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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
from collections.abc import Callable, Sequence
import torch
from monai.utils import ensure_tuple, ensure_tuple_rep
__all__ = ["generate_param_groups"]
def generate_param_groups(
network: torch.nn.Module,
layer_matches: Sequence[Callable],
match_types: Sequence[str],
lr_values: Sequence[float],
include_others: bool = True,
) -> list[dict]:
"""
Utility function to generate parameter groups with different LR values for optimizer.
The output parameter groups have the same order as `layer_match` functions.
Args:
network: source network to generate parameter groups from.
layer_matches: a list of callable functions to select or filter out network layer groups,
for "select" type, the input will be the `network`, for "filter" type,
the input will be every item of `network.named_parameters()`.
for "select", the parameters will be
`select_func(network).parameters()`.
for "filter", the parameters will be
`(x[1] for x in filter(f, network.named_parameters()))`
match_types: a list of tags to identify the matching type corresponding to the `layer_matches` functions,
can be "select" or "filter".
lr_values: a list of LR values corresponding to the `layer_matches` functions.
include_others: whether to include the rest layers as the last group, default to True.
It's mainly used to set different LR values for different network elements, for example:
.. code-block:: python
net = Unet(spatial_dims=3, in_channels=1, out_channels=3, channels=[2, 2, 2], strides=[1, 1, 1])
print(net) # print out network components to select expected items
print(net.named_parameters()) # print out all the named parameters to filter out expected items
params = generate_param_groups(
network=net,
layer_matches=[lambda x: x.model[0], lambda x: "2.0.conv" in x[0]],
match_types=["select", "filter"],
lr_values=[1e-2, 1e-3],
)
# the groups will be a list of dictionaries:
# [{'params': <generator object Module.parameters at 0x7f9090a70bf8>, 'lr': 0.01},
# {'params': <filter object at 0x7f9088fd0dd8>, 'lr': 0.001},
# {'params': <filter object at 0x7f9088fd0da0>}]
optimizer = torch.optim.Adam(params, 1e-4)
"""
layer_matches = ensure_tuple(layer_matches)
match_types = ensure_tuple_rep(match_types, len(layer_matches))
lr_values = ensure_tuple_rep(lr_values, len(layer_matches))
def _get_select(f):
def _select():
return f(network).parameters()
return _select
def _get_filter(f):
def _filter():
# should eventually generate a list of network parameters
return (x[1] for x in filter(f, network.named_parameters()))
return _filter
params = []
_layers = []
for func, ty, lr in zip(layer_matches, match_types, lr_values):
if ty.lower() == "select":
layer_params = _get_select(func)
elif ty.lower() == "filter":
layer_params = _get_filter(func)
else:
raise ValueError(f"unsupported layer match type: {ty}.")
params.append({"params": layer_params(), "lr": lr})
_layers.extend([id(x) for x in layer_params()])
if include_others:
params.append({"params": filter(lambda p: id(p) not in _layers, network.parameters())})
return params
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