import torchvision.models as models
from torchsummary import summary
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
vgg = models.vgg19().to(device)
summary(vgg, (3, 224, 224))
#print(vgg)
#print("Total number of paramerters in networks is {} ".format(sum(x.numel() for x in net.parameters())))
import torch
#import torch.optim as optim
import torch.nn as nn
#from network import SeeMotionInDarkNet
from network9 import VedioHigherResolutionNet
import cfg_raw as cfg
from default import _C as cfg2
from torchstat import stat
unet_model = VedioHigherResolutionNet(cfg2)
# n = Network()
stat(unet_model, (3, 400, 600))
