-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
6 changed files
with
156 additions
and
610 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,156 @@ | ||
import random | ||
|
||
import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as F | ||
|
||
from torchvision.transforms.functional import rotate | ||
|
||
from torchvision.transforms import InterpolationMode | ||
import cv2 | ||
from albumentations import PadIfNeeded,HorizontalFlip,Crop,CenterCrop,Compose,Resize,RandomCrop,VerticalFlip,OneOf | ||
import numpy as np | ||
from skimage.measure import label as label_fn | ||
|
||
def test_time_augmentation(data,i): | ||
if i == 0: | ||
x = data | ||
elif i == 1: | ||
x = torch.flip(data.clone(),dims=(-1,)) | ||
elif i == 2: | ||
x = torch.flip(data.clone(),dims=(-2,)) | ||
elif i == 3: | ||
x = torch.flip(data.clone(),dims=(-2,-1)) | ||
|
||
return x | ||
|
||
class TorchRandomRotate(nn.Module): | ||
def __init__(self, degrees, probability=1.0,interpolation=InterpolationMode.BILINEAR, center=None, fill=0,mask_fill=0): | ||
super().__init__() | ||
if not isinstance(degrees,(list,tuple)): | ||
degrees = (-abs(degrees),abs(degrees)) | ||
|
||
self.degrees = degrees | ||
self.interpolation = interpolation | ||
self.center = center | ||
self.fill_value = fill | ||
self.mask_fill_value = mask_fill | ||
self.proba = probability | ||
|
||
@staticmethod | ||
def get_params(degrees) -> float: | ||
"""Get parameters for ``rotate`` for a random rotation. | ||
Returns: | ||
float: angle parameter to be passed to ``rotate`` for random rotation. | ||
""" | ||
angle = float(torch.empty(1).uniform_(float(degrees[0]), float(degrees[1])).item()) | ||
return angle | ||
|
||
@torch.no_grad() | ||
def __call__(self,img,mask=None): | ||
|
||
# batch_size = img.shape[0] | ||
batch_size=img.width | ||
print(batch_size) | ||
for i in range(batch_size): | ||
|
||
if random.random() > self.proba: | ||
continue | ||
|
||
angle = self.get_params(self.degrees) | ||
img[i,...] = rotate(img[i,...], angle, self.interpolation, False, self.center, self.fill_value) | ||
#mask = mask.long() | ||
if mask is not None: | ||
mask[i,...] = rotate(mask[i,...], angle, self.interpolation, False, self.center, self.mask_fill_value) | ||
mask = mask.float() | ||
if mask is not None: | ||
mask[mask<0] = self.mask_fill_value | ||
return img,mask | ||
return | ||
|
||
|
||
class RandomMaskIgnore(nn.Module): | ||
|
||
def __init__(self,min_length=50,max_length=10,proba=0.5,ignore_index=-10): | ||
super().__init__() | ||
|
||
self.min_length = min_length | ||
self.max_length = max_length | ||
self.proba = proba | ||
self.ignore_index = ignore_index | ||
|
||
|
||
def generate_random_bbox(self,shape): | ||
H,W = shape | ||
L = random.randint(self.min_length,self.max_length) | ||
|
||
t = random.randint(0,H-L) | ||
b = t + L | ||
|
||
l = random.randint(0,W-L) | ||
r = l + L | ||
|
||
return (t,l,b,r) | ||
|
||
def mask_channel(self,bbox,channel): | ||
(t,l,b,r) = bbox | ||
channel[:,t:b,l:r] = self.ignore_index | ||
return channel | ||
|
||
@torch.no_grad() | ||
def __call__(self,mask): | ||
|
||
B,C,H,W = mask.shape | ||
for i in range(B): | ||
if random.random() > self.proba: | ||
continue | ||
bbox = self.generate_random_bbox((H,W)) | ||
mask[i,...] = self.mask_channel(bbox,mask[i,...]) | ||
|
||
return mask | ||
|
||
class MaskPixelDrop(nn.Module): | ||
|
||
def __init__(self,neg_drop=50,pos_drop=50,ignore_index=-10): | ||
super().__init__() | ||
|
||
if not isinstance(neg_drop,tuple): | ||
neg_drop = (0,neg_drop) | ||
if not isinstance(pos_drop,tuple): | ||
pos_drop = (0,pos_drop) | ||
|
||
self.neg_drop = neg_drop | ||
self.pos_drop = pos_drop | ||
|
||
self.ignore_index = ignore_index | ||
|
||
@staticmethod | ||
def get_drop_proba(_range): | ||
return random.randint(_range[0],_range[1]) / 100 | ||
|
||
def random_pixel_drop(self,gt,mask,_range): | ||
Cs,Hs,Ws = mask.nonzero(as_tuple=True) | ||
proba = self.get_drop_proba(_range) | ||
max_num = Cs.shape[0] | ||
drop_count = min(max_num,int(proba * max_num)) | ||
|
||
if drop_count == 0 or max_num == 0: | ||
return gt | ||
|
||
indexes = random.sample(range(0, max_num), drop_count) | ||
Cs,Hs,Ws = Cs[indexes].tolist(),Hs[indexes].tolist(),Ws[indexes].tolist() | ||
gt[Cs,Hs,Ws] = self.ignore_index | ||
return gt | ||
|
||
@torch.no_grad() | ||
def __call__(self,mask): | ||
B,C,H,W = mask.shape | ||
pos_mask = mask.gt(0) | ||
neg_mask = mask.eq(0) | ||
for i in range(B): | ||
mask[i] = self.random_pixel_drop(mask[i],pos_mask[i],self.pos_drop) | ||
mask[i] = self.random_pixel_drop(mask[i],neg_mask[i],self.neg_drop) | ||
return mask | ||
|
||
|
This file was deleted.
Oops, something went wrong.
Oops, something went wrong.