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