# 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 argparse



import torch

import sys

import torch.nn as nn

import torch.optim as optim

import apex.amp as amp

import apex

import logging

from torch.nn.parallel import DistributedDataParallel as DDP

from torch.utils.data.distributed import DistributedSampler

import torch.distributed as dist

import time

from torch.autograd import Variable

from torch.utils.data import DataLoader



from torch.nn.modules.loss import _Loss 

from models import ADNet

from dataset import prepare_data, Dataset

from utils import *





parser = argparse.ArgumentParser(description="DnCNN")

parser.add_argument("--preprocess", type=bool, default=False, help='run prepare_data or not')

parser.add_argument("--batchSize", type=int, default=128, help="Training batch size")

parser.add_argument("--num_of_layers", type=int, default=17, help="Number of total layers")

parser.add_argument("--epochs", type=int, default=70, help="Number of training epochs")

parser.add_argument("--milestone", type=int, default=30, help="When to decay learning rate; should be less than epochs")

parser.add_argument("--lr", type=float, default=1e-3, help="Initial learning rate")

parser.add_argument("--outf", type=str, default="logs", help='path of log files')

parser.add_argument("--mode", type=str, default="S", help='with known noise level (S) or blind training (B)')

parser.add_argument("--noiseL", type=float, default=15, help='noise level; ignored when mode=B')

parser.add_argument("--val_noiseL", type=float, default=15, help='noise level used on validation set')

parser.add_argument("--is_distributed", type=int, default=0, help='choose ddp or not')

parser.add_argument('--world_size', default=-1, type=int, help='number of nodes for distributed training')

parser.add_argument('--local_rank', type=int, default=0)

parser.add_argument('--DeviceID', type=str, default="0")

parser.add_argument("--num_gpus", default=1, type=int)

'''

parser.add_argument("--clip",type=float,default=0.005,help='Clipping Gradients. Default=0.4') #tcw201809131446tcw

parser.add_argument("--momentum",default=0.9,type='float',help = 'Momentum, Default:0.9') #tcw201809131447tcw

parser.add_argument("--weight-decay","-wd",default=1e-3,type=float,help='Weight decay, Default:1e-4') #tcw20180913347tcw

'''

opt = parser.parse_args()

if __name__ == "__main__":

    if opt.preprocess:

        if opt.mode == 'S':

            prepare_data(data_path='data', patch_size=50, stride=40, aug_times=1) 

        if opt.mode == 'B':

            prepare_data(data_path='data', patch_size=50, stride=10, aug_times=2)