Beispiel #1
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	def __init__(self, command):
		Test.__init__(self)
		self.command = command
		self.env = {}

		if isinstance(self.command, basestring):
			self.command = shlex.split(str(self.command))
Beispiel #2
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    def __init__(self, command):
        Test.__init__(self)
        self.command = command
        self.env = {}

        if isinstance(self.command, basestring):
            self.command = shlex.split(str(self.command))
Beispiel #3
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	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
Beispiel #4
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	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
Beispiel #5
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    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)
Beispiel #6
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    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)
Beispiel #7
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    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 = {}
Beispiel #8
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    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 = {}
Beispiel #9
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    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
Beispiel #10
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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)