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# Copyright (c) MONAI Consortium
# 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.
from __future__ import annotations
import torch.nn
from monai.networks.layers.factories import Act, Dropout, Norm, Pool, split_args
from monai.utils import has_option
__all__ = ["get_norm_layer", "get_act_layer", "get_dropout_layer", "get_pool_layer"]
def get_norm_layer(name: tuple | str, spatial_dims: int | None = 1, channels: int | None = 1):
"""
Create a normalization layer instance.
For example, to create normalization layers:
.. code-block:: python
from monai.networks.layers import get_norm_layer
g_layer = get_norm_layer(name=("group", {"num_groups": 1}))
n_layer = get_norm_layer(name="instance", spatial_dims=2)
Args:
name: a normalization type string or a tuple of type string and parameters.
spatial_dims: number of spatial dimensions of the input.
channels: number of features/channels when the normalization layer requires this parameter
but it is not specified in the norm parameters.
"""
if name == "":
return torch.nn.Identity()
norm_name, norm_args = split_args(name)
norm_type = Norm[norm_name, spatial_dims]
kw_args = dict(norm_args)
if has_option(norm_type, "num_features") and "num_features" not in kw_args:
kw_args["num_features"] = channels
if has_option(norm_type, "num_channels") and "num_channels" not in kw_args:
kw_args["num_channels"] = channels
return norm_type(**kw_args)
def get_act_layer(name: tuple | str):
"""
Create an activation layer instance.
For example, to create activation layers:
.. code-block:: python
from monai.networks.layers import get_act_layer
s_layer = get_act_layer(name="swish")
p_layer = get_act_layer(name=("prelu", {"num_parameters": 1, "init": 0.25}))
Args:
name: an activation type string or a tuple of type string and parameters.
"""
if name == "":
return torch.nn.Identity()
act_name, act_args = split_args(name)
act_type = Act[act_name]
return act_type(**act_args)
def get_dropout_layer(name: tuple | str | float | int, dropout_dim: int | None = 1):
"""
Create a dropout layer instance.
For example, to create dropout layers:
.. code-block:: python
from monai.networks.layers import get_dropout_layer
d_layer = get_dropout_layer(name="dropout")
a_layer = get_dropout_layer(name=("alphadropout", {"p": 0.25}))
Args:
name: a dropout ratio or a tuple of dropout type and parameters.
dropout_dim: the spatial dimension of the dropout operation.
"""
if name == "":
return torch.nn.Identity()
if isinstance(name, (int, float)):
# if dropout was specified simply as a p value, use default name and make a keyword map with the value
drop_name = Dropout.DROPOUT
drop_args = {"p": float(name)}
else:
drop_name, drop_args = split_args(name)
drop_type = Dropout[drop_name, dropout_dim]
return drop_type(**drop_args)
def get_pool_layer(name: tuple | str, spatial_dims: int | None = 1):
"""
Create a pooling layer instance.
For example, to create adaptiveavg layer:
.. code-block:: python
from monai.networks.layers import get_pool_layer
pool_layer = get_pool_layer(("adaptiveavg", {"output_size": (1, 1, 1)}), spatial_dims=3)
Args:
name: a pooling type string or a tuple of type string and parameters.
spatial_dims: number of spatial dimensions of the input.
"""
if name == "":
return torch.nn.Identity()
pool_name, pool_args = split_args(name)
pool_type = Pool[pool_name, spatial_dims]
return pool_type(**pool_args)
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