text stringlengths 1 93.6k |
|---|
member_map[n.name]=n;
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return member_map
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def split_io_list(io_list,new_names_all):
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#splits input/output list to identify removed, retained and totally new nodes
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removed_names=[]
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retained_names=[]
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for n in io_list:
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if n.name not in new_names_all:
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removed_names.append(n.name)
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if n.name in new_names_all:
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retained_names.append(n.name)
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new_names=list(set(new_names_all)-set(retained_names))
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return [removed_names,retained_names,new_names]
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def traceDependentNodes(graph,name,node_input_names,node_map, initializer_map):
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# recurisvely traces all dependent nodes for a given output nodes in a graph
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for n in graph.node:
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for noutput in n.output:
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if (noutput == name) and (n.name not in node_input_names):
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# give node "name" is node n's output, so add node "n" to node_input_names list
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node_input_names.append(n.name)
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if n.name in node_map.keys():
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for ninput in node_map[n.name].input:
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# trace input node's inputs
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node_input_names = traceDependentNodes(graph,ninput,node_input_names,node_map, initializer_map)
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# don't forget the initializers they can be terminal inputs on a path.
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if name in initializer_map.keys():
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node_input_names.append(name)
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return node_input_names
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def onnx_edit(input_model, output_model, new_input_node_names, input_shape_map, new_output_node_names, output_shape_map, verify):
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""" edits and modifies an onnx model to extract a subgraph based on input/output node names and shapes.
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Arguments:
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input_model: path of input onnx model
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output_model: path of output onnx model
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new_input_node_names: list of input node names including list of original input nodes if they are to be retained.
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If the list is empty original input nodes are assumed.
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input_shape_map: dictionary/map of input node names to corresponding shapes. Shapes are needed for model checker to pass.
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new_output_node_names: list of output node names, including list of original output nodes if they are to be retained
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If the list if empty original output nodes are assumed.
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output_shape_map: dictionary/map of output node names to corresponding shape. Shapes are needed for model checker to pass.
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verify: set to true if input and output models need to be verified.
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"""
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# LOAD MODEL AND PREP MAPS
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model = onnx.load(input_model)
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graph = model.graph
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if(verify):
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print("input model Errors: ", onnx.checker.check_model(model))
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#Generate a name for all node if they have none.
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nodeIdx = 0;
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for n in graph.node:
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if n.name == '':
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n.name = str(n.op_type) + str(nodeIdx)
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nodeIdx += 1
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node_map = createGraphMemberMap(graph.node)
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input_map = createGraphMemberMap(graph.input)
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output_map = createGraphMemberMap(graph.output)
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initializer_map = createGraphMemberMap(graph.initializer)
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if not new_input_node_names:
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new_input_node_names = list(input_map)
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if not new_output_node_names:
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new_output_node_names = list(output_map)
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# MODIFY INPUTS
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# Break the graph based on the new input node names
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[removed_names,retained_names,new_names]=split_io_list(graph.input,new_input_node_names)
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for name in removed_names:
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if name in input_map.keys():
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graph.input.remove(input_map[name])
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for name in new_names:
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# If a new input name corresponds to an existing node, it implies that original node in the graph needs to be replaced with an input node
|
# Exactly here the graph is broken
|
if name in node_map.keys():
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graph.node.remove(node_map[name])
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# Remove node where there output would match new input to avoid duplicate definitions
|
nodesToRemoveToAvoidDuplicateEntries = []
|
for n in graph.node:
|
for noutput in n.output:
|
if (noutput == name):
|
nodesToRemoveToAvoidDuplicateEntries.append(n)
|
for n in nodesToRemoveToAvoidDuplicateEntries:
|
graph.node.remove(n)
|
if(name in input_shape_map.keys()):
|
new_nv = helper.make_tensor_value_info(name, TensorProto.FLOAT, input_shape_map[name])
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else:
|
new_nv = helper.make_tensor_value_info(name, TensorProto.FLOAT, None)
|
graph.input.extend([new_nv])
|
node_map = createGraphMemberMap(graph.node)
|
input_map = createGraphMemberMap(graph.input)
|
# MODIFY OUTPUTS
|
# Break the graph based on the new output node names
|
[removed_names,retained_names,new_names]=split_io_list(graph.output,new_output_node_names)
|
for name in removed_names:
|
if name in output_map.keys():
|
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