def test():
model_stage1.eval();
model_select.eval();
global bestloss
test_loss=0
for sample in val_data.get_loader():
if (use_gpu):
sample['image']=sample['image'].to(device)
sample['label']=sample['label'].to(device)
sample['size'][0]=sample['size'][0].to(device);
sample['size'][1]=sample['size'][1].to(device);
stage1_label=model_stage1(sample['image'])
theta=model_select(stage1_label,sample['size'])
theta_label = torch.zeros((sample['image'].size()[0],6,2,3),device=device,requires_grad=False); #[batch_size,6,2,3]
W=1024.0;
H=1024.0;
'''
cens = torch.floor(calc_centroid_old(sample['label'])) #[batch_size,9,2]
for i in range(sample['image'].size()[0]):
for j in range(9):
cens[i,j,0]=cens[i,j,0]*(sample['size'][0][i]-1.0)/(128.0-1.0)
cens[i,j,1]=cens[i,j,1]*(sample['size'][1][i]-1.0)/(128.0-1.0)
points = torch.floor(torch.cat([cens[:, 1:6],cens[:, 6:9].mean(dim=1, keepdim=True)],dim=1)) #[batch_size,6,2]
'''
points=torch.floor(calc_centroid(sample['label_org']))
for i in range(6):
theta_label[:,i,0,0]=(81.0-1.0)/(W-1.0);
theta_label[:,i,1,1]=(81.0-1.0)/(H-1.0);
theta_label[:,i,0,2]=-1+2*points[:,i,0]/(W-1.0);
theta_label[:,i,1,2]=-1+2*points[:,i,1]/(H-1.0);
loss=fun.smooth_l1_loss(theta, theta_label);
test_loss+=fun.smooth_l1_loss(theta, theta_label).data;
test_loss/=len(val_data.get_loader().dataset)
print('\nTest set: {} Cases，Average loss: {:.8f}\n'.format(
len(test_data.get_loader().dataset),test_loss))
loss_list.append(test_loss.data.cpu().numpy());
if (test_loss<bestloss):
bestloss=test_loss
print("Best data Stage1 Updata\n");
torch.save(model_stage1,"./preBestNet_stage1")
torch.save(model_select,"./preBestNet_select")
def train(epoch):
model_stage2.train()
'''
part1_time=0;
part2_time=0;
part3_time=0;
prev_time=time.time();
'''
unloader = transforms.ToPILImage()
losstmp = 0
lossstep = 0
k = 0
for batch_idx, sample in enumerate(train_data.get_loader()):
'''
now_time=time.time();
part3_time+=now_time-prev_time;
prev_time=now_time;
'''
if (use_gpu):
sample['image'] = sample['image'].to(device)
sample['label'] = sample['label'].to(device)
sample['image_org'] = sample['image_org'].to(device)
sample['label_org'] = sample['label_org'].to(device)
sample['size'][0] = sample['size'][0].to(device)
sample['size'][1] = sample['size'][1].to(device)
'''
for i in range(batch_size):
image=sample['image_org'][i].cpu().clone();
image=transforms.ToPILImage()(image).convert('RGB')
plt.imshow(image);
plt.show(block=True);
'''
optimizer_stage2.zero_grad()
theta_label = torch.zeros((sample['image'].size()[0], 6, 2, 3),
device=device,
requires_grad=False)
#[batch_size,6,2,3]
W = 1024.0
H = 1024.0
points = torch.floor(calc_centroid(sample['label_org']))
for i in range(6):
theta_label[:, i, 0, 0] = (81.0 - 1.0) / (W - 1.0)
theta_label[:, i, 1, 1] = (81.0 - 1.0) / (H - 1.0)
theta_label[:, i, 0, 2] = -1 + 2 * points[:, i, 0] / (W - 1.0)
theta_label[:, i, 1, 2] = -1 + 2 * points[:, i, 1] / (H - 1.0)
stage2_label = model_stage2(sample['image_org'], theta_label)
parts2 = []
parts_label2 = []
loss = []
for i in range(6):
affine_stage2 = F.affine_grid(
theta_label[:, i], (sample['image'].size()[0], 3, 81, 81),
align_corners=True)
parts2.append(
F.grid_sample(sample['image_org'],
affine_stage2,
align_corners=True))
affine_stage2 = F.affine_grid(
theta_label[:, i],
(sample['image'].size()[0], label_channel[i], 81, 81),
align_corners=True)
parts_label2.append(
F.grid_sample(sample['label_org'][:, label_list[i]],
affine_stage2,
align_corners=True))
parts_label2[i][:, 0] += 0.00001
parts_label2[i] = parts_label2[i].detach()
'''
for j in range(sample['image'].size()[0]):
if (not os.path.exists("./data/trainimg_output/"+train_data.get_namelist()[(k+j)%2000])):
os.mkdir("./data/trainimg_output/"+train_data.get_namelist()[(k+j)%2000]);
image3=transforms.ToPILImage()(sample['image_org'][j].cpu().clone()).convert('RGB')
image3.save("./data/trainimg_output/"+train_data.get_namelist()[(k+j)%2000]+'/'+str((k+j)//2000)+'_orgimage'+'.jpg',quality=100);
image3=transforms.ToPILImage()(parts2[i][j].cpu().clone()).convert('RGB')
image3.save("./data/trainimg_output/"+train_data.get_namelist()[(k+j)%2000]+'/'+str((k+j)//2000)+'lbl0'+str(i)+'_thetalabel'+'.jpg',quality=100);
image3=unloader(np.uint8(parts_label2[i][j][1].cpu().detach().numpy()))
image3.save("./data/trainimg_output/"+train_data.get_namelist()[(k+j)%2000]+'/'+str((k+j)//2000)+'lbl0'+str(i)+'_label'+'_thetalabel'+'.jpg',quality=100);
'''
loss_tmp = fun.cross_entropy(
stage2_label[i], parts_label2[i].argmax(dim=1, keepdim=False))
loss.append(loss_tmp)
k += sample['image'].size()[0]
if (batch_idx % 100 == 0):
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(sample['image']),
len(train_data.get_loader().dataset),
100. * batch_idx / len(train_data.get_loader()),
torch.sum(torch.stack(loss))))
'''
now_time=time.time();
part1_time+=now_time-prev_time;
prev_time=now_time;
'''
loss = torch.stack(loss)
loss.backward(torch.ones(6, device=device, requires_grad=False))
losstmp += torch.sum(loss).item()
lossstep += sample['image'].size()[0]
optimizer_stage2.step()
'''
def printoutput():
model_stage2 = torch.load("./preBestNet_stage2", map_location="cpu")
if (use_gpu):
model_stage2 = model_stage2.to(device)
model_stage2.eval()
global bestloss, bestf1
test_loss = 0
hists = []
k = 0
for sample in test_data.get_loader():
if (use_gpu):
sample['image'] = sample['image'].to(device)
sample['label'] = sample['label'].to(device)
sample['image_org'] = sample['image_org'].to(device)
sample['label_org'] = sample['label_org'].to(device)
sample['size'][0] = sample['size'][0].to(device)
sample['size'][1] = sample['size'][1].to(device)
theta_label = torch.zeros((sample['image'].size()[0], 6, 2, 3),
device=device,
requires_grad=False)
#[batch_size,6,2,3]
W = 1024.0
H = 1024.0
points = torch.floor(calc_centroid(sample['label_org']))
for i in range(6):
theta_label[:, i, 0, 0] = (81.0 - 1.0) / (W - 1.0)
theta_label[:, i, 1, 1] = (81.0 - 1.0) / (H - 1.0)
theta_label[:, i, 0, 2] = -1 + 2 * points[:, i, 0] / (W - 1.0)
theta_label[:, i, 1, 2] = -1 + 2 * points[:, i, 1] / (H - 1.0)
stage2_label = model_stage2(sample['image_org'], theta_label)
#parts=[];
parts_label = []
for i in range(6):
#affine_stage2=F.affine_grid(theta[:,i],(sample['image'].size()[0],3,81,81),align_corners=True);
#parts.append(F.grid_sample(sample['image'],affine_stage2),align_corners=True);
affine_stage2 = F.affine_grid(
theta_label[:, i],
(sample['image'].size()[0], label_channel[i], 81, 81),
align_corners=True)
parts_label.append(
F.grid_sample(sample['label_org'][:, label_list[i]],
affine_stage2,
align_corners=True))
parts_label[i][:, 0] += 0.00001
test_loss += fun.cross_entropy(
stage2_label[i], parts_label[i].argmax(dim=1,
keepdim=False)).data
for j in range(sample['image'].shape[0]):
path = pre_stage2_output_path + '/' + test_data.get_namelist()[
k + j]
if not os.path.exists(path):
os.makedirs(path)
image = TF.to_pil_image(
parts_label[i][j][1].unsqueeze(0).cpu())
image.save(path + '/' + test_data.get_namelist()[k + j] +
'lbl0' + str(i) + '_label.jpg',
quality=100)
image = torch.softmax(stage2_label[i][j].cpu(),
dim=0).argmax(dim=0, keepdim=True)
image = torch.zeros(label_channel[i], 81,
81).scatter_(0, image, 1)
image = TF.to_pil_image(image[1].unsqueeze(0), mode="L")
image.save(path + '/' + test_data.get_namelist()[k + j] +
'lbl0' + str(i) + '_train.jpg',
quality=100)
output_2 = torch.softmax(stage2_label[i],
dim=1).argmax(dim=1, keepdim=False)
output_2 = output_2.cpu().clone()
target_2 = parts_label[i].argmax(dim=1, keepdim=False)
target_2 = target_2.cpu().clone()
hist = np.bincount(9 * target_2.reshape([-1]) +
output_2.reshape([-1]),
minlength=81).reshape(9, 9)
hists.append(hist)
k += sample['image'].shape[0]
hists_sum = np.sum(np.stack(hists, axis=0), axis=0)
tp = 0
tpfn = 0
tpfp = 0
f1score = 0.0
for i in range(9):
for j in range(9):
print(hists_sum[i][j], end=' ')
print()
for i in range(1, 9):
tp += hists_sum[i][i].sum()
tpfn += hists_sum[i, :].sum()
tpfp += hists_sum[:, i].sum()
f1score = 2 * tp / (tpfn + tpfp)
test_loss /= len(test_data.get_loader().dataset)
print('\nPrintoutput Average loss: {:.4f}\n'.format(test_loss))
print("STN-iCNN stage2 tp=", tp)
print("STN-iCNN stage2 tpfp=", tpfp)
print("STN-iCNN tstage2 pfn=", tpfn)
print('\nPrintoutputF1 Score: {:.4f}\n'.format(f1score))
print("printoutput Finish")
def test(epoch):
model_stage2.eval()
global bestloss, bestf1
test_loss = 0
hists = []
for sample in val_data.get_loader():
if (use_gpu):
sample['image'] = sample['image'].to(device)
sample['label'] = sample['label'].to(device)
sample['image_org'] = sample['image_org'].to(device)
sample['label_org'] = sample['label_org'].to(device)
sample['size'][0] = sample['size'][0].to(device)
sample['size'][1] = sample['size'][1].to(device)
theta_label = torch.zeros((sample['image'].size()[0], 6, 2, 3),
device=device,
requires_grad=False)
#[batch_size,6,2,3]
W = 1024.0
H = 1024.0
points = torch.floor(calc_centroid(sample['label_org']))
for i in range(6):
theta_label[:, i, 0, 0] = (81.0 - 1.0) / (W - 1.0)
theta_label[:, i, 1, 1] = (81.0 - 1.0) / (H - 1.0)
theta_label[:, i, 0, 2] = -1 + 2 * points[:, i, 0] / (W - 1.0)
theta_label[:, i, 1, 2] = -1 + 2 * points[:, i, 1] / (H - 1.0)
stage2_label = model_stage2(sample['image_org'], theta_label)
#parts=[];
parts_label = []
for i in range(6):
#affine_stage2=F.affine_grid(theta[:,i],(sample['image'].size()[0],3,81,81),align_corners=True);
#parts.append(F.grid_sample(sample['image'],affine_stage2),align_corners=True);
affine_stage2 = F.affine_grid(
theta_label[:, i],
(sample['image'].size()[0], label_channel[i], 81, 81),
align_corners=True)
parts_label.append(
F.grid_sample(sample['label_org'][:, label_list[i]],
affine_stage2,
align_corners=True))
parts_label[i][:, 0] += 0.00001
test_loss += fun.cross_entropy(
stage2_label[i], parts_label[i].argmax(dim=1,
keepdim=False)).data
output_2 = torch.softmax(stage2_label[i],
dim=1).argmax(dim=1, keepdim=False)
output_2 = output_2.cpu().clone()
target_2 = parts_label[i].argmax(dim=1, keepdim=False)
target_2 = target_2.cpu().clone()
hist = np.bincount(9 * target_2.reshape([-1]) +
output_2.reshape([-1]),
minlength=81).reshape(9, 9)
hists.append(hist)
hists_sum = np.sum(np.stack(hists, axis=0), axis=0)
for i in range(9):
for j in range(9):
print(hists_sum[i][j], end=' ')
print()
print()
tp = 0
tpfn = 0
tpfp = 0
f1score = 0.0
for i in range(1, 9):
tp += hists_sum[i][i].sum()
tpfn += hists_sum[i, :].sum()
tpfp += hists_sum[:, i].sum()
f1score = 2 * tp / (tpfn + tpfp)
test_loss /= len(test_data.get_loader().dataset)
print('\nTest set: {} Cases，Average loss: {:.4f}\n'.format(
len(test_data.get_loader().dataset), test_loss))
print("STN-iCNN tp=", tp)
print("STN-iCNN tpfp=", tpfp)
print("STN-iCNN tpfn=", tpfn)
print('\nTest set: {} Cases，F1 Score: {:.4f}\n'.format(
len(test_data.get_loader().dataset), f1score))
loss_list.append(test_loss.data.cpu().numpy())
f1_list.append(f1score)
if (UseF1):
if (f1score > bestf1):
bestf1 = f1score
print("Best data Updata\n")
torch.save(model_stage2, "./preBestNet_stage2")
else:
if (test_loss < bestloss):
bestloss = test_loss
print("Best data Updata\n")
torch.save(model_stage2, "./preBestNet_stage2")
def printoutput(in_data):
unloader = transforms.ToPILImage()
k = 0
hists = []
global label_list
for sample in in_data.get_loader():
if (use_gpu):
sample['image_org'] = sample['image_org'].to(device)
sample['label_org'] = sample['label_org'].to(device)
N = sample['image_org'].shape[0]
theta_label = torch.zeros((N, 6, 2, 3),
device=device,
requires_grad=False)
#[batch_size,6,2,3]
W = 1024.0
H = 1024.0
cens = torch.floor(calc_centroid(
sample['label_org'])) #[batch_size,9,2]
points = torch.floor(
torch.cat([cens[:, 1:6], cens[:, 6:9].mean(dim=1, keepdim=True)],
dim=1)) #[batch_size,6,2]
for i in range(6):
theta_label[:, i, 0, 0] = (81.0 - 1.0) / (W - 1.0)
theta_label[:, i, 1, 1] = (81.0 - 1.0) / (H - 1.0)
theta_label[:, i, 0, 2] = -1 + 2 * points[:, i, 0] / (W - 1.0)
theta_label[:, i, 1, 2] = -1 + 2 * points[:, i, 1] / (H - 1.0)
parts = []
parts_label = []
for i in range(6):
affine_stage2 = F.affine_grid(theta_label[:, i], (N, 3, 81, 81),
align_corners=True)
parts.append(
F.grid_sample(sample['image_org'],
affine_stage2,
align_corners=True))
affine_stage2 = F.affine_grid(theta_label[:, i],
(N, label_channel[i], 81, 81),
align_corners=True)
parts_label.append(
F.grid_sample(sample['label_org'][:, label_list[i]],
affine_stage2,
align_corners=True))
parts_label[i][:, 0] += 0.00001
for j in range(sample['image_org'].shape[0]):
parts_label_tmp = parts_label[i][j].argmax(dim=0, keepdim=True)
parts_label[i][j] = torch.zeros(label_channel[i], 81,
81).to(device).scatter_(
0, parts_label_tmp, 255)
for j in range(N):
path = "./data/facial_parts/" + in_data.get_namelist()[k + j]
if (not os.path.exists(path)):
os.mkdir(path)
image3 = transforms.ToPILImage()(
parts[i][j].cpu().clone()).convert('RGB')
image3.save(path + '/' + 'lbl0' + str(i) + '_img' + '.jpg',
quality=100)
for l in range(label_channel[i]):
image3 = unloader(
np.uint8(parts_label[i][j][l].cpu().detach().numpy()))
image3.save(path + '/' + 'lbl0' + str(i) + '_label0' +
str(l) + '.jpg',
quality=100)
k += N
if (k % 200 == 0): print(k)
print("Printoutput Finish!")
def train(epoch):
model_stage1.train();
model_select.train();
'''
part1_time=0;
part2_time=0;
part3_time=0;
prev_time=time.time();
'''
k=0;
for batch_idx,sample in enumerate(train_data.get_loader()):
'''
now_time=time.time();
part3_time+=now_time-prev_time;
prev_time=now_time;
'''
if (use_gpu):
sample['image']=sample['image'].to(device)
sample['label']=sample['label'].to(device)
sample['size'][0]=sample['size'][0].to(device);
sample['size'][1]=sample['size'][1].to(device);
optimizer_stage1.zero_grad();
optimizer_select.zero_grad();
stage1_label=model_stage1(sample['image'])
theta=model_select(stage1_label,sample['size'])
theta_label = torch.zeros((sample['image'].size()[0],6,2,3),device=device,requires_grad=False); #[batch_size,6,2,3]
W=1024.0;
H=1024.0;
'''
cens = torch.floor(calc_centroid_old(sample['label'])) #[batch_size,9,2]
for i in range(sample['image'].size()[0]):
for j in range(9):
cens[i,j,0]=cens[i,j,0]*(sample['size'][0][i]-1.0)/(128.0-1.0)
cens[i,j,1]=cens[i,j,1]*(sample['size'][1][i]-1.0)/(128.0-1.0)
points = torch.floor(torch.cat([cens[:, 1:6],cens[:, 6:9].mean(dim=1, keepdim=True)],dim=1)) #[batch_size,6,2]
'''
'''
points2 = torch.floor(calc_centroid(sample['label_org'])) #[batch_size,9,2]
print("cens resize:");
print(points);
print("cens org:");
print(points2);
print("delta");
print(points.cpu()-points2);
input("wait");
'''
points=torch.floor(calc_centroid(sample['label_org']))
for i in range(6):
theta_label[:,i,0,0]=(81.0-1.0)/(W-1.0);
theta_label[:,i,1,1]=(81.0-1.0)/(H-1.0);
theta_label[:,i,0,2]=-1+2*points[:,i,0]/(W-1.0);
theta_label[:,i,1,2]=-1+2*points[:,i,1]/(H-1.0);
if (torch.min(theta_label)<-1 or torch.max(theta_label)>1):
print("F**K");
print(k);
'''
for i in range(sample['image'].shape[0]):
if (not os.path.exists("./data/select_pre/"+train_data.get_namelist()[(k+i)%2000])):
os.mkdir("./data/select_pre/"+train_data.get_namelist()[(k+i)%2000]);
image=sample['image_org'][i].cpu().clone();
image=transforms.ToPILImage()(image).convert('RGB')
plt.imshow(image);
plt.show(block=True);
image.save('./data/select_pre/'+train_data.get_namelist()[(k+i)%2000]+'/'+str((k+i)//2000)+'_img'+'.jpg',quality=100);
for j in range(6):
affine_stage2=F.affine_grid(theta_label[i][j].unsqueeze(0),(1,1,81,81),align_corners=True);
image=F.grid_sample(sample['label_org'][i][label_list[j][1]].unsqueeze(0).unsqueeze(0).to(device),affine_stage2,align_corners=True);
image=image.squeeze(0).cpu();
image=transforms.ToPILImage()(image);
image.save('./data/select_pre/'+train_data.get_namelist()[(k+i)%2000]+'/'+str((k+i)//2000)+'_'+str(j)+'_thetalabel'+'.jpg',quality=100);
image=sample['label_org'][i][label_list[j][1]]
image=transforms.ToPILImage()(image);
image.save('./data/select_pre/'+train_data.get_namelist()[(k+i)%2000]+'/'+str((k+i)//2000)+'_'+str(j)+'_orglabel'+'.jpg',quality=100);
#plt.imshow(image);
#plt.show(block=True);
'''
k+=sample['image'].shape[0];
loss=fun.smooth_l1_loss(theta, theta_label);
'''
now_time=time.time();
part1_time+=now_time-prev_time;
prev_time=now_time;
'''
loss.backward()
optimizer_select.step();
optimizer_stage1.step();
'''
now_time=time.time();
part2_time+=now_time-prev_time;
prev_time=now_time;
'''
if (batch_idx%250==0):
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(sample['image']), len(train_data.get_loader().dataset),
100. * batch_idx / len(train_data.get_loader()),loss))
'''
def printoutput():
CheckBest=True;
if (CheckBest):
model_stage1=torch.load("./preBestnet_stage1",map_location="cpu")
model_select=torch.load("./preBestnet_select",map_location="cpu")
else:
model_stage1=torch.load("./preNetdata_stage1",map_location="cpu")
model_select=torch.load("./preNetdata_select",map_location="cpu")
model_select.select.change_device(device);
if (use_gpu):
model_stage1=model_stage1.to(device)
model_select=model_select.to(device)
unloader = transforms.ToPILImage()
k=0;
loss=0;
for sample in test_data.get_loader():
if (use_gpu):
sample['image']=sample['image'].to(device)
sample['label']=sample['label'].to(device)
sample['size'][0]=sample['size'][0].to(device);
sample['size'][1]=sample['size'][1].to(device);
sample['label_org']=sample['label_org'].to(device)
stage1_label=model_stage1(sample['image'])
theta=model_select(stage1_label,sample['size'])
theta_label = torch.zeros((sample['image'].size()[0],6,2,3),device=device,requires_grad=False); #[batch_size,6,2,3]
W=1024.0;
H=1024.0;
'''
cens = torch.floor(calc_centroid_old(sample['label'])) #[batch_size,9,2]
for i in range(sample['image'].size()[0]):
for j in range(9):
cens[i,j,0]=cens[i,j,0]*(sample['size'][0][i]-1.0)/(128.0-1.0)
cens[i,j,1]=cens[i,j,1]*(sample['size'][1][i]-1.0)/(128.0-1.0)
points = torch.floor(torch.cat([cens[:, 1:6],cens[:, 6:9].mean(dim=1, keepdim=True)],dim=1)) #[batch_size,6,2]
points2 = torch.floor(calc_centroid(sample['label_org'])) #[batch_size,9,2]
theta_label2 = torch.zeros((sample['image'].size()[0],6,2,3),device=device,requires_grad=False); #[batch_size,6,2,3]
for i in range(6):
theta_label2[:,i,0,0]=(81.0-1.0)/(W-1.0);
theta_label2[:,i,1,1]=(81.0-1.0)/(H-1.0);
theta_label2[:,i,0,2]=-1+2*points2[:,i,0]/(W-1.0);
theta_label2[:,i,1,2]=-1+2*points2[:,i,1]/(H-1.0);
if (abs(torch.max(points.cpu()-points2.cpu()))>20 or abs(torch.min(points.cpu()-points2.cpu()))>20):
print("points resize:");
print(points);
print("points org:");
print(points2);
print("delta");
print(points.cpu()-points2.cpu());
for i in range(sample['image'].size()[0]):
print(test_data.get_namelist()[k+i]);
input("wait");
'''
points=torch.floor(calc_centroid(sample['label_org']))
for i in range(6):
theta_label[:,i,0,0]=(81.0-1.0)/(W-1.0);
theta_label[:,i,1,1]=(81.0-1.0)/(H-1.0);
theta_label[:,i,0,2]=-1+2*points[:,i,0]/(W-1.0);
theta_label[:,i,1,2]=-1+2*points[:,i,1]/(H-1.0);
loss+=fun.smooth_l1_loss(theta, theta_label).detach().data;
'''
f=open("1.txt",mode='w');
print(sample['label'].size())
for k in range(9):
for i in range(sample['label'][0][k].size()[0]):
for j in range(sample['label'][0][k].size()[1]):
print(float(sample['label'][0][k][i][j].data),end=' ',file=f);
print(file=f);
f.close();
for i in range(batch_size):
for j in range(9):
image=sample['label'][i][j].cpu().clone();
image=transforms.ToPILImage()(image).convert('L')
plt.imshow(image);
plt.show(block=True);
input("check")
'''
'''
print(theta);
print(theta_label);
input("check")
'''
output=[];
for i in range(sample['image'].size()[0]):
'''
if (test_data.get_namelist()[k]=="13601661_1"):
print(cens[i]);
print(points[i]);
print(theta_label[i]);
input("wait")
'''
path=pre_output_path+'/'+test_data.get_namelist()[k];
if not os.path.exists(path):
os.makedirs(path);
image=sample['image'][i].cpu().clone();
image =unloader(image)
image.save(path+'/'+test_data.get_namelist()[k]+'_img.jpg',quality=100);
image=sample['image_org'][i].cpu().clone();
image2 =unloader(image)
image2.save(path+'/'+test_data.get_namelist()[k]+'_img_org.jpg',quality=100);
output2=stage1_label[i].cpu().clone();
output2=torch.softmax(output2, dim=0).argmax(dim=0, keepdim=False)
output2=output2.unsqueeze(0);
output2=torch.zeros(9,128,128).scatter_(0, output2, 255);
output.append(output2);
for j in range(9):
image3=unloader(np.uint8(output[i][j].numpy()))
#image3=transforms.ToPILImage()(output[i][j].cpu().detach()).convert('L')
image3.save(path+'/'+'stage1_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'.jpg',quality=100);
image3=transforms.ToPILImage()(sample['label_org'][i][j].cpu().detach()).convert('L')
image3.save(path+'/'+test_data.get_namelist()[k]+'lbl0'+str(j)+'_label'+'.jpg',quality=100);
image=image.to(device).unsqueeze(0);
for j in range(6):
affine_stage2=F.affine_grid(theta[i,j].unsqueeze(0),(1,3,81,81),align_corners=True);
image3=F.grid_sample(sample['image_org'][i].to(device).unsqueeze(0),affine_stage2,align_corners=True);
image3=unloader(image3[0].cpu());
image3.save(path+'/'+'select_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'.jpg',quality=100);
affine_stage2=F.affine_grid(theta_label[i,j].unsqueeze(0),(1,3,81,81),align_corners=True);
image3=F.grid_sample(sample['image_org'][i].to(device).unsqueeze(0),affine_stage2,align_corners=True);
image3=unloader(image3[0].cpu());
image3.save(path+'/'+'select_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'_thetalabel'+'.jpg',quality=100);
'''
affine_stage2=F.affine_grid(theta_label2[i,j].unsqueeze(0),(1,3,81,81),align_corners=True);
image3=F.grid_sample(sample['image_org'][i].to(device).unsqueeze(0),affine_stage2,align_corners=True);
image3=unloader(image3[0].cpu());
image3.save(path+'/'+'select_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'_thetalabel2'+'.jpg',quality=100);
'''
affine_stage2=F.affine_grid(theta[i,j].unsqueeze(0),(1,1,81,81),align_corners=True);
image3=F.grid_sample(sample['label_org'][i][label_list[j][1]].unsqueeze(0).unsqueeze(0),affine_stage2,align_corners=True);
image3=transforms.ToPILImage()(image3[0].squeeze(0).cpu().detach()).convert('L');
image3.save(path+'/'+'select_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'_label'+'.jpg',quality=100);
affine_stage2=F.affine_grid(theta_label[i,j].unsqueeze(0),(1,1,81,81),align_corners=True);
image3=F.grid_sample(sample['label_org'][i][label_list[j][1]].unsqueeze(0).unsqueeze(0),affine_stage2,align_corners=True);
image3=transforms.ToPILImage()(image3[0].squeeze(0).cpu().detach()).convert('L');
image3.save(path+'/'+'select_'+test_data.get_namelist()[k]+'lbl0'+str(j)+'_label'+'_thetalabel'+'.jpg',quality=100);
k+=1
if (k>=test_data.get_len()):break
if (k>=test_data.get_len()):break
loss/=len(val_data.get_loader().dataset);
print('\nTest set: {} Cases，Average loss: {:.8f}\n'.format(len(test_data.get_loader().dataset),loss))
print("printoutput Finish");