# -*- coding: utf-8 -*-

"""

@File    : trian_res34.py

@Time    : 2019/6/23 15:40

@Author  : Parker

@Email   : now_cherish@163.com

@Software: PyCharm

@Des     : 

"""

import torch

import torch.nn as nn

import torch.nn.functional as F

from torch.utils.data import DataLoader

import torch.optim as optim

from tensorboardX import SummaryWriter

import numpy as np

import time

import datetime

import argparse

import os

import os.path as osp

from rs_dataset import RSDataset_test

from get_logger import get_logger

from res_network import Resnet18, Resnet34, Resnet101

from tqdm import tqdm

def parse_args():

    parse = argparse.ArgumentParser()

    parse.add_argument('--epoch', type=int, default=15)

    parse.add_argument('--schedule_step', type=int, default=2)

    parse.add_argument('--test_batch_size', type=int, default=128)

    parse.add_argument('--num_workers', type=int, default=16)

    parse.add_argument('--eval_fre', type=int, default=1)

    parse.add_argument('--msg_fre', type=int, default=10)

    parse.add_argument('--save_fre', type=int, default=2)

    parse.add_argument('--name', type=str, default='res34_baseline',

                       help='unique out file name of this task include log/model_out/tensorboard log')

    parse.add_argument('--data_dir', type=str, default='/media/tiger/zzr/rsna')

    parse.add_argument('--log_dir', type=str, default='./logs')

    parse.add_argument('--tensorboard_dir', type=str, default='./tensorboard')

    parse.add_argument('--model_out_dir', type=str, default='./model_out')

    parse.add_argument('--model_out_name', type=str, default='final_model.pth')

    parse.add_argument('--seed', type=int, default=5, help='random seed')

    parse.add_argument('--eval_model_path', type=str, default='/media/tiger/zzr/rsna_script/model_out/191005-002929_temp/out_9.pth')

    return parse.parse_args()

def predict(args):

    test_set = RSDataset_test(rootpth=args.data_dir, mode='test')

    test_loader = DataLoader(test_set,

                            batch_size=args.test_batch_size,

                            drop_last=False,

                            shuffle=False,

                            pin_memory=True,

                            num_workers=args.num_workers)

    net = Resnet18()

    net.eval()

    net.load_state_dict(torch.load(args.eval_model_path))

    net.cuda()

    labels = ["epidural", "intraparenchymal", "intraventricular",

              "subarachnoid", "subdural", "any"]

    with open("result.csv", 'w') as fp:

        fp.write('ID,Label\n')

        with torch.no_grad():

            for img, name in tqdm(test_loader):

                img = img.cuda()

                outputs = net(img).cpu().numpy()

                for idx, i in enumerate(name):

                    for idxj, j in enumerate(labels):

                        fp.write(i + '_' + j + ',' + str(outputs[idx][idxj]) + '\n')

if __name__ == '__main__':

    args = parse_args()

    torch.manual_seed(args.seed)

    torch.cuda.manual_seed(args.seed)

    predict(args)