# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch
import time
import numpy as np
from model.model import parsingNet

# torch.backends.cudnn.deterministic = False

torch.backends.cudnn.benchmark = True
net = parsingNet(pretrained=False, backbone='18', cls_dim=(100 + 1, 56, 4), use_aux=False).cuda()
# net = parsingNet(pretrained = False, backbone='18',cls_dim = (200+1,18,4),use_aux=False).cuda()

net.eval()

x = torch.zeros((1, 3, 288, 800)).cuda() + 1
for i in range(10):
    y = net(x)

t_all = []
for i in range(100):
    t1 = time.time()
    y = net(x)
    t2 = time.time()
    t_all.append(t2 - t1)

print('average time:', np.mean(t_all) / 1)
print('average fps:', 1 / np.mean(t_all))

print('fastest time:', min(t_all) / 1)
print('fastest fps:', 1 / min(t_all))

print('slowest time:', max(t_all) / 1)
print('slowest fps:', 1 / max(t_all))