text: import torch.nn as nn
  from thop import profile
  from models import squeezenet, shufflenetv2, shufflenet, mobilenet, mobilenetv2, c3d, resnext, resnet
  
  # %%%%%%%%--------------------- SELECT THE MODEL BELOW ---------------------%%%%%%%%
  
  # model = shufflenet.get_model(groups=3, width_mult=0.5, num_classes=600)#1
  # model = shufflenetv2.get_model( width_mult=0.25, num_classes=600, sample_size = 112)#2
  # model = mobilenet.get_model( width_mult=0.5, num_classes=600, sample_size = 112)#3
  # model = mobilenetv2.get_model( width_mult=0.2, num_classes=600, sample_size = 112)#4
  # model = shufflenet.get_model(groups=3, width_mult=1.0, num_classes=600)#5
  # model = shufflenetv2.get_model( width_mult=1.0, num_classes=9, sample_size = 112)#6
  # model = mobilenet.get_model( width_mult=1.0, num_classes=600, sample_size = 112)#7
  # model = mobilenetv2.get_model( width_mult=0.45, num_classes=600, sample_size = 112)#8
  # model = shufflenet.get_model(groups=3, width_mult=1.5, num_classes=600)#9
  # model = shufflenetv2.get_model( width_mult=1.5, num_classes=600, sample_size = 112)#10
  # model = mobilenet.get_model( width_mult=1.5, num_classes=600, sample_size = 112)#11
  # model = mobilenetv2.get_model( width_mult=0.7, num_classes=600, sample_size = 112)#12
  # model = shufflenet.get_model(groups=3, width_mult=2.0, num_classes=600)#13
  # model = shufflenetv2.get_model( width_mult=2.0, num_classes=600, sample_size = 112)#14
  # model = mobilenet.get_model( width_mult=2.0, num_classes=600, sample_size = 112)#15
  # model = mobilenetv2.get_model( width_mult=1.0, num_classes=9, sample_size = 112)#16
  # model = squeezenet.get_model( version=1.1, num_classes=9, sample_size = 112, sample_duration = 16)
  model = resnext.resnext101( num_classes=9, shortcut_type='B', cardinality=32, sample_size=112, sample_duration=16)
  # model = resnet.resnet18( num_classes=9, shortcut_type='A', sample_size=112, sample_duration=16)
  # model = resnet.resnet50( num_classes=9, shortcut_type='B', sample_size=112, sample_duration=16)
  # model = resnet.resnet101( num_classes=9, shortcut_type='B', sample_size=112, sample_duration=16)
  # model = c3d.get_model( num_classes=9, sample_size=112, sample_duration=16)
  model = model.cuda()
  model = nn.DataParallel(model, device_ids=None)	
  print(model)
  
  pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
  print("Total number of trainable parameters: ", pytorch_total_params)
  
  flops, prms = profile(model, input_size=(1, 3, 16, 112, 112))
  print("Total number of FLOPs: ", flops)
  