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.
102 lines
4.1 KiB
102 lines
4.1 KiB
# 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.
|
|
|
|
from __future__ import print_function
|
|
|
|
import unittest
|
|
import numpy as np
|
|
from op_test import OpTest, skip_check_grad_ci, check_out_dtype
|
|
import paddle
|
|
import paddle.fluid.core as core
|
|
|
|
|
|
class ApiMaxTest(unittest.TestCase):
|
|
def setUp(self):
|
|
if core.is_compiled_with_cuda():
|
|
self.place = core.CUDAPlace(0)
|
|
else:
|
|
self.place = core.CPUPlace()
|
|
|
|
def test_api(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program(),
|
|
paddle.static.Program()):
|
|
data = paddle.static.data("data", shape=[10, 10], dtype="float32")
|
|
result_max = paddle.max(x=data, axis=1)
|
|
exe = paddle.static.Executor(self.place)
|
|
input_data = np.random.rand(10, 10).astype(np.float32)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
|
|
self.assertEqual((res == np.max(input_data, axis=1)).all(), True)
|
|
|
|
with paddle.static.program_guard(paddle.static.Program(),
|
|
paddle.static.Program()):
|
|
data = paddle.static.data("data", shape=[10, 10], dtype="int64")
|
|
result_max = paddle.max(x=data, axis=0)
|
|
exe = paddle.static.Executor(self.place)
|
|
input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
|
|
self.assertEqual((res == np.max(input_data, axis=0)).all(), True)
|
|
|
|
with paddle.static.program_guard(paddle.static.Program(),
|
|
paddle.static.Program()):
|
|
data = paddle.static.data("data", shape=[10, 10], dtype="int64")
|
|
result_max = paddle.max(x=data, axis=(0, 1))
|
|
exe = paddle.static.Executor(self.place)
|
|
input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
|
|
res, = exe.run(feed={"data": input_data}, fetch_list=[result_max])
|
|
self.assertEqual((res == np.max(input_data, axis=(0, 1))).all(), True)
|
|
|
|
def test_errors(self):
|
|
paddle.enable_static()
|
|
|
|
def test_input_type():
|
|
with paddle.static.program_guard(paddle.static.Program(),
|
|
paddle.static.Program()):
|
|
data = np.random.rand(10, 10)
|
|
result_max = paddle.max(x=data, axis=0)
|
|
|
|
self.assertRaises(TypeError, test_input_type)
|
|
|
|
def test_axis_type():
|
|
with paddle.static.program_guard(paddle.static.Program(),
|
|
paddle.static.Program()):
|
|
data = paddle.static.data("data", shape=[10, 10], dtype="int64")
|
|
axis = paddle.static.data("axis", shape=[10, 10], dtype="int64")
|
|
result_min = paddle.min(data, axis)
|
|
|
|
self.assertRaises(TypeError, test_axis_type)
|
|
|
|
def test_imperative_api(self):
|
|
paddle.disable_static()
|
|
np_x = np.array([10, 10]).astype('float64')
|
|
x = paddle.to_tensor(np_x)
|
|
z = paddle.max(x, axis=0)
|
|
np_z = z.numpy()
|
|
z_expected = np.array(np.max(np_x, axis=0))
|
|
self.assertEqual((np_z == z_expected).all(), True)
|
|
|
|
|
|
class TestOutDtype(unittest.TestCase):
|
|
def test_max(self):
|
|
api_fn = paddle.max
|
|
shape = [10, 16]
|
|
check_out_dtype(
|
|
api_fn,
|
|
in_specs=[(shape, )],
|
|
expect_dtypes=['float32', 'float64', 'int32', 'int64'])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|