| 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) |
|
|
| |
| 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) |
|
|
