mirror of https://github.com/kritiksoman/GIMP-ML
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
### 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.cpu().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))
|
|
try:
|
|
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
|