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130 lines
4.1 KiB
130 lines
4.1 KiB
#!/usr/bin/env python3 -W ignore::DeprecationWarning
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# prune model by output names
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import argparse
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import copy
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import sys
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import onnx
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--model',
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required=True,
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help='Path of directory saved the input model.')
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parser.add_argument(
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'--output_names',
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required=True,
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nargs='+',
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help='The outputs of pruned model.')
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parser.add_argument(
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'--save_file', required=True, help='Path to save the new onnx model.')
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return parser.parse_args()
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if __name__ == '__main__':
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args = parse_arguments()
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if len(set(args.output_names)) < len(args.output_names):
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print(
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"[ERROR] There's dumplicate name in --output_names, which is not allowed."
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)
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sys.exit(-1)
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model = onnx.load(args.model)
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# collect all node outputs and graph output
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output_tensor_names = set()
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for node in model.graph.node:
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for out in node.output:
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# may contain model output
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output_tensor_names.add(out)
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# for out in model.graph.output:
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# output_tensor_names.add(out.name)
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for output_name in args.output_names:
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if output_name not in output_tensor_names:
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print(
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"[ERROR] Cannot find output tensor name '{}' in onnx model graph.".
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format(output_name))
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sys.exit(-1)
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output_node_indices = set() # has output names
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output_to_node = dict() # all node outputs
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for i, node in enumerate(model.graph.node):
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for out in node.output:
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output_to_node[out] = i
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if out in args.output_names:
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output_node_indices.add(i)
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# from outputs find all the ancestors
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reserved_node_indices = copy.deepcopy(
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output_node_indices) # nodes need to keep
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reserved_inputs = set() # model input to keep
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new_output_node_indices = copy.deepcopy(output_node_indices)
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while True and len(new_output_node_indices) > 0:
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output_node_indices = copy.deepcopy(new_output_node_indices)
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new_output_node_indices = set()
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for out_node_idx in output_node_indices:
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# backtrace to parenet
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for ipt in model.graph.node[out_node_idx].input:
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if ipt in output_to_node:
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reserved_node_indices.add(output_to_node[ipt])
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new_output_node_indices.add(output_to_node[ipt])
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else:
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reserved_inputs.add(ipt)
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num_inputs = len(model.graph.input)
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num_outputs = len(model.graph.output)
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num_nodes = len(model.graph.node)
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print(
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f"old graph has {num_inputs} inputs, {num_outputs} outpus, {num_nodes} nodes"
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)
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print(f"{len(reserved_node_indices)} node to keep.")
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# del node not to keep
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for idx in range(num_nodes - 1, -1, -1):
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if idx not in reserved_node_indices:
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del model.graph.node[idx]
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# del graph input not to keep
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for idx in range(num_inputs - 1, -1, -1):
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if model.graph.input[idx].name not in reserved_inputs:
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del model.graph.input[idx]
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# del old graph outputs
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for i in range(num_outputs):
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del model.graph.output[0]
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# new graph output as user input
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for out in args.output_names:
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model.graph.output.extend([onnx.ValueInfoProto(name=out)])
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# infer shape
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try:
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from onnx_infer_shape import SymbolicShapeInference
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model = SymbolicShapeInference.infer_shapes(
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model,
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int_max=2**31 - 1,
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auto_merge=True,
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guess_output_rank=False,
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verbose=1)
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except Exception as e:
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print(f"skip infer shape step: {e}")
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# check onnx model
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onnx.checker.check_model(model)
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# save onnx model
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onnx.save(model, args.save_file)
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print("[Finished] The new model saved in {}.".format(args.save_file))
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print("[DEBUG INFO] The inputs of new model: {}".format(
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[x.name for x in model.graph.input]))
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print("[DEBUG INFO] The outputs of new model: {}".format(
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[x.name for x in model.graph.output]))
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