# -*- coding: utf-8 -*- 
# 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 torch
import torch.nn as nn

class DnCNN(nn.Module):
    """ DnCnn class """
    def __init__(self, channels, num_of_layers=17):
        super(DnCNN, self).__init__()
        kernel_size = 3
        padding = 1
        features = 64
        layers = []
        layers.append(nn.Conv2d(in_channels=channels, out_channels=features, \
                    kernel_size=kernel_size, padding=padding, bias=False))
        layers.append(nn.ReLU(inplace=True))
        for _ in range(num_of_layers - 2):
            layers.append(nn.Conv2d(in_channels=features, out_channels=features, \
                    kernel_size=kernel_size, padding=padding, bias=False))
            layers.append(nn.BatchNorm2d(features))
            layers.append(nn.ReLU(inplace=True))
        layers.append(nn.Conv2d(in_channels=features, out_channels=channels, \
                    kernel_size=kernel_size, padding=padding, bias=False))
        self.dncnn = nn.Sequential(*layers)
        
    def forward(self, x):
        """ forward train """
        out = self.dncnn(x)
        return out