|
|
|
import numpy as np |
|
import torch |
|
import torch.nn as nn |
|
from PIL import Image |
|
|
|
|
|
class Grid(object): |
|
|
|
def __init__(self, |
|
use_h, |
|
use_w, |
|
rotate=1, |
|
offset=False, |
|
ratio=0.5, |
|
mode=0, |
|
prob=1., |
|
length=1): |
|
self.use_h = use_h |
|
self.use_w = use_w |
|
self.rotate = rotate |
|
self.offset = offset |
|
self.ratio = ratio |
|
self.mode = mode |
|
self.st_prob = prob |
|
self.prob = prob |
|
self.length = length |
|
|
|
def set_prob(self, epoch, max_epoch): |
|
self.prob = self.st_prob * epoch / max_epoch |
|
|
|
def __call__(self, img, label): |
|
if np.random.rand() > self.prob: |
|
return img, label |
|
h = img.size(1) |
|
w = img.size(2) |
|
self.d1 = 2 |
|
self.d2 = min(h, w) |
|
hh = int(1.5 * h) |
|
ww = int(1.5 * w) |
|
d = np.random.randint(self.d1, self.d2) |
|
if self.ratio == 1: |
|
self.length = np.random.randint(1, d) |
|
else: |
|
self.length = min(max(int(d * self.ratio + 0.5), 1), d - 1) |
|
mask = np.ones((hh, ww), np.float32) |
|
st_h = np.random.randint(d) |
|
st_w = np.random.randint(d) |
|
if self.use_h: |
|
for i in range(hh // d): |
|
s = d * i + st_h |
|
t = min(s + self.length, hh) |
|
mask[s:t, :] *= 0 |
|
if self.use_w: |
|
for i in range(ww // d): |
|
s = d * i + st_w |
|
t = min(s + self.length, ww) |
|
mask[:, s:t] *= 0 |
|
|
|
r = np.random.randint(self.rotate) |
|
mask = Image.fromarray(np.uint8(mask)) |
|
mask = mask.rotate(r) |
|
mask = np.asarray(mask) |
|
mask = mask[(hh - h) // 2:(hh - h) // 2 + h, |
|
(ww - w) // 2:(ww - w) // 2 + w] |
|
|
|
mask = torch.from_numpy(mask).float() |
|
if self.mode == 1: |
|
mask = 1 - mask |
|
|
|
mask = mask.expand_as(img) |
|
if self.offset: |
|
offset = torch.from_numpy(2 * (np.random.rand(h, w) - 0.5)).float() |
|
offset = (1 - mask) * offset |
|
img = img * mask + offset |
|
else: |
|
img = img * mask |
|
|
|
return img, label |
|
|
|
|
|
class GridMask(nn.Module): |
|
|
|
def __init__(self, |
|
use_h, |
|
use_w, |
|
rotate=1, |
|
offset=False, |
|
ratio=0.5, |
|
mode=0, |
|
prob=1.): |
|
super(GridMask, self).__init__() |
|
self.use_h = use_h |
|
self.use_w = use_w |
|
self.rotate = rotate |
|
self.offset = offset |
|
self.ratio = ratio |
|
self.mode = mode |
|
self.st_prob = prob |
|
self.prob = prob |
|
|
|
def set_prob(self, epoch, max_epoch): |
|
self.prob = self.st_prob * epoch / max_epoch |
|
|
|
def forward(self, x): |
|
if np.random.rand() > self.prob or not self.training: |
|
return x |
|
n, c, h, w = x.size() |
|
x = x.view(-1, h, w) |
|
hh = int(1.5 * h) |
|
ww = int(1.5 * w) |
|
d = np.random.randint(2, h) |
|
self.length = min(max(int(d * self.ratio + 0.5), 1), d - 1) |
|
mask = np.ones((hh, ww), np.float32) |
|
st_h = np.random.randint(d) |
|
st_w = np.random.randint(d) |
|
if self.use_h: |
|
for i in range(hh // d): |
|
s = d * i + st_h |
|
t = min(s + self.length, hh) |
|
mask[s:t, :] *= 0 |
|
if self.use_w: |
|
for i in range(ww // d): |
|
s = d * i + st_w |
|
t = min(s + self.length, ww) |
|
mask[:, s:t] *= 0 |
|
|
|
r = np.random.randint(self.rotate) |
|
mask = Image.fromarray(np.uint8(mask)) |
|
mask = mask.rotate(r) |
|
mask = np.asarray(mask) |
|
mask = mask[(hh - h) // 2:(hh - h) // 2 + h, |
|
(ww - w) // 2:(ww - w) // 2 + w] |
|
|
|
mask = torch.from_numpy(mask).float().cuda() |
|
if self.mode == 1: |
|
mask = 1 - mask |
|
mask = mask.expand_as(x) |
|
if self.offset: |
|
offset = torch.from_numpy( |
|
2 * (np.random.rand(h, w) - 0.5)).float().cuda() |
|
x = x * mask + offset * (1 - mask) |
|
else: |
|
x = x * mask |
|
|
|
return x.view(n, c, h, w) |
|
|
