Spaces:
Sleeping
Sleeping
File size: 7,046 Bytes
087df0e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 |
# Copyright (c) 2023, NVIDIA Corporation & Affiliates. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, visit
# https://github.com/NVlabs/prismer/blob/main/LICENSE
import random
import numpy as np
import torch
from PIL import Image, ImageOps, ImageEnhance, ImageDraw
fillmask = {'depth': 0, 'normal': 0, 'edge': 0, 'seg_coco': 255, 'seg_ade': 255,
'obj_detection': 255, 'ocr_detection': 255}
fillcolor = (0, 0, 0)
def affine_transform(pair, affine_params):
img, label = pair
img = img.transform(img.size, Image.AFFINE, affine_params,
resample=Image.BILINEAR, fillcolor=fillcolor)
if label is not None:
for exp in label:
label[exp] = label[exp].transform(label[exp].size, Image.AFFINE, affine_params,
resample=Image.NEAREST, fillcolor=fillmask[exp])
return img, label
def ShearX(pair, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
if random.random() > 0.5:
v = -v
return affine_transform(pair, (1, v, 0, 0, 1, 0))
def ShearY(pair, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
if random.random() > 0.5:
v = -v
return affine_transform(pair, (1, 0, 0, v, 1, 0))
def TranslateX(pair, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert -0.45 <= v <= 0.45
if random.random() > 0.5:
v = -v
img, _ = pair
v = v * img.size[0]
return affine_transform(pair, (1, 0, v, 0, 1, 0))
def TranslateY(pair, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert -0.45 <= v <= 0.45
if random.random() > 0.5:
v = -v
img, _ = pair
v = v * img.size[1]
return affine_transform(pair, (1, 0, 0, 0, 1, v))
def TranslateXAbs(pair, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert 0 <= v <= 10
if random.random() > 0.5:
v = -v
return affine_transform(pair, (1, 0, v, 0, 1, 0))
def TranslateYAbs(pair, v): # [-150, 150] => percentage: [-0.45, 0.45]
assert 0 <= v <= 10
if random.random() > 0.5:
v = -v
return affine_transform(pair, (1, 0, 0, 0, 1, v))
def Rotate(pair, v): # [-30, 30]
assert -30 <= v <= 30
if random.random() > 0.5:
v = -v
img, label = pair
img = img.rotate(v, fillcolor=fillcolor)
if label is not None:
for exp in label:
label[exp] = label[exp].rotate(v, resample=Image.NEAREST, fillcolor=fillmask[exp])
return img, label
def AutoContrast(pair, _):
img, label = pair
return ImageOps.autocontrast(img), label
def Invert(pair, _):
img, label = pair
return ImageOps.invert(img), label
def Equalize(pair, _):
img, label = pair
return ImageOps.equalize(img), label
def Flip(pair, _): # not from the paper
img, label = pair
return ImageOps.mirror(img), ImageOps.mirror(label)
def Solarize(pair, v): # [0, 256]
img, label = pair
assert 0 <= v <= 256
return ImageOps.solarize(img, v), label
def Posterize(pair, v): # [4, 8]
img, label = pair
assert 4 <= v <= 8
v = int(v)
return ImageOps.posterize(img, v), label
def Posterize2(pair, v): # [0, 4]
img, label = pair
assert 0 <= v <= 4
v = int(v)
return ImageOps.posterize(img, v), label
def Contrast(pair, v): # [0.1,1.9]
img, label = pair
assert 0.1 <= v <= 1.9
return ImageEnhance.Contrast(img).enhance(v), label
def Color(pair, v): # [0.1,1.9]
img, label = pair
assert 0.1 <= v <= 1.9
return ImageEnhance.Color(img).enhance(v), label
def Brightness(pair, v): # [0.1,1.9]
img, label = pair
assert 0.1 <= v <= 1.9
return ImageEnhance.Brightness(img).enhance(v), label
def Sharpness(pair, v): # [0.1,1.9]
img, label = pair
assert 0.1 <= v <= 1.9
return ImageEnhance.Sharpness(img).enhance(v), label
def Cutout(pair, v): # [0, 60] => percentage: [0, 0.2]
assert 0.0 <= v <= 0.2
if v <= 0.:
return pair
img, label = pair
v = v * img.size[0]
return CutoutAbs(img, v), label
def CutoutAbs(img, v): # [0, 60] => percentage: [0, 0.2]
# assert 0 <= v <= 20
if v < 0:
return img
w, h = img.size
x0 = np.random.uniform(w)
y0 = np.random.uniform(h)
x0 = int(max(0, x0 - v / 2.))
y0 = int(max(0, y0 - v / 2.))
x1 = min(w, x0 + v)
y1 = min(h, y0 + v)
xy = (x0, y0, x1, y1)
color = (125, 123, 114)
# color = (0, 0, 0)
img = img.copy()
ImageDraw.Draw(img).rectangle(xy, color)
return img
def Identity(pair, v):
return pair
def augment_list(): # 16 oeprations and their ranges
# https://github.com/google-research/uda/blob/master/image/randaugment/policies.py#L57
l = [
(Identity, 0., 1.0),
(ShearX, 0., 0.3), # 0
(ShearY, 0., 0.3), # 1
(TranslateX, 0., 0.33), # 2
(TranslateY, 0., 0.33), # 3
(Rotate, 0, 30), # 4
(AutoContrast, 0, 1), # 5
# (Invert, 0, 1), # 6
(Equalize, 0, 1), # 7
# (Solarize, 0, 110), # 8
# (Posterize, 4, 8), # 9
# (Color, 0.1, 1.9), # 11
(Brightness, 0.1, 1.9), # 12
(Sharpness, 0.1, 1.9), # 13
]
return l
class Lighting(object):
"""Lighting noise(AlexNet - style PCA - based noise)"""
def __init__(self, alphastd, eigval, eigvec):
self.alphastd = alphastd
self.eigval = torch.Tensor(eigval)
self.eigvec = torch.Tensor(eigvec)
def __call__(self, img):
if self.alphastd == 0:
return img
alpha = img.new().resize_(3).normal_(0, self.alphastd)
rgb = self.eigvec.type_as(img).clone() \
.mul(alpha.view(1, 3).expand(3, 3)) \
.mul(self.eigval.view(1, 3).expand(3, 3)) \
.sum(1).squeeze()
return img.add(rgb.view(3, 1, 1).expand_as(img))
class CutoutDefault(object):
"""
Reference : https://github.com/quark0/darts/blob/master/cnn/utils.py
"""
def __init__(self, length):
self.length = length
def __call__(self, img):
h, w = img.size(1), img.size(2)
mask = np.ones((h, w), np.float32)
y = np.random.randint(h)
x = np.random.randint(w)
y1 = np.clip(y - self.length // 2, 0, h)
y2 = np.clip(y + self.length // 2, 0, h)
x1 = np.clip(x - self.length // 2, 0, w)
x2 = np.clip(x + self.length // 2, 0, w)
mask[y1: y2, x1: x2] = 0.
mask = torch.from_numpy(mask)
mask = mask.expand_as(img)
img *= mask
return img
class RandAugment:
def __init__(self, n, m):
self.n = n
self.m = m # [0, 10]
self.augment_list = augment_list()
def __call__(self, img, label):
pair = img, label
ops = random.choices(self.augment_list, k=self.n)
for op, minval, maxval in ops:
val = (float(self.m) / 10) * float(maxval - minval) + minval
pair = op(pair, val)
return pair
|