AntoineH/smart_augmentation
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Fix cast in Augmented Dataset

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Harle, Antoine (Contracteur) 2019-12-04 12:58:11 -05:00
parent 2ee8022c2f
commit adaac437b6
3 changed files with 29 additions and 822 deletions
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36
higher/datasets.py
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@ -34,6 +34,8 @@ from PIL import Image
import augmentation_transforms
import numpy as np
download_data=False
class AugmentedDataset(VisionDataset):
def __init__(self, root, train=True, transform=None, target_transform=None, download=False, subset=None):
@ -63,9 +65,21 @@ class AugmentedDataset(VisionDataset):
self._TF = [
'Invert', 'Cutout', 'Sharpness', 'AutoContrast', 'Posterize',
'ShearX', 'TranslateX', 'TranslateY', 'ShearY', 'Rotate',
'Equalize', 'Contrast', 'Color', 'Solarize', 'Brightness'
'Invert',
'Cutout',
'Sharpness',
'AutoContrast',
'Posterize',
'ShearX',
'TranslateX',
'TranslateY',
'ShearY',
'Rotate',
'Equalize',
'Contrast',
'Color',
'Solarize',
'Brightness'
]
self._op_list =[]
self.prob=0.5
@ -108,13 +122,13 @@ class AugmentedDataset(VisionDataset):
for _ in range(aug_copy):
chosen_policy = policies[np.random.choice(len(policies))]
aug_image = augmentation_transforms.apply_policy(chosen_policy, image)
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]
self.unsup_targets+=[self.sup_targets[idx]]
self.unsup_data=np.array(self.unsup_data).astype(self.sup_data.dtype)
self.unsup_data=(np.array(self.unsup_data)*255.).astype(self.sup_data.dtype) #Cast float image to uint8
self.data= np.concatenate((self.sup_data, self.unsup_data), axis=0)
self.targets= np.concatenate((self.sup_targets, self.unsup_targets), axis=0)
@ -133,12 +147,12 @@ class AugmentedDataset(VisionDataset):
return self.dataset_info['length']
def __str__(self):
return "CIFAR10(Sup:{}-Unsup:{})".format(self.dataset_info['sup'], self.dataset_info['unsup'])
return "CIFAR10(Sup:{}-Unsup:{}-{}TF)".format(self.dataset_info['sup'], self.dataset_info['unsup'], len(self._TF))
### Classic Dataset ###
data_train = torchvision.datasets.CIFAR10("./data", train=True, download=True, transform=transform)
#data_val = torchvision.datasets.CIFAR10("./data", train=True, download=True, transform=transform)
data_test = torchvision.datasets.CIFAR10("./data", train=False, download=True, transform=transform)
data_train = torchvision.datasets.CIFAR10("./data", train=True, download=download_data, transform=transform)
#data_val = torchvision.datasets.CIFAR10("./data", train=True, download=download_data, transform=transform)
data_test = torchvision.datasets.CIFAR10("./data", train=False, download=download_data, transform=transform)
train_subset_indices=range(int(len(data_train)/2))
@ -149,8 +163,8 @@ val_subset_indices=range(int(len(data_train)/2),len(data_train))
dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))
### Augmented Dataset ###
data_train_aug = AugmentedDataset("./data", train=True, download=True, transform=transform, subset=(0,int(len(data_train)/2)))
#data_train_aug.augement_data(aug_copy=1)
data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
data_train_aug.augement_data(aug_copy=1)
print(data_train_aug)
dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)