Minor changes in optim and networks

clinicadl/monai_networks/__init__.py

@@ -1,2 +1,2 @@
-from .config import create_network_config
+from .config import ImplementedNetworks, NetworkConfig, create_network_config
 from .factory import get_network

clinicadl/monai_networks/config/__init__.py

@@ -1,2 +1,3 @@
+from .base import NetworkConfig
 from .factory import create_network_config
 from .utils.enum import ImplementedNetworks

clinicadl/optim/lr_scheduler/factory.py

@@ -49,6 +49,11 @@ def get_lr_scheduler(
             config_dict_["min_lr"].append(
                 config_dict["min_lr"]["ELSE"]
             )  # ELSE must be the last group
+        else:
+            default_min_lr = get_args_and_defaults(scheduler_class.__init__)[1][
+                "min_lr"
+            ]
+            config_dict_["min_lr"].append(default_min_lr)
     scheduler = scheduler_class(optimizer, **config_dict_)
 
     updated_config = LRSchedulerConfig(scheduler=config.scheduler, **config_dict)

clinicadl/optim/optimizer/factory.py

@@ -1,12 +1,12 @@
-from typing import Any, Dict, Iterable, Iterator, List, Tuple
+from typing import Any, Dict, Tuple
 
-import torch
 import torch.nn as nn
 import torch.optim as optim
 
 from clinicadl.utils.factories import DefaultFromLibrary, get_args_and_defaults
 
 from .config import OptimizerConfig
+from .utils import get_params_in_groups, get_params_not_in_groups
 
 
 def get_optimizer(
@@ -45,23 +45,16 @@ def get_optimizer(
         list_args_groups = network.parameters()
     else:
         list_args_groups = []
-        union_groups = set()
         args_groups = sorted(args_groups.items())  # order in the list is important
         for group, args in args_groups:
-            params, params_names = _get_params_in_group(network, group)
+            params, _ = get_params_in_groups(network, group)
             args.update({"params": params})
             list_args_groups.append(args)
-            union_groups.update(set(params_names))
 
-        other_params = _get_params_not_in_group(network, union_groups)
-        try:
-            next(other_params)
-        except StopIteration:  # there is no other param in the network
-            pass
-        else:
-            other_params = _get_params_not_in_group(
-                network, union_groups
-            )  # reset the generator
+        other_params, params_names = get_params_not_in_groups(
+            network, [group for group, _ in args_groups]
+        )
+        if len(params_names) > 0:
             list_args_groups.append({"params": other_params})
 
     optimizer = optimizer_class(list_args_groups, **args_global)
@@ -126,80 +119,3 @@ def _regroup_args(
             args_global[arg] = value
 
     return args_groups, args_global
-
-
-def _get_params_in_group(
-    network: nn.Module, group: str
-) -> Tuple[Iterator[torch.Tensor], List[str]]:
-    """
-    Gets the parameters of a specific group of a neural network.
-
-    Parameters
-    ----------
-    network : nn.Module
-        The neural network.
-    group : str
-        The name of the group, e.g. a layer or a block.
-        If it is a sub-block, the hierarchy should be
-        specified with "." (see examples).
-        Will work even if the group is reduced to a base layer
-        (e.g. group = "dense.weight" or "dense.bias").
-
-    Returns
-    -------
-    Iterator[torch.Tensor]
-        A generator that contains the parameters of the group.
-    List[str]
-        The name of all the parameters in the group.
-
-    Examples
-    --------
-    >>> net = nn.Sequential(
-            OrderedDict(
-                [
-                    ("conv1", nn.Conv2d(1, 1, kernel_size=3)),
-                    ("final", nn.Sequential(OrderedDict([("dense1", nn.Linear(10, 10))]))),
-                ]
-            )
-        )
-    >>> generator, params_names = _get_params_in_group(network, "final.dense1")
-    >>> params_names
-    ["final.dense1.weight", "final.dense1.bias"]
-    """
-    group_hierarchy = group.split(".")
-    for name in group_hierarchy:
-        network = getattr(network, name)
-
-    try:
-        params = network.parameters()
-        params_names = [
-            ".".join([group, name]) for name, _ in network.named_parameters()
-        ]
-    except AttributeError:  # we already reached params
-        params = (param for param in [network])
-        params_names = [group]
-
-    return params, params_names
-
-
-def _get_params_not_in_group(
-    network: nn.Module, group: Iterable[str]
-) -> Iterator[torch.Tensor]:
-    """
-    Finds the parameters of a neural networks that
-    are not in a group.
-
-    Parameters
-    ----------
-    network : nn.Module
-        The neural network.
-    group : List[str]
-        The group of parameters.
-
-    Returns
-    -------
-    Iterator[torch.Tensor]
-        A generator of all the parameters that are not in the input
-        group.
-    """
-    return (param[1] for param in network.named_parameters() if param[0] not in group)

clinicadl/optim/optimizer/utils.py

@@ -0,0 +1,120 @@
+from itertools import chain
+from typing import Iterator, List, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+
+def get_params_in_groups(
+    network: nn.Module, groups: Union[str, List[str]]
+) -> Tuple[Iterator[torch.Tensor], List[str]]:
+    """
+    Gets the parameters of specific groups of a neural network.
+
+    Parameters
+    ----------
+    network : nn.Module
+        The neural network.
+    groups : Union[str, List[str]]
+        The name of the group(s), e.g. a layer or a block.
+        If the user refers to a sub-block, the hierarchy should be
+        specified with "." (see examples).
+        If a list is passed, the function will output the parameters
+        of all groups mentioned together.
+
+    Returns
+    -------
+    Iterator[torch.Tensor]
+        An iterator that contains the parameters of the group(s).
+    List[str]
+        The name of all the parameters in the group(s).
+
+    Examples
+    --------
+    >>> net = nn.Sequential(
+            OrderedDict(
+                [
+                    ("conv1", nn.Conv2d(1, 1, kernel_size=3)),
+                    ("final", nn.Sequential(OrderedDict([("dense1", nn.Linear(10, 10))]))),
+                ]
+            )
+        )
+    >>> params, params_names = get_params_in_groups(network, "final.dense1")
+    >>> params_names
+    ["final.dense1.weight", "final.dense1.bias"]
+    >>> params, params_names = get_params_in_groups(network, ["conv1.weight", "final"])
+    >>> params_names
+    ["conv1.weight", "final.dense1.weight", "final.dense1.bias"]
+    """
+    if isinstance(groups, str):
+        groups = [groups]
+
+    params = iter(())
+    params_names = []
+    for group in groups:
+        network_ = network
+        group_hierarchy = group.split(".")
+        for name in group_hierarchy:
+            network_ = getattr(network_, name)
+
+        try:
+            params = chain(params, network_.parameters())
+            params_names += [
+                ".".join([group, name]) for name, _ in network_.named_parameters()
+            ]
+        except AttributeError:  # we already reached params
+            params = chain(params, (param for param in [network_]))
+            params_names += [group]
+
+    return params, params_names
+
+
+def get_params_not_in_groups(
+    network: nn.Module, groups: Union[str, List[str]]
+) -> Tuple[Iterator[torch.Tensor], List[str]]:
+    """
+    Gets the parameters not in specific groups of a neural network.
+
+    Parameters
+    ----------
+    network : nn.Module
+        The neural network.
+    groups : Union[str, List[str]]
+        The name of the group(s), e.g. a layer or a block.
+        If the user refers to a sub-block, the hierarchy should be
+        specified with "." (see examples).
+        If a list is passed, the function will output the parameters
+        that are not in any group of that list.
+
+    Returns
+    -------
+    Iterator[torch.Tensor]
+        An iterator that contains the parameters not in the group(s).
+    List[str]
+        The name of all the parameters not in the group(s).
+
+    Examples
+    --------
+    >>> net = nn.Sequential(
+            OrderedDict(
+                [
+                    ("conv1", nn.Conv2d(1, 1, kernel_size=3)),
+                    ("final", nn.Sequential(OrderedDict([("dense1", nn.Linear(10, 10))]))),
+                ]
+            )
+        )
+    >>> params, params_names = get_params_in_groups(network, "final")
+    >>> params_names
+    ["conv1.weight", "conv1.bias"]
+    >>> params, params_names = get_params_in_groups(network, ["conv1.bias", "final"])
+    >>> params_names
+    ["conv1.weight"]
+    """
+    _, in_groups = get_params_in_groups(network, groups)
+    params = (
+        param[1] for param in network.named_parameters() if param[0] not in in_groups
+    )
+    params_names = list(
+        param[0] for param in network.named_parameters() if param[0] not in in_groups
+    )
+    return params, params_names

tests/unittests/optim/lr_scheduler/test_factory.py

@@ -17,6 +17,7 @@ def test_get_lr_scheduler():
             [
                 ("linear1", nn.Linear(4, 3)),
                 ("linear2", nn.Linear(3, 2)),
+                ("linear3", nn.Linear(2, 1)),
             ]
         )
     )
@@ -29,6 +30,9 @@ def test_get_lr_scheduler():
             {
                 "params": network.linear2.parameters(),
             },
+            {
+                "params": network.linear3.parameters(),
+            },
         ],
         lr=10.0,
     )
@@ -58,7 +62,7 @@ def test_get_lr_scheduler():
     assert scheduler.threshold == 1e-1
     assert scheduler.threshold_mode == "rel"
     assert scheduler.cooldown == 3
-    assert scheduler.min_lrs == [0.1, 0.01]
+    assert scheduler.min_lrs == [0.1, 0.01, 0.0]
     assert scheduler.eps == 1e-8
 
     assert updated_config.scheduler == "ReduceLROnPlateau"
@@ -71,12 +75,14 @@ def test_get_lr_scheduler():
     assert updated_config.min_lr == {"linear2": 0.01, "linear1": 0.1}
     assert updated_config.eps == 1e-8
 
-    network.add_module("linear3", nn.Linear(3, 2))
-    optimizer.add_param_group({"params": network.linear3.parameters()})
     config.min_lr = {"ELSE": 1, "linear2": 0.01, "linear1": 0.1}
     scheduler, updated_config = get_lr_scheduler(optimizer, config)
     assert scheduler.min_lrs == [0.1, 0.01, 1]
 
+    config.min_lr = 1
+    scheduler, updated_config = get_lr_scheduler(optimizer, config)
+    assert scheduler.min_lrs == [1.0, 1.0, 1.0]
+
     config = LRSchedulerConfig()
     scheduler, updated_config = get_lr_scheduler(optimizer, config)
     assert isinstance(scheduler, LambdaLR)