You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
206 lines
7.4 KiB
206 lines
7.4 KiB
# 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 logging
|
|
from paddle.fluid.op import Operator, DynamicRecurrentOp
|
|
import paddle.fluid.core as core
|
|
import unittest
|
|
import numpy as np
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid.framework import Program, program_guard
|
|
|
|
|
|
def create_tensor(scope, name, np_data):
|
|
tensor = scope.var(name).get_tensor()
|
|
tensor.set(np_data, core.CPUPlace())
|
|
return tensor
|
|
|
|
|
|
class BeamSearchOpTester(unittest.TestCase):
|
|
"""unittest of beam_search_op"""
|
|
|
|
def setUp(self):
|
|
self.scope = core.Scope()
|
|
self._create_ids()
|
|
self._create_pre_scores()
|
|
self._create_scores()
|
|
self._create_pre_ids()
|
|
self.scope.var('selected_ids').get_tensor()
|
|
self.scope.var('selected_scores').get_tensor()
|
|
self.scope.var('parent_idx').get_tensor()
|
|
|
|
def test_run(self):
|
|
op = Operator(
|
|
'beam_search',
|
|
pre_ids='pre_ids',
|
|
pre_scores='pre_scores',
|
|
ids='ids',
|
|
scores='scores',
|
|
selected_ids='selected_ids',
|
|
selected_scores='selected_scores',
|
|
parent_idx='parent_idx',
|
|
level=0,
|
|
beam_size=2,
|
|
end_id=0, )
|
|
op.run(self.scope, core.CPUPlace())
|
|
selected_ids = self.scope.find_var("selected_ids").get_tensor()
|
|
selected_scores = self.scope.find_var("selected_scores").get_tensor()
|
|
parent_idx = self.scope.find_var("parent_idx").get_tensor()
|
|
self.assertTrue(
|
|
np.allclose(
|
|
np.array(selected_ids), np.array([4, 2, 3, 8])[:, np.newaxis]))
|
|
self.assertTrue(
|
|
np.allclose(
|
|
np.array(selected_scores),
|
|
np.array([0.5, 0.6, 0.9, 0.7])[:, np.newaxis]))
|
|
self.assertEqual(selected_ids.lod(), [[0, 2, 4], [0, 1, 2, 3, 4]])
|
|
self.assertTrue(
|
|
np.allclose(np.array(parent_idx), np.array([0, 1, 2, 3])))
|
|
|
|
def _create_pre_ids(self):
|
|
np_data = np.array([[1, 2, 3, 4]], dtype='int64')
|
|
tensor = create_tensor(self.scope, 'pre_ids', np_data)
|
|
|
|
def _create_pre_scores(self):
|
|
np_data = np.array([[0.1, 0.2, 0.3, 0.4]], dtype='float32')
|
|
tensor = create_tensor(self.scope, 'pre_scores', np_data)
|
|
|
|
def _create_ids(self):
|
|
self.lod = [[0, 2, 4], [0, 1, 2, 3, 4]]
|
|
np_data = np.array(
|
|
[[4, 2, 5], [2, 1, 3], [3, 5, 2], [8, 2, 1]], dtype='int64')
|
|
tensor = create_tensor(self.scope, "ids", np_data)
|
|
tensor.set_lod(self.lod)
|
|
|
|
def _create_scores(self):
|
|
np_data = np.array(
|
|
[
|
|
[0.5, 0.3, 0.2],
|
|
[0.6, 0.3, 0.1],
|
|
[0.9, 0.5, 0.1],
|
|
[0.7, 0.5, 0.1],
|
|
],
|
|
dtype='float32')
|
|
tensor = create_tensor(self.scope, "scores", np_data)
|
|
tensor.set_lod(self.lod)
|
|
|
|
|
|
class TestBeamSearchOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
pre_ids = fluid.data(
|
|
name='pre_id', shape=[1], lod_level=2, dtype='int64')
|
|
pre_scores = fluid.data(
|
|
name='pre_scores', shape=[1], lod_level=2, dtype='float32')
|
|
probs = fluid.data(name='probs', shape=[10000], dtype='float32')
|
|
topk_scores, topk_indices = fluid.layers.topk(probs, k=4)
|
|
accu_scores = fluid.layers.elementwise_add(
|
|
x=fluid.layers.log(x=topk_scores),
|
|
y=fluid.layers.reshape(
|
|
pre_scores, shape=[-1]),
|
|
axis=0)
|
|
|
|
def test_preids_Variable():
|
|
# the input pre_ids must be Variable
|
|
preids_data = np.random.randint(1, 5, [5, 1]).astype("int64")
|
|
fluid.layers.beam_search(
|
|
pre_ids=preids_data,
|
|
pre_scores=pre_scores,
|
|
ids=topk_indices,
|
|
scores=accu_scores,
|
|
beam_size=4,
|
|
end_id=1)
|
|
|
|
self.assertRaises(TypeError, test_preids_Variable)
|
|
|
|
def test_prescores_Variable():
|
|
# the input pre_scores must be Variable
|
|
prescores_data = np.random.uniform(1, 5,
|
|
[5, 1]).astype("float32")
|
|
fluid.layers.beam_search(
|
|
pre_ids=pre_ids,
|
|
pre_scores=prescores_data,
|
|
ids=topk_indices,
|
|
scores=accu_scores,
|
|
beam_size=4,
|
|
end_id=1)
|
|
|
|
self.assertRaises(TypeError, test_prescores_Variable)
|
|
|
|
def test_ids_Variable():
|
|
# the input ids must be Variable or None
|
|
ids_data = np.random.randint(1, 5, [5, 1]).astype("int64")
|
|
fluid.layers.beam_search(
|
|
pre_ids=pre_ids,
|
|
pre_scores=pre_scores,
|
|
ids=ids_data,
|
|
scores=accu_scores,
|
|
beam_size=4,
|
|
end_id=1)
|
|
|
|
self.assertRaises(TypeError, test_ids_Variable)
|
|
|
|
def test_scores_Variable():
|
|
# the input scores must be Variable
|
|
scores_data = np.random.uniform(1, 5, [5, 1]).astype("float32")
|
|
fluid.layers.beam_search(
|
|
pre_ids=pre_ids,
|
|
pre_scores=pre_scores,
|
|
ids=topk_indices,
|
|
scores=scores_data,
|
|
beam_size=4,
|
|
end_id=1)
|
|
|
|
self.assertRaises(TypeError, test_scores_Variable)
|
|
|
|
def test_preids_dtype():
|
|
# the dtype of input pre_ids must be int64
|
|
preids_type_data = fluid.data(
|
|
name='preids_type_data',
|
|
shape=[1],
|
|
lod_level=2,
|
|
dtype='float32')
|
|
fluid.layers.beam_search(
|
|
pre_ids=preids_type_data,
|
|
pre_scores=pre_scores,
|
|
ids=topk_indices,
|
|
scores=accu_scores,
|
|
beam_size=4,
|
|
end_id=1)
|
|
|
|
self.assertRaises(TypeError, test_preids_dtype)
|
|
|
|
def test_prescores_dtype():
|
|
# the dtype of input pre_scores must be float32
|
|
prescores_type_data = fluid.data(
|
|
name='prescores_type_data',
|
|
shape=[1],
|
|
lod_level=2,
|
|
dtype='int64')
|
|
fluid.layers.beam_search(
|
|
pre_ids=pre_ids,
|
|
pre_scores=prescores_type_data,
|
|
ids=topk_indices,
|
|
scores=accu_scores,
|
|
beam_size=4,
|
|
end_id=1)
|
|
|
|
self.assertRaises(TypeError, test_prescores_dtype)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|