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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>
57 lines
2.3 KiB
Python
Executable File
57 lines
2.3 KiB
Python
Executable File
# 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 numpy as np
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import paddle
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from paddle.fluid import layers
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from paddle2onnx.legacy.op_mapper import CustomPaddleOp, register_custom_paddle_op
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from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
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from paddle2onnx.legacy.op_mapper import mapper_helper
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class BoxClip(CustomPaddleOp):
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def __init__(self, node, **kw):
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super(BoxClip, self).__init__(node)
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def forward(self):
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input = self.input('Input', 0)
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im_info = self.input('ImInfo', 0)
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im_info = paddle.reshape(im_info, shape=[3])
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h, w, s = paddle.tensor.split(im_info, axis=0, num_or_sections=3)
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tensor_one = paddle.full(shape=[1], dtype='float32', fill_value=1.0)
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tensor_zero = paddle.full(shape=[1], dtype='float32', fill_value=0.0)
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h = paddle.subtract(h, tensor_one)
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w = paddle.subtract(w, tensor_one)
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xmin, ymin, xmax, ymax = paddle.tensor.split(
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input, axis=-1, num_or_sections=4)
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xmin = paddle.maximum(paddle.minimum(xmin, w), tensor_zero)
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ymin = paddle.maximum(paddle.minimum(ymin, h), tensor_zero)
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xmax = paddle.maximum(paddle.minimum(xmax, w), tensor_zero)
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ymax = paddle.maximum(paddle.minimum(ymax, h), tensor_zero)
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cliped_box = paddle.concat([xmin, ymin, xmax, ymax], axis=-1)
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return {'Output': [cliped_box]}
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@op_mapper('box_clip')
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class Boxclip:
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@classmethod
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def opset_1(cls, graph, node, **kw):
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node = graph.make_node(
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'box_clip',
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inputs=node.input('Input')+node.input('ImInfo'),
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outputs=node.output('Output'),
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domain = 'custom')
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register_custom_paddle_op('box_clip', BoxClip)
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