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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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pytorch/augmentation/README.md
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@ -280,6 +280,8 @@ plt.show()
### Auto Augment ### Auto Augment
#### CIFAR10
```python ```python
random_auto1_transform = tv.transforms.AutoAugment( random_auto1_transform = tv.transforms.AutoAugment(
tv.transforms.AutoAugmentPolicy.CIFAR10 tv.transforms.AutoAugmentPolicy.CIFAR10
@ -293,38 +295,78 @@ plt.show()
![image20](image20.png) ![image20](image20.png)
#### IMAGENET
```python ```python
random_auto2_transform = tv.transforms.AutoAugment(
tv.transforms.AutoAugmentPolicy.IMAGENET
)
for i in range(1, 10):
new_image = random_auto2_transform((torch_image * 255).type(dtype=torch.uint8))
plt.subplot(3, 3, i)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()
``` ```
![image](image.png) ![image21](image21.png)
#### SVHN
```python ```python
random_auto3_transform = tv.transforms.AutoAugment(tv.transforms.AutoAugmentPolicy.SVHN)
for i in range(1, 10):
new_image = random_auto3_transform((torch_image * 255).type(dtype=torch.uint8))
plt.subplot(3, 3, i)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()
``` ```
![image](image.png) ![image22](image22.png)
## Sequential
```python ```python
sequential_transform = torch.nn.Sequential(
tv.transforms.RandomSolarize(threshold=0.5, p=1.0),
tv.transforms.RandomErasing(p=1.0),
)
new_image = sequential_transform((torch_image * 255).type(dtype=torch.uint8))
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()
``` ```
![image](image.png) ![image23](image23.png)
## Compose
```python ```python
compose_transform = tv.transforms.Compose(
[
tv.transforms.RandomSolarize(threshold=0.5, p=1.0),
tv.transforms.RandomErasing(p=1.0),
]
)
new_image = compose_transform((torch_image * 255).type(dtype=torch.uint8))
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()
``` ```
![image](image.png) ![image24](image24.png)
# -------------- # Random Apply
```python ```python
randomapply_transform = tv.transforms.RandomApply(
[
tv.transforms.RandomSolarize(threshold=0.5, p=1.0),
tv.transforms.RandomErasing(p=1.0),
],
p=0.5,
)
for i in range(1, 3):
plt.subplot(2, 1, i)
new_image = randomapply_transform((torch_image * 255).type(dtype=torch.uint8))
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()
``` ```
![image](image.png) ![image25](image25.png)