wehub-resource-sync
121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
# 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.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from op_test import get_device_place, get_places, is_custom_device
|
|
|
|
import paddle
|
|
from paddle import base
|
|
from paddle.base import core
|
|
from paddle.base.executor import Executor
|
|
|
|
|
|
class TestSquareErrorCost(unittest.TestCase):
|
|
def test_square_error_cost(self):
|
|
paddle.enable_static()
|
|
shape = [2, 3]
|
|
input_val = np.random.uniform(0.1, 0.5, shape).astype("float32")
|
|
label_val = np.random.uniform(0.1, 0.5, shape).astype("float32")
|
|
|
|
sub = input_val - label_val
|
|
np_result = sub * sub
|
|
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
input_var = paddle.static.data(
|
|
name="input", shape=shape, dtype="float32"
|
|
)
|
|
label_var = paddle.static.data(
|
|
name="label", shape=shape, dtype="float32"
|
|
)
|
|
output = paddle.nn.functional.square_error_cost(
|
|
input=input_var, label=label_var
|
|
)
|
|
|
|
place = get_device_place() if use_cuda else base.CPUPlace()
|
|
exe = Executor(place)
|
|
(result,) = exe.run(
|
|
paddle.static.default_main_program(),
|
|
feed={"input": input_val, "label": label_val},
|
|
fetch_list=[output],
|
|
)
|
|
|
|
np.testing.assert_allclose(np_result, result, rtol=1e-05)
|
|
|
|
|
|
class TestSquareErrorInvalidInput(unittest.TestCase):
|
|
def test_error(self):
|
|
paddle.enable_static()
|
|
|
|
def test_invalid_input():
|
|
input = [256, 3]
|
|
label = paddle.static.data(
|
|
name='label1', shape=[None, 3], dtype='float32'
|
|
)
|
|
loss = paddle.nn.functional.square_error_cost(input, label)
|
|
|
|
self.assertRaises(TypeError, test_invalid_input)
|
|
|
|
def test_invalid_label():
|
|
input = paddle.static.data(
|
|
name='input2', shape=[None, 3], dtype='float32'
|
|
)
|
|
label = [256, 3]
|
|
loss = paddle.nn.functional.square_error_cost(input, label)
|
|
|
|
self.assertRaises(TypeError, test_invalid_label)
|
|
|
|
|
|
class TestSquareErrorCost_ZeroSize(unittest.TestCase):
|
|
def init_shape(self):
|
|
self.shape = [0, 3]
|
|
|
|
def test_square_error_cost(self):
|
|
places = get_places()
|
|
self.init_shape()
|
|
shape = self.shape
|
|
input_val = np.random.uniform(0.1, 0.5, shape).astype("float32")
|
|
label_val = np.random.uniform(0.1, 0.5, shape).astype("float32")
|
|
|
|
sub = input_val - label_val
|
|
np_result = sub * sub
|
|
for place in places:
|
|
paddle.disable_static(place)
|
|
input = paddle.to_tensor(input_val)
|
|
input.stop_gradient = False
|
|
label = paddle.to_tensor(label_val)
|
|
output = paddle.nn.functional.square_error_cost(
|
|
input=input, label=label
|
|
)
|
|
np.testing.assert_allclose(np_result, output.numpy(), rtol=1e-05)
|
|
loss = paddle.sum(output)
|
|
loss.backward()
|
|
np.testing.assert_allclose(input.grad.shape, input.shape)
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestSquareErrorCost_ZeroSize2(TestSquareErrorCost_ZeroSize):
|
|
def init_shape(self):
|
|
self.shape = [0, 0]
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|