[Build] Support build with source code of Paddle2ONNX (#1559)

* 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>

paddle2onnx/legacy/op_mapper/sequence/im2sequence.py

@@ -0,0 +1,78 @@
+# Copyright (c) 2020  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import numpy as np
+from paddle2onnx.legacy.constant import dtypes
+from paddle2onnx.utils import logging
+from paddle2onnx.legacy.op_mapper import OpMapper as op_mapper
+
+
+@op_mapper('im2sequence')
+class Im2Sequence():
+    support_opset_verison_range = (1, 12)
+
+    @classmethod
+    def opset_1(cls, graph, node, **kw):
+        n, c, h, w = node.input_shape('X', 0)
+        assert h > 0 and w > 0, "Only supported fixed input shape for im2sequence operator."
+        stride_h, stride_w = node.attr('strides')
+        paddings = node.attr('paddings')
+        assert node.attr(
+            'out_stride'
+        ) != 1, "Only out_stride==1 is supported for im2sequence operator."
+        h = h + paddings[0] + paddings[1]
+        w = w + paddings[1] + paddings[2]
+        kernel_h, kernel_w = node.attr('kernels')
+        out_h = 1 + (h - kernel_h + stride_h - 1) // stride_h
+        out_w = 1 + (w - kernel_w + stride_w - 1) // stride_w
+        h_steps = list()
+        for i in range(out_h):
+            h_steps.append([i * stride_h, i * stride_h + kernel_h])
+        w_steps = list()
+        for i in range(out_w):
+            w_steps.append([i * stride_w, i * stride_w + kernel_w])
+
+        slice_node_blocks = list()
+        for i in range(out_h):
+            for j in range(out_w):
+                starts_node = graph.make_node(
+                    'Constant',
+                    dtype=dtypes.ONNX.INT64,
+                    dims=[4],
+                    value=[0, 0, h_steps[i][0], w_steps[j][0]])
+                ends_node = graph.make_node(
+                    'Constant',
+                    dtype=dtypes.ONNX.INT64,
+                    dims=[4],
+                    value=[999999, 999999, h_steps[i][1], w_steps[j][1]])
+                nodes.extend([starts_node, ends_node])
+
+                slice_block_node = graph.make_node(
+                    'Slice',
+                    inputs=[node.input('X', 0), starts_node, ends_node])
+                flatten_block_node = graph.make_node(
+                    "Flatten", inputs=[slice_block_node], axis=0)
+                nodes.extend([slice_block_node, flatten_block_node])
+        concat_block_node = graph.make_node(
+            "Concat",
+            inputs=slice_node_blocks,
+            outputs=node.output('Out'),
+            axis=0)
+        logging.info("==========Importance Notice===========")
+        logging.info(
+            "Since im2sequence operator is used in your paddlepaddle model, the translated onnx model only support input data with batch_size=1."
+        )
+        logging.info("======================================")