def eval_model(model_name, test_img_path, submit_path, save_flag=True):
if os.path.exists(submit_path):
shutil.rmtree(submit_path)
os.mkdir(submit_path)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = EAST(False).to(device)
checkpoint = torch.load(model_name)
model.load_state_dict(checkpoint['state_dict'])
print('model epoch :', checkpoint['epoch'])
# model.load_state_dict(torch.load(model_name))
model.eval()
start_time = time.time()
detect_dataset(model, device, test_img_path, submit_path)
os.chdir(submit_path)
res = subprocess.getoutput('zip -q submit13.zip *.txt')
res = subprocess.getoutput('mv submit13.zip ../')
os.chdir('../')
res = subprocess.getoutput(
'python ./evaluate/script.py –g=./evaluate/gt13.zip –s=./submit13.zip')
print(res)
os.remove('./submit13.zip')
print('eval time is {}'.format(time.time() - start_time))
if not save_flag:
shutil.rmtree(submit_path)
def eval_model(model, checkpoint, test_path, submit_path, save_flag=True):
test_img_path = os.path.join(test_path, 'images')
test_gt_path = os.path.join(test_path, 'gt')
proc_dir = os.getcwd()
if os.path.exists(submit_path):
shutil.rmtree(submit_path)
os.mkdir(submit_path)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
model.load_state_dict(torch.load(checkpoint)['model'])
model.eval()
start_time = time.time()
detect_dataset(model, device, test_img_path, submit_path)
os.chdir(submit_path)
res = subprocess.getoutput('zip -q submit.zip *.txt')
#print(res)
shutil.move('submit.zip', '../submit.zip')
os.chdir(test_gt_path)
res = subprocess.getoutput('zip -q gt.zip *.txt')
shutil.move('gt.zip', os.path.join(proc_dir, 'gt.zip'))
# res = subprocess.getoutput('mv submit.zip ../')
os.chdir(proc_dir)
res = subprocess.getoutput(
'python ./evaluate/script.py –g=./gt.zip –s=./submit.zip')
print(res)
os.remove('./submit.zip')
#print('eval time is {}'.format(time.time()-start_time))
if not save_flag:
shutil.rmtree(submit_path)