Nets.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Python version: 3.6

import torch
from torch import nn
import torch.nn.functional as F


# construct network
class Basicblock(nn.Module):
    def __init__(self, in_planes, planes, stride=1):
        super(Basicblock, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=in_planes, out_channels=planes, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(planes),
            nn.ReLU()
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels=planes, out_channels=planes, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(planes),
        )

        if stride != 1 or in_planes != planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels=in_planes, out_channels=planes, kernel_size=3, stride=stride, padding=1),
                nn.BatchNorm2d(planes)
            )
        else:
            self.shortcut = nn.Sequential()

    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class ResNetMnist(nn.Module):
    def __init__(self, args):
        super(ResNetMnist, self).__init__()
        self.in_planes = 16
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(16),
            nn.ReLU()
        )
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)

        self.block1 = self._make_layer(Basicblock, 16, 1, stride=1)
        self.block2 = self._make_layer(Basicblock, 32, 1, stride=2)
        self.block3 = self._make_layer(Basicblock, 64, 1, stride=2)
        # self.block4 = self._make_layer(block, 512, num_block[3], stride=2)

        self.outlayer = nn.Linear(64, args.num_classes)

    def _make_layer(self, block, planes, num_block, stride):
        layers = []
        for i in range(num_block):
            if i == 0:
                layers.append(block(self.in_planes, planes, stride))
            else:
                layers.append(block(planes, planes, 1))
        self.in_planes = planes
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.maxpool(self.conv1(x))
        x = self.block1(x)                       # [200, 64, 28, 28]
        x = self.block2(x)                       # [200, 128, 14, 14]
        x = self.block3(x)                       # [200, 256, 7, 7]
        # out = self.block4(out)
        x = F.avg_pool2d(x, 7)                   # [200, 256, 1, 1]
        x = x.view(x.size(0), -1)                # [200,256]
        out = self.outlayer(x)
        return out

class MLP(nn.Module):
    def __init__(self, dim_in, dim_hidden, dim_out):
        super(MLP, self).__init__()
        self.layer_input = nn.Linear(dim_in, dim_hidden)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout()
        self.layer_hidden = nn.Linear(dim_hidden, dim_out)

    def forward(self, x):
        x = x.view(-1, x.shape[1]*x.shape[-2]*x.shape[-1])
        x = self.layer_input(x)
        x = self.dropout(x)
        x = self.relu(x)
        x = self.layer_hidden(x)
        return x


class CNNMnist(nn.Module):
    def __init__(self, args):
        super(CNNMnist, self).__init__()
        self.conv1 = nn.Conv2d(args.num_channels, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, args.num_classes)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3])
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return x


class CNNCifar(nn.Module):
    def __init__(self, args):
        super(CNNCifar, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, args.num_classes)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x