wehub-resource-sync
711 lines
20 KiB
Python
711 lines
20 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 gradient_checker
|
|
import numpy as np
|
|
from decorator_helper import prog_scope
|
|
from op_test import get_places
|
|
from utils import dygraph_guard, static_guard
|
|
|
|
import paddle
|
|
import paddle.nn.functional as F
|
|
from paddle.base import core
|
|
|
|
|
|
class TestSigmoidTripleGradCheck(unittest.TestCase):
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0005
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = F.sigmoid(x)
|
|
x_arr = np.random.random(shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.002
|
|
gradient_checker.triple_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestSigmoidDoubleGradCheck(unittest.TestCase):
|
|
def sigmoid_wrapper(self, x):
|
|
return F.sigmoid(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0005
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = F.sigmoid(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.002
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.sigmoid_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestTanhTripleGradCheck(unittest.TestCase):
|
|
def tanh_wrapper(self, x):
|
|
return paddle.tanh(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0005
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.tanh(x)
|
|
x_arr = np.random.random(shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.002
|
|
from paddle.base import core
|
|
|
|
core._set_prim_backward_enabled(True)
|
|
gradient_checker.triple_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.tanh_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
core._set_prim_backward_enabled(False)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestTanhDoubleGradCheck(unittest.TestCase):
|
|
def tanh_wrapper(self, x):
|
|
return paddle.tanh(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0005
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.tanh(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.002
|
|
from paddle.base import core
|
|
|
|
core._set_prim_backward_enabled(True)
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.tanh_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
core._set_prim_backward_enabled(False)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestAbsDoubleGradCheck(unittest.TestCase):
|
|
def abs_wrapper(self, x):
|
|
return paddle.abs(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0005
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.abs(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.002
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.abs_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestReluDoubleGradCheck(unittest.TestCase):
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.005
|
|
dtype = np.float64
|
|
|
|
x = paddle.static.data('x', shape, dtype)
|
|
x.persistable = True
|
|
y = F.relu(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.02
|
|
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestLeakyReluDoubleGradCheck(unittest.TestCase):
|
|
def leaky_relu_wrapper(self, x):
|
|
return paddle.nn.functional.leaky_relu(x[0], negative_slope=0.2)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.005
|
|
alpha = 0.2
|
|
dtype = np.float64
|
|
|
|
x = paddle.static.data('x', shape, dtype)
|
|
x.persistable = True
|
|
|
|
y = paddle.nn.functional.leaky_relu(x, alpha)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.02
|
|
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.leaky_relu_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestELUDoubleGradCheck(unittest.TestCase):
|
|
def elu_wrapper(self, x):
|
|
return paddle.nn.functional.elu(x[0], alpha=0.2)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 4, 4, 4]
|
|
eps = 1e-6
|
|
alpha = 0.2
|
|
dtype = np.float64
|
|
SEED = 0
|
|
|
|
x = paddle.static.data('x', shape, dtype)
|
|
x.persistable = True
|
|
|
|
y = paddle.nn.functional.elu(x, alpha=alpha)
|
|
np.random.RandomState(SEED)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.elu_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestCELUDoubleGradCheck(unittest.TestCase):
|
|
def celu_wrapper(self, x):
|
|
return paddle.nn.functional.celu(x[0], alpha=0.2)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 4, 4, 4]
|
|
eps = 1e-6
|
|
alpha = 0.2
|
|
dtype = np.float64
|
|
SEED = 0
|
|
|
|
x = paddle.static.data('x', shape, dtype)
|
|
x.persistable = True
|
|
|
|
y = F.celu(x, alpha=alpha)
|
|
np.random.RandomState(SEED)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.celu_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestSoftplusDoubleGradCheck(unittest.TestCase):
|
|
def softplus_wrapper(self, x):
|
|
return F.softplus(x[0], beta=1, threshold=20)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 4, 4, 4]
|
|
eps = 1e-6
|
|
beta = 1
|
|
threshold = 20
|
|
dtype = np.float64
|
|
SEED = 0
|
|
|
|
x = paddle.static.data('x', shape, dtype)
|
|
x.persistable = True
|
|
|
|
y = F.softplus(x, beta=beta, threshold=threshold)
|
|
np.random.RandomState(SEED)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.softplus_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestSqrtDoubleGradCheck(unittest.TestCase):
|
|
def sqrt_wrapper(self, x):
|
|
return paddle.sqrt(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0001
|
|
dtype = np.float64
|
|
|
|
x = paddle.static.data('x', shape, dtype)
|
|
x.persistable = True
|
|
|
|
y = paddle.sqrt(x)
|
|
x_arr = np.random.uniform(0.1, 1, shape).astype(dtype)
|
|
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.sqrt_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestRsqrtDoubleGradCheck(unittest.TestCase):
|
|
def rsqrt_wrapper(self, x):
|
|
return paddle.rsqrt(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0001
|
|
dtype = np.float64
|
|
|
|
x = paddle.static.data('x', shape, dtype)
|
|
x.persistable = True
|
|
|
|
y = paddle.rsqrt(x)
|
|
x_arr = np.random.uniform(0.1, 1, shape).astype(dtype)
|
|
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.rsqrt_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestSquareDoubleGradCheck(unittest.TestCase):
|
|
def square_wrapper(self, x):
|
|
return paddle.square(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
# the shape of input variable should be clearly specified, not include -1.
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.005
|
|
dtype = np.float64
|
|
|
|
x = paddle.static.data('x', shape, dtype)
|
|
x.persistable = True
|
|
y = paddle.square(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.square_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestLogDoubleGradCheck(unittest.TestCase):
|
|
def log_wrapper(self, x):
|
|
return paddle.log(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 1e-6
|
|
dtype = np.float64
|
|
|
|
x = paddle.static.data('x', shape, dtype)
|
|
x.persistable = True
|
|
y = paddle.log(x)
|
|
|
|
x_arr = np.random.uniform(0.1, 1, shape).astype(dtype)
|
|
|
|
core._set_prim_all_enabled(True)
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.log_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
core._set_prim_all_enabled(False)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestSinDoubleGradCheck(unittest.TestCase):
|
|
def sin_wrapper(self, x):
|
|
return paddle.sin(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0005
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.sin(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.002
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.sin_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestCosDoubleGradCheck(unittest.TestCase):
|
|
def cos_wrapper(self, x):
|
|
return paddle.cos(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0005
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.cos(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.002
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.cos_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestCosDoubleGradCheck2(unittest.TestCase):
|
|
def _check_cos_double_dynamic(self, place):
|
|
with dygraph_guard():
|
|
x = paddle.randn([64, 64])
|
|
x = paddle.to_tensor(x, place=place, stop_gradient=False)
|
|
y = paddle.cos(x)
|
|
dx = paddle.grad(y, x, create_graph=True)
|
|
dxx_result = paddle.grad(dx, x)[0]
|
|
dxx_expected = -paddle.cos(x)
|
|
np.testing.assert_allclose(
|
|
dxx_result.numpy(),
|
|
dxx_expected.numpy(),
|
|
1e-6,
|
|
1e-6,
|
|
)
|
|
|
|
def _check_cos_double_static(self, place):
|
|
x_data = np.random.randn(64, 64).astype("float32")
|
|
with static_guard():
|
|
main_prog = paddle.static.Program()
|
|
startup_prog = paddle.static.Program()
|
|
with paddle.static.program_guard(main_prog, startup_prog):
|
|
x = paddle.assign(x_data)
|
|
x.stop_gradient = False
|
|
y = paddle.cos(x)
|
|
dx = paddle.static.gradients(y, x)
|
|
dxx = paddle.static.gradients(dx, x)[0]
|
|
|
|
exe = paddle.static.Executor(place)
|
|
exe.run(startup_prog)
|
|
(dxx_result,) = exe.run(main_prog, fetch_list=[dxx])
|
|
dxx_expected = -np.cos(x_data)
|
|
np.testing.assert_allclose(dxx_result, dxx_expected, 1e-6, 1e-6)
|
|
|
|
def test_cos_double_grad(self):
|
|
for place in get_places():
|
|
self._check_cos_double_dynamic(place)
|
|
self._check_cos_double_static(place)
|
|
|
|
|
|
class TestPowDoubleGradCheck1(unittest.TestCase):
|
|
def pow_wrapper(self, x):
|
|
return paddle.pow(x[0], 2)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 1e-6
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.pow(x, 2)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.pow_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestPowDoubleGradCheck2(unittest.TestCase):
|
|
def pow_wrapper(self, x):
|
|
return paddle.pow(x[0], 1)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 1e-6
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.pow(x, 1)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.pow_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestSinTripleGradCheck(unittest.TestCase):
|
|
def sin_wrapper(self, x):
|
|
return paddle.sin(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0005
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.sin(x)
|
|
x_arr = np.random.random(shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.002
|
|
gradient_checker.triple_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.sin_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestPowTripleGradCheck1(unittest.TestCase):
|
|
def pow_wrapper(self, x):
|
|
return paddle.pow(x[0], 1)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 1e-6
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.pow(x, 1)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.triple_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.pow_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestPowTripleGradCheck2(unittest.TestCase):
|
|
def pow_wrapper(self, x):
|
|
return paddle.pow(x[0], 2)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 1e-6
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.pow(x, 2)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.triple_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.pow_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestPowTripleGradCheck3(unittest.TestCase):
|
|
def pow_wrapper(self, x):
|
|
return paddle.pow(x[0], 4)
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 1e-6
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.pow(x, 4)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.triple_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.pow_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestCosTripleGradCheck(unittest.TestCase):
|
|
def cos_wrapper(self, x):
|
|
return paddle.cos(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
shape = [2, 3, 7, 9]
|
|
eps = 0.0005
|
|
dtype = np.float64
|
|
x = paddle.static.data('x', shape, dtype=dtype)
|
|
x.persistable = True
|
|
y = paddle.cos(x)
|
|
x_arr = np.random.random(shape).astype(dtype)
|
|
x_arr[np.abs(x_arr) < 0.005] = 0.002
|
|
gradient_checker.triple_grad_check(
|
|
[x], y, x_init=x_arr, place=place, eps=eps
|
|
)
|
|
gradient_checker.triple_grad_check_for_dygraph(
|
|
self.cos_wrapper, [x], y, x_init=x_arr, place=place
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|