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import os
import torch
import torch.nn as nn
from torch.nn import init
from torch.optim import lr_scheduler
class BaseModel(nn.Module):
def __init__(self, opt):
super(BaseModel, self).__init__()
self.opt = opt
self.total_steps = 0
self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
self.device = torch.device('cuda:{}'.format(opt.gpu_ids[0])) if opt.gpu_ids else torch.device('cpu')
def save_networks(self, save_filename):
save_path = os.path.join(self.save_dir, save_filename)
# serialize model and optimizer to dict
state_dict = {
'model': self.model.state_dict(),
'optimizer' : self.optimizer.state_dict(),
'total_steps' : self.total_steps,
}
torch.save(state_dict, save_path)
def eval(self):
self.model.eval()
def test(self):
with torch.no_grad():
self.forward()
def init_weights(net, init_type='normal', gain=0.02):
def init_func(m):
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
init.normal_(m.weight.data, 0.0, gain)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=gain)
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1:
init.normal_(m.weight.data, 1.0, gain)
init.constant_(m.bias.data, 0.0)
print('initialize network with %s' % init_type)
net.apply(init_func)
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