Merge branch 'master' into mhof_dev

domainlab/algos/trainers/a_trainer.py

@@ -5,6 +5,7 @@
 
 import torch
 from torch import optim
+from torch.optim import lr_scheduler 
 
 from domainlab.compos.pcr.p_chain_handler import AbstractChainNodeHandler
 
@@ -27,7 +28,12 @@ def mk_opt(model, aconf):
         #    {'params': model._decoratee.parameters()}
         # ], lr=aconf.lr)
         optimizer = optim.Adam(list_par, lr=aconf.lr)
-    return optimizer
+    if aconf.lr_scheduler is not None:
+        class_lr_scheduler = getattr(lr_scheduler, aconf.lr_scheduler)
+        scheduler = class_lr_scheduler(optimizer, T_max=aconf.epos)
+    else:
+        scheduler = None
+    return optimizer, scheduler
 
 
 class AbstractTrainer(AbstractChainNodeHandler, metaclass=abc.ABCMeta):
@@ -102,6 +108,8 @@ def __init__(self, successor_node=None, extend=None):
         self.dict_multiplier = {}
         # MIRO
         self.input_tensor_shape = None
+        # LR-scheduler
+        self.lr_scheduler = None
 
     @property
     def model(self):
@@ -178,7 +186,7 @@ def reset(self):
         """
         make a new optimizer to clear internal state
         """
-        self.optimizer = mk_opt(self.model, self.aconf)
+        self.optimizer, self.lr_scheduler = mk_opt(self.model, self.aconf)
 
     @abc.abstractmethod
     def tr_epoch(self, epoch):

domainlab/algos/trainers/train_basic.py

@@ -83,6 +83,8 @@ def tr_batch(self, tensor_x, tensor_y, tensor_d, others, ind_batch, epoch):
         loss, *_ = self.cal_loss(tensor_x, tensor_y, tensor_d, others)
         loss.backward()
         self.optimizer.step()
+        if self.lr_scheduler:
+            self.lr_scheduler.step()
         self.after_batch(epoch, ind_batch)
         self.counter_batch += 1
 

domainlab/arg_parser.py

@@ -264,6 +264,13 @@ def mk_parser_main():
         help="name of pytorch optimizer",
     )
 
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default=None,
+        help="name of pytorch learning rate scheduler",
+    )
+
     parser.add_argument(
         "--param_idx",
         type=bool,

scripts/generate_latex_table.py

@@ -0,0 +1,27 @@
+"""
+aggregate benchmark csv file to generate latex table
+"""
+import argparse
+import pandas as pd
+
+
+def gen_latex_table(raw_df, fname="table_perf.tex",
+                    group="method", str_perf="acc"):
+    """
+    aggregate benchmark csv file to generate latex table
+    """
+    df_result = raw_df.groupby(group)[str_perf].agg(["mean", "std", "count"])
+    latex_table = df_result.to_latex(float_format="%.3f")
+    str_table = df_result.to_string()
+    print(str_table)
+    with open(fname, 'w') as file:
+        file.write(latex_table)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Read a CSV file")
+    parser.add_argument("filename", help="Name of the CSV file to read")
+    args = parser.parse_args()
+
+    df = pd.read_csv(args.filename, index_col=False, skipinitialspace=True)
+    gen_latex_table(df)

scripts/sh_genplot.sh

@@ -1,2 +1,3 @@
-mkdir $2
-python main_out.py --gen_plots $1 --outp_dir $2
+# mkdir $2
+sh scripts/merge_csvs.sh $1
+python main_out.py --gen_plots merged_data.csv  --outp_dir partial_agg_plots

tests/test_lr_scheduler.py

@@ -0,0 +1,14 @@
+
+"""
+unit and end-end test for  lr scheduler
+"""
+from tests.utils_test import utils_test_algo
+
+
+def test_lr_scheduler():
+    """
+    train
+    """
+    args = "--te_d=2 --tr_d 0 1 --task=mnistcolor10 --debug --bs=100 --model=erm \
+        --nname=conv_bn_pool_2 --no_dump --lr_scheduler=CosineAnnealingLR"
+    utils_test_algo(args)