wehub-resource-sync
244 lines
8.0 KiB
Python
244 lines
8.0 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 sys
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import unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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from paddle.base.layer_helper_base import LayerHelperBase
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sys.path.append("../rnn")
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from rnn_numpy import (
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LSTMCell,
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rnn as numpy_rnn,
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)
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import paddle
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from paddle import base
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from paddle.base import core
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from paddle.base.executor import Executor
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from paddle.base.framework import Program, program_guard
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from paddle.nn.layer.rnn import rnn as dynamic_rnn
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paddle.enable_static()
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class TestRnnError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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batch_size = 4
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input_size = 16
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hidden_size = 16
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seq_len = 4
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inputs = paddle.static.data(
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name='inputs', shape=[None, input_size], dtype='float32'
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)
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pre_hidden = paddle.static.data(
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name='pre_hidden',
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shape=[None, hidden_size],
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dtype='float32',
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)
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inputs_basic_lstm = paddle.static.data(
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name='inputs_basic_lstm',
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shape=[None, None, input_size],
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dtype='float32',
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)
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sequence_length = paddle.static.data(
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name="sequence_length", shape=[None], dtype='int64'
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)
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inputs_dynamic_rnn = paddle.transpose(
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inputs_basic_lstm, perm=[1, 0, 2]
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)
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cell = paddle.nn.LSTMCell(
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input_size, hidden_size, name="LSTMCell_for_rnn"
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)
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np_inputs_dynamic_rnn = np.random.random(
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(seq_len, batch_size, input_size)
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).astype("float32")
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def test_input_Variable():
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dynamic_rnn(
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cell=cell,
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inputs=np_inputs_dynamic_rnn,
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sequence_length=sequence_length,
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is_reverse=False,
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)
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self.assertRaises(TypeError, test_input_Variable)
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def test_input_list():
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dynamic_rnn(
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cell=cell,
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inputs=[np_inputs_dynamic_rnn],
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sequence_length=sequence_length,
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is_reverse=False,
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)
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self.assertRaises(TypeError, test_input_list)
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def test_initial_states_type():
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cell = paddle.nn.GRUCell(
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input_size, hidden_size, name="GRUCell_for_rnn"
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)
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error_initial_states = np.random.random(
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(batch_size, hidden_size)
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).astype("float32")
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dynamic_rnn(
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cell=cell,
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inputs=inputs_dynamic_rnn,
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initial_states=error_initial_states,
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sequence_length=sequence_length,
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is_reverse=False,
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)
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self.assertRaises(TypeError, test_initial_states_type)
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def test_initial_states_list():
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error_initial_states = [
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np.random.random((batch_size, hidden_size)).astype(
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"float32"
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),
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np.random.random((batch_size, hidden_size)).astype(
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"float32"
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),
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]
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dynamic_rnn(
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cell=cell,
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inputs=inputs_dynamic_rnn,
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initial_states=error_initial_states,
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sequence_length=sequence_length,
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is_reverse=False,
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)
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self.assertRaises(TypeError, test_initial_states_type)
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def test_sequence_length_type():
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np_sequence_length = np.random.random(batch_size).astype(
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"float32"
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)
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dynamic_rnn(
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cell=cell,
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inputs=inputs_dynamic_rnn,
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sequence_length=np_sequence_length,
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is_reverse=False,
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)
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self.assertRaises(TypeError, test_sequence_length_type)
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class TestRnn(unittest.TestCase):
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def setUp(self):
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self.batch_size = 4
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self.input_size = 16
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self.hidden_size = 16
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self.seq_len = 4
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def test_run(self):
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numpy_cell = LSTMCell(self.input_size, self.hidden_size)
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LayerHelperBase.set_default_dtype("float64")
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dynamic_cell = paddle.nn.LSTMCell(self.input_size, self.hidden_size)
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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else:
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place = core.CPUPlace()
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exe = Executor(place)
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exe.run(paddle.static.default_startup_program())
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state = numpy_cell.parameters
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for k, v in dynamic_cell.named_parameters():
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param = np.random.uniform(-0.1, 0.1, size=state[k].shape).astype(
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'float64'
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)
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setattr(numpy_cell, k, param)
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base.global_scope().find_var(v.name).get_tensor().set(param, place)
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sequence_length = paddle.static.data(
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name="sequence_length", shape=[None], dtype='int64'
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)
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inputs_rnn = paddle.static.data(
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name='inputs_rnn',
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shape=[None, None, self.input_size],
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dtype='float64',
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)
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pre_hidden = paddle.static.data(
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name='pre_hidden', shape=[None, self.hidden_size], dtype='float64'
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)
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pre_cell = paddle.static.data(
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name='pre_cell', shape=[None, self.hidden_size], dtype='float64'
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)
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dynamic_output, dynamic_final_state = dynamic_rnn(
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cell=dynamic_cell,
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inputs=inputs_rnn,
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sequence_length=sequence_length,
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initial_states=(pre_hidden, pre_cell),
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is_reverse=False,
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)
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inputs_rnn_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.seq_len, self.input_size)
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).astype('float64')
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sequence_length_np = (
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np.ones(self.batch_size, dtype='int64') * self.seq_len
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)
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pre_hidden_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.hidden_size)
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).astype('float64')
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pre_cell_np = np.random.uniform(
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-0.1, 0.1, (self.batch_size, self.hidden_size)
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).astype('float64')
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o1, _ = numpy_rnn(
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cell=numpy_cell,
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inputs=inputs_rnn_np,
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initial_states=(pre_hidden_np, pre_cell_np),
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sequence_length=sequence_length_np,
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is_reverse=False,
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)
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o2 = exe.run(
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feed={
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'inputs_rnn': inputs_rnn_np,
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'sequence_length': sequence_length_np,
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'pre_hidden': pre_hidden_np,
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'pre_cell': pre_cell_np,
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},
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fetch_list=[dynamic_output],
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)[0]
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np.testing.assert_allclose(o1, o2, rtol=0.001)
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class TestRnnUtil(unittest.TestCase):
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"""
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Test cases for rnn apis' utility methods for coverage.
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"""
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def test_case(self):
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inputs = {"key1": 1, "key2": 2}
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func = lambda x: x + 1
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outputs = paddle.utils.map_structure(func, inputs)
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paddle.utils.assert_same_structure(inputs, outputs)
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try:
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inputs["key3"] = 3
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paddle.utils.assert_same_structure(inputs, outputs)
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except ValueError as identifier:
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pass
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if __name__ == '__main__':
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unittest.main()
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