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utils.py
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utils.py
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import scipy.io as sio
import os
import numpy as np
import matplotlib.pyplot as plt
import math
import torch
import logging
import random
import torch.nn.functional as F
from torch.autograd import Variable
from math import exp
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
def create_window(window_size, channel):
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
return window
def _ssim(img1, img2, window, window_size, channel, size_average = True):
mu1 = F.conv2d(img1, window, padding = window_size//2, groups = channel)
mu2 = F.conv2d(img2, window, padding = window_size//2, groups = channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = mu1*mu2
sigma1_sq = F.conv2d(img1*img1, window, padding = window_size//2, groups = channel) - mu1_sq
sigma2_sq = F.conv2d(img2*img2, window, padding = window_size//2, groups = channel) - mu2_sq
sigma12 = F.conv2d(img1*img2, window, padding = window_size//2, groups = channel) - mu1_mu2
C1 = 0.01**2
C2 = 0.03**2
ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
if size_average:
return ssim_map.mean()
else:
return ssim_map.mean(1).mean(1).mean(1)
def ssim(img1, img2, window_size = 11, size_average = True):
(_, channel, _, _) = img1.size()
window = create_window(window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
return _ssim(img1, img2, window, window_size, channel, size_average)
def generate_masks(mask_path, batch_size):
mask = sio.loadmat(mask_path + '/mask.mat')
mask = mask['mask']
mask3d = np.tile(mask[:,:,np.newaxis],(1,1,28))
mask3d = np.transpose(mask3d, [2, 0, 1])
mask3d = torch.from_numpy(mask3d)
[nC, H, W] = mask3d.shape
mask3d_batch = mask3d.expand([batch_size, nC, H, W]).cuda().float()
return mask3d_batch
def LoadTraining(path, arguement=False):
imgs = []
scene_list = os.listdir(path)
scene_list.sort()
print('training sences:', len(scene_list))
# for i in range(5):
for i in range(len(scene_list)):
scene_path = path + scene_list[i]
if 'mat' not in scene_path:
continue
img_dict = sio.loadmat(scene_path)
if "img_expand" in img_dict:
img = img_dict['img_expand']/65536.
elif "img" in img_dict:
img = img_dict['img']/65536.
if arguement:
rotTimes = random.randint(0, 3)
vFlip = random.randint(0, 1)
hFlip = random.randint(0, 1)
# Random rotation
for j in range(rotTimes):
img = np.rot90(img)
# Random vertical Flip
for j in range(vFlip):
img = img[:, ::-1, :].copy()
# Random horizontal Flip
for j in range(hFlip):
img = img[::-1, :, :].copy()
img = img.astype(np.float32)
imgs.append(img)
print('Sence {} is loaded. {}'.format(i, scene_list[i]))
return imgs
def LoadTraining_Real(path):
imgs = []
scene_list = os.listdir(path)
scene_list.sort()
print('training sences:', len(scene_list))
max_ = 0
# for i in range(5):
for i in range(len(scene_list)):
scene_path = path + scene_list[i]
if 'mat' not in scene_path:
continue
img_dict = sio.loadmat(scene_path)
if "img_expand" in img_dict:
img = img_dict['img_expand']/65536.
elif "img" in img_dict:
img = img_dict['img']/65536.
rot_angle = random.randint(1, 4)
img = np.rot90(img, rot_angle)
img = img.astype(np.float32)
imgs.append(img)
print('Sence {} is loaded. {}'.format(i, scene_list[i]))
return imgs
def LoadTest(path_test):
scene_list = os.listdir(path_test)
scene_list.sort()
test_data = np.zeros((len(scene_list), 256, 256, 28))
for i in range(len(scene_list)):
scene_path = path_test + scene_list[i]
img = sio.loadmat(scene_path)['img']
#img = img/img.max()
test_data[i,:,:,:] = img
# print(i, img.shape, img.max(), img.min())
test_data = torch.from_numpy(np.transpose(test_data, (0, 3, 1, 2)))
return test_data
def LoadTest_Real(path_test):
scene_list = os.listdir(path_test)
scene_list.sort()
test_data = np.zeros((len(scene_list), 660, 714))
for i in range(len(scene_list)):
scene_path = path_test + scene_list[i]
img = sio.loadmat(scene_path)['meas_real']
#img = img/img.max()
test_data[i,:,:] = img
print(i, img.shape, img.max(), img.min())
test_data = torch.from_numpy(test_data)
return test_data
def psnr(img1, img2):
psnr_list = []
for i in range(img1.shape[0]):
total_psnr = 0
#PIXEL_MAX = img2.max()
PIXEL_MAX = img2[i,:,:,:].max()
for ch in range(28):
mse = np.mean((img1[i,:,:,ch] - img2[i,:,:,ch])**2)
total_psnr += 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
psnr_list.append(total_psnr/img1.shape[3])
return psnr_list
def torch_psnr(img, ref): #input [28,256,256]
nC = img.shape[0]
pixel_max = torch.max(ref)
psnr = 0
for i in range(nC):
mse = torch.mean((img[i,:,:] - ref[i,:,:]) ** 2)
psnr += 20 * torch.log10(pixel_max / torch.sqrt(mse))
return psnr/nC
def torch_psnr_DGSMP(img, ref): #input [28,256,256]
nC = img.shape[0]
pixel_max = torch.max(ref)
# print(pixel_max)
psnr = 0
for i in range(nC):
mse = torch.mean((img[i,:,:] - ref[i,:,:]) ** 2)
psnr += 20 * torch.log10((255.0/256.0)/ torch.sqrt(mse))
return psnr/nC
def torch_ssim(img, ref): #input [28,256,256]
return ssim(torch.unsqueeze(img,0), torch.unsqueeze(ref,0))
def time2file_name(time):
year = time[0:4]
month = time[5:7]
day = time[8:10]
hour = time[11:13]
minute = time[14:16]
second = time[17:19]
time_filename = year + '_' + month + '_' + day + '_' + hour + '_' + minute + '_' + second
return time_filename
def shuffle_crop(train_data, batch_size, crop_size=256):
index = np.random.choice(range(len(train_data)), batch_size)
processed_data = np.zeros((batch_size, crop_size, crop_size, 28), dtype=np.float32)
for i in range(batch_size):
h, w, _ = train_data[index[i]].shape
x_index = np.random.randint(0, h - crop_size)
y_index = np.random.randint(0, w - crop_size)
processed_data[i, :, :, :] = train_data[index[i]][x_index:x_index + crop_size, y_index:y_index + crop_size, :]
gt_batch = torch.from_numpy(np.transpose(processed_data, (0, 3, 1, 2)))
return gt_batch
def gen_meas_torch_DGSMP(data_batch, mask3d_batch, is_training=True):
nC = data_batch.shape[1]
if is_training is False:
[batch_size, nC, H, W] = data_batch.shape
mask3d_batch = (mask3d_batch[0,:,:,:]).expand([batch_size, nC, H, W]).cuda().float() # [10,28,256,256]
# symbol = mask3d_batch==1
temp = shift(mask3d_batch*data_batch, 2) # eq(1)(2) # [10,28,256,310]
meas = torch.sum(temp, 1)
meas = meas/nC*2 # meas scale # eq(4) # [10,256,310]
return meas
def gen_meas_torch(data_batch, mask3d_batch, is_training=True):
nC = data_batch.shape[1]
if is_training is False:
[batch_size, nC, H, W] = data_batch.shape
mask3d_batch = (mask3d_batch[0,:,:,:]).expand([batch_size, nC, H, W]).cuda().float() # [10,28,256,256]
# symbol = mask3d_batch==1
temp = shift(mask3d_batch*data_batch, 2) # eq(1)(2) # [10,28,256,310]
meas = torch.sum(temp, 1)
meas = meas/nC*2 # meas scale # eq(4) # [10,256,310]
y_temp = shift_back(meas)
return y_temp
def gen_meas_torch_norm(data_batch, mask3d_batch, is_training=True):
nC = data_batch.shape[1]
if is_training is False:
[batch_size, nC, H, W] = data_batch.shape
mask3d_batch = (mask3d_batch[0,:,:,:]).expand([batch_size, nC, H, W]).cuda().float() # [10,28,256,256]
# symbol = mask3d_batch==1
temp = shift(mask3d_batch*data_batch, 2) # eq(1)(2) # [10,28,256,310]
meas = torch.sum(temp, 1)
meas = meas/nC*2 # meas scale # eq(4) # [10,256,310]
y_temp = shift_back(meas)
PhiTy = torch.mul(y_temp, mask3d_batch)
return PhiTy
def gen_meas_torch_real(data_batch, mask3d_batch, is_training=True):
if is_training:
nC = data_batch.shape[1]
# print(mask3d_batch.shape)
# print(data_batch.shape)
temp = shift(mask3d_batch*data_batch, 2) # eq(1)(2) # [10,28,256,310]
meas = torch.sum(temp, 1)/nC*2*1.2 # meas scale # eq(4) # [10,256,310]
# shot noise
QE, bit = 0.4, 2048
meas = torch.from_numpy(np.random.binomial((meas.cpu().numpy() * bit / QE).astype(int), QE)).cuda()
meas = meas // bit
else:
meas = data_batch/data_batch.max()*0.8
y_temp = shift_back(meas)
PhiTy = torch.mul(y_temp, mask3d_batch)
return PhiTy
def gen_meas_torch_real2(data_batch, mask3d_batch, is_training=True):
if is_training:
nC = data_batch.shape[1]
# print(mask3d_batch.shape)
# print(data_batch.shape)
temp = shift(mask3d_batch*data_batch, 2) # eq(1)(2) # [10,28,256,310]
meas = torch.sum(temp, 1)/nC*2*1.2 # meas scale # eq(4) # [10,256,310]
# shot noise
QE, bit = 0.4, 2048
meas = torch.from_numpy(np.random.binomial((meas.cpu().numpy() * bit / QE).astype(int), QE)).cuda()
meas = meas // bit
else:
meas = data_batch/data_batch.max()*0.8
y_temp = shift_back(meas)
#PhiTy = torch.mul(y_temp, mask3d_batch)
return y_temp
def shift(inputs, step=2):
[bs, nC, row, col] = inputs.shape
output = torch.zeros(bs, nC, row, col+(nC-1)*step).cuda().float()
for i in range(nC):
output[:,i,:,step*i:step*i+col] = inputs[:,i,:,:]
return output
def shift_back(inputs,step=2): # input [bs,256,310] output [bs, 28, 256, 256]
[bs, row, col] = inputs.shape
nC = 28
output = torch.zeros(bs, nC, row, col-(nC-1)*step).cuda().float()
for i in range(nC):
# print(f'output:{output[:,i,:,:].shape}')
# print(inputs[:,:,step*i:step*i+col-(nC-1)*step].shape)
output[:,i,:,:] = inputs[:,:,step*i:step*i+col-(nC-1)*step]
return output
def gen_log(model_path):
logger = logging.getLogger()
logger.setLevel(logging.INFO)
formatter = logging.Formatter("%(asctime)s - %(levelname)s: %(message)s")
log_file = model_path + '/log.txt'
fh = logging.FileHandler(log_file, mode='a')
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
return logger