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TEST_MRI_CS_ISTA_Net_plus.py
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TEST_MRI_CS_ISTA_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
try:
from skimage.metrics import structural_similarity as ssim
except ImportError:
from skimage.measure import compare_ssim as ssim
from argparse import ArgumentParser
import types
parser = ArgumentParser(description='ISTA-Net-plus')
parser.add_argument('--epoch_num', type=int, default=200, help='epoch number of model')
parser.add_argument('--layer_num', type=int, default=9, help='phase number of ISTA-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=20, help='from {1, 4, 10, 25, 40, 50}')
parser.add_argument('--gpu_list', type=str, default='0', help='gpu index')
parser.add_argument('--matrix_dir', type=str, default='sampling_matrix', help='sampling matrix directory')
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='BrainImages_test', 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
try:
# The flag below controls whether to allow TF32 on matmul. This flag defaults to True.
torch.backends.cuda.matmul.allow_tf32 = False
# The flag below controls whether to allow TF32 on cuDNN. This flag defaults to True.
torch.backends.cudnn.allow_tf32 = False
except:
pass
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")
# Load CS Sampling Matrix: phi
Phi_data_Name = './%s/mask_%d.mat' % (args.matrix_dir, cs_ratio)
Phi_data = sio.loadmat(Phi_data_Name)
mask_matrix = Phi_data['mask_matrix']
mask_matrix = torch.from_numpy(mask_matrix).type(torch.FloatTensor)
mask = torch.unsqueeze(mask_matrix, 2)
mask = torch.cat([mask, mask], 2)
mask = mask.to(device)
Training_data_Name = 'Training_BrainImages_256x256_100.mat'
Training_data = sio.loadmat('./%s/%s' % (args.data_dir, Training_data_Name))
Training_labels = Training_data['labels']
if isinstance(torch.fft, types.ModuleType):
class FFT_Mask_ForBack(torch.nn.Module):
def __init__(self):
super(FFT_Mask_ForBack, self).__init__()
def forward(self, x, full_mask):
full_mask = full_mask[..., 0]
x_in_k_space = torch.fft.fft2(x)
masked_x_in_k_space = x_in_k_space * full_mask.view(1, 1, *(full_mask.shape))
masked_x = torch.real(torch.fft.ifft2(masked_x_in_k_space))
return masked_x
else:
class FFT_Mask_ForBack(torch.nn.Module):
def __init__(self):
super(FFT_Mask_ForBack, self).__init__()
def forward(self, x, mask):
x_dim_0 = x.shape[0]
x_dim_1 = x.shape[1]
x_dim_2 = x.shape[2]
x_dim_3 = x.shape[3]
x = x.view(-1, x_dim_2, x_dim_3, 1)
y = torch.zeros_like(x)
z = torch.cat([x, y], 3)
fftz = torch.fft(z, 2)
z_hat = torch.ifft(fftz * mask, 2)
x = z_hat[:, :, :, 0:1]
x = x.view(x_dim_0, x_dim_1, x_dim_2, x_dim_3)
return x
# Define ISTA-Net-plus 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.conv_G = nn.Parameter(init.xavier_normal_(torch.Tensor(1, 32, 3, 3)))
def forward(self, x, fft_forback, PhiTb, mask):
x = x - self.lambda_step * fft_forback(x, mask)
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_G = F.conv2d(x_backward, self.conv_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 ISTA-Net-plus
class ISTANetplus(torch.nn.Module):
def __init__(self, LayerNo):
super(ISTANetplus, self).__init__()
onelayer = []
self.LayerNo = LayerNo
self.fft_forback = FFT_Mask_ForBack()
for i in range(LayerNo):
onelayer.append(BasicBlock())
self.fcs = nn.ModuleList(onelayer)
def forward(self, PhiTb, mask):
x = PhiTb
layers_sym = [] # for computing symmetric loss
for i in range(self.LayerNo):
[x, layer_sym] = self.fcs[i](x, self.fft_forback, PhiTb, mask)
layers_sym.append(layer_sym)
x_final = x
return [x_final, layers_sym]
model = ISTANetplus(layer_num)
model = nn.DataParallel(model)
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
model_dir = "./%s/MRI_CS_ISTA_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)))
def psnr(img1, img2):
img1.astype(np.float32)
img2.astype(np.float32)
mse = np.mean((img1 - img2) ** 2)
if mse == 0:
return 100
PIXEL_MAX = 255.0
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
test_dir = os.path.join(args.data_dir, test_name)
filepaths = glob.glob(test_dir + '/*.png')
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)
Init_PSNR_All = np.zeros([1, ImgNum], dtype=np.float32)
Init_SSIM_All = np.zeros([1, ImgNum], dtype=np.float32)
print('\n')
print("MRI CS Reconstruction Start")
with torch.no_grad():
for img_no in range(ImgNum):
imgName = filepaths[img_no]
Iorg = cv2.imread(imgName, 0)
Icol = Iorg.reshape(1, 1, 256, 256) / 255.0
Img_output = Icol
start = time()
batch_x = torch.from_numpy(Img_output)
batch_x = batch_x.type(torch.FloatTensor)
batch_x = batch_x.to(device)
PhiTb = FFT_Mask_ForBack()(batch_x, mask)
[x_output, loss_layers_sym] = model(PhiTb, mask)
end = time()
initial_result = PhiTb.cpu().data.numpy().reshape(256, 256)
Prediction_value = x_output.cpu().data.numpy().reshape(256, 256)
X_init = np.clip(initial_result, 0, 1).astype(np.float64)
X_rec = np.clip(Prediction_value, 0, 1).astype(np.float64)
init_PSNR = psnr(X_init * 255, Iorg.astype(np.float64))
init_SSIM = ssim(X_init * 255, Iorg.astype(np.float64), data_range=255)
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, Initial PSNR is %.2f, Initial SSIM is %.4f" % (img_no, ImgNum, imgName, (end - start), init_PSNR, init_SSIM))
print("[%02d/%02d] Run time for %s is %.4f, Proposed PSNR is %.2f, Proposed SSIM is %.4f" % (img_no, ImgNum, imgName, (end - start), rec_PSNR, rec_SSIM))
im_rec_rgb = np.clip(X_rec*255, 0, 255).astype(np.uint8)
resultName = imgName.replace(args.data_dir, args.result_dir)
cv2.imwrite("%s_ISTA_Net_plus_ratio_%d_epoch_%d_PSNR_%.2f_SSIM_%.4f.bmp" % (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
Init_PSNR_All[0, img_no] = init_PSNR
Init_SSIM_All[0, img_no] = init_SSIM
print('\n')
init_data = "CS ratio is %d, Avg Initial PSNR/SSIM for %s is %.2f/%.4f" % (cs_ratio, args.test_name, np.mean(Init_PSNR_All), np.mean(Init_SSIM_All))
output_data = "CS ratio is %d, Avg Proposed 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(init_data)
print(output_data)
output_file_name = "./%s/PSNR_SSIM_Results_MRI_CS_ISTA_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("MRI CS Reconstruction End")