Update on NAS examples (#3240)

examples/nas/cdarts/utils.py

@@ -14,7 +14,7 @@ import torch.nn as nn
 
 from genotypes import Genotype
 from ops import PRIMITIVES
-from nni.nas.pytorch.cdarts.utils import *
+from nni.algorithms.nas.pytorch.cdarts.utils import *
 
 
 def get_logger(file_path):

examples/nas/classic_nas-tf/train.py

@@ -7,7 +7,7 @@ from tensorflow.keras.optimizers import SGD
 
 import nni
 from nni.nas.tensorflow.mutables import LayerChoice, InputChoice
-from nni.nas.tensorflow.classic_nas import get_and_apply_next_architecture
+from nni.algorithms.nas.tensorflow.classic_nas import get_and_apply_next_architecture
 
 tf.get_logger().setLevel('ERROR')
 

examples/nas/enas-tf/search.py

@@ -5,7 +5,7 @@
 from tensorflow.keras.losses import Reduction, SparseCategoricalCrossentropy
 from tensorflow.keras.optimizers import SGD
 
-from nni.nas.tensorflow import enas
+from nni.algorithms.nas.tensorflow import enas
 
 import datasets
 from macro import GeneralNetwork

examples/nas/naive-tf/train.py

@@ -8,7 +8,7 @@ from tensorflow.keras.losses import Reduction, SparseCategoricalCrossentropy
 from tensorflow.keras.optimizers import SGD
 
 from nni.nas.tensorflow.mutables import LayerChoice, InputChoice
-from nni.nas.tensorflow.enas import EnasTrainer
+from nni.algorithms.nas.tensorflow.enas import EnasTrainer
 
 
 class Net(Model):
@@ -55,7 +55,7 @@ class Net(Model):
 
 
 def accuracy(truth, logits):
-    truth = tf.reshape(truth, -1)
+    truth = tf.reshape(truth, (-1, ))
     predicted = tf.cast(tf.math.argmax(logits, axis=1), truth.dtype)
     equal = tf.cast(predicted == truth, tf.int32)
     return tf.math.reduce_sum(equal).numpy() / equal.shape[0]

examples/nas/proxylessnas/main.py

+import json
 import logging
 import os
 import sys
@@ -102,6 +103,7 @@ if __name__ == "__main__":
                                    log_frequency=10)
         trainer.fit()
         print('Final architecture:', trainer.export())
+        json.dump(trainer.export(), open('checkpoint.json', 'w'))
     elif args.train_mode == 'search_v1':
         # this is architecture search
         logger.info('Creating ProxylessNasTrainer...')

examples/nas/proxylessnas/putils.py

@@ -85,7 +85,7 @@ def accuracy(output, target, topk=(1,)):
 
     res = dict()
     for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
+        correct_k = correct[:k].reshape(-1).float().sum(0)
         res["acc{}".format(k)] = correct_k.mul_(1.0 / batch_size).item()
     return res
 

examples/nas/search_space_zoo/darts_example.py

@@ -10,7 +10,7 @@ import torch.nn as nn
 
 import datasets
 from nni.nas.pytorch.callbacks import ArchitectureCheckpoint, LRSchedulerCallback
-from nni.nas.pytorch.darts import DartsTrainer
+from nni.algorithms.nas.pytorch.darts import DartsTrainer
 from utils import accuracy
 
 from nni.nas.pytorch.search_space_zoo import DartsCell

examples/nas/search_space_zoo/enas_macro_example.py

@@ -8,7 +8,7 @@ from torchvision import transforms
 from torchvision.datasets import CIFAR10
 
 from nni.nas.pytorch import mutables
-from nni.nas.pytorch import enas
+from nni.algorithms.nas.pytorch import enas
 from utils import accuracy, reward_accuracy
 from nni.nas.pytorch.callbacks import (ArchitectureCheckpoint,
                                        LRSchedulerCallback)

examples/nas/search_space_zoo/enas_micro_example.py

@@ -7,7 +7,7 @@ from argparse import ArgumentParser
 from torchvision import transforms
 from torchvision.datasets import CIFAR10
 
-from nni.nas.pytorch import enas
+from nni.algorithms.nas.pytorch import enas
 from utils import accuracy, reward_accuracy
 from nni.nas.pytorch.callbacks import (ArchitectureCheckpoint,
                                        LRSchedulerCallback)

examples/nas/search_space_zoo/nasbench201.py

@@ -10,13 +10,13 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader
-from nni.nas.pytorch import enas
+from nni.algorithms.nas.pytorch.darts import DartsTrainer
+from nni.algorithms.nas.pytorch import enas
 from nni.nas.pytorch.utils import AverageMeterGroup
 from nni.nas.pytorch.nasbench201 import NASBench201Cell
 from nni.nas.pytorch.fixed import apply_fixed_architecture
 from nni.nas.benchmarks.nasbench201 import query_nb201_trial_stats
 from nni.nas.pytorch.callbacks import ArchitectureCheckpoint, LRSchedulerCallback
-from nni.nas.pytorch.darts import DartsTrainer
 from utils import accuracy, reward_accuracy
 
 import datasets

examples/nas/spos/utils.py

@@ -36,6 +36,6 @@ def accuracy(output, target, topk=(1, 5)):
 
     res = dict()
     for k in topk:
-        correct_k = correct[:k].view(-1).float().sum(0)
+        correct_k = correct[:k].reshape(-1).float().sum(0)
         res["acc{}".format(k)] = correct_k.mul_(1.0 / batch_size).item()
     return res

examples/nas/textnas/run_retrain.sh

@@ -2,7 +2,6 @@
 # Licensed under the MIT license.
 
 export PYTHONPATH="$(pwd)"
-export CUDA_VISIBLE_DEVICES=0
 
 python3 -u retrain.py \
   --train_ratio=1.0 \

examples/nas/textnas/utils.py

@@ -14,7 +14,7 @@ logger = logging.getLogger("nni.textnas")
 
 def get_length(mask):
     length = torch.sum(mask, 1)
-    length = length.long()
+    length = length.long().cpu()
     return length