def unroll_ellipsis_for_inputs(graph: Graph, node: Node, ellipsis_start: int, num_insertions: int):
node_name = node.soft_get('name', node.id)
for i, input_name in [(1, 'begin'), (2, 'end'), (3, 'strides')]:
if i == 3 and not node.is_in_port_connected(3):
continue # no need to extend strides if they are not connected
blank_values_arr = np.zeros(num_insertions) if input_name != 'strides' else np.ones(num_insertions)
blank_values_node = Const(graph, {'name': node_name + '/const_to_unroll_{}_ellipsis'.format(input_name),
'value': int64_array(blank_values_arr)}).create_node()
concat_in_ports_count = 3 if ellipsis_start != 0 else 2
concat = Concat(graph, {'axis': 0, 'name': node_name + '/concat_{}'.format(input_name),
'in_ports_count': concat_in_ports_count}).create_node()
if ellipsis_start != 0:
split = create_op_with_const_inputs(graph, VariadicSplit, {1: int64_array(0),
2: int64_array([ellipsis_start, -1])},
{'name': node_name + '/split_for_{}_ellipsis'.format(input_name),
'out_ports_count': 2})
node.in_port(i).get_connection().set_destination(split.in_port(0))
concat.in_port(0).connect(split.out_port(0))
concat.in_port(1).connect(blank_values_node.out_port(0))
concat.in_port(2).connect(split.out_port(1))
else:
concat.in_port(0).connect(blank_values_node.out_port(0))
node.in_port(i).get_connection().set_destination(concat.in_port(1))
concat.out_port(0).get_connection().set_destination(node.in_port(i))
def extend_inputs(node: Node, num_insertions: int):
graph = node.graph
node_name = node.soft_get('name', node.id)
for i, input_name in [(1, 'begin'), (2, 'end'), (3, 'strides')]:
if i == 3 and not node.is_in_port_connected(3):
continue # no need to extend strides if they are not connected
blank_values_arr = np.zeros(num_insertions) if input_name != 'strides' else np.ones(num_insertions)
blank_values_node = Const(graph, {'name': node_name + '/extend_{}_const'.format(input_name),
'value': int64_array(blank_values_arr)}).create_node()
if node.in_port(i).get_source().node.soft_get('type') == 'Concat':
# concat already exists
concat = node.in_port(i).get_source().node
last_in_port = max(concat.in_ports().keys())
assert not concat.in_port(last_in_port).disconnected(), 'The last in_port of Concat node {}' \
'should be connected'. \
format(concat.soft_get('name', node.id))
concat.add_input_port(last_in_port + 1)
concat.in_port(last_in_port + 1).connect(blank_values_node.out_port(0))
else:
# have to create concat
concat = Concat(graph, {'axis': 0, 'name': node_name + '/concat_{}'.format(input_name),
'in_ports_count': 2}).create_node()
node.in_port(i).get_connection().set_destination(concat.in_port(0))
concat.in_port(1).connect(blank_values_node.out_port(0))
concat.out_port(0).get_connection().set_destination(node.in_port(i))
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_tdnn(self, graph: Graph, tdnn_node: Node):
tdnn_name = tdnn_node.soft_get('name', tdnn_node.id)
concat_node = Concat(graph, {'axis': 1}).create_node()
rename_nodes([(tdnn_node, tdnn_name + '/to_be_removed'),
(concat_node, tdnn_name)])
for offset_ind, t in enumerate(tdnn_node['time_offsets']):
concat_node.add_input_port(offset_ind)
if t != 0:
memory_name = tdnn_name + '/MemoryOffset/' + str(abs(t))
memoryoffset_node = MemoryOffset(
graph, {
'name': memory_name,
't': t,
'pair_name': memory_name + '_out',
'has_default': False,
'splitted': False
}).create_node()
tdnn_node.in_port(0).get_source().connect(
memoryoffset_node.in_port(0))
memoryoffset_node.out_port(0).connect(
concat_node.in_port(offset_ind))
else:
# 0 time delay is not allowed in IE, it's meaningless
# if time offset is 0 then connect input of tdnncomponent directly to Concat without memoryoffset
tdnn_node.in_port(0).get_source().connect(
concat_node.in_port(offset_ind))
weights = tdnn_node['weights']
fc_inputs = {1: weights}
bias_term = False
if tdnn_node.has_valid('biases'):
assert len(tdnn_node['biases']) == weights.shape[0]
fc_inputs.update({2: tdnn_node['biases']})
bias_term = True
fc_node = create_op_with_const_inputs(
graph, FullyConnected, fc_inputs, {
'name': tdnn_name + '/FC',
'out-size': weights.shape[0],
'transpose_weights': True,
'bias_term': bias_term
})
concat_node.out_port(0).connect(fc_node.in_port(0))
tdnn_node.in_port(0).disconnect()
tdnn_node.out_port(0).get_connection().set_source(fc_node.out_port(0))
def replace_pattern(graph: Graph, match: dict):
node = match['tile']
name = node.soft_get('name', node.id)
input_shape = node.in_port(0).data.get_shape()
assert input_shape is not None
tiles = node.in_port(1).data.get_value()
assert tiles is not None, "Undefined `repeats` (1st port input value) of Tile node '{}'".format(name)
if input_shape.size == tiles.size:
return
if input_shape.size < tiles.size:
unsqueeze = create_op_node_with_second_input(graph, Unsqueeze,
int64_array(list(range(tiles.size - input_shape.size))),
{'name': name + '/input_alignment',
'override_output_shape': True})
node.in_port(0).get_source().connect(unsqueeze.in_port(0))
node.in_port(0).get_connection().set_source(unsqueeze.out_port(0))
else:
const = Const(graph, {'name': name + '/tile_alignment_const',
'value': np.ones([input_shape.size - tiles.size], dtype=np.int64)}).create_node()
concat = Concat(graph, {'axis': 0, 'override_output_shape': True}).create_node()
concat.add_input_port(0)
concat.add_input_port(1)
node.in_port(1).get_source().connect(concat.in_port(1))
node.in_port(1).disconnect()
concat.in_port(0).connect(const.out_port(0))
node.in_port(1).connect(concat.out_port(0))
def replace_pattern(self, graph: Graph, match: Dict[str, Node]):
concat_node = match['concat']
concat_node['axis'] = 1
concat_name = concat_node.soft_get('name', concat_node.id)
concat_reshape = create_op_node_with_second_input(graph, Reshape, int64_array([1, 2, -1]), op_attrs=dict(
name=concat_name + '/Reshape'))
split_node = create_op_node_with_second_input(graph, Split, int64_array(1), op_attrs=dict(
name=concat_name + '/Split', num_splits=2), input_node=concat_reshape)
split_node_reshape = create_op_node_with_second_input(graph, Reshape, int64_array([-1, 4]), op_attrs=dict(
name=split_node.name + '/Reshape'))
split_node.out_port(0).connect(split_node_reshape.in_port(0))
value = create_op_node_with_second_input(graph, Split, int64_array(1), op_attrs=dict(
name=split_node_reshape.name + '/Split', num_splits=4), input_node=split_node_reshape)
xmin, xmax = calculate_prior_box_value(value, value_to_div=value.out_port(2), value_to_add=value.out_port(0))
ymin, ymax = calculate_prior_box_value(value, value_to_div=value.out_port(3), value_to_add=value.out_port(1))
concat_slice_value = Concat(graph, dict(name=value.name + '/Concat', in_ports_count=4, axis=1)).create_node()
for ind, node in enumerate([xmin, ymin, xmax, ymax]):
concat_slice_value.in_port(ind).connect(node.out_port(0))
reshape_concat_values = create_op_node_with_second_input(graph, Reshape, int64_array([1, 1, -1]),
op_attrs=dict(name=concat_slice_value.name + '/Reshape'),
input_node=concat_slice_value)
concat = Concat(graph, dict(name=reshape_concat_values.name + '/Concat', in_ports_count=2, axis=1)).create_node()
concat.in_port(0).connect(reshape_concat_values.out_port(0))
concat.in_port(1).connect(split_node.out_port(1))
match['detection_output'].in_port(2).get_connection().set_source(concat.out_port(0))
concat_node.out_port(0).get_connection().set_destination(concat_reshape.in_port(0))
def replace_with_split_concat(node):
graph = node.graph
name = node.soft_get('name', node.id)
axis = node.axis
order = node.order
split = create_op_with_const_inputs(graph, Split,
{1: int64_array(axis)}, {
'name': name + '/Split',
'num_splits': order.size
})
concat = Concat(graph, {
'name': name + '/Concat',
'axis': axis,
'in_ports_count': order.size
}).create_node()
for out_port_idx, in_port_idx in enumerate(order):
split.out_port(out_port_idx).connect(concat.in_port(in_port_idx))
node.out_port(0).get_connection().set_source(concat.out_port(0))
node.in_port(0).get_connection().set_destination(split.in_port(0))
graph.remove_node(node.id)
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 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': np.array([-2])}).create_node()
end = Const(graph, {'value': np.array([-1])}).create_node()
stride = Const(graph, {'value': np.array([1])}).create_node()
shape_part_for_tiling = StridedSlice(graph, {'name': name + '/get_-2_dim', 'begin_mask': np.array([1]),
'end_mask': np.array([1]), 'new_axis_mask': np.array([0]),
'shrink_axis_mask': np.array([0]),
'ellipsis_mask': np.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))
concat_value = Const(graph, {'value': np.array([4])}).create_node()
shape_concat = Concat(graph, {'name': name + '/shape_for_tiling', 'in_ports_count': 2,
'axis': np.array(0)}).create_node()
shape_part_for_tiling.out_port(0).connect(shape_concat.in_port(0))
concat_value.out_port(0).connect(shape_concat.in_port(1))
variance = Const(graph, {'name': name + '/variance', 'value': np.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': np.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 replace_pattern(self, graph: Graph, match: dict):
node = match['node']
node_name = node.soft_get('name', node.id)
connected_ports = [
port for port in node.in_ports().values()
if not port.disconnected()
]
if len(connected_ports) == 2:
axis = node.in_port(1).data.get_value()
else:
axis = node.axis
assert axis is not None, 'The "axis" should be defined for node "{}"'.format(
node_name)
assert node.has_and_set(
'output_type'), 'The data type is not set for node "{}"'.format(
node_name)
topk_mode = 'max' if node.op == 'ArgMax' else 'min'
topk_node = TopK(
graph, {
'axis': axis,
'mode': topk_mode,
'sort': 'index',
'remove_values_output':
node.has_and_set('remove_values_output'),
'index_element_type': node.output_type
}).create_node()
node.in_port(0).get_connection().set_destination(topk_node.in_port(0))
if node.has_and_set(
'out_max_val'
): # in this mode the ArgMax produces tuples (max_ind, max_value)
concat_node = Concat(graph, {
'axis': 1,
'name': node.name + '/Concat'
}).create_node()
concat_node.add_input_port(0, skip_if_exist=True)
concat_node.add_input_port(1, skip_if_exist=True)
topk_node.out_port(0).connect(concat_node.in_port(1)) # indices
topk_node.out_port(1).connect(concat_node.in_port(0)) # values
if not node.out_port(0).disconnected():
node.out_port(0).get_connection().set_source(
concat_node.out_port(0))
else:
if not node.out_port(0).disconnected():
node.out_port(0).get_connection().set_source(
topk_node.out_port(1))
topk_node.in_port(1).connect(
Const(graph, {
'name': node.soft_get('name') + '/TopK',
'value': node.top_k
}).create_node().out_port(0))
graph.remove_nodes_from([node.id, node.out_node(0).id])
def fuse_reduces(first_reduce, second_reduce):
first_reduce_name = first_reduce.soft_get('name', first_reduce.id)
second_reduce_name = second_reduce.soft_get('name', second_reduce.id)
reduce_type = first_reduce.type
assert first_reduce.type == second_reduce.type
if len(first_reduce.out_port(0).get_destinations()) != 1:
# data dependency
return
if first_reduce.keep_dims != second_reduce.keep_dims:
return
first_axes = first_reduce.in_port(1).data.get_value()
second_axes = second_reduce.in_port(1).data.get_value()
if first_axes is None or second_axes is None:
# dynamic axes merging is not supported
return
if not first_reduce.keep_dims:
if not np.all(first_axes > second_axes):
# indexing of upper reduce input dimensions changed
return
graph = second_reduce.graph
new_axes = Concat(
graph, {
'name': second_reduce_name + '/Axes',
'axis': int64_array(0),
'in_ports_count': 2,
'override_output_shape': True
}).create_node()
new_axes.in_port(0).connect(first_reduce.in_port(1).get_source())
new_axes.in_port(1).connect(second_reduce.in_port(1).get_source())
first_reduce.in_port(
0).get_source().node['need_shape_inference'] = True
first_reduce.in_port(
0).get_source().node['override_output_shape'] = True
second_reduce.in_port(1).get_connection().set_source(
new_axes.out_port(0))
first_reduce.out_port(0).get_connection().set_source(
first_reduce.in_port(0).get_connection().get_source())
first_reduce.in_port(1).disconnect()
graph.remove_node(first_reduce.id)
log.debug(
'{0} nodes {1} and {2} were fused to a single {2} node with updated axes input'
''.format(reduce_type, first_reduce_name, second_reduce_name))
def replace_pattern(graph: Graph, match: dict):
node = match['pool']
if node.pool_step is None:
node.stride = int64_array([1, 1, node.window[-1], node.window[-1]])
# create Reshape before convolution
# shape = [in_shape[0], in_shape[1]/patch_stride, 1, patch_stride]
shape = Shape(graph, {}).create_node()
shape.in_port(0).connect(node.in_port(0).get_source())
split = create_op_with_const_inputs(graph, VariadicSplit, {
1: int64_array(0),
2: int64_array([1, -1])
}, {'out_ports_count': 2}, shape)
node_pool_stride = Const(graph, {
'value': int64_array([node.pool_stride])
}).create_node()
pow_node = create_op_node_with_second_input(graph, Pow,
int64_array([-1]))
pow_node.in_port(0).connect(node_pool_stride.out_port(0))
mul = Mul(graph, {}).create_node()
mul.in_port(0).connect(split.out_port(1))
mul.in_port(1).connect(pow_node.out_port(0))
const_1 = Const(graph, {'value': int64_array([1])}).create_node()
concat = Concat(graph, {'in_ports_count': 4, 'axis': 0}).create_node()
concat.in_port(0).connect(split.out_port(0))
concat.in_port(3).connect(mul.out_port(0))
concat.in_port(2).connect(const_1.out_port(0))
concat.in_port(1).connect(node_pool_stride.out_port(0))
reshape_in = Reshape(graph, {
'name': '/Reshape/' + node.name
}).create_node()
reshape_in.in_port(1).connect(concat.out_port(0))
# create Reshape after Convolution
reshape_out = create_op_node_with_second_input(
graph, Reshape, int64_array([0, -1]),
{'name': node.name + '/Reshape/'})
# connect input_reshape_node
source = node.in_port(0).get_source()
node.in_port(0).get_connection().set_source(reshape_in.out_port(0))
reshape_in.in_port(0).connect(source)
# connect output_reshape_node
node.out_port(0).get_connection().set_source(reshape_out.out_port(0))
node.out_port(0).connect(reshape_out.in_port(0))
def create_zero_value_with_batch_from_input(input_out_port: Port,
second_dim,
precision=np.float):
# create init_graph connected to ReadValue
graph = input_out_port.node.graph
input_name = input_out_port.node.name
shape_of_input = Shape(graph, {
'name': 'shape/' + input_name
}).create_node()
shape_of_input.in_port(0).connect(input_out_port)
dim_for_get_batch = Const(
graph, {
'name': 'dim/crop_batch/' + shape_of_input.name,
'value': int64_array([1]),
'shape': int64_array([1])
}).create_node()
get_batch = Crop(
graph, {
'name': 'crop_batch/' + shape_of_input.name,
'axis': int64_array([0]),
'offset': int64_array([0])
}).create_node()
get_batch.in_port(0).connect(shape_of_input.out_port(0))
get_batch.in_port(1).connect(dim_for_get_batch.out_port(0))
mem_shape_2nd_dim = Const(
graph, {
'name': 'gifo_r_weights_shape/' + input_name,
'value': int64_array([second_dim]),
'shape': int64_array([1])
}).create_node()
mem_shape = Concat(
graph, {
'name': 'gather_memory_shape/' + input_name,
'axis': 0,
'in_ports_count': 2
}).create_node()
mem_shape.in_port(0).connect(get_batch.out_port(0))
mem_shape.in_port(1).connect(mem_shape_2nd_dim.out_port(0))
fill_value = Const(
graph, {
'name': 'fill_value/' + input_name,
'value': np.array([0.0], precision),
'shape': int64_array([1])
}).create_node()
init_value_prev_lstm_output = Broadcast(graph, {
'name': 'init_value/' + input_name,
}).create_node()
init_value_prev_lstm_output.in_port(0).connect(fill_value.out_port(0))
init_value_prev_lstm_output.in_port(1).connect(mem_shape.out_port(0))
return init_value_prev_lstm_output
def replace_pattern(self, graph: Graph, match: dict):
concat_node = match['concat']
sources_of_ports = [
concat_node.in_port(i).get_connection().get_source()
for i in concat_node.in_ports()
]
# If 'concat' is ConcatV2 layer from TF, then this layer initially had input 'axis' as the last input.
# But then this input was deleted and the attribute 'axis' was added. Hence, the last port source can
# be None in such case.
sources_of_ports = [s for s in sources_of_ports if s is not None]
input_nodes = [s.node for s in sources_of_ports]
if not all(n.has_valid('type') for n in input_nodes):
return
saved_ports = []
disconnected_ports = []
for port_num, node in enumerate(input_nodes):
if node.soft_get('type') == 'Const' and len(
node.shape) > 1 and any(i == 0 for i in node.shape):
disconnected_ports.append(port_num)
else:
saved_ports.append(port_num)
if not saved_ports or not disconnected_ports:
return
if len(saved_ports) == 1:
before_concat = concat_node.in_port(
saved_ports[0]).get_connection().get_source()
concat_node.out_port(0).get_connection().set_source(before_concat)
return
new_concat_attrs = concat_node.attrs().copy()
new_concat_attrs['name'] = concat_node.name + '/Concat_'
new_concat_attrs['in_ports_count'] = len(saved_ports)
new_concat_node = Concat(graph, attrs=new_concat_attrs).create_node()
for new_port_num, old_port_num in enumerate(saved_ports):
concat_node.in_port(old_port_num).get_connection().set_destination(
new_concat_node.in_port(new_port_num))
for p in disconnected_ports:
concat_node.in_port(p).disconnect()
concat_node.out_port(0).get_connection().set_source(
new_concat_node.out_port(0))
def replace_pattern(self, graph: Graph, match: dict):
merge = match['merge']
power = Pow(graph, {
'name': merge.name + '/reciprocal_',
'type': 'PNORM'
}).create_node()
const1 = Const(graph, {
'value': -1.0,
'name': merge.name + '/negate_const'
}).create_node()
merge.in_port(0).get_connection().set_destination(power.in_port(0))
const1.out_port(0).connect(power.in_port(1))
concat_node = Concat(
graph, {
'axis': 0,
'name': merge.name + '/Concat_',
'override_output_shape': True
}).create_node()
const3 = Const(graph, {
'name': merge.name + '/const_reduce',
'value': 0
}).create_node()
for ii, idx in enumerate(
range(merge.significant, merge.to_significant + 1, 1)):
const_node = Const(
graph, {
'value': float_array(math.pow(10.0, idx)),
'name': merge.name + '/Const_' + ii.__str__()
}).create_node()
mul_node = Mul(graph, {
'name': merge.name + '/Mul_' + ii.__str__()
}).create_node()
const_node.out_port(0).connect(mul_node.in_port(0))
power.out_port(0).connect(
mul_node.in_port(1)) # connect to the graph node
mul_node2 = Mul(graph, {
'name': merge.name + '/Mul_Div_' + ii.__str__()
}).create_node()
const_node2 = Const(
graph, {
'value': float_array(math.pow(10.0, -1 * idx)),
'name': merge.name + '/Const_Pow_' + ii.__str__()
}).create_node()
cast_node = Cast(
graph, {
'name': merge.name + '/Cast_' + idx.__str__(),
'dst_type': np.float32
}).create_node()
mul_node.out_port(0).connect(cast_node.in_port(0))
const_node2.out_port(0).connect(mul_node2.in_port(1))
cast_node.out_port(0).connect(mul_node2.in_port(0))
concat_node.add_input_port(ii, skip_if_exist=True)
concat_node.in_port(ii).get_connection().set_source(
mul_node2.out_port(0))
reducesum_node = ReduceMean(
graph, {
'name': merge.id + '/_pnorm_reduced_sum',
'keep_dims': False,
'in_ports_count': 2,
'need_shape_inference': None,
'infer': reduce_infer
}).create_node()
const3.out_port(0).connect(reducesum_node.in_port(1))
reducesum_node.in_port(0).get_connection().set_source(
concat_node.out_port(0))
reshape = Reshape(graph, {
'name': merge.name + '/Reshape_Node'
}).create_node()
reshape_dim = Const(graph, {
'value': np.array([1, 5]),
'name': merge.id + '/Reshape_Dim'
}).create_node()
reducesum_node.out_port(0).connect(reshape.in_port(0))
reshape.in_port(1).connect(reshape_dim.out_port(0))
merge.out_port(0).get_connection().set_source(reshape.out_port(0))
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 = Memory(
graph, {
'name': 'prev_splice_memory',
'id': memory_pair_id,
'index': 1,
'size': 2,
'shape': int64_array([memory_size])
}).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 = Memory(
graph, {
'name': 'out_splice_memory',
'id': memory_pair_id,
'index': 0,
'size': 2,
'shape': int64_array([memory_size])
}).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')
input_memory_const_dim = Memory(
graph, {
'name': 'const_dim_in_memory',
'id': memory_pair_id,
'index': 1,
'size': 2,
'shape': int64_array([memory_size_constdim])
}).create_node()
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 = Memory(
graph, {
'name': 'const_dim_out_memory',
'id': memory_pair_id,
'index': 0,
'size': 2,
'shape': int64_array([memory_size_constdim])
}).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))
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:
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)
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_sub_graph(self, graph: Graph, match: dict):
# obtain references to necessary nodes and their names
fill = match['fill']
dims = match['dims']
strided_slice = match['strided_slice']
strided_slice_1 = match['strided_slice_1']
ctc_greedy_decoder = match['decoder']
cast = match['cast']
sparse_to_dense = match['sparse_to_dense']
strided_slice_name = strided_slice.soft_get('name', strided_slice.id)
strided_slice_1_name = strided_slice_1.soft_get(
'name', strided_slice_1.id)
ctc_greedy_decoder_name = ctc_greedy_decoder.soft_get(
'name', ctc_greedy_decoder.id)
sparse_to_dense_name = sparse_to_dense.soft_get(
'name', sparse_to_dense.id)
# unsqueeze scalar values with batch size and time dimension
unsqueeze_batch_size = create_op_with_const_inputs(
graph, Unsqueeze, {1: int64_array(0)},
{'name': strided_slice_name + '/Unsqueeze'})
dims.in_port(0).get_connection().set_destination(
unsqueeze_batch_size.in_port(0))
unsqueeze_time_size = create_op_with_const_inputs(
graph, Unsqueeze, {1: int64_array(0)},
{'name': strided_slice_1_name + '/Unsqueeze'})
fill.in_port(1).get_connection().set_destination(
unsqueeze_time_size.in_port(0))
# compute a sequence mask shape [T, N] required for CTCGreedyDecoder
seq_mask_shape = Concat(
graph, {
'axis': 0,
'in_ports_count': 2,
'name': ctc_greedy_decoder_name + '/SequenceMaskShape'
}).create_node()
seq_mask_shape.in_port(0).connect(unsqueeze_time_size.out_port(0))
seq_mask_shape.in_port(1).connect(unsqueeze_batch_size.out_port(0))
# compute a sequence mask
sequence_mask = create_op_with_const_inputs(
graph, Broadcast, {0: np.array([1.0], dtype=np.float)}, {
'mode': 'numpy',
'name': ctc_greedy_decoder_name + '/SequenceMask'
})
sequence_mask.in_port(1).connect(seq_mask_shape.out_port(0))
# create CTCGreedyDecoder with the sequence mask instead of sequence length
ctc_greedy_decoder.in_port(1).disconnect()
ctc_greedy_decoder.in_port(1).connect(sequence_mask.out_port(0))
# remove fill and pack nodes since they are now in unconnected component
graph.remove_nodes_from([fill.id, dims.id])
# transform opset CTCGreedyDecoder output to TensorFlow's one that has a shape [N, T]
# opset CTCGreedyDecoder has an output with a shape [N, T, 1, 1]
squeeze_dec_seq = create_op_with_const_inputs(
graph, Squeeze, {1: int64_array([2, 3])},
{'name': sparse_to_dense_name})
squeeze_dec_seq.in_port(0).connect(ctc_greedy_decoder.out_port(0))
cast_to_int = Cast(graph, {
'name': sparse_to_dense_name + '/CastToInt',
'dst_type': np.int32
}).create_node()
cast_to_int.in_port(0).connect(squeeze_dec_seq.out_port(0))
# preserve output name from original graph
rename_nodes([(sparse_to_dense,
sparse_to_dense_name + '/AbandonedName'),
(cast_to_int, sparse_to_dense_name)])
# set output of the new sub-graph as a source for SparseToDense consumer
sparse_to_dense.out_port(0).get_connection().set_source(
cast_to_int.out_port(0))
# cleanup a graph
graph.remove_nodes_from([cast.id, sparse_to_dense.id])
def insert_select(graph: Graph, node: Node):
context_len = node.frame_time + 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 = create_const_with_batch_from_input(in_node_port, in_node_shape[1])
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_const_with_batch_from_input(in_node_port, context_len, precision=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 = create_const_with_batch_from_input(in_node_port, 1, 1, np.int32)
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']
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='Memory',
index=1,
shape=int64_array([context_len]))),
('mem_in_data', dict()),
('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='Memory',
index=0,
shape=int64_array([context_len]))),
('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:
input_port = Node(
graph,
inverse_dict(counter_match)['crop_out']).out_port(0)
else:
mem_out = Memory(
graph, {
'name': 'iteration_number',
'size': 2,
'index': 1,
'id': 'iteration_' + node.name,
'shape': int64_array([context_len]),
'dst_type': np.int32
}).create_node()
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 = Memory(
graph, {
'name': 'iteration_number_out',
'size': 2,
'index': 0,
'id': 'iteration_' + node.name,
'shape': int64_array([context_len])
}).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)
select_node.in_port(0).connect(input_port)
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']
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(self, graph: Graph, match: dict):
const = 0.99
merge = match['merge']
digits = significant_digits()
pnorm = Power(
graph, {
'name': merge.name + '/reciprocal_',
'type': 'PNORM',
'significant': digits[0],
'to_significant': digits[1],
'scale': 1,
'shift': 0,
'power': get_power_attr()
}).create_node()
merge.in_port(0).get_connection().set_destination(pnorm.in_port(0))
in_shape = pnorm.in_port(0).data.get_shape()
in_shape = list(in_shape)
in_shape.insert(0, 1)
reshape1 = Reshape(graph, {
'name': merge.name + '/Reshape_Node1'
}).create_node()
reshape_dim1 = Const(graph, {
'value': np.array(in_shape),
'name': merge.id + '/Reshape_Dim1'
}).create_node()
pnorm.out_port(0).connect(reshape1.in_port(0))
reshape1.in_port(1).connect(reshape_dim1.out_port(0))
concat_node = Concat(
graph, {
'axis': 0,
'name': merge.name + '/Concat_',
'override_output_shape': True
}).create_node()
const3 = Const(graph, {
'name': merge.name + '/const_reduce',
'value': 0
}).create_node()
for ii, idx in enumerate(
range(pnorm.significant, pnorm.to_significant + 1, 1)):
const_node = Const(
graph, {
'value': float_array(math.pow(const, idx)),
'name': merge.name + '/Const_' + ii.__str__()
}).create_node()
mul_node = Mul(graph, {
'name': merge.name + '/Mul_' + ii.__str__()
}).create_node()
const_node.out_port(0).connect(mul_node.in_port(0))
reshape1.out_port(0).connect(
mul_node.in_port(1)) # connect to the graph node
mul_node2 = Mul(graph, {
'name': merge.name + '/Mul_Div_' + ii.__str__()
}).create_node()
const_node2 = Const(
graph, {
'value': float_array(math.pow(const, -1 * idx)),
'name': merge.name + '/Const_Pow_' + ii.__str__()
}).create_node()
cast_node = ExpOp(graph, {
'name': merge.name + '/Exp_' + idx.__str__()
}).create_node()
mul_node.out_port(0).connect(cast_node.in_port(0))
const_node2.out_port(0).connect(mul_node2.in_port(1))
cast_node.out_port(0).connect(mul_node2.in_port(0))
concat_node.add_input_port(ii, skip_if_exist=True)
concat_node.in_port(ii).get_connection().set_source(
mul_node2.out_port(0))
in_shape = pnorm.in_port(0).data.get_shape()
in_shape = list(in_shape)
reducesum_node = ReduceMean(
graph, {
'name': merge.id + '/_pnorm_reduced_sum',
'keep_dims': True,
'in_ports_count': 2,
'shape': in_shape,
'axis': 0,
'need_shape_inference': None,
'infer': reduce_infer
}).create_node()
const3.out_port(0).connect(reducesum_node.in_port(1))
reducesum_node.in_port(0).get_connection().set_source(
concat_node.out_port(0))
reshape = Reshape(graph, {
'name': merge.name + '/Reshape_Node'
}).create_node()
reshape_dim = Const(graph, {
'value': np.array(in_shape),
'name': merge.id + '/Reshape_Dim'
}).create_node()
reducesum_node.out_port(0).connect(reshape.in_port(0))
reshape.in_port(1).connect(reshape_dim.out_port(0))
merge.out_port(0).get_connection().set_source(reshape.out_port(0))
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 replace_timeheightconv(self, graph: Graph, node: Node):
req_time_offsets = node.soft_get('time_offsets')
offsets = node.soft_get("offsets", [[]])
all_time_offsets = list(set(offsets[:, 0]))
all_time_offsets.sort()
in_name = node.soft_get('name', node.id)
rename_node(node, in_name + '/to_delete')
# create memoryoffsets for context gathering
# we need concat if time offsets more than 1
concat = Concat(graph,
attrs={
'name': in_name + '/Concat',
'in_ports_count': len(all_time_offsets)
}).create_node()
i = 0
for t in all_time_offsets:
# if time offset included in required_time_offsets we don't need default value
has_default = t not in req_time_offsets
memoff = MemoryOffset(graph,
attrs={
'name':
in_name + '/MemoryOffset_' + str(i),
't':
t,
'has_default':
has_default,
'splitted':
False,
'pair_name':
in_name + '/MemoryOffset_pair_' + str(i)
}).create_node()
concat.in_port(i).connect(memoff.out_port(0))
memoff.in_port(0).connect(node.in_port(0).get_source())
i = i + 1
stride = node.soft_get("height_subsample", 1)
kernel = int64_array([0, 0])
kernel[0] = len(set(offsets[:, 0]))
kernel[1] = len(set(offsets[:, 1]))
pad_h = int64_array([0, 0])
pad_h[0] = -min(offsets[:, 1]) if min(offsets[:, 1]) < 0 else 0
pad_h[1] = stride * node.height_out - (node.height_in -
max([max(offsets[:, 1]), 0]))
dilation_t = (max(offsets[:, 0]) - min(offsets[:, 0])) / (
kernel[0] - 1) if kernel[0] > 1 else 1
dilation_h = (max(offsets[:, 1]) - min(offsets[:, 1])) / (
kernel[1] - 1) if kernel[0] > 1 else 1
conv_attrs = {
'name':
in_name,
'output':
node['out_channels'],
'height_in':
node.height_in,
'bias_term':
None,
'pad':
int64_array([[0, 0], [0, 0], [0, 0], pad_h]),
'pad_spatial_shape':
int64_array([[0, 0], pad_h]),
'dilation':
int64_array([1, 1, dilation_t, dilation_h]),
'kernel':
int64_array(
[node.out_channels, node.in_channels, kernel[0], kernel[1]]),
'stride':
int64_array([1, 1, 1, stride]),
'kernel_spatial':
kernel,
'input_feature_channel':
1,
'output_feature_channel':
0,
'channel_dims':
int64_array([1]),
'spatial_dims':
int64_array([2, 3]),
'batch_dims':
int64_array([0]),
'kernel_spatial_idx':
int64_array([2, 3]),
'group':
1,
'reshape_kernel':
True,
'bias_addable':
True,
}
conv = Convolution(graph, attrs=conv_attrs).create_node()
conv.in_port(0).connect(concat.out_port(0))
conv.in_port(1).connect(node.in_port(1).get_source())
# change layout for weights from OHWI to OIHW
# in future should be replaced by common Permute mechanics
weights = conv.in_port(1).get_source().node.value
weights = weights.reshape(
int64_array([node.out_channels, -1, node.in_channels]))
weights = weights.transpose(int64_array([0, 2, 1]))
weights = weights.flatten()
conv.in_port(1).get_source().node.value = weights
conv.in_port(2).connect(node.in_port(2).get_source())
node.out_port(0).get_connection().set_source(conv.out_port(0))
graph.remove_node(node.id)