/
TransformLayer.py
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TransformLayer.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
import numbers
import random
import time
def rgb2hsv(rgb, eps=1e-8):
# Reference: https://www.rapidtables.com/convert/color/rgb-to-hsv.html
# Reference: https://github.com/scikit-image/scikit-image/blob/master/skimage/color/colorconv.py#L287
_device = rgb.device
r, g, b = rgb[:, 0, :, :], rgb[:, 1, :, :], rgb[:, 2, :, :]
Cmax = rgb.max(1)[0]
Cmin = rgb.min(1)[0]
delta = Cmax - Cmin
hue = torch.zeros((rgb.shape[0], rgb.shape[2], rgb.shape[3])).to(_device)
hue[Cmax== r] = (((g - b)/(delta + eps)) % 6)[Cmax == r]
hue[Cmax == g] = ((b - r)/(delta + eps) + 2)[Cmax == g]
hue[Cmax == b] = ((r - g)/(delta + eps) + 4)[Cmax == b]
hue[Cmax == 0] = 0.0
hue = hue / 6. # making hue range as [0, 1.0)
hue = hue.unsqueeze(dim=1)
saturation = (delta) / (Cmax + eps)
saturation[Cmax == 0.] = 0.
saturation = saturation.to(_device)
saturation = saturation.unsqueeze(dim=1)
value = Cmax
value = value.to(_device)
value = value.unsqueeze(dim=1)
return torch.cat((hue, saturation, value), dim=1)#.type(torch.FloatTensor).to(_device)
# return hue, saturation, value
def hsv2rgb(hsv):
# Reference: https://www.rapidtables.com/convert/color/hsv-to-rgb.html
# Reference: https://github.com/scikit-image/scikit-image/blob/master/skimage/color/colorconv.py#L287
_device = hsv.device
hsv = torch.clamp(hsv, 0, 1)
hue = hsv[:, 0, :, :] * 360.
saturation = hsv[:, 1, :, :]
value = hsv[:, 2, :, :]
c = value * saturation
x = - c * (torch.abs((hue / 60.) % 2 - 1) - 1)
m = (value - c).unsqueeze(dim=1)
rgb_prime = torch.zeros_like(hsv).to(_device)
inds = (hue < 60) * (hue >= 0)
rgb_prime[:, 0, :, :][inds] = c[inds]
rgb_prime[:, 1, :, :][inds] = x[inds]
inds = (hue < 120) * (hue >= 60)
rgb_prime[:, 0, :, :][inds] = x[inds]
rgb_prime[:, 1, :, :][inds] = c[inds]
inds = (hue < 180) * (hue >= 120)
rgb_prime[:, 1, :, :][inds] = c[inds]
rgb_prime[:, 2, :, :][inds] = x[inds]
inds = (hue < 240) * (hue >= 180)
rgb_prime[:, 1, :, :][inds] = x[inds]
rgb_prime[:, 2, :, :][inds] = c[inds]
inds = (hue < 300) * (hue >= 240)
rgb_prime[:, 2, :, :][inds] = c[inds]
rgb_prime[:, 0, :, :][inds] = x[inds]
inds = (hue < 360) * (hue >= 300)
rgb_prime[:, 2, :, :][inds] = x[inds]
rgb_prime[:, 0, :, :][inds] = c[inds]
rgb = rgb_prime + torch.cat((m, m, m), dim=1)
rgb = rgb.to(_device)
return torch.clamp(rgb, 0, 1)
class ColorJitterLayer(nn.Module):
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0, p=0, batch_size=128, stack_size=3):
super(ColorJitterLayer, self).__init__()
self.brightness = self._check_input(brightness, 'brightness')
self.contrast = self._check_input(contrast, 'contrast')
self.saturation = self._check_input(saturation, 'saturation')
self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5),
clip_first_on_zero=False)
self.prob = p
self.batch_size = batch_size
self.stack_size = stack_size
def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
if isinstance(value, numbers.Number):
if value < 0:
raise ValueError("If {} is a single number, it must be non negative.".format(name))
value = [center - value, center + value]
if clip_first_on_zero:
value[0] = max(value[0], 0)
elif isinstance(value, (tuple, list)) and len(value) == 2:
if not bound[0] <= value[0] <= value[1] <= bound[1]:
raise ValueError("{} values should be between {}".format(name, bound))
else:
raise TypeError("{} should be a single number or a list/tuple with lenght 2.".format(name))
# if value is 0 or (1., 1.) for brightness/contrast/saturation
# or (0., 0.) for hue, do nothing
if value[0] == value[1] == center:
value = None
return value
def adjust_contrast(self, x):
"""
Args:
x: torch tensor img (rgb type)
Factor: torch tensor with same length as x
0 gives gray solid image, 1 gives original image,
Returns:
torch tensor image: Brightness adjusted
"""
_device = x.device
factor = torch.empty(self.batch_size, device=_device).uniform_(*self.contrast)
factor = factor.reshape(-1,1).repeat(1, self.stack_size).reshape(-1)
means = torch.mean(x, dim=(2, 3), keepdim=True)
return torch.clamp((x - means)
* factor.view(len(x), 1, 1, 1) + means, 0, 1)
def adjust_hue(self, x):
_device = x.device
factor = torch.empty(self.batch_size, device=_device).uniform_(*self.hue)
factor = factor.reshape(-1,1).repeat(1, self.stack_size).reshape(-1)
h = x[:, 0, :, :]
h += (factor.view(len(x), 1, 1) * 255. / 360.)
h = (h % 1)
x[:, 0, :, :] = h
return x
def adjust_brightness(self, x):
"""
Args:
x: torch tensor img (hsv type)
Factor:
torch tensor with same length as x
0 gives black image, 1 gives original image,
2 gives the brightness factor of 2.
Returns:
torch tensor image: Brightness adjusted
"""
_device = x.device
factor = torch.empty(self.batch_size, device=_device).uniform_(*self.brightness)
factor = factor.reshape(-1,1).repeat(1, self.stack_size).reshape(-1)
x[:, 2, :, :] = torch.clamp(x[:, 2, :, :]
* factor.view(len(x), 1, 1), 0, 1)
return torch.clamp(x, 0, 1)
def adjust_saturate(self, x):
"""
Args:
x: torch tensor img (hsv type)
Factor:
torch tensor with same length as x
0 gives black image and white, 1 gives original image,
2 gives the brightness factor of 2.
Returns:
torch tensor image: Brightness adjusted
"""
_device = x.device
factor = torch.empty(self.batch_size, device=_device).uniform_(*self.saturation)
factor = factor.reshape(-1,1).repeat(1, self.stack_size).reshape(-1)
x[:, 1, :, :] = torch.clamp(x[:, 1, :, :]
* factor.view(len(x), 1, 1), 0, 1)
return torch.clamp(x, 0, 1)
def transform(self, inputs):
hsv_transform_list = [rgb2hsv, self.adjust_brightness,
self.adjust_hue, self.adjust_saturate,
hsv2rgb]
rgb_transform_list = [self.adjust_contrast]
# Shuffle transform
if random.uniform(0,1) >= 0.5:
transform_list = rgb_transform_list + hsv_transform_list
else:
transform_list = hsv_transform_list + rgb_transform_list
for t in transform_list:
inputs = t(inputs)
return inputs
def forward(self, inputs):
_device = inputs.device
random_inds = np.random.choice(
[True, False], len(inputs), p=[self.prob, 1 - self.prob])
inds = torch.tensor(random_inds).to(_device)
if random_inds.sum() > 0:
inputs[inds] = self.transform(inputs[inds])
return inputs