SCAR的pytorch实现

本文所实现的网络来源于SCAR:Spatial-/Channel-wise Attention Regression Networks for Crowd Counting（Neurocompting 2019）

import torch;from torchvision import models
from torchvision.models import vgg16
import warnings;from torch import nn
warnings.filterwarnings("ignore")
vgg16 = vgg16(pretrained=True)
def initialize_weights(models):
    for model in models:
        real_init_weights(model)
import warnings
warnings.filterwarnings("ignore")
def real_init_weights(m):

    if isinstance(m, list):
        for mini_m in m:
            real_init_weights(mini_m)
    else:
        if isinstance(m, nn.Conv2d):
            nn.init.normal_(m.weight, std=0.01)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.Linear):
            m.weight.data.normal_(0.0, std=0.01)
        elif isinstance(m, nn.BatchNorm2d):
            nn.init.constant_(m.weight, 1)
            nn.init.constant_(m.bias, 0)
        elif isinstance(m,nn.Module):
            for mini_m in m.children():
                real_init_weights(mini_m)
        else:
            print( m )
class SCAR(torch.nn.Module):
    def __init__(self,loadwieght=False):
        super(SCAR,self).__init__()
        self.vgg10=vgg10
        if loadwieght==False:
            mod = models.vgg16(pretrained=True)
            initialize_weights(self.modules())
            self.vgg10.load_state_dict(mod.features[0:23].state_dict(