wehub-resource-sync
180 lines
6.1 KiB
Python
180 lines
6.1 KiB
Python
# Copyright (c) 2022 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 unittest
|
|
|
|
import numpy as np
|
|
from op_test import get_device_place
|
|
|
|
import paddle
|
|
|
|
np.random.seed(10)
|
|
|
|
|
|
class TestNanmeanAPI(unittest.TestCase):
|
|
# test paddle.tensor.math.nanmean
|
|
|
|
def setUp(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
|
|
self.x[0, :, :, :] = np.nan
|
|
self.x_grad = np.array(
|
|
[[np.nan, np.nan, 3.0], [0.0, np.nan, 2.0]]
|
|
).astype(np.float32)
|
|
self.place = get_device_place()
|
|
|
|
def test_api_static(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data('X', self.x_shape)
|
|
out1 = paddle.nanmean(x)
|
|
out2 = paddle.tensor.nanmean(x)
|
|
out3 = paddle.tensor.math.nanmean(x)
|
|
axis = np.arange(len(self.x_shape)).tolist()
|
|
out4 = paddle.nanmean(x, axis)
|
|
out5 = paddle.nanmean(x, tuple(axis))
|
|
exe = paddle.static.Executor(self.place)
|
|
res = exe.run(
|
|
feed={'X': self.x}, fetch_list=[out1, out2, out3, out4, out5]
|
|
)
|
|
out_ref = np.nanmean(self.x)
|
|
for out in res:
|
|
np.testing.assert_allclose(out, out_ref, rtol=0.0001)
|
|
|
|
def test_api_dygraph(self):
|
|
paddle.disable_static(self.place)
|
|
|
|
def test_case(x, axis=None, keepdim=False):
|
|
x_tensor = paddle.to_tensor(x)
|
|
out = paddle.nanmean(x_tensor, axis, keepdim)
|
|
if isinstance(axis, list):
|
|
axis = tuple(axis)
|
|
if len(axis) == 0:
|
|
axis = None
|
|
|
|
out_ref = np.nanmean(x, axis, keepdims=keepdim)
|
|
if np.isnan(out_ref).sum():
|
|
nan_mask = np.isnan(out_ref)
|
|
out_ref[nan_mask] = 0
|
|
out_np = out.numpy()
|
|
out_np[nan_mask] = 0
|
|
np.testing.assert_allclose(out_np, out_ref, rtol=0.0001)
|
|
else:
|
|
np.testing.assert_allclose(out.numpy(), out_ref, rtol=0.0001)
|
|
|
|
test_case(self.x)
|
|
test_case(self.x, [])
|
|
test_case(self.x, -1)
|
|
test_case(self.x, keepdim=True)
|
|
test_case(self.x, 2, keepdim=True)
|
|
test_case(self.x, [0, 2])
|
|
test_case(self.x, (0, 2))
|
|
test_case(self.x, [0, 1, 2, 3])
|
|
paddle.enable_static()
|
|
|
|
def test_errors(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = paddle.static.data('X', [10, 12], 'int32')
|
|
self.assertRaises(TypeError, paddle.nanmean, x)
|
|
|
|
def test_api_dygraph_grad(self):
|
|
paddle.disable_static(self.place)
|
|
|
|
def test_case(x, axis=None, keepdim=False):
|
|
if isinstance(axis, list):
|
|
axis = list(axis)
|
|
if len(axis) == 0:
|
|
axis = None
|
|
x_tensor = paddle.to_tensor(x, stop_gradient=False)
|
|
y = paddle.nanmean(x_tensor, axis, keepdim)
|
|
dx = paddle.grad(y, x_tensor)[0].numpy()
|
|
sum_dx_ref = np.prod(y.shape)
|
|
if np.isnan(y.numpy()).sum():
|
|
sum_dx_ref -= np.isnan(y.numpy()).sum()
|
|
cnt = paddle.sum(
|
|
~paddle.isnan(x_tensor), axis=axis, keepdim=keepdim
|
|
)
|
|
if (cnt == 0).sum():
|
|
dx[np.isnan(dx)] = 0
|
|
sum_dx = dx.sum()
|
|
np.testing.assert_allclose(sum_dx, sum_dx_ref, rtol=0.0001)
|
|
|
|
test_case(self.x)
|
|
test_case(self.x, [])
|
|
test_case(self.x, -1)
|
|
test_case(self.x, keepdim=True)
|
|
test_case(self.x, 2, keepdim=True)
|
|
test_case(self.x, [0, 2])
|
|
test_case(self.x, (0, 2))
|
|
test_case(self.x, [0, 1, 2, 3])
|
|
|
|
test_case(self.x_grad)
|
|
test_case(self.x_grad, [])
|
|
test_case(self.x_grad, -1)
|
|
test_case(self.x_grad, keepdim=True)
|
|
test_case(self.x_grad, 0, keepdim=True)
|
|
test_case(self.x_grad, 1)
|
|
test_case(self.x_grad, (0, 1))
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestNanmeanAPI_ZeroSize(unittest.TestCase):
|
|
# test paddle.tensor.math.nanmean
|
|
|
|
def setUp(self):
|
|
self.x_shape = [2, 0, 4, 5]
|
|
self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
|
|
self.x[0, :, :, :] = np.nan
|
|
self.place = get_device_place()
|
|
|
|
def test_api_dygraph(self):
|
|
paddle.disable_static(self.place)
|
|
|
|
def test_case(x, axis=None, keepdim=False):
|
|
x_tensor = paddle.to_tensor(x)
|
|
out = paddle.nanmean(x_tensor, axis, keepdim)
|
|
if isinstance(axis, list):
|
|
axis = tuple(axis)
|
|
if len(axis) == 0:
|
|
axis = None
|
|
|
|
out_ref = np.nanmean(x, axis, keepdims=keepdim)
|
|
np.testing.assert_allclose(out.numpy(), out_ref, rtol=0.0001)
|
|
|
|
test_case(self.x)
|
|
paddle.enable_static()
|
|
|
|
def test_api_dygraph_grad(self):
|
|
paddle.disable_static(self.place)
|
|
|
|
def test_case(x, axis=None, keepdim=False):
|
|
if isinstance(axis, list):
|
|
axis = list(axis)
|
|
if len(axis) == 0:
|
|
axis = None
|
|
x_tensor = paddle.to_tensor(x, stop_gradient=False)
|
|
x_tensor.stop_gradient = False
|
|
y = paddle.nanmean(x_tensor, axis, keepdim)
|
|
loss = paddle.sum(y)
|
|
loss.backward()
|
|
np.testing.assert_allclose(x_tensor.grad.shape, x_tensor.shape)
|
|
|
|
test_case(self.x)
|
|
paddle.enable_static()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|