def setUp(self):
test_utils.setup_proxy()
setup_imports()
replace_with_jit()
model_name = "visual_bert"
args = test_utils.dummy_args(model=model_name)
configuration = Configuration(args)
config = configuration.get_config()
model_config = config.model_config[model_name]
model_config.model = model_name
self.pretrain_model = build_model(model_config)
def setUp(self):
test_utils.setup_proxy()
setup_imports()
replace_with_jit()
model_name = "visual_bert"
args = test_utils.dummy_args(model=model_name)
configuration = Configuration(args)
config = configuration.get_config()
model_config = config.model_config[model_name]
model_config["training_head_type"] = "classification"
model_config["num_labels"] = 2
model_config.model = model_name
self.finetune_model = build_model(model_config)
def setUp(self):
setup_imports()
replace_with_jit()
model_name = "visual_bert"
args = test_utils.dummy_args(model=model_name)
configuration = Configuration(args)
config = configuration.get_config()
model_class = registry.get_model_class(model_name)
self.pretrain_model = model_class(config.model_config[model_name])
self.pretrain_model.build()
config.model_config[model_name][
"training_head_type"] = "classification"
config.model_config[model_name]["num_labels"] = 2
self.finetune_model = model_class(config.model_config[model_name])
self.finetune_model.build()
def __init__(self, config, *args, **kwargs):
super().__init__(config, *args, **kwargs)
# Replace transformer layers with scriptable JIT layers
replace_with_jit()
def test_undo_replace_with_jit(self):
original_function = BertSelfAttention.forward
replace_with_jit()
undo_replace_with_jit()
self.assertTrue(BertSelfAttention.forward is original_function)
