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__init__.py
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__init__.py
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import torch
import torch.nn as nn
from torch.nn import Sequential, Linear, ReLU, BatchNorm1d
import os
MODEL_PATH = "models"
CHECKPOINT_PATH = os.path.join(MODEL_PATH, 'checkpoints')
LATENT_CODES_FILENAME = os.path.join(MODEL_PATH, "sdf_net_latent_codes.to")
LATENT_CODE_SIZE = 128
class Lambda(nn.Module):
def __init__(self, function):
super(Lambda, self).__init__()
self.function = function
def forward(self, x):
return self.function(x)
class SavableModule(nn.Module):
def __init__(self, filename):
super(SavableModule, self).__init__()
self.filename = filename
def get_filename(self, epoch=None, filename=None):
if filename is None:
filename = self.filename
if epoch is None:
return os.path.join(MODEL_PATH, filename)
else:
filename = filename.split('.')
filename[-2] += '-epoch-{:05d}'.format(epoch)
filename = '.'.join(filename)
return os.path.join(CHECKPOINT_PATH, filename)
def load(self, epoch=None):
self.load_state_dict(torch.load(self.get_filename(epoch=epoch)), strict=False)
def save(self, epoch=None):
if epoch is not None and not os.path.exists(CHECKPOINT_PATH):
os.mkdir(CHECKPOINT_PATH)
torch.save(self.state_dict(), self.get_filename(epoch=epoch))
@property
def device(self):
return next(self.parameters()).device