AntoineH/smart_augmentation
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RandAugment

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Harle, Antoine (Contracteur) 2020-02-05 12:24:20 -05:00
parent 7221142a9a
commit 6277e268c1
3 changed files with 407 additions and 73 deletions
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higher/smart_aug/datasets.py
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@ -1,6 +1,6 @@
""" Dataset definition.
MNIST / CIFAR10
MNIST / CIFAR10 / CIFAR100 / SVHN / ImageNet
"""
import torch
from torch.utils.data.dataset import ConcatDataset
@ -37,9 +37,16 @@ transform_train = torchvision.transforms.Compose([
#transforms.RandomVerticalFlip(),
torchvision.transforms.ToTensor(),
])
#from RandAugment import RandAugment
## RandAugment ##
from RandAugment import RandAugment
# Add RandAugment with N, M(hyperparameter)
#transform_train.transforms.insert(0, RandAugment(n=2, m=30))
rand_aug={'N': 2, 'M': 1}
#rand_aug={'N': 2, 'M': 9./30} #RN-ImageNet
#rand_aug={'N': 3, 'M': 5./30} #WRN-CIFAR10
#rand_aug={'N': 2, 'M': 14./30} #WRN-CIFAR100
#rand_aug={'N': 3, 'M': 7./30} #WRN-SVHN
transform_train.transforms.insert(0, RandAugment(n=rand_aug['N'], m=rand_aug['M']))
### Classic Dataset ###
@ -50,7 +57,7 @@ transform_train = torchvision.transforms.Compose([
#CIFAR
data_train = torchvision.datasets.CIFAR10(dataroot, train=True, download=download_data, transform=transform_train)
#data_val = torchvision.datasets.CIFAR10(dataroot, train=True, download=download_data, transform=transform)
data_val = torchvision.datasets.CIFAR10(dataroot, train=True, download=download_data, transform=transform)
data_test = torchvision.datasets.CIFAR10(dataroot, train=False, download=download_data, transform=transform)
#data_train = torchvision.datasets.CIFAR100(dataroot, train=True, download=download_data, transform=transform_train)
@ -72,32 +79,18 @@ data_test = torchvision.datasets.CIFAR10(dataroot, train=False, download=downloa
#Validation set size [0, 1]
valid_size=0.1
#train_subset_indices=range(int(len(data_train)*(1-valid_size)))
#val_subset_indices=range(int(len(data_train)*(1-valid_size)),len(data_train))
train_subset_indices=range(int(len(data_train)*(1-valid_size)))
val_subset_indices=range(int(len(data_train)*(1-valid_size)),len(data_train))
#train_subset_indices=range(BATCH_SIZE*10)
#val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)
#from torch.utils.data import SubsetRandomSampler
#dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
#dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
from torch.utils.data import SubsetRandomSampler
dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
dl_val = torch.utils.data.DataLoader(data_val, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
dl_test = torch.utils.data.DataLoader(data_test, batch_size=TEST_SIZE, shuffle=False, num_workers=num_workers, pin_memory=pin_memory)
#Cross Validation
'''
from skorch.dataset import CVSplit
import numpy as np
cvs = CVSplit(cv=valid_size, stratified=True) #Stratified =True for unbalanced dataset #ShuffleSplit
def next_CVSplit():
train_subset, val_subset = cvs(data_train, y=np.array(data_train.targets))
dl_train = torch.utils.data.DataLoader(train_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
dl_val = torch.utils.data.DataLoader(val_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
return dl_train, dl_val
dl_train, dl_val = next_CVSplit()
'''
import numpy as np
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import StratifiedShuffleSplit
@ -134,7 +127,7 @@ class CVSplit(object):
else:
cv_cls = ShuffleSplit
self._cv= cv_cls(test_size=val_size, random_state=0)
self._cv= cv_cls(test_size=val_size, random_state=0) #Random state w/ fixed seed
def next_split(self):
""" Get next cross-validation split.
@ -157,4 +150,21 @@ class CVSplit(object):
return dl_train, dl_val
cvs = CVSplit(data_train, val_size=valid_size)
dl_train, dl_val = cvs.next_split()
dl_train, dl_val = cvs.next_split()
'''
'''
from skorch.dataset import CVSplit
import numpy as np
cvs = CVSplit(cv=valid_size, stratified=True) #Stratified =True for unbalanced dataset #ShuffleSplit
def next_CVSplit():
train_subset, val_subset = cvs(data_train, y=np.array(data_train.targets))
dl_train = torch.utils.data.DataLoader(train_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
dl_val = torch.utils.data.DataLoader(val_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
return dl_train, dl_val
dl_train, dl_val = next_CVSplit()
'''