wehub-resource-sync
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
# 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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import unittest
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import numpy as np
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import paddle
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from paddle import base
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from paddle.incubate.layers.nn import search_pyramid_hash
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class TestPyramidHashOpApi(unittest.TestCase):
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def test_api(self):
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paddle.enable_static()
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num_voc = 128
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embed_dim = 64
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x_shape, x_lod = [16, 10], [[3, 5, 2, 6]]
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x = paddle.static.data(name='x', shape=x_shape, dtype='int32')
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hash_embed = search_pyramid_hash(
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input=x,
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num_emb=embed_dim,
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space_len=num_voc * embed_dim,
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pyramid_layer=4,
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rand_len=16,
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drop_out_percent=0.5,
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is_training=True,
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use_filter=False,
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white_list_len=6400,
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black_list_len=2800,
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seed=3,
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lr=0.002,
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param_attr=base.ParamAttr(
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name="PyramidHash_emb_0",
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learning_rate=0,
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),
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param_attr_wl=base.ParamAttr(
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name="Filter",
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learning_rate=0,
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),
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param_attr_bl=None,
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distribute_update_vars=["PyramidHash_emb_0"],
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name=None,
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)
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place = base.CPUPlace()
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x_tensor = base.create_lod_tensor(
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np.random.randint(0, num_voc, x_shape).astype('int32'), x_lod, place
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)
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exe = base.Executor(place)
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exe.run(base.default_startup_program())
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ret = exe.run(
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feed={'x': x_tensor}, fetch_list=[hash_embed], return_numpy=False
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)
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if __name__ == "__main__":
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unittest.main()
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