ai_modules/models/modules.py

import torch
from torch import nn


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, bn_momentum=0.1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=bn_momentum)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


# class Bottleneck_Tranpose(nn.Module):
#     expansion = 4

#     def __init__(self, inplanes, planes, stride=1, downsample=None, bn_momentum=0.1):
#         super(Bottleneck, self).__init__()
#         nn.ConvTranspose2d(c, 64, (3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)),
        
#         self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
#         self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)
#         self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
#         self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)
#         self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
#         self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=bn_momentum)
#         self.relu = nn.ReLU(inplace=True)
#         self.downsample = downsample
#         self.stride = stride

#     def forward(self, x):
#         residual = x

#         out = self.conv1(x)
#         out = self.bn1(out)
#         out = self.relu(out)

#         out = self.conv2(out)
#         out = self.bn2(out)
#         out = self.relu(out)

#         out = self.conv3(out)
#         out = self.bn3(out)

#         if self.downsample is not None:
#             residual = self.downsample(x)

#         out += residual
#         out = self.relu(out)

#         return out
    
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, bn_momentum=0.1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out