def setUpClass(cls) -> None:
cls._tmpdir = tempfile.mkdtemp()
args = argparse.Namespace()
args.opts = [
f"env.save_dir={cls._tmpdir}", f"model=cnn_lstm", f"dataset=clevr"
]
args.config_override = None
configuration = Configuration(args)
configuration.freeze()
cls.config = configuration.get_config()
registry.register("config", cls.config)
setup_output_folder.cache_clear()
setup_logger.cache_clear()
cls.writer = setup_logger()
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 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_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()
self.assertTrue(
model_key in config.model_config,
"Key for model {} doesn't exists in its configuration".
format(model_key),
)
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_init_processors(self):
path = os.path.join(
os.path.abspath(__file__),
"../../../mmf/configs/datasets/vqa2/defaults.yaml",
)
args = dummy_args()
args.opts.append(f"config={path}")
configuration = Configuration(args)
answer_processor = (
configuration.get_config().dataset_config.vqa2.processors.answer_processor
)
vocab_path = os.path.join(
os.path.abspath(__file__), "..", "..", "data", "vocab.txt"
)
answer_processor.params.vocab_file = os.path.abspath(vocab_path)
self._fix_configuration(configuration)
configuration.freeze()
base_dataset = BaseDataset(
"vqa2", configuration.get_config().dataset_config.vqa2, "train"
)
expected_processors = [
"answer_processor",
"ocr_token_processor",
"bbox_processor",
]
# Check no processors are initialized before init_processors call
self.assertFalse(any(hasattr(base_dataset, key) for key in expected_processors))
for processor in expected_processors:
self.assertIsNone(registry.get("{}_{}".format("vqa2", processor)))
# Check processors are initialized after init_processors
base_dataset.init_processors()
self.assertTrue(all(hasattr(base_dataset, key) for key in expected_processors))
for processor in expected_processors:
self.assertIsNotNone(registry.get("{}_{}".format("vqa2", processor)))
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")