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c6dfc69 | 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 | import random
import matplotlib.pyplot as plt
import numpy
import torch
import torchvision.transforms.functional as F
import torchvision.transforms as transforms
class Augmentation(object):
def __init__(self, image_mean, image_std, image_width, image_height, scale_list, ignore_index=255):
self.image_size = (image_height, image_width)
# self.image_norm = (image_mean, image_std)
# self.get_crop_pos = transforms.RandomCrop(self.image_size)
self.color_jitter = transforms.ColorJitter(brightness=.5, contrast=.5, saturation=.5, hue=.25)
self.gaussian_blurring = transforms.GaussianBlur((3, 3))
self.scale_list = scale_list
self.normalise = transforms.Normalize(mean=image_mean, std=image_std)
self.to_tensor = transforms.ToTensor()
self.ignore_index = ignore_index
# self.normalise = transforms.Normalize(mean=image_mean, std=image_std)
# if setup == "avs" or setup == "avss" or setup == "avss_binary":
# # AVS
# self.scale_list = [.5, .75, 1.]
# self.color_jitter = None
# else:
# # COCO
# # self.scale_list = [.75, 1., 1.25, 1.5, 1.75, 2.]
# self.scale_list = [0.5,0.75,1.0,1.25,1.5,1.75,2.0]
# def normalise(self, image):
# image = image / 255.0
# image = image - self.image_norm[0]
# image = image / self.image_norm[1]
# return image
def resize(self, image_, label_, size=None):
h_, w_ = self.image_size if size is None else size
image_ = F.resize(image_, (h_, w_), transforms.InterpolationMode.BICUBIC)
label_ = F.resize(label_, (h_, w_), transforms.InterpolationMode.NEAREST)
return image_, label_
def random_crop_with_padding(self, image_, label_):
w_, h_ = image_.size
if min(h_, w_) < min(self.image_size):
res_w_ = max(self.image_size[0] - w_, 0)
res_h_ = max(self.image_size[1] - h_, 0)
image_ = F.pad(image_, [0, 0, res_w_, res_h_], fill=(numpy.array(self.image_norm[0]) * 255.).tolist())
# image_ = F.pad(image_, [0, 0, res_w_, res_h_], fill=self.ignore_index) # if error, define the padding value.
label_ = F.pad(label_, [0, 0, res_w_, res_h_], fill=self.ignore_index)
pos_ = self.get_crop_pos.get_params(image_, self.image_size)
image_ = F.crop(image_, *pos_)
label_ = F.crop(label_, *pos_)
return image_, label_
# @staticmethod
def random_scales(self, image_, label_):
w_, h_ = image_.size
chosen_scale = random.choice(self.scale_list)
w_, h_ = int(w_ * chosen_scale), int(h_ * chosen_scale)
image_ = F.resize(image_, (h_, w_), transforms.InterpolationMode.BICUBIC)
label_ = F.resize(label_, (h_, w_), transforms.InterpolationMode.NEAREST)
return image_, label_
@staticmethod
def random_flip_h(image_, label_):
chosen_flip = random.random() > 0.5
image_ = F.hflip(image_) if chosen_flip else image_
label_ = F.hflip(label_) if chosen_flip else label_
return image_, label_
def augment_entire_clip(self, x_list, y_list):
degree_ = float(torch.empty(1).uniform_(float(-25.), float(25.)).item())
shear_ = [float(torch.empty(1).uniform_(float(-20.), float(20.)).item()),
torch.empty(1).uniform_(float(-20.), float(20.)).item()]
dice = random.random()
for index, single_x in enumerate(x_list):
if dice <= 0.1:
single_x = F.rgb_to_grayscale(single_x, num_output_channels=3)
single_x = F.affine(single_x, angle=degree_, shear=shear_, translate=[0,0], scale=1.,
interpolation=transforms.InterpolationMode.BILINEAR, fill=[0., 0., 0.])
single_y = F.affine(y_list[index], angle=degree_, shear=shear_, translate=[0,0], scale=1.,
interpolation=transforms.InterpolationMode.NEAREST, fill=[0.])
x_list[index] = single_x
y_list[index] = single_y
return x_list, y_list
def train_aug(self, x_, y_):
x_, y_ = self.random_flip_h(x_, y_)
# # x, y = self.random_scales(x, y)
x_, y_ = self.resize(x_, y_)
if self.color_jitter is not None and random.random() < 0.5:
x_ = self.color_jitter(x_)
if self.gaussian_blurring is not None and random.random() < 0.5:
x_ = self.gaussian_blurring(x_)
# x, y = self.random_crop_with_padding(x, y)
x_ = self.normalise(self.to_tensor(x_)).type(torch.float32)
# receive pseudo labels.
y_ = torch.tensor(numpy.array(y_)[numpy.newaxis, ...], dtype=torch.float)
return x_, y_
def test_process(self, x_, y_):
# x = self.to_tensor(x)
# y = torch.tensor(numpy.asarray(y)).long()
# following AVSbench setup, we fix image size (224, 224)
x_, y_ = self.resize(x_, y_)
x_ = self.normalise(self.to_tensor(x_)).type(torch.float32)
y_ = torch.tensor(numpy.array(y_)[numpy.newaxis, ...], dtype=torch.float)
return x_, y_
def __call__(self, x, y, split):
return self.train_aug(x, y) if split == "train" \
else self.test_process(x, y)
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