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import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
__all__ = ['vgg19']
model_urls = {
'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
}
class VGG(nn.Module):
def __init__(self, features):
super(VGG, self).__init__()
self.features = features
self.reg_layer = nn.Sequential(
nn.Conv2d(512, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
)
self.density_layer = nn.Sequential(nn.Conv2d(128, 1, 1), nn.ReLU())
def forward(self, x):
x = self.features(x)
x = F.upsample_bilinear(x, scale_factor=2)
x = self.reg_layer(x)
mu = self.density_layer(x)
B, C, H, W = mu.size()
mu_sum = mu.view([B, -1]).sum(1).unsqueeze(1).unsqueeze(2).unsqueeze(3)
mu_normed = mu / (mu_sum + 1e-6)
return mu, mu_normed
def make_layers(cfg, batch_norm=False):
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
cfg = {
'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512]
}
def vgg19():
"""VGG 19-layer model (configuration "E")
model pre-trained on ImageNet
"""
model = VGG(make_layers(cfg['E']))
model.load_state_dict(model_zoo.load_url(model_urls['vgg19']), strict=False)
return model
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