def getGraph(onnx_path):
model = onnx.load(onnx_path)
model = shape_inference.infer_shapes(model)
model_graph = model.graph
graph = Graph.from_onnx(model_graph)
graph = graph.transformed(transformers)
graph.channel_dims = {}
return graph
def getGraph(onnx_path):
model = onnx.load(onnx_path)
output_names = [node.name for node in model.graph.output]
model = shape_inference.infer_shapes(model)
model_graph = model.graph
graph = Graph.from_onnx(model_graph)
graph = graph.transformed(transformers)
graph.channel_dims = {}
return graph, output_names
def getGraph(onnx_path, with_opt=False):
model = onnx.load(onnx_path)
if with_opt:
opt_passes = ['eliminate_nop_pad', 'eliminate_identity']
model = optimizer.optimize(model, opt_passes)
model = shape_inference.infer_shapes(model)
model_graph = model.graph
graph = Graph.from_onnx(model_graph)
graph = graph.transformed(transformers)
graph.channel_dims = {}
return graph
def getGraph(onnx_path):
model = onnx.load(onnx_path)
opset_version = model.opset_import[
0].version # 获取 opset version ,不同的 opset version 下 onnx的 op解析方式不同
model = shape_inference.infer_shapes(model)
model_graph = model.graph
graph = Graph.from_onnx(model_graph)
graph = graph.transformed(transformers)
graph.channel_dims = {}
return graph, opset_version
