pad_value=config1['pad_value'])
dataset = DataBowl3Detector(testsplit,
config1,
phase='test',
split_comber=split_comber)
test_loader = DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=32,
pin_memory=False,
collate_fn=collate)
test_detect(test_loader,
nod_net,
get_pbb,
bbox_result_path,
config1,
n_gpu=config_submit['n_gpu'])
dataset = DataBowl3Detector(valsplit,
config1,
phase='test',
split_comber=split_comber)
test_loader = DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=32,
pin_memory=False,
collate_fn=collate)
test_detect(test_loader,
config1, nod_net, loss, get_pbb = nodmodel.get_model()
checkpoint = torch.load('./2_nodule_detection/detector.ckpt')
nod_net.load_state_dict(checkpoint['state_dict'])
nod_net = nod_net
nod_net = nod_net
bbox_result_path = args.bbox_root
if not os.path.exists(bbox_result_path):
os.mkdir(bbox_result_path)
split_comber = SplitComb(config1['sidelen'],
config1['max_stride'],
config1['stride'],
config1['margin'],
pad_value=config1['pad_value'])
dataset = DataBowl3Detector(config['testsplit'],
config1,
phase='test',
split_comber=split_comber)
test_loader = DataLoader(dataset,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=False,
collate_fn=collate)
test_detect(test_loader, nod_net, get_pbb, bbox_result_path, config1)
import os
import sys
os.environ['PYTHONPATH'] = '%s:%s' % ('/home/caffe/python', '/workspace/pai')
import sys
sys.path.append('/home/caffe/python')
sys.path.append('/workspace/pai')
import caffe
from data import DataBowl3Detector
from test_detect import test_detect
from split_combine import SplitComb
from test_config import test_config as config
process = 'test'
if config['detector']:
net = caffe.Net(config['test_prototxt'], config['caffe_model'], caffe.TEST)
split_comber = SplitComb(config)
dataset = DataBowl3Detector(config,
process=process,
split_comber=split_comber)
test_detect(dataset, net, config=config, process=process)
bbox_result_path = './bbox_result'
if not os.path.exists(bbox_result_path):
os.mkdir(bbox_result_path)
#testsplit = [f.split('_clean')[0] for f in os.listdir(prep_result_path) if '_clean' in f]
if not skip_detect:
margin = 32
sidelen = 144
config1['datadir'] = prep_result_path
split_comber = SplitComb(sidelen,config1['max_stride'],config1['stride'],margin,pad_value= config1['pad_value'])
dataset = DataBowl3Detector(testsplit,config1,phase='test',split_comber=split_comber)
test_loader = DataLoader(dataset,batch_size = 1,
shuffle = False,num_workers = 32,pin_memory=False,collate_fn =collate)
test_detect(test_loader, nod_net, get_pbb, bbox_result_path,config1,n_gpu=config_submit['n_gpu'])
casemodel = import_module(config_submit['classifier_model'].split('.py')[0])
casenet = casemodel.CaseNet(topk=5)
config2 = casemodel.config
checkpoint = torch.load(config_submit['classifier_param'])
casenet.load_state_dict(checkpoint['state_dict'])
torch.cuda.set_device(0)
casenet = casenet.cuda()
cudnn.benchmark = True
casenet = DataParallel(casenet)
