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TEST_CS_OPINE_Net_plus.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import scipy.io as sio
import numpy as np
import os
import glob
from time import time
import math
from torch.nn import init
import copy
import cv2
from skimage.measure import compare_ssim as ssim
from argparse import ArgumentParser
from utility_for_opinenet import *
parser = ArgumentParser(description='OPINE-Net-plus')
parser.add_argument('--epoch_num', type=int, default=170, help='epoch number of model')
parser.add_argument('--layer_num', type=int, default=9, help='phase number of OPINE-Net-plus')
parser.add_argument('--learning_rate', type=float, default=1e-4, help='learning rate')
parser.add_argument('--group_num', type=int, default=1, help='group number for training')
parser.add_argument('--cs_ratio', type=int, default=50, help='from {1, 4, 10, 25, 40, 50}')
parser.add_argument('--gpu_list', type=str, default='0', help='gpu index')
parser.add_argument('--model_dir', type=str, default='model', help='trained or pre-trained model directory')
parser.add_argument('--data_dir', type=str, default='data', help='training or test data directory')
parser.add_argument('--log_dir', type=str, default='log', help='log directory')
parser.add_argument('--result_dir', type=str, default='result', help='result directory')
parser.add_argument('--test_name', type=str, default='Set11', help='name of test set')
args = parser.parse_args()
epoch_num = args.epoch_num
learning_rate = args.learning_rate
layer_num = args.layer_num
group_num = args.group_num
cs_ratio = args.cs_ratio
gpu_list = args.gpu_list
test_name = args.test_name
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = gpu_list
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
ratio_dict = {1: 10, 4: 43, 10: 109, 25: 272, 30: 327, 40: 436, 50: 545}
n_input = ratio_dict[cs_ratio]
n_output = 1089
nrtrain = 88912
batch_size = 64
class MySign(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
output = input.new(input.size())
output[input >= 0] = 1
output[input < 0] = -1
return output
@staticmethod
def backward(ctx, grad_output):
grad_input = grad_output.clone()
return grad_input
MyBinarize = MySign.apply
# Define OPINE-Net Block
class BasicBlock(torch.nn.Module):
def __init__(self):
super(BasicBlock, self).__init__()
self.lambda_step = nn.Parameter(torch.Tensor([0.5]))
self.soft_thr = nn.Parameter(torch.Tensor([0.01]))
self.conv_D = nn.Parameter(init.xavier_normal_(torch.Tensor(32, 1, 3, 3)))
self.conv1_forward = nn.Parameter(init.xavier_normal_(torch.Tensor(32, 32, 3, 3)))
self.conv2_forward = nn.Parameter(init.xavier_normal_(torch.Tensor(32, 32, 3, 3)))
self.conv1_backward = nn.Parameter(init.xavier_normal_(torch.Tensor(32, 32, 3, 3)))
self.conv2_backward = nn.Parameter(init.xavier_normal_(torch.Tensor(32, 32, 3, 3)))
self.conv1_G = nn.Parameter(init.xavier_normal_(torch.Tensor(32, 32, 3, 3)))
self.conv2_G = nn.Parameter(init.xavier_normal_(torch.Tensor(32, 32, 3, 3)))
self.conv3_G = nn.Parameter(init.xavier_normal_(torch.Tensor(1, 32, 3, 3)))
def forward(self, x, PhiWeight, PhiTWeight, PhiTb):
x = x - self.lambda_step * PhiTPhi_fun(x, PhiWeight, PhiTWeight)
x = x + self.lambda_step * PhiTb
x_input = x
x_D = F.conv2d(x_input, self.conv_D, padding=1)
x = F.conv2d(x_D, self.conv1_forward, padding=1)
x = F.relu(x)
x_forward = F.conv2d(x, self.conv2_forward, padding=1)
x = torch.mul(torch.sign(x_forward), F.relu(torch.abs(x_forward) - self.soft_thr))
x = F.conv2d(x, self.conv1_backward, padding=1)
x = F.relu(x)
x_backward = F.conv2d(x, self.conv2_backward, padding=1)
x = F.conv2d(F.relu(x_backward), self.conv1_G, padding=1)
x = F.conv2d(F.relu(x), self.conv2_G, padding=1)
x_G = F.conv2d(x, self.conv3_G, padding=1)
x_pred = x_input + x_G
x = F.conv2d(x_forward, self.conv1_backward, padding=1)
x = F.relu(x)
x_D_est = F.conv2d(x, self.conv2_backward, padding=1)
symloss = x_D_est - x_D
return [x_pred, symloss]
# Define OPINE-Net-plus
class OPINENetplus(torch.nn.Module):
def __init__(self, LayerNo, n_input):
super(OPINENetplus, self).__init__()
self.Phi = nn.Parameter(init.xavier_normal_(torch.Tensor(n_input, 1089)))
self.Phi_scale = nn.Parameter(torch.Tensor([0.01]))
onelayer = []
self.LayerNo = LayerNo
for i in range(LayerNo):
onelayer.append(BasicBlock())
self.fcs = nn.ModuleList(onelayer)
def forward(self, x):
# Sampling-subnet
Phi_ = MyBinarize(self.Phi)
Phi = self.Phi_scale * Phi_
PhiWeight = Phi.contiguous().view(n_input, 1, 33, 33)
Phix = F.conv2d(x, PhiWeight, padding=0, stride=33, bias=None) # Get measurements
# Initialization-subnet
PhiTWeight = Phi.t().contiguous().view(n_output, n_input, 1, 1)
PhiTb = F.conv2d(Phix, PhiTWeight, padding=0, bias=None)
PhiTb = torch.nn.PixelShuffle(33)(PhiTb)
x = PhiTb # Conduct initialization
# Recovery-subnet
layers_sym = [] # for computing symmetric loss
for i in range(self.LayerNo):
[x, layer_sym] = self.fcs[i](x, PhiWeight, PhiTWeight, PhiTb)
layers_sym.append(layer_sym)
x_final = x
return [x_final, layers_sym, Phi]
def PhiTPhi_fun(x, PhiW, PhiTW):
temp = F.conv2d(x, PhiW, padding=0,stride=33, bias=None)
temp = F.conv2d(temp, PhiTW, padding=0, bias=None)
return torch.nn.PixelShuffle(33)(temp)
model = OPINENetplus(layer_num, n_input)
model = nn.DataParallel(model)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
model_dir = "./%s/CS_OPINE_Net_plus_layer_%d_group_%d_ratio_%d" % (args.model_dir, layer_num, group_num, cs_ratio)
# Load pre-trained model with epoch number
model.load_state_dict(torch.load('./%s/net_params_%d.pkl' % (model_dir, epoch_num)))
test_dir = os.path.join(args.data_dir, test_name)
filepaths = glob.glob(test_dir + '/*.tif')
result_dir = os.path.join(args.result_dir, test_name)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
ImgNum = len(filepaths)
PSNR_All = np.zeros([1, ImgNum], dtype=np.float32)
SSIM_All = np.zeros([1, ImgNum], dtype=np.float32)
print('\n')
print("CS Sampling and Reconstruction by OPINE-Net plus Start")
print('\n')
with torch.no_grad():
for img_no in range(ImgNum):
imgName = filepaths[img_no]
Img = cv2.imread(imgName, 1)
Img_yuv = cv2.cvtColor(Img, cv2.COLOR_BGR2YCrCb)
Img_rec_yuv = Img_yuv.copy()
Iorg_y = Img_yuv[:,:,0]
[Iorg, row, col, Ipad, row_new, col_new] = imread_CS_py(Iorg_y)
Img_output = Ipad.reshape(1, 1, row_new, col_new)/255.0
start = time()
batch_x = torch.from_numpy(Img_output)
batch_x = batch_x.type(torch.FloatTensor)
batch_x = batch_x.to(device)
[x_output, loss_layers_sym, Phi] = model(batch_x)
end = time()
Prediction_value = x_output.cpu().data.numpy().squeeze()
X_rec = np.clip(Prediction_value[:row,:col], 0, 1).astype(np.float64)
rec_PSNR = psnr(X_rec*255, Iorg.astype(np.float64))
rec_SSIM = ssim(X_rec*255, Iorg.astype(np.float64), data_range=255)
print("[%02d/%02d] Run time for %s is %.4f, PSNR is %.2f, SSIM is %.4f" % (img_no, ImgNum, imgName, (end - start), rec_PSNR, rec_SSIM))
Img_rec_yuv[:,:,0] = X_rec*255
im_rec_rgb = cv2.cvtColor(Img_rec_yuv, cv2.COLOR_YCrCb2BGR)
im_rec_rgb = np.clip(im_rec_rgb, 0, 255).astype(np.uint8)
resultName = imgName.replace(args.data_dir, args.result_dir)
cv2.imwrite("%s_OPINE_Net_plus_ratio_%d_epoch_%d_PSNR_%.2f_SSIM_%.4f.png" % (resultName, cs_ratio, epoch_num, rec_PSNR, rec_SSIM), im_rec_rgb)
del x_output
PSNR_All[0, img_no] = rec_PSNR
SSIM_All[0, img_no] = rec_SSIM
print('\n')
output_data = "CS ratio is %d, Avg PSNR/SSIM for %s is %.2f/%.4f, Epoch number of model is %d \n" % (cs_ratio, args.test_name, np.mean(PSNR_All), np.mean(SSIM_All), epoch_num)
print(output_data)
output_file_name = "./%s/PSNR_SSIM_Results_CS_OPINE_Net_plus_layer_%d_group_%d_ratio_%d.txt" % (args.log_dir, layer_num, group_num, cs_ratio)
output_file = open(output_file_name, 'a')
output_file.write(output_data)
output_file.close()
print("CS Sampling and Reconstruction by OPINE-Net plus End")