test_entry_attr.py

#   Copyright (c) 2018 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 unittest
import paddle.fluid as fluid
from paddle.fluid.entry_attr import ProbabilityEntry, CountFilterEntry


class EntryAttrChecks(unittest.TestCase):
    def base(self):
        with self.assertRaises(NotImplementedError):
            import paddle.fluid.entry_attr as entry
            base = entry.EntryAttr()
            base.to_attr()

    def probability_entry(self):
        prob = ProbabilityEntry(0.5)
        ss = prob.to_attr()
        self.assertEqual("probability_entry:0.5", ss)

        with self.assertRaises(ValueError):
            prob1 = ProbabilityEntry("none")

        with self.assertRaises(ValueError):
            prob2 = ProbabilityEntry(-1)

    def countfilter_entry(self):
        counter = CountFilterEntry(20)
        ss = counter.to_attr()
        self.assertEqual("count_filter_entry:20", ss)

        with self.assertRaises(ValueError):
            counter1 = CountFilterEntry("none")

        with self.assertRaises(ValueError):
            counter2 = CountFilterEntry(-1)

    def spaese_layer(self):
        prog = fluid.Program()
        scope = fluid.core.Scope()

        with fluid.scope_guard(scope):
            with fluid.program_guard(prog):
                input = fluid.layers.data(
                    name="dnn_data",
                    shape=[-1, 1],
                    dtype="int64",
                    lod_level=1,
                    append_batch_size=False)
                prob = ProbabilityEntry(0.5)
                emb = fluid.contrib.layers.sparse_embedding(
                    input=input,
                    size=[100, 10],
                    is_test=False,
                    entry=prob,
                    param_attr=fluid.ParamAttr(name="deep_embedding"))
                pool = fluid.layers.sequence_pool(input=emb, pool_type="sum")
                predict = fluid.layers.fc(input=pool, size=2, act='softmax')

        block = prog.global_block()
        for op in block.ops:
            if op.type == "lookup_table":
                entry = op.attr("entry")
                is_test = op.attr("is_test")
                is_sparse = op.attr("is_sparse")
                is_distributed = op.attr("is_distributed")

                self.assertEqual(entry, "probability_entry:0.5")
                self.assertTrue(is_distributed)
                self.assertTrue(is_sparse)
                self.assertFalse(is_test)


class TestEntryAttrs(EntryAttrChecks):
    def test_base(self):
        self.base()

    def test_prob(self):
        self.probability_entry()

    def test_counter(self):
        self.countfilter_entry()

    def test_spaese_embedding_layer(self):
        self.spaese_layer()


if __name__ == '__main__':
    unittest.main()