def get_model(cfg, dataset, num_classes):
"""
Builds and returns a model and associated hyper parameters according to the config object passed.
Args:
cfg: A YACS config object.
dataset: A multi domain dataset consisting of source and target datasets.
num_classes: The class number of specific dataset.
"""
# setup feature extractor
feature_network, class_feature_dim, domain_feature_dim = get_video_feat_extractor(
cfg.MODEL.METHOD.upper(), cfg.DATASET.IMAGE_MODALITY, cfg.MODEL.ATTENTION, num_classes
)
# setup classifier
classifier_network = ClassNetVideo(input_size=class_feature_dim, n_class=num_classes)
config_params = get_config(cfg)
train_params = config_params["train_params"]
train_params_local = deepcopy(train_params)
method_params = {}
method = domain_adapter.Method(cfg.DAN.METHOD)
if method.is_mmd_method():
model = video_domain_adapter.create_mmd_based_video(
method=method,
dataset=dataset,
image_modality=cfg.DATASET.IMAGE_MODALITY,
feature_extractor=feature_network,
task_classifier=classifier_network,
**method_params,
**train_params_local,
)
else:
critic_input_size = domain_feature_dim
# setup critic network
if method.is_cdan_method():
if cfg.DAN.USERANDOM:
critic_input_size = cfg.DAN.RANDOM_DIM
else:
critic_input_size = domain_feature_dim * num_classes
critic_network = DomainNetVideo(input_size=critic_input_size)
if cfg.DAN.METHOD == "CDAN":
method_params["use_random"] = cfg.DAN.USERANDOM
# The following calls kale.loaddata.dataset_access for the first time
model = video_domain_adapter.create_dann_like_video(
method=method,
dataset=dataset,
image_modality=cfg.DATASET.IMAGE_MODALITY,
feature_extractor=feature_network,
task_classifier=classifier_network,
critic=critic_network,
**method_params,
**train_params_local,
)
return model, train_params
def get_model(cfg, dataset, num_channels):
"""
Builds and returns a model and associated hyper parameters according to the config object passed.
Args:
cfg: A YACS config object.
dataset: A multi domain dataset consisting of source and target datasets.
num_channels: The number of image channels.
"""
# setup feature extractor
feature_network, feature_dim = get_feat_extractor(cfg.MODEL.METHOD.upper(),
cfg.DATASET.NUM_CLASSES,
num_channels)
# setup classifier
classifier_network = ClassNetSmallImage(feature_dim,
cfg.DATASET.NUM_CLASSES)
config_params = get_config(cfg)
train_params = config_params["train_params"]
train_params_local = deepcopy(train_params)
method_params = {}
method = domain_adapter.Method(cfg.DAN.METHOD)
if method.is_mmd_method():
model = domain_adapter.create_mmd_based(
method=method,
dataset=dataset,
feature_extractor=feature_network,
task_classifier=classifier_network,
**method_params,
**train_params_local,
)
else:
critic_input_size = feature_dim
# setup critic network
if method.is_cdan_method():
if cfg.DAN.USERANDOM:
critic_input_size = cfg.DAN.RANDOM_DIM
else:
critic_input_size = feature_dim * cfg.DATASET.NUM_CLASSES
critic_network = DomainNetSmallImage(critic_input_size)
if cfg.DAN.METHOD == "CDAN":
method_params["use_random"] = cfg.DAN.USERANDOM
# The following calls kale.loaddata.dataset_access for the first time
model = domain_adapter.create_dann_like(
method=method,
dataset=dataset,
feature_extractor=feature_network,
task_classifier=classifier_network,
critic=critic_network,
**method_params,
**train_params_local,
)
return model, train_params
def test_domain_adaptor(da_method, n_fewshot, download_path, testing_cfg):
if n_fewshot is None:
if da_method in ["FSDANN", "MME", "Source"]:
return
else:
if da_method in [
"DANN", "CDAN", "CDAN-E", "WDGRL", "WDGRLMod", "DAN", "JAN"
]:
return
source, target, num_channels = DigitDataset.get_source_target(
DigitDataset(SOURCE), DigitDataset(TARGET), download_path)
dataset = MultiDomainDatasets(source,
target,
config_weight_type=WEIGHT_TYPE,
config_size_type=DATASIZE_TYPE,
n_fewshot=n_fewshot)
# setup feature extractor
feature_network = SmallCNNFeature(num_channels)
# setup classifier
feature_dim = feature_network.output_size()
classifier_network = ClassNetSmallImage(feature_dim, NUM_CLASSES)
train_params = testing_cfg["train_params"]
method_params = {}
da_method = domain_adapter.Method(da_method)
if da_method.is_mmd_method():
model = domain_adapter.create_mmd_based(
method=da_method,
dataset=dataset,
feature_extractor=feature_network,
task_classifier=classifier_network,
**method_params,
**train_params,
)
else: # All other non-mmd DA methods are dann like with critic
critic_input_size = feature_dim
# setup critic network
if da_method.is_cdan_method():
critic_input_size = 1024
method_params["use_random"] = True
critic_network = DomainNetSmallImage(critic_input_size)
# The following calls kale.loaddata.dataset_access for the first time
model = domain_adapter.create_dann_like(
method=da_method,
dataset=dataset,
feature_extractor=feature_network,
task_classifier=classifier_network,
critic=critic_network,
**method_params,
**train_params,
)
ModelTestHelper.test_model(model, train_params)
def test_video_domain_adapter(source_cfg, target_cfg, image_modality,
da_method, testing_cfg, testing_training_cfg):
source_name, source_n_class, source_trainlist, source_testlist = source_cfg.split(
";")
target_name, target_n_class, target_trainlist, target_testlist = target_cfg.split(
";")
# get cfg parameters
cfg = testing_cfg
cfg.DATASET.SOURCE = source_name
cfg.DATASET.SRC_TRAINLIST = source_trainlist
cfg.DATASET.SRC_TESTLIST = source_testlist
cfg.DATASET.TARGET = target_name
cfg.DATASET.TGT_TRAINLIST = target_trainlist
cfg.DATASET.TGT_TESTLIST = target_testlist
cfg.DATASET.IMAGE_MODALITY = image_modality
cfg.DATASET.WEIGHT_TYPE = WEIGHT_TYPE
cfg.DATASET.SIZE_TYPE = DATASIZE_TYPE
cfg.DAN.USERANDOM = False
# download example data
download_file_by_url(
url=url,
output_directory=str(Path(cfg.DATASET.ROOT).parent.absolute()),
output_file_name="video_test_data.zip",
file_format="zip",
)
# build dataset
source, target, num_classes = VideoDataset.get_source_target(
VideoDataset(source_name), VideoDataset(target_name), seed, cfg)
dataset = VideoMultiDomainDatasets(
source,
target,
image_modality=cfg.DATASET.IMAGE_MODALITY,
seed=seed,
config_weight_type=cfg.DATASET.WEIGHT_TYPE,
config_size_type=cfg.DATASET.SIZE_TYPE,
)
# setup feature extractor
if cfg.DATASET.IMAGE_MODALITY in ["rgb", "flow"]:
class_feature_dim = 1024
domain_feature_dim = class_feature_dim
if cfg.DATASET.IMAGE_MODALITY == "rgb":
feature_network = {"rgb": VideoBoringModel(3), "flow": None}
else:
feature_network = {"rgb": None, "flow": VideoBoringModel(2)}
else:
class_feature_dim = 2048
domain_feature_dim = int(class_feature_dim / 2)
feature_network = {
"rgb": VideoBoringModel(3),
"flow": VideoBoringModel(2)
}
# setup classifier
classifier_network = ClassNetVideo(input_size=class_feature_dim,
n_class=num_classes)
train_params = testing_training_cfg["train_params"]
method_params = {}
method = domain_adapter.Method(da_method)
# setup DA method
if method.is_mmd_method():
model = video_domain_adapter.create_mmd_based_video(
method=method,
dataset=dataset,
image_modality=cfg.DATASET.IMAGE_MODALITY,
feature_extractor=feature_network,
task_classifier=classifier_network,
**method_params,
**train_params,
)
else:
critic_input_size = domain_feature_dim
# setup critic network
if method.is_cdan_method():
if cfg.DAN.USERANDOM:
critic_input_size = 1024
else:
critic_input_size = domain_feature_dim * num_classes
critic_network = DomainNetVideo(input_size=critic_input_size)
if da_method == "CDAN":
method_params["use_random"] = cfg.DAN.USERANDOM
model = video_domain_adapter.create_dann_like_video(
method=method,
dataset=dataset,
image_modality=cfg.DATASET.IMAGE_MODALITY,
feature_extractor=feature_network,
task_classifier=classifier_network,
critic=critic_network,
**method_params,
**train_params,
)
ModelTestHelper.test_model(model, train_params)