# 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 os

import sys

import torch

import numpy as np

import argparse

from tqdm import tqdm

import models.provider

from models.ModelNetDataLoader import ModelNetDataLoader

import models.pointnet2_cls_ssg as models



def inplace_relu(m):

    classname = m.__class__.__name__

    if classname.find('ReLU') != -1:

        m.inplace=True

        

def parse_args():

    '''PARAMETERS'''

    parser = argparse.ArgumentParser('training')

    parser.add_argument('--batch_size', type=int, default=24, help='batch size in training')

    parser.add_argument('--num_category', default=40, type=int, choices=[10, 40],  help='training on ModelNet10/40')

    parser.add_argument('--epoch', default=2, type=int, help='number of epoch in training')

    parser.add_argument('--num_points', type=int, default=1024, help='Point Number')

    parser.add_argument('--use_normals', action='store_true', default=False, help='use normals')

    parser.add_argument('--process_data', action='store_true', default=True, help='save data offline')

    parser.add_argument('--use_uniform_sample', action='store_true', default=False, help='use uniform sampiling')

    parser.add_argument('--device', type=str,default='cpu',help='which device to use')

    parser.add_argument('--data',type=str, default='./modelnet40_normal_resampled', help='data_path')

    parser.add_argument('--num_class',type=int,default=41,help='num of class')

    parser.add_argument('--num_point', type=int, default=1024, help='Point Number')

    parser.add_argument('--model_pth', type=str, default='./log/classification/pointnet2_cls_ssg/checkpoints/', help='Point Number')

    parser.add_argument('--worker', type=int, default=1, help='number ofs workers')



    return parser.parse_args()



def main(args):

    test_dataset = ModelNetDataLoader(root=args.data, args=args, split='test', process_data=args.process_data)

    testDataLoader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.worker)

    model_dict = torch.load(str(args.model_pth)+'best_model.pth', map_location='cpu')['model_state_dict']

    model_dict.pop('fc3.weight')

    model_dict.pop('fc3.bias')

    classifier = models.get_model(args.num_class, normal_channel=args.use_normals)

    classifier.apply(inplace_relu)

    if args.device !='cpu':

        classifier = classifier.npu()

    classifier.load_state_dict(model_dict, strict=False)



    for epoch in range(args.epoch):

        for batch_id,(points, target) in tqdm(enumerate(testDataLoader, 0), total=len(testDataLoader)):

            points = points.transpose(2, 1)

            if args.device !='cpu':

                points, target = points.npu(), target.npu()

            pred, trans_feat = classifier(points)

            pred_choice = pred.data.max(1)[1]

            print("output class is",pred_choice)



if __name__ == '__main__':

    args = parse_args()

    main(args)