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100 lines
3.7 KiB
100 lines
3.7 KiB
### Copyright (C) 2020 Roy Or-El. All rights reserved.
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### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
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import os
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import torch
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import torch.nn as nn
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import sys
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class BaseModel(torch.nn.Module):
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def name(self):
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return 'BaseModel'
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def initialize(self, opt):
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self.opt = opt
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self.gpu_ids = opt.gpu_ids
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self.isTrain = opt.isTrain
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self.Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.Tensor
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self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)
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def set_input(self, input):
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self.input = input
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def forward(self):
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pass
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# used in test time, no backprop
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def test(self):
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pass
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def get_image_paths(self):
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pass
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def optimize_parameters(self):
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pass
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def get_current_visuals(self):
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return self.input
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def get_current_errors(self):
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return {}
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def save(self, label):
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pass
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# helper saving function that can be used by subclasses
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def save_network(self, network, network_label, epoch_label, gpu_ids):
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save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
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save_path = os.path.join(self.save_dir, save_filename)
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if isinstance(network,nn.DataParallel):
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torch.save(network.module.state_dict(), save_path)
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else:
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torch.save(network.state_dict(), save_path)
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# helper loading function that can be used by subclasses
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def load_network(self, network, network_label, epoch_label, save_dir=''):
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save_filename = '%s_net_%s.pth' % (epoch_label, network_label)
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if not save_dir:
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save_dir = self.save_dir
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save_path = os.path.join(save_dir, save_filename)
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if not os.path.isfile(save_path):
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print('%s not exists yet!' % save_path)
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if 'G' in network_label:
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raise('Generator must exist!')
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else:
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try:
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if isinstance(network,nn.DataParallel):
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network.module.load_state_dict(torch.load(save_path))
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else:
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network.load_state_dict(torch.load(save_path))
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except:
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pretrained_dict = torch.load(save_path)
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if isinstance(network,nn.DataParallel):
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model_dict = network.module.state_dict()
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else:
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model_dict = network.state_dict()
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try:
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pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
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network.load_state_dict(pretrained_dict)
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if self.opt.verbose:
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print('Pretrained network %s has excessive layers; Only loading layers that are used' % network_label)
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except:
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print('Pretrained network %s has fewer layers; The following are not initialized:' % network_label)
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if sys.version_info >= (3,0):
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not_initialized = set()
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else:
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from sets import Set
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not_initialized = Set()
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for k, v in pretrained_dict.items():
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if v.size() == model_dict[k].size():
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model_dict[k] = v
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for k, v in model_dict.items():
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if k not in pretrained_dict or v.size() != pretrained_dict[k].size():
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not_initialized.add(k.split('.')[0])
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if self.opt.verbose:
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print(sorted(not_initialized))
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network.load_state_dict(model_dict)
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def update_learning_rate():
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pass
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