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https://github.com/AntoineHX/smart_augmentation.git
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Fix cast in Augmented Dataset
This commit is contained in:
Harle, Antoine (Contracteur)
parent
2ee8022c2f
commit
adaac437b6
3 changed files with 29 additions and 822 deletions
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@ -34,6 +34,8 @@ from PIL import Image
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import augmentation_transforms
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import numpy as np
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download_data=False
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class AugmentedDataset(VisionDataset):
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def __init__(self, root, train=True, transform=None, target_transform=None, download=False, subset=None):
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@ -63,9 +65,21 @@ class AugmentedDataset(VisionDataset):
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self._TF = [
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'Invert', 'Cutout', 'Sharpness', 'AutoContrast', 'Posterize',
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'ShearX', 'TranslateX', 'TranslateY', 'ShearY', 'Rotate',
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'Equalize', 'Contrast', 'Color', 'Solarize', 'Brightness'
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'Invert',
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'Cutout',
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'Sharpness',
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'AutoContrast',
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'Posterize',
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'ShearX',
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'TranslateX',
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'TranslateY',
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'ShearY',
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'Rotate',
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'Equalize',
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'Contrast',
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'Color',
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'Solarize',
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'Brightness'
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]
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self._op_list =[]
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self.prob=0.5
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@ -108,13 +122,13 @@ class AugmentedDataset(VisionDataset):
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for _ in range(aug_copy):
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chosen_policy = policies[np.random.choice(len(policies))]
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aug_image = augmentation_transforms.apply_policy(chosen_policy, image)
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aug_image = augmentation_transforms.apply_policy(chosen_policy, image, use_mean_std=False) #Cast en float image
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#aug_image = augmentation_transforms.cutout_numpy(aug_image)
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self.unsup_data+=[aug_image]
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self.unsup_targets+=[self.sup_targets[idx]]
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self.unsup_data=np.array(self.unsup_data).astype(self.sup_data.dtype)
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self.unsup_data=(np.array(self.unsup_data)*255.).astype(self.sup_data.dtype) #Cast float image to uint8
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self.data= np.concatenate((self.sup_data, self.unsup_data), axis=0)
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self.targets= np.concatenate((self.sup_targets, self.unsup_targets), axis=0)
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@ -133,12 +147,12 @@ class AugmentedDataset(VisionDataset):
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return self.dataset_info['length']
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def __str__(self):
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return "CIFAR10(Sup:{}-Unsup:{})".format(self.dataset_info['sup'], self.dataset_info['unsup'])
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return "CIFAR10(Sup:{}-Unsup:{}-{}TF)".format(self.dataset_info['sup'], self.dataset_info['unsup'], len(self._TF))
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### Classic Dataset ###
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data_train = torchvision.datasets.CIFAR10("./data", train=True, download=True, transform=transform)
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#data_val = torchvision.datasets.CIFAR10("./data", train=True, download=True, transform=transform)
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data_test = torchvision.datasets.CIFAR10("./data", train=False, download=True, transform=transform)
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data_train = torchvision.datasets.CIFAR10("./data", train=True, download=download_data, transform=transform)
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#data_val = torchvision.datasets.CIFAR10("./data", train=True, download=download_data, transform=transform)
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data_test = torchvision.datasets.CIFAR10("./data", train=False, download=download_data, transform=transform)
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train_subset_indices=range(int(len(data_train)/2))
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@ -149,8 +163,8 @@ val_subset_indices=range(int(len(data_train)/2),len(data_train))
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dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))
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### Augmented Dataset ###
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data_train_aug = AugmentedDataset("./data", train=True, download=True, transform=transform, subset=(0,int(len(data_train)/2)))
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#data_train_aug.augement_data(aug_copy=1)
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data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
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data_train_aug.augement_data(aug_copy=1)
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print(data_train_aug)
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dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
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@ -1,810 +0,0 @@
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{
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"Accuracy": 59.15,
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2.891577087839999,
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],
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}
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||||
|
|
@ -69,7 +69,7 @@ if __name__ == "__main__":
|
|||
'aug_dataset',
|
||||
#'aug_model'
|
||||
}
|
||||
n_inner_iter = 0
|
||||
n_inner_iter = 1
|
||||
epochs = 100
|
||||
dataug_epoch_start=0
|
||||
|
||||
|
|
@ -104,6 +104,9 @@ if __name__ == "__main__":
|
|||
|
||||
#### Augmented Dataset ####
|
||||
if 'aug_dataset' in tasks:
|
||||
|
||||
xs, ys = next(iter(dl_train))
|
||||
viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
|
||||
t0 = time.process_time()
|
||||
model = LeNet(3,10).to(device)
|
||||
#model = WideResNet(num_classes=10, wrn_size=16).to(device)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue