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utils.py
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utils.py
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# -*-coding:utf-8 -*-
'''
Created on 19/10/2022
@author: Carlos
'''
from pathlib import Path
import torch
import os, sys
import numpy as np
import torch.nn as nn
from math import ceil, floor
from datetime import date
import nibabel as nib
import patcher
class Util():
'''Class that includes multiple useful functions'''
def __init__(self,outputroot,e):
self.output=os.path.join(outputroot,e)
def weights_init(self, m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
def preprocessdata(self, d, bs):
if len(d[0].shape)==5 and len(d[1].shape)<=2:
x=torch.permute(d[0],(0,4,3,1,2))
y=torch.unsqueeze(d[1], axis=-1)
y=torch.unsqueeze(y, axis=-1); y=torch.unsqueeze(y, axis=-1)
return x, y
elif len(d[0].shape)==5 and len(d[1].shape)==5:
x=torch.permute(d[0],(0,4,3,1,2))
y=torch.permute(d[1],(0,4,3,1,2))
return x, y
elif len(d[0].shape)==4 and len(d[1].shape)==4:
x=torch.permute(d[0],(0,3,1,2))
y=torch.permute(d[1],(0,3,1,2))
return x, y
elif len(d[0].shape)==4 and len(d[1].shape)==3:
x=torch.permute(d[0],(0,3,1,2))
y=torch.permute(d[1],(0,2,1))
return x, y
elif len(d[0].shape)<2 and bs==1:
x=torch.unsqueeze(d[0],axis=0)
y=torch.unsqueeze(d[1],axis=0)
return x, y
else:
return d
def padding(self, data, p, p2v):
l=data.shape[0]; w=data.shape[1]; h=data.shape[2]
maxpad = l if l>w else w
maxhpad = h
while maxpad%p!=0: maxpad+=1
while maxhpad%p!=0: maxhpad+=1
self.pad=((ceil((maxpad-l)/2), floor((maxpad-l)/2)), (ceil((maxpad-w)/2), floor((maxpad-w)/2)), (ceil((maxhpad-h)/2), floor((maxhpad-h)/2)))
if p2v: self.pad=tuple([(i[0]+1,i[1]+1) for i in self.pad])
return np.pad(data, (self.pad[0], self.pad[1], self.pad[2], (0,0)))
def unpadding(self, padded_data, pad):
self.pad=pad
lright=padded_data.shape[0]-self.pad[0][0]
lleft=self.pad[0][1]
wright=padded_data.shape[1]-self.pad[1][0]
wleft=self.pad[1][1]
hright=padded_data.shape[2]-self.pad[2][1]
hleft=self.pad[2][0]
return padded_data[lleft:lright,wleft:wright,hleft:hright,:]
def save_current_model(self, net, epoch, model):
model_file=os.path.join(self.output,'models', 'model_epoch_'+str(epoch)+'.pkl')
latest_model_file=os.path.join(self.output,'models', model+'.pkl')
torch.save(net.state_dict(), Path(model_file))
torch.save(net.state_dict(), Path(latest_model_file))
def load_model(self, net,model):
latest_model_file=os.path.join(self.output,'models',model+'.pkl')
net.load_state_dict(torch.load(latest_model_file))
def save_current_errors(self, epoch,n_epochs,i,n_batches,loss):
message = 'Loss at epoch [{}/{}] and batch [{}/{}]: {}'.format(epoch+1,n_epochs,i+1,n_batches,round(loss,6))
sys.stdout.flush()
if i+1!=n_batches: sys.stdout.write(message+' \r')
else: print(message)
log_name=os.path.join(self.output,'logs','training_loss.txt')
with open(log_name, "a+") as log_file:
if epoch==0 and i==0:
log_file.write('---------------Training session {}---------------\n'.format(date.today()))
log_file.write('%s\n' % message)
def mkdirs(self):
os.makedirs(os.path.join(self.output,'models'), exist_ok=True)
os.makedirs(os.path.join(self.output,'logs'), exist_ok=True)
os.makedirs(os.path.join(self.output,'results'), exist_ok=True)
def save_nifti(self, data, fn, info, method, data_info):
if method=='m2m':
for p,name in zip(range(data.shape[-1]),data_info):
if not name.startswith('_'): name='_'+name
nifti_file=os.path.join(self.output,'results', fn+name)
im = nib.Nifti1Image(data[:,:,:,p], info.affine, info.header)
nib.save(im, nifti_file)
else:
nifti_file=os.path.join(self.output,'results', fn)
im = nib.Nifti1Image(data, info.affine, info.header)
nib.save(im, nifti_file)
def batch_data(self, train_data, batch_size): #Deprecated
while len(train_data)%batch_size!=0: train_data.append((np.zeros(5,), np.zeros(16,)))
batch_size = batch_size if len(train_data)%batch_size==0 else self.find_bs(train_data,batch_size)
return [(np.stack([train_data[i+b][0] for b in range(batch_size)], axis=0),
np.stack([train_data[i+b][1] for b in range(batch_size)], axis=0))
for i in range(0,len(train_data),batch_size)]
def find_bs(self, train, bs): #Deprecated
for i in range(bs,0,-1):
if len(train)%i==0:
return i
def postpatch2vox(self, opt, data):
data = data.cpu()
result = np.zeros((opt.data_info[1][0]-2,opt.data_info[1][1]-2,opt.data_info[1][2]-2,opt.out_channels))
i=0
for z in range(opt.data_info[1][2]-2):
for x in range(opt.data_info[1][0]-2):
for y in range(opt.data_info[1][1]-2):
result[x,y,z,:]=data[i,:]
i+=1
return self.unpadding(result, tuple([(i[0]-1, i[1]-1) for i in opt.data_info[2]]))
def postprocessing(self, opt, tm):
self.mask=tm
if opt.patch_size:
tm_p=tm[:,:opt.out_channels]; tm_i=tm[:,:opt.in_channels]
# Prediction
predicted_data = torch.cat(opt.predicted_patches,0)
if opt.patch2vox: predicted_data = self.postpatch2vox(opt, predicted_data)
else:
predicted_data = patcher.reconstruct(predicted_data, opt.data_info[0], opt.data_info[1], opt.stride)
predicted_data = self.unpadding(predicted_data, opt.data_info[2])
predicted_data = predicted_data*np.reshape(tm_p, predicted_data.shape)
# Ground truth
real_data=torch.cat(opt.real_patches,0)
if opt.patch2vox: real_data = self.postpatch2vox(opt, real_data)
else:
real_data = patcher.reconstruct(real_data, opt.data_info[0], opt.data_info[1], opt.stride)
real_data = self.unpadding(real_data, opt.data_info[2])
real_data = real_data*np.reshape(tm_p, real_data.shape)
# Input
input_data = torch.cat(opt.input_patches,0)
input_data = patcher.reconstruct(input_data, opt.data_info[0], opt.data_info[1], opt.stride)
input_data = self.unpadding(input_data, opt.data_info[2])
input_data = input_data*np.reshape(tm_i, input_data.shape)
else:
tm_p=tm_r=tm.copy(); tm_i=tm[:,:opt.in_channels]
# Prediction
predicted_patches=torch.cat(opt.predicted_patches,0)
predicted_data = predicted_patches.cpu().detach().numpy()
tm_p[tm_p==1] = predicted_data.flatten()[:len(tm_p[tm_p==1])]
predicted_data = np.reshape(tm_p,(opt.data_info[1][0],opt.data_info[1][1],opt.data_info[1][2],opt.out_channels))
# Gorund truth
real_patches=torch.cat(opt.real_patches,0)
real_data = real_patches.cpu().detach().numpy()
tm_r[tm_r==True] = real_data.flatten()[:len(tm_r[tm_r==1])]
real_data = np.reshape(tm_r,(opt.data_info[1][0],opt.data_info[1][1],opt.data_info[1][2],opt.out_channels))
#Input
input_patches=torch.cat(opt.input_patches,0)
input_data = input_patches.cpu().detach().numpy()
tm_i[tm_i==1] = input_data.flatten()[:len(tm_i[tm_i==1])]
input_data = np.reshape(tm_i,(opt.data_info[1][0],opt.data_info[1][1],opt.data_info[1][2],opt.in_channels))
self.errors(predicted_data, real_data)
# Concatenate input and prediction if signal-to-signal approach (to save the 21 b-values as one NIFTI file)
if opt.method!='m2m': to_save = np.concatenate((input_data, predicted_data), axis=-1)
else: to_save = predicted_data
# Recover normalization
if opt.norm != 'non':
for bval in range(to_save.shape[3]):
to_save[:,:,:,bval]=to_save[:,:,:,bval]*opt.data_info[3][bval]
# Save results
self.save_nifti(to_save, opt.file_name, opt.data_info[4], opt.method, opt.data_info[5])
print('Test finished!\n')
def errors(self, pred, real):
print('\nCalculating errors...')
if torch.is_tensor(pred): pred = pred.numpy()
if torch.is_tensor(real): real = real.numpy()
e=np.abs((real[np.where(real!=0)]-pred[np.where(real!=0)]).flatten())
message='\nTesting error: {:.4f} [{:.4f} - {:.4f}]\n'.format(np.mean(e),np.min(e), np.max(e))
print(message)
log_name=os.path.join(self.output,'logs','test_res_error.txt')
with open(log_name, "a+") as log_file:
log_file.write('---------------Testing session {}---------------\n'.format(date.today()))
log_file.write('%s\n' % message)