def test_mxnet_wrapper_thinc_model_subclass(mx_model):
class CustomModel(Model):
def fn(self) -> int:
return 1337
model = MXNetWrapper(mx_model, model_class=CustomModel)
assert isinstance(model, CustomModel)
assert model.fn() == 1337
def test_mxnet_wrapper_gluon_sequential():
import mxnet as mx
mx_model = mx.gluon.nn.Sequential()
mx_model.add(mx.gluon.nn.Dense(12))
wrapped = MXNetWrapper(mx_model)
assert isinstance(wrapped, Model)
def test_mxnet_wrapper_convert_inputs(data, n_args, kwargs_keys):
import mxnet as mx
mx_model = mx.gluon.nn.Sequential()
mx_model.add(mx.gluon.nn.Dense(12))
mx_model.initialize()
model = MXNetWrapper(mx_model)
convert_inputs = model.attrs["convert_inputs"]
Y, backprop = convert_inputs(model, data, is_train=True)
check_input_converters(Y, backprop, data, n_args, kwargs_keys,
mx.nd.NDArray)
def model(mx_model) -> Model[Array2d, Array2d]:
return MXNetWrapper(mx_model)
def test_mxnet_wrapper_thinc_set_model_name(mx_model):
model = MXNetWrapper(mx_model, model_name="cool")
assert model.name == "cool"
def test_mxnet_wrapper_to_cpu(mx_model, X: Array2d):
model = MXNetWrapper(mx_model)
model.predict(X)
model.to_cpu()