def loadModels(self, ):
try:
generator_name = util.fcnt_load(self.sheckpoints_dir,
"pix2pix_generator", "pth")
print(generator_name)
self.generator = torch.load(
generator_name,
map_location=lambda storage, loc: storage.cuda(
convertDevice(self.gpu_ids[0])))
self.generator.setSetting(self.setting)
except:
print("Checkpoint directory or files could not be found." +
"New directory {} will be created.".format(
self.sheckpoints_dir))
import traceback
traceback.print_exc()
return False
if self.setting['data']['base']['isTrain']:
try:
discriminator_name = util.fcnt_load(self.sheckpoints_dir,
"pix2pix_discriminator",
"pth")
print(discriminator_name)
self.discriminator = torch.load(discriminator_name).to(
self.gpu_ids[0])
self.discriminator.setSetting(self.setting)
except:
print("Checkpoint directory or files could not be found." +
"New directory {} will be created.".format(
self.sheckpoints_dir))
import traceback
traceback.print_exc()
return False
return True
def loadModels(self, ):
import aimaker.models.model_factory as mf
model_factory = mf.ModelFactory(self.setting)
generator_name = self.setting['controllers']['pix2pix'][
'generatorModel']
discriminator_name = self.setting['controllers']['pix2pix'][
'discriminatorModel']
try:
generator_path = util.fcnt_load(self.sheckpoints_dir,
"pix2pix_generator", "pth")
self.generator = self._loadModel(generator_path, self.gpu_ids[0])
except:
import traceback
traceback.print_exc()
self.generator = model_factory.create(generator_name).to(
self.gpu_ids[0])
try:
discriminator_path = util.fcnt_load(self.sheckpoints_dir,
"pix2pix_discriminator", "pth")
self.discriminator = self._loadModel(discriminator_path,
self.gpu_ids[0])
except:
import traceback
traceback.print_exc()
self.discriminator = model_factory.create(discriminator_name).to(
self.gpu_ids[0])
return True
def loadModels(self,):
try:
self.generator = torch.load(util.fcnt_load(self.checkpoints_dir, "starGAN_generator", "pth")).cuda(self.gpu_ids[0])
self.discriminator = torch.load(util.fcnt_load(self.checkpoints_dir, "starGAN_discriminator", "pth")).cuda(self.gpu_ids[0])
self.generator.setConfig(self.config)
self.discriminator.setConfig(self.config)
return True
except:
print("Checkpoint directory or files could not be found."+
"New directory {} will be created.".format(self.checkpoints_dir))
return False
def loadModels(self, ):
try:
generator_model = util.fcnt_load(self.checkpoints_dir,
"voxel_flow_generator", "pth")
discriminator_model = util.fcnt_load(self.checkpoints_dir,
"voxel_flow_discriminator",
"pth")
self.generator = torch.load(generator_model).cuda(self.gpu_ids[0])
self.discriminator = torch.load(discriminator_model).cuda(
self.gpu_ids[0])
self.generator.setConfig(self.config)
self.discriminator.setConfig(self.config)
except:
print("Checkpoint directory or files could not be found." +
"New directory {} will be created.".format(
self.checkpoints_dir))