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import numpy as np
import random
import torch
import torchvision.transforms.functional as F
from torchvision.transforms import (
Resize, Compose, ToTensor, Normalize, CenterCrop, RandomCrop, ColorJitter,
RandomApply, GaussianBlur, RandomGrayscale, RandomResizedCrop,
RandomHorizontalFlip
)
from torchvision.transforms.functional import InterpolationMode
from .autoaugment import SVHNPolicy, CIFAR10Policy, ImageNetPolicy
from .randaugment import RandAugment, RandAugment2, RandAugmentFixMatch
AVAI_CHOICES = [
"random_flip",
"random_resized_crop",
"normalize",
"instance_norm",
"random_crop",
"random_translation",
"center_crop", # This has become a default operation during testing
"cutout",
"imagenet_policy",
"cifar10_policy",
"svhn_policy",
"randaugment",
"randaugment_fixmatch",
"randaugment2",
"gaussian_noise",
"colorjitter",
"randomgrayscale",
"gaussian_blur",
]
INTERPOLATION_MODES = {
"bilinear": InterpolationMode.BILINEAR,
"bicubic": InterpolationMode.BICUBIC,
"nearest": InterpolationMode.NEAREST,
}
class Random2DTranslation:
"""Given an image of (height, width), we resize it to
(height*1.125, width*1.125), and then perform random cropping.
Args:
height (int): target image height.
width (int): target image width.
p (float, optional): probability that this operation takes place.
Default is 0.5.
interpolation (int, optional): desired interpolation. Default is
``torchvision.transforms.functional.InterpolationMode.BILINEAR``
"""
def __init__(
self, height, width, p=0.5, interpolation=InterpolationMode.BILINEAR
):
self.height = height
self.width = width
self.p = p
self.interpolation = interpolation
def __call__(self, img):
if random.uniform(0, 1) > self.p:
return F.resize(
img=img,
size=[self.height, self.width],
interpolation=self.interpolation
)
new_width = int(round(self.width * 1.125))
new_height = int(round(self.height * 1.125))
resized_img = F.resize(
img=img,
size=[new_height, new_width],
interpolation=self.interpolation
)
x_maxrange = new_width - self.width
y_maxrange = new_height - self.height
x1 = int(round(random.uniform(0, x_maxrange)))
y1 = int(round(random.uniform(0, y_maxrange)))
croped_img = F.crop(
img=resized_img,
top=y1,
left=x1,
height=self.height,
width=self.width
)
return croped_img
class InstanceNormalization:
"""Normalize data using per-channel mean and standard deviation.
Reference:
- Ulyanov et al. Instance normalization: The missing in- gredient
for fast stylization. ArXiv 2016.
- Shu et al. A DIRT-T Approach to Unsupervised Domain Adaptation.
ICLR 2018.
"""
def __init__(self, eps=1e-8):
self.eps = eps
def __call__(self, img):
C, H, W = img.shape
img_re = img.reshape(C, H * W)
mean = img_re.mean(1).view(C, 1, 1)
std = img_re.std(1).view(C, 1, 1)
return (img-mean) / (std + self.eps)
class Cutout:
"""Randomly mask out one or more patches from an image.
https://github.com/uoguelph-mlrg/Cutout
Args:
n_holes (int, optional): number of patches to cut out
of each image. Default is 1.
length (int, optinal): length (in pixels) of each square
patch. Default is 16.
"""
def __init__(self, n_holes=1, length=16):
self.n_holes = n_holes
self.length = length
def __call__(self, img):
"""
Args:
img (Tensor): tensor image of size (C, H, W).
Returns:
Tensor: image with n_holes of dimension
length x length cut out of it.
"""
h = img.size(1)
w = img.size(2)
mask = np.ones((h, w), np.float32)
for n in range(self.n_holes):
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] = 0.0
mask = torch.from_numpy(mask)
mask = mask.expand_as(img)
return img * mask
class GaussianNoise:
"""Add gaussian noise."""
def __init__(self, mean=0, std=0.15, p=0.5):
self.mean = mean
self.std = std
self.p = p
def __call__(self, img):
if random.uniform(0, 1) > self.p:
return img
noise = torch.randn(img.size()) * self.std + self.mean
return img + noise
def build_transform(cfg, is_train=True, choices=None):
"""Build transformation function.
Args:
cfg (CfgNode): config.
is_train (bool, optional): for training (True) or test (False).
Default is True.
choices (list, optional): list of strings which will overwrite
cfg.INPUT.TRANSFORMS if given. Default is None.
"""
if cfg.INPUT.NO_TRANSFORM:
print("Note: no transform is applied!")
return None
if choices is None:
choices = cfg.INPUT.TRANSFORMS
for choice in choices:
assert choice in AVAI_CHOICES
target_size = f"{cfg.INPUT.SIZE[0]}x{cfg.INPUT.SIZE[1]}"
normalize = Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD)
if is_train:
return _build_transform_train(cfg, choices, target_size, normalize)
else:
return _build_transform_test(cfg, choices, target_size, normalize)
def _build_transform_train(cfg, choices, target_size, normalize):
print("Building transform_train")
tfm_train = []
interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION]
input_size = cfg.INPUT.SIZE
# Make sure the image size matches the target size
conditions = []
conditions += ["random_crop" not in choices]
conditions += ["random_resized_crop" not in choices]
if all(conditions):
print(f"+ resize to {target_size}")
tfm_train += [Resize(input_size, interpolation=interp_mode)]
if "random_translation" in choices:
print("+ random translation")
tfm_train += [Random2DTranslation(input_size[0], input_size[1])]
if "random_crop" in choices:
crop_padding = cfg.INPUT.CROP_PADDING
print(f"+ random crop (padding = {crop_padding})")
tfm_train += [RandomCrop(input_size, padding=crop_padding)]
if "random_resized_crop" in choices:
s_ = cfg.INPUT.RRCROP_SCALE
print(f"+ random resized crop (size={input_size}, scale={s_})")
tfm_train += [
RandomResizedCrop(input_size, scale=s_, interpolation=interp_mode)
]
if "random_flip" in choices:
print("+ random flip")
tfm_train += [RandomHorizontalFlip()]
if "imagenet_policy" in choices:
print("+ imagenet policy")
tfm_train += [ImageNetPolicy()]
if "cifar10_policy" in choices:
print("+ cifar10 policy")
tfm_train += [CIFAR10Policy()]
if "svhn_policy" in choices:
print("+ svhn policy")
tfm_train += [SVHNPolicy()]
if "randaugment" in choices:
n_ = cfg.INPUT.RANDAUGMENT_N
m_ = cfg.INPUT.RANDAUGMENT_M
print(f"+ randaugment (n={n_}, m={m_})")
tfm_train += [RandAugment(n_, m_)]
if "randaugment_fixmatch" in choices:
n_ = cfg.INPUT.RANDAUGMENT_N
print(f"+ randaugment_fixmatch (n={n_})")
tfm_train += [RandAugmentFixMatch(n_)]
if "randaugment2" in choices:
n_ = cfg.INPUT.RANDAUGMENT_N
print(f"+ randaugment2 (n={n_})")
tfm_train += [RandAugment2(n_)]
if "colorjitter" in choices:
b_ = cfg.INPUT.COLORJITTER_B
c_ = cfg.INPUT.COLORJITTER_C
s_ = cfg.INPUT.COLORJITTER_S
h_ = cfg.INPUT.COLORJITTER_H
print(
f"+ color jitter (brightness={b_}, "
f"contrast={c_}, saturation={s_}, hue={h_})"
)
tfm_train += [
ColorJitter(
brightness=b_,
contrast=c_,
saturation=s_,
hue=h_,
)
]
if "randomgrayscale" in choices:
print("+ random gray scale")
tfm_train += [RandomGrayscale(p=cfg.INPUT.RGS_P)]
if "gaussian_blur" in choices:
print(f"+ gaussian blur (kernel={cfg.INPUT.GB_K})")
gb_k, gb_p = cfg.INPUT.GB_K, cfg.INPUT.GB_P
tfm_train += [RandomApply([GaussianBlur(gb_k)], p=gb_p)]
print("+ to torch tensor of range [0, 1]")
tfm_train += [ToTensor()]
if "cutout" in choices:
cutout_n = cfg.INPUT.CUTOUT_N
cutout_len = cfg.INPUT.CUTOUT_LEN
print(f"+ cutout (n_holes={cutout_n}, length={cutout_len})")
tfm_train += [Cutout(cutout_n, cutout_len)]
if "normalize" in choices:
print(
f"+ normalization (mean={cfg.INPUT.PIXEL_MEAN}, std={cfg.INPUT.PIXEL_STD})"
)
tfm_train += [normalize]
if "gaussian_noise" in choices:
print(
f"+ gaussian noise (mean={cfg.INPUT.GN_MEAN}, std={cfg.INPUT.GN_STD})"
)
tfm_train += [GaussianNoise(cfg.INPUT.GN_MEAN, cfg.INPUT.GN_STD)]
if "instance_norm" in choices:
print("+ instance normalization")
tfm_train += [InstanceNormalization()]
tfm_train = Compose(tfm_train)
return tfm_train
def _build_transform_test(cfg, choices, target_size, normalize):
print("Building transform_test")
tfm_test = []
interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION]
input_size = cfg.INPUT.SIZE
print(f"+ resize the smaller edge to {max(input_size)}")
tfm_test += [Resize(max(input_size), interpolation=interp_mode)]
print(f"+ {target_size} center crop")
tfm_test += [CenterCrop(input_size)]
print("+ to torch tensor of range [0, 1]")
tfm_test += [ToTensor()]
if "normalize" in choices:
print(
f"+ normalization (mean={cfg.INPUT.PIXEL_MEAN}, std={cfg.INPUT.PIXEL_STD})"
)
tfm_test += [normalize]
if "instance_norm" in choices:
print("+ instance normalization")
tfm_test += [InstanceNormalization()]
tfm_test = Compose(tfm_test)
return tfm_test
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