def align_frame_time(graph: Graph, node: Node, frame_time_max):
for inp in node.in_ports():
if node.in_port(inp).disconnected():
continue
in_node = node.in_port(inp).get_source().node
in_node_out_port = node.in_port(inp).get_source()
in_port = node.in_port(inp)
# Adding MemoryOffset for Const does not make sense
if in_node.frame_time < frame_time_max and in_node.op != 'Const':
# Change existing MemoryOffset to avoid adding new one
if in_node.op == 'MemoryOffset':
in_node.t = in_node.frame_time - frame_time_max
in_node.frame_time = in_node.t
else:
mem_name = graph.unique_id("align_" + node.id)
memory_align = MemoryOffset(
graph,
attrs={
'id': mem_name,
'name': mem_name,
'pair_name': mem_name + "_pair",
't': in_node.frame_time - frame_time_max,
'splitted': False
}).create_node()
# add element_size for MemoryOffset after Parameter for infer
if in_node.op == 'Parameter':
memory_align['element_size'] = in_node.shape
in_port.get_connection().set_source(memory_align.out_port(0))
memory_align.in_port(0).connect(in_node_out_port)
memory_align['frame_time'] = memory_align.t
# remove MemoryOffset with maximum delay
elif in_node.frame_time == frame_time_max and in_node.op == 'MemoryOffset':
in_node_out_port.get_connection().set_source(
in_node.in_port(0).get_source())
graph.remove_node(in_node.id)
def find_and_replace_pattern(self, graph: Graph):
for offset_node in graph.get_op_nodes(op='MemoryOffset',
splitted=False):
paired_node = MemoryOffset(
graph, {
'name': offset_node.pair_name,
'splitted': True,
'pair_name': offset_node.id,
't': offset_node.t,
'has_default': offset_node.has_default
}).create_node()
offset_node['splitted'] = True
offset_node.out_port(0).get_connection().set_source(
paired_node.out_port(0))
res_node = Result(graph, {
'name': offset_node.id + "_output"
}).create_node()
offset_node.out_port(0).connect(res_node.in_port(0))
# If 'element_size' is previously copied from Parameter of from node with defined dim
if offset_node.has_valid('element_size'):
paired_node['element_size'] = offset_node['element_size']
# Copy shape from previous node. Typically (but not always) for TDNN blocks this is the case
else:
paired_node['element_size'] = offset_node.in_port(
0).data.get_shape()[1]
def replace_pattern(graph: Graph, match: dict):
offset_node = match['mem_offset']
paired_node = MemoryOffset(graph, {'name': offset_node.pair_name, 'splitted': True, 'pair_name': offset_node.id,
't': offset_node.t, 'has_default': offset_node.has_default}).create_node()
offset_node['splitted'] = True
offset_node.out_port(0).get_connection().set_source(paired_node.out_port(0))
res_node = Result(graph, {'name': offset_node.id+"_output"}).create_node()
offset_node.out_port(0).connect(res_node.in_port(0))
def extract(cls, node):
pb = node.parameters
mapping_rule = {
'pair_name': pb['pair_name'],
't': pb['t'],
'has_default': pb['has_default'],
'splitted': False,
}
if 'element_size' in pb:
mapping_rule['element_size'] = pb['element_size']
MemoryOffset.update_node_stat(node, mapping_rule)
return cls.enabled
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 split_offset(offset_node: Node):
paired_node = MemoryOffset(
offset_node.graph, {
'name': offset_node.pair_name,
'splitted': True,
'pair_name': offset_node.id,
'element_size': offset_node['element_size'],
't': offset_node.t,
'has_default': offset_node.has_default
}).create_node()
offset_node['splitted'] = True
offset_node.out_port(0).get_connection().set_source(
paired_node.out_port(0))
res_node = Result(offset_node.graph, {
'name': offset_node.id + '_output'
}).create_node()
offset_node.out_port(0).connect(res_node.in_port(0))
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)
