wehub-resource-sync
414 lines
14 KiB
Python
414 lines
14 KiB
Python
# 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 copy
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from op_test import get_device_place, is_custom_device
|
|
|
|
import paddle
|
|
|
|
paddle.enable_static()
|
|
|
|
np.random.seed(10)
|
|
paddle.seed(10)
|
|
|
|
|
|
class TestNormalAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
self.mean = 1.0
|
|
self.std = 0.0
|
|
self.shape = None
|
|
self.repeat_num = 2000
|
|
self.set_attrs()
|
|
self.dtype = self.get_dtype()
|
|
self.place = (
|
|
get_device_place()
|
|
if (
|
|
(paddle.base.core.is_compiled_with_cuda() or is_custom_device())
|
|
or is_custom_device()
|
|
)
|
|
else paddle.CPUPlace()
|
|
)
|
|
|
|
def set_attrs(self):
|
|
self.shape = [8, 12]
|
|
|
|
def get_shape(self):
|
|
if isinstance(self.mean, np.ndarray):
|
|
shape = self.mean.shape
|
|
elif isinstance(self.std, np.ndarray):
|
|
shape = self.std.shape
|
|
else:
|
|
shape = self.shape
|
|
return list(shape)
|
|
|
|
def get_dtype(self):
|
|
if isinstance(self.mean, np.ndarray):
|
|
return self.mean.dtype
|
|
elif isinstance(self.std, np.ndarray):
|
|
return self.std.dtype
|
|
else:
|
|
return 'float32'
|
|
|
|
def static_api(self):
|
|
paddle.enable_static()
|
|
shape = self.get_shape()
|
|
ret_all_shape = copy.deepcopy(shape)
|
|
ret_all_shape.insert(0, self.repeat_num)
|
|
ret_all = np.zeros(ret_all_shape, self.dtype)
|
|
main_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program):
|
|
if isinstance(self.mean, np.ndarray) and isinstance(
|
|
self.std, np.ndarray
|
|
):
|
|
mean = paddle.static.data(
|
|
'Mean', self.mean.shape, self.mean.dtype
|
|
)
|
|
std = paddle.static.data('Std', self.std.shape, self.std.dtype)
|
|
out = paddle.normal(mean, std, self.shape)
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
for i in range(self.repeat_num):
|
|
ret = exe.run(
|
|
feed={
|
|
'Mean': self.mean,
|
|
'Std': self.std.reshape(shape),
|
|
},
|
|
fetch_list=[out],
|
|
)
|
|
ret_all[i] = ret[0]
|
|
elif isinstance(self.mean, np.ndarray):
|
|
mean = paddle.static.data(
|
|
'Mean', self.mean.shape, self.mean.dtype
|
|
)
|
|
out = paddle.normal(mean, self.std, self.shape)
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
for i in range(self.repeat_num):
|
|
ret = exe.run(feed={'Mean': self.mean}, fetch_list=[out])
|
|
ret_all[i] = ret[0]
|
|
elif isinstance(self.std, np.ndarray):
|
|
std = paddle.static.data('Std', self.std.shape, self.std.dtype)
|
|
out = paddle.normal(self.mean, std, self.shape)
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
for i in range(self.repeat_num):
|
|
ret = exe.run(feed={'Std': self.std}, fetch_list=[out])
|
|
ret_all[i] = ret[0]
|
|
else:
|
|
out = paddle.normal(self.mean, self.std, self.shape)
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
for i in range(self.repeat_num):
|
|
ret = exe.run(fetch_list=[out])
|
|
ret_all[i] = ret[0]
|
|
paddle.disable_static()
|
|
return ret_all
|
|
|
|
def dygraph_api(self):
|
|
paddle.disable_static(self.place)
|
|
shape = self.get_shape()
|
|
ret_all_shape = copy.deepcopy(shape)
|
|
ret_all_shape.insert(0, self.repeat_num)
|
|
ret_all = np.zeros(ret_all_shape, self.dtype)
|
|
|
|
mean = (
|
|
paddle.to_tensor(self.mean)
|
|
if isinstance(self.mean, np.ndarray)
|
|
else self.mean
|
|
)
|
|
std = (
|
|
paddle.to_tensor(self.std)
|
|
if isinstance(self.std, np.ndarray)
|
|
else self.std
|
|
)
|
|
for i in range(self.repeat_num):
|
|
out = paddle.normal(mean, std, self.shape)
|
|
ret_all[i] = out.numpy()
|
|
paddle.enable_static()
|
|
return ret_all
|
|
|
|
def test_api(self):
|
|
ret_static = self.static_api()
|
|
ret_dygraph = self.dygraph_api()
|
|
for ret in [ret_static, ret_dygraph]:
|
|
shape_ref = self.get_shape()
|
|
self.assertEqual(shape_ref, list(ret[0].shape))
|
|
|
|
ret = ret.flatten().reshape([self.repeat_num, -1])
|
|
mean = np.mean(ret, axis=0)
|
|
std = np.std(ret, axis=0)
|
|
mean_ref = (
|
|
self.mean.flatten()
|
|
if isinstance(self.mean, np.ndarray)
|
|
else self.mean
|
|
)
|
|
std_ref = (
|
|
self.std.flatten()
|
|
if isinstance(self.std, np.ndarray)
|
|
else self.std
|
|
)
|
|
np.testing.assert_allclose(mean_ref, mean, rtol=0.2, atol=0.2)
|
|
np.testing.assert_allclose(std_ref, std, rtol=0.2, atol=0.2)
|
|
|
|
|
|
class TestNormalAPI_mean_is_tensor(TestNormalAPI):
|
|
def set_attrs(self):
|
|
self.mean = np.random.uniform(-2, -1, [2, 3, 4, 5]).astype('float64')
|
|
|
|
|
|
class TestNormalAPI_std_is_tensor(TestNormalAPI):
|
|
def set_attrs(self):
|
|
self.std = np.random.uniform(0.7, 1, [2, 3, 17]).astype('float64')
|
|
|
|
|
|
class TestNormalAPI_mean_std_are_tensor(TestNormalAPI):
|
|
def set_attrs(self):
|
|
self.mean = np.random.uniform(1, 2, [1, 100]).astype('float64')
|
|
self.std = np.random.uniform(0.5, 1, [1, 100]).astype('float64')
|
|
|
|
|
|
class TestNormalAPI_mean_std_are_tensor_with_different_dtype(TestNormalAPI):
|
|
def set_attrs(self):
|
|
self.mean = np.random.uniform(1, 2, [100]).astype('float64')
|
|
self.std = np.random.uniform(1, 2, [100]).astype('float32')
|
|
|
|
|
|
class TestNormalAlias(unittest.TestCase):
|
|
def test_alias(self):
|
|
paddle.disable_static()
|
|
shape = [1, 2, 3]
|
|
out1 = paddle.normal(shape=shape)
|
|
out2 = paddle.tensor.normal(shape=shape)
|
|
out3 = paddle.tensor.random.normal(shape=shape)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestNormalErrors(unittest.TestCase):
|
|
def test_errors(self):
|
|
main_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program):
|
|
mean = [1, 2, 3]
|
|
self.assertRaises(TypeError, paddle.normal, mean)
|
|
|
|
std = [1, 2, 3]
|
|
self.assertRaises(TypeError, paddle.normal, std=std)
|
|
|
|
mean = paddle.static.data('Mean', [100], 'int32')
|
|
self.assertRaises(TypeError, paddle.normal, mean)
|
|
|
|
std = paddle.static.data('Std', [100], 'int32')
|
|
self.assertRaises(TypeError, paddle.normal, mean=1.0, std=std)
|
|
|
|
self.assertRaises(TypeError, paddle.normal, shape=1)
|
|
self.assertRaises(TypeError, paddle.normal, shape=[1.0])
|
|
|
|
shape = paddle.static.data('Shape', [100], 'float32')
|
|
self.assertRaises(TypeError, paddle.normal, shape=shape)
|
|
|
|
|
|
class TestNormalAPIComplex(unittest.TestCase):
|
|
def setUp(self):
|
|
self.mean = 1.0 + 1.0j
|
|
self.std = 1.0
|
|
self.shape = None
|
|
self.repeat_num = 2000
|
|
self.set_attrs()
|
|
self.dtype = self.get_dtype()
|
|
self.place = (
|
|
get_device_place()
|
|
if (
|
|
(paddle.base.core.is_compiled_with_cuda() or is_custom_device())
|
|
or is_custom_device()
|
|
)
|
|
else paddle.CPUPlace()
|
|
)
|
|
|
|
def set_attrs(self):
|
|
self.shape = [8, 12]
|
|
|
|
def get_shape(self):
|
|
if isinstance(self.mean, np.ndarray):
|
|
shape = self.mean.shape
|
|
elif isinstance(self.std, np.ndarray):
|
|
shape = self.std.shape
|
|
else:
|
|
shape = self.shape
|
|
return list(shape)
|
|
|
|
def get_dtype(self):
|
|
if isinstance(self.mean, np.ndarray):
|
|
return self.mean.dtype
|
|
else:
|
|
return 'complex64'
|
|
|
|
def static_api(self):
|
|
paddle.enable_static()
|
|
shape = self.get_shape()
|
|
ret_all_shape = copy.deepcopy(shape)
|
|
ret_all_shape.insert(0, self.repeat_num)
|
|
ret_all = np.zeros(ret_all_shape, self.dtype)
|
|
main_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program):
|
|
if isinstance(self.mean, np.ndarray) and isinstance(
|
|
self.std, np.ndarray
|
|
):
|
|
mean = paddle.static.data(
|
|
'Mean', self.mean.shape, self.mean.dtype
|
|
)
|
|
std = paddle.static.data('Std', self.std.shape, self.std.dtype)
|
|
out = paddle.normal(mean, std, self.shape)
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
for i in range(self.repeat_num):
|
|
ret = exe.run(
|
|
feed={
|
|
'Mean': self.mean,
|
|
'Std': self.std.reshape(shape),
|
|
},
|
|
fetch_list=[out],
|
|
)
|
|
ret_all[i] = ret[0]
|
|
elif isinstance(self.mean, np.ndarray):
|
|
mean = paddle.static.data(
|
|
'Mean', self.mean.shape, self.mean.dtype
|
|
)
|
|
out = paddle.normal(mean, self.std, self.shape)
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
for i in range(self.repeat_num):
|
|
ret = exe.run(feed={'Mean': self.mean}, fetch_list=[out])
|
|
ret_all[i] = ret[0]
|
|
elif isinstance(self.std, np.ndarray):
|
|
mean = paddle.static.data('Mean', self.std.shape, 'complex128')
|
|
std = paddle.static.data('Std', self.std.shape, self.std.dtype)
|
|
out = paddle.normal(mean, std, self.shape)
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
for i in range(self.repeat_num):
|
|
ret = exe.run(
|
|
feed={
|
|
'Std': self.std,
|
|
'Mean': np.broadcast_to(
|
|
np.array(self.mean), self.std.shape
|
|
),
|
|
},
|
|
fetch_list=[out],
|
|
)
|
|
ret_all[i] = ret[0]
|
|
else:
|
|
mean = paddle.static.data('Mean', (), 'complex128')
|
|
out = paddle.normal(mean, self.std, self.shape)
|
|
|
|
exe = paddle.static.Executor(self.place)
|
|
for i in range(self.repeat_num):
|
|
ret = exe.run(
|
|
feed={'Mean': np.array(self.mean)}, fetch_list=[out]
|
|
)
|
|
ret_all[i] = ret[0]
|
|
paddle.disable_static()
|
|
return ret_all
|
|
|
|
def dygraph_api(self):
|
|
paddle.disable_static(self.place)
|
|
shape = self.get_shape()
|
|
ret_all_shape = copy.deepcopy(shape)
|
|
ret_all_shape.insert(0, self.repeat_num)
|
|
ret_all = np.zeros(ret_all_shape, self.dtype)
|
|
|
|
mean = (
|
|
paddle.to_tensor(self.mean)
|
|
if isinstance(self.mean, np.ndarray)
|
|
else self.mean
|
|
)
|
|
std = (
|
|
paddle.to_tensor(self.std)
|
|
if isinstance(self.std, np.ndarray)
|
|
else self.std
|
|
)
|
|
for i in range(self.repeat_num):
|
|
out = paddle.normal(mean, std, self.shape)
|
|
ret_all[i] = out.numpy()
|
|
paddle.enable_static()
|
|
return ret_all
|
|
|
|
def test_api(self):
|
|
ret_static = self.static_api()
|
|
ret_dygraph = self.dygraph_api()
|
|
for ret in [ret_static, ret_dygraph]:
|
|
shape_ref = self.get_shape()
|
|
self.assertEqual(shape_ref, list(ret[0].shape))
|
|
|
|
mean = np.mean(ret, axis=0)
|
|
var = np.var(ret, axis=0)
|
|
var_real = np.var(ret.real, axis=0)
|
|
var_imag = np.var(ret.imag, axis=0)
|
|
mean_ref = self.mean
|
|
var_ref = np.power(self.std, 2)
|
|
np.testing.assert_allclose(mean_ref, mean, rtol=0.2, atol=0.2)
|
|
np.testing.assert_allclose(var_ref, var, rtol=0.2, atol=0.2)
|
|
np.testing.assert_allclose(
|
|
var_ref / 2.0, var_real, rtol=0.2, atol=0.2
|
|
)
|
|
np.testing.assert_allclose(
|
|
var_ref / 2.0, var_imag, rtol=0.2, atol=0.2
|
|
)
|
|
|
|
|
|
class TestNormalAPIComplex_mean_is_tensor(TestNormalAPIComplex):
|
|
def set_attrs(self):
|
|
self.mean = np.vectorize(complex)(
|
|
np.random.uniform(-2, -1, [2, 3]), np.random.uniform(-2, -1, [2, 3])
|
|
)
|
|
|
|
|
|
class TestNormalAPIComplex_std_is_tensor(TestNormalAPIComplex):
|
|
def set_attrs(self):
|
|
self.std = np.random.uniform(0.7, 1, [2, 5]).astype('float64')
|
|
|
|
|
|
class TestNormalAPIComplex_mean_std_are_tensor(TestNormalAPIComplex):
|
|
def set_attrs(self):
|
|
self.mean = np.vectorize(complex)(
|
|
np.random.uniform(1, 2, [1, 100]), np.random.uniform(1, 2, [1, 100])
|
|
)
|
|
self.std = np.random.uniform(0.5, 1, [1, 100]).astype('float64')
|
|
|
|
|
|
class TestNormalAPIComplex_mean_std_are_tensor_with_different_dtype(
|
|
TestNormalAPIComplex
|
|
):
|
|
def set_attrs(self):
|
|
self.mean = np.vectorize(complex)(
|
|
np.random.uniform(1, 2, [100]), np.random.uniform(1, 2, [100])
|
|
).astype('complex64')
|
|
self.std = np.random.uniform(1, 2, [100]).astype('float32')
|
|
|
|
|
|
class TestNormalComplexErrors(unittest.TestCase):
|
|
def test_errors(self):
|
|
main_program = paddle.static.Program()
|
|
with paddle.static.program_guard(main_program):
|
|
mean = 1 + 1j
|
|
self.assertRaises(TypeError, paddle.normal, mean, dtype='float32')
|
|
|
|
mean = 2 + 0.5j
|
|
self.assertRaises(ValueError, paddle.normal, mean)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|