def test(cfg, writer, logger):
torch.manual_seed(cfg.get('seed', 1337))
torch.cuda.manual_seed(cfg.get('seed', 1337))
np.random.seed(cfg.get('seed', 1337))
random.seed(cfg.get('seed', 1337))
## create dataset
default_gpu = cfg['model']['default_gpu']
device = torch.device(
"cuda:{}".format(default_gpu) if torch.cuda.is_available() else 'cpu')
datasets = create_dataset(
cfg, writer, logger
) #source_train\ target_train\ source_valid\ target_valid + _loader
model = CustomModel(cfg, writer, logger)
running_metrics_val = runningScore(cfg['data']['target']['n_class'])
source_running_metrics_val = runningScore(cfg['data']['target']['n_class'])
val_loss_meter = averageMeter()
source_val_loss_meter = averageMeter()
time_meter = averageMeter()
loss_fn = get_loss_function(cfg)
path = cfg['test']['path']
checkpoint = torch.load(path)
model.adaptive_load_nets(model.BaseNet,
checkpoint['DeepLab']['model_state'])
validation(
model, logger, writer, datasets, device, running_metrics_val, val_loss_meter, loss_fn,\
source_val_loss_meter, source_running_metrics_val, iters = model.iter
)
def __init__(self, numbers=19):
self.class_numbers = numbers
self.classes_recall_thr = np.zeros([19, 3])
self.classes_recall_thr_num = np.zeros([19])
self.classes_recall_clu = np.zeros([19, 3])
self.classes_recall_clu_num = np.zeros([19])
self.running_metrics_val_threshold = runningScore(self.class_numbers)
self.running_metrics_val_clusters = runningScore(self.class_numbers)
self.clu_threshold = np.full((19), 2.5)
def train(data_loader,model,epoch,num_batch,learning_rate):
#### Model One
#resnet = PM.ResNet(PM.BottleBlock(), FLAGS.kernal_num, True, 0.5)
#logites = resnet(tf_image) ## (batch, 7, size, size)
#Loss = PM.Dec_Loss_2(logites=logites, gt_texts=tf_gt, gt_kernels=tf_kernal, training_masks=tf_mask)
running_metric_text = runningScore(2)
running_metric_kernel = runningScore(2)
'''
learning_rate = tf.train.exponential_decay(
learning_rate=FLAGS.learning_rate,
global_step= step,
decay_steps=num_batch * 200,
decay_rate=0.5,
staircase=True)
'''
#optim = tf.train.AdamOptimizer(learning_rate=learning_rate)
optim = tf.optimizers.Adam(learning_rate=learning_rate)
step = epoch * num_batch
for i in range(num_batch):
images, gt_texts, train_masks, kernal_images = pre_tools.batch_data(data_loader, i, FLAGS.batch_size)
images = tf.convert_to_tensor(images, dtype=tf.float32)
gt_texts = tf.convert_to_tensor(gt_texts, dtype=tf.float32)
train_masks = tf.convert_to_tensor(train_masks, dtype=tf.float32)
kernal_images = tf.convert_to_tensor(kernal_images, dtype=tf.float32)
with tf.GradientTape() as tape:
logites = model(images) ### (32,320,320,3)
logites = tf.transpose(logites, (0, 3, 1, 2)) ### (32,3,320,320)
Loss = PM.Dec_Loss_2(logites=logites, gt_texts=gt_texts,
gt_kernels=kernal_images, training_masks=train_masks,kernal=FLAGS.kernal_num)
# 计算梯度 tape模式，保持跟踪
grads = tape.gradient(Loss, model.trainable_weights)
optim.apply_gradients(zip(grads, model.trainable_weights))
texts = logites[:, 0, :, :]
kernels = logites[:, 1:, :, :]
score_text = cal_text_score(texts, gt_texts, train_masks, running_metric_text)
score_kernel = cal_kernel_score(kernels, kernal_images, gt_texts, train_masks, running_metric_kernel)
acc = score_text['Mean Acc']
iou_t = score_text['Mean IoU']
iou_k = score_kernel['Mean IoU']
step = step + 1
if i % 20 == 0:
information = '## Epoch:{:d} Step_Train / Total_Batch: {:d} / {:d} train_loss= {:5f} train_acc= {:5f} IOU_t={:5f} IOU_k={:5f}'. \
format(epoch,step, num_batch, Loss, acc,iou_t,iou_k)
print(information) ### 输出到屏幕
def __init__(self, numbers=19, modal_num=3, model=None):
self.class_numbers = numbers
self.classes_recall_thr = np.zeros([19, 3])
self.classes_recall_thr_num = np.zeros([19])
self.classes_recall_clu = np.zeros([19, 3])
self.classes_recall_clu_num = np.zeros([19])
self.running_metrics_val_threshold = runningScore(self.class_numbers)
self.running_metrics_val_clusters = runningScore(self.class_numbers)
self.clu_threshold = torch.full((modal_num + 1, 19), 3.0).cuda()
self.multimodal_merger = CustomMetricsMultimodalMerger(
modal_num=modal_num + 1, category_num=numbers, model=model
)
def boxplotvis(cfg):
# device = torch.device("cuda:{}".format(cfg["other"]["gpu_idx"]) if torch.cuda.is_available() else "cpu")
data_loader = get_loader(cfg["data"]["dataset"])
data_path = cfg["data"]["path"]
v_loader = data_loader(data_path, split='val')
n_classes = v_loader.n_classes
n_val = len(v_loader.files['val'])
# test differnet models' prediction
vgg16lstm_metric = runningScore(n_classes, n_val)
vgg16gru_metric = runningScore(n_classes, n_val)
segnet_metric = runningScore(n_classes, n_val)
with torch.no_grad():
for i_val, (images_val, labels_val,
img_name_val) in tqdm(enumerate(v_loader)):
gt = np.squeeze(labels_val.data.cpu().numpy())
vgg16lstm_pred = m.imread(
pjoin(cfg["data"]["pred_path"], 'vgg16_lstm_brainweb',
img_name_val + '.bmp'))
vgg16gru_pred = m.imread(
pjoin(cfg["data"]["pred_path"], 'vgg16_gru_brainweb',
img_name_val + '.bmp'))
segnet_pred = m.imread(
pjoin(cfg["data"]["pred_path"], 'segnet_brainweb',
img_name_val + '.bmp'))
vgg16lstm_encode = v_loader.encode_segmap(vgg16lstm_pred)
vgg16gru_encode = v_loader.encode_segmap(vgg16gru_pred)
segnet_encode = v_loader.encode_segmap(segnet_pred)
vgg16lstm_metric.update(gt, vgg16lstm_encode, i_val)
vgg16gru_metric.update(gt, vgg16gru_encode, i_val)
segnet_metric.update(gt, segnet_encode, i_val)
vgg16lstm_acc_all, vgg16lstm_dsc_cls = vgg16lstm_metric.get_list()
vgg16gru_acc_all, vgg16gru_dsc_cls = vgg16gru_metric.get_list()
segnet_acc_all, segnet_dsc_cls = segnet_metric.get_list()
# dsc_list = [vgg16lstm_dsc_cls.transpose(), vgg16gru_dsc_cls.transpose(), segnet_dsc_cls.transpose()]
data0 = array2dataframe(vgg16lstm_dsc_cls)
data0['Method'] = 'VGG16-LSTM'
data1 = array2dataframe(vgg16gru_dsc_cls)
data1['Method'] = 'VGG16-GRU'
data2 = array2dataframe(segnet_dsc_cls)
data2['Method'] = 'SegNet'
data = pd.concat([data0, data1, data2])
def val(args, model, dataloader):
segmiou_cal = runningScore(n_classes=args.num_classes)
posmiou_cal = runningScore(n_classes=args.num_char)
model.eval()
with torch.no_grad():
for i, (data, seg, pos) in enumerate(dataloader):
seg_pred, pos_pred = model(data)
seg_pred = seg_pred.cpu().numpy()
seg_pred = np.argmax(seg_pred, axis=1)
seg = seg.numpy()
segmiou_cal.update(seg, seg_pred)
pos_pred = pos_pred.cpu().numpy()
pos_pred = np.argmax(pos_pred, axis=1)
pos = pos.numpy()
posmiou_cal.update(pos, pos_pred)
segmiou = segmiou_cal.get_scores()
posmiou = posmiou_cal.get_scores()
print('segmiou:{}'.format(segmiou))
print('posmiou:{}'.format(posmiou))
return segmiou, posmiou
def main(test_args):
testset = "/mnt/iusers01/eee01/mchiwml4/CamVid/test"
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(mean, std)])
test_dataset = DataLoader(Loaddata(testset,
transform=transform,
target_transform=MaskToTensor()),
batch_size=1,
shuffle=False,
num_workers=8)
label_num = 11
model = segnetmodel.segnet(label_num)
model = model.cuda()
model.load_state_dict(torch.load(test_args.load_param))
model.eval()
total = np.zeros((label_num, ))
running_metrics = runningScore(label_num)
for j, data in enumerate(test_dataset):
inputs, labels = data
inputs = Variable(inputs.cuda())
outputs = model(inputs)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels.numpy()
running_metrics.update(gt, pred)
for i in range(label_num):
mask = gt == i # ground truth mask of class i
total[i] += np.sum(
mask) # total number of pixels of class i (tp+fn)
score, class_iou, class_acc = running_metrics.get_scores()
for k, v in score.items():
print(k, v)
print('class iou: ')
for i in range(label_num):
print(i, class_iou[i])
print('class acc: ')
for i in range(label_num):
print(i, class_acc[i])
print('number of pixels:')
print(total)
def test(opt, logger):
torch.manual_seed(opt.seed)
torch.cuda.manual_seed(opt.seed)
np.random.seed(opt.seed)
random.seed(opt.seed)
## create dataset
device = torch.device("cuda:0" if torch.cuda.is_available() else 'cpu')
datasets = create_dataset(opt, logger)
if opt.model_name == 'deeplabv2':
checkpoint = torch.load(opt.resume_path)['ResNet101']["model_state"]
model = adaptation_modelv2.CustomModel(opt, logger)
model.BaseNet.load_state_dict(checkpoint)
running_metrics_val = runningScore(opt.n_class)
validation(model, logger, datasets, device, running_metrics_val)
def validate(val_loader, model, criterion):
with torch.no_grad():
model.eval()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
running_metric_text = runningScore(2)
running_metric_kernel = runningScore(2)
end = time.time()
for batch_idx, (imgs, gt_texts, gt_kernels,
training_masks) in enumerate(val_loader):
data_time.update(time.time() - end)
imgs = Variable(imgs.cuda())
gt_texts = Variable(gt_texts.cuda())
gt_kernels = Variable(gt_kernels.cuda())
training_masks = Variable(training_masks.cuda())
outputs = model(imgs)
texts = outputs[:, 0, :, :]
kernels = outputs[:, 1:, :, :]
selected_masks = ohem_batch(texts, gt_texts, training_masks)
selected_masks = Variable(selected_masks.cuda())
loss_text = criterion(texts, gt_texts, selected_masks)
loss_kernels = []
mask0 = torch.sigmoid(texts).data.cpu().numpy()
mask1 = training_masks.data.cpu().numpy()
selected_masks = ((mask0 > 0.5) & (mask1 > 0.5)).astype('float32')
selected_masks = torch.from_numpy(selected_masks).float()
selected_masks = Variable(selected_masks.cuda())
for i in range(6):
kernel_i = kernels[:, i, :, :]
gt_kernel_i = gt_kernels[:, i, :, :]
loss_kernel_i = criterion(kernel_i, gt_kernel_i,
selected_masks)
loss_kernels.append(loss_kernel_i)
loss_kernel = sum(loss_kernels) / len(loss_kernels)
loss = 0.7 * loss_text + 0.3 * loss_kernel
losses.update(loss.item(), imgs.size(0))
score_text = cal_text_score(texts, gt_texts, training_masks,
running_metric_text)
score_kernel = cal_kernel_score(kernels, gt_kernels, gt_texts,
training_masks,
running_metric_kernel)
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % 5 == 0:
output_log = '({batch}/{size}) Batch: {bt:.3f}s | TOTAL: {total:.0f}min | ETA: {eta:.0f}min '.format(
batch=batch_idx + 1,
size=len(val_loader),
bt=batch_time.avg,
total=batch_time.avg * batch_idx / 60.0,
eta=batch_time.avg * (len(val_loader) - batch_idx) / 60.0)
print(output_log)
sys.stdout.flush()
return (float(losses.avg), float(score_text['Mean Acc']),
float(score_kernel['Mean Acc']), float(score_text['Mean IoU']),
float(score_kernel['Mean IoU']))
def train(args):
os.environ["CUDA_VISIBLE_DEVICES"]=str(args.gpu_id)
#torch.manual_seed(1337)
print(args)
# setup dataloader
t_loader=MR18loader_CV(root=args.data_path,val_num=args.val_num,is_val=False,is_transform=True,is_flip=True,is_rotate=True,is_crop=True,is_histeq=True,forest=args.num_forest)
v_loader=MR18loader_CV(root=args.data_path,val_num=args.val_num,is_val=True,is_transform=True,is_flip=False,is_rotate=False,is_crop=True,is_histeq=True,forest=args.num_forest)
n_classes = t_loader.n_classes
trainloader = data.DataLoader(t_loader, batch_size=args.batch_size, num_workers=1, shuffle=True)
valloader = data.DataLoader(v_loader, batch_size=1, num_workers=1,shuffle=True)
# setup Metrics
running_metrics_single = runningScore(n_classes)
running_metrics_single_test = runningScore(4)
# setup Model
model=fcn_mul(n_classes=n_classes)
vgg16 = models.vgg16(pretrained=True)
model.init_vgg16_params(vgg16)
model.cuda()
# setup optimizer and loss
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.99, weight_decay=5e-4)
loss_ce = cross_entropy2d
#loss_ce_weight = weighted_loss
#loss_dc = dice_loss
#loss_hed= bce2d_hed
# resume
best_iou=-100.0
if args.resume is not None:
if os.path.isfile(args.resume):
print("Loading model and optimizer from checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
best_iou=checkpoint['best_iou']
model.load_state_dict(checkpoint['model_state'])
optimizer.load_state_dict(checkpoint['optimizer_state'])
print("Loaded checkpoint '{}' (epoch {}), best_iou={}"
.format(args.resume, checkpoint['epoch'],best_iou))
else:
best_iou=-100.0
print("No checkpoint found at '{}'".format(args.resume))
# visualization
t = []
loss_seg_list=[]
loss_hed_list=[]
Dice_mean=[]
Dice_CSF=[]
Dice_GM=[]
Dice_WM=[]
t_pre=time.time()
print('training prepared, cost {} seconds\n\n'.format(t_pre-t_begin))
for epoch in range(args.n_epoch):
t.append(epoch+1)
model.train()
adjust_learning_rate(optimizer,epoch)
#loss_sum=0.0
loss_epoch=0.0
t_epoch=time.time()
for i_train, (regions,T1s,IRs,T2s,lbls) in enumerate(trainloader):
T1s=Variable(T1s.cuda())
IRs,T2s=Variable(IRs.cuda()),Variable(T2s.cuda())
lbls=Variable(lbls.cuda()[:,int(args.num_forest/2),:,:].unsqueeze(1))
#edges=Variable(edges.cuda()[:,int(args.num_forest/2),:,:].unsqueeze(1))
optimizer.zero_grad()
outputs=model(T1s,IRs,T2s)
seg_out=F.log_softmax(outputs,dim=1)
max_prob,_=torch.max(seg_out,dim=1)
max_prob=-max_prob.detach().unsqueeze(1)
loss_seg_value=loss_ce(input=outputs,target=lbls)
#+0.5*loss_dc(input=outputs,target=lbls)
#+0.5*loss_ce_weight(input=outputs,target=lbls,weight=max_prob)\
#+0.5*loss_ce_weight(input=outputs,target=lbls,weight=edges)\
#loss_hed_value=loss_hed(input=outputs[1],target=edges)
#+0.5*loss_hed(input=outputs[2],target=edges) \
#+0.5*loss_hed(input=outputs[3],target=edges) \
#+0.5*loss_hed(input=outputs[4],target=edges) \
#+0.5*loss_hed(input=outputs[5],target=edges)
loss=loss_seg_value
#loss=loss_seg_value+loss_hed_value
# loss average
#loss_sum+=loss
#if (i_train+1)%args.loss_avg==0:
# loss_sum/=args.loss_avg
# loss_sum.backward()
# optimizer.step()
# loss_sum=0.0
loss.backward()
optimizer.step()
loss_epoch+=loss.item()
# visualization
if i_train==40:
ax1=plt.subplot(241)
ax1.imshow(T1s[0,1,:,:].data.cpu().numpy(),cmap ='gray')
ax1.set_title('train_img')
ax1.axis('off')
ax2=plt.subplot(242)
ax2.imshow(t_loader.decode_segmap(lbls[0,0,:,:].data.cpu().numpy()).astype(np.uint8))
ax2.set_title('train_label')
ax2.axis('off')
ax3=plt.subplot(243)
model.eval()
train_show=model(T1s,IRs,T2s)
ax3.imshow(t_loader.decode_segmap(train_show[0].data.max(0)[1].cpu().numpy()).astype(np.uint8))
ax3.set_title('train_predict')
ax3.axis('off')
ax4=plt.subplot(244)
ax4.imshow(max_prob[0,0].cpu().numpy())
ax4.set_title('uncertainty')
ax4.axis('off')
model.train()
loss_epoch/=i_train
loss_seg_list.append(loss_epoch)
loss_hed_list.append(0)
t_train=time.time()
print('epoch: ',epoch+1)
print('--------------------------------Training--------------------------------')
print('average loss in this epoch: ',loss_epoch)
print('final loss in this epoch: ',loss.data.item())
print('cost {} seconds up to now'.format(t_train-t_begin))
print('cost {} seconds in this train epoch'.format(t_train-t_epoch))
model.eval()
for i_val, (regions_val,T1s_val,IRs_val,T2s_val,lbls_val) in enumerate(valloader):
T1s_val=Variable(T1s_val.cuda())
IRs_val,T2s_val=Variable(IRs_val.cuda()),Variable(T2s_val.cuda())
with torch.no_grad():
outputs_single=model(T1s_val,IRs_val,T2s_val)[0,:,:,:]
# get predict
pred_single=outputs_single.data.max(0)[1].cpu().numpy()
# pad to 240
pred_pad=np.zeros((240,240),np.uint8)
pred_pad[regions_val[0]:regions_val[1],regions_val[2]:regions_val[3]]= \
pred_single[0:regions_val[1]-regions_val[0],0:regions_val[3]-regions_val[2]]
# convert to 3 classes
pred_single_test=np.zeros((240,240),np.uint8)
pred_single_test=v_loader.lbl_totest(pred_pad)
# get gt
gt = lbls_val[0][int(args.num_forest/2)].numpy()
# pad to 240
gt_pad=np.zeros((240,240),np.uint8)
gt_pad[regions_val[0]:regions_val[1],regions_val[2]:regions_val[3]]= \
gt[0:regions_val[1]-regions_val[0],0:regions_val[3]-regions_val[2]]
# convert to 3 classes
gt_test=np.zeros((240,240),np.uint8)
gt_test=v_loader.lbl_totest(gt_pad)
# metrics update
running_metrics_single.update(gt_pad, pred_pad)
running_metrics_single_test.update(gt_test, pred_single_test)
# visualization
if i_val==40:
ax5=plt.subplot(245)
ax5.imshow((T1s_val[0,int(args.num_forest/2),:,:].data.cpu().numpy()*255+t_loader.T1mean).astype(np.uint8),cmap ='gray')
ax5.set_title('src_img')
ax5.axis('off')
ax6=plt.subplot(246)
ax6.imshow(t_loader.decode_segmap(gt).astype(np.uint8))
ax6.set_title('gt')
ax6.axis('off')
ax7=plt.subplot(247)
ax7.imshow(t_loader.decode_segmap(pred_single).astype(np.uint8))
ax7.set_title('pred_single')
ax7.axis('off')
ax8=plt.subplot(248)
ax8.imshow(pred_single_test[regions_val[0]:regions_val[1],regions_val[2]:regions_val[3]].astype(np.uint8))
ax8.set_title('pred_single_test')
ax8.axis('off')
plt.tight_layout()
plt.subplots_adjust(wspace=.1,hspace=.3)
plt.savefig('./fig_out/val_{}_out_{}.png'.format(str(args.val_num),epoch+1))
# compute dice coefficients during validation
score_single, class_iou_single = running_metrics_single.get_scores()
score_single_test, class_iou_single_test = running_metrics_single_test.get_scores()
Dice_mean.append(score_single['Mean Dice : \t'])
Dice_CSF.append(score_single_test['Dice : \t'][1])
Dice_GM.append(score_single_test['Dice : \t'][2])
Dice_WM.append(score_single_test['Dice : \t'][3])
print('--------------------------------All tissues--------------------------------')
print('Back: Background,')
print('GM: Cortical GM(red), Basal ganglia(green),')
print('WM: WM(yellow), WM lesions(blue),')
print('CSF: CSF(pink), Ventricles(light blue),')
print('Back: Cerebellum(white), Brainstem(dark red)')
print('single predict: ')
for k, v in score_single.items():
print(k, v)
print('--------------------------------Only tests--------------------------------')
print('tissue : Back , CSF , GM , WM')
print('single predict: ')
for k, v in score_single_test.items():
print(k, v)
t_test=time.time()
print('cost {} seconds up to now'.format(t_test-t_begin))
print('cost {} seconds in this validation epoch'.format(t_test-t_train))
# save model at best validation metrics
if score_single['Mean Dice : \t'] >= best_iou:
best_iou = score_single['Mean Dice : \t']
state = {'epoch': epoch+1,
'model_state': model.state_dict(),
'optimizer_state' : optimizer.state_dict(),
'best_iou':best_iou}
torch.save(state, "val_{}_best.pkl".format(str(args.val_num)))
print('model saved!!!')
# save model every 10 epochs
if (epoch+1)%10==0:
state = {'epoch': epoch+1,
'model_state': model.state_dict(),
'optimizer_state' : optimizer.state_dict(),
'score':score_single}
torch.save(state, "val_{}_e_{}.pkl".format(str(args.val_num),epoch+1))
# plot curve
ax1=plt.subplot(211)
ax1.plot(t,loss_seg_list,'g')
ax1.plot(t,loss_hed_list,'r')
ax1.set_title('train loss')
ax2=plt.subplot(212)
ax2.plot(t,Dice_mean,'k')
ax2.plot(t,Dice_CSF,'r')
ax2.plot(t,Dice_GM,'g')
ax2.plot(t,Dice_WM,'b')
ax2.set_title('validate Dice, R/G/B for CSF/GM/WM')
plt.tight_layout()
plt.subplots_adjust(wspace=0,hspace=.3)
plt.savefig('./fig_out/val_{}_curve.png'.format(str(args.val_num)))
# metric reset
running_metrics_single.reset()
running_metrics_single_test.reset()
print('\n\n')
def train(train_loader, model, criterion, optimizer, epoch):
#model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
running_metric_text = runningScore(2)
running_metric_kernel = runningScore(2)
end = time.time()
for batch_idx, (imgs, gt_texts, gt_kernels, training_masks,
data_length) in enumerate(train_loader):
with tf.GradientTape() as tape:
data_time.update(time.time() - end)
outputs = model(imgs)
outputs = tf.transpose(outputs, (0, 3, 1, 2))
texts = outputs[:, 0, :, :]
kernels = outputs[:, 1:, :, :]
selected_masks = ohem_batch(texts, gt_texts, training_masks)
loss_text = criterion(texts, gt_texts, selected_masks)
loss_kernels = []
mask0 = tf.sigmoid(texts).numpy()
mask1 = training_masks.numpy()
selected_masks = ((mask0 > 0.5) & (mask1 > 0.5)).astype('float32')
#selected_masks = torch.from_numpy(selected_masks).float()
selected_masks = tf.convert_to_tensor(selected_masks,
dtype=tf.float32)
#selected_masks = Variable(selected_masks.cuda())
for i in range(6):
kernel_i = kernels[:, i, :, :]
gt_kernel_i = gt_kernels[:, i, :, :]
loss_kernel_i = criterion(kernel_i, gt_kernel_i,
selected_masks)
loss_kernels.append(loss_kernel_i)
loss_kernel = sum(loss_kernels) / len(loss_kernels)
loss = 0.7 * loss_text + 0.3 * loss_kernel
#反向计算各层loss
losses.update(loss.numpy(), imgs.shape[0])
#计算梯度 tape模式，保持跟踪
grads = tape.gradient(loss, model.trainable_weights)
#
optimizer.apply_gradients(zip(grads, model.trainable_weights))
score_text = cal_text_score(texts, gt_texts, training_masks,
running_metric_text)
score_kernel = cal_kernel_score(kernels, gt_kernels, gt_texts,
training_masks, running_metric_kernel)
batch_time.update(time.time() - end)
end = time.time()
size = data_length / args.batch_size
if batch_idx % 20 == 0:
output_log = '({batch}/{size}) Batch: {bt:.3f}s | TOTAL: {total:.0f}min \
| ETA: {eta:.0f}min | Loss: {loss:.4f} | Acc_t: {acc: .4f} | IOU_t: {iou_t: .4f}\
| IOU_k: {iou_k: .4f}'.format(
batch=batch_idx + 1,
#size=len(train_loader),
size=data_length / args.batch_size,
bt=batch_time.avg,
total=batch_time.avg * batch_idx / 60.0,
#eta=batch_time.avg * (len(train_loader) - batch_idx) / 60.0,
eta=batch_time.avg * (size - batch_idx) / 60.0,
loss=losses.avg,
acc=score_text['Mean Acc'],
iou_t=score_text['Mean IoU'],
iou_k=score_kernel['Mean IoU'])
print(output_log)
sys.stdout.flush()
return (losses.avg, score_text['Mean Acc'], score_kernel['Mean Acc'],
score_text['Mean IoU'], score_kernel['Mean IoU'])
def train(cfg):
# Setup seeds
torch.manual_seed(cfg.get('seed', 1337))
torch.cuda.manual_seed(cfg.get('seed', 1337))
np.random.seed(cfg.get('seed', 1337))
random.seed(cfg.get('seed', 1337))
# Setup device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Setup Augmentations
augmentations = cfg['training'].get('augmentations', None)
data_aug = get_composed_augmentations(augmentations)
# Setup Dataloader
data_loader = get_loader(cfg['data']['dataset'])
data_path = cfg['data']['path']
t_loader = data_loader(
data_path,
is_transform=True,
split=cfg['data']['train_split'],
#img_size=(cfg['data']['img_rows'], cfg['data']['img_cols']),
augmentations=data_aug)
v_loader = data_loader(
data_path,
is_transform=True,
split=cfg['data']['val_split'],
#img_size=(cfg['data']['img_rows'], cfg['data']['img_cols']),
)
n_classes = t_loader.n_classes
trainloader = data.DataLoader(t_loader,
batch_size=cfg['training']['batch_size'],
num_workers=cfg['training']['n_workers'],
shuffle=True)
valloader = data.DataLoader(v_loader,
batch_size=cfg['training']['batch_size'],
num_workers=cfg['training']['n_workers'])
# Setup Metrics
running_metrics_val = runningScore(n_classes)
# Setup Model
model = get_model(cfg['model'], n_classes).to(device)
model = torch.nn.DataParallel(model, device_ids=range(torch.cuda.device_count()))
# Setup optimizer, lr_scheduler and loss function
optimizer_cls = get_optimizer(cfg)
optimizer_params = {k:v for k, v in cfg['training']['optimizer'].items()
if k != 'name'}
optimizer = optimizer_cls(model.parameters(), **optimizer_params)
scheduler = get_scheduler(optimizer, cfg['training']['lr_schedule'])
loss_fn = get_loss_function(cfg)
start_iter = 0
if cfg['training']['resume'] is not None:
if os.path.isfile(cfg['training']['resume']):
checkpoint = torch.load(cfg['training']['resume'])
model.load_state_dict(checkpoint["model_state"])
optimizer.load_state_dict(checkpoint["optimizer_state"])
scheduler.load_state_dict(checkpoint["scheduler_state"])
start_iter = checkpoint["epoch"]
print("=====>",
"Loaded checkpoint '{}' (iter {})".format(
cfg['training']['resume'], checkpoint["epoch"]
)
)
else:
print("=====>","No checkpoint found at '{}'".format(cfg['training']['resume']))
val_loss_meter = averageMeter()
time_meter = averageMeter()
best_iou = -100.0
i = start_iter
flag = True
while i <= cfg['training']['train_iters'] and flag:
for (images, labels) in trainloader:
i += 1
start_ts = time.time()
scheduler.step()
model.train()
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = loss_fn(input=outputs, target=labels)
loss.backward()
optimizer.step()
time_meter.update(time.time() - start_ts)
if (i + 1) % cfg['training']['print_interval'] == 0:
fmt_str = "Iter [{:d}/{:d}] Loss: {:.4f} Time/Image: {:.4f}"
print_str = fmt_str.format(i + 1,
cfg['training']['train_iters'],
loss.item(),
time_meter.avg / cfg['training']['batch_size'])
print(print_str)
time_meter.reset()
if (i + 1) % cfg['training']['val_interval'] == 0 or \
(i + 1) == cfg['training']['train_iters']:
model.eval()
with torch.no_grad():
for i_val, (images_val, labels_val) in tqdm(enumerate(valloader)):
images_val = images_val.to(device)
labels_val = labels_val.to(device)
outputs = model(images_val)
val_loss = loss_fn(input=outputs, target=labels_val)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels_val.data.cpu().numpy()
running_metrics_val.update(gt, pred)
val_loss_meter.update(val_loss.item())
print("Iter %d Loss: %.4f" % (i + 1, val_loss_meter.avg))
score, class_iou = running_metrics_val.get_scores()
for k, v in score.items():
print(k,':',v)
for k, v in class_iou.items():
print('{}: {}'.format(k, v))
val_loss_meter.reset()
running_metrics_val.reset()
if score["Mean IoU : \t"] >= best_iou:
best_iou = score["Mean IoU : \t"]
state = {
"epoch": i + 1,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict(),
"best_iou": best_iou,
}
save_path = os.path.join('./checkpoint',
"{}_{}_best_model.pkl".format(
cfg['model']['arch'],
cfg['data']['dataset']))
print("saving···")
torch.save(state, save_path)
if (i + 1) == cfg['training']['train_iters']:
flag = False
break
input = input.transpose(1, 2).transpose(2, 3).contiguous().view(-1, c)
target = target.view(-1)
loss = F.cross_entropy(input,
target,
weight=weight,
size_average=size_average,
ignore_index=250)
return loss
# optimier
optimizer = SGD(model.parameters(), lr=0.01, momentum=0.9)
#optimizer = Adam(model.parameters(), lr = 0.01)
# Setup Metrics
running_metrics_val = runningScore(n_classes)
val_loss_meter = averageMeter()
num_epochs = 30
step = 0
epoch = 0
if load_model_file is not None:
step = start_step
epoch = start_epoch
score_list = []
while epoch <= num_epochs:
epoch += 1
print("Starting epoch %s" % epoch)
for (images, labels) in trainloader:
step += 1
def train(train_loader, model, criterion, optimizer, epoch,writer):
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
losses_Lagg = AverageMeter()
losses_Ldis = AverageMeter()
running_metric_text = runningScore(2)
running_metric_kernel = runningScore(2)
end = time.time()
for batch_idx, (imgs, gt_texts, gt_kernels, training_masks,gt_text_key,gt_kernels_key) in enumerate(train_loader):
data_time.update(time.time() - end)
# print(imgs.shape,gt_texts.shape,gt_kernels.shape,training_masks.shape,gt_text_key.shape,gt_kernels_key.shape)
imgs = Variable(imgs.cuda()) # batch_size*channel*w*h
gt_texts = Variable(gt_texts.cuda())# batch_size*w*h
gt_kernels = Variable(gt_kernels.cuda())# batch_size*1*w*h
gt_text_key = Variable(gt_text_key.cuda())# batch_size*w*h
gt_kernels_key = Variable(gt_kernels_key.cuda())# batch_size*w*h
training_masks = Variable(training_masks.cuda())# batch_size*w*h
outputs = model(imgs)
ind = 'cat_34'
cv2.imwrite('text'+str(ind)+'.jpg',torch.sigmoid(outputs[0, 0, :, :]).data.cpu().numpy().astype(np.uint8)*255)
cv2.imwrite('kernel'+str(ind)+'.jpg',torch.sigmoid(outputs[0, 1, :, :]).data.cpu().numpy().astype(np.uint8)*255)
if batch_idx % 20 == 0:
writer.add_image('/data/ori_image', torchvision.utils.make_grid(imgs,nrow=8, padding=10,normalize=True).cpu(),0)
writer.add_image('/data/label_text', torchvision.utils.make_grid(gt_texts.cpu().unsqueeze(1),nrow=8, padding=10,normalize=True),0)
writer.add_image('/data/predict_text', torchvision.utils.make_grid(torch.sigmoid(outputs[:, 0, :, :]).unsqueeze(1),nrow=8, padding=10,normalize=True).cpu(),0)
writer.add_image('/data/label_kernel', torchvision.utils.make_grid(gt_kernels,nrow=8, padding=10,normalize=True).cpu(),0)
writer.add_image('/data/predict_kernel', torchvision.utils.make_grid(torch.sigmoid(outputs[:, 1, :, :]).unsqueeze(1),nrow=8, padding=10,normalize=True).cpu(),0)
texts = outputs[:, 0, :, :]
kernels = outputs[:, 1:2, :, :]
similarity_vector=outputs[:,2:,:,:]#torch.sigmoid(outputs[:,2:,:,:])
selected_masks = ohem_batch(texts, gt_texts, training_masks)
selected_masks = Variable(selected_masks.cuda())
loss_text = criterion(texts, gt_texts, selected_masks)
mask0 = torch.sigmoid(texts).data.cpu().numpy()
mask1 = training_masks.data.cpu().numpy()
selected_masks = ((mask0 > 0.5) & (mask1 > 0.5)).astype('float32')
selected_masks = torch.from_numpy(selected_masks).float()
selected_masks = Variable(selected_masks.cuda())
loss_kernels = []
for i in range(1):
kernel_i = kernels[:, i, :, :]
gt_kernel_i = gt_kernels[:, i, :, :]
loss_kernel_i = criterion(kernel_i, gt_kernel_i, selected_masks)
loss_kernels.append(loss_kernel_i)
loss_kernel = sum(loss_kernels) / len(loss_kernels)
# total_tag,total_label_kernel,total_label_text = get_batch_tag(outputs,outputs.shape[0],gt_texts,gt_kernels)
# loss_agg = cal_Lagg(similarity_vector,total_tag,total_label_kernel,total_label_text)
# loss_ldis = cal_Ldis(similarity_vector,total_tag,total_label_kernel)
loss_agg = cal_Lagg_gt(similarity_vector,gt_kernels_key,gt_text_key,training_masks)
loss_ldis = cal_Ldis_gt(similarity_vector,gt_kernels_key,training_masks)
# loss_agg,loss_ldis = agg_dis_loss(outputs[:, 0, :, :], outputs[:, 1, :, :], gt_text_key, gt_kernels_key, similarity_vector)
# loss_agg = loss_agg.mean()
# loss_ldis = loss_ldis.mean()
loss = loss_text + 0.5*loss_kernel+0.25*(loss_agg+loss_ldis)
writer.add_scalar('Loss/total_loss',loss,batch_idx+epoch*(1000/8))
writer.add_scalar('Loss/loss_text',loss_text,batch_idx+epoch*(1000/8))
writer.add_scalar('Loss/loss_kernel',loss_kernel,batch_idx+epoch*(1000/8))
writer.add_scalar('Loss/loss_agg',loss_agg,batch_idx+epoch*(1000/8))
writer.add_scalar('Loss/loss_ldis',loss_ldis,batch_idx+epoch*(1000/8))
losses.update(loss.item(), imgs.size(0))
losses_Lagg.update(loss_agg.item(), imgs.size(0))
losses_Ldis.update(loss_ldis.item(), imgs.size(0))
optimizer.zero_grad()
loss.backward()
# print('loss_text',loss_text.grad)
# print('loss_kernel',loss_kernel.grad)
# print('loss_agg',loss_agg.grad)
# print('loss_ldis',loss_ldis.grad)
# print('loss',loss.grad)
optimizer.step()
score_text = cal_text_score(texts, gt_texts, training_masks, running_metric_text)
score_kernel = cal_kernel_score(kernels, gt_kernels, gt_texts, training_masks, running_metric_kernel)
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % 20 == 0:
output_log = '({batch}/{size}) Batch: {bt:.3f}s | TOTAL: {total:.0f}min | ETA: {eta:.0f}min | Loss: {loss:.4f} | Acc_t: {acc: .4f} | IOU_t: {iou_t: .4f} | IOU_k: {iou_k: .4f} | Lagg: {lagg:.4f} | Ldis: {ldis:.4f}'.format(
batch=batch_idx + 1,
size=len(train_loader),
bt=batch_time.avg,
total=batch_time.avg * batch_idx / 60.0,
eta=batch_time.avg * (len(train_loader) - batch_idx) / 60.0,
loss=losses.avg,
acc=score_text['Mean Acc'],
iou_t=score_text['Mean IoU'],
iou_k=score_kernel['Mean IoU'],
lagg=losses_Lagg.avg,
ldis=losses_Ldis.avg
)
print(output_log)
sys.stdout.flush()
return (losses.avg, score_text['Mean Acc'], score_kernel['Mean Acc'], score_text['Mean IoU'], score_kernel['Mean IoU'],losses_Lagg.avg,losses_Ldis.avg)
def train(data_path, models_path, backend, snapshot, crop_x, crop_y,
batch_size, alpha, epochs, start_lr, milestones, gpu):
os.environ["CUDA_VISIBLE_DEVICES"] = gpu
net, starting_epoch = build_network(snapshot, backend)
data_path = os.path.abspath(os.path.expanduser(data_path))
models_path = os.path.abspath(os.path.expanduser(models_path))
os.makedirs(models_path, exist_ok=True)
'''
To follow this training routine you need a DataLoader that yields the tuples of the following format:
(Bx3xHxW FloatTensor x, BxHxW LongTensor y, BxN LongTensor y_cls) where
x - batch of input images,
y - batch of groung truth seg maps,
y_cls - batch of 1D tensors of dimensionality N: N total number of classes,
y_cls[i, T] = 1 if class T is present in image i, 0 otherwise
'''
voc_data = pascalVOCLoader(root=data_path,
is_transform=True,
augmentations=None)
# train_loader, class_weights, n_images = None, None, None
train_loader = DataLoader(voc_data,
batch_size=batch_size,
shuffle=True,
num_workers=0)
max_steps = len(voc_data)
class_weights = None
optimizer = optim.Adam(net.parameters(), lr=start_lr)
scheduler = MultiStepLR(optimizer,
milestones=[int(x) for x in milestones.split(',')],
gamma=0.1)
running_score = runningScore(21)
for epoch in range(starting_epoch, starting_epoch + epochs):
seg_criterion = nn.NLLLoss2d(weight=class_weights)
cls_criterion = nn.BCEWithLogitsLoss(weight=class_weights)
epoch_losses = []
# train_iterator = tqdm(train_loader, total=max_steps // batch_size + 1)
net.train()
print('------------epoch[{}]----------'.format(epoch + 1))
for i, (x, y, y_cls) in enumerate(train_loader):
optimizer.zero_grad()
x, y, y_cls = Variable(x).cuda(), Variable(y).cuda(), Variable(
y_cls).float().cuda()
out, out_cls = net(x)
pred = out.data.max(1)[1].cpu().numpy()
seg_loss, cls_loss = seg_criterion(out, y), cls_criterion(
out_cls, y_cls)
loss = seg_loss + alpha * cls_loss
epoch_losses.append(loss.item())
running_score.update(y.data.cpu().numpy(), pred)
if (i + 1) % 138 == 0:
score, class_iou = running_score.get_scores()
for k, v in score.items():
print(k, v)
logger.info('{}:{}'.format(k, v))
running_score.reset()
print_format_str = "Epoch[{}] batch[{}] loss = {:.4f} LR = {}"
print_str = print_format_str.format(epoch + 1, i + 1, loss.item(),
scheduler.get_lr()[0])
print(print_str)
logger.info(print_str)
'''
status = '[{}] loss = {:.4f} avg = {:.4f}, LR = {}'.format(
epoch + 1, loss.item(), np.mean(epoch_losses), scheduler.get_lr()[0])
train_iterator.set_description(status)
'''
loss.backward()
optimizer.step()
scheduler.step()
if epoch + 1 > 20:
train_loss = ('%.4f' % np.mean(epoch_losses))
torch.save(
net.state_dict(),
os.path.join(
models_path,
'_'.join(["PSPNet", str(epoch + 1), train_loss]) + '.pth'))
def train(cycle_num,
dirs,
path_to_net,
plotter,
batch_size=12,
test_split=0.3,
random_state=666,
epochs=100,
learning_rate=0.0001,
momentum=0.9,
num_folds=5,
num_slices=155,
n_classes=4):
"""
Applies training on the network
Args:
cycle_num (int): number of cycle in n-fold (num_folds) cross validation
dirs (string): path to dataset subject directories
path_to_net (string): path to directory where to save network
plotter (callable): visdom plotter
batch_size - default (int): batch size
test_split - default (float): percentage of test split
random_state - default (int): seed for k-fold cross validation
epochs - default (int): number of epochs
learning_rate - default (float): learning rate
momentum - default (float): momentum
num_folds - default (int): number of folds in cross validation
num_slices - default (int): number of slices per volume
n_classes - default (int): number of classes (regions)
"""
print('Setting started', flush=True)
# Creating data indices
# arange len of list of subject dirs
indices = np.arange(len(glob.glob(dirs + '*')))
test_indices, trainset_indices = get_test_indices(indices, test_split)
# kfold index generator
for cv_num, (train_indices, val_indices) in enumerate(
get_train_cv_indices(trainset_indices, num_folds, random_state)):
# splitted the 5-fold CV in 5 jobs
if cv_num != int(cycle_num):
continue
net = U_Net()
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
num_GPU = torch.cuda.device_count()
if num_GPU > 1:
print('Let us use {} GPUs!'.format(num_GPU), flush=True)
net = nn.DataParallel(net)
net.to(device)
criterion = nn.CrossEntropyLoss()
if cycle_num % 2 == 0:
optimizer = optim.SGD(net.parameters(),
lr=learning_rate,
momentum=momentum)
else:
optimizer = optim.Adam(net.parameters(), lr=learning_rate)
scheduler = ReduceLROnPlateau(optimizer, threshold=1e-6, patience=0)
print('cv cycle number: ', cycle_num, flush=True)
start = time.time()
print('Start Train and Val loading', flush=True)
MRIDataset_train = dataset.MRIDataset(dirs, train_indices)
MRIDataset_val = dataset.MRIDataset(dirs, val_indices)
datalengths = {
'train': len(MRIDataset_train),
'val': len(MRIDataset_val)
}
dataloaders = {
'train': get_dataloader(MRIDataset_train, batch_size, num_GPU),
'val': get_dataloader(MRIDataset_val, batch_size, num_GPU)
}
print('Train and Val loading took: ', time.time() - start, flush=True)
# make loss and acc history for train and val separatly
# Setup Metrics
running_metrics_val = runningScore(n_classes)
running_metrics_train = runningScore(n_classes)
val_loss_meter = averageMeter()
train_loss_meter = averageMeter()
itr = 0
iou_best = 0.
for epoch in tqdm(range(epochs), desc='Epochs'):
print('Epoch: ', epoch + 1, flush=True)
phase = 'train'
print('Phase: ', phase, flush=True)
start = time.time()
# Set model to training mode
net.train()
# Iterate over data.
for i, data in tqdm(enumerate(dataloaders[phase]),
desc='Data Iteration ' + phase):
if (i + 1) % 100 == 0:
print('Number of Iteration [{}/{}]'.format(
i + 1, int(datalengths[phase] / batch_size)),
flush=True)
# get the inputs
inputs = data['mri_data'].to(device)
GT = data['seg'].to(device)
subject_slice_path = data['subject_slice_path']
# Clear all accumulated gradients
optimizer.zero_grad()
# Predict classes using inputs from the train set
SR = net(inputs)
# Compute the loss based on the predictions and
# actual segmentation
loss = criterion(SR, GT)
# Backpropagate the loss
loss.backward()
# Adjust parameters according to the computed
# gradients
# -- weight update
optimizer.step()
# Trake and plot metrics and loss, and save network
predictions = SR.data.max(1)[1].cpu().numpy()
GT_cpu = GT.data.cpu().numpy()
running_metrics_train.update(GT_cpu, predictions)
train_loss_meter.update(loss.item(), n=1)
if (i + 1) % 100 == 0:
itr += 1
score, class_iou = running_metrics_train.get_scores()
for k, v in score.items():
plotter.plot(k, 'itr', phase, k, itr, v)
for k, v in class_iou.items():
print('Class {} IoU: {}'.format(k, v), flush=True)
plotter.plot(
str(k) + ' Class IoU', 'itr', phase,
str(k) + ' Class IoU', itr, v)
print('Loss Train', train_loss_meter.avg, flush=True)
plotter.plot('Loss', 'itr', phase, 'Loss Train', itr,
train_loss_meter.avg)
print('Phase {} took {} s for whole {}set!'.format(
phase,
time.time() - start, phase),
flush=True)
# Validation Phase
phase = 'val'
print('Phase: ', phase, flush=True)
start = time.time()
# Set model to evaluation mode
net.eval()
start = time.time()
with torch.no_grad():
# Iterate over data.
for i, data in tqdm(enumerate(dataloaders[phase]),
desc='Data Iteration ' + phase):
if (i + 1) % 100 == 0:
print('Number of Iteration [{}/{}]'.format(
i + 1, int(datalengths[phase] / batch_size)),
flush=True)
# get the inputs
inputs = data['mri_data'].to(device)
GT = data['seg'].to(device)
subject_slice_path = data['subject_slice_path']
# Clear all accumulated gradients
optimizer.zero_grad()
# Predict classes using inputs from the train set
SR = net(inputs)
# Compute the loss based on the predictions and
# actual segmentation
loss = criterion(SR, GT)
# Trake and plot metrics and loss
predictions = SR.data.max(1)[1].cpu().numpy()
GT_cpu = GT.data.cpu().numpy()
running_metrics_val.update(GT_cpu, predictions)
val_loss_meter.update(loss.item(), n=1)
if (i + 1) % 100 == 0:
itr += 1
score, class_iou = running_metrics_val.get_scores()
for k, v in score.items():
plotter.plot(k, 'itr', phase, k, itr, v)
for k, v in class_iou.items():
print('Class {} IoU: {}'.format(k, v), flush=True)
plotter.plot(
str(k) + ' Class IoU', 'itr', phase,
str(k) + ' Class IoU', itr, v)
print('Loss Val', val_loss_meter.avg, flush=True)
plotter.plot('Loss ', 'itr', phase, 'Loss Val', itr,
val_loss_meter.avg)
if (epoch + 1) % 10 == 0:
if score['Mean IoU'] > iou_best:
save_net(path_to_net, batch_size, epoch, cycle_num,
train_indices, val_indices, test_indices, net,
optimizer)
iou_best = score['Mean IoU']
save_output(epoch, path_to_net, subject_slice_path,
SR.data.cpu().numpy(), GT_cpu)
print('Phase {} took {} s for whole {}set!'.format(
phase,
time.time() - start, phase),
flush=True)
# Call the learning rate adjustment function after every epoch
scheduler.step(val_loss_meter.avg)
# save network after training
save_net(path_to_net,
batch_size,
epochs,
cycle_num,
train_indices,
val_indices,
test_indices,
net,
optimizer,
iter_num=None)
def train(cfg, logger):
# Setup Seeds
torch.manual_seed(cfg.get("seed", 1337))
torch.cuda.manual_seed(cfg.get("seed", 1337))
np.random.seed(cfg.get("seed", 1337))
random.seed(cfg.get("seed", 1337))
# Setup Device
device = torch.device("cuda:{}".format(cfg["training"]["gpu_idx"])
if torch.cuda.is_available() else "cpu")
# Setup Augmentations
augmentations = cfg["training"].get("augmentations", None)
# Setup Dataloader
data_loader = get_loader(cfg["data"]["dataset"])
data_path = cfg["data"]["path"]
t_loader = data_loader(
data_path,
split=cfg["data"]["train_split"],
)
v_loader = data_loader(
data_path,
split=cfg["data"]["val_split"],
)
n_classes = t_loader.n_classes
n_val = len(v_loader.files['val'])
trainloader = data.DataLoader(
t_loader,
batch_size=cfg["training"]["batch_size"],
num_workers=cfg["training"]["n_workers"],
shuffle=True,
)
valloader = data.DataLoader(v_loader,
batch_size=cfg["training"]["batch_size"],
num_workers=cfg["training"]["n_workers"])
# Setup Metrics
running_metrics_val = runningScore(n_classes, n_val)
# Setup Model
model = get_model(cfg["model"], n_classes).to(device)
model = torch.nn.DataParallel(model,
device_ids=[cfg["training"]["gpu_idx"]])
# Setup Optimizer, lr_scheduler and Loss Function
optimizer_cls = get_optimizer(cfg)
optimizer_params = {
k: v
for k, v in cfg["training"]["optimizer"].items() if k != "name"
}
optimizer = optimizer_cls(model.parameters(), **optimizer_params)
logger.info("Using optimizer {}".format(optimizer))
scheduler = get_scheduler(optimizer, cfg["training"]["lr_schedule"])
loss_fn = get_loss_function(cfg)
logger.info("Using loss {}".format(loss_fn))
# Resume Trained Model
if cfg["training"]["resume"] is not None:
if os.path.isfile(cfg["training"]["resume"]):
logger.info(
"Loading model and optimizer from checkpoint '{}'".format(
cfg["training"]["resume"]))
checkpoint = torch.load(cfg["training"]["resume"])
model.load_state_dict(checkpoint["model_state"])
optimizer.load_state_dict(checkpoint["optimizer_state"])
scheduler.load_state_dict(checkpoint["scheduler_state"])
start_iter = checkpoint["epoch"]
logger.info("Loaded checkpoint '{}' (iter {})".format(
cfg["training"]["resume"], checkpoint["epoch"]))
else:
logger.info("No checkpoint found at '{}'".format(
cfg["training"]["resume"]))
# Start Training
val_loss_meter = averageMeter()
time_meter = averageMeter()
start_iter = 0
best_dice = -100.0
i = start_iter
flag = True
while i <= cfg["training"]["train_iters"] and flag:
for (images, labels, img_name) in trainloader:
i += 1
start_ts = time.time()
scheduler.step()
model.train()
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = loss_fn(input=outputs, target=labels)
loss.backward()
optimizer.step()
time_meter.update(time.time() - start_ts)
# print train loss
if (i + 1) % cfg["training"]["print_interval"] == 0:
fmt_str = "Iter [{:d}/{:d}] Loss: {:.4f} Time/Image: {:.4f}"
print_str = fmt_str.format(
i + 1,
cfg["training"]["train_iters"],
loss.item(),
time_meter.avg / cfg["training"]["batch_size"],
)
print(print_str)
logger.info(print_str)
time_meter.reset()
# validation
if (i + 1) % cfg["training"]["val_interval"] == 0 or (
i + 1) == cfg["training"]["train_iters"]:
model.eval()
with torch.no_grad():
for i_val, (images_val, labels_val,
img_name_val) in tqdm(enumerate(valloader)):
images_val = images_val.to(device)
labels_val = labels_val.to(device)
outputs = model(images_val)
val_loss = loss_fn(input=outputs, target=labels_val)
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels_val.data.cpu().numpy()
running_metrics_val.update(gt, pred, i_val)
val_loss_meter.update(val_loss.item())
logger.info("Iter %d Loss: %.4f" % (i + 1, val_loss_meter.avg))
# print val metrics
score, class_dice = running_metrics_val.get_scores()
for k, v in score.items():
print(k, v)
logger.info("{}: {}".format(k, v))
for k, v in class_dice.items():
logger.info("{}: {}".format(k, v))
val_loss_meter.reset()
running_metrics_val.reset()
# save model
if score["Dice : \t"] >= best_dice:
best_dice = score["Dice : \t"]
state = {
"epoch": i + 1,
"model_state": model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict(),
"best_dice": best_dice,
}
save_path = os.path.join(
cfg["training"]["model_dir"],
"{}_{}.pkl".format(cfg["model"]["arch"],
cfg["data"]["dataset"]),
)
torch.save(state, save_path)
if (i + 1) == cfg["training"]["train_iters"]:
flag = False
break
def train(train_loader, model, criterion, optimizer, epoch):
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
running_metric_text = runningScore(2)
running_metric_kernel = runningScore(2)
end = time.time()
for batch_idx, (imgs, gt_texts, gt_kernels,
training_masks) in enumerate(train_loader):
data_time.update(time.time() - end)
imgs = imgs.to(device)
gt_texts = gt_texts.to(device)
gt_kernels = gt_kernels.to(device)
training_masks = training_masks.to(device)
outputs = model(imgs)
texts = outputs[:, 0, :, :]
kernels = outputs[:, 1:, :, :]
selected_masks = ohem_batch(texts, gt_texts, training_masks)
selected_masks = selected_masks.to(device)
loss_text = criterion(texts, gt_texts, selected_masks)
loss_kernels = []
mask0 = torch.sigmoid(texts).data.cpu().numpy()
mask1 = training_masks.data.cpu().numpy()
selected_masks = ((mask0 > 0.5) & (mask1 > 0.5)).astype('float32')
selected_masks = torch.from_numpy(selected_masks).float()
selected_masks = selected_masks.to(device)
for i in range(args.kernelnum - 1):
kernel_i = kernels[:, i, :, :]
gt_kernel_i = gt_kernels[:, i, :, :]
loss_kernel_i = criterion(kernel_i, gt_kernel_i, selected_masks)
loss_kernels.append(loss_kernel_i)
loss_kernel = sum(loss_kernels) / len(loss_kernels)
loss = 0.7 * loss_text + 0.3 * loss_kernel
losses.update(loss.item(), imgs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
score_text = cal_text_score(texts, gt_texts, training_masks,
running_metric_text)
score_kernel = cal_kernel_score(kernels, gt_kernels, gt_texts,
training_masks, running_metric_kernel)
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % 20 == 0:
output_log = '[{epoch}/{allepoch}][{batch}/{size}] Batch: {bt:.3f}s | TOTAL: {total:.0f}min | ETA: {eta:.0f}min | Loss: {loss:.4f} | Acc_t: {acc: .4f} | IOU_t: {iou_t: .4f} | IOU_k: {iou_k: .4f}'.format(
epoch=epoch,
allepoch=args.n_epoch,
batch=batch_idx + 1,
size=len(train_loader),
bt=batch_time.avg,
total=batch_time.avg * batch_idx / 60.0,
eta=batch_time.avg * (len(train_loader) - batch_idx) / 60.0,
loss=losses.avg,
acc=score_text['Mean Acc'],
iou_t=score_text['Mean IoU'],
iou_k=score_kernel['Mean IoU'])
print(output_log)
sys.stdout.flush()
torch.save(model.state_dict(), 'training_model')
return (losses.avg, score_text['Mean Acc'], score_kernel['Mean Acc'],
score_text['Mean IoU'], score_kernel['Mean IoU'])
def test(cfg):
device = torch.device("cuda:{}".format(cfg["training"]["gpu_idx"])
if torch.cuda.is_available() else "cpu")
data_loader = get_loader(cfg["data"]["dataset"])
data_path = cfg["data"]["path"]
v_loader = data_loader(data_path, split='val')
n_classes = v_loader.n_classes
n_val = len(v_loader.files['val'])
valLoader = data.DataLoader(v_loader,
batch_size=1,
num_workers=cfg["training"]["n_workers"])
model = get_model(cfg["model"], n_classes).to(device)
state = convert_state_dict(
torch.load(cfg["testing"]["trained_model"],
map_location=device)["model_state"])
model.load_state_dict(state)
model.eval()
model.to(device)
running_metrics_val = runningScore(n_classes, n_val)
with torch.no_grad():
for i_val, (images_val, labels_val,
img_name_val) in tqdm(enumerate(valLoader)):
images_val = images_val.to(device)
labels_val = labels_val.to(device)
outputs = model(images_val)
pred = np.squeeze(outputs.data.max(1)[1].cpu().numpy())
gt = np.squeeze(labels_val.data.cpu().numpy())
running_metrics_val.update(gt, pred, i_val)
decoded = v_loader.decode_segmap(pred, plot=False)
m.imsave(
pjoin(cfg["testing"]["path"],
'{}.bmp'.format(img_name_val[0])), decoded)
score = running_metrics_val.get_scores()
acc_all, dsc_cls = running_metrics_val.get_list()
for k, v in score[0].items():
print(k, v)
if cfg["testing"]["boxplot"] == True:
sns.set_style("whitegrid")
labels = ['CSF', 'Gray Matter', 'White Matter']
fig1, ax1 = plt.subplots()
ax1.set_title('Basic Plot')
# ax1.boxplot(dsc_cls.transpose()[:,1:n_classes], showfliers=False, labels=labels)
ax1 = sns.boxplot(data=dsc_cls.transpose()[:, 1:n_classes])
# ax1.yaxis.grid(True)
ax1.set_xlabel('Three separate samples')
ax1.set_ylabel('Dice Score')
# path to save boxplot
plt.savefig('/home/jwliu/disk/kxie/CNN_LSTM/test_results/box.pdf')
def train(cfg, writer, logger):
torch.manual_seed(cfg.get('seed', 1337))
torch.cuda.manual_seed(cfg.get('seed', 1337))
np.random.seed(cfg.get('seed', 1337))
random.seed(cfg.get('seed', 1337))
## create dataset
default_gpu = cfg['model']['default_gpu']
device = torch.device(
"cuda:{}".format(default_gpu) if torch.cuda.is_available() else 'cpu')
datasets = create_dataset(cfg, writer, logger)
use_pseudo_label = False
model = CustomModel(cfg, writer, logger, use_pseudo_label, modal_num=3)
# Setup Metrics
running_metrics_val = runningScore(cfg['data']['target']['n_class'])
source_running_metrics_val = runningScore(cfg['data']['target']['n_class'])
val_loss_meter = averageMeter()
source_val_loss_meter = averageMeter()
time_meter = averageMeter()
loss_fn = get_loss_function(cfg)
flag_train = True
epoches = cfg['training']['epoches']
source_train_loader = datasets.source_train_loader
target_train_loader = datasets.target_train_loader
logger.info('source train batchsize is {}'.format(
source_train_loader.args.get('batch_size')))
print('source train batchsize is {}'.format(
source_train_loader.args.get('batch_size')))
logger.info('target train batchsize is {}'.format(
target_train_loader.batch_size))
print('target train batchsize is {}'.format(
target_train_loader.batch_size))
val_loader = None
if cfg.get('valset') == 'gta5':
val_loader = datasets.source_valid_loader
logger.info('valset is gta5')
print('valset is gta5')
else:
val_loader = datasets.target_valid_loader
logger.info('valset is cityscapes')
print('valset is cityscapes')
logger.info('val batchsize is {}'.format(val_loader.batch_size))
print('val batchsize is {}'.format(val_loader.batch_size))
# load category anchors
"""
objective_vectors = torch.load('category_anchors')
model.objective_vectors = objective_vectors['objective_vectors']
model.objective_vectors_num = objective_vectors['objective_num']
"""
# begin training
model.iter = 0
for epoch in range(epoches):
if not flag_train:
break
if model.iter > cfg['training']['train_iters']:
break
if use_pseudo_label:
# monitoring the accuracy and recall of CAG-based PLA and probability-based PLA
score_cl, _ = model.metrics.running_metrics_val_clusters.get_scores(
)
print('clus_IoU: {}'.format(score_cl["Mean IoU : \t"]))
logger.info('clus_IoU: {}'.format(score_cl["Mean IoU : \t"]))
logger.info('clus_Recall: {}'.format(
model.metrics.calc_mean_Clu_recall()))
logger.info(model.metrics.classes_recall_clu[:, 0] /
model.metrics.classes_recall_clu[:, 1])
logger.info('clus_Acc: {}'.format(
np.mean(model.metrics.classes_recall_clu[:, 0] /
model.metrics.classes_recall_clu[:, 1])))
logger.info(model.metrics.classes_recall_clu[:, 0] /
model.metrics.classes_recall_clu[:, 2])
score_cl, _ = model.metrics.running_metrics_val_threshold.get_scores(
)
logger.info('thr_IoU: {}'.format(score_cl["Mean IoU : \t"]))
logger.info('thr_Recall: {}'.format(
model.metrics.calc_mean_Thr_recall()))
logger.info(model.metrics.classes_recall_thr[:, 0] /
model.metrics.classes_recall_thr[:, 1])
logger.info('thr_Acc: {}'.format(
np.mean(model.metrics.classes_recall_thr[:, 0] /
model.metrics.classes_recall_thr[:, 1])))
logger.info(model.metrics.classes_recall_thr[:, 0] /
model.metrics.classes_recall_thr[:, 2])
model.metrics.reset()
for (target_image, target_label,
target_img_name) in datasets.target_train_loader:
model.iter += 1
i = model.iter
if i > cfg['training']['train_iters']:
break
source_batchsize = cfg['data']['source']['batch_size']
# load source data
images, labels, source_img_name = datasets.source_train_loader.next(
)
start_ts = time.time()
images = images.to(device)
labels = labels.to(device)
# load target data
target_image = target_image.to(device)
target_label = target_label.to(device)
#model.scheduler_step()
model.train(logger=logger)
if cfg['training'].get('freeze_bn') == True:
model.freeze_bn_apply()
model.optimizer_zerograd()
# Switch on modals
source_modal_ids = []
for _img_name in source_img_name:
if 'gtav2cityscapes' in _img_name:
source_modal_ids.append(0)
elif 'gtav2cityfoggy' in _img_name:
source_modal_ids.append(1)
elif 'gtav2cityrain' in _img_name:
source_modal_ids.append(2)
else:
assert False, "[ERROR] unknown image source, neither gtav2cityscapes, gtav2cityfoggy!"
target_modal_ids = []
for _img_name in target_img_name:
if 'Cityscapes_foggy' in _img_name:
target_modal_ids.append(1)
elif 'Cityscapes_rain' in _img_name:
target_modal_ids.append(2)
else:
target_modal_ids.append(0)
loss, loss_cls_L2, loss_pseudo = model.step(
images, labels, source_modal_ids, target_image, target_label,
target_modal_ids, use_pseudo_label)
# scheduler step
model.scheduler_step()
if loss_cls_L2 > 10:
logger.info('loss_cls_l2 abnormal!!')
time_meter.update(time.time() - start_ts)
if (i + 1) % cfg['training']['print_interval'] == 0:
unchanged_cls_num = 0
if use_pseudo_label:
fmt_str = "Epoches [{:d}/{:d}] Iter [{:d}/{:d}] Loss: {:.4f} Loss_L2: {:.4f} Loss_pseudo: {:.4f} Time/Image: {:.4f}"
else:
fmt_str = "Epoches [{:d}/{:d}] Iter [{:d}/{:d}] Loss_GTA: {:.4f} Loss_adv: {:.4f} Loss_D: {:.4f} Time/Image: {:.4f}"
print_str = fmt_str.format(
epoch + 1, epoches, i + 1, cfg['training']['train_iters'],
loss.item(), loss_cls_L2.item(), loss_pseudo.item(),
time_meter.avg / cfg['data']['source']['batch_size'])
print(print_str)
logger.info(print_str)
logger.info(
'unchanged number of objective class vector: {}'.format(
unchanged_cls_num))
if use_pseudo_label:
loss_names = [
'train_loss', 'train_L2Loss', 'train_pseudoLoss'
]
else:
loss_names = [
'train_loss_GTA', 'train_loss_adv', 'train_loss_D'
]
writer.add_scalar('loss/{}'.format(loss_names[0]), loss.item(),
i + 1)
writer.add_scalar('loss/{}'.format(loss_names[1]),
loss_cls_L2.item(), i + 1)
writer.add_scalar('loss/{}'.format(loss_names[2]),
loss_pseudo.item(), i + 1)
time_meter.reset()
if use_pseudo_label:
score_cl, _ = model.metrics.running_metrics_val_clusters.get_scores(
)
logger.info('clus_IoU: {}'.format(
score_cl["Mean IoU : \t"]))
logger.info('clus_Recall: {}'.format(
model.metrics.calc_mean_Clu_recall()))
logger.info('clus_Acc: {}'.format(
np.mean(model.metrics.classes_recall_clu[:, 0] /
model.metrics.classes_recall_clu[:, 2])))
score_cl, _ = model.metrics.running_metrics_val_threshold.get_scores(
)
logger.info('thr_IoU: {}'.format(
score_cl["Mean IoU : \t"]))
logger.info('thr_Recall: {}'.format(
model.metrics.calc_mean_Thr_recall()))
logger.info('thr_Acc: {}'.format(
np.mean(model.metrics.classes_recall_thr[:, 0] /
model.metrics.classes_recall_thr[:, 2])))
# evaluation
if (i + 1) % cfg['training']['val_interval'] == 0 or \
(i + 1) == cfg['training']['train_iters']:
validation(
model, logger, writer, datasets, device, running_metrics_val, val_loss_meter, loss_fn,\
source_val_loss_meter, source_running_metrics_val, iters = model.iter
)
torch.cuda.empty_cache()
logger.info('Best iou until now is {}'.format(model.best_iou))
if (i + 1) == cfg['training']['train_iters']:
flag = False
break
def main(test_args):
testset = "/mnt/iusers01/eee01/mchiwml4/CamVid/test"
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(mean, std)])
test_dataset = DataLoader(Loaddata(testset,
transform=transform,
target_transform=MaskToTensor()),
batch_size=1,
shuffle=False,
num_workers=8)
label_num = 11
model = deeplab_v2.Deeplab_Resnet(label_num)
model = model.cuda()
model.load_state_dict(torch.load(test_args.load_param))
model.eval()
total = np.zeros((label_num, ))
running_metrics = runningScore(label_num)
for j, data in enumerate(test_dataset):
inputs, labels = data
inputs = Variable(inputs.cuda())
outputs = model(inputs)
H = inputs.size()[2]
W = inputs.size()[3]
interp_resize = nn.Upsample(size=(int(H), int(W)), mode='bilinear')
output = interp_resize(outputs[3])
output = F.softmax(output, dim=1)
output = output.data.cpu().numpy()
if test_args.crf:
crf_output = np.zeros(output.shape)
images = inputs.data.cpu().numpy().astype(np.uint8)
for i, (image, prob_map) in enumerate(zip(images, output)):
image = image.transpose(1, 2, 0)
crf_output[i] = dense_crf(image, prob_map)
output = crf_output
pred = np.argmax(output, axis=1)
gt = labels.numpy()
running_metrics.update(gt, pred)
for i in range(label_num):
mask = gt == i # ground truth mask of class i
total[i] += np.sum(
mask) # total number of pixels of class i (tp+fn)
score, class_iou, class_acc = running_metrics.get_scores()
for k, v in score.items():
print(k, v)
print('class iou: ')
for i in range(label_num):
print(i, class_iou[i])
print('class acc: ')
for i in range(label_num):
print(i, class_acc[i])
print('number of pixels:')
print(total)
def train(train_loader, model, criterion, optimizer, epoch):
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
running_metric_text = runningScore(2)
running_metric_kernel = runningScore(2)
end = time.time()
for batch_idx, (imgs, gt_texts, gt_kernels,
training_masks) in enumerate(train_loader):
data_time.update(time.time() - end)
imgs = Variable(imgs.cuda())
gt_texts = Variable(gt_texts.cuda())
gt_kernels = Variable(gt_kernels.cuda())
training_masks = Variable(training_masks.cuda())
outputs = model(imgs)
texts = outputs[:, 0, :, :]
kernels = outputs[:, 1:, :, :]
selected_masks = ohem_batch(texts, gt_texts, training_masks)
selected_masks = Variable(selected_masks.cuda())
loss_text = criterion(texts, gt_texts, selected_masks) #dice loss
#loss_text = (F.binary_cross_entropy_with_logits(texts, gt_texts, reduce=False) * selected_masks).mean()#bce loss
loss_kernels = []
mask0 = torch.sigmoid(texts).data.cpu().numpy()
mask1 = training_masks.data.cpu().numpy()
selected_masks = ((mask0 > 0.5) & (mask1 > 0.5)).astype('float32')
selected_masks = torch.from_numpy(selected_masks).float()
selected_masks = Variable(selected_masks.cuda())
for i in range(6):
kernel_i = kernels[:, i, :, :]
gt_kernel_i = gt_kernels[:, i, :, :]
loss_kernel_i = criterion(kernel_i, gt_kernel_i, selected_masks)
loss_kernels.append(loss_kernel_i)
loss_kernel = sum(loss_kernels) / len(loss_kernels)
loss = 0.7 * loss_text + 0.3 * loss_kernel
losses.update(loss.item(), imgs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
score_text = cal_text_score(texts, gt_texts, training_masks,
running_metric_text)
score_kernel = cal_kernel_score(kernels, gt_kernels, gt_texts,
training_masks, running_metric_kernel)
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % 20 == 0:
output_log = '({batch}/{size}) Batch: {bt:.3f}s | TOTAL: {total:.0f}min | ETA: {eta:.0f}min | Loss: {loss:.4f} | Acc_t: {acc: .4f} | IOU_t: {iou_t: .4f} | IOU_k: {iou_k: .4f}'.format(
batch=batch_idx + 1,
size=len(train_loader),
bt=batch_time.avg,
total=batch_time.avg * batch_idx / 60.0,
eta=batch_time.avg * (len(train_loader) - batch_idx) / 60.0,
loss=losses.avg,
acc=score_text['Mean Acc'],
iou_t=score_text['Mean IoU'],
iou_k=score_kernel['Mean IoU'])
print(output_log)
sys.stdout.flush()
return (losses.avg, score_text['Mean Acc'], score_kernel['Mean Acc'],
score_text['Mean IoU'], score_kernel['Mean IoU'])
net = FCN_Dilated()
net.load_state_dict(torch.load('best_seg_dcgan.wts'))
net.to(device)
net.eval()
dst = kittiLoader(
'/home-local/rohitrishabh/utilScripts/Segmentation/FCN_Dilated/data_road/',
split="val")
valloader = data.DataLoader(dst,
batch_size=1,
shuffle=True,
num_workers=1,
pin_memory=True)
score = runningScore(2)
for i, data in enumerate(valloader):
imgs, labels = data
imgs, labels = imgs.to(device), labels.to(device)
with torch.no_grad():
out = net(imgs)
pred = out.data.max(1)[1].cpu().numpy()
plt.imshow(pred[0])
plt.savefig('output_test/' + str(i) + '_out.png')
plt.imshow(labels[0])
plt.savefig('output_test/' + str(i) + '_orig.png')
print(np.sum(pred))
gt = labels.data.cpu().numpy()
def train(train_loader, model, criterion, optimizer, epoch, summary_writer):
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
running_metric_text = runningScore(2)
L1_loss = torch.nn.L1Loss()
end = time.time()
for batch_idx, (imgs, gt_texts, gt_kernels, training_masks, ori_imgs,
mask_kernels) in enumerate(train_loader):
data_time.update(time.time() - end)
imgs = Variable(imgs.cuda())
gt_texts = Variable(gt_texts.cuda())
gt_kernels = Variable(gt_kernels.cuda())
training_masks = Variable(training_masks.cuda())
outputs = model(imgs)
probability_map, threshold_map, binarization_map = outputs
# loss for probability_map
selected_masks = ohem_batch(probability_map, gt_texts, training_masks)
selected_masks = Variable(selected_masks.cuda())
loss_probability = criterion(probability_map, gt_texts, selected_masks)
# loss for binary_map
binary_selected_masks = ohem_batch(binarization_map, gt_texts,
training_masks)
binary_selected_masks = Variable(binary_selected_masks.cuda())
loss_binary = criterion(binarization_map, gt_texts,
binary_selected_masks)
# loss for threshold_map
loss_thresh = L1_loss(threshold_map * mask_kernels,
gt_kernels * mask_kernels)
loss = loss_probability + loss_binary + 10 * loss_thresh
losses.update(loss.item(), imgs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
score_prob = cal_text_score(probability_map, gt_texts, training_masks,
running_metric_text)
score_binary = cal_text_score(binarization_map, gt_texts,
training_masks, running_metric_text)
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % 20 == 0:
# visualization
global_step = epoch * len(train_loader) + batch_idx
summary_writer.add_images('gt/img',
ori_imgs[:2],
global_step=global_step)
summary_writer.add_images('gt/score_map',
torch.unsqueeze(gt_texts[:2], 1),
global_step=global_step)
summary_writer.add_images('gt/thresh_map',
torch.unsqueeze(gt_kernels[:2], 1),
global_step=global_step)
summary_writer.add_images('predicition/score_map',
torch.sigmoid(probability_map[:2]),
global_step=global_step)
summary_writer.add_images('predicition/binary_map',
torch.sigmoid(binarization_map[:2]),
global_step=global_step)
summary_writer.add_images('predicition/threshold_map',
torch.sigmoid(threshold_map[:2]),
global_step=global_step)
summary_writer.add_scalar('loss/text_loss',
loss_probability,
global_step=global_step)
summary_writer.add_scalar('loss/binary_loss',
loss_binary,
global_step=global_step)
summary_writer.add_scalar('loss/thresh_loss',
loss_thresh,
global_step=global_step)
summary_writer.add_scalar('metric/acc_t',
score_prob['Mean Acc'],
global_step=global_step)
summary_writer.add_scalar('metric/acc_b',
score_binary['Mean Acc'],
global_step=global_step)
summary_writer.add_scalar('metric/iou_t',
score_prob['Mean IoU'],
global_step=global_step)
summary_writer.add_scalar('metric/iou_b',
score_binary['Mean IoU'],
global_step=global_step)
output_log = '({batch}/{size}) Batch: {bt:.3f}s | TOTAL: {total:.0f}min | ETA: {eta:.0f}min | Loss: {loss:.4f} | Acc_t: {acc_t: .4f} | Acc_b: {acc_b: .4f} | IOU_t: {iou_t: .4f} | IOU_b: {iou_k: .4f}'.format(
batch=batch_idx + 1,
size=len(train_loader),
bt=batch_time.avg,
total=batch_time.avg * batch_idx / 60.0,
eta=batch_time.avg * (len(train_loader) - batch_idx) / 60.0,
loss=losses.avg,
acc_t=score_prob['Mean Acc'],
acc_b=score_binary['Mean Acc'],
iou_t=score_prob['Mean IoU'],
iou_k=score_binary['Mean IoU'])
print(output_log)
sys.stdout.flush()
return (losses.avg, score_prob['Mean Acc'], score_binary['Mean Acc'],
score_prob['Mean IoU'], score_binary['Mean IoU'])
def train(train_loader, model, criterion, optimizer, epoch, summary_writer):
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
running_metric_text = runningScore(2)
L1_loss = torch.nn.L1Loss()
end = time.time()
for batch_idx, (imgs, gt_texts, training_masks, ori_imgs,
border_map) in enumerate(train_loader):
data_time.update(time.time() - end)
imgs = Variable(imgs.cuda())
gt_texts = Variable(gt_texts[:, ::4, ::4].cuda())
training_masks = Variable(training_masks[:, ::4, ::4].cuda())
border_map = Variable(border_map.cuda())
outputs = model(imgs)
gaussian_map = outputs
# gaussian_map, center_map, region_map = outputs
weighted_mse_loss, mse_region_loss, loss_center = weighted_regression(
gaussian_map, gt_texts, training_masks)
center_gt = torch.where(gt_texts > 0.7, gt_texts,
torch.zeros_like(gt_texts))
# center_mask = torch.where(gt_texts > 0.7, torch.ones_like(gt_texts), torch.zeros_like(gt_texts))
region_gt = torch.where(gt_texts > 0.4, gt_texts,
torch.zeros_like(gt_texts))
# region_mask = torch.where(gt_texts > 0.4, torch.ones_like(gt_texts), torch.zeros_like(gt_texts))
# loss for center_map
# loss_center_dice = criterion(gaussian_map, center_gt, training_masks)
# loss for region_map
loss_region_dice = criterion(gaussian_map, region_gt, training_masks)
# loss for border_map
# border_mask = 1. - (center_other - border_map)
# loss_border = criterion(gaussian_map, gt_texts, training_masks)
loss = loss_center + weighted_mse_loss + mse_region_loss + loss_region_dice
# print("loss:", loss_center, "loss_region:", loss_region, "weighted_mse_loss:", weighted_mse_loss)
losses.update(loss.item(), imgs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
score_center = cal_text_score(gaussian_map, gt_texts, training_masks,
running_metric_text, 0, 0.8)
# score_region = cal_text_score(gaussian_map, gt_texts, training_masks * region_mask, running_metric_text, 0, 0.2)
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % 20 == 0:
# visualization
global_step = epoch * len(train_loader) + batch_idx
maps = torch.sigmoid(gaussian_map[0:1])
center_map = torch.where(maps > 0.8, maps, torch.zeros_like(maps))
text_map = torch.where(maps > 0.4, maps, torch.zeros_like(maps))
summary_writer.add_images('gt/img',
ori_imgs[0:1],
global_step=global_step)
summary_writer.add_images('gt/score_map',
torch.unsqueeze(gt_texts[0:1], 1),
global_step=global_step)
summary_writer.add_images('gt/center_map',
torch.unsqueeze(center_gt[0:1], 1),
global_step=global_step)
summary_writer.add_images('gt/region_map',
torch.unsqueeze(region_gt[0:1], 1),
global_step=global_step)
# summary_writer.add_images('gt/border_map', torch.unsqueeze(border_mask[0:1], 1), global_step=global_step)
summary_writer.add_images('predicition/score_map',
torch.sigmoid(gaussian_map[0:1]),
global_step=global_step)
summary_writer.add_images('predicition/center_map',
torch.sigmoid(center_map[0:1]),
global_step=global_step)
summary_writer.add_images('predicition/region_map',
torch.sigmoid(text_map[0:1]),
global_step=global_step)
summary_writer.add_scalar('loss/reg_loss',
weighted_mse_loss,
global_step=global_step)
summary_writer.add_scalar('loss/reg_center_loss',
loss_center,
global_step=global_step)
# summary_writer.add_scalar('loss/center_dice_loss', loss_center_dice, global_step=global_step)
summary_writer.add_scalar('loss/region_dice_loss',
loss_region_dice,
global_step=global_step)
# summary_writer.add_scalar('loss/border_loss', loss_border, global_step=global_step)
summary_writer.add_scalar('loss/text_region_loss',
mse_region_loss,
global_step=global_step)
summary_writer.add_scalar('metric/acc_c',
score_center['Mean Acc'],
global_step=global_step)
summary_writer.add_scalar('metric/iou_c',
score_center['Mean IoU'],
global_step=global_step)
# summary_writer.add_scalar('metric/acc_t', score_region['Mean Acc'], global_step=global_step)
# summary_writer.add_scalar('metric/iou_t', score_region['Mean IoU'], global_step=global_step)
output_log = '({batch}/{size}) Batch: {bt:.3f}s | TOTAL: {total:.0f}min | ETA: {eta:.0f}min | Loss: {loss:.4f} | Acc_c: {acc_c: .4f} | IOU_c: {iou_c: .4f} '.format(
batch=batch_idx + 1,
size=len(train_loader),
bt=batch_time.avg,
total=batch_time.avg * batch_idx / 60.0,
eta=batch_time.avg * (len(train_loader) - batch_idx) / 60.0,
loss=losses.avg,
acc_c=score_center['Mean Acc'],
iou_c=score_center['Mean IoU'],
# acc_t=score_region['Mean Acc'],
# iou_t=score_region['Mean IoU'],
)
print(output_log)
sys.stdout.flush()
return (losses.avg, score_center['Mean Acc'], score_center['Mean IoU'])
def train(
models_path,
backend,
snapshot,
alpha,
epochs,
init_lr,
):
# os.environ["CUDA_VISIBLE_DEVICES"] = gpu
net, starting_epoch = build_network(snapshot, backend)
# net.train()
models_path = os.path.abspath(os.path.expanduser(models_path))
os.makedirs(models_path, exist_ok=True)
class_weights = torch.ones(num_classes).cuda()
class_weight = torch.ones(batch_size, num_classes).cuda()
optimizer = optim.Adam(net.parameters(), lr=start_lr, weight_decay=0.0001)
# Setup Metrics
running_metrics = runningScore(num_classes)
best_iou = -100.0
#从断点出恢复训练
for epoch in range(starting_epoch, starting_epoch + epochs):
seg_criterion = nn.NLLLoss2d(weight=class_weights)
# cls_criterion = nn.BCEWithLogitsLoss(weight=class_weights)#二分类
epoch_losses = []
train_iterator = tqdm(train_loader, total=len(train_loader))
net.train()
for x, y, y_cls in train_iterator:
optimizer.zero_grad()
x, y, y_cls = Variable(x).cuda(), Variable(y).cuda(), Variable(
y_cls).cuda()
# #y:torch.Size([16, 1, 256, 256])
out, out_cls = net(x)
# print('out_cls:',out_cls.size())#16,150,256,256
seg_loss = seg_criterion(out, y.squeeze(1))
cls_loss = seg_criterion(out_cls, y.squeeze(1))
loss = seg_loss + alpha * cls_loss
epoch_losses.append(loss.data[0])
status = '[{0}] loss = {1:0.5f} avg = {2:0.5f}, '.format(
epoch + 1, loss.data[0], np.mean(epoch_losses))
train_iterator.set_description(status) #tadm中可以打印信息
loss.backward()
optimizer.step()
net.eval()
for i_val, (images_val, labels_val,
label_cls) in tqdm(enumerate(val_loader)):
images_val = Variable(images_val.cuda(), volatile=True)
labels_val = Variable(labels_val.cuda(), volatile=True)
outputs, outputs_cls = net(
images_val) #outputs=batch,num_classes,H,W
pred = outputs.data.max(1)[1].cpu().numpy()
gt = labels_val.data.cpu().numpy()
running_metrics.update(gt, pred)
score, class_iou = running_metrics.get_scores()
running_metrics.reset()
if score['Mean IoU : \t'] >= best_iou:
best_iou = score['Mean IoU : \t']
print("{}_{}_best_model.pkl".format(
os.path.join(models_path, 'PSPNet'), 'ADEK'))
torch.save(
net.state_dict(), "{}_{}_best_model.pkl".format(
os.path.join(models_path, 'PSPNet'), 'ADEK'))
poly_lr_scheduler(optimizer,
init_lr,
epoch,
lr_decay_iter=10,
max_iter=100,
power=0.9)
IMG_Path = Path("C:\\Users\dell\Desktop\\tt\img")
IMG_File = natsort.natsorted(list(IMG_Path.glob("*.png")), alg=natsort.PATH)
IMG_Str = []
for i in IMG_File:
IMG_Str.append(str(i))
GT_Path = Path("C:\\Users\dell\Desktop\\tt\gt")
# GT_Path = Path("I:\\DVS_dataset\scnn_result\\vgg_SCNN_merge\merge")
GT_File = natsort.natsorted(list(GT_Path.glob("*.png")), alg=natsort.PATH)
GT_Str = []
for j in GT_File:
GT_Str.append(str(j))
t = time.time()
running_metrics_val = runningScore(5)
label_values = [[0, 0, 0], [100, 100, 100], [150, 150, 150], [200, 200, 200],
[250, 250, 250]]
def compute_two(img_path, gt_path, img_path2, gt_path2):
out = load_image(img_path)
# gt = load_image(gt_path)
# 不要用interpolation=cv.INTER_NEAREST,不然结果不一样，估计opencv bug
gt = cv.resize(load_image(gt_path), (512, 256), cv.INTER_NEAREST)
# val_gt_erode paired with [0,0,0]label value
# label order: R G B
# num_classes = len(label_values)
gt = util.reverse_one_hot(util.one_hot_it(gt, label_values))
output_image = util.reverse_one_hot(util.one_hot_it(out, label_values))
def validate(self):
visualizations = []
val_metrics = runningScore(self.n_classes)
val_loss_meter = averageMeter()
with torch.no_grad():
self.model.eval()
for rgb, ir, target in tqdm.tqdm(
self.val_loader, total=len(self.val_loader),
desc=f'Valid epoch={self.epoch}', ncols=80, leave=False):
rgb, ir, target = rgb.to(self.device), ir.to(self.device), target.to(self.device)
score = self.model(rgb, ir)
# score = self.model(rgb)
weight = self.val_loader.dataset.class_weight
if weight:
weight = torch.Tensor(weight).to(self.device)
loss = CrossEntropyLoss(score, target, weight=weight, reduction='mean', ignore_index=-1)
loss_data = loss.data.item()
if np.isnan(loss_data):
raise ValueError('loss is nan while validating')
val_loss_meter.update(loss_data)
rgbs = rgb.data.cpu()
irs = ir.data.cpu()
if isinstance(score, (tuple, list)):
lbl_pred = score[0].data.max(1)[1].cpu().numpy()
else:
lbl_pred = score.data.max(1)[1].cpu().numpy()
lbl_true = target.data.cpu()
for rgb, ir, lt, lp in zip(rgbs, irs, lbl_true, lbl_pred):
rgb, ir, lt = self.val_loader.dataset.untransform(rgb, ir, lt)
val_metrics.update(lt, lp)
if len(visualizations) < 9:
viz = visualize_segmentation(
lbl_pred=lp, lbl_true=lt, img=rgb, ir=ir,
n_classes=self.n_classes, dataloader=self.train_loader)
visualizations.append(viz)
acc, acc_cls, mean_iou, fwavacc, cls_iu = val_metrics.get_scores()
metrics = [acc, acc_cls, mean_iou, fwavacc]
print(f'\nEpoch: {self.epoch}', f'loss: {val_loss_meter.avg}, mIoU: {mean_iou}')
out = osp.join(self.out, 'visualization_viz')
if not osp.exists(out):
os.makedirs(out)
out_file = osp.join(out, 'epoch{:0>5d}.jpg'.format(self.epoch))
scipy.misc.imsave(out_file, get_tile_image(visualizations))
with open(osp.join(self.out, 'log.csv'), 'a') as f:
elapsed_time = (
datetime.datetime.now(pytz.timezone('UTC')) -
self.timestamp_start).total_seconds()
log = [self.epoch] + [''] * 5 + \
[val_loss_meter.avg] + metrics + [elapsed_time]
log = map(str, log)
f.write(','.join(log) + '\n')
mean_iu = metrics[2]
is_best = mean_iu > self.best_mean_iu
if is_best:
self.best_mean_iu = mean_iu
torch.save({
'epoch': self.epoch,
'arch': self.model.__class__.__name__,
'optim_state_dict': self.optim.state_dict(),
'model_state_dict': self.model.state_dict(),
'best_mean_iu': self.best_mean_iu,
}, osp.join(self.out, 'checkpoint.pth.tar'))
if is_best:
shutil.copy(osp.join(self.out, 'checkpoint.pth.tar'),
osp.join(self.out, 'model_best.pth.tar'))
val_loss_meter.reset()
val_metrics.reset()
class_name = self.val_loader.dataset.class_names
if class_name is not None:
for index, value in enumerate(cls_iu.values()):
offset = 20 - len(class_name[index])
print(class_name[index] + ' ' * offset + f'{value * 100:>.2f}')
else:
print("\nyou don't specify class_names, use number instead")
for key, value in cls_iu.items():
print(key, f'{value * 100:>.2f}')
def train(train_loader, model, criterion, optimizer, epoch, tflogger):
model.train()
#taglist = ['module.conv1.weight','module.bn1.weight','module.bn1.bias','module.conv2.weight','module.conv2.bias','module.bn2.weight','module.bn2.bias','module.conv3.weight','module.conv3.bias','module.bn3.weight','module.bn3.bia','module.conv4.weight','module.conv4.bias']
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
running_metric_text = runningScore(2)
running_metric_kernel = runningScore(2)
global globalcounter
end = time.time()
for batch_idx, (imgs, gt_texts, gt_kernels,
training_masks) in enumerate(train_loader):
data_time.update(time.time() - end)
imgs = Variable(imgs.cuda())
gt_texts = Variable(gt_texts.cuda())
gt_kernels = Variable(gt_kernels.cuda())
training_masks = Variable(training_masks.cuda())
outputs = model(imgs)
texts = outputs[:, 0, :, :]
kernels = outputs[:, 1:, :, :]
selected_masks = ohem_batch(texts, gt_texts, training_masks)
selected_masks = Variable(selected_masks.cuda())
loss_text = criterion(texts, gt_texts, selected_masks)
loss_kernels = []
mask0 = torch.sigmoid(texts).data.cpu().numpy()
mask1 = training_masks.data.cpu().numpy()
selected_masks = ((mask0 > 0.5) & (mask1 > 0.5)).astype('float32')
selected_masks = torch.from_numpy(selected_masks).float()
selected_masks = Variable(selected_masks.cuda())
for i in range(6):
kernel_i = kernels[:, i, :, :]
gt_kernel_i = gt_kernels[:, i, :, :]
loss_kernel_i = criterion(kernel_i, gt_kernel_i, selected_masks)
loss_kernels.append(loss_kernel_i)
loss_kernel = sum(loss_kernels) / len(loss_kernels)
loss = 0.7 * loss_text + 0.3 * loss_kernel
losses.update(loss.item(), imgs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
score_text = cal_text_score(texts, gt_texts, training_masks,
running_metric_text)
score_kernel = cal_kernel_score(kernels, gt_kernels, gt_texts,
training_masks, running_metric_kernel)
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % 20 == 0:
output_log = '({batch}/{size}) Batch: {bt:.3f}s | TOTAL: {total:.0f}min | ETA: {eta:.0f}min | Loss: {loss:.4f} | Acc_t: {acc: .4f} | IOU_t: {iou_t: .4f} | IOU_k: {iou_k: .4f}'.format(
batch=batch_idx + 1,
size=len(train_loader),
bt=batch_time.avg,
total=batch_time.avg * batch_idx / 60.0,
eta=batch_time.avg * (len(train_loader) - batch_idx) / 60.0,
loss=losses.avg,
acc=score_text['Mean Acc'],
iou_t=score_text['Mean IoU'],
iou_k=score_kernel['Mean IoU'])
print(output_log)
sys.stdout.flush()
if batch_idx % 100 == 0:
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
tflogger.histo_summary(tag,
value.data.detach().cpu().numpy(),
globalcounter)
tflogger.histo_summary(tag + '/grad',
value.grad.data.detach().cpu().numpy(),
globalcounter)
globalcounter += 1
return (float(losses.avg), float(score_text['Mean Acc']),
float(score_kernel['Mean Acc']), float(score_text['Mean IoU']),
float(score_kernel['Mean IoU']))
def train_epoch(self):
if self.epoch % self.val_epoch == 0 or self.epoch == 1:
self.validate()
self.model.train()
train_metrics = runningScore(self.n_classes)
train_loss_meter = averageMeter()
self.optim.zero_grad()
for rgb, ir, target in tqdm.tqdm(
self.train_loader, total=len(self.train_loader),
desc=f'Train epoch={self.epoch}', ncols=80, leave=False):
self.iter += 1
assert self.model.training
rgb, ir, target = rgb.to(self.device), ir.to(self.device), target.to(self.device)
score = self.model(rgb, ir)
# score = self.model(rgb)
weight = self.train_loader.dataset.class_weight
if weight:
weight = torch.Tensor(weight).to(self.device)
loss = CrossEntropyLoss(score, target, weight=weight, ignore_index=-1, reduction='mean')
loss_data = loss.data.item()
train_loss_meter.update(loss_data)
if np.isnan(loss_data):
raise ValueError('loss is nan while training')
# loss.backward(retain_graph=True)
loss.backward()
self.optim.step()
self.optim.zero_grad()
if isinstance(score, (tuple, list)):
lbl_pred = score[0].data.max(1)[1].cpu().numpy()
else:
lbl_pred = score.data.max(1)[1].cpu().numpy()
lbl_true = target.data.cpu().numpy()
train_metrics.update(lbl_true, lbl_pred)
acc, acc_cls, mean_iou, fwavacc, _ = train_metrics.get_scores()
metrics = [acc, acc_cls, mean_iou, fwavacc]
with open(osp.join(self.out, 'log.csv'), 'a') as f:
elapsed_time = (
datetime.datetime.now(pytz.timezone('UTC')) -
self.timestamp_start).total_seconds()
log = [self.epoch] + [train_loss_meter.avg] + \
metrics + [''] * 5 + [elapsed_time]
log = map(str, log)
f.write(','.join(log) + '\n')
if self.scheduler:
self.scheduler.step()
if self.epoch % self.val_epoch == 0 or self.epoch == 1:
lr = self.optim.param_groups[0]['lr']
print(f'\nCurrent base learning rate of epoch {self.epoch}: {lr:.7f}')
train_loss_meter.reset()
train_metrics.reset()
