def evalute_model(self, model, loader, gpu_id, input_size, num_classes, whole):
mean_IU, IU_array = evaluate_main(model=model, loader = loader,
gpu_id = gpu_id,
input_size = input_size,
num_classes = num_classes,
whole = whole)
return mean_IU, IU_array
from torch.utils import data
from networks.pspnet_combine_S import Res_pspnet, BasicBlock, Bottleneck
from networks.evaluate import evaluate_main
from dataset.datasets import CSDataTestSet
from utils.train_options import TrainOptionsForTest
import torch
from utils.utils import *
if __name__ == '__main__':
args = TrainOptionsForTest().initialize()
testloader = data.DataLoader(CSDataTestSet(
args.data_dir,
'./dataset/list/cityscapes/test.lst',
crop_size=(1024, 2048)),
batch_size=2,
shuffle=False,
pin_memory=True)
# student = Res_pspnet(BasicBlock, [2, 2, 2, 2], num_classes = 19)
# student.load_state_dict(torch.load(args.resume_from))
# evaluate_main(student, testloader, '0', '512,512', 19, True, type = 'test')
teacher = Res_pspnet(Bottleneck, [3, 4, 23, 3], num_classes=19)
# teacher.load_state_dict(torch.load(args.resume_from))
teacher.cuda()
load_T_model(teacher, args.resume_from)
evaluate_main(teacher, testloader, '0', '512,512', 19, True, type='test')
from torch.utils import data
from networks.pspnet_combine import Res_pspnet, BasicBlock, Bottleneck
from networks.evaluate import evaluate_main
from dataset.datasets import CSDataTestSet
from utils.train_options import TrainOptionsForTest
import torch
if __name__ == '__main__':
args = TrainOptionsForTest().initialize()
testloader = data.DataLoader(CSDataTestSet(
args.data_dir,
'./dataset/list/cityscapes/test.lst',
crop_size=(1024, 2048)),
batch_size=1,
shuffle=False,
pin_memory=True)
student = Res_pspnet(BasicBlock, [2, 2, 2, 2], num_classes=19) # resnet
student.load_state_dict(torch.load(args.resume_from))
evaluate_main(student, testloader, '512,512', 19, True, type='test')