Add ONNX Exporter (#27831)

* add onnx export module, test=develop

* add unit test for paddle.onnx.export

* adjust api & doc

* fix some typo

python/paddle/__init__.py

@@ -268,6 +268,7 @@ from .fluid.layers import crop_tensor as crop  #DEFINE_ALIAS
 from . import jit
 from . import static
 from . import amp
+from . import onnx
 
 # high-level api
 from .hapi import Model

python/paddle/fluid/tests/unittests/test_onnx_export.py

+# 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 print_function
+
+import os
+import pickle
+import unittest
+import numpy as np
+import paddle
+from paddle.static import InputSpec
+
+
+class LinearNet(paddle.nn.Layer):
+    def __init__(self):
+        super(LinearNet, self).__init__()
+        self._linear = paddle.nn.Linear(128, 10)
+
+    def forward(self, x):
+        return self._linear(x)
+
+
+class Logic(paddle.nn.Layer):
+    def __init__(self):
+        super(Logic, self).__init__()
+
+    def forward(self, x, y, z):
+        if z:
+            return x
+        else:
+            return y
+
+
+class TestExportWithTensor(unittest.TestCase):
+    def setUp(self):
+        self.x_spec = paddle.static.InputSpec(
+            shape=[None, 128], dtype='float32')
+
+    def test_with_tensor():
+        model = LinearNet()
+        paddle.onnx.export(model, 'linear_net', input_spec=[self.x_spec])
+
+
+class TestExportWithTensor(unittest.TestCase):
+    def setUp(self):
+        self.x = paddle.to_tensor(np.random.random((1, 128)))
+
+    def test_with_tensor(self):
+        model = LinearNet()
+        paddle.onnx.export(model, 'linear_net', input_spec=[self.x])
+
+
+class TestExportPrunedGraph(unittest.TestCase):
+    def setUp(self):
+        self.x = paddle.to_tensor(np.array([1]))
+        self.y = paddle.to_tensor(np.array([-1]))
+
+    def test_prune_graph(self):
+        model = Logic()
+        paddle.jit.to_static(model)
+        out = model(self.x, self.y, z=True)
+        paddle.onnx.export(
+            model, 'pruned', input_spec=[self.x], output_spec=[out])
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/onnx/__init__.py

+# 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 print_function
+from .export import export
+
+__all__ = ['export']

python/paddle/onnx/export.py

+# 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.
+
+import os
+from paddle.utils import try_import
+
+__all__ = ['export']
+
+
+def export(layer, path, input_spec=None, opset_version=9, **configs):
+    """
+    Export Layer to ONNX format, which can use for inference via onnxruntime or other backends.
+    For more details, Please refer to `paddle2onnx <https://github.com/PaddlePaddle/paddle2onnx>`_ .
+
+    Args:
+        layer (Layer): The Layer to be exported.
+        path (str): The path prefix to export model. The format is ``dirname/file_prefix`` or ``file_prefix`` ,
+            and the exported ONNX file suffix is ``.onnx`` . 
+        input_spec (list[InputSpec|Tensor], optional): Describes the input of the exported model's forward 
+            method, which can be described by InputSpec or example Tensor. If None, all input variables of 
+            the original Layer's forward method would be the inputs of the exported ``ONNX`` model. Default: None.
+        opset_version(int, optional): Opset version of exported ONNX model.
+            Now, stable supported opset version include 9, 10, 11. Default: 9.
+        **configs (dict, optional): Other export configuration options for compatibility. We do not 
+            recommend using these configurations, they may be removed in the future. If not necessary, 
+            DO NOT use them. Default None.
+            The following options are currently supported:
+            (1) output_spec (list[Tensor]): Selects the output targets of the exported model.
+            By default, all return variables of original Layer's forward method are kept as the 
+            output of the exported model. If the provided ``output_spec`` list is not all output variables, 
+            the exported model will be pruned according to the given ``output_spec`` list. 
+    Returns:
+        None
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import numpy as np
+
+            class LinearNet(paddle.nn.Layer):
+                def __init__(self):
+                    super(LinearNet, self).__init__()
+                    self._linear = paddle.nn.Linear(128, 10)
+
+                def forward(self, x):
+                    return self._linear(x)
+
+            # Export model with 'InputSpec' to support dynamic input shape.
+            def export_linear_net():
+                model = LinearNet()
+                x_spec = paddle.static.InputSpec(shape=[None, 128], dtype='float32')
+                paddle.onnx.export(model, 'linear_net', input_spec=[x_spec])
+
+            export_linear_net()
+
+            class Logic(paddle.nn.Layer):
+                def __init__(self):
+                    super(Logic, self).__init__()
+
+                def forward(self, x, y, z):
+                    if z:
+                        return x
+                    else:
+                        return y
+
+            # Export model with 'Tensor' to support pruned model by set 'output_spec'.
+            def export_logic():
+                model = Logic()
+                x = paddle.to_tensor(np.array([1]))
+                y = paddle.to_tensor(np.array([2]))
+                # Static and run model.
+                paddle.jit.to_static(model)
+                out = model(x, y, z=True)
+                paddle.onnx.export(model, 'pruned', input_spec=[x], output_spec=[out])
+
+            export_logic()
+    """
+
+    p2o = try_import('paddle2onnx')
+
+    file_prefix = os.path.basename(path)
+    if file_prefix == "":
+        raise ValueError("The input path MUST be format of dirname/file_prefix "
+                         "[dirname\\file_prefix in Windows system], but "
+                         "the file_prefix is empty in received path: {}".format(
+                             path))
+    save_file = path + '.onnx'
+
+    p2o.dygraph2onnx(
+        layer,
+        save_file,
+        input_spec=input_spec,
+        opset_version=opset_version,
+        **configs)

python/setup.py.in

@@ -213,6 +213,7 @@ packages=['paddle',
           'paddle.static',
           'paddle.static.nn',
           'paddle.tensor',
+          'paddle.onnx',
           ]
 
 with open('@PADDLE_SOURCE_DIR@/python/requirements.txt') as f:

python/unittest_py/requirements.txt

@@ -4,3 +4,4 @@ pycrypto ; platform_system != "Windows"
 mock
 opencv-python<=4.2.0.32
 visualdl ; python_version>="3.5"
+paddle2onnx>=0.4