# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
#
# 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.
#
# **************************************************************************
# Modified from github (https://github.com/WenmuZhou/DBNet.pytorch)
# Copyright (c) WenmuZhou
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# https://github.com/WenmuZhou/DBNet.pytorch/blob/master/LICENSE.md
# **************************************************************************
"""Basic module."""
from torch import nn


class ConvBnRelu(nn.Module):
    """ConvBnRelu class."""

    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', inplace=True):
        """Initialize."""
        super().__init__()
        self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation,
                              groups=groups, bias=bias, padding_mode=padding_mode)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=inplace)

    def forward(self, x):
        """Forward."""
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x
