import argparse
import torch
from models import vgg, resnet, densenet
parser = argparse.ArgumentParser(description='Tiny Imagenet Classification')
parser.add_argument('--model', choices=['vgg', 'resnet', 'densenet'],
    default='vgg')
args = parser.parse_args()
if args.model == 'vgg':
    model = vgg.VGG('VGG16')
elif args.model == 'resnet':
    model = resnet.ResNet18()
elif args.model == 'densenet':
    model = densenet.densenet_timgnet()
a = torch.rand(10, 3, 64, 64)
b = model.forward(a)
print(b.size())

e6c8f8229dcf4e221ddc470f862021589faed0c5
<mask token>

<mask token>
parser.add_argument('--model', choices=['vgg', 'resnet', 'densenet'],
    default='vgg')
<mask token>
if args.model == 'vgg':
    model = vgg.VGG('VGG16')
elif args.model == 'resnet':
    model = resnet.ResNet18()
elif args.model == 'densenet':
    model = densenet.densenet_timgnet()
<mask token>
print(b.size())

<mask token>
parser = argparse.ArgumentParser(description='Tiny Imagenet Classification')
parser.add_argument('--model', choices=['vgg', 'resnet', 'densenet'],
    default='vgg')
args = parser.parse_args()
if args.model == 'vgg':
    model = vgg.VGG('VGG16')
elif args.model == 'resnet':
    model = resnet.ResNet18()
elif args.model == 'densenet':
    model = densenet.densenet_timgnet()
a = torch.rand(10, 3, 64, 64)
b = model.forward(a)
print(b.size())

import argparse
import torch
from models import vgg, resnet, densenet
parser = argparse.ArgumentParser(description='Tiny Imagenet Classification')
parser.add_argument('--model', choices=['vgg', 'resnet', 'densenet'],
    default='vgg')
args = parser.parse_args()
if args.model == 'vgg':
    model = vgg.VGG('VGG16')
elif args.model == 'resnet':
    model = resnet.ResNet18()
elif args.model == 'densenet':
    model = densenet.densenet_timgnet()
a = torch.rand(10, 3, 64, 64)
b = model.forward(a)
print(b.size())
