File size: 9,364 Bytes
7f43945 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 |
"""
Created on 2020/9/8
@author: Boyun Li
"""
import os
import numpy as np
import torch
import random
import torch.nn as nn
from torch.nn import init
from PIL import Image
class EdgeComputation(nn.Module):
def __init__(self, test=False):
super(EdgeComputation, self).__init__()
self.test = test
def forward(self, x):
if self.test:
x_diffx = torch.abs(x[:, :, :, 1:] - x[:, :, :, :-1])
x_diffy = torch.abs(x[:, :, 1:, :] - x[:, :, :-1, :])
# y = torch.Tensor(x.size()).cuda()
y = torch.Tensor(x.size())
y.fill_(0)
y[:, :, :, 1:] += x_diffx
y[:, :, :, :-1] += x_diffx
y[:, :, 1:, :] += x_diffy
y[:, :, :-1, :] += x_diffy
y = torch.sum(y, 1, keepdim=True) / 3
y /= 4
return y
else:
x_diffx = torch.abs(x[:, :, 1:] - x[:, :, :-1])
x_diffy = torch.abs(x[:, 1:, :] - x[:, :-1, :])
y = torch.Tensor(x.size())
y.fill_(0)
y[:, :, 1:] += x_diffx
y[:, :, :-1] += x_diffx
y[:, 1:, :] += x_diffy
y[:, :-1, :] += x_diffy
y = torch.sum(y, 0) / 3
y /= 4
return y.unsqueeze(0)
# randomly crop a patch from image
def crop_patch(im, pch_size):
H = im.shape[0]
W = im.shape[1]
ind_H = random.randint(0, H - pch_size)
ind_W = random.randint(0, W - pch_size)
pch = im[ind_H:ind_H + pch_size, ind_W:ind_W + pch_size]
return pch
# crop an image to the multiple of base
def crop_img(image, base=64):
h = image.shape[0]
w = image.shape[1]
crop_h = h % base
crop_w = w % base
return image[crop_h // 2:h - crop_h + crop_h // 2, crop_w // 2:w - crop_w + crop_w // 2, :]
# image (H, W, C) -> patches (B, H, W, C)
def slice_image2patches(image, patch_size=64, overlap=0):
assert image.shape[0] % patch_size == 0 and image.shape[1] % patch_size == 0
H = image.shape[0]
W = image.shape[1]
patches = []
image_padding = np.pad(image, ((overlap, overlap), (overlap, overlap), (0, 0)), mode='edge')
for h in range(H // patch_size):
for w in range(W // patch_size):
idx_h = [h * patch_size, (h + 1) * patch_size + overlap]
idx_w = [w * patch_size, (w + 1) * patch_size + overlap]
patches.append(np.expand_dims(image_padding[idx_h[0]:idx_h[1], idx_w[0]:idx_w[1], :], axis=0))
return np.concatenate(patches, axis=0)
# patches (B, H, W, C) -> image (H, W, C)
def splice_patches2image(patches, image_size, overlap=0):
assert len(image_size) > 1
assert patches.shape[-3] == patches.shape[-2]
H = image_size[0]
W = image_size[1]
patch_size = patches.shape[-2] - overlap
image = np.zeros(image_size)
idx = 0
for h in range(H // patch_size):
for w in range(W // patch_size):
image[h * patch_size:(h + 1) * patch_size, w * patch_size:(w + 1) * patch_size, :] = patches[idx,
overlap:patch_size + overlap,
overlap:patch_size + overlap,
:]
idx += 1
return image
# def data_augmentation(image, mode):
# if mode == 0:
# # original
# out = image.numpy()
# elif mode == 1:
# # flip up and down
# out = np.flipud(image)
# elif mode == 2:
# # rotate counterwise 90 degree
# out = np.rot90(image, axes=(1, 2))
# elif mode == 3:
# # rotate 90 degree and flip up and down
# out = np.rot90(image, axes=(1, 2))
# out = np.flipud(out)
# elif mode == 4:
# # rotate 180 degree
# out = np.rot90(image, k=2, axes=(1, 2))
# elif mode == 5:
# # rotate 180 degree and flip
# out = np.rot90(image, k=2, axes=(1, 2))
# out = np.flipud(out)
# elif mode == 6:
# # rotate 270 degree
# out = np.rot90(image, k=3, axes=(1, 2))
# elif mode == 7:
# # rotate 270 degree and flip
# out = np.rot90(image, k=3, axes=(1, 2))
# out = np.flipud(out)
# else:
# raise Exception('Invalid choice of image transformation')
# return out
def data_augmentation(image, mode):
if mode == 0:
# original
out = image.numpy()
elif mode == 1:
# flip up and down
out = np.flipud(image)
elif mode == 2:
# rotate counterwise 90 degree
out = np.rot90(image)
elif mode == 3:
# rotate 90 degree and flip up and down
out = np.rot90(image)
out = np.flipud(out)
elif mode == 4:
# rotate 180 degree
out = np.rot90(image, k=2)
elif mode == 5:
# rotate 180 degree and flip
out = np.rot90(image, k=2)
out = np.flipud(out)
elif mode == 6:
# rotate 270 degree
out = np.rot90(image, k=3)
elif mode == 7:
# rotate 270 degree and flip
out = np.rot90(image, k=3)
out = np.flipud(out)
else:
raise Exception('Invalid choice of image transformation')
return out
# def random_augmentation(*args):
# out = []
# if random.randint(0, 1) == 1:
# flag_aug = random.randint(1, 7)
# for data in args:
# out.append(data_augmentation(data, flag_aug).copy())
# else:
# for data in args:
# out.append(data)
# return out
def random_augmentation(*args):
out = []
flag_aug = random.randint(1, 7)
for data in args:
out.append(data_augmentation(data, flag_aug).copy())
return out
def weights_init_normal_(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.uniform(m.weight.data, 0.0, 0.02)
elif classname.find('Linear') != -1:
init.uniform(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm2d') != -1:
init.uniform(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
m.apply(weights_init_normal_)
elif classname.find('Linear') != -1:
init.uniform(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm2d') != -1:
init.uniform(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def weights_init_xavier(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_normal(m.weight.data, gain=1)
elif classname.find('Linear') != -1:
init.xavier_normal(m.weight.data, gain=1)
elif classname.find('BatchNorm2d') != -1:
init.uniform(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def weights_init_kaiming(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
elif classname.find('Linear') != -1:
init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
elif classname.find('BatchNorm2d') != -1:
init.uniform(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def weights_init_orthogonal(m):
classname = m.__class__.__name__
print(classname)
if classname.find('Conv') != -1:
init.orthogonal(m.weight.data, gain=1)
elif classname.find('Linear') != -1:
init.orthogonal(m.weight.data, gain=1)
elif classname.find('BatchNorm2d') != -1:
init.uniform(m.weight.data, 1.0, 0.02)
init.constant(m.bias.data, 0.0)
def init_weights(net, init_type='normal'):
print('initialization method [%s]' % init_type)
if init_type == 'normal':
net.apply(weights_init_normal)
elif init_type == 'xavier':
net.apply(weights_init_xavier)
elif init_type == 'kaiming':
net.apply(weights_init_kaiming)
elif init_type == 'orthogonal':
net.apply(weights_init_orthogonal)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
def np_to_torch(img_np):
"""
Converts image in numpy.array to torch.Tensor.
From C x W x H [0..1] to C x W x H [0..1]
:param img_np:
:return:
"""
return torch.from_numpy(img_np)[None, :]
def torch_to_np(img_var):
"""
Converts an image in torch.Tensor format to np.array.
From 1 x C x W x H [0..1] to C x W x H [0..1]
:param img_var:
:return:
"""
return img_var.detach().cpu().numpy()
# return img_var.detach().cpu().numpy()[0]
def save_image(name, image_np, output_path="output/normal/"):
if not os.path.exists(output_path):
os.mkdir(output_path)
p = np_to_pil(image_np)
p.save(output_path + "{}.png".format(name))
def np_to_pil(img_np):
"""
Converts image in np.array format to PIL image.
From C x W x H [0..1] to W x H x C [0...255]
:param img_np:
:return:
"""
ar = np.clip(img_np * 255, 0, 255).astype(np.uint8)
if img_np.shape[0] == 1:
ar = ar[0]
else:
assert img_np.shape[0] == 3, img_np.shape
ar = ar.transpose(1, 2, 0)
return Image.fromarray(ar) |