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“5e8ba7d7c043570e313eb02a37f0845f81d022b8”上不存在“develop/doc/design/executor.html”
未验证 提交 a914d68e 编写于 作者: G GGBond8488 提交者： GitHub
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【fluid clean】 move out LayerList, ParameterList, Sequential from fluid (#48197) 

* fluid clean move out LayerList, ParameterList, Sequential from fluid

* copy Sequential from fluid to paddle.nn

* replace fluid LayerList,ParamterList, Sequential in unit test file

* add omit unit test file use paddle.nn.Sequential

* remove numpy and use paddle.uniform instead
上级 1066094a
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Showing with 329 addition and 279 deletion
python/paddle/fluid/contrib/slim/tests/imperative_test_utils.py
浏览文件 @ a914d68e
......@@ -17,7 +17,7 @@ import logging
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.dygraph.container import Sequential
from paddle.nn import Sequential
from paddle.nn import ReLU, ReLU6, LeakyReLU, Sigmoid, Softmax, PReLU
from paddle.nn import Linear, Conv2D, Softmax, BatchNorm2D, MaxPool2D
from paddle.nn import BatchNorm1D
......
python/paddle/fluid/contrib/slim/tests/test_imperative_out_scale.py
浏览文件 @ a914d68e
......@@ -27,7 +27,7 @@ from paddle.fluid import core
from paddle.fluid.optimizer import AdamOptimizer
from paddle.fluid.framework import IrGraph, _test_eager_guard
from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
from paddle.fluid.dygraph.container import Sequential
from paddle.nn import Sequential
from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
from paddle.nn.layer import ReLU, LeakyReLU, Sigmoid, Softmax, PReLU
from paddle.nn import Linear, Conv2D, Softmax, BatchNorm2D, MaxPool2D
......
python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py
浏览文件 @ a914d68e
......@@ -25,7 +25,7 @@ import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.optimizer import AdamOptimizer
from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
from paddle.fluid.dygraph.container import Sequential
from paddle.nn import Sequential
from paddle.nn import Linear, Conv2D, Softmax, Conv2DTranspose
from paddle.fluid.log_helper import get_logger
from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
......
python/paddle/fluid/contrib/slim/tests/test_imperative_qat_lsq.py
浏览文件 @ a914d68e
......@@ -29,7 +29,7 @@ from paddle.fluid.optimizer import (
MomentumOptimizer,
)
from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
from paddle.fluid.dygraph.container import Sequential
from paddle.nn import Sequential
from paddle.nn import ReLU, ReLU6, LeakyReLU, Sigmoid, Softmax, PReLU
from paddle.nn import Linear, Conv2D, Softmax, BatchNorm2D, MaxPool2D
from paddle.fluid.log_helper import get_logger
......
python/paddle/fluid/dygraph/container.py
浏览文件 @ a914d68e
......@@ -12,15 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict
from ..framework import Parameter
from .layers import Layer
from .base import param_guard
__all__ = [
'Sequential',
'ParameterList',
'LayerList',
]
......@@ -97,240 +92,3 @@ class Sequential(Layer):
for layer in self._sub_layers.values():
input = layer(input)
return input
class ParameterList(Layer):
"""ParameterList Container.
This container acts like a Python list, but parameters it contains will be properly added.
Parameters:
parameters (iterable, optional): Iterable Parameters to be added
Examples:
.. code-block:: python
import paddle
import numpy as np
class MyLayer(paddle.nn.Layer):
def __init__(self, num_stacked_param):
super().__init__()
# create ParameterList with iterable Parameters
self.params = paddle.nn.ParameterList(
[paddle.create_parameter(
shape=[2, 2], dtype='float32')] * num_stacked_param)
def forward(self, x):
for i, p in enumerate(self.params):
tmp = self._helper.create_variable_for_type_inference('float32')
self._helper.append_op(
type="mul",
inputs={"X": x,
"Y": p},
outputs={"Out": tmp},
attrs={"x_num_col_dims": 1,
"y_num_col_dims": 1})
x = tmp
return x
data_np = np.random.uniform(-1, 1, [5, 2]).astype('float32')
x = paddle.to_tensor(data_np)
num_stacked_param = 4
model = MyLayer(num_stacked_param)
print(len(model.params)) # 4
res = model(x)
print(res.shape) # [5, 2]
replaced_param = paddle.create_parameter(shape=[2, 3], dtype='float32')
model.params[num_stacked_param - 1] = replaced_param # replace last param
res = model(x)
print(res.shape) # [5, 3]
model.params.append(paddle.create_parameter(shape=[3, 4], dtype='float32')) # append param
print(len(model.params)) # 5
res = model(x)
print(res.shape) # [5, 4]
"""
def __init__(self, parameters=None):
super().__init__()
if parameters is not None:
for idx, param in enumerate(parameters):
assert isinstance(param, Parameter)
self.add_parameter(str(idx), param)
def __getitem__(self, idx):
with param_guard(self._parameters):
return self._parameters[str(idx)]
def __setitem__(self, idx, param):
assert isinstance(param, Parameter)
setattr(self, str(idx), param)
def __len__(self):
return len(self._parameters)
def __iter__(self):
with param_guard(self._parameters):
return iter(self._parameters.values())
def append(self, parameter):
"""Appends a given parameter at the end of the list.
Parameters:
parameter (Parameter): parameter to append
"""
idx = len(self._parameters)
self.add_parameter(str(idx), parameter)
return self
class LayerList(Layer):
"""
LayerList holds sublayers, and sublayers it contains are properly registered.
Holded sublayers can be indexed like a regular python list.
Parameters:
sublayers (iterable of Layer, optional): sublayers to hold
Examples:
.. code-block:: python
import paddle
import numpy as np
class MyLayer(paddle.nn.Layer):
def __init__(self):
super().__init__()
self.linears = paddle.nn.LayerList(
[paddle.nn.Linear(10, 10) for i in range(10)])
def forward(self, x):
# LayerList can act as an iterable, or be indexed using ints
for i, l in enumerate(self.linears):
x = self.linears[i // 2](x) + l(x)
return x
"""
def __init__(self, sublayers=None):
super().__init__()
if sublayers is not None:
for idx, layer in enumerate(sublayers):
self.add_sublayer(str(idx), layer)
def _get_abs_idx(self, idx):
if isinstance(idx, int):
if not (-len(self) <= idx < len(self)):
raise IndexError(
'index {} is out of range, should be an integer in range [{}, {})'.format(
idx, -len(self), len(self)
)
)
if idx < 0:
idx += len(self)
return idx
def __getitem__(self, idx):
if isinstance(idx, slice):
return self.__class__(list(self._sub_layers.values())[idx])
else:
idx = self._get_abs_idx(idx)
return self._sub_layers[str(idx)]
def __setitem__(self, idx, sublayer):
idx = self._get_abs_idx(idx)
return setattr(self, str(idx), sublayer)
def __delitem__(self, idx):
if isinstance(idx, slice):
for k in range(len(self._sub_layers))[idx]:
delattr(self, str(k))
else:
idx = self._get_abs_idx(idx)
delattr(self, str(idx))
str_indices = [str(i) for i in range(len(self._sub_layers))]
self._sub_layers = OrderedDict(
list(zip(str_indices, self._sub_layers.values()))
)
def __len__(self):
return len(self._sub_layers)
def __iter__(self):
return iter(self._sub_layers.values())
def append(self, sublayer):
"""
Appends a sublayer to the end of the list.
Parameters:
sublayer (Layer): sublayer to append
Examples:
.. code-block:: python
import paddle
linears = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(10)])
another = paddle.nn.Linear(10, 10)
linears.append(another)
print(len(linears)) # 11
"""
self.add_sublayer(str(len(self)), sublayer)
return self
def insert(self, index, sublayer):
"""
Insert a sublayer before a given index in the list.
Parameters:
index (int): index to insert.
sublayer (Layer): sublayer to insert
Examples:
.. code-block:: python
import paddle
linears = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(10)])
another = paddle.nn.Linear(10, 10)
linears.insert(3, another)
print(linears[3] is another) # True
another = paddle.nn.Linear(10, 10)
linears.insert(-1, another)
print(linears[-2] is another) # True
"""
assert isinstance(index, int) and -len(self._sub_layers) <= index < len(
self._sub_layers
), "index should be an integer in range [{}, {})".format(
-len(self), len(self)
)
index = self._get_abs_idx(index)
for i in range(len(self._sub_layers), index, -1):
self._sub_layers[str(i)] = self._sub_layers[str(i - 1)]
self._sub_layers[str(index)] = sublayer
def extend(self, sublayers):
"""
Appends sublayers to the end of the list.
Parameters:
sublayers (iterable of Layer): iterable of sublayers to append
Examples:
.. code-block:: python
import paddle
linears = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(10)])
another_list = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(5)])
linears.extend(another_list)
print(len(linears)) # 15
print(another_list[0] is linears[10]) # True
"""
offset = len(self)
for i, sublayer in enumerate(sublayers):
idx = str(offset + i)
self.add_sublayer(idx, sublayer)
return self
python/paddle/fluid/tests/unittests/collective/fleet/hybrid_parallel_pp_embedding.py
浏览文件 @ a914d68e
......@@ -19,7 +19,7 @@ import random
import paddle
import paddle.distributed as dist
import paddle.distributed.fleet as fleet
from paddle.fluid.dygraph.container import Sequential
from paddle.nn import Sequential
from paddle.distributed.fleet.meta_parallel import PipelineLayer
from paddle.fluid.dygraph.layers import Layer
import paddle.nn as nn
......
python/paddle/fluid/tests/unittests/hybrid_parallel_pp_layer.py
浏览文件 @ a914d68e
......@@ -15,7 +15,7 @@
import unittest
import numpy as np
from paddle.distributed import fleet
from paddle.fluid.dygraph.container import Sequential
from paddle.nn import Sequential
import paddle.nn as nn
from paddle.fluid.dygraph.layers import Layer
from paddle.distributed.fleet.meta_parallel import LayerDesc, PipelineLayer
......
python/paddle/fluid/tests/unittests/test_imperative_container_layerlist.py
浏览文件 @ a914d68e
......@@ -31,11 +31,6 @@ class MyLayer(fluid.Layer):
class TestImperativeContainer(unittest.TestCase):
def fluid_dygraph_list(self):
return fluid.dygraph.LayerList(
[fluid.dygraph.Linear(2**i, 2 ** (i + 1)) for i in range(6)]
)
def paddle_imperative_list(self):
return paddle.nn.LayerList(
[fluid.dygraph.Linear(2**i, 2 ** (i + 1)) for i in range(6)]
......@@ -45,11 +40,7 @@ class TestImperativeContainer(unittest.TestCase):
data_np = np.random.uniform(-1, 1, [5, 1]).astype('float32')
with fluid.dygraph.guard():
x = fluid.dygraph.to_variable(data_np)
layerlist = (
self.fluid_dygraph_list()
if use_fluid_api
else self.paddle_imperative_list()
)
layerlist = self.paddle_imperative_list()
size = len(layerlist)
model = MyLayer(layerlist)
......
python/paddle/fluid/tests/unittests/test_imperative_container_parameterlist.py
浏览文件 @ a914d68e
......@@ -24,17 +24,7 @@ class MyLayer(fluid.Layer):
def __init__(self, num_stacked_param, use_fluid_api):
super().__init__()
# create ParameterList with iterable Parameters
self.params = (
self.fluid_dygraph_ParameterList(num_stacked_param)
if use_fluid_api
else self.paddle_imperative_ParameterList(num_stacked_param)
)
def fluid_dygraph_ParameterList(self, num_stacked_param):
return fluid.dygraph.ParameterList(
[fluid.layers.create_parameter(shape=[2, 2], dtype='float32')]
* num_stacked_param
)
self.params = self.paddle_imperative_ParameterList(num_stacked_param)
def paddle_imperative_ParameterList(self, num_stacked_param):
return paddle.nn.ParameterList(
......
python/paddle/fluid/tests/unittests/test_imperative_named_members.py
浏览文件 @ a914d68e
......@@ -37,7 +37,7 @@ class TestImperativeNamedSubLayers(unittest.TestCase):
fc1 = fluid.Linear(10, 3)
fc2 = fluid.Linear(3, 10, bias_attr=False)
custom = MyLayer(3, 10)
model = fluid.dygraph.Sequential(fc1, fc2, custom)
model = paddle.nn.Sequential(fc1, fc2, custom)
named_sublayers = model.named_sublayers()
list_named_sublayers = list(named_sublayers)
......
python/paddle/fluid/tests/unittests/test_imperative_star_gan_with_gradient_penalty.py
浏览文件 @ a914d68e
......@@ -268,7 +268,7 @@ class Generator(fluid.dygraph.Layer):
cur_channels *= 2
sub_layers.append(sub_layer)
self._conv0 = fluid.dygraph.Sequential(*sub_layers)
self._conv0 = paddle.nn.Sequential(*sub_layers)
repeat_num = cfg.g_repeat_num
sub_layers = []
......@@ -278,7 +278,7 @@ class Generator(fluid.dygraph.Layer):
)
sub_layers.append(res_block)
self._res_block = fluid.dygraph.Sequential(*sub_layers)
self._res_block = paddle.nn.Sequential(*sub_layers)
cur_channels = cfg.g_base_dims * 4
sub_layers = []
......@@ -296,7 +296,7 @@ class Generator(fluid.dygraph.Layer):
cur_channels = cfg.g_base_dims * rate
sub_layers.append(deconv)
self._deconv = fluid.dygraph.Sequential(*sub_layers)
self._deconv = paddle.nn.Sequential(*sub_layers)
self._conv1 = Conv2DLayer(
num_channels=cur_channels,
......@@ -353,7 +353,7 @@ class Discriminator(fluid.dygraph.Layer):
cur_dim *= 2
sub_layers.append(sub_layer)
self._conv0 = fluid.dygraph.Sequential(*sub_layers)
self._conv0 = paddle.nn.Sequential(*sub_layers)
kernel_size = int(cfg.image_size / np.power(2, repeat_num))
......
python/paddle/nn/__init__.py
浏览文件 @ a914d68e
......@@ -15,9 +15,9 @@
# TODO: import all neural network related api under this directory,
# including layers, linear, conv, rnn etc.
from ..fluid.dygraph.layers import Layer # noqa: F401
from ..fluid.dygraph.container import LayerList # noqa: F401
from ..fluid.dygraph.container import ParameterList # noqa: F401
from ..fluid.dygraph.container import Sequential # noqa: F401
from .layer.container import LayerList # noqa: F401
from .layer.container import ParameterList # noqa: F401
from .layer.container import Sequential # noqa: F401
from .clip import ClipGradByGlobalNorm # noqa: F401
from .clip import ClipGradByNorm # noqa: F401
......
python/paddle/nn/layer/container.py
浏览文件 @ a914d68e
......@@ -15,6 +15,8 @@
from collections import OrderedDict
from .. import Layer
from collections.abc import Iterable, Mapping
from ...fluid.framework import Parameter
from ...fluid.dygraph.base import param_guard
__all__ = []
......@@ -295,3 +297,310 @@ class LayerDict(Layer):
+ ", which must be 2."
)
self.add_sublayer(kv[0], kv[1])
class Sequential(Layer):
"""Sequential container.
Sub layers will be added to this container in the order of argument in the constructor.
The argument passed to the constructor can be iterable Layers or iterable name Layer pairs.
Parameters:
layers(Layer|list|tuple): Layer or list/tuple of iterable name Layer pair.
Examples:
.. code-block:: python
import paddle
data = paddle.uniform(shape=[30, 10], dtype='float32')
# create Sequential with iterable Layers
model1 = paddle.nn.Sequential(
paddle.nn.Linear(10, 1), paddle.nn.Linear(1, 2)
)
model1[0] # access the first layer
res1 = model1(data) # sequential execution
# create Sequential with name Layer pairs
model2 = paddle.nn.Sequential(
('l1', paddle.nn.Linear(10, 2)),
('l2', paddle.nn.Linear(2, 3))
)
model2['l1'] # access l1 layer
model2.add_sublayer('l3', paddle.nn.Linear(3, 3)) # add sublayer
res2 = model2(data) # sequential execution
"""
def __init__(self, *layers):
super().__init__()
if len(layers) > 0 and isinstance(layers[0], (list, tuple)):
for name, layer in layers:
self.add_sublayer(name, layer)
else:
for idx, layer in enumerate(layers):
self.add_sublayer(str(idx), layer)
def __getitem__(self, name):
if isinstance(name, slice):
return self.__class__(*(list(self._sub_layers.values())[name]))
elif isinstance(name, str):
return self._sub_layers[name]
else:
if name >= len(self._sub_layers):
raise IndexError('index {} is out of range'.format(name))
elif name < 0 and name >= -len(self._sub_layers):
name += len(self._sub_layers)
elif name < -len(self._sub_layers):
raise IndexError('index {} is out of range'.format(name))
return list(self._sub_layers.values())[name]
def __setitem__(self, name, layer):
assert isinstance(layer, Layer)
setattr(self, str(name), layer)
def __delitem__(self, name):
name = str(name)
assert name in self._sub_layers
del self._sub_layers[name]
def __len__(self):
return len(self._sub_layers)
def forward(self, input):
for layer in self._sub_layers.values():
input = layer(input)
return input
class ParameterList(Layer):
"""ParameterList Container.
This container acts like a Python list, but parameters it contains will be properly added.
Parameters:
parameters (iterable, optional): Iterable Parameters to be added
Examples:
.. code-block:: python
import paddle
class MyLayer(paddle.nn.Layer):
def __init__(self, num_stacked_param):
super().__init__()
# create ParameterList with iterable Parameters
self.params = paddle.nn.ParameterList(
[paddle.create_parameter(
shape=[2, 2], dtype='float32')] * num_stacked_param)
def forward(self, x):
for i, p in enumerate(self.params):
tmp = self._helper.create_variable_for_type_inference('float32')
self._helper.append_op(
type="mul",
inputs={"X": x,
"Y": p},
outputs={"Out": tmp},
attrs={"x_num_col_dims": 1,
"y_num_col_dims": 1})
x = tmp
return x
x = paddle.uniform(shape=[5, 2], dtype='float32')
num_stacked_param = 4
model = MyLayer(num_stacked_param)
print(len(model.params)) # 4
res = model(x)
print(res.shape) # [5, 2]
replaced_param = paddle.create_parameter(shape=[2, 3], dtype='float32')
model.params[num_stacked_param - 1] = replaced_param # replace last param
res = model(x)
print(res.shape) # [5, 3]
model.params.append(paddle.create_parameter(shape=[3, 4], dtype='float32')) # append param
print(len(model.params)) # 5
res = model(x)
print(res.shape) # [5, 4]
"""
def __init__(self, parameters=None):
super().__init__()
if parameters is not None:
for idx, param in enumerate(parameters):
assert isinstance(param, Parameter)
self.add_parameter(str(idx), param)
def __getitem__(self, idx):
with param_guard(self._parameters):
return self._parameters[str(idx)]
def __setitem__(self, idx, param):
assert isinstance(param, Parameter)
setattr(self, str(idx), param)
def __len__(self):
return len(self._parameters)
def __iter__(self):
with param_guard(self._parameters):
return iter(self._parameters.values())
def append(self, parameter):
"""Appends a given parameter at the end of the list.
Parameters:
parameter (Parameter): parameter to append
"""
idx = len(self._parameters)
self.add_parameter(str(idx), parameter)
return self
class LayerList(Layer):
"""
LayerList holds sublayers, and sublayers it contains are properly registered.
Holded sublayers can be indexed like a regular python list.
Parameters:
sublayers (iterable of Layer, optional): sublayers to hold
Examples:
.. code-block:: python
import paddle
class MyLayer(paddle.nn.Layer):
def __init__(self):
super().__init__()
self.linears = paddle.nn.LayerList(
[paddle.nn.Linear(10, 10) for i in range(10)])
def forward(self, x):
# LayerList can act as an iterable, or be indexed using ints
for i, l in enumerate(self.linears):
x = self.linears[i // 2](x) + l(x)
return x
"""
def __init__(self, sublayers=None):
super().__init__()
if sublayers is not None:
for idx, layer in enumerate(sublayers):
self.add_sublayer(str(idx), layer)
def _get_abs_idx(self, idx):
if isinstance(idx, int):
if not (-len(self) <= idx < len(self)):
raise IndexError(
'index {} is out of range, should be an integer in range [{}, {})'.format(
idx, -len(self), len(self)
)
)
if idx < 0:
idx += len(self)
return idx
def __getitem__(self, idx):
if isinstance(idx, slice):
return self.__class__(list(self._sub_layers.values())[idx])
else:
idx = self._get_abs_idx(idx)
return self._sub_layers[str(idx)]
def __setitem__(self, idx, sublayer):
idx = self._get_abs_idx(idx)
return setattr(self, str(idx), sublayer)
def __delitem__(self, idx):
if isinstance(idx, slice):
for k in range(len(self._sub_layers))[idx]:
delattr(self, str(k))
else:
idx = self._get_abs_idx(idx)
delattr(self, str(idx))
str_indices = [str(i) for i in range(len(self._sub_layers))]
self._sub_layers = OrderedDict(
list(zip(str_indices, self._sub_layers.values()))
)
def __len__(self):
return len(self._sub_layers)
def __iter__(self):
return iter(self._sub_layers.values())
def append(self, sublayer):
"""
Appends a sublayer to the end of the list.
Parameters:
sublayer (Layer): sublayer to append
Examples:
.. code-block:: python
import paddle
linears = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(10)])
another = paddle.nn.Linear(10, 10)
linears.append(another)
print(len(linears)) # 11
"""
self.add_sublayer(str(len(self)), sublayer)
return self
def insert(self, index, sublayer):
"""
Insert a sublayer before a given index in the list.
Parameters:
index (int): index to insert.
sublayer (Layer): sublayer to insert
Examples:
.. code-block:: python
import paddle
linears = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(10)])
another = paddle.nn.Linear(10, 10)
linears.insert(3, another)
print(linears[3] is another) # True
another = paddle.nn.Linear(10, 10)
linears.insert(-1, another)
print(linears[-2] is another) # True
"""
assert isinstance(index, int) and -len(self._sub_layers) <= index < len(
self._sub_layers
), "index should be an integer in range [{}, {})".format(
-len(self), len(self)
)
index = self._get_abs_idx(index)
for i in range(len(self._sub_layers), index, -1):
self._sub_layers[str(i)] = self._sub_layers[str(i - 1)]
self._sub_layers[str(index)] = sublayer
def extend(self, sublayers):
"""
Appends sublayers to the end of the list.
Parameters:
sublayers (iterable of Layer): iterable of sublayers to append
Examples:
.. code-block:: python
import paddle
linears = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(10)])
another_list = paddle.nn.LayerList([paddle.nn.Linear(10, 10) for i in range(5)])
linears.extend(another_list)
print(len(linears)) # 15
print(another_list[0] is linears[10]) # True
"""
offset = len(self)
for i, sublayer in enumerate(sublayers):
idx = str(offset + i)
self.add_sublayer(idx, sublayer)
return self
python/paddle/nn/layer/rnn.py
浏览文件 @ a914d68e
......@@ -20,7 +20,8 @@ import paddle
from paddle import framework
from paddle.nn import functional as F
from paddle.nn import initializer as I
from paddle.nn import Layer, LayerList
from paddle.nn import Layer
from .container import LayerList
from paddle.fluid.layers import utils
from paddle.fluid.layers.utils import flatten, map_structure
from paddle import _C_ops, _legacy_C_ops
......
python/paddle/nn/layer/transformer.py
浏览文件 @ a914d68e
......@@ -24,7 +24,8 @@ from .norm import LayerNorm
from .. import functional as F
from ... import tensor
from ...fluid import layers
from .. import Layer, LayerList
from .. import Layer
from .container import LayerList
from ...framework import ParamAttr
from paddle.fluid.data_feeder import convert_dtype
......
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