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test.py
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import argparse
import logging
import os
import random
import sys
from typing import Iterable
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torchvision import transforms
from networks.unet_model_avg import UNet
from dataloaders.dataloader import FundusSegmentation, ProstateSegmentation, MNMSSegmentation
import dataloaders.custom_transforms as tr
from utils import losses, metrics, ramps, util
from medpy.metric import binary
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='fundus', choices=['fundus', 'prostate', 'MNMS'])
parser.add_argument("--save_name", type=str, default="BCMDA", help="experiment_name")
parser.add_argument("--overwrite", action='store_true')
parser.add_argument("--model", type=str, default="unet", help="model_name")
parser.add_argument("--gpu", type=str, default='0')
parser.add_argument('--eval', type=bool, default=True)
parser.add_argument('--lb_num', type=int, default=20)
parser.add_argument("--test_bs", type=int, default=1)
parser.add_argument('--domain_num', type=int, default=4)
parser.add_argument('--lb_domain', type=int, default=1)
parser.add_argument("--temp", default=0.05, type=float)
parser.add_argument('--data_path', type=str, default='../data/ProstateSlice')
parser.add_argument('--save_img', action='store_true')
args = parser.parse_args()
def to_2d(input_tensor):
input_tensor = input_tensor.unsqueeze(1)
tensor_list = []
temp_prob = input_tensor == torch.ones_like(input_tensor)
tensor_list.append(temp_prob)
temp_prob2 = input_tensor > torch.zeros_like(input_tensor)
tensor_list.append(temp_prob2)
output_tensor = torch.cat(tensor_list, dim=1)
return output_tensor.float()
def cycle(iterable: Iterable):
"""Make an iterator returning elements from the iterable.
.. note::
**DO NOT** use `itertools.cycle` on `DataLoader(shuffle=True)`.\n
Because `itertools.cycle` saves a copy of each element, batches are shuffled only at the first epoch. \n
See https://docs.python.org/3/library/itertools.html#itertools.cycle for more details.
"""
while True:
for x in iterable:
yield x
def to_3d(input_tensor):
input_tensor = input_tensor.unsqueeze(1)
tensor_list = []
for i in range(1, 4):
temp_prob = input_tensor == i * torch.ones_like(input_tensor)
tensor_list.append(temp_prob)
output_tensor = torch.cat(tensor_list, dim=1)
return output_tensor.float()
if args.dataset == 'fundus':
part = ['cup', 'disc']
dataset = FundusSegmentation
elif args.dataset == 'prostate':
part = ['base']
dataset = ProstateSegmentation
elif args.dataset == 'MNMS':
part = ['lv', 'myo', 'rv']
dataset = MNMSSegmentation
n_part = len(part)
dice_calcu = {'fundus': metrics.dice_coeff_2label, 'prostate': metrics.dice_coeff, 'MNMS': metrics.dice_coeff_3label}
@torch.no_grad()
def test(args, model, test_dataloader, epoch):
model.eval()
val_dice = [0.0] * n_part
val_dc, val_jc, val_hd, val_asd = [0.0] * n_part, [0.0] * n_part, [0.0] * n_part, [0.0] * n_part
domain_num = len(test_dataloader)
num = 0
for i in range(domain_num):
cur_dataloader = test_dataloader[i]
domain_val_dice = [0.0] * n_part
domain_val_dc, domain_val_jc, domain_val_hd, domain_val_asd = [0.0] * n_part, [0.0] * n_part, [0.0] * n_part, [
0.0] * n_part
domain_code = i + 1
for batch_num, sample in enumerate(cur_dataloader):
data = sample['image'].cuda()
mask = sample['label'].cuda()
if args.dataset == 'fundus':
cup_mask = mask.eq(0).float()
disc_mask = mask.le(128).float()
mask = torch.cat((cup_mask.unsqueeze(1), disc_mask.unsqueeze(1)), dim=1)
elif args.dataset == 'prostate':
mask = mask.eq(0).long()
elif args.dataset == 'MNMS':
mask_ = mask[:, ..., 0].eq(255).float()
mask_[mask[:, ..., 1].eq(255)] = 2
mask_[mask[:, ..., 2].eq(255)] = 3
mask = mask_.long()
res_test = model(data)
output_linear = model.classify_linear(res_test['last_fts'])
output = output_linear
if args.dataset == 'fundus':
p_linear = torch.sigmoid(output_linear)
pred_prob = p_linear
elif args.dataset == 'prostate':
p_linear = torch.softmax(output_linear, dim=1)
pred_prob = p_linear
elif args.dataset == 'MNMS':
p_linear = torch.softmax(output_linear, dim=1)
pred_prob = p_linear
pred_prob = pred_prob.cpu()
mask = mask.cpu()
output = output.cpu()
if args.dataset == 'fundus':
pred_label = pred_prob.ge(0.5)
pred_onehot = pred_label.clone()
mask_onehot = mask.clone()
elif args.dataset == 'prostate':
pred_label = torch.max(pred_prob, dim=1)[1]
pred_onehot = pred_label.clone().unsqueeze(1)
mask_onehot = mask.clone().unsqueeze(1)
elif args.dataset == 'MNMS':
pred_label = torch.max(pred_prob, dim=1)[1]
pred_onehot = to_3d(pred_label)
mask_onehot = to_3d(mask)
dice = dice_calcu[args.dataset](np.asarray(pred_label), mask)
avg_dice = sum(dice) / len(dice)
pred_path = './img/save/{}/pred/'.format(args.save_name)
if not os.path.exists(pred_path):
os.makedirs(pred_path)
if args.save_img:
for j in range(len(data)):
num += 1
util.draw_contour_and_save(data[j], pred_onehot[j], mask_onehot[j],
pred_path + '{}_{}_{}.png'.format(domain_code, num, round(avg_dice, 4)))
dc, jc, hd, asd = [0.0] * n_part, [0.0] * n_part, [0.0] * n_part, [0.0] * n_part
for j in range(len(data)):
for i, p in enumerate(part):
dc[i] += binary.dc(np.asarray(pred_onehot[j, i], dtype=bool),
np.asarray(mask_onehot[j, i], dtype=bool))
jc[i] += binary.jc(np.asarray(pred_onehot[j, i], dtype=bool),
np.asarray(mask_onehot[j, i], dtype=bool))
if pred_onehot[j, i].float().sum() < 1e-4:
hd[i] += 100
asd[i] += 100
else:
hd[i] += binary.hd95(np.asarray(pred_onehot[j, i], dtype=bool),
np.asarray(mask_onehot[j, i], dtype=bool))
asd[i] += binary.asd(np.asarray(pred_onehot[j, i], dtype=bool),
np.asarray(mask_onehot[j, i], dtype=bool))
for i, p in enumerate(part):
dc[i] /= len(data)
jc[i] /= len(data)
hd[i] /= len(data)
asd[i] /= len(data)
for i in range(len(domain_val_dice)):
domain_val_dice[i] += dice[i]
domain_val_dc[i] += dc[i]
domain_val_jc[i] += jc[i]
domain_val_hd[i] += hd[i]
domain_val_asd[i] += asd[i]
for i in range(len(domain_val_dice)):
domain_val_dice[i] /= len(cur_dataloader)
val_dice[i] += domain_val_dice[i]
domain_val_dc[i] /= len(cur_dataloader)
val_dc[i] += domain_val_dc[i]
domain_val_jc[i] /= len(cur_dataloader)
val_jc[i] += domain_val_jc[i]
domain_val_hd[i] /= len(cur_dataloader)
val_hd[i] += domain_val_hd[i]
domain_val_asd[i] /= len(cur_dataloader)
val_asd[i] += domain_val_asd[i]
text = 'domain%d lb_domain %d :' % (domain_code, epoch)
text += '\n\t'
for n, p in enumerate(part):
text += 'val_%s_dice: %f, ' % (p, domain_val_dice[n])
text += '\n\t'
for n, p in enumerate(part):
text += 'val_%s_dc: %f, ' % (p, domain_val_dc[n])
text += '\t'
for n, p in enumerate(part):
text += 'val_%s_jc: %f, ' % (p, domain_val_jc[n])
text += '\n\t'
for n, p in enumerate(part):
text += 'val_%s_hd: %f, ' % (p, domain_val_hd[n])
text += '\t'
for n, p in enumerate(part):
text += 'val_%s_asd: %f, ' % (p, domain_val_asd[n])
logging.info(text)
model.train()
for i in range(len(val_dice)):
val_dice[i] /= domain_num
val_dc[i] /= domain_num
val_jc[i] /= domain_num
val_hd[i] /= domain_num
val_asd[i] /= domain_num
text = 'lb_domain %d :' % (epoch)
text += '\n\t'
avg = 0.0
for n, p in enumerate(part):
text += 'val_%s_dice: %f, ' % (p, val_dice[n])
avg += val_dice[n]
avg = avg / len(val_dice)
text += 'val_avg_dice: %f, ' % (avg)
text += '\n\t'
avg = 0.0
for n, p in enumerate(part):
text += 'val_%s_dc: %f, ' % (p, val_dc[n])
avg += val_dc[n]
avg = avg / len(val_dc)
text += 'val_avg_dc: %f, ' % (avg)
text += '\t'
avg = 0.0
for n, p in enumerate(part):
text += 'val_%s_jc: %f, ' % (p, val_jc[n])
avg += val_jc[n]
avg = avg / len(val_jc)
text += 'val_avg_jc: %f, ' % (avg)
text += '\n\t'
avg = 0.0
for n, p in enumerate(part):
text += 'val_%s_hd: %f, ' % (p, val_hd[n])
avg += val_hd[n]
avg = avg / len(val_hd)
text += 'val_avg_hd: %f, ' % (avg)
text += '\t'
avg = 0.0
for n, p in enumerate(part):
text += 'val_%s_asd: %f, ' % (p, val_asd[n])
avg += val_asd[n]
avg = avg / len(val_asd)
text += 'val_avg_asd: %f, ' % (avg)
logging.info(text)
return val_dice, val_dc, val_jc, val_hd, val_asd
def main(args, snapshot_path):
if args.dataset == 'fundus':
num_channels = 3
num_classes = 2
if args.domain_num >= 4:
args.domain_num = 4
elif args.dataset == 'prostate':
num_channels = 1
num_classes = 2
if args.domain_num >= 6:
args.domain_num = 6
elif args.dataset == 'MNMS':
num_channels = 1
num_classes = 4
if args.domain_num >= 4:
args.domain_num = 4
normal_toTensor = transforms.Compose([
tr.Normalize_tf(),
tr.ToTensor()
])
domain_num = args.domain_num
test_dataset = []
test_dataloader = []
for i in range(1, domain_num + 1):
cur_dataset = dataset(base_dir=train_data_path, phase='test', splitid=-1, domain=[i],
normal_toTensor=normal_toTensor)
test_dataset.append(cur_dataset)
for i in range(0, domain_num):
cur_dataloader = DataLoader(test_dataset[i], batch_size=args.test_bs, shuffle=False, num_workers=0,
pin_memory=True)
test_dataloader.append(cur_dataloader)
def create_model(ema=False):
# Network definition
if args.model == 'unet':
model = UNet(n_channels=num_channels, n_classes=num_classes, temp=args.temp)
if ema:
for param in model.parameters():
param.detach_()
return model.cuda()
model = create_model()
if args.eval:
model.load_state_dict(
torch.load('../model/{}/{}/unet_avg_dice_best_model.pth'.format(args.dataset, args.save_name)))
test(args, model, test_dataloader, args.lb_domain)
exit()
if __name__ == "__main__":
snapshot_path = "../model/" + args.dataset + "/" + args.save_name + "/"
train_data_path = args.data_path
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if not os.path.exists(snapshot_path):
os.makedirs(snapshot_path)
elif not args.overwrite:
raise Exception('file {} is exist!'.format(snapshot_path))
logging.basicConfig(filename=snapshot_path + "/log.txt", level=logging.INFO,
format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S')
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
cmd = " ".join(["python"] + sys.argv)
logging.info(cmd)
logging.info(str(args))
main(args, snapshot_path)