54 lines
1.8 KiB
54 lines
1.8 KiB
# Copyright (c) 2019 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 unittest
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import paddle.fluid as fluid
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import numpy as np
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class TestImperativePartitialBackward(unittest.TestCase):
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def test_partitial_backward(self):
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with fluid.dygraph.guard():
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x = np.random.randn(2, 4, 5).astype("float32")
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x = fluid.dygraph.to_variable(x)
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fc1 = fluid.dygraph.FC("fc1", 10, num_flatten_dims=2)
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fc2 = fluid.dygraph.FC("fc2", 10, num_flatten_dims=2)
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y = fc1(x[:, :2])
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z = fc2(x[:, 2:])
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loss = fluid.layers.reduce_mean(y)
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loss.backward()
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for param in fc1.parameters():
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self.assertIsNotNone(param._grad_ivar())
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for param in fc2.parameters():
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self.assertIsNone(param._grad_ivar())
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optimizer = fluid.optimizer.AdamOptimizer()
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_, params_grads = optimizer.minimize(loss)
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self.assertListEqual(
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sorted([p.name for p in fc1.parameters()]),
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sorted([p_g[0].name for p_g in params_grads]))
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fc1.clear_gradients()
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fc2.clear_gradients()
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if __name__ == '__main__':
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unittest.main()
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