def main():
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--deepsave", help="A path to save deepfeature", type=str,
# default='re/cap_vs_covid.npy')
default='deep_f')
parser.add_argument("-e", "--exclude_list",
help="A path to a txt file for excluded data list. If no file need to be excluded, "
"it should be 'none'.", type=str,
default='none')
parser.add_argument("-v", "--invert_exclude", help="Whether to invert exclude to include", type=bool,
default=False)
parser.add_argument("-k", "--topk", help="gpuid", type=int,
default=5)
parser.add_argument("-s", "--savenpy", help="gpuid", type=str,
default='top1.npy')
args = parser.parse_args()
os.makedirs(args.deepsave, exist_ok=True)
print("Loading options...")
with open('test.toml', 'r') as optionsFile:
options = toml.loads(optionsFile.read())
if (options["general"]["usecudnnbenchmark"] and options["general"]["usecudnn"]):
print("Running cudnn benchmark...")
torch.backends.cudnn.benchmark = True
os.environ['CUDA_VISIBLE_DEVICES'] = options["general"]['gpuid']
torch.manual_seed(options["general"]['random_seed'])
# Create the model.
if options['general']['use_plus']:
model = resnet152_plus(options['general']['class_num'])
else:
model = resnet152(options['general']['class_num'])
if 'R' in options['general'].keys():
model = resnet152_R(options['general']['class_num'])
pretrained_dict = torch.load(options['general']['pretrainedmodelpath'])
# load only exists weights
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if
k in model_dict.keys() and v.size() == model_dict[k].size()}
print('matched keys:', len(pretrained_dict))
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
tester = Validator(options, 'test',model,options['validation']['saves'],args)
result, re_all = tester()
print (tester.savenpy)
print('-' * 21)
print('All acc:' + str(re_all))
print('{:<10}|{:>10}'.format('Cls #', 'Accuracy'))
for i in range(result.shape[0]):
print('{:<10}|{:>10}'.format(i, result[i]))
print('-' * 21)
def model_get():
model = resnet152(2)
pretrained_dict = torch.load('../res152_lungattention_2train_lidc.pt')
# load only exists weights
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if
k in model_dict.keys() and v.size() == model_dict[k].size()}
#print('matched keys:', len(pretrained_dict))
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model
def model_get(path):
model = resnet152(4)
pretrained_dict = torch.load(path)
# load only exists weights
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items()
if k in model_dict.keys() and v.size() == model_dict[k].size()
}
# print('matched keys:', len(pretrained_dict))
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model
print("Loading options...")
with open('options_lip.toml', 'r') as optionsFile:
options = toml.loads(optionsFile.read())
if (options["general"]["usecudnnbenchmark"]
and options["general"]["usecudnn"]):
print("Running cudnn benchmark...")
torch.backends.cudnn.benchmark = True
os.environ['CUDA_VISIBLE_DEVICES'] = options["general"]['gpuid']
torch.manual_seed(options["general"]['random_seed'])
# Create the model.
model = resnet152(2)
pretrained_dict = torch.load(options["general"]["pretrainedmodelpath"])
# load only exists weights
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items()
if k in model_dict.keys() and v.size() == model_dict[k].size()
}
print('matched keys:', len(pretrained_dict))
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
tester = Validator(options, 'test')
os.environ['CUDA_VISIBLE_DEVICES'] = options["general"]['gpuid']
torch.manual_seed(options["general"]['random_seed'])
# Create the model.
if options['general']['use_3d']:
model = Dense3D(options) ##TODO:1
elif options['general']['use_slice']:
if options['general']['use_plus']:
model = resnet152_plus(options['general']['class_num'],
asinput=options['general']['plus_as_input'],
USE_25D=options['general']['use25d'])
else:
model = resnet152(options['general']['class_num'],
USE_25D=options['general']
['use25d']) # vgg19_bn(2)#squeezenet1_1(2)
if 'R' in options['general'].keys():
model = resnet152_R(options['general']['class_num'])
else:
model = densenet161(2)
if (options["general"]["loadpretrainedmodel"]):
# remove paralle module
pretrained_dict = torch.load(options["general"]["pretrainedmodelpath"])
# load only exists weights
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items()
if k in model_dict.keys() and v.size() == model_dict[k].size()
and options["general"]["usecudnn"]):
print("Running cudnn benchmark...")
torch.backends.cudnn.benchmark = True
os.environ['CUDA_VISIBLE_DEVICES'] = options["general"]['gpuid']
torch.manual_seed(options["general"]['random_seed'])
#Create the model.
if options['general']['use_3d']:
model = Dense3D(options) ##TODO:1
elif options['general']['use_slice']:
if options['general']['use_plus']:
model = resnet152_plus(2)
else:
model = resnet152(2) #vgg19_bn(2)#squeezenet1_1(2)
else:
model = densenet161(2)
if (options["general"]["loadpretrainedmodel"]):
# remove paralle module
pretrained_dict = torch.load(options["general"]["pretrainedmodelpath"])
# load only exists weights
model_dict = model.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items()
if k in model_dict.keys() and v.size() == model_dict[k].size()
}
print('matched keys:', len(pretrained_dict))
model_dict.update(pretrained_dict)
if(options["general"]["usecudnnbenchmark"] and options["general"]["usecudnn"]):
print("Running cudnn benchmark...")
torch.backends.cudnn.benchmark = True
os.environ['CUDA_VISIBLE_DEVICES'] = options["general"]['gpuid']
torch.manual_seed(options["general"]['random_seed'])
#Create the model.
if options['general']['use_3d']:
model = Dense3D(options)##TODO:1
elif options['general']['use_slice']:
if options['general']['use_plus']:
model = resnet152_plus(options['general']['class_num'])
else:
model = resnet152(options['general']['class_num'])#vgg19_bn(2)#squeezenet1_1(2)
else:
model=densenet161(2)
if(options["general"]["loadpretrainedmodel"]):
# remove paralle module
pretrained_dict = torch.load(options["general"]["pretrainedmodelpath"])
# load only exists weights
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict.keys() and v.size() == model_dict[k].size()}
print('matched keys:',len(pretrained_dict))
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
#Move the model to the GPU.
#criterion = model.loss()
def main():
parser = argparse.ArgumentParser()
mod='AB-in'
parser.add_argument("-m", "--maskpath", help="A list of paths for lung segmentation data", # type=list,
default=['/mnt/data6/CAP/seg_test',
'/mnt/data7/ILD/resampled_seg',
# '/mnt/data7/examples/seg',
# '/mnt/data7/reader_ex/resampled_seg',
# '/mnt/data7/LIDC/resampled_seg',
'/mnt/data7/resampled_seg/test1', '/mnt/data7/resampled_seg/test2',
'/mnt/data7/resampled_seg/test3'
# '/mnt/data7/slice_test_seg/mask_re',
# '/mnt/data7/resampled_seg/test3']
])
parser.add_argument("-i", "--imgpath", help="A list of paths for image data",
default=['/mnt/data6/CAP/data_test',
'/mnt/data7/ILD/resampled_data',
# '/mnt/data7/examples/data',
# '/mnt/data7/reader_ex/resampled_data',
# '/mnt/data7/LIDC/resampled_data',
'/mnt/data7/resampled_data/test1', '/mnt/data7/resampled_data/test2',
'/mnt/data7/resampled_data/test3'
# '/mnt/data7/slice_test_seg/data_re',
# '/mnt/data7/resampled_data/resampled_test_3']
])
parser.add_argument("-o", "--savenpy", help="A path to save record", type=str,
#default='re/reader_healthy_vs_ill.npy')
#default = 're/reader_cap_vs_covid.npy')
default = 're/reader_influenza_vs_covid.npy')
#default='re/test_2.npy')
parser.add_argument("-d", "--deepsave", help="A path to save deepfeature", type=str,
# default='re/cap_vs_covid.npy')
default='deep_f')
parser.add_argument("-e", "--exclude_list",
help="A path to a txt file for excluded data list. If no file need to be excluded, "
"it should be 'none'.", type=str,
default='none')
parser.add_argument("-v", "--invert_exclude", help="Whether to invert exclude to include", type=bool,
default=False)
parser.add_argument("-p", "--model_path", help="Whether to invert exclude to include", type=str,
default='weights/new_4cls_pure.pt')
# default='weights/healthy_or_not.pt')
parser.add_argument("-g", "--gpuid", help="gpuid", type=str,
default='1')
args = parser.parse_args()
os.makedirs(args.deepsave, exist_ok=True)
print("Loading options...")
with open('test.toml', 'r') as optionsFile:
options = toml.loads(optionsFile.read())
if (options["general"]["usecudnnbenchmark"] and options["general"]["usecudnn"]):
print("Running cudnn benchmark...")
torch.backends.cudnn.benchmark = True
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpuid
if isinstance(args.imgpath,str):
args.imgpath=eval(args.imgpath)
args.maskpath=eval(args.maskpath)
torch.manual_seed(options["general"]['random_seed'])
# Create the model.
if options['general']['use_plus']:
model = resnet152_plus(options['general']['class_num'])
else:
model = resnet152(options['general']['class_num'])
if 'R' in options['general'].keys():
model = resnet152_R(options['general']['class_num'])
pretrained_dict = torch.load(args.model_path)
# load only exists weights
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if
k in model_dict.keys() and v.size() == model_dict[k].size()}
print('matched keys:', len(pretrained_dict))
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
tester = Validator(options, 'test',model,mod,savenpy=args.savenpy)
result, re_all = tester()
print (tester.savenpy)
print('-' * 21)
print('All acc:' + str(re_all))
print('{:<10}|{:>10}'.format('Cls #', 'Accuracy'))
for i in range(result.shape[0]):
print('{:<10}|{:>10}'.format(i, result[i]))
print('-' * 21)
