| import os |
| import numpy as np |
| import torch |
| from torch.autograd import Variable |
| from pdb import set_trace as st |
| from IPython import embed |
|
|
| class BaseModel(): |
| def __init__(self): |
| pass; |
| |
| def name(self): |
| return 'BaseModel' |
|
|
| def initialize(self, use_gpu=True, gpu_ids=[0]): |
| self.use_gpu = use_gpu |
| self.gpu_ids = gpu_ids |
|
|
| def forward(self): |
| pass |
|
|
| def get_image_paths(self): |
| pass |
|
|
| def optimize_parameters(self): |
| pass |
|
|
| def get_current_visuals(self): |
| return self.input |
|
|
| def get_current_errors(self): |
| return {} |
|
|
| def save(self, label): |
| pass |
|
|
| |
| def save_network(self, network, path, network_label, epoch_label): |
| save_filename = '%s_net_%s.pth' % (epoch_label, network_label) |
| save_path = os.path.join(path, save_filename) |
| torch.save(network.state_dict(), save_path) |
|
|
| |
| def load_network(self, network, network_label, epoch_label): |
| save_filename = '%s_net_%s.pth' % (epoch_label, network_label) |
| save_path = os.path.join(self.save_dir, save_filename) |
| print('Loading network from %s'%save_path) |
| network.load_state_dict(torch.load(save_path)) |
|
|
| def update_learning_rate(): |
| pass |
|
|
| def get_image_paths(self): |
| return self.image_paths |
|
|
| def save_done(self, flag=False): |
| np.save(os.path.join(self.save_dir, 'done_flag'),flag) |
| np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i') |
|
|
