model.py

# -*- coding: utf-8 -*-
# model.py

# Copyright (c) 2018, Eric Liang Yang @chiyangliang@gmail.com
# Produced at the Robotics Laboratory of the City College of New York
# All rights reserved.
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#   notice, this list of conditions and the following disclaimer.
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## please switch to any model you prefer
import torch
from torch import nn

#from models import resnet as resnet
from models import resnet as resnet
#from models import vgg as resnet
#from models.alexnet import *

def generate_model(opt):

    model = resnet.resnet34(pretrained = True, num_classes=opt.n_classes)


    if not opt.no_cuda:
        model = model.cuda()
        model = nn.DataParallel(model, device_ids=None)

        if opt.pretrain_path:
            print('loading pretrained model {}'.format(opt.pretrain_path))
            pretrain = torch.load(opt.pretrain_path)
            assert opt.arch == pretrain['arch']

            model.load_state_dict(pretrain['state_dict'])

            return model, model.parameters()
    else:
        if opt.pretrain_path:
            print('loading pretrained model {}'.format(opt.pretrain_path))
            pretrain = torch.load(opt.pretrain_path)
            assert opt.arch == pretrain['arch']

            model.load_state_dict(pretrain['state_dict'])


            return model, model.parameters()

    return model, model.parameters()