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test.py
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test.py
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from options.test_options import TestOptions
import data as Dataset
from model import create_model
from util import visualizer
from itertools import islice
import numpy as np
import torch
import random
import os
def Init_Seed(arg):
'''
Disable cudnn to maximize reproducibility
'''
# torch.cuda.cudnn_enabled = False
random.seed(arg.seed)
np.random.seed(arg.seed)
torch.manual_seed(arg.seed)
torch.cuda.manual_seed(arg.seed)
torch.cuda.manual_seed_all(arg.seed)
os.environ['PYTHONHASHSEED'] = str(arg.seed)
if __name__=='__main__':
# get testing options
opt = TestOptions().parse(0)
print(opt.phase)
#Init_Seed(opt)
dataset = Dataset.create_dataloader(opt)
model = create_model(opt)
#exit()
# creat a dataset
#dataset = Dataset.create_dataloader(opt)
dataset_size = len(dataset) * opt.batchSize
print('testing images = %d' % dataset_size)
# create a model
#model = create_model(opt)
iou=0
#if hasattr(model,'par_metric'):
# iou=1
with torch.no_grad():
for i, data in enumerate(dataset):
model.set_input(data)
#model.test_dtd()
model.test()
#model.get_pic()
#if iou==1:
#class_iou,miou=model.par_metric.value()
#print(class_iou,miou)