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add norm 2.0 api, test=develop (#26465)

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* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop

* add norm 2.0 api, test=develop
revert-26856-strategy_example2
zhang wenhui 5 years ago committed by GitHub
parent a8b5741fb4
commit 0679678212
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8 changed files with 1583 additions and 13 deletions
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129
python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py
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# Copyright (c) 2020 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 os
import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from op_test import OpTest, _set_use_system_allocator
from paddle.fluid.framework import grad_var_name
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import paddle
class TestBatchNorm(unittest.TestCase):
def test_name(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
with fluid.dygraph.guard(p):
batch_norm1d = paddle.nn.BatchNorm1d(1, name="test")
def test_error(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
#paddle.disable_static()
x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
def error1d():
x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
batch_norm1d = paddle.nn.BatchNorm1d(1)
batch_norm1d(fluid.dygraph.to_variable(x_data_4))
def error2d():
x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
batch_norm2d = paddle.nn.BatchNorm2d(1)
batch_norm2d(fluid.dygraph.to_variable(x_data_3))
def error3d():
x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
batch_norm3d = paddle.nn.BatchNorm3d(1)
batch_norm3d(fluid.dygraph.to_variable(x_data_4))
with fluid.dygraph.guard(p):
self.assertRaises(ValueError, error1d)
self.assertRaises(ValueError, error2d)
self.assertRaises(ValueError, error3d)
def test_dygraph(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
shape = [4, 10, 4, 4]
def compute_v1(x, is_test, trainable_statistics):
with fluid.dygraph.guard(p):
bn = fluid.dygraph.BatchNorm(
shape[1],
is_test=is_test,
trainable_statistics=trainable_statistics)
y = bn(fluid.dygraph.to_variable(x))
return y.numpy()
def compute_v2(x):
with fluid.dygraph.guard(p):
bn = paddle.nn.BatchNorm2d(shape[1])
y = bn(fluid.dygraph.to_variable(x))
return y.numpy()
x = np.random.randn(*shape).astype("float32")
y1 = compute_v1(x, False, False)
y2 = compute_v2(x)
self.assertTrue(np.allclose(y1, y2))
def test_static(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
exe = fluid.Executor(p)
shape = [4, 10, 16, 16]
def compute_v1(x_np, is_test, trainable_statistics):
with program_guard(Program(), Program()):
bn = fluid.dygraph.BatchNorm(
shape[1],
is_test=is_test,
trainable_statistics=trainable_statistics)
x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
y = bn(x)
exe.run(fluid.default_startup_program())
r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
return r
def compute_v2(x_np):
with program_guard(Program(), Program()):
bn = paddle.nn.BatchNorm2d(shape[1])
x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
y = bn(x)
exe.run(fluid.default_startup_program())
r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
return r
x = np.random.randn(*shape).astype("float32")
y1 = compute_v1(x, False, False)
y2 = compute_v2(x)
self.assertTrue(np.allclose(y1, y2))
if __name__ == '__main__':
unittest.main()

86
python/paddle/fluid/tests/unittests/test_group_norm_op_v2.py
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# Copyright (c) 2020 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 os
import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from op_test import OpTest, _set_use_system_allocator
from paddle.fluid.framework import grad_var_name
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import paddle
class TestDygraphGroupNormv2(unittest.TestCase):
def test_dygraph(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu("group_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
shape = [2, 6, 2, 2]
def compute_v1(x):
with fluid.dygraph.guard(p):
gn = fluid.dygraph.GroupNorm(channels=2, groups=2)
y = gn(fluid.dygraph.to_variable(x))
return y.numpy()
def compute_v2(x):
with fluid.dygraph.guard(p):
gn = paddle.nn.GroupNorm(num_channels=2, num_groups=2)
y = gn(fluid.dygraph.to_variable(x))
return y.numpy()
x = np.random.randn(*shape).astype("float32")
y1 = compute_v1(x)
y2 = compute_v2(x)
self.assertTrue(np.allclose(y1, y2))
def test_static(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu("layer_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
exe = fluid.Executor(p)
shape = [2, 6, 2, 2]
def compute_v1(x_np):
with program_guard(Program(), Program()):
gn = fluid.dygraph.GroupNorm(channels=2, groups=2)
x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
y = gn(x)
exe.run(fluid.default_startup_program())
r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
return r
def compute_v2(x_np):
with program_guard(Program(), Program()):
gn = paddle.nn.GroupNorm(num_channels=2, num_groups=2)
x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
y = gn(x)
exe.run(fluid.default_startup_program())
r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
return r
x = np.random.randn(*shape).astype("float32")
y1 = compute_v1(x)
y2 = compute_v2(x)
self.assertTrue(np.allclose(y1, y2))
if __name__ == '__main__':
unittest.main()

115
python/paddle/fluid/tests/unittests/test_instance_norm_op_v2.py
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# Copyright (c) 2020 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 os
import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from op_test import OpTest, _set_use_system_allocator
from paddle.fluid.framework import grad_var_name
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import paddle
class TestInstanceNorm(unittest.TestCase):
def test_error(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu(
"instance_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
def error1d():
x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
instance_norm1d = paddle.nn.InstanceNorm1d(1)
instance_norm1d(fluid.dygraph.to_variable(x_data_4))
def error2d():
x_data_3 = np.random.random(size=(2, 1, 3)).astype('float32')
instance_norm2d = paddle.nn.InstanceNorm2d(1)
instance_norm2d(fluid.dygraph.to_variable(x_data_3))
def error3d():
x_data_4 = np.random.random(size=(2, 1, 3, 3)).astype('float32')
instance_norm3d = paddle.nn.BatchNorm3d(1)
instance_norm3d(fluid.dygraph.to_variable(x_data_4))
with fluid.dygraph.guard(p):
self.assertRaises(ValueError, error1d)
self.assertRaises(ValueError, error2d)
self.assertRaises(ValueError, error3d)
def test_dygraph(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu(
"instance_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
shape = [4, 10, 4, 4]
def compute_v1(x):
with fluid.dygraph.guard(p):
bn = fluid.dygraph.InstanceNorm(shape[1])
y = bn(fluid.dygraph.to_variable(x))
return y.numpy()
def compute_v2(x):
with fluid.dygraph.guard(p):
bn = paddle.nn.InstanceNorm2d(shape[1])
y = bn(fluid.dygraph.to_variable(x))
return y.numpy()
x = np.random.randn(*shape).astype("float32")
y1 = compute_v1(x)
y2 = compute_v2(x)
self.assertTrue(np.allclose(y1, y2))
def test_static(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu(
"instance_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
exe = fluid.Executor(p)
shape = [4, 10, 16, 16]
def compute_v1(x_np):
with program_guard(Program(), Program()):
ins = fluid.dygraph.InstanceNorm(shape[1])
x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
y = ins(x)
exe.run(fluid.default_startup_program())
r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
return r
def compute_v2(x_np):
with program_guard(Program(), Program()):
ins = paddle.nn.InstanceNorm2d(shape[1])
x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
y = ins(x)
exe.run(fluid.default_startup_program())
r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
return r
x = np.random.randn(*shape).astype("float32")
y1 = compute_v1(x)
y2 = compute_v2(x)
self.assertTrue(np.allclose(y1, y2))
if __name__ == '__main__':
unittest.main()

86
python/paddle/fluid/tests/unittests/test_layer_norm_op_v2.py
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# Copyright (c) 2020 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 os
import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from op_test import OpTest, _set_use_system_allocator
from paddle.fluid.framework import grad_var_name
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import paddle
class TestDygraphLayerNormv2(unittest.TestCase):
def test_dygraph(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu("layer_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
shape = [4, 10, 4, 4]
def compute_v1(x):
with fluid.dygraph.guard(p):
ln = fluid.dygraph.LayerNorm(shape[1:])
y = ln(fluid.dygraph.to_variable(x))
return y.numpy()
def compute_v2(x):
with fluid.dygraph.guard(p):
ln = paddle.nn.LayerNorm(shape[1:])
y = ln(fluid.dygraph.to_variable(x))
return y.numpy()
x = np.random.randn(*shape).astype("float32")
y1 = compute_v1(x)
y2 = compute_v2(x)
self.assertTrue(np.allclose(y1, y2))
def test_static(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu("layer_norm"):
places.append(fluid.CUDAPlace(0))
for p in places:
exe = fluid.Executor(p)
shape = [4, 10, 16, 16]
def compute_v1(x_np):
with program_guard(Program(), Program()):
ln = fluid.dygraph.LayerNorm(shape[1:])
x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
y = ln(x)
exe.run(fluid.default_startup_program())
r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
return r
def compute_v2(x_np):
with program_guard(Program(), Program()):
ln = paddle.nn.LayerNorm(shape[1:])
x = fluid.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
y = ln(x)
exe.run(fluid.default_startup_program())
r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
return r
x = np.random.randn(*shape).astype("float32")
y1 = compute_v1(x)
y2 = compute_v2(x)
self.assertTrue(np.allclose(y1, y2))
if __name__ == '__main__':
unittest.main()

6
python/paddle/nn/__init__.py
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@ -128,6 +128,12 @@ from .layer.norm import GroupNorm #DEFINE_ALIAS
from .layer.norm import LayerNorm #DEFINE_ALIAS
from .layer.norm import SpectralNorm #DEFINE_ALIAS
from .layer.norm import InstanceNorm #DEFINE_ALIAS
from .layer.norm import InstanceNorm1d #DEFINE_ALIAS
from .layer.norm import InstanceNorm2d #DEFINE_ALIAS
from .layer.norm import InstanceNorm3d #DEFINE_ALIAS
from .layer.norm import BatchNorm1d #DEFINE_ALIAS
from .layer.norm import BatchNorm2d #DEFINE_ALIAS
from .layer.norm import BatchNorm3d #DEFINE_ALIAS
# from .layer.rnn import RNNCell #DEFINE_ALIAS
# from .layer.rnn import GRUCell #DEFINE_ALIAS
# from .layer.rnn import LSTMCell #DEFINE_ALIAS

6
python/paddle/nn/functional/__init__.py
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@ -160,12 +160,12 @@ from .loss import square_error_cost #DEFINE_ALIAS
from .loss import ssd_loss #DEFINE_ALIAS
from .loss import teacher_student_sigmoid_loss #DEFINE_ALIAS
from .loss import ctc_loss #DEFINE_ALIAS
# from .norm import batch_norm #DEFINE_ALIAS
# from .norm import data_norm #DEFINE_ALIAS
# from .norm import group_norm #DEFINE_ALIAS
# from .norm import instance_norm #DEFINE_ALIAS
from .norm import l2_normalize #DEFINE_ALIAS
# from .norm import layer_norm #DEFINE_ALIAS
from .norm import batch_norm #DEFINE_ALIAS
from .norm import instance_norm #DEFINE_ALIAS
from .norm import layer_norm #DEFINE_ALIAS
from .norm import lrn #DEFINE_ALIAS
from .norm import normalize #DEFINE_ALIAS
# from .norm import spectral_norm #DEFINE_ALIAS

294
python/paddle/nn/functional/norm.py
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872
python/paddle/nn/layer/norm.py
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