<reponame>vfdev-5/segmentation_models.pytorch<filename>segmentation_models_pytorch/common/blocks.py
import torch.nn as nn


class Conv2dReLU(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, padding=0,
                 stride=1, use_batchnorm=True, **batchnorm_params):

        super().__init__()

        layers = [
            nn.Conv2d(in_channels, out_channels, kernel_size,
                              stride=stride, padding=padding, bias=not (use_batchnorm)),
            nn.ReLU(inplace=True),
        ]

        if use_batchnorm:
            layers.insert(1, nn.BatchNorm2d(out_channels, **batchnorm_params))

        self.block = nn.Sequential(*layers)

    def forward(self, x):
        return self.block(x)

import torch.nn as nn


class Conv2dReLU(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, padding=0,
                 stride=1, use_batchnorm=True, **batchnorm_params):

        super().__init__()

        layers = [
            nn.Conv2d(in_channels, out_channels, kernel_size,
                              stride=stride, padding=padding, bias=not (use_batchnorm)),
            nn.ReLU(inplace=True),
        ]

        if use_batchnorm:
            layers.insert(1, nn.BatchNorm2d(out_channels, **batchnorm_params))

        self.block = nn.Sequential(*layers)

    def forward(self, x):
        return self.block(x)