/
test_nanmean_api.py
137 lines (119 loc) · 4.91 KB
/
test_nanmean_api.py
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# 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.
from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid import Program, program_guard
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., np.nan, 2.]]).astype(np.float32)
self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_api_static(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.fluid.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:
self.assertEqual(np.allclose(out, out_ref, rtol=1e-04), True)
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
self.assertEqual(np.allclose(out_np, out_ref, rtol=1e-04), True)
else:
self.assertEqual(
np.allclose(
out.numpy(), out_ref, rtol=1e-04), True)
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.fluid.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()
self.assertEqual(np.allclose(sum_dx, sum_dx_ref, rtol=1e-04), True)
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()
if __name__ == "__main__":
unittest.main()