def test_tensor_converter_3():
converter = TensorConverter()
np_ = np.asarray([[1, 2, 3], [4, 5, 6]])
tensor_ = torch.from_numpy(np_)
y, y_ = converter.output_proc(tensor_, None, training=True)
assert y_ is None
assert isinstance(y, torch.Tensor)
assert y.shape == (2, 3)
assert torch.equal(y, tensor_)
y, y_ = converter.output_proc(tensor_, tensor_, training=True)
assert isinstance(y, torch.Tensor)
assert isinstance(y_, torch.Tensor)
assert y.equal(y_)
assert y.shape == (2, 3)
assert torch.equal(y, tensor_)
y, _ = converter.output_proc((tensor_, ), None, training=True)
assert isinstance(y, tuple)
assert isinstance(y[0], torch.Tensor)
assert torch.equal(y[0], tensor_)
y, y_ = converter.output_proc(tensor_, tensor_, training=False)
assert isinstance(y, np.ndarray)
assert isinstance(y_, np.ndarray)
assert np.all(y == y_)
assert y.shape == (2, 3)
assert np.all(y == tensor_.numpy())
y, _ = converter.output_proc((tensor_, ), None, training=False)
assert isinstance(y, tuple)
assert isinstance(y[0], np.ndarray)
assert np.all(y[0] == tensor_.numpy())
def test_tensor_converter_3():
np_ = np.asarray([[1, 2, 3], [4, 5, 6]])
tensor_ = torch.from_numpy(np_)
converter = TensorConverter()
y, y_ = converter.output_proc(tensor_, None, is_training=True)
assert y_ is None
assert isinstance(y, torch.Tensor)
assert y.shape == (2, 3)
assert torch.equal(y, tensor_)
y, y_ = converter.output_proc(tensor_, tensor_, is_training=True)
assert isinstance(y, torch.Tensor)
assert isinstance(y_, torch.Tensor)
assert y.equal(y_)
assert y.shape == (2, 3)
assert torch.equal(y, tensor_)
y, _ = converter.output_proc((tensor_, ), None, is_training=True)
assert isinstance(y, tuple)
assert isinstance(y[0], torch.Tensor)
assert torch.equal(y[0], tensor_)
y, y_ = converter.output_proc(tensor_, tensor_, is_training=False)
assert isinstance(y, np.ndarray)
assert isinstance(y_, np.ndarray)
assert np.all(y == y_)
assert y.shape == (2, 3)
assert np.all(y == tensor_.numpy())
y, _ = converter.output_proc((tensor_, ), None, is_training=False)
assert isinstance(y, tuple)
assert isinstance(y[0], np.ndarray)
assert np.all(y[0] == tensor_.numpy())
converter = TensorConverter(argmax=True)
y, y_ = converter.output_proc(tensor_, tensor_, is_training=False)
assert isinstance(y, np.ndarray)
assert isinstance(y_, np.ndarray)
assert y.shape == (2, )
assert y_.shape == (2, 3)
assert np.all(y == np.argmax(np_, 1))
y, y_ = converter.output_proc((tensor_, tensor_), None, is_training=False)
assert isinstance(y, tuple)
assert y_ is None
assert y[0].shape == (2, )
assert y[0].shape == y[1].shape
assert np.all(y[0] == np.argmax(np_, 1))
converter = TensorConverter(probability=True)
y, y_ = converter.output_proc(tensor_, tensor_, is_training=False)
assert isinstance(y, np.ndarray)
assert isinstance(y_, np.ndarray)
assert y.shape == (2, 3)
assert y_.shape == (2, 3)
assert np.all(y == softmax(np_, 1))
y, y_ = converter.output_proc((tensor_, tensor_), None, is_training=False)
assert isinstance(y, tuple)
assert y_ is None
assert y[0].shape == (2, 3)
assert y[0].shape == y[1].shape
assert np.all(y[0] == softmax(np_, 1))