def replace_sub_graph(self, graph: Graph, match: [dict, SubgraphMatch]):
node = match['op']
name = node.soft_get('name', node.id)
# biases normalization
bias_node = Add(graph, {'name': name + '/Bias_', 'can_be_scaleshift': False}).create_node()
if not graph.graph['cmd_params'].generate_deprecated_IR_V7:
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))
if node.has_valid('alpha') and not math.isclose(node.alpha, 1):
bias_node.insert_op_on_input_port(in_port_idx=0, new_op_class=Mul, value=np.array(node.alpha),
new_op_attrs={'name': name + '/Alpha_', 'can_be_scaleshift': False})
del node['alpha']
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=np.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_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()
if not graph.graph['cmd_params'].generate_deprecated_IR_V7:
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'] in ['caffe', 'mxnet']:
node.insert_op_on_input_port(in_port_idx=0, new_op_class=Reshape,
new_op_attrs={'name': name + '/flatten_fc_input'}, value=int64_array([0, -1]))
MatMul.update_node_stat(node, {})
def extract(cls, node):
attr = node.pb.attr
attrs = {
'transpose_a': int(attr['adj_x'].b),
'transpose_b': int(attr['adj_y'].b),
}
MatMul.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node: Node):
unsupported_attrs = []
for attr_name in [
'adjoint_a', 'adjoint_b', 'a_is_sparse', 'b_is_sparse'
]:
if attr_name in node.pb.attr and node.pb.attr[attr_name].b:
unsupported_attrs.append(attr_name)
if len(unsupported_attrs) != 0:
raise Error('MatMul operation {} use unsupported attrs: {}'.format(
node.id, unsupported_attrs))
MatMul.update_node_stat(
node, {
'transpose_a': node.pb.attr['transpose_a'].b,
'transpose_b': node.pb.attr['transpose_b'].b,
})
return cls.enabled
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: np.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=np.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 extract(cls, node):
MatMul.update_node_stat(node)
return cls.enabled