update scripts

lib/datasets/__init__.py

@@ -1,3 +1,4 @@
 from .MetaBatchSampler import MetaBatchSampler
 from .TieredImageNet import TieredImageNet
 from .LanguageDataset import Corpus
+from .get_dataset_with_transform import get_datasets

lib/datasets/get_dataset_with_transform.py

@@ -0,0 +1,74 @@
+import os, sys, torch
+import os.path as osp
+import torchvision.datasets as dset
+import torch.backends.cudnn as cudnn
+import torchvision.transforms as transforms
+
+from utils import Cutout
+from .TieredImageNet import TieredImageNet
+
+Dataset2Class = {'cifar10' : 10,
+                 'cifar100': 100,
+                 'tiered'  : -1,
+                 'imagnet-1k'  : 1000,
+                 'imagenet-100': 100}
+
+
+def get_datasets(name, root, cutout):
+
+  # Mean + Std
+  if name == '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 name == '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]]
+  elif name == 'tiered':
+    mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
+  elif name == 'imagnet-1k' or name == 'imagenet-100':
+    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+  else: raise TypeError("Unknow dataset : {:}".format(name))
+
+
+  # Data Argumentation
+  if name == 'cifar10' or name == 'cifar100':
+    lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(32, padding=4), transforms.ToTensor(),
+             transforms.Normalize(mean, std)]
+    if cutout > 0 : lists += [Cutout(cutout)]
+    train_transform = transforms.Compose(lists)
+    test_transform  = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
+  elif name == 'tiered':
+    lists = [transforms.RandomHorizontalFlip(), transforms.RandomCrop(80, padding=4), transforms.ToTensor(), transforms.Normalize(mean, std)]
+    if cutout > 0 : lists += [Cutout(cutout)]
+    train_transform = transforms.Compose(lists)
+    test_transform  = transforms.Compose([transforms.CenterCrop(80), transforms.ToTensor(), transforms.Normalize(mean, std)])
+  elif name == 'imagnet-1k' or name == 'imagenet-100':
+    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    train_transform = transforms.Compose([
+      transforms.RandomResizedCrop(224),
+      transforms.RandomHorizontalFlip(),
+      transforms.ColorJitter(
+        brightness=0.4,
+        contrast=0.4,
+        saturation=0.4,
+        hue=0.2),
+      transforms.ToTensor(),
+      normalize,
+    ])
+    test_transform = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), normalize])
+  else: raise TypeError("Unknow dataset : {:}".format(name))
+    train_data = TieredImageNet(root, 'train-val', train_transform)
+    test_data = None
+  if name == 'cifar10':
+    train_data = dset.CIFAR10(root, train=True, transform=train_transform, download=True)
+    test_data  = dset.CIFAR10(root, train=True, transform=test_transform , download=True)
+  elif name == 'cifar100':
+    train_data = dset.CIFAR100(root, train=True, transform=train_transform, download=True)
+    test_data  = dset.CIFAR100(root, train=True, transform=test_transform , download=True)
+  elif name == 'imagnet-1k' or name == 'imagenet-100':
+    train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
+    test_data  = dset.ImageFolder(osp.join(root, 'val'), train_transform)
+  else: raise TypeError("Unknow dataset : {:}".format(name))
+  
+  class_num = Dataset2Class[name]
+  return train_data, test_data, class_num

lib/move.sh

@@ -1,4 +0,0 @@
-rm -rf pytorch
-git clone https://github.com/pytorch/pytorch.git
-cp -r ./pytorch/torch/nn xnn
-rm -rf pytorch

lib/nas/__init__.py

@@ -11,8 +11,6 @@ from .CifarNet import NetworkCIFAR
 from .ImageNet import NetworkImageNet
 
 # genotypes
-from .genotypes import DARTS_V1, DARTS_V2
-from .genotypes import NASNet, PNASNet, AmoebaNet, ENASNet
-from .genotypes import DMS_V1, DMS_F1, GDAS_CC
+from .genotypes import model_types
 
 from .construct_utils import return_alphas_str

lib/nas/genotypes.py

@@ -179,7 +179,7 @@ ENASNet = Genotype(
 DARTS = DARTS_V2
 
 # Search by normal and reduce
-DMS_V1 = Genotype(
+GDAS_V1 = Genotype(
   normal=[('skip_connect', 0, 0.13017432391643524), ('skip_connect', 1, 0.12947972118854523), ('skip_connect', 0, 0.13062666356563568), ('sep_conv_5x5', 2, 0.12980839610099792), ('sep_conv_3x3', 3, 0.12923765182495117), ('skip_connect', 0, 0.12901571393013), ('sep_conv_5x5', 4, 0.12938997149467468), ('sep_conv_3x3', 3, 0.1289220005273819)],
   normal_concat=range(2, 6),
   reduce=[('sep_conv_5x5', 0, 0.12862831354141235), ('sep_conv_3x3', 1, 0.12783904373645782), ('sep_conv_5x5', 2, 0.12725995481014252), ('sep_conv_5x5', 1, 0.12705285847187042), ('dil_conv_5x5', 2, 0.12797553837299347), ('sep_conv_3x3', 1, 0.12737272679805756), ('sep_conv_5x5', 0, 0.12833961844444275), ('sep_conv_5x5', 1, 0.12758426368236542)],
@@ -187,7 +187,7 @@ DMS_V1 = Genotype(
 )
 
 # Search by normal and fixing reduction
-DMS_F1 = Genotype(
+GDAS_F1 = Genotype(
   normal=[('skip_connect', 0, 0.16), ('skip_connect', 1, 0.13), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.16), ('sep_conv_3x3', 2, 0.15)],
   normal_concat=[2, 3, 4, 5],
   reduce=None,
@@ -201,3 +201,13 @@ GDAS_CC = Genotype(
   reduce=None,
   reduce_concat=range(2, 6)
 )
+
+model_types = {'DARTS_V1': DARTS_V1,
+               'DARTS_V2': DARTS_V2,
+               'NASNet'  : NASNet,
+               'PNASNet' : PNASNet, 
+               'AmoebaNet': AmoebaNet,
+               'ENASNet' : ENASNet,
+               'GDAS_V1' : GDAS_V1,
+               'GDAS_F1' : GDAS_F1,
+               'GDAS_CC' : GDAS_CC}