def __init__(self, command):
Test.__init__(self)
self.command = command
self.env = {}
if isinstance(self.command, basestring):
self.command = shlex.split(str(self.command))
def __init__(self, command):
Test.__init__(self)
self.command = command
self.env = {}
if isinstance(self.command, basestring):
self.command = shlex.split(str(self.command))
def __init__(self, filepath, runConcurrent = True): """ :filepath: Must end in one '.vert', '.geom', or '.frag'. """ Test.__init__(self, runConcurrent) self.__config = None self.__command = None self.__filepath = filepath self.result = None
def __init__(self, filepath, runConcurrent = True): """ :filepath: Must end in one '.vert', '.geom', or '.frag'. """ Test.__init__(self, runConcurrent) self.__config = None self.__command = None self.__filepath = filepath self.result = None
def __init__(self, command):
Test.__init__(self)
self.command = command
self.split_command = os.path.split(self.command[0])[1]
self.env = {}
if isinstance(self.command, basestring):
self.command = shlex.split(str(self.command))
self.skip_test = self.check_for_skip_scenario(command)
def __init__(self, command):
Test.__init__(self)
self.command = command
self.split_command = os.path.split(self.command[0])[1]
self.env = {}
if isinstance(self.command, basestring):
self.command = shlex.split(str(self.command))
self.skip_test = self.check_for_skip_scenario(command)
def __init__(self, shader_runner_args, run_standalone=False):
"""run_standalone: Run the test outside the Python framework."""
Test.__init__(self, runConcurrent=True)
assert(isinstance(shader_runner_args, list))
assert(isinstance(shader_runner_args[0], str) or \
isinstance(shader_runner_args[0], unicode))
self.__run_standalone = run_standalone
self.__shader_runner_args = shader_runner_args
self.__test_filepath = shader_runner_args[0]
self.__result = None
self.__command = None
self.__gl_api = None
self.env = {}
def __init__(self, shader_runner_args, run_standalone=False):
"""run_standalone: Run the test outside the Python framework."""
Test.__init__(self, runConcurrent=True)
assert (isinstance(shader_runner_args, list))
assert (isinstance(shader_runner_args[0], str)
or isinstance(shader_runner_args[0], unicode))
self.__run_standalone = run_standalone
self.__shader_runner_args = shader_runner_args
self.__test_filepath = shader_runner_args[0]
self.__result = None
self.__command = None
self.__gl_api = None
self.env = {}
def load_tests(self, tests_dir, name):
core.log_open_sec(name.capitalize() + " tests")
tests_subdir = os.path.join(tests_dir, name)
tests = []
for td in os.listdir(tests_subdir):
tests.append(Test(os.path.join(tests_subdir, td), td))
core.log_end_sec()
return tests
def main(args):
if args.training_type is 'Train':
savefilename = osp.join(args.dataset1 + args.dataset2 + args.dataset3 +
'1')
elif args.training_type is 'Test':
savefilename = osp.join(
args.tstfile,
args.tstdataset + 'to' + args.dataset_target + args.snapshotnum)
args.seed = init_random_seed(args.manual_seed)
if args.training_type in ['Train', 'Test']:
summary_writer = SummaryWriter(
osp.join(args.results_path, 'log', savefilename))
saver = Saver(args, savefilename)
saver.print_config()
##################### load seed#####################
#####################load datasets#####################
if args.training_type is 'Train':
data_loader1_real = get_dataset_loader(name=args.dataset1,
getreal=True,
batch_size=args.batchsize)
data_loader1_fake = get_dataset_loader(name=args.dataset1,
getreal=False,
batch_size=args.batchsize)
data_loader2_real = get_dataset_loader(name=args.dataset2,
getreal=True,
batch_size=args.batchsize)
data_loader2_fake = get_dataset_loader(name=args.dataset2,
getreal=False,
batch_size=args.batchsize)
data_loader3_real = get_dataset_loader(name=args.dataset3,
getreal=True,
batch_size=args.batchsize)
data_loader3_fake = get_dataset_loader(name=args.dataset3,
getreal=False,
batch_size=args.batchsize)
data_loader_target = get_tgtdataset_loader(name=args.dataset_target,
batch_size=args.batchsize)
elif args.training_type is 'Test':
data_loader_target = get_tgtdataset_loader(name=args.dataset_target,
batch_size=args.batchsize)
##################### load models#####################
FeatExtmodel = models.create(args.arch_FeatExt)
DepthEstmodel = models.create(args.arch_DepthEst)
FeatEmbdmodel = models.create(args.arch_FeatEmbd,
momentum=args.bn_momentum)
if args.training_type is 'Train':
FeatExt_restore = None
DepthEst_restore = None
FeatEmbd_restore = None
elif args.training_type is 'Test':
FeatExt_restore = osp.join('results', args.tstfile, 'snapshots',
args.tstdataset,
'FeatExtor-' + args.snapshotnum + '.pt')
FeatEmbd_restore = osp.join('results', args.tstfile, 'snapshots',
args.tstdataset,
'FeatEmbder-' + args.snapshotnum + '.pt')
DepthEst_restore = None
else:
raise NotImplementedError('method type [%s] is not implemented' %
args.training_type)
FeatExtor = init_model(net=FeatExtmodel,
init_type=args.init_type,
restore=FeatExt_restore,
parallel_reload=True)
DepthEstor = init_model(net=DepthEstmodel,
init_type=args.init_type,
restore=DepthEst_restore,
parallel_reload=True)
FeatEmbder = init_model(net=FeatEmbdmodel,
init_type=args.init_type,
restore=FeatEmbd_restore,
parallel_reload=False)
print(">>> FeatExtor <<<")
print(FeatExtor)
print(">>> DepthEstor <<<")
print(DepthEstor)
print(">>> FeatEmbder <<<")
print(FeatEmbder)
##################### tarining models#####################
if args.training_type == 'Train':
Train(args, FeatExtor, DepthEstor, FeatEmbder, data_loader1_real,
data_loader1_fake, data_loader2_real, data_loader2_fake,
data_loader3_real, data_loader3_fake, data_loader_target,
summary_writer, saver, savefilename)
elif args.training_type in ['Test']:
Test(args, FeatExtor, FeatEmbder, data_loader_target, savefilename)
else:
raise NotImplementedError('method type [%s] is not implemented' %
args.training_type)