cnn_with_spp.py

import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
from spp_layer import spatial_pyramid_pool
class SPP_NET(nn.Module):
    '''
    A CNN model which adds spp layer so that we can input multi-size tensor
    '''
    def __init__(self, opt, input_nc, ndf=64,  gpu_ids=[]):
        super(SPP_NET, self).__init__()
        self.gpu_ids = gpu_ids
        self.output_num = [4,2,1]
        
        self.conv1 = nn.Conv2d(input_nc, ndf, 4, 2, 1, bias=False)
        
        self.conv2 = nn.Conv2d(ndf, ndf * 2, 4, 1, 1, bias=False)
        self.BN1 = nn.BatchNorm2d(ndf * 2)

        self.conv3 = nn.Conv2d(ndf * 2, ndf * 4, 4, 1, 1, bias=False)
        self.BN2 = nn.BatchNorm2d(ndf * 4)

        self.conv4 = nn.Conv2d(ndf * 4, ndf * 8, 4, 1, 1, bias=False)
        self.BN3 = nn.BatchNorm2d(ndf * 8)

        self.conv5 = nn.Conv2d(ndf * 8, 64, 4, 1, 0, bias=False)
        self.fc1 = nn.Linear(10752,4096)
        self.fc2 = nn.Linear(4096,1000)

    def forward(self,x):
        x = self.conv1(x)
        x = self.LReLU1(x)

        x = self.conv2(x)
        x = F.leaky_relu(self.BN1(x))

        x = self.conv3(x)
        x = F.leaky_relu(self.BN2(x))
        
        x = self.conv4(x)
        # x = F.leaky_relu(self.BN3(x))
        # x = self.conv5(x)
        spp = spatial_pyramid_pool(x,1,[int(x.size(2)),int(x.size(3))],self.output_num)
        # print(spp.size())
        fc1 = self.fc1(spp)
        fc2 = self.fc2(fc1)
        s = nn.Sigmoid()
        output = s(fc2)
        return output