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test_gather_tree_op.py
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test_gather_tree_op.py
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# Copyright (c) 2019 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 numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
from paddle.fluid.framework import program_guard, Program
class TestGatherTreeOp(OpTest):
def setUp(self):
self.op_type = "gather_tree"
self.python_api = paddle.nn.functional.gather_tree
max_length, batch_size, beam_size = 5, 2, 2
ids = np.random.randint(0,
high=10,
size=(max_length, batch_size, beam_size))
parents = np.random.randint(0,
high=beam_size,
size=(max_length, batch_size, beam_size))
self.inputs = {"Ids": ids, "Parents": parents}
self.outputs = {'Out': self.backtrace(ids, parents)}
def test_check_output(self):
self.check_output(check_eager=True)
@staticmethod
def backtrace(ids, parents):
out = np.zeros_like(ids)
(max_length, batch_size, beam_size) = ids.shape
for batch in range(batch_size):
for beam in range(beam_size):
out[max_length - 1, batch, beam] = ids[max_length - 1, batch,
beam]
parent = parents[max_length - 1, batch, beam]
for step in range(max_length - 2, -1, -1):
out[step, batch, beam] = ids[step, batch, parent]
parent = parents[step, batch, parent]
return out
class TestGatherTreeOpAPI(unittest.TestCase):
def test_case(self):
paddle.enable_static()
ids = fluid.layers.data(name='ids',
shape=[5, 2, 2],
dtype='int64',
append_batch_size=False)
parents = fluid.layers.data(name='parents',
shape=[5, 2, 2],
dtype='int64',
append_batch_size=False)
final_sequences = fluid.layers.gather_tree(ids, parents)
paddle.disable_static()
def test_case2(self):
ids = paddle.to_tensor([[[2, 2], [6, 1]], [[3, 9], [6, 1]],
[[0, 1], [9, 0]]])
parents = paddle.to_tensor([[[0, 0], [1, 1]], [[1, 0], [1, 0]],
[[0, 0], [0, 1]]])
final_sequences = paddle.nn.functional.gather_tree(ids, parents)
class TestGatherTreeOpError(unittest.TestCase):
def test_errors(self):
paddle.enable_static()
with program_guard(Program(), Program()):
ids = fluid.layers.data(name='ids',
shape=[5, 2, 2],
dtype='int64',
append_batch_size=False)
parents = fluid.layers.data(name='parents',
shape=[5, 2, 2],
dtype='int64',
append_batch_size=False)
def test_Variable_ids():
# the input type must be Variable
np_ids = np.random.random((5, 2, 2), dtype='int64')
fluid.layers.gather_tree(np_ids, parents)
self.assertRaises(TypeError, test_Variable_ids)
def test_Variable_parents():
# the input type must be Variable
np_parents = np.random.random((5, 2, 2), dtype='int64')
fluid.layers.gather_tree(ids, np_parents)
self.assertRaises(TypeError, test_Variable_parents)
def test_type_ids():
# dtype must be int32 or int64
bad_ids = fluid.layers.data(name='bad_ids',
shape=[5, 2, 2],
dtype='float32',
append_batch_size=False)
fluid.layers.gather_tree(bad_ids, parents)
self.assertRaises(TypeError, test_type_ids)
def test_type_parents():
# dtype must be int32 or int64
bad_parents = fluid.layers.data(name='bad_parents',
shape=[5, 2, 2],
dtype='float32',
append_batch_size=False)
fluid.layers.gather_tree(ids, bad_parents)
self.assertRaises(TypeError, test_type_parents)
def test_ids_ndim():
bad_ids = fluid.layers.data(name='bad_test_ids',
shape=[5, 2],
dtype='int64',
append_batch_size=False)
paddle.nn.functional.gather_tree(bad_ids, parents)
self.assertRaises(ValueError, test_ids_ndim)
def test_parents_ndim():
bad_parents = fluid.layers.data(name='bad_test_parents',
shape=[5, 2],
dtype='int64',
append_batch_size=False)
paddle.nn.functional.gather_tree(ids, bad_parents)
self.assertRaises(ValueError, test_parents_ndim)
paddle.disable_static()
if __name__ == "__main__":
paddle.enable_static()
unittest.main()