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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>
79 lines
3.2 KiB
Python
79 lines
3.2 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 numpy as np
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from paddle2onnx.legacy.constant import dtypes
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from paddle2onnx.utils import logging
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from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
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@op_mapper('im2sequence')
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class Im2Sequence():
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support_opset_verison_range = (1, 12)
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@classmethod
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def opset_1(cls, graph, node, **kw):
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n, c, h, w = node.input_shape('X', 0)
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assert h > 0 and w > 0, "Only supported fixed input shape for im2sequence operator."
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stride_h, stride_w = node.attr('strides')
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paddings = node.attr('paddings')
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assert node.attr(
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'out_stride'
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) != 1, "Only out_stride==1 is supported for im2sequence operator."
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h = h + paddings[0] + paddings[1]
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w = w + paddings[1] + paddings[2]
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kernel_h, kernel_w = node.attr('kernels')
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out_h = 1 + (h - kernel_h + stride_h - 1) // stride_h
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out_w = 1 + (w - kernel_w + stride_w - 1) // stride_w
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h_steps = list()
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for i in range(out_h):
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h_steps.append([i * stride_h, i * stride_h + kernel_h])
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w_steps = list()
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for i in range(out_w):
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w_steps.append([i * stride_w, i * stride_w + kernel_w])
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slice_node_blocks = list()
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for i in range(out_h):
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for j in range(out_w):
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starts_node = graph.make_node(
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'Constant',
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dtype=dtypes.ONNX.INT64,
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dims=[4],
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value=[0, 0, h_steps[i][0], w_steps[j][0]])
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ends_node = graph.make_node(
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'Constant',
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dtype=dtypes.ONNX.INT64,
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dims=[4],
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value=[999999, 999999, h_steps[i][1], w_steps[j][1]])
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nodes.extend([starts_node, ends_node])
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slice_block_node = graph.make_node(
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'Slice',
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inputs=[node.input('X', 0), starts_node, ends_node])
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flatten_block_node = graph.make_node(
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"Flatten", inputs=[slice_block_node], axis=0)
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nodes.extend([slice_block_node, flatten_block_node])
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concat_block_node = graph.make_node(
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"Concat",
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inputs=slice_node_blocks,
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outputs=node.output('Out'),
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axis=0)
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logging.info("==========Importance Notice===========")
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logging.info(
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"Since im2sequence operator is used in your paddlepaddle model, the translated onnx model only support input data with batch_size=1."
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)
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logging.info("======================================")
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