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test.py
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#!/usr/bin/env python
import argparse
import os
import os.path as osp
import torch.nn.functional as F
import torch
from torch.autograd import Variable
import tqdm
from dataloaders import fundus_dataloader as DL
from torch.utils.data import DataLoader
from dataloaders import custom_transforms as tr
from torchvision import transforms
from scipy.misc import imsave
from utils.Utils import *
from utils.metrics import *
from datetime import datetime
import pytz
from networks.deeplabv3 import *
import cv2
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model-file', type=str, default='./logs/train2/20181202_160326.365442/checkpoint_9.pth.tar',
help='Model path')
parser.add_argument(
'--dataset', type=str, default='Drishti-GS', help='test folder id contain images ROIs to test'
)
parser.add_argument('-g', '--gpu', type=int, default=0)
parser.add_argument(
'--data-dir',
default='/home/sjwang/ssd1T/fundus/domain_adaptation/',
help='data root path'
)
parser.add_argument(
'--out-stride',
type=int,
default=16,
help='out-stride of deeplabv3+',
)
parser.add_argument(
'--save-root-ent',
type=str,
default='./results/ent/',
help='path to save ent',
)
parser.add_argument(
'--save-root-mask',
type=str,
default='./results/mask/',
help='path to save mask',
)
parser.add_argument(
'--sync-bn',
type=bool,
default=True,
help='sync-bn in deeplabv3+',
)
parser.add_argument(
'--freeze-bn',
type=bool,
default=False,
help='freeze batch normalization of deeplabv3+',
)
parser.add_argument('--test-prediction-save-path', type=str,
default='./results/baseline/',
help='Path root for test image and mask')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
model_file = args.model_file
# 1. dataset
composed_transforms_test = transforms.Compose([
tr.Normalize_tf(),
tr.ToTensor()
])
db_test = DL.FundusSegmentation(base_dir=args.data_dir, dataset=args.dataset, split='test',
transform=composed_transforms_test)
test_loader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=1)
# 2. model
model = DeepLab(num_classes=2, backbone='mobilenet', output_stride=args.out_stride,
sync_bn=args.sync_bn, freeze_bn=args.freeze_bn).cuda()
if torch.cuda.is_available():
model = model.cuda()
print('==> Loading %s model file: %s' %
(model.__class__.__name__, model_file))
checkpoint = torch.load(model_file)
try:
model.load_state_dict(model_data)
pretrained_dict = checkpoint['model_state_dict']
model_dict = model_gen.state_dict()
# 1. filter out unnecessary keys
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. overwrite entries in the existing state dict
model_dict.update(pretrained_dict)
# 3. load the new state dict
model_gen.load_state_dict(model_dict)
except Exception:
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
print('==> Evaluating with %s' % (args.dataset))
val_cup_dice = 0.0
val_disc_dice = 0.0
timestamp_start = \
datetime.now(pytz.timezone('Asia/Hong_Kong'))
for batch_idx, (sample) in tqdm.tqdm(enumerate(test_loader),
total=len(test_loader),
ncols=80, leave=False):
data = sample['image']
target = sample['map']
img_name = sample['img_name']
if torch.cuda.is_available():
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
prediction, boundary = model(data)
prediction = torch.nn.functional.interpolate(prediction, size=(target.size()[2], target.size()[3]),
mode="bilinear")
boundary = torch.nn.functional.interpolate(boundary, size=(target.size()[2], target.size()[3]),
mode="bilinear")
data = torch.nn.functional.interpolate(data, size=(target.size()[2], target.size()[3]), mode="bilinear")
prediction = torch.sigmoid(prediction)
boundary = torch.sigmoid(boundary)
draw_ent(prediction.data.cpu()[0].numpy(), os.path.join(args.save_root_ent, args.dataset), img_name[0])
draw_mask(prediction.data.cpu()[0].numpy(), os.path.join(args.save_root_mask, args.dataset), img_name[0])
draw_boundary(boundary.data.cpu()[0].numpy(), os.path.join(args.save_root_mask, args.dataset), img_name[0])
prediction = postprocessing(prediction.data.cpu()[0], dataset=args.dataset)
target_numpy = target.data.cpu()
cup_dice = dice_coefficient_numpy(prediction[0, ...], target_numpy[0, 0, ...])
disc_dice = dice_coefficient_numpy(prediction[1, ...], target_numpy[0, 1, ...])
val_cup_dice += cup_dice
val_disc_dice += disc_dice
imgs = data.data.cpu()
for img, lt, lp in zip(imgs, target_numpy, [prediction]):
img, lt = untransform(img, lt)
save_per_img(img.numpy().transpose(1, 2, 0), os.path.join(args.test_prediction_save_path, args.dataset),
img_name[0],
lp, mask_path=None, ext="bmp")
val_cup_dice /= len(test_loader)
val_disc_dice /= len(test_loader)
print('''\n==>val_cup_dice : {0}'''.format(val_cup_dice))
print('''\n==>val_disc_dice : {0}'''.format(val_disc_dice))
with open(osp.join(args.test_prediction_save_path, 'test_log.csv'), 'a') as f:
elapsed_time = (
datetime.now(pytz.timezone('Asia/Hong_Kong')) -
timestamp_start).total_seconds()
log = [[args.model_file] + ['cup dice coefficence: '] + \
[val_cup_dice] + ['disc dice coefficence: '] + \
[val_disc_dice] + [elapsed_time]]
log = map(str, log)
f.write(','.join(log) + '\n')
if __name__ == '__main__':
main()