def replace_pattern(graph: Graph, match: dict):
node = match['op']
pair_node = Node(graph, node.pair_name)
if node.t >= 0:
raise Error('Does not support IfDefined with t > 0')
if node.in_port(0).get_source() is not None:
input_port = node.in_port(0).get_source()
op_output_id = node.out_port(0).get_destination().node.id
out_port = pair_node.out_port(0)
node_name = node.name
pair_name = pair_node.name
else:
input_port = pair_node.in_port(0).get_source()
op_output_id = pair_node.out_port(0).get_destination().node.id
out_port = node.out_port(0)
node_name = pair_node.name
pair_name = node.name
in_shape = input_port.data.get_shape()
node_t = abs(node.t)
init_value_memory_out = create_zero_value_with_batch_from_input(input_port, in_shape[1]*node_t)
memory_out = ReadValue(graph, {'name': pair_name, 'variable_id': node_name+pair_name}).create_node()
init_value_memory_out.out_port(0).connect(memory_out.in_port(0))
if node_t > 1:
crop_concat = Crop(graph, {'name': 'Memory_crop', 'dim': np.array([in_shape[1]*(node_t-1)]),
'offset': np.array([in_shape[1]]), 'axis': np.array([1])}).create_node()
memory_out.out_port(0).connect(crop_concat.in_port(0))
concat = Concat(graph, {'name': 'Memory_concat'}).create_node()
concat.add_sequence_of_ports('in', range(2))
crop_concat.out_port(0).connect(concat.in_port(0))
concat.in_port(1).connect(input_port)
memory_in = Assign(graph, {'name': node_name, 'variable_id': node_name + pair_name}).create_node()
concat.out_port(0).connect(memory_in.in_port(0))
out = Result(graph, {'name': 'Memory_output'}).create_node()
memory_in.out_port(0).connect(out.in_port(0))
crop_out = Crop(graph, {'name': 'Memory_crop_out', 'dim': np.array([in_shape[1]]),
'offset': np.array([0]), 'axis': np.array([1])}).create_node()
memory_out.out_port(0).connect(crop_out.in_port(0))
out_port.get_connection().set_source(crop_out.out_port(0))
else:
memory_in = Assign(graph, {'name': node_name, 'variable_id': node_name + pair_name}).create_node()
memory_in.in_port(0).connect(input_port)
out = Result(graph, {'name': 'Memory_output'}).create_node()
memory_in.out_port(0).connect(out.in_port(0))
out_port.get_connection().set_source(memory_out.out_port(0))
graph.remove_node(op_output_id)
graph.remove_node(node.id)
graph.remove_node(pair_node.id)
def replace_pattern(graph: Graph, match: dict):
node = match['op']
pair_node = Node(graph, node.pair_name)
if node.t >= 0:
raise Error('Does not support IfDefined with t > 0')
if node.in_port(0).get_source() is not None:
input_port = node.in_port(0).get_source()
op_output_id = node.out_port(0).get_destination().node.id
out_port = pair_node.out_port(0)
node_name = node.name
pair_name = pair_node.name
else:
input_port = pair_node.in_port(0).get_source()
op_output_id = pair_node.out_port(0).get_destination().node.id
out_port = node.out_port(0)
node_name = pair_node.name
pair_name = node.name
in_shape = input_port.data.get_shape()
node_t = abs(node.t)
init_value_memory_out = create_zero_value_with_batch_from_input(
input_port, in_shape[1] * node_t)
memory_out = ReadValue(graph, {
'name': pair_name,
'variable_id': node_name + pair_name
}).create_node()
init_value_memory_out.out_port(0).connect(memory_out.in_port(0))
if node_t > 1:
crop_concat = Crop(
graph, {
'name': 'Memory_crop',
'dim': np.array([in_shape[1] * (node_t - 1)]),
'offset': np.array([in_shape[1]]),
'axis': np.array([1])
}).create_node()
memory_out.out_port(0).connect(crop_concat.in_port(0))
concat = Concat(graph, {'name': 'Memory_concat'}).create_node()
concat.add_sequence_of_ports('in', range(2))
crop_concat.out_port(0).connect(concat.in_port(0))
concat.in_port(1).connect(input_port)
memory_in = Assign(graph, {
'name': node_name,
'variable_id': node_name + pair_name
}).create_node()
concat.out_port(0).connect(memory_in.in_port(0))
out = Result(graph, {'name': 'Memory_output'}).create_node()
memory_in.out_port(0).connect(out.in_port(0))
crop_out = Crop(
graph, {
'name': 'Memory_crop_out',
'dim': np.array([in_shape[1]]),
'offset': np.array([0]),
'axis': np.array([1])
}).create_node()
memory_out.out_port(0).connect(crop_out.in_port(0))
out_port.get_connection().set_source(crop_out.out_port(0))
else:
memory_in = Assign(graph, {
'name': node_name,
'variable_id': node_name + pair_name
}).create_node()
memory_in.in_port(0).connect(input_port)
out = Result(graph, {'name': 'Memory_output'}).create_node()
memory_in.out_port(0).connect(out.in_port(0))
out_port.get_connection().set_source(memory_out.out_port(0))
if not graph.graph['cmd_params'].static_shape:
log.error(
"Model can not be translated in a reshape-able way.\n"
"Model Optimizer key static_shape was turned on to prevent related errors.\n"
"There will be no success changing input shapes of the model with the help of "
"InferenceEngine reshape method",
extra={'is_warning': True})
graph.graph['cmd_params'].static_shape = True
graph.remove_node(op_output_id)
graph.remove_node(node.id)
graph.remove_node(pair_node.id)
def replace_pattern(graph: Graph, match: dict):
node = match['op']
if node.name == 'iteration_number_out':
return
# calculate length of context when state of inference becomes meaningful
inputs = []
for n in graph.get_op_nodes(**{'op': 'Parameter'}):
inputs.append(n)
in_nodes = []
for inp in inputs:
for ins in inp.out_port(0).get_destinations():
in_nodes.append(ins.node.name)
context_len = 1
try:
subgraph = invert_sub_graph_between_nodes(
graph, [node.in_port(0).get_source().node.name], in_nodes)
except Error:
return
for n in subgraph:
n_node = Node(graph, n)
if n_node.kind == 'op' and n_node.op == 'Splice':
context_len += len(n_node.context) - 1
if context_len == 1:
return
in_node_port = node.in_port(0).get_source()
in_node_shape = node.in_port(0).data.get_shape()
node.in_port(0).disconnect()
# add Select before saving state to avoid saving garbage
select_node = Select(graph, {
'name': 'select_' + node.name
}).create_node()
zero_else = Const(graph, {
'name': 'zero_else',
'value': np.zeros(in_node_shape)
}).create_node()
select_node.in_port(1).connect(in_node_port)
select_node.in_port(2).connect(zero_else.out_port(0))
# check if we have already appropriate iteration counter
existing_counters = find_pattern_matches(
graph,
nodes=[('mem_in', dict(op='ReadValue')),
('mem_in_data', dict(shape=int64_array([context_len]))),
('crop_mem_in',
dict(op='Crop',
axis=int64_array([1]),
offset=int64_array([1]),
dim=int64_array([context_len - 1]))),
('crop_mem_in_data', dict()),
('concat', dict(op='Concat', axis=1)),
('concat_data', dict()), ('const_1', dict(op='Const')),
('const_1_data', dict()), ('mem_out', dict(op='Assign')),
('crop_out',
dict(op='Crop',
axis=int64_array([1]),
offset=int64_array([0]),
dim=int64_array([1]))), ('crop_out_data', dict()),
('select', dict(op='Select'))],
edges=[('mem_in', 'mem_in_data'), ('mem_in_data', 'crop_mem_in'),
('crop_mem_in', 'crop_mem_in_data'),
('crop_mem_in_data', 'concat', {
'in': 0
}), ('const_1', 'const_1_data'),
('const_1_data', 'concat', {
'in': 1
}), ('concat', 'concat_data'), ('concat_data', 'mem_out'),
('concat_data', 'crop_out'), ('crop_out', 'crop_out_data'),
('crop_out_data', 'select')])
counter_match = next(existing_counters, None)
if counter_match is not None:
ones = Node(graph, inverse_dict(counter_match)['const_1'])
input_port = Node(
graph,
inverse_dict(counter_match)['crop_out']).out_port(0)
else:
init_value_mem_out = create_zero_value_with_batch_from_input(
in_node_port, context_len, np.int32)
mem_out = ReadValue(
graph, {
'name': 'iteration_number',
'variable_id': 'iteration_' + node.name
}).create_node()
mem_out.in_port(0).connect(init_value_mem_out.out_port(0))
cut_first = Crop(
graph, {
'name': 'cut_first',
'axis': int64_array([1]),
'offset': int64_array([1]),
'dim': int64_array([context_len - 1])
}).create_node()
cut_first.in_port(0).connect(mem_out.out_port(0))
ones = Const(graph, {
'name': 'ones',
'value': np.ones([1, 1], dtype=np.int32)
}).create_node()
concat = Concat(graph, {
'name': 'concat_ones',
'in_ports_count': 2,
'axis': 1
}).create_node()
concat.in_port(0).connect(cut_first.out_port(0))
concat.in_port(1).connect(ones.out_port(0))
mem_in = Assign(
graph, {
'name': 'iteration_number_out',
'variable_id': 'iteration_' + node.name
}).create_node()
mem_in.in_port(0).connect(concat.out_port(0))
res = Result(graph, {}).create_node()
mem_in.out_port(0).connect(res.in_port(0))
cut_last = Crop(
graph, {
'name': 'cut_last',
'axis': int64_array([1]),
'offset': int64_array([0]),
'dim': int64_array([1])
}).create_node()
cut_last.in_port(0).connect(concat.out_port(0))
input_port = cut_last.out_port(0)
# Check if data from memory is 1
# if it is True, we have correct data and should proceed with saving it to memory
# else we have not gathered context and have garbage here, shouldn't change initial state of memory
cast_in = Equal(graph, {
'name': input_port.node.name + '/cast_to_bool'
}).create_node()
cast_in.in_port(0).connect(ones.out_port(0))
cast_in.in_port(1).connect(input_port)
select_node.in_port(0).connect(cast_in.out_port(0))
select_node.out_port(0).connect(node.in_port(0))
select_node.out_port(0).data.set_shape(in_node_shape)
def replace_pattern(graph: Graph, match: dict):
node = match['op']
in_shape = node.in_port(0).data.get_shape().copy()
memory_element = in_shape[1] - node.const_dim
memory_size = memory_element * len(node.context)
memory_pair_id = unique_id('id')
# Memory(in)
input_memory = ReadValue(graph, {
'name': 'prev_splice_memory',
'variable_id': memory_pair_id
}).create_node()
# Memory(in) \
# Crop
# Input(temp) /
crop = Crop(
graph, {
'name': 'Splice_Crop',
'axis': int64_array([1]),
'offset': int64_array([memory_element]),
'dim': int64_array([memory_size - memory_element])
}).create_node()
crop.in_port(0).connect(input_memory.out_port(0))
# Crop \
# Concat
# Input /
concat_node = Concat(graph, {
'name': 'Splice_Concat',
'in_ports_count': 2,
'axis': 1
}).create_node()
concat_node.in_port(0).connect(crop.out_port(0))
# Concat -> Memory(out)
mem_out = Assign(graph, {
'name': 'out_splice_memory',
'variable_id': memory_pair_id
}).create_node()
mem_out.in_port(0).connect(concat_node.out_port(0))
Result(graph).create_node().in_port(0).connect(mem_out.out_port(0))
if node.const_dim != 0:
memory_element_constdim = node.const_dim
memory_size_constdim = memory_element_constdim * len(node.context)
split = create_op_with_const_inputs(
graph, VariadicSplit, {
1: int64_array(1),
2: int64_array([memory_element, memory_element_constdim])
}, {
'name': node.id + '_split_const',
'out_ports_count': 2
})
split.out_port(0).connect(concat_node.in_port(1))
# create separate splice construction for const_dim
memory_pair_id = unique_id('memory_for_const_dim')
init_value_input_memory_const_dim = create_zero_value_with_batch_from_input(
split.out_port(1), memory_size_constdim)
input_memory_const_dim = ReadValue(graph, {
'name': 'const_dim_in_memory',
'variable_id': memory_pair_id
}).create_node()
init_value_input_memory_const_dim.out_port(0).connect(
input_memory_const_dim.in_port(0))
crop_const_dim = Crop(
graph, {
'name':
'const_dim_crop',
'axis':
int64_array([1]),
'offset':
int64_array([memory_element_constdim]),
'dim':
int64_array(
[memory_size_constdim - memory_element_constdim])
}).create_node()
crop_const_dim.in_port(0).connect(
input_memory_const_dim.out_port(0))
concat_node_const_dim = Concat(graph, {
'name': 'const_dim_concat',
'in_ports_count': 2,
'axis': 1
}).create_node()
concat_node_const_dim.in_port(0).connect(
crop_const_dim.out_port(0))
mem_out_const_dim = Assign(graph, {
'name': 'const_dim_out_memory',
'variable_id': memory_pair_id
}).create_node()
mem_out_const_dim.in_port(0).connect(
concat_node_const_dim.out_port(0))
Result(graph).create_node().in_port(0).connect(
mem_out_const_dim.out_port(0))
# connect splice to Split as begin and Concat as the end
split.out_port(1).connect(concat_node_const_dim.in_port(1))
crop_first = Crop(
graph, {
'name': 'const_dim_crop_first',
'axis': int64_array([1]),
'offset': int64_array([0]),
'dim': int64_array([memory_element_constdim])
}).create_node()
crop_first.in_port(0).connect(concat_node_const_dim.out_port(0))
concat_const = Concat(graph, {
'name': node.id + '_concat_const',
'axis': 1,
'in_ports_count': 2
}).create_node()
concat_const.in_port(1).connect(crop_first.out_port(0))
concat_const.in_port(0).connect(concat_node.out_port(0))
init_value_input_memory = create_zero_value_with_batch_from_input(
split.out_port(0), memory_size)
init_value_input_memory.out_port(0).connect(
input_memory.in_port(0))
node.in_port(0).get_connection().set_destination(split.in_port(0))
node.out_port(0).get_connection().set_source(
concat_const.out_port(0))
else:
init_value_input_memory = create_zero_value_with_batch_from_input(
node.in_port(0).get_source(), memory_size)
init_value_input_memory.out_port(0).connect(
input_memory.in_port(0))
node.in_port(0).get_connection().set_destination(
concat_node.in_port(1))
node.out_port(0).get_connection().set_source(
concat_node.out_port(0))
# to avoid re-inference of shape and touching in next replacements
graph.remove_node(node.id)
