master: Fixed hyperparameter collisions

docs/doc_usage_cmd.md

@@ -21,7 +21,34 @@ To run DomainLab, the minimum necessary parameters are:
 ### Advanced Configuration
 
 - **Learning Rate (`--lr`):** Set the training learning rate.
-- **Regularization (`--gamma_reg`):** Weight of regularization loss.
+- **Regularization (`--gamma_reg`):** Sets the weight of the regularization
+ loss. This parameter can be configured either as
+ a single value applied to individual classes,
+ or using a dictionary to specify different
+ weights for different models and trainers.
+
+  - **Command Line Usage:**
+    - For a single value: `python script.py --gamma_reg=0.1`
+    - For multiple values: `python script.py --gamma_reg='default=0.1,dann=0.05,jigen=0.2'`
+
+  - **YAML Configuration:**
+    - For a single value:
+
+      ```yaml
+      gamma_reg: 0.1
+      ```
+
+    - For different values:
+
+      ```yaml
+      gamma_reg:
+        dann: 0.05
+        dial: 0.2
+        default: 0.1 # value for every other instance
+      ```  
+Gamma reg is available for the trainers, as well as the 
+dann and jigen model. 
+
 - **Early Stopping (`--es`):** Steps for early stopping.
 - **Random Seed (`--seed`):** Seed for reproducibility.
 - **CUDA Options (`--nocu`, `--device`):** Configure CUDA usage and device settings.

domainlab/algos/builder_dann.py

@@ -13,6 +13,7 @@
 from domainlab.compos.zoo_nn import FeatExtractNNBuilderChainNodeGetter
 from domainlab.models.model_dann import mk_dann
 from domainlab.utils.utils_cuda import get_device
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class NodeAlgoBuilderDANN(NodeAlgoBuilder):
@@ -55,7 +56,7 @@ def init_business(self, exp):
         model = mk_dann(list_str_y=task.list_str_y,
                         net_classifier=net_classifier)(
             list_d_tr=task.list_domain_tr,
-            alpha=args.gamma_reg,
+            alpha=get_gamma_reg(args, 'dann'),
             net_encoder=net_encoder,
             net_discriminator=net_discriminator,
             builder=self)

domainlab/algos/builder_jigen1.py

@@ -15,6 +15,7 @@
 from domainlab.dsets.utils_wrapdset_patches import WrapDsetPatches
 from domainlab.models.model_jigen import mk_jigen
 from domainlab.utils.utils_cuda import get_device
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class NodeAlgoBuilderJiGen(NodeAlgoBuilder):
@@ -56,7 +57,7 @@ def init_business(self, exp):
         model = mk_jigen(
             list_str_y=task.list_str_y,
             net_classifier=net_classifier)(
-            coeff_reg=args.gamma_reg,
+            coeff_reg=get_gamma_reg(args, 'jigen'),
             net_encoder=net_encoder,
             net_classifier_permutation=net_classifier_perm,
             n_perm=args.nperm,

domainlab/algos/trainers/a_trainer.py

@@ -258,3 +258,11 @@ def dset_decoration_args_algo(self, args, ddset):
         if self._decoratee is not None:
             return self._decoratee.dset_decoration_args_algo(args, ddset)
         return ddset
+
+    def print_parameters(self):
+        """
+        Function to print all parameters of the object. 
+        Can be used to print the parameters of any child class
+        """
+        params = vars(self)
+        print(f"Parameters of {type(self).__name__}: {params}")

domainlab/algos/trainers/train_dial.py

@@ -5,6 +5,7 @@
 from torch.autograd import Variable
 
 from domainlab.algos.trainers.train_basic import TrainerBasic
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class TrainerDIAL(TrainerBasic):
@@ -49,4 +50,4 @@ def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others=None):
         tensor_x_adv = self.gen_adversarial(self.device, tensor_x, tensor_y)
         tensor_x_batch_adv_no_grad = Variable(tensor_x_adv, requires_grad=False)
         loss_dial = self.model.cal_task_loss(tensor_x_batch_adv_no_grad, tensor_y)
-        return [loss_dial], [self.aconf.gamma_reg]
+        return [loss_dial], [get_gamma_reg(self.aconf, self.name)]

domainlab/algos/trainers/train_fishr.py

@@ -13,6 +13,7 @@
     backpack = None
 
 from domainlab.algos.trainers.train_basic import TrainerBasic
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class TrainerFishr(TrainerBasic):
@@ -39,7 +40,7 @@ def tr_epoch(self, epoch):
             dict_layerwise_var_var_grads_sum = \
                 {key: val.sum() for key, val in dict_layerwise_var_var_grads.items()}
             loss_fishr = sum(dict_layerwise_var_var_grads_sum.values())
-            loss = sum(list_loss_erm) + self.aconf.gamma_reg * loss_fishr
+            loss = sum(list_loss_erm) + get_gamma_reg(self.aconf, self.name) * loss_fishr
             loss.backward()
             self.optimizer.step()
             self.epo_loss_tr += loss.detach().item()

domainlab/algos/trainers/train_matchdg.py

@@ -13,6 +13,7 @@
 )
 from domainlab.tasks.utils_task_dset import DsetIndDecorator4XYD
 from domainlab.utils.logger import Logger
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class TrainerMatchDG(AbstractTrainer):
@@ -36,7 +37,7 @@ def init_business(
         self.base_domain_size = get_base_domain_size4match_dg(self.task)
         self.epo_loss_tr = 0
         self.flag_erm = flag_erm
-        self.lambda_ctr = self.aconf.gamma_reg
+        self.lambda_ctr = get_gamma_reg(aconf, self.name)
         self.mk_match_tensor(epoch=0)
         self.flag_match_tensor_sweep_over = False
         self.tuple_tensor_ref_domain2each_y = None

domainlab/algos/trainers/train_mldg.py

@@ -10,6 +10,7 @@
 from domainlab.algos.trainers.train_basic import TrainerBasic
 from domainlab.tasks.utils_task import mk_loader
 from domainlab.tasks.utils_task_dset import DsetZip
+from domainlab.utils.hyperparameter_retrieval import get_gamma_reg
 
 
 class TrainerMLDG(AbstractTrainer):
@@ -108,7 +109,7 @@ def tr_epoch(self, epoch):
             loss = (
                 loss_source_task.sum()
                 + source_reg_tr.sum()
-                + self.aconf.gamma_reg * loss_look_forward.sum()
+                + get_gamma_reg(self.aconf, self.name) * loss_look_forward.sum()
             )
             #
             loss.backward()

domainlab/arg_parser.py

@@ -12,6 +12,37 @@
 from domainlab.models.args_vae import add_args2parser_vae
 from domainlab.utils.logger import Logger
 
+class ParseValuesOrKeyValuePairs(argparse.Action):
+    """Class used for arg parsing where values are provided in a key value format"""
+
+    def __call__(self, parser: argparse.ArgumentParser,
+    namespace: argparse.Namespace, values: str, option_string: str = None):
+        """
+            Handle parsing of key value pairs, or a single value instead
+
+            Args:
+                parser (argparse.ArgumentParser): The ArgumentParser object.
+                namespace (argparse.Namespace): The namespace object to store parsed values.
+                values (str): The string containing key=value pairs or a single float value.
+                option_string (str, optional): The option string that triggered this action (unused).
+
+            Raises:
+                ValueError: If the values cannot be parsed to float.
+        """
+        if "=" in values:
+            my_dict = {}
+            for kv in values.split(","):
+                k, v = kv.split("=")
+                try:
+                    my_dict[k.strip()] = float(v.strip())
+                except ValueError:
+                    raise ValueError(f"Invalid value in key-value pair: '{kv}', must be float")
+            setattr(namespace, self.dest, my_dict)
+        else:
+            try:
+                setattr(namespace, self.dest, float(values))
+            except ValueError:
+                raise ValueError(f"Invalid value for {self.dest}: '{values}', must be float")
 
 def mk_parser_main():
     """
@@ -31,7 +62,13 @@ def mk_parser_main():
     parser.add_argument("--lr", type=float, default=1e-4, help="learning rate")
 
     parser.add_argument(
-        "--gamma_reg", type=float, default=0.1, help="weight of regularization loss"
+        "--gamma_reg",
+        default=0.1,
+        help="weight of regularization loss in the form of $$\ell(\cdot) + \mu \times R(\cdot)$$ \
+        can specify per model as 'default=3.0, dann=1.0,jigen=2.0', where default refer to gamma for trainer \
+        note diva is implemented $$\ell(\cdot) + \mu \times R(\cdot)$$ \
+        so diva does not have gamma_reg",
+        action=ParseValuesOrKeyValuePairs
     )
 
     parser.add_argument("--es", type=int, default=1, help="early stop steps")

domainlab/models/a_model.py

@@ -178,3 +178,35 @@ def dset_decoration_args_algo(self, args, ddset):
         if self._decoratee is not None:
             return self._decoratee.dset_decoration_args_algo(args, ddset)
         return ddset
+
+    @property
+    def p_na_prefix(self):
+        """
+        common prefix for Models
+        """
+        return "Model"
+
+    @property
+    def name(self):
+        """
+        get the name of the algorithm
+        """
+        na_prefix = self.p_na_prefix
+        len_prefix = len(na_prefix)
+        na_class = type(self).__name__
+        if na_class[:len_prefix] != na_prefix:
+            raise RuntimeError(
+                "Model builder node class must start with ",
+                na_prefix,
+                "the current class is named: ",
+                na_class,
+            )
+        return type(self).__name__[len_prefix:].lower()
+
+    def print_parameters(self):
+        """
+        Function to print all parameters of the object. 
+        Can be used to print the parameters of every child class.
+        """
+        params = vars(self)
+        print(f"Parameters of {type(self).__name__}: {params}")

domainlab/models/model_dann.py

@@ -85,13 +85,15 @@ def hyper_update(self, epoch, fun_scheduler):
             dict_rst = fun_scheduler(
                 epoch
             )  # the __call__ method of hyperparameter scheduler
-            self.alpha = dict_rst["alpha"]
+            self.alpha = dict_rst[self.name + "_alpha"]
 
         def hyper_init(self, functor_scheduler):
             """hyper_init.
             :param functor_scheduler:
             """
-            return functor_scheduler(trainer=None, alpha=self.alpha)
+            parameters = {}
+            parameters[self.name + "_alpha"] = self.alpha
+            return functor_scheduler(trainer=None, **parameters)
 
         def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others):
             _ = others

domainlab/models/model_diva.py

@@ -98,18 +98,22 @@ def hyper_update(self, epoch, fun_scheduler):
             :param fun_scheduler:
             """
             dict_rst = fun_scheduler(epoch)
-            self.beta_d = dict_rst["beta_d"]
-            self.beta_y = dict_rst["beta_y"]
-            self.beta_x = dict_rst["beta_x"]
+            self.beta_d = dict_rst[self.name + "_beta_d"]
+            self.beta_y = dict_rst[self.name + "_beta_x"]
+            self.beta_x = dict_rst[self.name + "_beta_y"]
 
         def hyper_init(self, functor_scheduler):
             """
             initiate a scheduler object via class name and things inside this model
 
             :param functor_scheduler: the class name of the scheduler
             """
+            parameters = {}
+            parameters[self.name + "_beta_d"] = self.beta_d
+            parameters[self.name + "_beta_y"] = self.beta_y
+            parameters[self.name + "_beta_x"] = self.beta_x
             return functor_scheduler(
-                trainer=None, beta_d=self.beta_d, beta_y=self.beta_y, beta_x=self.beta_x
+                trainer=None, **parameters
             )
 
         def _cal_reg_loss(self, tensor_x, tensor_y, tensor_d, others=None):

domainlab/models/model_hduva.py

@@ -66,10 +66,10 @@ def hyper_update(self, epoch, fun_scheduler):
             dict_rst = fun_scheduler(
                 epoch
             )  # the __call__ function of hyper-para-scheduler object
-            self.beta_d = dict_rst["beta_d"]
-            self.beta_y = dict_rst["beta_y"]
-            self.beta_x = dict_rst["beta_x"]
-            self.beta_t = dict_rst["beta_t"]
+            self.beta_d = dict_rst[self.name + "_beta_d"]
+            self.beta_y = dict_rst[self.name + "_beta_y"]
+            self.beta_x = dict_rst[self.name + "_beta_x"]
+            self.beta_t = dict_rst[self.name + "_beta_t"]
 
         def hyper_init(self, functor_scheduler):
             """hyper_init.
@@ -78,12 +78,13 @@ def hyper_init(self, functor_scheduler):
             # calling the constructor of the hyper-parameter-scheduler class, so that this scheduler
             # class build a dictionary {"beta_d":self.beta_d, "beta_y":self.beta_y}
             # constructor signature is def __init__(self, **kwargs):
+            parameters = {}
+            parameters[self.name + "_beta_d"] = self.beta_d
+            parameters[self.name + "_beta_y"] = self.beta_y
+            parameters[self.name + "_beta_x"] = self.beta_x
+            parameters[self.name + "_beta_t"] = self.beta_t
             return functor_scheduler(
-                trainer=None,
-                beta_d=self.beta_d,
-                beta_y=self.beta_y,
-                beta_x=self.beta_x,
-                beta_t=self.beta_t,
+                trainer=None, **parameters
             )
 
         @store_args

domainlab/utils/hyperparameter_retrieval.py

@@ -0,0 +1,18 @@
+"""
+retrieval for hyperparameters
+"""
+
+def get_gamma_reg(args, component_name):
+    """
+    Retrieves either a shared gamma regularization, or individual ones for each specified object
+    """
+    gamma_reg = args.gamma_reg
+    if isinstance(gamma_reg, dict):
+        if component_name in gamma_reg:
+            return gamma_reg[component_name]
+        if 'default' in gamma_reg:
+            return gamma_reg['default']
+        raise ValueError("""If a gamma_reg dict is specified,
+                              but no value set for every model and trainer,
+                              a default value must be specified.""")
+    return gamma_reg  # Return the single value if it's not a dictionary

examples/conf/vlcs_diva_mldg_dial.yaml

@@ -5,7 +5,10 @@ val_threshold: 0.8                  # threashold before which training does not
 model: dann_diva                    # combine model DANN with DIVA
 epos: 1                             # number of epochs
 trainer: mldg_dial                  # combine trainer MLDG and DIAL
-gamma_reg: 1.0                      # hyperparameter of DANN
+gamma_reg: 
+  default: 1.0
+  dann: 1.5
+# in this case, mldg and dial get the default gamma_reg value 1.0
 gamma_y: 700000.0                   # hyperparameter of diva
 gamma_d: 100000.0                   # hyperparameter of diva
 npath: examples/nets/resnet.py      # neural network for class classification

run_benchmark_slurm.sh

@@ -32,4 +32,4 @@ echo "Number of GPUs: $NUMBER_GPUS"
 echo "Results will be stored in: $results_dir"
 
 # Helmholtz
-snakemake --profile "examples/yaml/slurm" --config yaml_file="$CONFIGFILE" --keep-going --keep-incomplete --notemp --cores 3 -s "domainlab/exp_protocol/benchmark.smk" --configfile "$CONFIGFILE" --config output_dir="$results_dir" 2>&1 | tee "$logfile"
+snakemake --profile "examples/yaml/slurm" --config yaml_file="$CONFIGFILE" --keep-going --keep-incomplete --notemp --cores 3 -s "domainlab/exp_protocol/benchmark.smk" --configfile "$CONFIGFILE" --config output_dir="$results_dir" 2>&1 | tee "$logfile"