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mc_rec_train_and_test_dudornet.py
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import numpy as np
import torch
import torch.nn as nn
import os
from tqdm import tqdm
import torchvision.utils as vutils
from torch.nn.utils import clip_grad_norm
import data.Mc_Rec_Dataloader_Dataset as Dataloader_Dataset
from utils.averagemeter import AverageMeter
from utils.save_image import save_image
from skimage.metrics import structural_similarity
from skimage.metrics import peak_signal_noise_ratio
from skimage.metrics import normalized_root_mse
import matplotlib.pyplot as plt
import csv
n_iter = 0
def train(train_loader, model, loss, optimizer, epoch, train_writer, device, logger, args):
global n_iter
train_losses = AverageMeter()
loss_ = torch.FloatTensor([0.]).to(device)
loss_.requires_grad_(True)
loss_sr = torch.FloatTensor([0.]).to(device)
loss_sr.requires_grad_(True)
loss_rec = torch.FloatTensor([0.]).to(device)
loss_rec.requires_grad_(True)
# ------------------------------------------------------------------------------------------------------------------
# total image numbers
if args.data_name == 'IXI':
total = len(Dataloader_Dataset.IXI_dataset(args, 'train'))
elif args.data_name == 'fastMRI':
total = len(Dataloader_Dataset.fastMRI_dataset(args, 'train'))
model.train()
# ------------------------------------------------------------------------------------------------------------------
logger.info('Epoch [{}/{}]\tLearning Rate: {:.3e}'.format(
epoch, args.n_epochs, optimizer.state_dict()['param_groups'][0]['lr']))
for idx, (name_rec, image_rec, fully, under_sample, mask, name_ref, image_ref, fully_ref) in enumerate(train_loader):
image_rec = image_rec.float().to(device) # (bs, 1, 256, 256)
fully = fully.float().to(device) # (bs, 2, 256, 256) k-space torch.cat([fully_real, fully_imag], dim=0)
under_sample = under_sample.float().to(device) # (bs, 2, 256, 256)
mask = mask.float().to(device) # (bs, 1, 256, 256)
image_ref = image_ref.float().to(device)
fully_ref = fully_ref.float().to(device) # (bs, 2, 256, 256)
# # --------------------------------------------------------------------------------------------------------------
# # 将under_sample变为复数形式，并进行数据截断
# fully_real = torch.unsqueeze(fully[:, 0, :, :], 1)
# fully_imag = torch.unsqueeze(fully[:, 1, :, :], 1)
# fully = torch.complex(fully_real, fully_imag)
# 将under_sample变为复数形式
under_sample_real = torch.unsqueeze(under_sample[:, 0, :, :], 1)
under_sample_imag = torch.unsqueeze(under_sample[:, 1, :, :], 1)
under_sample = torch.complex(under_sample_real, under_sample_imag) # bs ,1, 256, 256
under_image_rec = torch.abs(torch.fft.ifft2(under_sample))
# fully = torch.fft.fftshift(torch.fft.fft2(image_rec)) # [b, 1, H, W]
# under_sample = fully * mask
# under_image_rec = torch.abs(torch.fft.ifft2(torch.fft.ifftshift(under_sample)))
# fully_ref = torch.fft.fftshift(torch.fft.fft2(image_ref)) # [B, 1, H, W]
# plt.subplot(131), plt.imshow(np.array(image_ref[0, 0, :, :].cpu()), 'gray')
# plt.subplot(132), plt.imshow(np.array(image_rec[0, 0, :, :].cpu()), 'gray')
# plt.subplot(133), plt.imshow(np.array(under_image_rec[0, 0, :, :].cpu()), 'gray')
# plt.subplot(133), plt.imshow(np.array(under_image_rec[0, 0, :, :].cpu()), 'gray')
# plt.show()
# plt.savefig('y.png')
# 参考图片，欠采样图片，欠采样k-space，掩码
net, image_Rec = model(under_image_rec, under_sample, mask)
# loss_ = loss(image_Rec, image_rec)
# --------------------------------------------------------------------------------------------------------------
# 更新损失并作记录
train_losses.update(loss_.item(), args.batch_size) # 整个epoch的平均损失
train_writer.add_scalar('Train_loss', loss_.item(), n_iter)
# --------------------------------------------------------------------------------------------------------------
# 计算梯度并更新参数
loss_G_L1 = 0
optimizer.zero_grad()
# criterion= nn.MSELoss()
criterion= nn.L1Loss()
# Image domain
n_recurrent=5
loss_img_dc = 0
for j in range(1, n_recurrent + 1):
loss_img_dc = loss_img_dc + criterion(net['r%d_img_dc_pred' % j], image_rec)
# Kspace domain
loss_kspc = 0
for j in range(1, n_recurrent + 1):
loss_kspc = loss_kspc + criterion(net['r%d_kspc_pred' % j], fully)
loss_ = loss_img_dc + loss_kspc
# loss_G_L1 = loss_G_L1.item()
# loss_img = loss_img_dc.item()
# loss_kspc = loss_kspc.item()
# loss_ = loss_G_L1
loss_.backward()
optimizer.step()
# 梯度裁剪
if args.clip_grad_norm:
clip_grad_norm(model.parameters(), max_norm=10, norm_type=2)
n_iter += 1
# --------------------------------------------------------------------------------------------------------------
# 输出已经处理的数据及相应batch的loss_
# print(loss_) # TODO
if (idx + 1) % 60 == 0:
logger.info(
'Epoch-{}-[{}/{}]\t\tLoss: {:.4f}'.format(epoch, (idx + 1) * args.batch_size, total, loss_.item()))
# ------------------------------------------------------------------------------------------------------------------
# 返回一个周期的平均损失
return train_losses.avg
def validate(valid_loader, model, loss, epoch, output_writers, device, logger, args):
logger.info('\n{}: [{}/{}/X{}] Validation:'.format(args.data_name, args.model, args.modal, args.acceleration))
loss_ = torch.FloatTensor([0.]).to(device)
loss_.requires_grad_(True)
loss_sr = torch.FloatTensor([0.]).to(device)
loss_sr.requires_grad_(True)
loss_rec = torch.FloatTensor([0.]).to(device)
loss_rec.requires_grad_(True)
valid_losses = AverageMeter()
valid_psnr = AverageMeter()
valid_ssim = AverageMeter()
valid_nrmse = AverageMeter()
# ------------------------------------------------------------------------------------------------------------------
# total image numbers
if args.data_name == 'IXI':
total = len(Dataloader_Dataset.IXI_dataset(args, 'valid'))
# print(total) # TODO
elif args.data_name == 'fastMRI':
total = len(Dataloader_Dataset.fastMRI_dataset(args, 'valid'))
model.eval()
# ------------------------------------------------------------------------------------------------------------------
image_recs = []
image_Recs = []
image_refs = []
with torch.no_grad():
for idx, (name_rec, image_rec, fully, under_sample, mask, name_ref, image_ref, fully_ref) in tqdm(enumerate(valid_loader), desc='Batch',
total=len(valid_loader), ncols=80):
image_rec = image_rec.float().to(device) # (bs, 1, 256, 256)
fully = fully.float().to(device) # (bs, 2, 256, 256)
under_sample = under_sample.float().to(device) # (bs, 2, 256, 256)
mask = mask.float().to(device) # (bs, 1, 256, 256)
image_ref = image_ref.float().to(device)
fully_ref = fully_ref.float().to(device) # (bs, 2, 256, 256)
# # --------------------------------------------------------------------------------------------------------------
# # 将under_sample变为复数形式，并进行数据截断
# fully_real = torch.unsqueeze(fully[:, 0, :, :], 1)
# fully_imag = torch.unsqueeze(fully[:, 1, :, :], 1)
# fully = torch.complex(fully_real, fully_imag)
#
# 将under_sample变为复数形式
under_sample_real = torch.unsqueeze(under_sample[:, 0, :, :], 1)
under_sample_imag = torch.unsqueeze(under_sample[:, 1, :, :], 1)
under_sample = torch.complex(under_sample_real, under_sample_imag) # bs ,1, 256, 256
under_image_rec = torch.abs(torch.fft.ifft2(under_sample))
# fully = torch.fft.fftshift(torch.fft.fft2(image_rec)) # [b, 1, H, W]
# under_sample = fully * mask
# under_image_rec = torch.abs(torch.fft.ifft2(torch.fft.ifftshift(under_sample)))
# fully_ref = torch.fft.fftshift(torch.fft.fft2(image_ref)) # [B, 1, H, W]
# plt.subplot(131), plt.imshow(np.array(image_ref[0, 0, :, :].cpu()), 'gray')
# plt.subplot(132), plt.imshow(np.array(image_rec[0, 0, :, :].cpu()), 'gray')
# plt.subplot(133), plt.imshow(np.array(under_image_rec[0, 0, :, :].cpu()), 'gray')
# plt.show()
# 参考图片，欠采样图片，欠采样k-space，掩码
net, image_Rec = model(under_image_rec, under_sample, mask)
loss_ = loss(image_Rec, image_rec)
valid_losses.update(loss_.item(), image_rec.size(0)) # 整个epoch的平均损失
# ----------------------------------------------------------------------------------------------------------
# 计算PSNR，SSIM，NRMSE
psnr = peak_signal_noise_ratio(np.array(image_rec[0, 0, :, :].data.cpu()),
np.array(image_Rec[0, 0, :, :].clamp(0, 1).data.cpu()),
data_range=np.max(np.array(image_rec[0, 0, :, :].data.cpu())))
ssim = structural_similarity(np.array(image_rec[0, 0, :, :].data.cpu()),
np.array(image_Rec[0, 0, :, :].clamp(0, 1).data.cpu()))
nrmse = normalized_root_mse(np.array(image_rec[0, 0, :, :].data.cpu()),
np.array(image_Rec[0, 0, :, :].clamp(0, 1).data.cpu()))
valid_psnr.update(psnr, image_rec.size(0))
valid_ssim.update(ssim, image_rec.size(0))
valid_nrmse.update(nrmse, image_rec.size(0))
# ----------------------------------------------------------------------------------------------------------
# 可视化图片质量-周期数的变化
step = int(total / 8)
image_idx = [x for x in range(0, 8 * step, step)]
if idx in image_idx:
image_recs.append(image_rec)
image_Recs.append(image_Rec)
image_refs.append(image_ref)
image_recs = torch.cat(image_recs, dim=0) # (8, 1, 256, 256)
image_Recs = torch.cat(image_Recs, dim=0)
image_refs = torch.cat(image_refs, dim=0)
image_recs = vutils.make_grid(image_recs, normalize=True, scale_each=True) # (3, 256, 2048)
image_Recs = vutils.make_grid(image_Recs, normalize=True, scale_each=True)
image_refs = vutils.make_grid(image_refs, normalize=True, scale_each=True)
output_writers[0].add_image('ground_truth images', torch.unsqueeze(image_recs[0], dim=0), 0)
output_writers[1].add_image('Rec images ', torch.unsqueeze(image_Recs[0], dim=0), epoch)
output_writers[2].add_image('Ref images ', torch.unsqueeze(image_refs[0], dim=0), 0)
# --------------------------------------------------------------------------------------------------------------
return valid_losses.avg, valid_psnr.avg, valid_ssim.avg, valid_nrmse.avg
def test(test_loader, model, loss, output_writers, device, args):
print("======================== Start testing! ========================")
test_psnr = AverageMeter()
test_ssim = AverageMeter()
test_nrmse = AverageMeter()
test_losses = AverageMeter()
# ------------------------------------------------------------------------------------------------------------------
# total image numbers
if args.data_name == 'IXI':
total = len(Dataloader_Dataset.IXI_dataset(args, 'test'))
elif args.data_name == 'fastMRI':
total = len(Dataloader_Dataset.fastMRI_dataset(args, 'test'))
model.eval()
# ------------------------------------------------------------------------------------------------------------------
# /home/shx/My_Projects/My_Net/experiment/IXI/T2/random/MBMSN/X8/test_reconstruction_images
save_dir = os.path.join(args.root_path, args.save_dir, args.data_name, args.modal,
args.mask,
args.model,
'X{}'.format(args.acceleration), 'test_reconstruction_images')
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
print("The reconstructed images will be saved in {}".format(save_dir))
save_path = os.path.join(args.root_path, args.save_dir, args.data_name, args.modal,
args.mask,
args.model,
'X{}'.format(args.acceleration))
if not os.path.exists(save_path):
os.makedirs(save_path, exist_ok=True)
csv_filename = 'instance_metrics.csv'
field_names = ['name', 'psnr', 'ssim', 'nrmse']
with open(os.path.join(save_path, csv_filename), 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=field_names, delimiter=',')
writer.writeheader()
# ------------------------------------------------------------------------------------------------------------------
image_recs = []
image_Recs = []
image_refs = []
with torch.no_grad():
# image_rec_128 = None
for idx, (name_rec, image_rec, fully, under_sample, mask, name_ref, image_ref, fully_ref) in tqdm(enumerate(test_loader), desc='Batch',
total=len(test_loader), ncols=80):
image_rec = image_rec.float().to(device) # (bs, 1, 256, 256)
fully = fully.float().to(device) # (bs, 2, 256, 256)
under_sample = under_sample.float().to(device) # (bs, 2, 256, 256)
mask = mask.float().to(device) # (bs, 1, 256, 256)
image_ref = image_ref.float().to(device)
fully_ref = fully_ref.float().to(device) # (bs, 2, 256, 256)
# # --------------------------------------------------------------------------------------------------------------
# # 将under_sample变为复数形式，并进行数据截断
# fully_real = torch.unsqueeze(fully[:, 0, :, :], 1)
# fully_imag = torch.unsqueeze(fully[:, 1, :, :], 1)
# fully = torch.complex(fully_real, fully_imag)
# 将under_sample变为复数形式
under_sample_real = torch.unsqueeze(under_sample[:, 0, :, :], 1)
under_sample_imag = torch.unsqueeze(under_sample[:, 1, :, :], 1)
under_sample = torch.complex(under_sample_real, under_sample_imag) # bs ,1, 256, 256
under_image_rec = torch.abs(torch.fft.ifft2(under_sample))
# fully = torch.fft.fftshift(torch.fft.fft2(image_rec)) # [b, 1, H, W]
# under_sample = fully * mask
# under_image_rec = torch.abs(torch.fft.ifft2(torch.fft.ifftshift(under_sample)))
# fully_ref = torch.fft.fftshift(torch.fft.fft2(image_ref)) # [B, 1, H, W]
# plt.subplot(131), plt.imshow(np.array(image_ref[0, 0, :, :].cpu()), 'gray')
# plt.subplot(132), plt.imshow(np.array(image_rec[0, 0, :, :].cpu()), 'gray')
# plt.subplot(133), plt.imshow(np.array(under_image_rec[0, 0, :, :].cpu()), 'gray')
# plt.show()
# 参考图片，欠采样图片，欠采样k-space，掩码
net, image_Rec = model(under_image_rec, under_sample, mask)
loss_ = loss(image_Rec, image_rec)
test_losses.update(loss_.item(), image_rec.size(0)) # 整个epoch的平均损失
# ----------------------------------------------------------------------------------------------------------
# 计算PSNR，SSIM，NRMSE
psnr = peak_signal_noise_ratio(np.array(image_rec[0, 0, :, :].data.cpu()),
np.array(image_Rec[0, 0, :, :].clamp(0, 1).data.cpu()),
data_range=np.max(np.array(image_rec[0, 0, :, :].data.cpu())))
ssim = structural_similarity(np.array(image_rec[0, 0, :, :].data.cpu()),
np.array(image_Rec[0, 0, :, :].clamp(0, 1).data.cpu()))
nrmse = normalized_root_mse(np.array(image_rec[0, 0, :, :].data.cpu()),
np.array(image_Rec[0, 0, :, :].clamp(0, 1).data.cpu()))
test_psnr.update(psnr, image_rec.size(0))
test_ssim.update(ssim, image_rec.size(0))
test_nrmse.update(nrmse, image_rec.size(0))
# write the metrics into a csv File and visualize
with open(os.path.join(save_path, csv_filename), 'a', newline='') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=field_names, delimiter=',')
row_data = [name_rec, psnr, ssim, nrmse]
writer.writerow(dict(zip(field_names, row_data)))
# ----------------------------------------------------------------------------------------------------------
# 可视化图片质量-周期数的变化
step = int(total / 8)
image_idx = [x for x in range(0, 8 * step, step)]
if idx in image_idx:
image_recs.append(image_rec)
image_Recs.append(image_Rec)
image_refs.append(image_ref)
# ----------------------------------------------------------------------------------------------------------
# 保存生成的图像数据
name_rec = name_rec[0].split('.')[0]
if not os.path.exists(os.path.join(save_dir, 'image_under')):
os.makedirs(os.path.join(save_dir, 'image_under'), exist_ok=True)
if not os.path.exists(os.path.join(save_dir, 'image_Rec')):
os.makedirs(os.path.join(save_dir, 'image_Rec'), exist_ok=True)
save_path_under = os.path.join(save_dir, 'image_under', name_rec+'_under.jpg')
save_path_rec = os.path.join(save_dir, 'image_Rec', name_rec+'_Rec.jpg')
image_Rec = np.array(image_Rec[0, 0, :, :].clamp(0, 1).cpu())
image_under = np.array(under_image_rec[0, 0, :, :].cpu())
# np.save(save_path, image_sr_256)
save_image(save_path_under, image_under)
save_image(save_path_rec, image_Rec)
image_recs = torch.cat(image_recs, dim=0) # (8, 1, 256, 256)
image_Recs = torch.cat(image_Recs, dim=0)
image_refs = torch.cat(image_refs, dim=0)
image_recs = vutils.make_grid(image_recs, normalize=True, scale_each=True) # (3, 256, 2048)
image_Recs = vutils.make_grid(image_Recs, normalize=True, scale_each=True)
image_refs = vutils.make_grid(image_refs, normalize=True, scale_each=True)
output_writers[0].add_image('ground_truth images', torch.unsqueeze(image_recs[0], dim=0), 0)
output_writers[1].add_image('Rec images ', torch.unsqueeze(image_Recs[0], dim=0), 0)
output_writers[2].add_image('Ref images ', torch.unsqueeze(image_refs[0], dim=0), 0)
return test_losses.avg, test_psnr.avg, test_ssim.avg, test_nrmse.avg