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import sys
from tqdm import tqdm
from tensorboardX import SummaryWriter
import logging
import time
import torch.optim as optim
from torch.utils.data import DataLoader
from networks.mynet import TwoBranch
from networks_time.mynet import DiffTwoBranch
from utils.option import args
import matplotlib.pyplot as plt
use_new_dataloader = True
if use_new_dataloader:
from dataloaders.m4raw_std_dataloader import M4Raw_TestSet, M4Raw_TrainSet, normalize, normalize_instance_dim
else:
from dataloaders.m4raw_dataloader import M4Raw_TestSet, M4Raw_TrainSet, normalize, normalize_instance_dim
from frequency_diffusion.degradation.k_degradation import get_ksu_kernel, apply_tofre, apply_to_spatial
from utils.lpips import LPIPS
from utils.metric import nmse, psnr, ssim, AverageMeter
from collections import defaultdict
# import imsave
from skimage.io import imsave
train_data_path = args.root_path
test_data_path = args.root_path
snapshot_path = "model/" + args.exp + "/"
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
batch_size = args.batch_size * len(args.gpu.split(','))
max_iterations = args.max_iterations
base_lr = args.base_lr
from utils.utils import *
frequency_distortion = True
num_timesteps = args.num_timesteps
image_size = args.image_size
distortion_sigma = 10/255
DEBUG = args.DEBUG
use_kspace = args.use_kspace
use_time_model = args.use_time_model
# Baseline ------------------------------------
# NMSE: 3.3329 ± 0.4915
# PSNR: 34.0418 ± 0.6790
# SSIM: 0.8699 ± 0.0143
# ------------------------------------
# Save Path: model/FSMNet_m4raw_4x//result_case/
# ------------------------------------
# NMSE: 3.1966 ± 0.3939
# PSNR: 34.2115 ± 0.5978
# SSIM: 0.8910 ± 0.0118
# ------------------------------------
# Save Path: model/FSMNet_m4raw_4x_kspace//result_case/
# num_timesteps = 5
image_size = 240
# if args.phase == 'test':
# kspace_masks = np.load(f"./dataloaders/example_mask/m4raw_{args.ACCELERATIONS[0]}_mask.npy")
# kspace_masks = torch.from_numpy(np.asarray(kspace_masks)).cuda()
#
# else:
use_in_mean_std = False
# Output a list of k-space kernels
kspace_masks = get_ksu_kernel(num_timesteps, image_size,
ksu_routine="LogSamplingRate",
accelerated_factor=args.ACCELERATIONS[0],
) # args.ACCELERATIONS = [4] or [8]
np.save(f"./dataloaders/example_mask/m4raw_{args.ACCELERATIONS[0]}_mask.npy", kspace_masks)
kspace_masks = torch.from_numpy(np.asarray(kspace_masks)).cuda()
print("kspace kernels shape:", kspace_masks.shape) # (1, 1, 320, 320)
@torch.no_grad()
def evaluate(model, data_loader, device):
model.eval()
print_i = 1
nmse_meter = AverageMeter()
psnr_meter = AverageMeter()
ssim_meter = AverageMeter()
output_dic = defaultdict(dict)
target_dic = defaultdict(dict)
input_dic = defaultdict(dict)
for id, sampled_batch in enumerate(data_loader):
if use_new_dataloader:
t1_img, t1_in = sampled_batch['t1'], sampled_batch['t1_in']
t2_img, t2_in = sampled_batch['t2'], sampled_batch['t2_in']
else:
t1_img, t1_in = sampled_batch['ref_image_full'], sampled_batch['ref_image_sub']
t2_img, t2_in = sampled_batch['tag_image_full'], sampled_batch['tag_image_sub']
t1_img = t1_img.to(device)
# t1_in = t1_in.to(device)
t2_img = t2_img.to(device)
t2_in = t2_in.to(device)
mean, std = sampled_batch['t2_mean'], sampled_batch['t2_std']
fname = sampled_batch['fname']
slice_num = sampled_batch['slice']
mean = mean.unsqueeze(1).to(device)
std = std.unsqueeze(1).to(device)
t2_in_origin = t2_in.clone()
# Degradation
if use_kspace:
b = 1
t = torch.randint(num_timesteps - 1, num_timesteps, (b,), device=device).long() # t-1
mask = kspace_masks[t]
fft, mask = apply_tofre(t2_in.clone(), mask) # t2_img
fft = fft * mask + 0.0
t2_in = apply_to_spatial(fft)
# Save for debug:
# put t2_in and t2_in_oringin side by side
t2_in_origin = t2_in.clone()
# print("test fft/mask = ", fft.shape, fft.shape)
if use_in_mean_std:
t2_in = t2_in * std + mean
t2_img = t2_img * std + mean
# print("Test After restore:", t2_in.shape, t2_img.shape)
t2_in, mean, std = normalize_instance_dim(t2_in, eps=1e-11)
t2_img = normalize(t2_img, mean=mean, stddev=std, eps=1e-11)
t2_in = t2_in.float()
t2_img = t2_img.float()
mean = mean[0]
std = std[0]
# print("Test Re-normalize:", t2_in.shape, t2_img.shape)
# print("in put t2_in shape:", t2_in.shape, t2_in_origin.shape)
while t >= 0:
if use_time_model:
outputs = model(t2_in, t1_img, t)['img_out']
else:
outputs = model(t2_in, t1_img)['img_out']
if t == 0:
mask = kspace_masks[0] # last one
t2_in = outputs
else:
k_full = kspace_masks[-1]
faded_recon_sample_fre, k_full = apply_tofre(t2_in, k_full)
with torch.no_grad():
kt_sub_1 = kspace_masks[t-1] #get_kspace_kernels(t - 2).cuda()
kt = kspace_masks[t] #self.get_kspace_kernels(t - 1).cuda() # last one
k_residual = kt_sub_1 - kt
recon_sample_fre, k_residual = apply_tofre(outputs, k_residual)
fre_amend = recon_sample_fre * k_residual
faded_recon_sample_fre = faded_recon_sample_fre + fre_amend # * (1-k_residual)
# faded_recon_sample_fre = faded_recon_sample_fre * kt_sub_1
outputs = apply_to_spatial(faded_recon_sample_fre)
t2_in = outputs
t = t-1
else:
outputs = model(t2_in, t1_img)['img_out']
if print_i:
t2_in_save = torch.cat([t2_in, t2_in_origin, t2_img], dim=3).cpu().numpy()[0, 0]
t2_in_save = (t2_in_save - t2_in_save.min()) / (t2_in_save.max() - t2_in_save.min())
# t2_in_save = np.stack([t2_in_save, t2_in_save, t2_in_save], axis=2)
# save to file
os.makedirs("./debug", exist_ok=True)
save_path = f"./debug/{use_kspace}_{fname[0]}_{slice_num[0]}.png"
plt.imsave(save_path, t2_in_save, cmap='gray')
print_i = 0
print("print_i")
t2_img = t2_img.squeeze(1) * std + mean
inputs = t2_in_origin.squeeze(1) * std + mean
outputs = outputs.squeeze(1) * std + mean
# print("output:", t2_img.shape, outputs.shape, inputs.shape)
for i, f in enumerate(fname):
output_dic[f][slice_num[i]] = outputs[i]
target_dic[f][slice_num[i]] = t2_img[i]
input_dic[f][slice_num[i]] = inputs[i]
if id > 100:
break
for name in output_dic.keys():
f_output = torch.stack([v for _, v in output_dic[name].items()])
f_target = torch.stack([v for _, v in target_dic[name].items()])
our_nmse = nmse(f_target.cpu().numpy(), f_output.cpu().numpy())
our_psnr = psnr(f_target.cpu().numpy(), f_output.cpu().numpy())
our_ssim = ssim(f_target.cpu().numpy(), f_output.cpu().numpy())
nmse_meter.update(our_nmse, 1)
psnr_meter.update(our_psnr, 1)
ssim_meter.update(our_ssim, 1)
print("==> Evaluate Metric")
print("Results ----------")
print("NMSE: {:.4}".format(nmse_meter.avg))
print("PSNR: {:.4}".format(psnr_meter.avg))
print("SSIM: {:.4}".format(ssim_meter.avg))
print("------------------")
model.train()
return {'NMSE': nmse_meter.avg, 'PSNR': psnr_meter.avg, 'SSIM':ssim_meter.avg}
if __name__ == "__main__":
## make logger file
if use_kspace:
if use_new_dataloader:
snapshot_path = snapshot_path.rstrip("/") + f'_t{num_timesteps}_new_kspace/'
else:
snapshot_path = snapshot_path.rstrip("/") + f'_t{num_timesteps}/'
if not isinstance(args.test_tag, type(None)):
snapshot_path = snapshot_path.rstrip("/") + f'_{args.test_tag}/'
if use_time_model:
snapshot_path = snapshot_path.rstrip("/") + '_time/'
if not frequency_distortion:
snapshot_path = snapshot_path.rstrip("/") + 'no_distortion/'
if not os.path.exists(snapshot_path):
os.makedirs(snapshot_path)
logging.basicConfig(filename=snapshot_path + "/log.txt", level=logging.INFO,
format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S')
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
logging.info(str(args))
if use_time_model:
model = DiffTwoBranch(args).cuda()
else:
model = TwoBranch(args).cuda()
# model = build_model_from_name(args).cuda()
device = torch.device('cuda')
model.to(device)
lpips_loss = LPIPS().eval().to(device)
if len(args.gpu.split(',')) > 1:
model = nn.DataParallel(model)
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('number of params: %.2f M' % (n_parameters / 1024 / 1024))
# db_train = M4Raw_TrainSet(args.root_path, args.MRIDOWN, use_kspace=use_kspace) # build_dataset(args, mode='train')
# db_test = M4Raw_TestSet(args.root_path, args.MRIDOWN, use_kspace=use_kspace) # build_dataset(args, mode='val')
db_train = M4Raw_TrainSet(args, use_kspace=use_kspace, DEBUG=DEBUG) # build_dataset(args, mode='train')
db_test = M4Raw_TestSet(args, use_kspace=use_kspace, DEBUG=DEBUG) #
trainloader = DataLoader(db_train, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=4, pin_memory=True)
if args.phase == 'train':
model.train()
params = list(model.parameters())
optimizer1 = optim.AdamW(params, lr=base_lr, betas=(0.9, 0.999), weight_decay=1e-4)
scheduler1 = optim.lr_scheduler.StepLR(optimizer1, step_size=20000, gamma=0.5)
writer = SummaryWriter(snapshot_path + '/log')
iter_num = 0
max_epoch = max_iterations // len(trainloader) + 1
best_status = {'NMSE': 10000000, 'PSNR': 0, 'SSIM': 0}
fft_weight=0.01
criterion = nn.L1Loss().to(device, non_blocking=True)
freloss = Frequency_Loss().to(device, non_blocking=True)
t = 0
for epoch_num in tqdm(range(max_epoch), ncols=70):
time1 = time.time()
start_time = time.time()
for i_batch, sampled_batch in enumerate(trainloader):
time2 = time.time()
# T1 is the reference image, T2 is the target image
t1_img, t1_in = sampled_batch['t1'], sampled_batch['t1_in']
t2_img, t2_in = sampled_batch['t2'], sampled_batch['t2_in']
t1_img = t1_img.to(device)
t1_in = t1_in.to(device)
t2_img = t2_img.to(device)
t2_in = t2_in.to(device)
time3 = time.time()
# Degradation
if use_kspace:
t2_origin = t2_in.clone()
b = t1_in.size(0)
t = torch.randint(0, num_timesteps, (b,), device=device).long()
mask = kspace_masks[t]
fft, mask = apply_tofre(t2_in.clone(), mask) # TODO t2_img
fft = fft * mask
# Frequency Noise
if frequency_distortion:
fft_magnitude = torch.abs(fft) # 幅度
fft_phase = torch.angle(fft) # 相位
# Add noise to unmasked frequencies to maintain the stochasticity that diffusion models typically rely on.
sigma = distortion_sigma * torch.abs(torch.randn(1)).item()
noise = torch.randn_like(fft_magnitude) * sigma
noise_magnitude = noise * fft_magnitude * mask # + noise * (1 - mask)
fft_magnitude += noise_magnitude
sigma = distortion_sigma / 2 * torch.abs(torch.randn(1)).item()
noise = torch.randn_like(fft_phase) * sigma
noise_pha = noise * fft_phase * mask # + noise * (1 - mask)
fft_phase += noise_pha
fft = fft_magnitude * torch.exp(1j * fft_phase)
t2_in = apply_to_spatial(fft)
if use_in_mean_std:
mean, std = sampled_batch['t2_mean'], sampled_batch['t2_std']
mean = mean.unsqueeze(1).unsqueeze(1).to(device)
std = std.unsqueeze(1).unsqueeze(1).to(device)
t2_in = t2_in * std + mean
t2_img = t2_img * std + mean
# print("Train After restore:", t2_in.shape, t2_img.shape, mean.shape, std.shape)
t2_in, mean, std = normalize_instance_dim(t2_in, eps=1e-11)
t2_img = normalize(t2_img, mean=mean, stddev=std, eps=1e-11).detach()
t2_in = t2_in.float()
t2_img = t2_img.float()
# print("Train After restore:", t2_in.shape, t2_img.shape)
# breakpoint()
if use_time_model:
outputs = model(t2_in, t1_img, t) # ['img_out']
else:
outputs = model(t2_in, t1_img) # ['img_out']
loss = criterion(outputs['img_out'], t2_img) + \
fft_weight * freloss(outputs['img_fre'], t2_img) + \
criterion(outputs['img_fre'], t2_img)
# 0.01 * lpips_loss(outputs['img_out'], target).mean()
time4 = time.time()
optimizer1.zero_grad()
loss.backward()
if args.clip_grad == "True":
### clip the gradients to a small range.
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.01)
optimizer1.step()
scheduler1.step()
time5 = time.time()
# summary
iter_num = iter_num + 1
print_iter = 100 #if not DEBUG else 5
if iter_num % print_iter == 0:
logging.info('iteration %d [%.2f sec]: learning rate : %f loss : %f ' % (iter_num, time.time() - start_time, scheduler1.get_lr()[0], loss.item()))
if DEBUG:
break
if iter_num % 20000 == 0:
save_mode_path = os.path.join(snapshot_path, 'iter_' + str(iter_num) + '.pth')
torch.save({'network': model.state_dict()}, save_mode_path)
logging.info("save model to {}".format(save_mode_path))
if iter_num > max_iterations:
break
time1 = time.time()
## ================ Evaluate ================
logging.info(f'Epoch {epoch_num} Evaluation:')
# print()
eval_result = evaluate(model, testloader, device)
if eval_result['PSNR'] > best_status['PSNR']:
best_status = {'NMSE': eval_result['NMSE'], 'PSNR': eval_result['PSNR'], 'SSIM': eval_result['SSIM']}
best_checkpoint_path = os.path.join(snapshot_path, 'best_checkpoint.pth')
torch.save({'network': model.state_dict()}, best_checkpoint_path)
print('New Best Network saved:', best_checkpoint_path)
logging.info(f"average MSE: {eval_result['NMSE']} average PSNR: {eval_result['PSNR']} average SSIM: {eval_result['SSIM']}")
print("snapshot_path=", snapshot_path)
if iter_num > max_iterations:
break
print(best_status)
save_mode_path = os.path.join(snapshot_path, 'iter_' + str(max_iterations) + '.pth')
torch.save({'network': model.state_dict()},
save_mode_path)
logging.info("save model to {}".format(save_mode_path))
writer.close()
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