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#!/usr/bin/env python3
"""
Function used for visualization of data and results
Author: Shilpaj Bhalerao
Date: Jul 23, 2023
"""
# Third-Party Imports
import torch
import albumentations as A
from albumentations.pytorch import ToTensorV2
# Train Phase transformations
train_set_transforms = {
'randomcrop': A.RandomCrop(height=32, width=32, p=0.2),
'horizontalflip': A.HorizontalFlip(),
'cutout': A.CoarseDropout(max_holes=1, max_height=16, max_width=16, min_holes=1, min_height=1, min_width=1, fill_value=[0.49139968*255, 0.48215827*255 ,0.44653124*255], mask_fill_value=None),
'normalize': A.Normalize((0.49139968, 0.48215827, 0.44653124), (0.24703233, 0.24348505, 0.26158768)),
'standardize': ToTensorV2(),
}
# Test Phase transformations
test_set_transforms = {
'normalize': A.Normalize((0.49139968, 0.48215827, 0.44653124), (0.24703233, 0.24348505, 0.26158768)),
'standardize': ToTensorV2()
}
class AddGaussianNoise(object):
"""
Class for custom augmentation strategy
"""
def __init__(self, mean=0., std=1.):
"""
Constructor
"""
self.std = std
self.mean = mean
def __call__(self, tensor):
"""
Augmentation strategy to be implemented when called
"""
return tensor + torch.randn(tensor.size()) * self.std + self.mean
def __repr__(self):
"""
Method to print more infor about the strategy
"""
return f"{self.__class__.__name__}(mean={self.mean}, std={self.std})"
# Usage details
# transforms = transforms.Compose([
# transforms.ToTensor(),
# AddGaussianNoise(0., 1.0),
# ])
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