mc.py

import torch
from torch import nn


class MCNet(nn.Module):
    def __init__(self, input_size=224, in_channels=3, n_classes=3):
        super(MCNet, self).__init__()
        fc_in_channels = (input_size // 32) * (input_size // 32) * 64
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, 64, kernel_size=3, stride=2, padding=1, bias=True),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=True),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=True),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
            )
        self.fc = nn.Sequential(
            nn.Linear(fc_in_channels, 512),
            nn.BatchNorm1d(512),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.Linear(512, n_classes),
            # nn.Softmax(dim=1)
            )
        self._init_weight()

    def forward(self, x):
        x = self.conv(x)
        x = torch.flatten(x, 1)
        out = self.fc(x)
        return out

    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.xavier_normal_(m.weight)
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)