mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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6343b0db47
* Add notes for tensors * Optimize some apis * move some warnings * Support build with Paddle2ONNX * Add protobuf support * Fix compile on mac * add clearn package script * Add paddle2onnx code * remove submodule * Add onnx ocde * remove softlink * add onnx code * fix error * Add cmake file * fix patchelf * update paddle2onnx * Delete .gitmodules --------- Co-authored-by: PaddleCI <paddle_ci@example.com> Co-authored-by: pangyoki <pangyoki@126.com> Co-authored-by: jiangjiajun <jiangjiajun@baidu.lcom>
84 lines
3.1 KiB
Python
84 lines
3.1 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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import os
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import paddle
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import numpy as np
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from paddle.fluid import core
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from paddle.fluid.framework import Variable, program_guard
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from paddle2onnx.utils import logging
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def prepend_feed_ops(inference_program,
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feed_target_names,
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feed_holder_name='feed'):
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if len(feed_target_names) == 0:
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return
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global_block = inference_program.global_block()
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feed_var = global_block.create_var(
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name=feed_holder_name,
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type=core.VarDesc.VarType.FEED_MINIBATCH,
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persistable=True)
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for i, name in enumerate(feed_target_names):
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if not global_block.has_var(name):
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raise ValueError(
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"The feed_var_names[{i}]: '{name}' doesn't exist in pruned inference program. "
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"Please check whether '{name}' is a valid feed_var name, or remove it from feed_var_names "
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"if '{name}' is not involved in the fetch_vars calculation.".
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format(
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i=i, name=name))
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out = global_block.var(name)
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global_block._prepend_op(
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type='feed',
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inputs={'X': [feed_var]},
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outputs={'Out': [out]},
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attrs={'col': i})
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def append_fetch_ops(inference_program,
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fetch_target_names,
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fetch_holder_name='fetch'):
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global_block = inference_program.global_block()
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fetch_var = global_block.create_var(
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name=fetch_holder_name,
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type=core.VarDesc.VarType.FETCH_LIST,
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persistable=True)
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for i, name in enumerate(fetch_target_names):
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global_block.append_op(
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type='fetch',
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inputs={'X': [name]},
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outputs={'Out': [fetch_var]},
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attrs={'col': i})
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def get_program(program, feed_var_names, fetch_vars):
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global_block = program.global_block()
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need_to_remove_op_index = []
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for i, op in enumerate(global_block.ops):
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op.desc.set_is_target(False)
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if op.type == "feed" or op.type == "fetch":
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need_to_remove_op_index.append(i)
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for index in need_to_remove_op_index[::-1]:
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global_block._remove_op(index)
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program.desc.flush()
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program = program._prune_with_input(
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feeded_var_names=feed_var_names, targets=fetch_vars)
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program = program._inference_optimize(prune_read_op=True)
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fetch_var_names = [v.name for v in fetch_vars]
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prepend_feed_ops(program, feed_var_names)
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append_fetch_ops(program, fetch_var_names)
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return program
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