AntoineH/smart_augmentation
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Stockage code inutile dans old

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Harle, Antoine (Contracteur) 2020-01-22 11:15:56 -05:00
parent c1ad787d97
commit f2019aae4a
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120
higher/datasets.py
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@ -38,126 +38,6 @@ from PIL import Image
import augmentation_transforms
import numpy as np
class AugmentedDataset(VisionDataset):
def __init__(self, root, train=True, transform=None, target_transform=None, download=False, subset=None):
super(AugmentedDataset, self).__init__(root, transform=transform, target_transform=target_transform)
supervised_dataset = torchvision.datasets.CIFAR10(root, train=train, download=download, transform=transform)
self.sup_data = supervised_dataset.data if not subset else supervised_dataset.data[subset[0]:subset[1]]
self.sup_targets = supervised_dataset.targets if not subset else supervised_dataset.targets[subset[0]:subset[1]]
assert len(self.sup_data)==len(self.sup_targets)
for idx, img in enumerate(self.sup_data):
self.sup_data[idx]= Image.fromarray(img) #to PIL Image
self.unsup_data=[]
self.unsup_targets=[]
self.data= self.sup_data
self.targets= self.sup_targets
self.dataset_info= {
'name': 'CIFAR10',
'sup': len(self.sup_data),
'unsup': len(self.unsup_data),
'length': len(self.sup_data)+len(self.unsup_data),
}
self._TF = [
## Geometric TF ##
'Rotate',
'TranslateX',
'TranslateY',
'ShearX',
'ShearY',
'Cutout',
## Color TF ##
'Contrast',
'Color',
'Brightness',
'Sharpness',
#'Posterize',
#'Solarize',
'Invert',
'AutoContrast',
'Equalize',
]
self._op_list =[]
self.prob=0.5
for tf in self._TF:
for mag in range(1, 10):
self._op_list+=[(tf, self.prob, mag)]
self._nb_op = len(self._op_list)
def __getitem__(self, index):
"""
Args:
index (int): Index
Returns:
tuple: (image, target) where target is index of the target class.
"""
img, target = self.data[index], self.targets[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
#img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
return img, target
def augement_data(self, aug_copy=1):
policies = []
for op_1 in self._op_list:
for op_2 in self._op_list:
policies += [[op_1, op_2]]
for idx, image in enumerate(self.sup_data):
if (idx/self.dataset_info['sup'])%0.2==0: print("Augmenting data... ", idx,"/", self.dataset_info['sup'])
#if idx==10000:break
for _ in range(aug_copy):
chosen_policy = policies[np.random.choice(len(policies))]
aug_image = augmentation_transforms.apply_policy(chosen_policy, image, use_mean_std=False) #Cast en float image
#aug_image = augmentation_transforms.cutout_numpy(aug_image)
self.unsup_data+=[(aug_image*255.).astype(self.sup_data.dtype)]#Cast float image to uint8
self.unsup_targets+=[self.sup_targets[idx]]
#self.unsup_data=(np.array(self.unsup_data)*255.).astype(self.sup_data.dtype) #Cast float image to uint8
self.unsup_data=np.array(self.unsup_data)
self.data= np.concatenate((self.sup_data, self.unsup_data), axis=0)
self.targets= np.concatenate((self.sup_targets, self.unsup_targets), axis=0)
assert len(self.unsup_data)==len(self.unsup_targets)
assert len(self.data)==len(self.targets)
self.dataset_info['unsup']=len(self.unsup_data)
self.dataset_info['length']=self.dataset_info['sup']+self.dataset_info['unsup']
def len_supervised(self):
return self.dataset_info['sup']
def len_unsupervised(self):
return self.dataset_info['unsup']
def __len__(self):
return self.dataset_info['length']
def __str__(self):
return "CIFAR10(Sup:{}-Unsup:{}-{}TF)".format(self.dataset_info['sup'], self.dataset_info['unsup'], len(self._TF))
class AugmentedDatasetV2(VisionDataset):
def __init__(self, root, train=True, transform=None, target_transform=None, download=False, subset=None):