aDPtorch/src/models.py

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

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


# 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)
#         self.softmax = nn.Softmax(dim=1)

#     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 self.softmax(x)

# Changed MLP model to 2 hidden layers with 200 units
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_hidden1 = nn.Linear(dim_hidden, dim_hidden)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout()
        self.layer_hidden2 = nn.Linear(dim_hidden, dim_out)
        self.softmax = nn.Softmax(dim=1)

    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_hidden1(x)
        x = self.dropout(x)
        x = self.relu(x)
        x = self.layer_hidden2(x)
        return self.softmax(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 F.log_softmax(x, dim=1)


class CNNFashion_Mnist(nn.Module):
    def __init__(self, args):
        super(CNNFashion_Mnist, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, padding=2),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, padding=2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(2))
        self.fc = nn.Linear(7*7*32, 10)

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out


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 F.log_softmax(x, dim=1)