datasets/augmentations.py

import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms


class ToOneHot(object):
	""" Convert the input PIL image to a one-hot torch tensor """
	def __init__(self, n_classes=None):
		self.n_classes = n_classes

	def onehot_initialization(self, a):
		if self.n_classes is None:
			self.n_classes = len(np.unique(a))
		out = np.zeros(a.shape + (self.n_classes, ), dtype=int)
		out[self.__all_idx(a, axis=2)] = 1
		return out

	def __all_idx(self, idx, axis):
		grid = np.ogrid[tuple(map(slice, idx.shape))]
		grid.insert(axis, idx)
		return tuple(grid)

	def __call__(self, img):
		img = np.array(img)
		one_hot = self.onehot_initialization(img)
		return one_hot


class BilinearResize(object):
	def __init__(self, factors=[1, 2, 4, 8, 16, 32]):
		self.factors = factors

	def __call__(self, image):
		factor = np.random.choice(self.factors, size=1)[0]
		D = BicubicDownSample(factor=factor, cuda=False)
		img_tensor = transforms.ToTensor()(image).unsqueeze(0)
		img_tensor_lr = D(img_tensor)[0].clamp(0, 1)
		img_low_res = transforms.ToPILImage()(img_tensor_lr)
		return img_low_res


class BicubicDownSample(nn.Module):
	def bicubic_kernel(self, x, a=-0.50):
		"""
		This equation is exactly copied from the website below:
		https://clouard.users.greyc.fr/Pantheon/experiments/rescaling/index-en.html#bicubic
		"""
		abs_x = torch.abs(x)
		if abs_x <= 1.:
			return (a + 2.) * torch.pow(abs_x, 3.) - (a + 3.) * torch.pow(abs_x, 2.) + 1
		elif 1. < abs_x < 2.:
			return a * torch.pow(abs_x, 3) - 5. * a * torch.pow(abs_x, 2.) + 8. * a * abs_x - 4. * a
		else:
			return 0.0

	def __init__(self, factor=4, cuda=True, padding='reflect'):
		super().__init__()
		self.factor = factor
		size = factor * 4
		k = torch.tensor([self.bicubic_kernel((i - torch.floor(torch.tensor(size / 2)) + 0.5) / factor)
						  for i in range(size)], dtype=torch.float32)
		k = k / torch.sum(k)
		k1 = torch.reshape(k, shape=(1, 1, size, 1))
		self.k1 = torch.cat([k1, k1, k1], dim=0)
		k2 = torch.reshape(k, shape=(1, 1, 1, size))
		self.k2 = torch.cat([k2, k2, k2], dim=0)
		self.cuda = '.cuda' if cuda else ''
		self.padding = padding
		for param in self.parameters():
			param.requires_grad = False

	def forward(self, x, nhwc=False, clip_round=False, byte_output=False):
		filter_height = self.factor * 4
		filter_width = self.factor * 4
		stride = self.factor

		pad_along_height = max(filter_height - stride, 0)
		pad_along_width = max(filter_width - stride, 0)
		filters1 = self.k1.type('torch{}.FloatTensor'.format(self.cuda))
		filters2 = self.k2.type('torch{}.FloatTensor'.format(self.cuda))

		# compute actual padding values for each side
		pad_top = pad_along_height // 2
		pad_bottom = pad_along_height - pad_top
		pad_left = pad_along_width // 2
		pad_right = pad_along_width - pad_left

		# apply mirror padding
		if nhwc:
			x = torch.transpose(torch.transpose(x, 2, 3), 1, 2)   # NHWC to NCHW

		# downscaling performed by 1-d convolution
		x = F.pad(x, (0, 0, pad_top, pad_bottom), self.padding)
		x = F.conv2d(input=x, weight=filters1, stride=(stride, 1), groups=3)
		if clip_round:
			x = torch.clamp(torch.round(x), 0.0, 255.)

		x = F.pad(x, (pad_left, pad_right, 0, 0), self.padding)
		x = F.conv2d(input=x, weight=filters2, stride=(1, stride), groups=3)
		if clip_round:
			x = torch.clamp(torch.round(x), 0.0, 255.)

		if nhwc:
			x = torch.transpose(torch.transpose(x, 1, 3), 1, 2)
		if byte_output:
			return x.type('torch.ByteTensor'.format(self.cuda))
		else:
			return x