def test_number_input_feature(
number_config: Dict,
) -> None:
# setup image input feature definition
number_def = deepcopy(number_config)
# pickup any other missing parameters
NumberInputFeature.populate_defaults(number_def)
# ensure no exceptions raised during build
input_feature_obj = ECD.build_single_input(number_def, None).to(DEVICE)
# check one forward pass through input feature
input_tensor = torch.rand(2, dtype=torch.float32).to(DEVICE)
encoder_output = input_feature_obj(input_tensor)
assert encoder_output["encoder_output"].shape == (BATCH_SIZE, *input_feature_obj.output_shape)
def test_image_input_feature(image_config: Dict, encoder: str, height: int, width: int, num_channels) -> None:
# setup image input feature definition
image_def = deepcopy(image_config)
image_def["encoder"] = encoder
image_def["height"] = height
image_def["width"] = width
image_def["num_channels"] = num_channels
# pickup any other missing parameters
ImageInputFeature.populate_defaults(image_def)
# ensure no exceptions raised during build
input_feature_obj = ECD.build_single_input(image_def, None)
# check one forward pass through input feature
input_tensor = torch.randint(0, 256, size=(BATCH_SIZE, num_channels, height, width), dtype=torch.uint8)
encoder_output = input_feature_obj(input_tensor)
assert encoder_output["encoder_output"].shape == (BATCH_SIZE, *input_feature_obj.output_shape)
def test_category_input_feature(
category_config: Dict,
encoder: str,
) -> None:
# setup image input feature definition
category_def = deepcopy(category_config)
category_def["encoder"] = encoder
# pickup any other missing parameters
CategoryInputFeature.populate_defaults(category_def)
# ensure no exceptions raised during build
input_feature_obj = ECD.build_single_input(category_def, None)
# check one forward pass through input feature
input_tensor = torch.randint(0, 3, size=(BATCH_SIZE,), dtype=torch.int32).to(DEVICE)
encoder_output = input_feature_obj(input_tensor)
assert encoder_output["encoder_output"].shape == (BATCH_SIZE, *input_feature_obj.output_shape)
def test_set_input_feature(set_config: Dict, ) -> None:
# setup image input feature definition
set_def = deepcopy(set_config)
# pickup any other missing parameters
SetInputFeature.populate_defaults(set_def)
# ensure no exceptions raised during build
input_feature_obj = ECD.build_single_input(set_def, None).to(DEVICE)
# check one forward pass through input feature
input_tensor = torch.randint(0,
2,
size=(BATCH_SIZE, len(set_def["vocab"])),
dtype=torch.int64).to(DEVICE)
encoder_output = input_feature_obj(input_tensor)
assert encoder_output["encoder_output"].shape == (
BATCH_SIZE, *input_feature_obj.output_shape)