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| # Copyright (C) 2017 NVIDIA Corporation. All rights reserved. | |
| # Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). | |
| import os | |
| import torch | |
| import sys | |
| class BaseModel(torch.nn.Module): | |
| def name(self): | |
| return 'BaseModel' | |
| def initialize(self, opt): | |
| self.opt = opt | |
| self.gpu_ids = opt.gpu_ids | |
| self.isTrain = opt.isTrain | |
| self.Tensor = torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor | |
| self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) | |
| def set_input(self, input): | |
| self.input = input | |
| def forward(self): | |
| pass | |
| # used in test time, no backprop | |
| def test(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 | |
| # helper saving function that can be used by subclasses | |
| def save_network(self, network, network_label, epoch_label, gpu_ids): | |
| save_filename = '%s_net_%s.pth' % (epoch_label, network_label) | |
| save_path = os.path.join(self.save_dir, save_filename) | |
| torch.save(network.state_dict(), save_path) | |
| # if len(gpu_ids) and torch.cuda.is_available(): | |
| # network.cuda() | |
| # helper loading function that can be used by subclasses | |
| def load_network(self, network, network_label, epoch_label, save_dir=''): | |
| save_filename = '%s_net_%s.pth' % (epoch_label, network_label) | |
| print(save_filename) | |
| if not save_dir: | |
| save_dir = self.save_dir | |
| save_path = os.path.join(save_dir, save_filename) | |
| if not os.path.isfile(save_path): | |
| print('%s not exists yet!' % save_path) | |
| if network_label == 'G': | |
| raise('Generator must exist!') | |
| else: | |
| # network.load_state_dict(torch.load(save_path)) | |
| network.load_state_dict(torch.load(save_path)) | |
| # except: | |
| # pretrained_dict = torch.load(save_path) | |
| # model_dict = network.state_dict() | |
| # try: | |
| # pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict} | |
| # network.load_state_dict(pretrained_dict) | |
| # if self.opt.verbose: | |
| # print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label) | |
| # except: | |
| # print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label) | |
| # for k, v in pretrained_dict.items(): | |
| # if v.size() == model_dict[k].size(): | |
| # model_dict[k] = v | |
| # | |
| # if sys.version_info >= (3,0): | |
| # not_initialized = set() | |
| # else: | |
| # from sets import Set | |
| # not_initialized = Set() | |
| # | |
| # for k, v in model_dict.items(): | |
| # if k not in pretrained_dict or v.size() != pretrained_dict[k].size(): | |
| # not_initialized.add(k.split('.')[0]) | |
| # | |
| # print(sorted(not_initialized)) | |
| # network.load_state_dict(model_dict) | |
| def update_learning_rate(): | |
| pass | |