bilinear_model.py

import torch
import torchvision
import torch.optim as optim
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F

# vgg16 = torchvision.models.vgg16(pretrained=True)
# import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "2"



class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            # nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0),


            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
            # nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0),



            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            # nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0),


            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            # nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2, padding=0),

            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),


            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),

            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            # nn.MaxPool2d(kernel_size=2, stride=2, padding=0),

        )

        self.classifiers = nn.Sequential(
            nn.Linear(512 ** 2, 200),
        )

    def forward(self, x):
        x = self.features(x)
        batch_size = x.size(0)
        x = x.view(batch_size, 512, 28 ** 2)

        x = (torch.bmm(x, torch.transpose(x, 1, 2)) / 28 ** 2).view(batch_size, -1)

        x = torch.nn.functional.normalize(torch.sign(x) * torch.sqrt(torch.abs(x) + 1e-10))

        # feature = feature.view(feature.size(0), -1)
        x = self.classifiers(x)
        return x