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TFAL_visualization.py
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TFAL_visualization.py
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import os,tqdm,sys,time,argparse,tqdm
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'lib'))
import numpy as np
import torch.cuda.amp as amp
scaler = amp.GradScaler()
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.functional import one_hot
import torch.utils.data
import torch.distributed as dist
from net.Ours.TFAL_Module import TFAL_get_affinity,TFAL_select_Mask_test
from utils.summary import DisablePrint
from utils.LoadModel import load_model_full_fortest
from skimage import io
from sklearn.preprocessing import MinMaxScaler
##------------------------------ Training settings ------------------------------##
parser = argparse.ArgumentParser(description='real-time segmentation')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--dist', action='store_true')
parser.add_argument('--root_dir', type=str, default='./results/endo18')
parser.add_argument('--dataset', type=str, default='endovis2018')
parser.add_argument('--data_tag', type=str, default='type')
parser.add_argument('--log_name', type=str, default='Uncertainty_test')
parser.add_argument('--data_type', type=str, choices=['clean','noisy'], default='noisy')
parser.add_argument('--data_ver', type=int, default=4 )
parser.add_argument('--arch', type=str, choices=['puredeeplab18','swinPlus'], default='puredeeplab18')
parser.add_argument('--pre_log_name', type=str, default='DLV3PLUS_clean_ver_0')
parser.add_argument('--pre_checkpoint', type=str, default=None) #!!
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--weight_decay', type=float, default=1e-4)
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('--num_epochs', type=int, default=100)
parser.add_argument('--loss', type=str, default='ohem')
parser.add_argument('--gpus', type=str, default='2')
parser.add_argument('--downsample', type=int, default=1)
parser.add_argument('--h', type=int, default=256)
parser.add_argument('--w', type=int, default=320)
parser.add_argument('--log_interval', type=int, default=50)
parser.add_argument('--val_interval', type=int, default=1)
parser.add_argument('--num_workers', type=int, default=3)
parser.add_argument('--t', type=int, default=1)
parser.add_argument('--step', type=int, default=1)
parser.add_argument('--ver', type=int, default=0)
parser.add_argument('--tag', type=int, default=1)
parser.add_argument('--global_n', type=int, default=0)
parser.add_argument('--pretrain_ep', type=int, default=None)
parser.add_argument('--decay', type=int, default=2)
parser.add_argument('--reset', type=str, default=None)
parser.add_argument('--reset_ep', type=int)
cfg = parser.parse_args()
color_map = {
0: [0,0,0], # background-tissue
1: [0,255,0], # instrument-shaft
2: [0,255,255], # instrument-clasper
3: [125,255,12], # instrument-wrist
4: [255,55,0], # kidney-parenchyma，
5: [24,55,125], # covered-kidney，
6: [187,155,25], # thread，
7: [0,255,125], # clamps，
8: [255,255,125], # suturing-needle
9: [123,15,175], # suction-instrument，
10: [124,155,5], # small-intestine
11: [12,255,141] # ultrasound-probe,
}
def label2rgb(ind_im, color_map=color_map):
rgb_im = np.zeros((ind_im.shape[0], ind_im.shape[1], 3))
for i, rgb in color_map.items():
rgb_im[(ind_im==i)] = rgb
return rgb_im
def main():
################################################ def part ################################################
##------------------------------ compute feature based affinity confidence ------------------------------##
def affinity_confidence():
print('\n computing affinity confidence test...')
model.eval()
Procedures = np.array([1,2,3,4,5,6,7,9,10,11,12,13,14,15,16])
weight = np.array([0.4,0.4,0.4,0.4,0.4,0.4,0.5,0.6,0.7,0.8,1,1,1,1,1])
p_sum_for_each_vedio = np.zeros((15,))
n_sum_for_each_vedio = np.zeros((15,))
count = np.zeros((15,))
weight_final = np.zeros((15,))
tic = time.perf_counter()
for batch_idx, batch in tqdm.tqdm(enumerate(train_loader)):
# if batch_idx < 6:
# continue
for k in batch:
if not k=='path':
batch[k] = batch[k].to(device=cfg.device).float()
#print('shape:', batch['image'].shape) #4, 3, 272, 480
with torch.no_grad():
#print(batch['image'].shape)
outputs , feature = model(batch['image'])
outputs_1 , feature_1 = model(batch['image_1'])
B, C, H, W = feature_1.shape
label_ds = F.interpolate(batch['label'].unsqueeze(0), size=[H,W], mode='nearest').squeeze(0)
label_1_ds = F.interpolate(batch['label_1'].unsqueeze(0), size=[H,W], mode='nearest').squeeze(0)
_,p,n,_,_,_,_,_ = TFAL_select_Mask_test(feature, feature_1, label_ds, label_1_ds, class_num =classes , p_thershold = 0.5, n_thershold = 0.5, select = 'intersection', H = cfg.h, W = cfg.w)
if batch['path'][0] < 9:
ins = batch['path'][0].numpy() - 1
else:
ins = batch['path'][0].numpy() - 2
p_sum_for_each_vedio[ins] += p.cpu().numpy()
n_sum_for_each_vedio[ins] += n.cpu().numpy()
count[ins] += 1
print('Frame number for each video:')
print(count)
AC_pn = (p_sum_for_each_vedio + count - n_sum_for_each_vedio) / count
sort_p = np.argsort(p_sum_for_each_vedio)
sort_n = np.argsort(count-n_sum_for_each_vedio)
sort_pn = np.argsort(AC_pn)
for i in range(len(weight_final)):
weight_final[sort_pn[i]] = weight[i]
print('Positive affinity for each video:')
print(p_sum_for_each_vedio / count)
print('Negative affinity for each video:')
print((count-n_sum_for_each_vedio) / count)
print('Affinity confidence for each video:')
print(AC_pn)
p_thershold = np.mean(p_sum_for_each_vedio / count)
n_thershold = np.mean((count-n_sum_for_each_vedio) / count)
print('p_thershold:',p_thershold)
print('n_thershold:',n_thershold)
print('Sort according to positive affinity from small to large:',Procedures[sort_p])
print('Sort according to negative affinity from small to large:',Procedures[sort_n])
print('Sort according to affinity confidence from small to large:',Procedures[sort_pn])
print('weight for each video:',weight_final)
print(' compute uncertainty finished.')
return
##------------------------------ generate samples figures related to temporal affinity ------------------------------##
def feature_based_affinity_confidence_test():
print('\n computing sample affinity confidence test...')
model.eval()
Procedures = np.array([1,2,3,4,5,6,7,9,10,11,12,13,14,15,16])
weight = np.array([0.2,0.2,0.2,0.2,0.2,0.4,0.5,0.6,0.7,0.8,1,1,1,1,1])
p_sum_for_each_vedio = np.zeros((15,))
n_sum_for_each_vedio = np.zeros((15,))
count = np.zeros((15,))
weight_final = np.zeros((15,))
label_diff_output = []
p_thershold = 0.48319209465377816
n_thershold = 0.78678705171334
for batch_idx, batch in tqdm.tqdm(enumerate(train_loader)):
if batch_idx < 0:
continue
for k in batch:
if not k=='path':
# batch[k] = batch[k].to(device=cfg.device, nonw_blocking=True).float()
batch[k] = batch[k].to(device=cfg.device).float()
## get the difference between noisy label and ground truth label ##
a,b,c = batch['label'].shape
label_clean = torch.zeros(a,b,c).to(device=cfg.device).float()
label_diff_output = np.zeros((a,b,c))
print(label_clean.shape)
print('batch %d testing' % batch_idx)
for i in range(cfg.batch_size):
print(train_clean_dataset[i+cfg.batch_size * batch_idx]['path'])
label_clean[i] = train_clean_dataset[i+cfg.batch_size * batch_idx]['label']
# get the noise variance map
for i in range(cfg.batch_size):
label_diff = one_hot(label_clean[i].to(torch.int64), num_classes=12)* one_hot(batch['label'][i].to(torch.int64), num_classes=12)
label_diff = 1 - torch.sum(label_diff,dim=2)
label_diff_output[i] = label_diff.cpu().numpy().astype(np.uint8)
## get the difference between noisy label and ground truth label ##
outputs , feature = model(batch['image'])
outputs_1 , feature_1 = model(batch['image_1'])
ins,frame = batch['path']
B, C, H, W = feature_1.shape
for i in range(B):
print('the image is seq_%d frame%03d' %(ins[i],frame[i]))
output = F.softmax(outputs,dim=1)
output_output = torch.argmax(output,dim=1).cpu().numpy().astype(np.uint8)
label_ds = F.interpolate(batch['label'].unsqueeze(0), size=[H,W], mode='nearest').squeeze(0)
label_1_ds = F.interpolate(batch['label_1'].unsqueeze(0), size=[H,W], mode='nearest').squeeze(0)
pos_pix_p,p,n,confidence_map,mask1comwith2_p,dist1comwith2_p,dist1comwith2_n,logit1comwith2 = TFAL_select_Mask_test(feature, feature_1, label_ds, label_1_ds, class_num = 12 ,p_thershold = p_thershold, n_thershold = n_thershold, select = 'p',H=h,W=w)
pos_pix_n,_,_,_,_,_,_,_= TFAL_select_Mask_test(feature, feature_1, label_ds, label_1_ds, class_num = 12 ,p_thershold = p_thershold, n_thershold = n_thershold, select = 'n',H=h,W=w)
pos_pix_i,_,_,_,_,_,_,_ = TFAL_select_Mask_test(feature, feature_1, label_ds, label_1_ds, class_num = 12 ,p_thershold = p_thershold, n_thershold = n_thershold, select = 'intersection',H=h,W=w)
pos_pix_u,_,_,_,_,_,_,_ = TFAL_select_Mask_test(feature, feature_1, label_ds, label_1_ds, class_num = 12 ,p_thershold = p_thershold, n_thershold = n_thershold, select = 'union',H=h,W=w)
mask1comwith2_n = 1 - mask1comwith2_p
pos_pix_p_output = pos_pix_p.cpu().numpy().astype(np.uint8)
pos_pix_n_output = pos_pix_n.cpu().numpy().astype(np.uint8)
pos_pix_i_output = pos_pix_i.cpu().numpy().astype(np.uint8)
pos_pix_u_output = pos_pix_u.cpu().numpy().astype(np.uint8) # 1, 256, 320
pos_pix_i_n_output = 1 - pos_pix_i_output
mask1comwith2_p_output = mask1comwith2_p.cpu().numpy().astype(np.uint8)
mask1comwith2_n_output = mask1comwith2_n.cpu().numpy().astype(np.uint8)
min_max_scaler = MinMaxScaler()
confidence_map = confidence_map.cpu().numpy()
dist1comwith2_p = dist1comwith2_p.cpu().numpy()
dist1comwith2_n = dist1comwith2_n.cpu().numpy()
logit1comwith2 = logit1comwith2.cpu().detach().numpy()
for i in range(B):
confidence_map[i] = min_max_scaler.fit_transform(confidence_map[i].reshape(-1, 1)).squeeze(1)
dist1comwith2_p[i] = min_max_scaler.fit_transform(dist1comwith2_p[i].reshape(-1, 1)).squeeze(1)
dist1comwith2_n[i] = min_max_scaler.fit_transform(dist1comwith2_n[i].reshape(-1, 1)).squeeze(1)
confidence_map = confidence_map.reshape(B, h, w)
dist1comwith2_p = dist1comwith2_p.reshape(B, h, w)
dist1comwith2_n = dist1comwith2_n.reshape(B, h, w)
label_gt_output = batch['label'].cpu().numpy().astype(np.uint8) # 1, 256, 320
label_corrected = pos_pix_i_n_output * batch['label'].cpu().detach().numpy() + pos_pix_i_output * output_output
image_output = batch['image'].permute(0,2,3,1).cpu().numpy() # 256, 320, 3
# --------------------------------- save the images ---------------------------------- $
cfg.pix_p_vis_path = os.path.join(cfg.test_dir,'pos_pix_p_seq_{}_frame{:03d}.png')
cfg.pix_n_vis_path = os.path.join(cfg.test_dir,'pos_pix_n_seq_{}_frame{:03d}.png')
cfg.pix_i_vis_path = os.path.join(cfg.test_dir,'pos_pix_i_seq_{}_frame{:03d}.png')
cfg.pix_u_vis_path = os.path.join(cfg.test_dir,'pos_pix_u_seq_{}_frame{:03d}.png')
cfg.labelgt_vis_path = os.path.join(cfg.test_dir,'labelnoisy_seq_{}_frame{:03d}.png')
cfg.image_vis_path = os.path.join(cfg.test_dir,'image_seq_{}_frame{:03d}.png')
cfg.labeldiff_vis_path = os.path.join(cfg.test_dir,'labeldiff_seq_{}_frame{:03d}.png')
cfg.modelpred_vis_path = os.path.join(cfg.test_dir,'modelpred_seq_{}_frame{:03d}.png')
cfg.labelcorrected_vis_path = os.path.join(cfg.test_dir,'labelcorrected_seq_{}_frame{:03d}.png')
cfg.confidence_map_vis_path = os.path.join(cfg.test_dir,'affinity_confidence_map_seq_{}_frame{:03d}.png')
cfg.p_map_vis_path = os.path.join(cfg.test_dir,'p_map_seq_{}_frame{:03d}.png')
cfg.n_map_vis_path = os.path.join(cfg.test_dir,'n_map_seq_{}_frame{:03d}.png')
cfg.p_affinity_map_vis_path = os.path.join(cfg.test_dir,'p_affinity_map_seq_{}_frame{:03d}.png')
cfg.n_affinity_map_vis_path = os.path.join(cfg.test_dir,'n_affinity_map_seq_{}_frame{:03d}.png')
cfg.cos_sim_map_vis_path = os.path.join(cfg.test_dir,'cos_sim__map_seq_{}_frame{:03d}.png')
for i in range(B):
save_pix_p_pth = cfg.pix_p_vis_path.format(ins[i],frame[i])
save_pix_n_pth = cfg.pix_n_vis_path.format(ins[i],frame[i])
save_pix_i_pth = cfg.pix_i_vis_path.format(ins[i],frame[i])
save_pix_u_pth = cfg.pix_u_vis_path.format(ins[i],frame[i])
save_labelgt_pth = cfg.labelgt_vis_path.format(ins[i],frame[i])
save_image_pth = cfg.image_vis_path.format(ins[i],frame[i])
save_labeldiff_pth = cfg.labeldiff_vis_path.format(ins[i],frame[i])
save_modelpred_pth = cfg.modelpred_vis_path.format(ins[i],frame[i])
save_labelcorrected_pth = cfg.labelcorrected_vis_path.format(ins[i],frame[i])
save_confidence_map_path = cfg.confidence_map_vis_path.format(ins[i],frame[i])
save_p_map_path = cfg.p_map_vis_path.format(ins[i],frame[i])
save_n_map_path = cfg.n_map_vis_path.format(ins[i],frame[i])
save_p_affinity_map_path = cfg.p_affinity_map_vis_path.format(ins[i],frame[i])
save_n_affinity_map_path = cfg.n_affinity_map_vis_path.format(ins[i],frame[i])
save_cos_sim_map_path = cfg.cos_sim_map_vis_path.format(ins[i],frame[i])
predict = label2rgb(label_gt_output[i]).astype(np.uint8)
predict_model = label2rgb(output_output[i]).astype(np.uint8)
predict_corrected = label2rgb(label_corrected[i]).astype(np.uint8)
io.imsave(save_pix_p_pth, pos_pix_p_output[i] * 255)
io.imsave(save_pix_n_pth, pos_pix_n_output[i] * 255)
io.imsave(save_pix_i_pth, pos_pix_i_output[i] * 255)
io.imsave(save_pix_u_pth, pos_pix_u_output[i] * 255)
io.imsave(save_labelgt_pth, predict)
io.imsave(save_image_pth, image_output[i])
io.imsave(save_labeldiff_pth, label_diff_output[i] * 255)
io.imsave(save_modelpred_pth, predict_model)
io.imsave(save_labelcorrected_pth, predict_corrected)
io.imsave(save_confidence_map_path, confidence_map[i])
io.imsave(save_p_map_path, mask1comwith2_p_output[i] * 255)
io.imsave(save_n_map_path, mask1comwith2_n_output[i] * 255)
io.imsave(save_p_affinity_map_path, dist1comwith2_p[i])
io.imsave(save_n_affinity_map_path, dist1comwith2_n[i])
io.imsave(save_cos_sim_map_path, logit1comwith2[i] * 255)
print('feature based uncertainty test finished.')
if batch_idx >= 0:
print('testing break.')
break
return
################################################ def part ################################################
################################################ main part ################################################
##------------------------------ Enviroment ------------------------------##
os.environ['CUDA_VISIBLE_DEVICES']=cfg.gpus
torch.backends.cudnn.benchmark = True # disable this if OOM at beginning of training
num_gpus = torch.cuda.device_count()
if cfg.dist:
cfg.device = torch.device('cuda:%d' % cfg.local_rank)
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl', init_method='env://',
world_size=num_gpus, rank=cfg.local_rank)
else:
cfg.device = torch.device('cuda')
cfg.log_name += '_ver_' + str(cfg.ver)
# logger
cfg.log_dir = os.path.join(cfg.root_dir, cfg.log_name, 'logs')
cfg.ckpt_dir = os.path.join(cfg.root_dir, cfg.log_name, 'ckpt')
cfg.test_dir = os.path.join(cfg.root_dir, cfg.log_name, 'sample_test')
os.makedirs(cfg.test_dir, exist_ok=True)
print(cfg)
##------------------------------ dataset ------------------------------##
print('Setting up data...')
if cfg.dataset=='endovis2018':
h,w = [cfg.h,cfg.w]
ori_h, ori_w = [1024, 1280]
print('size of data %d, %d.' %(h,w))
if cfg.data_type=='clean':
from dataset.Endovis2018_backbone import endovis2018
train_dataset = endovis2018('train_clean', t=cfg.t, arch='swinPlus',rate=1, global_n=cfg.global_n,h = h, w = w)
val_dataset = endovis2018('test_part', t=cfg.t,arch='swinPlus', rate=1, global_n=cfg.global_n,h = h, w = w)
classes = train_dataset.class_num
elif cfg.data_type=='noisy':
from dataset.Endovis2018_backbone import endovis2018
train_dataset = endovis2018('train', t=cfg.t, arch='swinPlus',rate=1, global_n=cfg.global_n, data_ver=cfg.data_ver,h = h, w = w)
train_clean_dataset = endovis2018('train_clean', t=cfg.t, arch='swinPlus',rate=1, global_n=cfg.global_n, data_ver=cfg.data_ver,h = h, w = w)
val_dataset = endovis2018('test_part', t=cfg.t,arch='swinPlus', rate=1, global_n=cfg.global_n, data_ver=cfg.data_ver,h = h, w = w)
classes = train_dataset.class_num
##------------------------------ build model ------------------------------##
if 'puredeeplab' in cfg.arch:
from net.Ours.base18 import DeepLabV3Plus
model = DeepLabV3Plus(train_dataset.class_num, 18)
elif 'swin' in cfg.arch:
from net.Ours.base18 import TswinPlus
model = TswinPlus(train_dataset.class_num,h,w)
else:
raise NotImplementedError
# load pretrain model
if cfg.pre_log_name is not None:
cfg.pre_ckpt_path = os.path.join(cfg.root_dir, cfg.pre_log_name, 'ckpt', 'epoch_1_checkpoint.t7')
print('initialize the model from:', cfg.pre_ckpt_path)
model = load_model_full_fortest(model, cfg.pre_ckpt_path)
##------------------------------ combile model ------------------------------##
torch.cuda.empty_cache()
print('Starting computing...')
gpus = cfg.gpus.split(',')
if len(cfg.gpus)>1:
model = nn.DataParallel(model, device_ids=list(map(int,gpus))).cuda()
else:
model = model.to(cfg.device)
##------------------------------ dataloader ------------------------------##
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=cfg.batch_size,
shuffle= False,
num_workers=cfg.num_workers,
pin_memory=True,
drop_last=True)
##------------------------------ compute feature based affinity confidence ------------------------------##
# enable this esction if you want to compute the affinity confidence for the dataset
affinity_confidence()
##------------------------------ generate samples figures related to temporal affinity ------------------------------##
# enable this esction if you want to generate samples figures related to temporal affinity
feature_based_affinity_confidence_test()
################################################ main part ################################################
if __name__ == '__main__':
with DisablePrint(local_rank=cfg.local_rank):
main()