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utils.py
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utils.py
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import matlab.engine
eng = matlab.engine.start_matlab()
import math
import cv2
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch
from skimage.measure import compare_psnr, compare_ssim
def weights_init_kaiming(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
elif classname.find('Linear') != -1:
nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(mean=0, std=math.sqrt(2./9./64.)).clamp_(-0.025,0.025)
nn.init.constant(m.bias.data, 0.0)
def batch_PSNR(img, imclean, data_range):
Img = img.data.cpu().numpy().astype(np.float32)
Iclean = imclean.data.cpu().numpy().astype(np.float32)
PSNR = 0
for i in range(Img.shape[0]):
PSNR += compare_psnr(Iclean[i,:,:,:], Img[i,:,:,:], data_range=data_range)
return (PSNR/Img.shape[0])
def batch_SSIM(img, imclean, data_range):
img = torch.transpose(img, 1, 3)
imclean = torch.transpose(imclean, 1, 3)
Img = img.data.cpu().numpy().astype(np.float32)
Iclean = imclean.data.cpu().numpy().astype(np.float32)
SSIM = 0
for i in range(Img.shape[0]):
SSIM += compare_ssim(Iclean[i,:,:,:], Img[i,:,:,:], data_range=data_range, multichannel=True)
return (SSIM/Img.shape[0])
def batch_NIQE(img):
# eng = matlab.engine.start_matlab()
img_ = torch.transpose(img, 1, 3).data.cpu().numpy().astype(np.float32)
NIQE = 0
for i in range(img_.shape[0]):
NIQE += eng.niqe(matlab.double(img_[i,:,:,:].tolist()))
# eng.quit()
return (NIQE/img_.shape[0])
def data_augmentation(image, mode):
out = np.transpose(image, (1,2,0))
if mode == 0:
# original
out = out
elif mode == 1:
# flip up and down
out = np.flipud(out)
elif mode == 2:
# rotate counterwise 90 degree
out = np.rot90(out)
elif mode == 3:
# rotate 90 degree and flip up and down
out = np.rot90(out)
out = np.flipud(out)
elif mode == 4:
# rotate 180 degree
out = np.rot90(out, k=2)
elif mode == 5:
# rotate 180 degree and flip
out = np.rot90(out, k=2)
out = np.flipud(out)
elif mode == 6:
# rotate 270 degree
out = np.rot90(out, k=3)
elif mode == 7:
# rotate 270 degree and flip
out = np.rot90(out, k=3)
out = np.flipud(out)
return np.transpose(out, (2,0,1))
def resize_target(target, size):
new_target = np.zeros((target.shape[0], size, size), np.int32)
for i, t in enumerate(target.numpy()):
new_target[i, ...] = cv2.resize(t, (size,) * 2, interpolation=cv2.INTER_CUBIC)
return new_target
def resize_output(target, size):
new_target = np.zeros((target.shape[0], target.shape[1], size, size), np.int32)
for j in range(target.shape[0]):
for i, t in enumerate(target[j,:,:,:].data.cpu().numpy()):
# new_target[j, i, :, :] = cv2.resize(t, (size,) * 2, interpolation=cv2.INTER_NEAREST)
new_target[j, i, :, :] = cv2.resize(t, (size,) * 2, interpolation=cv2.INTER_CUBIC)
return new_target
class FocalLoss(nn.Module):
# def __init__(self, device, gamma=0, eps=1e-7, size_average=True):
def __init__(self, gamma=0, eps=1e-7, size_average=True, reduce=True):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.eps = eps
self.size_average = size_average
self.reduce = reduce
# self.device = device
def forward(self, input, target):
# y = one_hot(target, input.size(1), self.device)
y = one_hot(target, input.size(1))
probs = F.softmax(input, dim=1)
probs = (probs * y).sum(1) # dimension ???
probs = probs.clamp(self.eps, 1. - self.eps)
log_p = probs.log()
# print('probs size= {}'.format(probs.size()))
# print(probs)
batch_loss = -(torch.pow((1 - probs), self.gamma)) * log_p
# print('-----bacth_loss------')
# print(batch_loss)
if self.reduce:
if self.size_average:
loss = batch_loss.mean()
else:
loss = batch_loss.sum()
else:
loss = batch_loss
return loss
def one_hot(index, classes):
size = index.size()[:1] + (classes,) + index.size()[1:]
view = index.size()[:1] + (1,) + index.size()[1:]
# mask = torch.Tensor(size).fill_(0).to(device)
mask = torch.Tensor(size).fill_(0).cuda()
index = index.view(view)
ones = 1.
return mask.scatter_(1, index, ones)
def get_NoGT_target(inputs):
sfmx_inputs = F.log_softmax(inputs, dim=1)
target = torch.argmax(sfmx_inputs, dim=1)
return target
def get_SegLoss_Wt(loss, coef=1):
return nn.Tanh()(coef * loss)