update scripts

exps-cnn/DARTS-Search.py

@@ -1,3 +1,4 @@
+# DARTS First Order, Refer to https://github.com/quark0/darts
 import os, sys, time, glob, random, argparse
 import numpy as np
 from copy import deepcopy

exps-cnn/train_base.py

@@ -13,25 +13,11 @@ if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
 from utils import AverageMeter, time_string, convert_secs2time
 from utils import print_log, obtain_accuracy
 from utils import Cutout, count_parameters_in_MB
-from nas import DARTS_V1, DARTS_V2, NASNet, PNASNet, AmoebaNet, ENASNet
-from nas import DMS_V1, DMS_F1, GDAS_CC
-from meta_nas import META_V1, META_V2
+from nas import model_types as models
 from train_utils import main_procedure
 from train_utils_imagenet import main_procedure_imagenet
 from scheduler import load_config
 
-models = {'DARTS_V1': DARTS_V1,
-          'DARTS_V2': DARTS_V2,
-          'NASNet'  : NASNet,
-          'PNASNet' : PNASNet,
-          'ENASNet' : ENASNet,
-          'DMS_V1'  : DMS_V1,
-          'DMS_F1'  : DMS_F1,
-          'GDAS_CC' : GDAS_CC,
-          'META_V1' : META_V1,
-          'META_V2' : META_V2,
-          'AmoebaNet' : AmoebaNet}
-
 
 parser = argparse.ArgumentParser("cifar")
 parser.add_argument('--data_path',         type=str,   help='Path to dataset')

exps-cnn/train_utils.py

@@ -10,6 +10,7 @@ from utils import time_string, convert_secs2time
 from utils import count_parameters_in_MB
 from utils import Cutout
 from nas import NetworkCIFAR as Network
+from datasets import get_datasets
 
 def obtain_best(accuracies):
   if len(accuracies) == 0: return (0, 0)
@@ -17,38 +18,10 @@ def obtain_best(accuracies):
   s2b = sorted( tops )
   return s2b[-1]
 
+
 def main_procedure(config, dataset, data_path, args, genotype, init_channels, layers, log):
   
-  # Mean + Std
-  if dataset == 'cifar10':
-    mean = [x / 255 for x in [125.3, 123.0, 113.9]]
-    std = [x / 255 for x in [63.0, 62.1, 66.7]]
-  elif dataset == 'cifar100':
-    mean = [x / 255 for x in [129.3, 124.1, 112.4]]
-    std = [x / 255 for x in [68.2, 65.4, 70.4]]
-  else:
-    raise TypeError("Unknow dataset : {:}".format(dataset))
-  # Dataset Transformation
-  if dataset == 'cifar10' or dataset == 'cifar100':
-    lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(),
-             transforms.Normalize(mean, std)]
-    if config.cutout > 0 : lists += [Cutout(config.cutout)]
-    train_transform = transforms.Compose(lists)
-    test_transform  = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
-  else:
-    raise TypeError("Unknow dataset : {:}".format(dataset))
-  # Dataset Defination
-  if dataset == 'cifar10':
-    train_data = dset.CIFAR10(data_path, train= True, transform=train_transform, download=True)
-    test_data  = dset.CIFAR10(data_path, train=False, transform=test_transform , download=True)
-    class_num  = 10
-  elif dataset == 'cifar100':
-    train_data = dset.CIFAR100(data_path, train= True, transform=train_transform, download=True)
-    test_data  = dset.CIFAR100(data_path, train=False, transform=test_transform , download=True)
-    class_num  = 100
-  else:
-    raise TypeError("Unknow dataset : {:}".format(dataset))
-
+  train_data, test_data, class_num = get_datasets(dataset, data_path, args.cutout)
 
   print_log('-------------------------------------- main-procedure', log)
   print_log('config        : {:}'.format(config), log)

exps-cnn/train_utils_imagenet.py

@@ -12,6 +12,7 @@ from utils import count_parameters_in_MB
 from utils import print_FLOPs
 from utils import Cutout
 from nas import NetworkImageNet as Network
+from datasets import get_datasets
 
 
 def obtain_best(accuracies):
@@ -40,30 +41,7 @@ class CrossEntropyLabelSmooth(nn.Module):
 def main_procedure_imagenet(config, data_path, args, genotype, init_channels, layers, log):
   
   # training data and testing data
-  traindir = os.path.join(data_path, 'train')
-  validdir = os.path.join(data_path, 'val')
-  normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-  train_data = dset.ImageFolder(
-    traindir,
-    transforms.Compose([
-      transforms.RandomResizedCrop(224),
-      transforms.RandomHorizontalFlip(),
-      transforms.ColorJitter(
-        brightness=0.4,
-        contrast=0.4,
-        saturation=0.4,
-        hue=0.2),
-      transforms.ToTensor(),
-      normalize,
-    ]))
-  valid_data = dset.ImageFolder(
-    validdir,
-    transforms.Compose([
-      transforms.Resize(256),
-      transforms.CenterCrop(224),
-      transforms.ToTensor(),
-      normalize,
-    ]))
+  train_data, valid_data, class_num = get_datasets('imagenet-1k', data_path, -1)
 
   train_queue = torch.utils.data.DataLoader(
     train_data, batch_size=config.batch_size, shuffle= True, pin_memory=True, num_workers=args.workers)
@@ -73,7 +51,6 @@ def main_procedure_imagenet(config, data_path, args, genotype, init_channels, la
 
   class_num   = 1000
 
-
   print_log('-------------------------------------- main-procedure', log)
   print_log('config        : {:}'.format(config), log)
   print_log('genotype      : {:}'.format(genotype), log)
@@ -98,8 +75,7 @@ def main_procedure_imagenet(config, data_path, args, genotype, init_channels, la
   criterion_smooth = CrossEntropyLabelSmooth(class_num, config.label_smooth).cuda()
 
 
-  optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay)
-  #optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay, nestero=True)
+  optimizer = torch.optim.SGD(model.parameters(), config.LR, momentum=config.momentum, weight_decay=config.decay, nestero=True)
   if config.type == 'cosine':
     scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(config.epochs))
   elif config.type == 'steplr':