_prev_state_attrs = {
'name': 'prev_memory_output_fullyconnected',
'out-size': node.gifo_r_weights_shape[0],
'transpose_weights': True,
'bias_term': False,
}
fc_layer_from_prev_state = FullyConnected(
graph, fc_layer_from_prev_state_attrs).create_node()
fc_layer_from_prev_state.in_port(0).connect(
prev_lstm_output.out_port(0))
input_as_const(fc_layer_from_prev_state,
fc_layer_from_prev_state_attrs, 1, 'weights',
node.gifo_r_weights)
# Memory -> FullyConnected \
# *Eltwise(sum)
# Input -> FullyConnected /
join_input_prev_state_sum = Add(graph, {
'name': 'join_input_eltwise'
}).create_node()
join_input_prev_state_sum.in_port(0).connect(
fc_layer_from_prev_state.out_port(0))
join_input_prev_state_sum.in_port(1).connect(
fc_layer_after_input.out_port(0))
# *Eltwise(sum) -> Split
# it is split into 4 nodes: Act, Eltw*3
# the following order is mandatory
# ___Tanh
# /
# Split ---(2)Eltwise(sum)
# |\
# | \__(3)Eltwise(sum)
# |____(4)Eltwise(sum)
split_joined_input_axis = Const(graph, {
'value': np.int64(1)
}).create_node()
split_joined_input = Split(graph, {
'name': 'join_input_split',
'num_splits': 4,
'out_ports_count': 4
}).create_node()
split_joined_input.in_port(0).connect(
join_input_prev_state_sum.out_port(0))
split_joined_input.in_port(1).connect(
split_joined_input_axis.out_port(0))
init_value_prev_lstm_state = create_const_with_batch_from_input(
split_joined_input.out_port(0), node.input_gate_weights.shape[0])
prev_lstm_state = ReadValue(graph, {
'name': 'prev_memory_state',
'variable_id': memory_pair_output
}).create_node()
prev_lstm_state.in_port(0).connect(
init_value_prev_lstm_state.out_port(0))
# *Memory(state) -> *ScaleShift(input)
state_input_scaleshift_attrs = {
'name': 'input_scaleshift',
'bias_term': False
}
state_input_scaleshift = ScaleShiftOp(
graph, state_input_scaleshift_attrs).create_node()
state_input_scaleshift.in_port(0).connect(prev_lstm_state.out_port(0))
input_as_const(state_input_scaleshift, state_input_scaleshift_attrs, 1,
'weights', node.input_gate_weights)
# *Memory(state) -> *ScaleShift(forget)
state_forget_scaleshift_attrs = {
'name': 'forget_scaleshift',
'bias_term': False
}
state_forget_scaleshift = ScaleShiftOp(
graph, state_forget_scaleshift_attrs).create_node()
state_forget_scaleshift.in_port(0).connect(prev_lstm_state.out_port(0))
input_as_const(state_forget_scaleshift, state_forget_scaleshift_attrs,
1, 'weights', node.forget_gate_weights)
# Split \
# (2)Eltwise(sum)
# Memory(state) -> *ScaleShift(input) /
join_prev_lstm_input_joined_input_sum = Add(
graph, {
'name': 'join_prev_lstm_input_joined_input_eltwise'
}).create_node()
join_prev_lstm_input_joined_input_sum.in_port(0).connect(
split_joined_input.out_port(1))
join_prev_lstm_input_joined_input_sum.in_port(1).connect(
state_input_scaleshift.out_port(0))
# Split \
# (3)Eltwise(sum)
# Memory(state) -> *ScaleShift(forget) /
join_prev_lstm_input_joined_forget_sum = Add(
graph, {
'name': 'join_prev_lstm_input_joined_forget_sum',
}).create_node()
join_prev_lstm_input_joined_forget_sum.in_port(0).connect(
split_joined_input.out_port(2))
join_prev_lstm_input_joined_forget_sum.in_port(1).connect(
state_forget_scaleshift.out_port(0))
# Split -> Tanh
remember_tahn = Tanh(graph, {'name': 'remember_tahnv'}).create_node()
remember_tahn.in_port(0).connect(split_joined_input.out_port(0))
# Split -> (2)Eltwise(sum) -> *Sigmoid
remember_sigmoid = Sigmoid(graph, {
'name': 'remember_sigmoid'
}).create_node()
remember_sigmoid.in_port(0).connect(
join_prev_lstm_input_joined_input_sum.out_port(0))
# Split -> (3)Eltwise(sum) -> **Sigmoid
forget_sigmoid = Sigmoid(graph, {
'name': 'forget_sigmoid'
}).create_node()
forget_sigmoid.in_port(0).connect(
join_prev_lstm_input_joined_forget_sum.out_port(0))
# *Memory(state) \
# (6)Eltwise(mul)
# Split -> (3)Eltwise(sum) -> **Sigmoid /
join_forget_prev_state_mul = Mul(graph, {
'name': 'join_forget_prev_state_mul'
}).create_node()
join_forget_prev_state_mul.in_port(0).connect(
forget_sigmoid.out_port(0))
join_forget_prev_state_mul.in_port(1).connect(
prev_lstm_state.out_port(0))
# Split -> Tahn \
# (5)Eltwise(mul)
# Split -> (2)Eltwise(sum) -> *Sigmoid /
join_remember_candidates_mul = Mul(
graph, {
'name': 'join_remember_candidates_mul'
}).create_node()
join_remember_candidates_mul.in_port(0).connect(
remember_tahn.out_port(0))
join_remember_candidates_mul.in_port(1).connect(
remember_sigmoid.out_port(0))
# (5)Eltwise(mul) \
# (7)Eltwise(sum)
# (6)Eltwise(mul) /
join_forget_remember_sum = Add(graph, {
'name': 'join_forget_remember_sum'
}).create_node()
join_forget_remember_sum.in_port(0).connect(
join_forget_prev_state_mul.out_port(0))
join_forget_remember_sum.in_port(1).connect(
join_remember_candidates_mul.out_port(0))
# (7)Eltwise(sum) -> Clamp
join_forget_clamp = create_op_with_const_inputs(
graph, Clamp, {
1: np.array(-node.clip_value, dtype=np.float32),
2: np.array(node.clip_value, dtype=np.float32)
}, {'name': 'join_forget_clamp'}, join_forget_remember_sum)
#
# Clamp -> (2)Memory(state)
next_lstm_state = Assign(graph, {
'name': 'next_lstm_state',
'variable_id': memory_pair_output
}).create_node()
next_lstm_state.in_port(0).connect(join_forget_clamp.out_port(0))
res_node = Result(graph, {'name': 'next_lstm_state_out'}).create_node()
res_node.in_port(0).connect(next_lstm_state.out_port(0))
# Clamp -> (2)Tahn
state_filtered_tahn = Tanh(graph, {
'name': 'state_filtered_tahn'
}).create_node()
state_filtered_tahn.in_port(0).connect(join_forget_clamp.out_port(0))
# Clamp -> (2)ScaleShift
clamp_scaleshift_attrs = {
'name': 'clamp_scaleshift',
'bias_term': False
}
clamp_scaleshift = ScaleShiftOp(graph,
clamp_scaleshift_attrs).create_node()
clamp_scaleshift.in_port(0).connect(join_forget_clamp.out_port(0))
input_as_const(clamp_scaleshift, clamp_scaleshift_attrs, 1, 'weights',
node.output_gate_weights)
# Split \
# (4)Eltwise(sum)
# Clamp -> (2)ScaleShift /
join_next_lstm_input_joined_input_sum = Add(
graph, {
'name': 'join_next_lstm_input_joined_input_sum',
}).create_node()
join_next_lstm_input_joined_input_sum.in_port(0).connect(
split_joined_input.out_port(3))
join_next_lstm_input_joined_input_sum.in_port(1).connect(
clamp_scaleshift.out_port(0))
# (4)Eltwise(sum) -> (3)Sigmoid
output_sigmoid = Sigmoid(graph, {
'name': 'output_sigmoid'
}).create_node()
output_sigmoid.in_port(0).connect(
join_next_lstm_input_joined_input_sum.out_port(0))
# (4)Eltwise(sum) -> (3)Sigmoid \
# (5)Eltwise(mul)
# Clamp -> (2)Tahn /
joined_output_mul = Mul(graph, {
'name': 'joined_output_mul'
}).create_node()
joined_output_mul.in_port(0).connect(state_filtered_tahn.out_port(0))
joined_output_mul.in_port(1).connect(output_sigmoid.out_port(0))
# (5)Eltwise(mul) -> (3)FullyConnected
fc_output_attrs = {
'name': 'FullyConnected',
'out-size': node.projection_weights_shape[0],
'transpose_weights': True,
'bias_term': False
}
fc_output = FullyConnected(graph, fc_output_attrs).create_node()
fc_output.in_port(0).connect(joined_output_mul.out_port(0))
input_as_const(fc_output, fc_output_attrs, 1, 'weights',
node.projection_weights)
# / (2)Memory(output)
# (3)FullyConnected
# \ Output (any next node) (edge created automatically after replacement)
next_lstm_output = Assign(graph, {
'name': 'next_lstm_output',
'variable_id': memory_pair_input
}).create_node()
next_lstm_output.in_port(0).connect(fc_output.out_port(0))
res_node_lstm_output = Result(graph, {
'name': 'next_lstm_output_out'
}).create_node()
res_node_lstm_output.in_port(0).connect(next_lstm_output.out_port(0))
return [fc_output.id]
def replace_op(self, graph: Graph, node: Node):
node_name = node.soft_get('name', node.id)
# check if we have dropout
input_port = node.in_port(0)
if node.has_and_set('use_dropout'):
split_dropout = AttributedVariadicSplit(graph,
{'name': node_name + '/split_dropout',
'size_splits': int64_array([-1, 1, 1, 1]),
'axis': int64_array(1)}).create_node()
input_port.get_connection().set_destination(split_dropout.in_port(0))
input_port = split_dropout.out_port(0)
i_drop_scale = split_dropout.out_port(1)
f_drop_scale = split_dropout.out_port(2)
o_drop_scale = split_dropout.out_port(3)
# split input to (i_part, f_part, c_part, o_part, ct_1)
split_node = create_op_with_const_inputs(graph, Split, {1: np.int64(1)},
{'name': node_name + '/split_lstm_input',
'num_splits': 5})
input_port.get_connection().set_destination(split_node.in_port(0))
i_part = split_node.out_port(0)
f_part = split_node.out_port(1)
c_part = split_node.out_port(2)
o_part = split_node.out_port(3)
ct_1 = split_node.out_port(4)
# i_t = Sigmoid(i_part + w_ic*ct_1)
i_scale_attrs = {'name': node_name + '/i_scaleshift',
'bias_term': False}
i_scale = ScaleShiftOp(graph, i_scale_attrs).create_node()
input_as_const(i_scale, i_scale_attrs, 1, 'weights', node.i_weights)
ct_1.connect(i_scale.in_port(0))
sum_i_c = Add(graph, {'name': node_name + '/sum_i_c_'}).create_node()
i_part.connect(sum_i_c.in_port(0))
i_scale.out_port(0).connect(sum_i_c.in_port(1))
i_sigmoid = Sigmoid(graph, {'name': node_name + '/i_sigmoid'}).create_node()
sum_i_c.out_port(0).connect(i_sigmoid.in_port(0))
if node['use_dropout']:
mul_dropout_i = Mul(graph, {'name': split_node.soft_get('name', split_node.id) + '/mul_i'}).create_node()
mul_dropout_i.in_port(0).connect(i_sigmoid.out_port(0))
mul_dropout_i.in_port(1).connect(i_drop_scale)
i_sigmoid = mul_dropout_i
# f_t = Sigmoid(f_part + w_fc*ct_1)
f_scale_attrs = {'name': node_name + '/f_scaleshift',
'bias_term': False}
f_scale = ScaleShiftOp(graph, f_scale_attrs).create_node()
input_as_const(f_scale, f_scale_attrs, 1, 'weights', node.f_weights)
ct_1.connect(f_scale.in_port(0))
sum_f_c = Add(graph, {'name': node_name + '/sum_f_c_'}).create_node()
f_part.connect(sum_f_c.in_port(0))
f_scale.out_port(0).connect(sum_f_c.in_port(1))
f_sigmoid = Sigmoid(graph, {'name': node_name + '/f_sigmoid'}).create_node()
sum_f_c.out_port(0).connect(f_sigmoid.in_port(0))
if node['use_dropout']:
mul_dropout_f = Mul(graph, {'name': split_node.soft_get('name', split_node.id) + '/mul_f'}).create_node()
mul_dropout_f.in_port(0).connect(f_sigmoid.out_port(0))
mul_dropout_f.in_port(1).connect(f_drop_scale)
f_sigmoid = mul_dropout_f
# c_t = f_t*ct_1 + i_t * tanh(c_part)
c_tanh = Tanh(graph, {'name': node_name + '/c_tanh'}).create_node()
c_part.connect(c_tanh.in_port(0))
prod_i_c_tanh = Mul(graph, {'name': node_name + '/prod_i_c_tanh_'}).create_node()
i_sigmoid.out_port(0).connect(prod_i_c_tanh.in_port(0))
c_tanh.out_port(0).connect(prod_i_c_tanh.in_port(1))
prod_f_ct_1 = Mul(graph, {'name': node_name + '/prod_f_ct_1_'}).create_node()
f_sigmoid.out_port(0).connect(prod_f_ct_1.in_port(0))
ct_1.connect(prod_f_ct_1.in_port(1))
sum_f_i = Add(graph, {'name': node_name + '/sum_f_i_'}).create_node()
prod_f_ct_1.out_port(0).connect(sum_f_i.in_port(0))
prod_i_c_tanh.out_port(0).connect(sum_f_i.in_port(1))
# o_t = Sigmoid(o_part + w_oc*c_t)
o_scale_attrs = {'name': node_name + '/o_scaleshift',
'bias_term': False}
o_scale = ScaleShiftOp(graph, o_scale_attrs).create_node()
input_as_const(o_scale, o_scale_attrs, 1, 'weights', node.o_weights)
sum_f_i.out_port(0).connect(o_scale.in_port(0))
sum_o_c = Add(graph, {'name': node_name + '/sum_o_c_'}).create_node()
o_part.connect(sum_o_c.in_port(0))
o_scale.out_port(0).connect(sum_o_c.in_port(1))
o_sigmoid = Sigmoid(graph, {'name': node_name + '/o_sigmoid'}).create_node()
sum_o_c.out_port(0).connect(o_sigmoid.in_port(0))
if node['use_dropout']:
mul_dropout_o = Mul(graph, {'name': split_node.soft_get('name', split_node.id) + '/mul_o'}).create_node()
mul_dropout_o.in_port(0).connect(o_sigmoid.out_port(0))
mul_dropout_o.in_port(1).connect(o_drop_scale)
o_sigmoid = mul_dropout_o
# m_t = o_t * Tanh(c_t)
c_t_tanh = Tanh(graph, {'name': node_name + '/c_t_tanh'}).create_node()
sum_f_i.out_port(0).connect(c_t_tanh.in_port(0))
prod_o_c_t_tanh = Mul(graph, {'name': node_name + '/prod_o_c_t_tanh_'}).create_node()
o_sigmoid.out_port(0).connect(prod_o_c_t_tanh.in_port(0))
c_t_tanh.out_port(0).connect(prod_o_c_t_tanh.in_port(1))
# add concat to create 1 output
concat = Concat(graph, {'name': node_name + '/concat_c_m'}).create_node()
concat.add_sequence_of_ports('in', range(2))
sum_f_i.out_port(0).connect(concat.in_port(0))
prod_o_c_t_tanh.out_port(0).connect(concat.in_port(1))
return [concat.id]
def replace_pattern(self, graph: Graph, match: [str, Node]):
node = match['crop']
assert node.has_valid('axis')
node_axis = self.list_to_ndarray(node.axis)
in_shape = node.in_port(0).data.get_shape()
shape_rank = in_shape.size
axis_mask = int64_array(
[1 if i in node_axis else 0 for i in range(shape_rank)])
begin_mask = axis_mask.copy()
end_mask = axis_mask.copy()
ss = StridedSlice(
graph, {
'name': node.soft_get('name', node.id) + '/strided_slice',
'begin_mask': begin_mask,
'end_mask': end_mask,
'new_axis_mask': np.zeros(len(end_mask)),
'shrink_axis_mask': np.zeros(len(end_mask)),
'ellipsis_mask': np.zeros(len(end_mask))
}).create_node()
if len(node.in_nodes()) == 2 and node.has_valid('offset'):
# Crop Type 1
begin = Const(
graph, {
'value':
self.mask_normalizer(shape_rank, node_axis, node.offset),
'name':
ss.name + '/begin'
}).create_node()
shape = Shape(graph, {
'name': ss.name + '/shape_of_crop'
}).create_node()
end = Add(graph, {'name': ss.name + '/end'}).create_node()
node.in_port(1).get_connection().get_source().connect(
shape.in_port(0))
node.in_port(1).disconnect()
shape.out_port(0).connect(end.in_port(0))
begin.out_port(0).connect(end.in_port(1))
elif node.has_valid('dim') and node.has_valid('offset'):
# Crop Type 2
node_dim = self.list_to_ndarray(node.dim)
node_offset = self.list_to_ndarray(node.offset)
assert node_dim.size == node_offset.size == node_axis.size
begin = Const(
graph, {
'value':
self.mask_normalizer(shape_rank, node_axis, node_offset),
'name':
ss.name + '/begin'
}).create_node()
end_values = mo_array(
[node_offset[i] + node_dim[i] for i in range(len(node_dim))])
end = Const(
graph, {
'value':
self.mask_normalizer(shape_rank, node_axis, end_values),
'name':
ss.name + '/end'
}).create_node()
elif node.has_valid('crop_begin') and node.has_valid('crop_end'):
# Crop Type 3
node_crop_begin = self.list_to_ndarray(node.crop_begin)
node_crop_end = self.list_to_ndarray(node.crop_end)
assert len(node_crop_begin) == len(node_crop_end) == len(node_axis)
begin = Const(
graph, {
'value':
self.mask_normalizer(shape_rank, node_axis,
node_crop_begin),
'name':
ss.name + '/begin'
}).create_node()
shape = Shape(graph, {'name': ss.name + '/shape'}).create_node()
end = Add(graph, {'name': ss.name + '/end'}).create_node()
const = Const(
graph, {
'value':
-1 *
self.mask_normalizer(shape_rank, node_axis, node_crop_end),
'name':
ss.name + '/const'
}).create_node()
node.in_port(0).get_connection().get_source().connect(
shape.in_port(0))
shape.out_port(0).connect(end.in_port(0))
const.out_port(0).connect(end.in_port(1))
else:
raise Exception("Unknown type of Crop")
source = node.in_port(0).get_connection().get_source()
stride = Const(
graph, {
'value': np.ones(shape_rank, dtype=np.int64),
'name': ss.name + '/stride'
}).create_node()
source.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))
node.in_port(0).disconnect()
node.out_port(0).get_connection().set_source(ss.out_port(0))
ss['force_precision_in_ports'] = {1: 'int64', 2: 'int64', 3: 'int64'}
