Spaces:
Running
on
T4
Running
on
T4
File size: 1,618 Bytes
ac4ce84 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
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
# helper saving function that can be used by subclasses
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)
# helper loading function that can be used by subclasses
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')
|