wehub-resource-sync
398 lines
13 KiB
Python
398 lines
13 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
|
|
|
|
import paddle
|
|
from paddle import base
|
|
|
|
|
|
class TestInstanceNormDoubleGradCheck(unittest.TestCase):
|
|
@prog_scope()
|
|
def func(self, place):
|
|
prog = base.Program()
|
|
with base.program_guard(prog):
|
|
np.random.seed()
|
|
shape = [2, 3, 4, 5]
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 1e-4
|
|
x = paddle.create_parameter(dtype=dtype, shape=shape, name='x')
|
|
z = paddle.nn.InstanceNorm2D(3)(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], z, x_init=x_arr, atol=atol, place=place, eps=eps
|
|
)
|
|
|
|
@prog_scope()
|
|
def func_pir(self, place):
|
|
prog = paddle.static.Program()
|
|
with paddle.static.program_guard(prog):
|
|
np.random.seed()
|
|
shape = [2, 3, 4, 5]
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 1e-4
|
|
x = paddle.static.data(dtype=dtype, shape=shape, name='x')
|
|
z = paddle.nn.functional.instance_norm(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], z, x_init=x_arr, atol=atol, place=place, eps=eps
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
with paddle.pir_utils.OldIrGuard():
|
|
self.func(p)
|
|
self.func_pir(p)
|
|
|
|
|
|
class TestInstanceNormDoubleGradCheckWithoutParamBias(
|
|
TestInstanceNormDoubleGradCheck
|
|
):
|
|
@prog_scope()
|
|
def func(self, place):
|
|
prog = base.Program()
|
|
with base.program_guard(prog):
|
|
np.random.seed()
|
|
shape = [2, 3, 4, 5]
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 1e-4
|
|
x = paddle.create_parameter(dtype=dtype, shape=shape, name='x')
|
|
z = paddle.nn.InstanceNorm2D(3, weight_attr=False, bias_attr=False)(
|
|
x
|
|
)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], z, x_init=x_arr, atol=atol, place=place, eps=eps
|
|
)
|
|
|
|
@prog_scope()
|
|
def func_pir(self, place):
|
|
prog = paddle.static.Program()
|
|
with paddle.static.program_guard(prog):
|
|
np.random.seed()
|
|
shape = [2, 3, 4, 5]
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 1e-4
|
|
x = paddle.static.data(dtype=dtype, shape=shape, name='x')
|
|
z = paddle.nn.functional.instance_norm(x, bias=None)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], z, x_init=x_arr, atol=atol, place=place, eps=eps
|
|
)
|
|
|
|
|
|
class TestInstanceNormDoubleGradEagerCheck(unittest.TestCase):
|
|
def instance_norm_wrapper(self, x):
|
|
return paddle.nn.functional.instance_norm(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
prog = base.Program()
|
|
with base.program_guard(prog):
|
|
np.random.seed()
|
|
shape = [2, 3, 4, 5]
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 1e-4
|
|
x = paddle.static.data(dtype=dtype, shape=shape, name='x')
|
|
z = paddle.nn.functional.instance_norm(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
# check for static graph mode
|
|
gradient_checker.double_grad_check(
|
|
[x], z, x_init=x_arr, atol=atol, place=place, eps=eps
|
|
)
|
|
# check for eager mode
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.instance_norm_wrapper,
|
|
[x],
|
|
z,
|
|
x_init=x_arr,
|
|
atol=atol,
|
|
place=place,
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
self.func(p)
|
|
|
|
|
|
class TestInstanceNormDoubleGradEagerCheckWithParams(
|
|
TestInstanceNormDoubleGradEagerCheck
|
|
):
|
|
def instance_norm_wrapper(self, x):
|
|
instance_norm = paddle.nn.InstanceNorm2D(3)
|
|
return instance_norm(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
prog = paddle.static.Program()
|
|
with paddle.static.program_guard(prog):
|
|
np.random.seed()
|
|
shape = [2, 3, 4, 5]
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 1e-4
|
|
x = paddle.static.data(dtype=dtype, shape=shape, name='x')
|
|
z = paddle.nn.InstanceNorm2D(3)(x)
|
|
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
|
|
# check for static graph mode
|
|
gradient_checker.double_grad_check(
|
|
[x], z, x_init=x_arr, atol=atol, place=place, eps=eps
|
|
)
|
|
# check for eager mode
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.instance_norm_wrapper,
|
|
[x],
|
|
z,
|
|
x_init=x_arr,
|
|
atol=atol,
|
|
place=place,
|
|
)
|
|
|
|
|
|
class TestBatchNormDoubleGradCheck(unittest.TestCase):
|
|
def setUp(self):
|
|
self.init_test()
|
|
|
|
def init_test(self):
|
|
self.data_layout = 'NCHW'
|
|
self.use_global_stats = False
|
|
self.shape = [2, 3, 4, 5]
|
|
self.channel_index = 1
|
|
|
|
def batch_norm_wrapper(self, x):
|
|
batch_norm = paddle.nn.BatchNorm2D(
|
|
self.shape[self.channel_index],
|
|
data_format=self.data_layout,
|
|
use_global_stats=self.use_global_stats,
|
|
)
|
|
return batch_norm(x[0])
|
|
|
|
@prog_scope()
|
|
def func(self, place):
|
|
prog = base.Program()
|
|
with base.program_guard(prog):
|
|
np.random.seed()
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 1e-4
|
|
x = paddle.create_parameter(dtype=dtype, shape=self.shape, name='x')
|
|
z = paddle.static.nn.batch_norm(
|
|
input=x,
|
|
data_layout=self.data_layout,
|
|
use_global_stats=self.use_global_stats,
|
|
)
|
|
x_arr = np.random.uniform(-1, 1, self.shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], z, x_init=x_arr, atol=atol, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.batch_norm_wrapper,
|
|
[x],
|
|
z,
|
|
x_init=x_arr,
|
|
atol=atol,
|
|
place=place,
|
|
)
|
|
|
|
@prog_scope()
|
|
def func_pir(self, place):
|
|
prog = base.Program()
|
|
with base.program_guard(prog):
|
|
np.random.seed()
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 1e-4
|
|
x = paddle.static.data(dtype=dtype, shape=self.shape, name='x')
|
|
bn = paddle.nn.BatchNorm2D(
|
|
self.shape[self.channel_index],
|
|
data_format=self.data_layout,
|
|
use_global_stats=self.use_global_stats,
|
|
)
|
|
z = bn(x)
|
|
x_arr = np.random.uniform(-1, 1, self.shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], z, x_init=x_arr, atol=atol, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.batch_norm_wrapper,
|
|
[x],
|
|
z,
|
|
x_init=x_arr,
|
|
atol=atol,
|
|
place=place,
|
|
)
|
|
|
|
def test_grad(self):
|
|
paddle.enable_static()
|
|
for p in get_places():
|
|
with paddle.pir_utils.OldIrGuard():
|
|
self.func(p)
|
|
self.func_pir(p)
|
|
|
|
|
|
class TestBatchNormDoubleGradCheckCase1(TestBatchNormDoubleGradCheck):
|
|
def init_test(self):
|
|
self.data_layout = 'NHWC'
|
|
self.use_global_stats = False
|
|
self.shape = [2, 3, 4, 5]
|
|
self.channel_index = 3
|
|
|
|
|
|
class TestBatchNormDoubleGradCheckCase2(TestBatchNormDoubleGradCheck):
|
|
def init_test(self):
|
|
self.data_layout = 'NCHW'
|
|
self.use_global_stats = True
|
|
self.shape = [2, 3, 4, 5]
|
|
self.channel_index = 1
|
|
|
|
|
|
class TestBatchNormDoubleGradCheckCase3(TestBatchNormDoubleGradCheck):
|
|
def init_test(self):
|
|
self.data_layout = 'NHWC'
|
|
self.use_global_stats = True
|
|
self.shape = [2, 3, 4, 5]
|
|
self.channel_index = 3
|
|
|
|
|
|
class TestBatchNormDoubleGradCheckCase4(TestBatchNormDoubleGradCheck):
|
|
def init_test(self):
|
|
self.data_layout = 'NCHW'
|
|
self.use_global_stats = False
|
|
self.shape = [2, 2, 3, 4, 5]
|
|
self.channel_index = 1
|
|
|
|
def batch_norm_wrapper(self, x):
|
|
batch_norm = paddle.nn.BatchNorm3D(
|
|
self.shape[self.channel_index],
|
|
data_format=self.data_layout,
|
|
use_global_stats=self.use_global_stats,
|
|
)
|
|
return batch_norm(x[0])
|
|
|
|
@prog_scope()
|
|
def func_pir(self, place):
|
|
prog = base.Program()
|
|
with base.program_guard(prog):
|
|
np.random.seed()
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 1e-4
|
|
x = paddle.static.data(dtype=dtype, shape=self.shape, name='x')
|
|
bn = paddle.nn.BatchNorm3D(
|
|
self.shape[self.channel_index],
|
|
data_format=self.data_layout,
|
|
use_global_stats=self.use_global_stats,
|
|
)
|
|
z = bn(x)
|
|
x_arr = np.random.uniform(-1, 1, self.shape).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x], z, x_init=x_arr, atol=atol, place=place, eps=eps
|
|
)
|
|
gradient_checker.double_grad_check_for_dygraph(
|
|
self.batch_norm_wrapper,
|
|
[x],
|
|
z,
|
|
x_init=x_arr,
|
|
atol=atol,
|
|
place=place,
|
|
)
|
|
|
|
|
|
class TestBatchNormDoubleGradCheckCase5(TestBatchNormDoubleGradCheck):
|
|
@prog_scope()
|
|
def func(self, place):
|
|
prog = base.Program()
|
|
with base.program_guard(prog):
|
|
np.random.seed(37)
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 2e-4
|
|
chn = (
|
|
self.shape[1] if self.data_layout == 'NCHW' else self.shape[-1]
|
|
)
|
|
x = paddle.create_parameter(dtype=dtype, shape=self.shape, name='x')
|
|
z = paddle.static.nn.batch_norm(
|
|
input=x,
|
|
data_layout=self.data_layout,
|
|
use_global_stats=self.use_global_stats,
|
|
)
|
|
x_arr = np.random.uniform(-1, 1, self.shape).astype(dtype)
|
|
w, b = prog.global_block().all_parameters()[1:3]
|
|
w_arr = np.ones(chn).astype(dtype)
|
|
b_arr = np.zeros(chn).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x, w, b],
|
|
z,
|
|
x_init=[x_arr, w_arr, b_arr],
|
|
atol=atol,
|
|
place=place,
|
|
eps=eps,
|
|
)
|
|
|
|
@prog_scope()
|
|
def func_pir(self, place):
|
|
prog = base.Program()
|
|
with base.program_guard(prog):
|
|
np.random.seed(37)
|
|
dtype = "float32"
|
|
eps = 0.005
|
|
atol = 2e-4
|
|
chn = (
|
|
self.shape[1] if self.data_layout == 'NCHW' else self.shape[-1]
|
|
)
|
|
x = paddle.static.data(dtype=dtype, shape=self.shape, name='x')
|
|
w = paddle.static.data(dtype=dtype, shape=[chn], name='w')
|
|
b = paddle.static.data(dtype=dtype, shape=[chn], name='b')
|
|
bn = paddle.nn.BatchNorm2D(
|
|
self.shape[self.channel_index],
|
|
data_format=self.data_layout,
|
|
use_global_stats=self.use_global_stats,
|
|
)
|
|
z = bn(x)
|
|
x_arr = np.random.uniform(-1, 1, self.shape).astype(dtype)
|
|
w_arr = np.ones(chn).astype(dtype)
|
|
b_arr = np.zeros(chn).astype(dtype)
|
|
gradient_checker.double_grad_check(
|
|
[x, w, b],
|
|
z,
|
|
x_init=[x_arr, w_arr, b_arr],
|
|
atol=atol,
|
|
place=place,
|
|
eps=eps,
|
|
)
|
|
|
|
|
|
class TestBatchNormDoubleGradCheckCase6(TestBatchNormDoubleGradCheckCase5):
|
|
def init_test(self):
|
|
self.data_layout = 'NCHW'
|
|
self.use_global_stats = True
|
|
self.shape = [2, 3, 4, 5]
|
|
self.channel_index = 1
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|