vgg.py

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

class VGG19(nn.Module):
    ''' 
    input tensor should range in [0,1]
    '''

    def __init__(self, pool='max'):
        super(VGG19, self).__init__()
        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv3_4 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv4_4 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv5_4 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        if pool == 'max':
            self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
            self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
            self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
            self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
            self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
        elif pool == 'avg':
            self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2)
            self.pool2 = nn.AvgPool2d(kernel_size=2, stride=2)
            self.pool3 = nn.AvgPool2d(kernel_size=2, stride=2)
            self.pool4 = nn.AvgPool2d(kernel_size=2, stride=2)
            self.pool5 = nn.AvgPool2d(kernel_size=2, stride=2)

    def forward(self, x, out_keys, preprocess=True):
        ''' 
        NOTE: input tensor should range in [0,1]
        '''
        out = {}
        if preprocess:
            x = vgg_preprocess(x)
        out['r11'] = F.relu(self.conv1_1(x))
        out['r12'] = F.relu(self.conv1_2(out['r11']))
        out['p1'] = self.pool1(out['r12'])
        out['r21'] = F.relu(self.conv2_1(out['p1']))
        out['r22'] = F.relu(self.conv2_2(out['r21']))
        out['p2'] = self.pool2(out['r22'])
        out['r31'] = F.relu(self.conv3_1(out['p2']))
        out['r32'] = F.relu(self.conv3_2(out['r31']))
        out['r33'] = F.relu(self.conv3_3(out['r32']))
        out['r34'] = F.relu(self.conv3_4(out['r33']))
        out['p3'] = self.pool3(out['r34'])
        out['r41'] = F.relu(self.conv4_1(out['p3']))
        out['r42'] = F.relu(self.conv4_2(out['r41']))
        out['r43'] = F.relu(self.conv4_3(out['r42']))
        out['r44'] = F.relu(self.conv4_4(out['r43']))
        out['p4'] = self.pool4(out['r44'])
        out['r51'] = F.relu(self.conv5_1(out['p4']))
        out['r52'] = F.relu(self.conv5_2(out['r51']))
        out['r53'] = F.relu(self.conv5_3(out['r52']))
        out['r54'] = F.relu(self.conv5_4(out['r53']))
        out['p5'] = self.pool5(out['r54'])
        return [out[key] for key in out_keys]