/
inference_abdomen_igma.py
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/
inference_abdomen_igma.py
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import copy
import glob
import nibabel as nib
import numpy as np
import os
import torch
from einops import rearrange
from omegaconf import OmegaConf
from torch.utils.data import DataLoader
from tqdm import tqdm
from ldm.util import instantiate_from_config
from volumentations import *
def get_augmentation():
return Compose([
Rotate((-2.5, 2.5), (-2.5, 2.5), (-2.5, 2.5), interpolation=0, p=1.0),
], p=1.0)
aug = get_augmentation()
resume_path = './infer_model/abdomen'
paths = resume_path.split("/")
idx = len(paths)-paths[::-1].index("infer_model")+1
logdir = "/".join(paths[:idx])
logdir = resume_path.rstrip("/")
ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))[1]
configs = OmegaConf.load(base_configs)
model = instantiate_from_config(configs.model)
model.init_from_ckpt(ckpt)
model.eval()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("Using device", device)
model = model.to(device)
resume_path = './infer_model/abdomen_slice'
paths = resume_path.split("/")
idx = len(paths)-paths[::-1].index("infer_model")+1
slice_logdir = resume_path.rstrip("/")
ckpt = os.path.join(slice_logdir, "checkpoints", "last.ckpt")
base_configs = sorted(glob.glob(os.path.join(slice_logdir, "configs/*.yaml")))[1]
configs = OmegaConf.load(base_configs)
slice_model = instantiate_from_config(configs.model)
slice_model.init_from_ckpt(ckpt)
slice_model.eval()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("Using device", device)
slice_model = slice_model.to(device)
config = OmegaConf.load('./configs/latent-diffusion/inference_abdomen.yaml')
data = instantiate_from_config(config.data)
data.prepare_data()
data.setup()
save_path = 'ig+ma'
save_path = os.path.join(logdir, save_path)
if not os.path.exists(save_path):
os.makedirs(save_path)
val_dataset = data.datasets['validation']
batch_size = 1
valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
val_num = len(val_dataset)
save_gt = True
for idx, data in tqdm(enumerate(valloader)):
if idx >= val_num:
break
name = data['name']
gt = data['volume_data']
data_seg = data['volume_seg']
slice_num = gt.shape[1]
aug_seg = copy.deepcopy(data_seg)
assert aug_seg.shape[0] == 1
assert aug_seg.shape[4] == 1
aug_seg_tmp = torch.ones_like(aug_seg) * (-1)
aug_seg_tmp = aug_seg_tmp + 0.1
aug_seg = aug_seg + 0.1
aug_seg = torch.from_numpy(aug(**{'image':aug_seg.squeeze().numpy()})['image']).unsqueeze(0).unsqueeze(-1)
mask = copy.deepcopy(aug_seg)
aug_seg_tmp[(mask!=0).bool()] = aug_seg[(mask!=0).bool()]
aug_seg = aug_seg_tmp - 0.1
seg = rearrange(aug_seg, 'b z h w c -> b z c h w')
seg = seg.to(memory_format=torch.contiguous_format).float()
seg = seg.to(model.device)
start_slice = np.random.randint(0, slice_num)
pos_id = torch.Tensor([start_slice / slice_num]).to(model.device)
slice_c = slice_model.get_learned_conditioning(pos_id)
ref_slice, _ = slice_model.sample_log(cond=slice_c, batch_size=1, ddim=True, eta=1., ddim_steps=200)
z_ref_0 = ref_slice.detach()
z_ref = z_ref_0.clone()
result = torch.zeros((batch_size, slice_num, 4, 64, 64)).cuda()
window_length = 16
h = 1
# sample in bi-directional with conditional diffusion model
upper_iters = (slice_num-start_slice-h) // (window_length-h)+1 if (slice_num-start_slice-h)%(window_length-h) != 0 else (slice_num-start_slice-h) // (window_length-h)
for i in range(upper_iters):
if i == upper_iters-1:
seg_i = seg[:, -window_length:]
else:
seg_i = seg[:, start_slice+i*window_length-i*h:start_slice+(i+1)*window_length-i*h]
seg_i = rearrange(seg_i, 'b z c h w -> (b z) c h w')
seg_i = seg_i.repeat(1,3,1,1)
encoder_posterior = model.encode_first_stage(seg_i)
seg_i = model.get_first_stage_encoding(encoder_posterior).detach()
z_ref = z_ref.unsqueeze(1).repeat(1,window_length,1,1,1)
z_ref = rearrange(z_ref, 'b z c h w -> (b z) c h w')
c = torch.cat([seg_i, z_ref], dim=1)
if i == 0:
samples_i, _ = model.sample_log(cond=c, batch_size=z_ref.shape[0], ddim=True, eta=1., ddim_steps=200)
else:
samples_i, _ = model.sample_log(cond=c, batch_size=z_ref.shape[0], ddim=True, eta=1., ddim_steps=200,
previous=x_minus1)
samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=window_length)
if i == upper_iters-1:
result[:, -window_length+h:] = samples_i[:,h:,...]
else:
if i == 0:
result[:, start_slice:start_slice+window_length] = samples_i
else:
result[:, start_slice+i*window_length-i*h+h:start_slice+(i+1)*window_length-i*h] = samples_i[:, h:]
z_ref = result[:,start_slice+(i+1)*window_length-i*h-h,...]
x_minus1 = samples_i[:, -h:,...]
z_ref = z_ref_0.clone()
# sample the other direction, note the direction
lower_iters = (start_slice-h) // (window_length-h)+1 if (start_slice-h)%(window_length-h) != 0 else (start_slice-h) // (window_length-h)
for i in range(lower_iters):
if i == lower_iters-1:
seg_i = seg[:, :window_length]
else:
seg_i = seg[:, start_slice-(i+1)*window_length+i*h+1:start_slice-i*window_length+i*h+1]
seg_i = rearrange(seg_i, 'b z c h w -> (b z) c h w')
seg_i = seg_i.repeat(1,3,1,1)
encoder_posterior = model.encode_first_stage(seg_i)
seg_i = model.get_first_stage_encoding(encoder_posterior).detach()
z_ref = z_ref.unsqueeze(1).repeat(1,window_length,1,1,1)
z_ref = rearrange(z_ref, 'b z c h w -> (b z) c h w')
c = torch.cat([seg_i, z_ref], dim=1)
if i == 0:
samples_i, _ = model.sample_log(cond=c, batch_size=z_ref.shape[0], ddim=True, eta=1., ddim_steps=200)
else:
samples_i, _ = model.sample_log(cond=c, batch_size=z_ref.shape[0], ddim=True, eta=1., ddim_steps=200,
previous=x_minus1, previous_reverse=True)
samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=window_length)
if i == lower_iters-1:
result[:, :window_length-h] = samples_i[:, :window_length-h,...]
else:
if i == 0:
result[:, start_slice-window_length+1:start_slice+1] = samples_i
else:
result[:, start_slice-(i+1)*window_length+i*h+1:start_slice-i*window_length+i*h-h+1] = samples_i[:, :-h]
z_ref = result[:,start_slice-(i+1)*window_length+i*h+1,...]
x_minus1 = samples_i[:, :h,...]
result = rearrange(result, 'b z c h w -> (b z) c h w')
x_result = torch.zeros((result.shape[0],3,512,512))
dec_unit = 16
num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
for i in range(num_dec_iter):
if i == num_dec_iter - 1:
x_result[-dec_unit:] = model.decode_first_stage(result[-dec_unit:])
x_result[i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[i*dec_unit:(i+1)*dec_unit])
x_result[x_result>1.0] = 1.0
x_result[x_result<-1.0] = -1.0
x_result = (x_result+1)/2
x_result = rearrange(x_result, '(b z) c h w -> b z c h w', z=slice_num)
x_result = x_result[0,:,0,...].detach().cpu().numpy()
x_result = x_result.transpose(1,2,0)
x_result = np.rot90(x_result, k=3, axes=(0,1))
data_path = os.path.join(save_path, str(f'{name[0]}_output.nii'))
data_nii = nib.Nifti1Image(x_result, np.identity(4))
nib.save(data_nii, data_path)
if save_gt:
gt = (gt+1)/2
gt = gt[0,:,:,:,0].detach().cpu().numpy()
gt = gt.transpose(1,2,0)
gt = np.rot90(gt, k=3, axes=(0,1))
data_path = os.path.join(save_path, str(f'{name[0]}_gt.nii'))
data_nii = nib.Nifti1Image(gt, np.identity(4))
nib.save(data_nii, data_path)
data_seg = (data_seg+1)/2 * 4
data_seg = data_seg[0,:,:,:,0].detach().cpu().numpy()
data_seg = data_seg.transpose(1,2,0)
data_seg = np.rot90(data_seg, k=3, axes=(0,1))
data_path = os.path.join(save_path, str(f'{name[0]}_seg.nii'))
data_nii = nib.Nifti1Image(data_seg, np.identity(4))
nib.save(data_nii, data_path)
aug_seg = (aug_seg+1)/2 * 4
aug_seg = aug_seg[0,:,:,:,0].detach().cpu().numpy()
aug_seg = aug_seg.transpose(1,2,0)
aug_seg = np.rot90(aug_seg, k=3, axes=(0,1))
data_path = os.path.join(save_path, str(f'{name[0]}_aug_seg.nii'))
data_nii = nib.Nifti1Image(aug_seg, np.identity(4))
nib.save(data_nii, data_path)