clarify restrictions

exps/algos/DARTS-V1.py

@@ -105,20 +105,13 @@ def main(xargs):
   logger = prepare_logger(args)
 
   train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
-  if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
+  #config_path = 'configs/nas-benchmark/algos/DARTS.config'
+  config = load_config(xargs.config_path, {'class_num': class_num, 'xshape': xshape}, logger)
+  if xargs.dataset == 'cifar10':
     split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
     cifar_split = load_config(split_Fpath, None, None)
-    train_split, valid_split = cifar_split.train, cifar_split.valid
+    train_split, valid_split = cifar_split.train, cifar_split.valid # search over the proposed training and validation set
-    logger.log('Load split file from {:}'.format(split_Fpath))
+    logger.log('Load split file from {:}'.format(split_Fpath))      # they are two disjoint groups in the original CIFAR-10 training set
-  elif xargs.dataset.startswith('ImageNet16'):
-    split_Fpath = 'configs/nas-benchmark/{:}-split.txt'.format(xargs.dataset)
-    imagenet16_split = load_config(split_Fpath, None, None)
-    train_split, valid_split = imagenet16_split.train, imagenet16_split.valid
-    logger.log('Load split file from {:}'.format(split_Fpath))
-  else:
-    raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
-  config_path = 'configs/nas-benchmark/algos/DARTS.config'
-  config = load_config(config_path, {'class_num': class_num, 'xshape': xshape}, logger)
     # To split data
     train_data_v2 = deepcopy(train_data)
     train_data_v2.transform = valid_data.transform
@@ -126,7 +119,13 @@ def main(xargs):
     search_data   = SearchDataset(xargs.dataset, train_data, train_split, valid_split)
     # data loader
     search_loader = torch.utils.data.DataLoader(search_data, batch_size=config.batch_size, shuffle=True , num_workers=xargs.workers, pin_memory=True)
-  valid_loader  = torch.utils.data.DataLoader(valid_data, batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
+    valid_loader  = torch.utils.data.DataLoader(valid_data , batch_size=config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(valid_split), num_workers=xargs.workers, pin_memory=True)
+  elif xargs.dataset == 'cifar100':
+    raise ValueError('not support yet : {:}'.format(xargs.dataset))
+  elif xargs.dataset.startswith('ImageNet16'):
+    raise ValueError('not support yet : {:}'.format(xargs.dataset))
+  else:
+    raise ValueError('invalid dataset : {:}'.format(xargs.dataset))
   logger.log('||||||| {:10s} ||||||| Search-Loader-Num={:}, Valid-Loader-Num={:}, batch size={:}'.format(xargs.dataset, len(search_loader), len(valid_loader), config.batch_size))
   logger.log('||||||| {:10s} ||||||| Config={:}'.format(xargs.dataset, config))
 
@@ -231,6 +230,7 @@ if __name__ == '__main__':
   parser.add_argument('--data_path',          type=str,   help='Path to dataset')
   parser.add_argument('--dataset',            type=str,   choices=['cifar10', 'cifar100', 'ImageNet16-120'], help='Choose between Cifar10/100 and ImageNet-16.')
   # channels and number-of-cells
+  parser.add_argument('--config_path',        type=str,   help='The config paths.')
   parser.add_argument('--search_space_name',  type=str,   help='The search space name.')
   parser.add_argument('--max_nodes',          type=int,   help='The maximum number of nodes.')
   parser.add_argument('--channel',            type=int,   help='The number of channels.')

exps/algos/ENAS.py

@@ -184,6 +184,7 @@ def main(xargs):
   logger = prepare_logger(args)
 
   train_data, test_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
+  assert xargs.dataset == 'cifar10', 'currently only support CIFAR-10'
   if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
     split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
     cifar_split = load_config(split_Fpath, None, None)

exps/algos/GDAS.py

@@ -81,6 +81,7 @@ def main(xargs):
   logger = prepare_logger(args)
 
   train_data, _, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
+  assert xargs.dataset == 'cifar10', 'currently only support CIFAR-10'
   if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
     split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
     cifar_split = load_config(split_Fpath, None, None)

exps/algos/SETN.py

@@ -135,6 +135,7 @@ def main(xargs):
   logger = prepare_logger(args)
 
   train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, xargs.data_path, -1)
+  assert xargs.dataset == 'cifar10', 'currently only support CIFAR-10'
   if xargs.dataset == 'cifar10' or xargs.dataset == 'cifar100':
     split_Fpath = 'configs/nas-benchmark/cifar-split.txt'
     cifar_split = load_config(split_Fpath, None, None)

lib/datasets/get_dataset_with_transform.py

@@ -5,7 +5,6 @@ import os, sys, torch
 import os.path as osp
 import numpy as np
 import torchvision.datasets as dset
-import torch.backends.cudnn as cudnn
 import torchvision.transforms as transforms
 from PIL import Image
 from .DownsampledImageNet import ImageNet16

scripts-search/algos/DARTS-V1.sh

@@ -33,6 +33,7 @@ OMP_NUM_THREADS=4 python ./exps/algos/DARTS-V1.py \
 	--save_dir ${save_dir} --max_nodes ${max_nodes} --channel ${channel} --num_cells ${num_cells} \
 	--dataset ${dataset} --data_path ${data_path} \
 	--search_space_name ${space} \
+	--config_path configs/nas-benchmark/algos/DARTS.config \
 	--arch_nas_dataset ${TORCH_HOME}/NAS-Bench-102-v1_0-e61699.pth \
 	--arch_learning_rate 0.0003 --arch_weight_decay 0.001 \
 	--workers 4 --print_freq 200 --rand_seed ${seed}