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75854b3 2af0e94 75854b3 2af0e94 75854b3 2af0e94 75854b3 2af0e94 75854b3 2af0e94 75854b3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 | import torch
import torchvision
from torch import nn
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torch.optim import Adam
from torchvision.utils import make_grid
from Diffusion.diffuser import DeformDDPM
from Diffusion.networks import get_net, STN
from torchvision.transforms import Lambda
import random
import os
import utils
from Dataloader.dataloader0 import get_dataloader
from Dataloader.dataLoader import *
from torchvision.utils import save_image
from einops import rearrange, reduce, repeat
# import matplotlib.image
import numpy as np
import nibabel as nib
from tqdm import tqdm
import yaml
import argparse
EPS = 10e-8
parser = argparse.ArgumentParser()
parser.add_argument(
"--config",
"-C",
help="Path for the config file",
type=str,
default="Config/config_cmr.yaml",
# default="Config/config_lct.yaml",
required=False,
)
args = parser.parse_args()
#=======================================================================================================================
# config_path = 'Config/config_cmr.yaml'
# config_path = 'Config/config_lct.yaml'
# Load the YAML file into a dictionary
with open(args.config, 'r') as file:
hyp_parameters = yaml.safe_load(file)
print(hyp_parameters)
# hyp_parameters["aug_img_savepath"] = os.path.join(hyp_parameters["aug_img_savepath"],hyp_parameters["data_name"],'')
if not os.path.exists(hyp_parameters["aug_img_savepath"]):
os.makedirs(hyp_parameters["aug_img_savepath"])
if not os.path.exists(hyp_parameters["aug_msk_savepath"]):
os.makedirs(hyp_parameters["aug_msk_savepath"])
if not os.path.exists(hyp_parameters["aug_ddf_savepath"]):
os.makedirs(hyp_parameters["aug_ddf_savepath"])
print(hyp_parameters["aug_img_savepath"])
hyp_parameters['batchsize'] = 1
# =======================================================================================================================
# min_crop_ratio = 0.5
min_crop_ratio = 0.9
# dataset = OminiDataset_v1(transform=None,min_crop_ratio=min_crop_ratio)
# Infer_Loader = DataLoader(
# dataset,
# batch_size=hyp_parameters['batchsize'],
# shuffle=False
# )
# label_keys = ['heart']
label_keys = ['brain']
# label_keys = ['pancreas']
# database = ['MSD']
database = ['Brats2019']
dataset = OminiDataset_inference_w_all(transform=None,min_crop_ratio=min_crop_ratio,label_key = label_keys, database=database)
Infer_Loader = DataLoader(
dataset,
batch_size=hyp_parameters['batchsize'],
shuffle=False
)
# =======================================================================================================================
# Data_Loader=get_dataloader(hyp_parameters['data_name'],mode='aug')
# transformer = utils.get_transformer(img_sz=hyp_parameters["ndims"]*[hyp_parameters['img_size']])
# dataset = Data_Loader(patient_index = hyp_parameters["patients_list"])
# train_loader = DataLoader(dataset, batch_size = hyp_parameters['batchsize'], shuffle = False)
epoch=f'{hyp_parameters["model_id_str"]}_{hyp_parameters["data_name"]}_{hyp_parameters["net_name"]}'
model_save_path = f'Models/{hyp_parameters["data_name"]}_{hyp_parameters["net_name"]}/'
model_save_path = os.path.join(model_save_path, str(epoch)+'.pth')
Net = get_net(hyp_parameters["net_name"])
Deformddpm = DeformDDPM(
network=Net(n_steps = hyp_parameters["timesteps"],
ndims = hyp_parameters["ndims"],
num_input_chn = hyp_parameters["num_input_chn"],
res = hyp_parameters['img_size']
),
n_steps = hyp_parameters["timesteps"],
image_chw = [hyp_parameters["num_input_chn"]] + [hyp_parameters["img_size"]]*hyp_parameters["ndims"],
device = hyp_parameters["device"],
batch_size = hyp_parameters["batchsize"],
img_pad_mode = hyp_parameters["img_pad_mode"],
ddf_pad_mode = hyp_parameters["ddf_pad_mode"],
padding_mode = hyp_parameters["padding_mode"],
v_scale = hyp_parameters["v_scale"],
resample_mode = hyp_parameters["resample_mode"],
inf_mode = True, # set to True for inference, which will use fixed slice num and slice idx for better evaluation
)
Deformddpm.to(hyp_parameters["device"])
ddf_stn = STN(
img_sz = hyp_parameters["img_size"],
ndims = hyp_parameters["ndims"],
padding_mode = hyp_parameters['padding_mode'],
device = hyp_parameters["device"],
)
ddf_stn.to(hyp_parameters["device"])
print("Loading model from:", model_save_path)
# Deformddpm.load_state_dict(torch.load(model_save_path))
checkpoint = torch.load(model_save_path, map_location='cpu')
Deformddpm.load_state_dict(checkpoint['model_state_dict'])
Deformddpm.eval()
os.makedirs(hyp_parameters['reg_img_savepath'], exist_ok=True)
os.makedirs(hyp_parameters['reg_msk_savepath'], exist_ok=True)
os.makedirs(hyp_parameters['reg_ddf_savepath'], exist_ok=True)
print("total num of image:", len(Infer_Loader))
for e, d in tqdm(enumerate(Infer_Loader)):
# for e, d in enumerate(Infer_Loader):
# img, mask, pid = d
# img = d
# mask = d
img = d['img']
mask = d['labels']
# pid = pid.cpu().detach().numpy()
# pid = pid[0]
pid = e
print('Processing to patient:', pid, ' image:',e)
img = img.to(hyp_parameters["device"])
img = img.type(torch.float32)
image_original = img.cpu().detach().numpy()
#
#
if e <= 0:
target_img = img.clone().detach() # save the first image as target image for conditioning
mask = mask.to(hyp_parameters["device"])
mask = mask.type(torch.float32)
mask_original = mask.cpu().detach().numpy()
# print(pid, image_original.shape, mask_original.max())
nifti_img = utils.converet_to_nibabel(image_original, ndims=hyp_parameters["ndims"])
nifti_mask = utils.converet_to_nibabel(mask_original, ndims=hyp_parameters["ndims"])
# Saving original (undeformed image)
# CMR: format: Patient0001_Slice0001_ORG_NA.nii.gz
# Lung CT: Patient0001_Slice0001_ORG_NA.nii.gz
nib.save(nifti_img, os.path.join(hyp_parameters['reg_img_savepath'],utils.get_barcode([pid,e])+'.nii.gz'))
# Saving original (undeformed image)
# CMR: format: Patient0001_Slice0001_ORG_NA_GT.nii.gz
# Lung CT: ...
nib.save(nifti_img, os.path.join(hyp_parameters['reg_msk_savepath'],utils.get_barcode([pid,e])+'_GT.nii.gz'))
noise_step = hyp_parameters["start_noise_step"]
with torch.no_grad():
for im in range(1):
# # Permute
# if hyp_parameters["ndims"] == 2:
# [img, mask] = utils.random_permute([img, mask], select_dims=[-1, -2]) # add random rotation to image
# elif hyp_parameters["ndims"] == 3:
# [img, mask] = utils.random_permute([img, mask], select_dims=[-1, -2, -3]) # add random rotation to image
print('Generating - >', 'Subject-',pid,', Scan-',e,' (',im,'/',hyp_parameters["aug_coe"],')', end='\r')
[ddf_comp,ddf_rand],[img_rec,img_diff,img_save],[msk_rec,msk_diff,msk_save] = Deformddpm.diff_recover(img_org=img,cond_imgs=target_img.clone().detach(),msk_org=mask,T=[None,hyp_parameters["timesteps"]],v_scale=hyp_parameters["v_scale"],t_save=None,proc_type=hyp_parameters["condition_type"])
denoise_imgs = img_rec.cpu().detach().numpy()
denoise_msks = msk_rec.cpu().detach().numpy()
noisy_imgs_np = img_diff.cpu().detach().numpy()
noisy_msks_np = msk_diff.cpu().detach().numpy()
nifti_img_aug = utils.converet_to_nibabel(denoise_imgs, ndims=hyp_parameters["ndims"])
nifti_mask_aug = utils.converet_to_nibabel(denoise_msks, ndims=hyp_parameters["ndims"])
nifti_img = utils.converet_to_nibabel(noisy_imgs_np, ndims=hyp_parameters["ndims"])
nifti_mask = utils.converet_to_nibabel(noisy_msks_np, ndims=hyp_parameters["ndims"])
nib.save(nifti_img_aug, os.path.join(hyp_parameters['reg_img_savepath'],utils.get_barcode([pid,e,im,noise_step])+'.nii.gz'))
nib.save(nifti_mask_aug, os.path.join(hyp_parameters['reg_msk_savepath'],utils.get_barcode([pid,e,im,noise_step])+'_GT.nii.gz'))
# Saving noisy image to nifti
# CMR: format: Patient0001_Slice0001_NosieImg0001_NoiseStep0070.nii.gz
# Lung CT: ...
nib.save(nifti_img, os.path.join(hyp_parameters['reg_img_savepath'],utils.get_barcode([pid,e,im,noise_step],header=['Patient','Slice','NoiseImg','NoiseStep'])+'.nii.gz'))
nib.save(nifti_mask, os.path.join(hyp_parameters['reg_msk_savepath'],utils.get_barcode([pid,e,im,noise_step],header=['Patient','Slice','NoiseImg','NoiseStep'])+'_GT.nii.gz'))
if (im - hyp_parameters["start_noise_step"])%2 == 0:
noise_step = noise_step + hyp_parameters["noise_step"]
# break # for testing
if e > 5:
break
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