[NAS] simplify log, and fix a bug on pdarts exporting (#1777)

docs/en_US/NAS/Overview.md

@@ -51,8 +51,8 @@ cd examples/nas/pdarts
 python3 search.py
 
 # train the best architecture, it's the same progress as darts.
-cd examples/nas/darts
-python3 retrain.py --arc-checkpoint ./checkpoints/epoch_2.json
+cd ../darts
+python3 retrain.py --arc-checkpoint ../pdarts/checkpoints/epoch_2.json
 ```
 
 ## Use NNI API

examples/nas/darts/retrain.py

@@ -4,24 +4,16 @@ from argparse import ArgumentParser
 
 import torch
 import torch.nn as nn
-from nni.nas.pytorch.fixed import apply_fixed_architecture
-from nni.nas.pytorch.utils import AverageMeter
 from torch.utils.tensorboard import SummaryWriter
 
 import datasets
 import utils
 from model import CNN
+from nni.nas.pytorch.fixed import apply_fixed_architecture
+from nni.nas.pytorch.utils import AverageMeter
 
-logger = logging.getLogger()
-
-fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
-logging.Formatter.converter = time.localtime
-formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
+logger = logging.getLogger('nni')
 
-std_out_info = logging.StreamHandler()
-std_out_info.setFormatter(formatter)
-logger.setLevel(logging.INFO)
-logger.addHandler(std_out_info)
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 writer = SummaryWriter()

examples/nas/darts/search.py

@@ -11,16 +11,7 @@ from nni.nas.pytorch.callbacks import ArchitectureCheckpoint, LRSchedulerCallbac
 from nni.nas.pytorch.darts import DartsTrainer
 from utils import accuracy
 
-logger = logging.getLogger()
-
-fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
-logging.Formatter.converter = time.localtime
-formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
-
-std_out_info = logging.StreamHandler()
-std_out_info.setFormatter(formatter)
-logger.setLevel(logging.INFO)
-logger.addHandler(std_out_info)
+logger = logging.getLogger('nni')
 
 if __name__ == "__main__":
     parser = ArgumentParser("darts")

examples/nas/enas/search.py

@@ -9,19 +9,12 @@ import datasets
 from macro import GeneralNetwork
 from micro import MicroNetwork
 from nni.nas.pytorch import enas
-from nni.nas.pytorch.callbacks import LRSchedulerCallback, ArchitectureCheckpoint
+from nni.nas.pytorch.callbacks import (ArchitectureCheckpoint,
+                                       LRSchedulerCallback)
 from utils import accuracy, reward_accuracy
 
-logger = logging.getLogger()
+logger = logging.getLogger('nni')
 
-fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
-logging.Formatter.converter = time.localtime
-formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
-
-std_out_info = logging.StreamHandler()
-std_out_info.setFormatter(formatter)
-logger.setLevel(logging.INFO)
-logger.addHandler(std_out_info)
 
 if __name__ == "__main__":
     parser = ArgumentParser("enas")

examples/nas/pdarts/search.py

@@ -19,16 +19,9 @@ if True:
     from model import CNN
     import datasets
 
-logger = logging.getLogger()
 
-fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
-logging.Formatter.converter = time.localtime
-formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
+logger = logging.getLogger('nni')
 
-std_out_info = logging.StreamHandler()
-std_out_info.setFormatter(formatter)
-logger.setLevel(logging.INFO)
-logger.addHandler(std_out_info)
 
 if __name__ == "__main__":
     parser = ArgumentParser("pdarts")

src/sdk/pynni/nni/nas/pytorch/mutables.py

@@ -5,7 +5,6 @@ import torch.nn as nn
 from nni.nas.pytorch.utils import global_mutable_counting
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
 
 
 class Mutable(nn.Module):

src/sdk/pynni/nni/nas/pytorch/pdarts/trainer.py

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
+import json
 import logging
 
 from nni.nas.pytorch.callbacks import LRSchedulerCallback
 from nni.nas.pytorch.darts import DartsTrainer
-from nni.nas.pytorch.trainer import BaseTrainer
+from nni.nas.pytorch.trainer import BaseTrainer, TorchTensorEncoder
 
 from .mutator import PdartsMutator
 
 logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
 
 
 class PdartsTrainer(BaseTrainer):
@@ -55,7 +55,7 @@ class PdartsTrainer(BaseTrainer):
 
             self.trainer = DartsTrainer(model, mutator=self.mutator, loss=criterion, optimizer=optim,
                                         callbacks=darts_callbacks, **self.darts_parameters)
-            logger.info("start pdarts training %s...", epoch)
+            logger.info("start pdarts training epoch %s...", epoch)
 
             self.trainer.train()
 
@@ -67,5 +67,10 @@ class PdartsTrainer(BaseTrainer):
     def validate(self):
         self.model.validate()
 
+    def export(self, file):
+        mutator_export = self.mutator.export()
+        with open(file, "w") as f:
+            json.dump(mutator_export, f, indent=2, sort_keys=True, cls=TorchTensorEncoder)
+
     def checkpoint(self):
         raise NotImplementedError("Not implemented yet")

src/sdk/pynni/nni/nas/pytorch/trainer.py

@@ -7,7 +7,6 @@ import torch
 from .base_trainer import BaseTrainer
 
 _logger = logging.getLogger(__name__)
-_logger.setLevel(logging.INFO)
 
 
 class TorchTensorEncoder(json.JSONEncoder):