add a parameter base_criterion to models (#217)

skada/deep/_adversarial.py

@@ -82,6 +82,7 @@ def DANN(
     reg=1,
     domain_classifier=None,
     num_features=None,
+    base_criterion=None,
     domain_criterion=None,
     **kwargs,
 ):
@@ -109,6 +110,9 @@ def DANN(
         the feature extractor.
         If domain_classifier is None, num_features has to be
         provided.
+    base_criterion : torch criterion (class)
+        The base criterion used to compute the loss with source
+        labels. If None, the default is `torch.nn.CrossEntropyLoss`.
     domain_criterion : torch criterion (class)
         The criterion (loss) used to compute the
         DANN loss. If None, a BCELoss is used.
@@ -127,14 +131,17 @@ def DANN(
             )
         domain_classifier = DomainClassifier(num_features=num_features)
 
+    if base_criterion is None:
+        base_criterion = torch.nn.CrossEntropyLoss()
+
     net = DomainAwareNet(
         module=DomainAwareModule,
         module__base_module=module,
         module__layer_name=layer_name,
         module__domain_classifier=domain_classifier,
         iterator_train=DomainBalancedDataLoader,
         criterion=DomainAwareCriterion,
-        criterion__criterion=nn.CrossEntropyLoss(),
+        criterion__base_criterion=base_criterion,
         criterion__reg=reg,
         criterion__adapt_criterion=DANNLoss(domain_criterion=domain_criterion),
         **kwargs,
@@ -319,6 +326,7 @@ def CDAN(
     domain_classifier=None,
     num_features=None,
     n_classes=None,
+    base_criterion=None,
     domain_criterion=None,
     **kwargs,
 ):
@@ -351,6 +359,9 @@ def CDAN(
     n_classes : int, default None
         Number of output classes.
         If domain_classifier is None, n_classes has to be provided.
+    base_criterion : torch criterion (class)
+        The base criterion used to compute the loss with source
+        labels. If None, the default is `torch.nn.CrossEntropyLoss`.
     domain_criterion : torch criterion (class)
         The criterion (loss) used to compute the
         CDAN loss. If None, a BCELoss is used.
@@ -372,6 +383,9 @@ def CDAN(
         num_features = np.min([num_features * n_classes, max_features])
         domain_classifier = DomainClassifier(num_features=num_features)
 
+    if base_criterion is None:
+        base_criterion = torch.nn.CrossEntropyLoss()
+
     net = DomainAwareNet(
         module=CDANModule,
         module__base_module=module,
@@ -380,7 +394,7 @@ def CDAN(
         module__max_features=max_features,
         iterator_train=DomainBalancedDataLoader,
         criterion=DomainAwareCriterion,
-        criterion__criterion=nn.CrossEntropyLoss(),
+        criterion__base_criterion=base_criterion,
         criterion__reg=reg,
         criterion__adapt_criterion=CDANLoss(domain_criterion=domain_criterion),
         **kwargs,

skada/deep/_divergence.py

@@ -50,7 +50,7 @@ def forward(
         return loss
 
 
-def DeepCoral(module, layer_name, reg=1, **kwargs):
+def DeepCoral(module, layer_name, reg=1, base_criterion=None, **kwargs):
     """DeepCORAL domain adaptation method.
 
     From [12]_.
@@ -64,20 +64,26 @@ def DeepCoral(module, layer_name, reg=1, **kwargs):
         collected during the training for the adaptation.
     reg : float, optional (default=1)
         The regularization parameter of the covariance estimator.
+    base_criterion : torch criterion (class)
+        The base criterion used to compute the loss with source
+        labels. If None, the default is `torch.nn.CrossEntropyLoss`.
 
     References
     ----------
     .. [12]  Baochen Sun and Kate Saenko. Deep coral:
             Correlation alignment for deep domain
             adaptation. In ECCV Workshops, 2016.
     """
+    if base_criterion is None:
+        base_criterion = torch.nn.CrossEntropyLoss()
+
     net = DomainAwareNet(
         module=DomainAwareModule,
         module__base_module=module,
         module__layer_name=layer_name,
         iterator_train=DomainBalancedDataLoader,
         criterion=DomainAwareCriterion,
-        criterion__criterion=torch.nn.CrossEntropyLoss(),
+        criterion__base_criterion=base_criterion,
         criterion__reg=reg,
         criterion__adapt_criterion=DeepCoralLoss(),
         **kwargs,
@@ -123,7 +129,7 @@ def forward(
         return loss
 
 
-def DAN(module, layer_name, reg=1, sigmas=None, **kwargs):
+def DAN(module, layer_name, reg=1, sigmas=None, base_criterion=None, **kwargs):
     """DAN domain adaptation method.
 
     See [14]_.
@@ -139,22 +145,26 @@ def DAN(module, layer_name, reg=1, sigmas=None, **kwargs):
         The regularization parameter of the covariance estimator.
     sigmas : array-like, optional (default=None)
         The sigmas for the Gaussian kernel.
+    base_criterion : torch criterion (class)
+        The base criterion used to compute the loss with source
+        labels. If None, the default is `torch.nn.CrossEntropyLoss`.
 
     References
     ----------
     .. [14]  Mingsheng Long et. al. Learning Transferable
             Features with Deep Adaptation Networks.
             In ICML, 2015.
     """
+    if base_criterion is None:
+        base_criterion = torch.nn.CrossEntropyLoss()
+
     net = DomainAwareNet(
         module=DomainAwareModule,
         module__base_module=module,
         module__layer_name=layer_name,
         iterator_train=DomainBalancedDataLoader,
-        criterion=DomainAwareCriterion(
-            torch.nn.CrossEntropyLoss(), DANLoss(sigmas=sigmas), reg=reg
-        ),
-        criterion__criterion=torch.nn.CrossEntropyLoss(),
+        criterion=DomainAwareCriterion,
+        criterion__base_criterion=base_criterion,
         criterion__reg=reg,
         criterion__adapt_criterion=DANLoss(sigmas=sigmas),
         **kwargs,

skada/deep/_optimal_transport.py

@@ -1,7 +1,7 @@
 # Author: Theo Gnassounou <[email protected]>
 #
 # License: BSD 3-Clause
-from torch import nn
+import torch
 
 from skada.deep.base import (
     BaseDALoss,
@@ -67,7 +67,15 @@ def forward(
         return loss
 
 
-def DeepJDOT(module, layer_name, reg=1, reg_cl=1, target_criterion=None, **kwargs):
+def DeepJDOT(
+    module,
+    layer_name,
+    reg=1,
+    reg_cl=1,
+    base_criterion=None,
+    target_criterion=None,
+    **kwargs,
+):
     """DeepJDOT.
 
        See [13]_.
@@ -83,6 +91,9 @@ def DeepJDOT(module, layer_name, reg=1, reg_cl=1, target_criterion=None, **kwarg
         Regularization parameter.
     reg_cl : float, default=1
         Class distance term regularization parameter.
+    base_criterion : torch criterion (class)
+        The base criterion used to compute the loss with source
+        labels. If None, the default is `torch.nn.CrossEntropyLoss`.
     target_criterion : torch criterion (class)
         The uninitialized criterion (loss) used to compute the
         DeepJDOT loss. The criterion should support reduction='none'.
@@ -96,13 +107,16 @@ def DeepJDOT(module, layer_name, reg=1, reg_cl=1, target_criterion=None, **kwarg
             15th European Conference on Computer Vision,
             September 2018. Springer.
     """
+    if base_criterion is None:
+        base_criterion = torch.nn.CrossEntropyLoss()
+
     net = DomainAwareNet(
         module=DomainAwareModule,
         module__base_module=module,
         module__layer_name=layer_name,
         iterator_train=DomainBalancedDataLoader,
         criterion=DomainAwareCriterion,
-        criterion__criterion=nn.CrossEntropyLoss(),
+        criterion__base_criterion=base_criterion,
         criterion__adapt_criterion=DeepJDOTLoss(reg_cl, target_criterion),
         criterion__reg=reg,
         **kwargs,

skada/deep/base.py

@@ -22,7 +22,7 @@ class DomainAwareCriterion(torch.nn.Module):
 
     Parameters
     ----------
-    criterion : torch criterion (class)
+    base_criterion : torch criterion (class)
         The initialized criterion (loss) used to optimize the
         module with prediction on source.
     adapt_criterion : torch criterion (class)
@@ -32,9 +32,9 @@ class DomainAwareCriterion(torch.nn.Module):
         Regularization parameter.
     """
 
-    def __init__(self, criterion, adapt_criterion, reg=1):
+    def __init__(self, base_criterion, adapt_criterion, reg=1):
         super(DomainAwareCriterion, self).__init__()
-        self.criterion = criterion
+        self.base_criterion = base_criterion
         self.adapt_criterion = adapt_criterion
         self.reg = reg
 
@@ -73,7 +73,7 @@ def forward(
         features_t = features[~source_idx]
 
         # predict
-        return self.criterion(
+        return self.base_criterion(
             y_pred_s, y_true[source_idx]
         ) + self.reg * self.adapt_criterion(
             y_true[source_idx],