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test.py
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import os,tqdm,sys,time,argparse
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'lib'))
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import torch.distributed as dist
from utils.EndoMetric import general_dice, general_jaccard
from utils.summary import create_logger, DisablePrint
from utils.LoadModel import load_model_full_fortest
from skimage import io
# 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, choices=['endovis2018','colon_oct'],default='endovis2018')
parser.add_argument('--data_tag', type=str, default='type')
parser.add_argument('--log_name', type=str, default='DLV3PLUS_clean_ver_0')
parser.add_argument('--checkpoint', type=str,default='1')
parser.add_argument('--layer', type=int, default=18)
parser.add_argument('--load_model', type=str, default=None)
parser.add_argument('--arch', type=str, choices=['puredeeplab18','RAUNet','swinPlus'], default='puredeeplab18') #!!
parser.add_argument('--gpus', type=str, default='1')
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('--num_workers', type=int, default=3)
parser.add_argument('--test_bs', type=int, default=1)
parser.add_argument('--t', type=int, default=1)
parser.add_argument('--step', type=int, default=1)
parser.add_argument('--global_n', type=int, default=0)
cfg = parser.parse_args()
#bg = black
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,
}
color_map_oct = {
0: [0,0,0], # background-tissue
1: [0,255,0], # instrument-shaft
2: [0,255,255], # instrument-clasper
3: [255,220,100], # instrument-wrist
4: [255,55,0], # kidney-parenchyma，
5: [62,110,218] # covered-kidney，
}
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():
##------------------------------ 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')
# logger
cfg.log_dir = os.path.join(cfg.root_dir, cfg.log_name, 'logs_test_time')
os.makedirs(cfg.log_dir, exist_ok=True)
cfg.ckpt_dir = os.path.join(cfg.root_dir, cfg.log_name, 'ckpt')
if cfg.dataset=='endovis2018':
for k in range(1,5):
cfg.vis_dir = os.path.join(cfg.log_dir, 'visualization_'+str(cfg.checkpoint),'seq_'+str(k))
os.makedirs(cfg.vis_dir, exist_ok=True)
cfg.vis_path = os.path.join(cfg.log_dir, 'visualization_'+str(cfg.checkpoint), 'seq_{}/frame{:03d}.png')
elif cfg.dataset=='colon_oct':
Procedures_mini = {'train':['2T1','3C1','3T1','3T2','7C','10C','13C','15C'],'test':['C1','C4','T1']}
for k in range(0,3):
cfg.vis_dir = os.path.join(cfg.log_dir, 'visualization_'+str(cfg.checkpoint),Procedures_mini['test'][k])
os.makedirs(cfg.vis_dir, exist_ok=True)
cfg.vis_path = os.path.join(cfg.log_dir, 'visualization_'+str(cfg.checkpoint), '{}/{}.png')
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
print = logger.info
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 endovis2018 data %d, %d.' %(h,w))
from dataset.Endovis2018_backbone import endovis2018
test_dataset = endovis2018('test', t=cfg.t, rate=1, global_n=cfg.global_n,h = h, w = w)
classes = test_dataset.class_num
elif cfg.dataset=='colon_oct':
h,w = [cfg.h,cfg.w]
ori_h, ori_w = [1024, 1024]
from dataset.Colon_OCT import Colon_OCT
test_dataset = Colon_OCT('test', t=cfg.t, rate=1, global_n=cfg.global_n,h = h, w = w)
classes = test_dataset.class_num
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1,
shuffle=False, num_workers=cfg.num_workers, pin_memory=True, drop_last=False)
##------------------------------ build model ------------------------------##
if 'puredeeplab' in cfg.arch:
from net.Ours.base18 import DeepLabV3Plus
model = DeepLabV3Plus(test_dataset.class_num, int(cfg.arch[-2:]))
elif 'swinPlus' in cfg.arch:
from net.Ours.base18 import TswinPlus
model = TswinPlus(test_dataset.class_num,h,w)
elif 'RAUNet' in cfg.arch:
from net.Ours.RAUNet import RAUNet
model = RAUNet(test_dataset.class_num)
else:
raise NotImplementedError
# combile model
torch.cuda.empty_cache()
gpus = cfg.gpus.split(',')
if len(cfg.gpus)>1:
model = nn.DataParallel(model, device_ids=gpus).cuda()
else:
model = model.to(cfg.device)
if cfg.load_model is None:
cfg.load_model = os.path.join(cfg.ckpt_dir, 'epoch_{}_checkpoint.t7'.format(cfg.checkpoint))
print('model path: %s' % cfg.load_model)
model = load_model_full_fortest(model, cfg.load_model)
################################################ def part ################################################
def val_map_endo(epoch):
print('\n Val@Epoch: %d' % epoch)
model.eval()
torch.cuda.empty_cache()
metrics = np.zeros((2,))
metrics_seq = np.zeros((2, 4))
count_seq = np.zeros((4,))
dice_each = np.zeros((12,))
iou_each = np.zeros((12,))
tool_eac = np.zeros((12,))
count = 0
with torch.no_grad():
for inputs in tqdm.tqdm(test_loader):
inputs['image'] = inputs['image'].to(cfg.device).float()
#print('shape:', inputs['image'].shape) #1,3,256,480
tic = time.perf_counter()
output,_ = model(inputs['image'])
output = F.interpolate(output, (ori_h,ori_w), mode='bilinear', align_corners=True)
output = F.softmax(output,dim=1)
output = torch.argmax(output,dim=1)
output = output.cpu().numpy()
duration = time.perf_counter() - tic
# print('duration: %f' % duration)
# #=====visualize figure======
predict = output.astype(np.uint8)
ins = int(inputs['path'][0])
i = int(inputs['path'][1])
save_pth = cfg.vis_path.format(ins, i)
# print('input path:', save_pth)
predict = label2rgb(predict[0]).astype(np.uint8)
io.imsave(save_pth, predict)
dice = general_dice(inputs['label'].numpy(),output) # dice containing each tool class
iou = general_jaccard(inputs['label'].numpy(), output)
for i in range(len(dice)):
tool_id = dice[i][0]
dice_each[tool_id] += dice[i][1]
iou_each[tool_id] += iou[i][1]
tool_eac[tool_id] += 1
frame_dice = np.mean([dice[i][1] for i in range(len(dice))])
frame_iou = np.mean([iou[i][1] for i in range(len(dice))])
#overall
metrics[0] += frame_dice # dice of each frame
metrics[1] += frame_iou
count += 1
#----for seq
seq_ind = int(inputs['path'][0]) - 1 #seq: 0-3
metrics_seq[0][seq_ind] += frame_dice
metrics_seq[1][seq_ind] += frame_iou
count_seq[seq_ind] += 1
print(count)
metrics[0] /= count
metrics[1] /= count
print(metrics)
dc, jc = metrics[0], metrics[1]
metrics_seq[0] /= count_seq
dice_seq = [float('{:.4f}'.format(i)) for i in metrics_seq[0]]
metrics_seq[1] /= count_seq
iou_seq = [float('{:.4f}'.format(i)) for i in metrics_seq[1]]
print('Dice:{:.4f} IoU:{:.4f} Time:{:.4f}'.format(dc, jc, duration))
print('Dice_seq1:{:.4f}, seq2:{:.4f}, seq3:{:.4f}, seq4:{:.4f}'.format(dice_seq[0], dice_seq[1], dice_seq[2],dice_seq[3]))
print('IOU_seq1:{:.4f}, seq2:{:.4f}, seq3:{:.4f}, seq4:{:.4f}'.format(iou_seq[0], iou_seq[1], iou_seq[2],iou_seq[3]))
return jc
def val_map_oct(epoch):
print('\n Val@Epoch: %d' % epoch)
model.eval()
torch.cuda.empty_cache()
metrics = np.zeros((2,))
count = 0
with torch.no_grad():
for inputs in tqdm.tqdm(test_loader):
inputs['image'] = inputs['image'].to(cfg.device).float()
# print('shape:', inputs['image'].shape) #1,3,256,480
tic = time.perf_counter()
output,_ = model(inputs['image'])
output = F.interpolate(output, (ori_h,ori_w), mode='bilinear', align_corners=True)
output = F.softmax(output,dim=1)
output = torch.argmax(output,dim=1)
output = output.cpu().numpy()
duration = time.perf_counter() - tic
# #=====visualize figure======
# predict = output.astype(np.uint8)
# ins = inputs['path'][0][0]
# i = int(inputs['path'][1])
# save_pth = cfg.vis_path.format(ins, i)
# # print('input path:', save_pth)
# predict = label2rgb(predict[0],color_map=color_map_oct).astype(np.uint8)
# io.imsave(save_pth, predict)
dice = general_dice(inputs['label'].numpy(),
output) # dice containing each tool class
iou = general_jaccard(inputs['label'].numpy(), output)
frame_dice = np.mean([dice[i][1] for i in range(len(dice))])
frame_iou = np.mean([iou[i][1] for i in range(len(dice))])
#overall
metrics[0] += frame_dice # dice of each frame
metrics[1] += frame_iou
count += 1
print(count)
metrics[0] /= count
metrics[1] /= count
print(metrics)
dc, jc = metrics[0], metrics[1]
print('Dice:{:.4f} IoU:{:.4f} Time:{:.4f}'.format(dc, jc, duration))
return jc
################################################ def part ################################################
if cfg.dataset=='endovis2018':
val_map_endo(0)
elif cfg.dataset=='colon_oct':
val_map_oct(0)
if __name__ == '__main__':
with DisablePrint(local_rank=cfg.local_rank):
main()