test_basic_rnn_name.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 unittest

from test_imperative_base import new_program_scope

import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.contrib.layers import basic_gru, basic_lstm


class TestBasicGRUApiName(unittest.TestCase):
    def setUp(self):
        self.name_set = set(
            [
                "test1_fw_w_0_gate",
                "test1_fw_w_0_candidate",
                "test1_fw_b_0_gate",
                "test1_fw_b_0_candidate",
                "test1_bw_w_0_gate",
                "test1_bw_w_0_candidate",
                "test1_bw_b_0_gate",
                "test1_bw_b_0_candidate",
            ]
        )

    def test_name(self):
        batch_size = 20
        input_size = 128
        hidden_size = 256
        num_layers = 1
        dropout = 0.5
        bidirectional = True
        batch_first = False

        with new_program_scope():
            input = layers.data(
                name="input",
                shape=[-1, batch_size, input_size],
                dtype='float32',
            )
            pre_hidden = layers.data(
                name="pre_hidden", shape=[-1, hidden_size], dtype='float32'
            )
            sequence_length = layers.data(
                name="sequence_length", shape=[-1], dtype='int32'
            )

            rnn_out, last_hidden = basic_gru(
                input,
                pre_hidden,
                hidden_size,
                num_layers=num_layers,
                sequence_length=sequence_length,
                dropout_prob=dropout,
                bidirectional=bidirectional,
                batch_first=batch_first,
                param_attr=fluid.ParamAttr(name="test1"),
                bias_attr=fluid.ParamAttr(name="test1"),
                name="basic_gru",
            )

            var_list = fluid.io.get_program_parameter(
                fluid.default_main_program()
            )

            for var in var_list:
                self.assertTrue(var.name in self.name_set)


class TestBasicLSTMApiName(unittest.TestCase):
    def setUp(self):
        self.name_set = set(
            [
                "test1_fw_w_0",
                "test1_fw_b_0",
                "test1_fw_w_1",
                "test1_fw_b_1",
                "test1_bw_w_0",
                "test1_bw_b_0",
                "test1_bw_w_1",
                "test1_bw_b_1",
            ]
        )

    def test_name(self):
        batch_size = 20
        input_size = 128
        hidden_size = 256
        num_layers = 2
        dropout = 0.5
        bidirectional = True
        batch_first = False

        with new_program_scope():
            input = layers.data(
                name="input",
                shape=[-1, batch_size, input_size],
                dtype='float32',
            )
            pre_hidden = layers.data(
                name="pre_hidden", shape=[-1, hidden_size], dtype='float32'
            )
            pre_cell = layers.data(
                name="pre_cell", shape=[-1, hidden_size], dtype='float32'
            )
            sequence_length = layers.data(
                name="sequence_length", shape=[-1], dtype='int32'
            )

            rnn_out, last_hidden, last_cell = basic_lstm(
                input,
                pre_hidden,
                pre_cell,
                hidden_size,
                num_layers=num_layers,
                sequence_length=sequence_length,
                dropout_prob=dropout,
                bidirectional=bidirectional,
                param_attr=fluid.ParamAttr(name="test1"),
                bias_attr=fluid.ParamAttr(name="test1"),
                batch_first=batch_first,
            )

            var_list = fluid.io.get_program_parameter(
                fluid.default_main_program()
            )

            for var in var_list:
                self.assertTrue(var.name in self.name_set)


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