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Code Issues Releases Activity

dnn_backend_native_layer_mathbinary: add sub support

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more math binary operations will be added here

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
This commit is contained in:
Guo, Yejun
2020-03-20 20:55:38 +08:00
parent 2114c42418
commit ffa1561608
7 changed files with 219 additions and 4 deletions
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55
tools/python/convert_from_tensorflow.py
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@ -70,7 +70,8 @@ class TFConverter:
self.converted_nodes = set()
self.conv2d_scope_names = set()
self.conv2d_scopename_inputname_dict = {}
self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4}
self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5}
self.mathbin2code = {'Sub':0}
self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
self.name_operand_dict = {}
@ -113,6 +114,8 @@ class TFConverter:
# if activation is None, and BiasAdd.next is the last op which is Identity
if conv2d_scope_name + '/BiasAdd' in self.edges:
anode = self.edges[conv2d_scope_name + '/BiasAdd'][0]
if anode.op not in self.conv_activations:
anode = None
else:
anode = None
return knode, bnode, dnode, anode
@ -252,14 +255,47 @@ class TFConverter:
np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
def dump_sub_to_file(self, node, f):
assert(node.op == 'Sub')
self.layer_number = self.layer_number + 1
self.converted_nodes.add(node.name)
i0_node = self.name_node_dict[node.input[0]]
i1_node = self.name_node_dict[node.input[1]]
np.array([self.op2code['MathBinary'], self.mathbin2code[node.op]], dtype=np.uint32).tofile(f)
if i0_node.op == 'Const':
scalar = i0_node.attr['value'].tensor.float_val[0]
assert(i0_node.name.find('sub/x'))
np.array([1], dtype=np.uint32).tofile(f)
np.array([scalar], dtype=np.float32).tofile(f)
np.array([0], dtype=np.uint32).tofile(f)
input_operand_index = self.add_operand(i1_node.name, Operand.IOTYPE_INPUT)
np.array([input_operand_index], dtype=np.uint32).tofile(f)
elif i1_node.op == 'Const':
scalar = i1_node.attr['value'].tensor.float_val[0]
assert(i1_node.name.find('sub/y'))
np.array([0], dtype=np.uint32).tofile(f)
input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT)
np.array([input_operand_index], dtype=np.uint32).tofile(f)
np.array([1], dtype=np.uint32).tofile(f)
np.array([scalar], dtype=np.float32).tofile(f)
else:
np.array([0], dtype=np.uint32).tofile(f)
input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT)
np.array([input_operand_index], dtype=np.uint32).tofile(f)
np.array([0], dtype=np.uint32).tofile(f)
input_operand_index = self.add_operand(i1_node.name, Operand.IOTYPE_INPUT)
np.array([input_operand_index], dtype=np.uint32).tofile(f)
output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
np.array([output_operand_index], dtype=np.uint32).tofile(f)
def dump_layers_to_file(self, f):
for node in self.nodes:
if node.name in self.converted_nodes:
continue
# conv2d with dilation generates very complex nodes, so handle it in special
scope_name = TFConverter.get_scope_name(node.name)
if scope_name in self.conv2d_scope_names:
if self.in_conv2d_scope(node.name):
if node.op == 'Conv2D':
self.dump_complex_conv2d_to_file(node, f)
continue
@ -272,6 +308,8 @@ class TFConverter:
self.dump_mirrorpad_to_file(node, f)
elif node.op == 'Maximum':
self.dump_maximum_to_file(node, f)
elif node.op == 'Sub':
self.dump_sub_to_file(node, f)
def dump_operands_to_file(self, f):
@ -352,6 +390,17 @@ class TFConverter:
return name[0:index]
def in_conv2d_scope(self, name):
inner_scope = TFConverter.get_scope_name(name)
if inner_scope == "":
return False;
for scope in self.conv2d_scope_names:
index = inner_scope.find(scope)
if index == 0:
return True
return False
def generate_conv2d_scope_info(self):
# mostly, conv2d is a sub block in graph, get the scope name
for node in self.nodes: