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test.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#@Author:ShawnWang
##### System library #####
import os
import os.path as osp
from os.path import exists
import argparse
import json
import logging
import time
import numpy as np
##### pytorch library #####
import torch
from torch import nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
##### My own library #####
import data.seg_transforms as st
from data.Seg_dataset import SegList
from utils.logger import Logger
from models.net_builder import net_builder
from utils.utils import AverageMeter, zip_dir
FORMAT = "[%(asctime)-15s %(filename)s:%(lineno)d %(funcName)s] %(message)s"
logging.basicConfig(format=FORMAT)
logger_vis = logging.getLogger(__name__)
logger_vis.setLevel(logging.DEBUG)
###### test ########
def test(args, test_data_loader, model,result_path):
model.eval()
batch_time = AverageMeter()
end = time.time()
for iter, (image,image_name) in enumerate(test_data_loader):
# batchsize = 1 ,so squeeze dim 1
image = image.squeeze()
image_name = image_name[0]
with torch.no_grad():
# batch test for memory reduce
batch = 8
pred_seg = torch.zeros(image.shape[0],image.shape[2],image.shape[3])
pred_cls = torch.zeros(image.shape[0],3)
for i in range(0,image.shape[0],batch):
start_id = i
end_id = i + batch
if end_id > image.shape[0]:
end_id = image.shape[0]
image_batch = image[start_id:end_id,:,:,:]
image_var = Variable(image_batch).cuda()
# model forward
output_seg,output_cls = model(image_var)
_, pred_batch = torch.max(output_seg, 1)
pred_seg[start_id:end_id,:,:] = pred_batch.cpu().data
pred_cls[start_id:end_id,:] = output_cls.cpu().data
pred_seg = pred_seg.numpy().astype('uint8') # predict label
pred_det = pred_cls.numpy().astype('float32')
batch_time.update(time.time() - end)
# save seg result
if args.seg:
save_dir = osp.join(result_path, 'segment')
if not exists(save_dir):
os.makedirs(save_dir)
np.save(osp.join(save_dir, image_name+'_labelMark_volumes'), pred_seg)
print('save segment result, finished!')
# save cls result
if args.det:
save_dir = osp.join(result_path, 'segment')
if not exists(save_dir):
os.makedirs(save_dir)
np.save(osp.join(save_dir, image_name+'_labelMark_detections'), pred_det)
print('save detection result, finished!')
end = time.time()
logger_vis.info('Eval: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
.format(iter, len(test_data_loader), batch_time=batch_time))
def test_fusion(args, test_data_loader, model_list,result_path):
for model in model_list:
model.eval()
batch_time = AverageMeter()
end = time.time()
for iter, (image,image_name) in enumerate(test_data_loader):
# batchsize = 1 ,so squeeze dim 1
image = image.squeeze()
image_name = image_name[0]
with torch.no_grad():
# batch test for memory reduce
batch = 8
pred_seg = torch.zeros(image.shape[0],image.shape[2],image.shape[3])
pred_cls = torch.zeros(image.shape[0],3)
for i in range(0,image.shape[0],batch):
start_id = i
end_id = i + batch
if end_id > image.shape[0]:
end_id = image.shape[0]
image_batch = image[start_id:end_id,:,:,:]
image_var = Variable(image_batch).cuda()
Output_Seg = Variable(torch.zeros(batch,4,image.shape[2],image.shape[3])).cuda()
Output_Cls = Variable(torch.zeros(batch,3)).cuda()
# wangshen model forward
weight = torch.tensor([0.5,0.5]).cuda()
for j,model in enumerate(model_list):
output_seg,output_cls = model(image_var)
Output_Seg += weight[j]*torch.exp(output_seg)
Output_Cls += weight[j]*output_cls
_, pred_batch = torch.max(Output_Seg, 1)
pred_seg[start_id:end_id,:,:] = pred_batch.cpu().data
pred_cls[start_id:end_id,:] = Output_Cls.cpu().data
pred_seg = pred_seg.numpy().astype('uint8') # predict label
pred_det = pred_cls.numpy().astype('float32')
batch_time.update(time.time() - end)
if args.seg:
save_dir = osp.join(result_path, 'segment')
if not exists(save_dir):
os.makedirs(save_dir)
np.save(osp.join(save_dir, image_name+'_labelMark_volumes'), pred_seg)
print('save segment result, finished!')
if args.det:
save_dir = osp.join(result_path, 'segment')
if not exists(save_dir):
os.makedirs(save_dir)
np.save(osp.join(save_dir, image_name+'_labelMark_detections'), pred_det)
print('save detection result, finished!')
end = time.time()
logger_vis.info('Eval: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
.format(iter, len(test_data_loader), batch_time=batch_time))
def test_seg(args,result_path):
print('Loading test model ...')
if args.fusion:
# 1
net_1 = net_builder('unet_nested')
net_1 = nn.DataParallel(net_1).cuda()
checkpoint_1 = torch.load('result/ori_3D/train/unet_nested/checkpoint/model_best.pth.tar')
net_1.load_state_dict(checkpoint_1['state_dict'])
# 2
net_2 = net_builder('unet')
net_2 = nn.DataParallel(net_2).cuda()
checkpoint_2 = torch.load('result/ori_3D/train/unet/checkpoint/model_best.pth.tar')
net_2.load_state_dict(checkpoint_2['state_dict'])
net = [net_1,net_2]
else:
net = net_builder(args.seg_name)
net = nn.DataParallel(net).cuda()
checkpoint = torch.load(args.seg_path)
net.load_state_dict(checkpoint['state_dict'])
#print('model loaded!')
info = json.load(open(osp.join(args.list_dir,'info_test.json'), 'r'))
normalize = st.Normalize(mean=info['mean'], std=info['std'])
t = []
if args.resize:
t.append(st.Resize(args.resize))
t.extend([st.ToTensor(),
normalize])
dataset = SegList(args.data_dir, 'test', st.Compose(t), list_dir=args.list_dir)
test_loader = torch.utils.data.DataLoader(
dataset,batch_size=1, shuffle=False, num_workers=args.workers,pin_memory=False
)
cudnn.benchmark = True
if args.fusion:
test_fusion(args, test_loader,net,result_path)
else:
test(args, test_loader,net,result_path)
def parse_args():
# Testing settings
parser = argparse.ArgumentParser(description='')
parser.add_argument('-d', '--data-dir', default=None, required=True)
parser.add_argument('-l', '--list-dir', default=None,
help='List dir to look for train_images.txt etc. '
'It is the same with --data-dir if not set.')
parser.add_argument('-j', '--workers', type=int, default=8)
parser.add_argument('--seg-name', dest='seg_name',help='seg model',default=None, type=str)
parser.add_argument('--det-name', dest='det_name',help='det model',default=None, type=str)
parser.add_argument('--seg-path',help='pretrained model test',default='./', type=str)
parser.add_argument('--det-path',help='pretrained model test',default='./', type=str)
parser.add_argument('--seg',action='store_true')
parser.add_argument('--det',action='store_true')
parser.add_argument('--fusion',action='store_true')
parser.add_argument('--resize', default=0, type=int)
args = parser.parse_args()
return args
def main():
args = parse_args()
task_name = args.list_dir.split('/')[-1]
##### logger setting #####
result_path = osp.join('result',task_name,'test', 'final_CHUNXIN')
#result_path = osp.join('result',data_name,'comp', args.name)
if not exists(result_path):
os.makedirs(result_path)
test_seg(args,result_path)
# zip submission
print('Submission zip generating ... ')
zip_dir(osp.join(result_path,'segment'),osp.join(result_path,'submission.zip'))
print('Submission zip generated ^_^ ')
if __name__ == '__main__':
main()