# BSD 3-Clause License

# Copyright (c) Soumith Chintala 2016,
# Copyright 2020 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 argparse
import torch.utils.data
from torch.autograd import Variable
from pointnet.dataset import ShapeNetDataset
from pointnet.model import PointNetCls
import torch.nn.functional as F

parser = argparse.ArgumentParser()

parser.add_argument('--model', type=str, default='', help='model path')
parser.add_argument('--num_points', type=int, default=2500, help='input batch size')

opt = parser.parse_args()
print(opt)

test_dataset = ShapeNetDataset(
    root='shapenetcore_partanno_segmentation_benchmark_v0',
    split='test',
    classification=True,
    npoints=opt.num_points,
    data_augmentation=False)

testdataloader = torch.utils.data.DataLoader(
    test_dataset, batch_size=32, shuffle=True)

classifier = PointNetCls(k=len(test_dataset.classes))
classifier.cuda()
classifier.load_state_dict(torch.load(opt.model))
classifier.eval()

for i, data in enumerate(testdataloader, 0):
    points, target = data
    points, target = Variable(points), Variable(target[:, 0])
    points = points.transpose(2, 1)
    points, target = points.cuda(), target.cuda()
    pred, _, _ = classifier(points)
    loss = F.nll_loss(pred, target)

    pred_choice = pred.data.max(1)[1]
    correct = pred_choice.eq(target.data).cpu().sum()
    print('i:%d  loss: %f accuracy: %f' % (i, loss.data.item(), correct / float(32)))