def setUp(self):
test_utils.setup_proxy()
setup_imports()
model_name = "vilbert"
args = test_utils.dummy_args(model=model_name)
configuration = Configuration(args)
config = configuration.get_config()
model_class = registry.get_model_class(model_name)
self.vision_feature_size = 1024
self.vision_target_size = 1279
config.model_config[model_name]["training_head_type"] = "pretraining"
config.model_config[model_name][
"visual_embedding_dim"] = self.vision_feature_size
config.model_config[model_name][
"v_feature_size"] = self.vision_feature_size
config.model_config[model_name][
"v_target_size"] = self.vision_target_size
config.model_config[model_name]["dynamic_attention"] = False
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 test_model_configs_for_keys(self):
models_mapping = registry.mapping["model_name_mapping"]
for model_key, model_cls in models_mapping.items():
if model_cls.config_path() is None:
warnings.warn(
("Model {} has no default configuration defined. " +
"Skipping it. Make sure it is intentional"
).format(model_key))
continue
with contextlib.redirect_stdout(StringIO()):
args = dummy_args(model=model_key)
configuration = Configuration(args)
configuration.freeze()
config = configuration.get_config()
if model_key == "mmft":
continue
self.assertTrue(
model_key in config.model_config,
"Key for model {} doesn't exists in its configuration".
format(model_key),
)
def setUp(self):
test_utils.setup_proxy()
setup_imports()
model_name = "vinvl"
args = test_utils.dummy_args(model=model_name, dataset="test")
configuration = Configuration(args)
config = configuration.get_config()
model_config = config.model_config[model_name]
model_config.model = model_name
model_config.do_pretraining = False
classification_config_dict = {
"do_pretraining": False,
"heads": {"mlp": {"num_labels": 3129}},
"ce_loss": {"ignore_index": -1},
}
self.classification_config = OmegaConf.create(
{**model_config, **classification_config_dict}
)
pretraining_config_dict = {
"do_pretraining": True,
"heads": {"mlm": {"hidden_size": 768}},
}
self.pretraining_config = OmegaConf.create(
{**model_config, **pretraining_config_dict}
)
self.sample_list = self._get_sample_list()
def setUp(self):
test_utils.setup_proxy()
setup_imports()
self.model_name = "mmf_transformer"
args = test_utils.dummy_args(model=self.model_name)
configuration = Configuration(args)
self.config = configuration.get_config()
self.config.model_config[self.model_name].model = self.model_name
def setUp(self):
test_utils.setup_proxy()
setup_imports()
model_name = "vilt"
args = test_utils.dummy_args(model=model_name, dataset="test")
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()
self.model_name = "multimodelity_transformer"
args = test_utils.dummy_args(model=self.model_name)
configuration = Configuration(args)
self.config = configuration.get_config()
self.config.model_config[self.model_name].model = self.model_name
self.finetune_model = build_model(
self.config.model_config[self.model_name])
def setUp(self):
setup_imports()
self.model_name = "mmf_transformer"
args = test_utils.dummy_args(model=self.model_name)
configuration = Configuration(args)
self.config = configuration.get_config()
self.model_class = registry.get_model_class(self.model_name)
self.finetune_model = self.model_class(
self.config.model_config[self.model_name])
self.finetune_model.build()
def setUp(self):
setup_imports()
model_name = "mmbt"
args = test_utils.dummy_args(model=model_name)
configuration = Configuration(args)
config = configuration.get_config()
model_class = registry.get_model_class(model_name)
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 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()
model_name = "mmbt"
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):
test_utils.setup_proxy()
setup_imports()
model_name = "uniter"
args = test_utils.dummy_args(model=model_name, dataset="vqa2")
configuration = Configuration(args)
config = configuration.get_config()
model_config = config.model_config[model_name]
model_config.model = model_name
model_config.losses = {"vqa2": "logit_bce"}
model_config.do_pretraining = False
model_config.tasks = "vqa2"
classification_config_dict = {
"do_pretraining": False,
"tasks": "vqa2",
"heads": {
"vqa2": {
"type": "mlp",
"num_labels": 3129
}
},
"losses": {
"vqa2": "logit_bce"
},
}
classification_config = OmegaConf.create({
**model_config,
**classification_config_dict
})
pretraining_config_dict = {
"do_pretraining": True,
"tasks": "wra",
"heads": {
"wra": {
"type": "wra"
}
},
}
pretraining_config = OmegaConf.create({
**model_config,
**pretraining_config_dict
})
self.model_for_classification = build_model(classification_config)
self.model_for_pretraining = build_model(pretraining_config)
def setUp(self):
setup_imports()
torch.manual_seed(1234)
config_path = os.path.join(
get_mmf_root(),
"..",
"projects",
"butd",
"configs",
"coco",
"beam_search.yaml",
)
config_path = os.path.abspath(config_path)
args = dummy_args(model="butd", dataset="coco")
args.opts.append(f"config={config_path}")
configuration = Configuration(args)
configuration.config.datasets = "coco"
configuration.freeze()
self.config = configuration.config
registry.register("config", self.config)
def setUp(self):
test_utils.setup_proxy()
setup_imports()
model_name = "vilbert"
args = test_utils.dummy_args(model=model_name)
configuration = Configuration(args)
config = configuration.get_config()
self.vision_feature_size = 1024
self.vision_target_size = 1279
model_config = config.model_config[model_name]
model_config["training_head_type"] = "pretraining"
model_config["visual_embedding_dim"] = self.vision_feature_size
model_config["v_feature_size"] = self.vision_feature_size
model_config["v_target_size"] = self.vision_target_size
model_config["dynamic_attention"] = False
model_config.model = model_name
model_config["training_head_type"] = "classification"
model_config["num_labels"] = 2
self.model_config = model_config
def setUp(self):
torch.manual_seed(1234)
registry.register("clevr_text_vocab_size", 80)
registry.register("clevr_num_final_outputs", 32)
config_path = os.path.join(
get_mmf_root(),
"..",
"projects",
"others",
"cnn_lstm",
"clevr",
"defaults.yaml",
)
config_path = os.path.abspath(config_path)
args = dummy_args(model="cnn_lstm", dataset="clevr")
args.opts.append(f"config={config_path}")
configuration = Configuration(args)
configuration.config.datasets = "clevr"
configuration.freeze()
self.config = configuration.config
registry.register("config", self.config)
def setUp(self):
setup_imports()
torch.manual_seed(1234)
config_path = os.path.join(
get_mmf_root(),
"..",
"projects",
"butd",
"configs",
"coco",
"nucleus_sampling.yaml",
)
config_path = os.path.abspath(config_path)
args = dummy_args(model="butd", dataset="coco")
args.opts.append(f"config={config_path}")
configuration = Configuration(args)
configuration.config.datasets = "coco"
configuration.config.model_config.butd.inference.params.sum_threshold = 0.5
configuration.freeze()
self.config = configuration.config
registry.register("config", self.config)
def test_dataset_configs_for_keys(self):
builder_name = registry.mapping["builder_name_mapping"]
for builder_key, builder_cls in builder_name.items():
if builder_cls.config_path() is None:
warnings.warn(
("Dataset {} has no default configuration defined. " +
"Skipping it. Make sure it is intentional"
).format(builder_key))
continue
with contextlib.redirect_stdout(StringIO()):
args = dummy_args(dataset=builder_key)
configuration = Configuration(args)
configuration.freeze()
config = configuration.get_config()
self.assertTrue(
builder_key in config.dataset_config,
"Key for dataset {} doesn't exists in its configuration".
format(builder_key),
)
def test_config_overrides(self):
config_path = os.path.join(
get_mmf_root(),
"..",
"projects",
"m4c",
"configs",
"textvqa",
"defaults.yaml",
)
config_path = os.path.abspath(config_path)
args = dummy_args(model="m4c", dataset="textvqa")
args.opts += [
f"config={config_path}", "training.lr_steps[1]=10000",
"dataset_config.textvqa.zoo_requirements[0]=\"test\""
]
configuration = Configuration(args)
configuration.freeze()
config = configuration.get_config()
self.assertEqual(config.training.lr_steps[1], 10000)
self.assertEqual(config.dataset_config.textvqa.zoo_requirements[0],
"test")