def test_tensor_converter_2():
class _Trainer(BaseRunner):
def __init__(self):
super().__init__()
self.non_blocking = False
def predict(self, x_, y_):
return x_, y_
trainer = _Trainer()
converter = TensorConverter()
np_ = np.asarray([[1, 2, 3], [4, 5, 6]])
pd_ = pd.DataFrame(np_)
tensor_ = torch.Tensor(np_) # noqa
x, y = converter.input_proc(np_, np_[0], trainer=trainer) # noqa
assert isinstance(y, torch.Tensor)
assert y.shape == (3, 1)
assert torch.equal(y, tensor_[0].unsqueeze(-1))
x, y = converter.input_proc(pd_, pd_.iloc[0], trainer=trainer) # noqa
assert isinstance(y, torch.Tensor)
assert y.shape == (3, 1)
assert torch.equal(y, tensor_[0].unsqueeze(-1))
x, y = converter.input_proc(tensor_, tensor_[0], trainer=trainer) # noqa
assert isinstance(y, torch.Tensor)
assert y.shape == (3,)
assert torch.equal(y, tensor_[0])
def test_tensor_converter_1():
class _Trainer(BaseRunner):
def __init__(self):
super().__init__()
self.non_blocking = False
def predict(self, x_, y_):
return x_, y_
trainer = _Trainer()
converter = TensorConverter()
np_ = np.asarray([[1, 2, 3], [4, 5, 6]])
pd_ = pd.DataFrame(np_)
tensor_ = torch.Tensor(np_)
x, y = converter.input_proc(np_, None, trainer=trainer)
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert y is None
x, y = converter.input_proc(pd_, None, trainer=trainer)
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert y is None
x, y = converter.input_proc(tensor_, None, trainer=trainer)
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert y is None
x, y = converter.input_proc(np_, np_, trainer=trainer)
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert torch.equal(y, tensor_)
x, y = converter.input_proc(pd_, pd_, trainer=trainer)
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert torch.equal(y, tensor_)
x, y = converter.input_proc(tensor_, tensor_, trainer=trainer)
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert torch.equal(y, tensor_)
def test_tensor_converter_1():
class _Trainer(BaseRunner):
def __init__(self):
super().__init__()
self.non_blocking = False
def predict(self, x_, y_): # noqa
return x_, y_
trainer = _Trainer()
arr_1 = [1, 2, 3]
np_1 = np.asarray(arr_1)
se_1 = pd.Series(arr_1)
pd_1 = pd.DataFrame(arr_1)
np_ = np.asarray([arr_1, arr_1])
pd_ = pd.DataFrame(np_)
tensor_ = torch.Tensor(np_)
# test auto reshape; #189
converter = TensorConverter(auto_reshape=False)
x, y = converter.input_proc(np_1, None, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (3,)
x, y = converter.input_proc(se_1, None, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (3,)
x, y = converter.input_proc(pd_1, None, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (3, 1)
converter = TensorConverter()
x, y = converter.input_proc(np_1, None, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (3, 1)
x, y = converter.input_proc(se_1, None, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (3, 1)
x, y = converter.input_proc(pd_1, None, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (3, 1)
# normal tests
x, y = converter.input_proc(np_, None, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert y is None
x, y = converter.input_proc(pd_, None, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert y is None
x, y = converter.input_proc(tensor_, None, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert y is None
x, y = converter.input_proc(np_, np_, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert torch.equal(y, tensor_)
x, y = converter.input_proc(pd_, pd_, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert torch.equal(y, tensor_)
x, y = converter.input_proc(tensor_, tensor_, trainer=trainer) # noqa
assert isinstance(x, torch.Tensor)
assert x.shape == (2, 3)
assert torch.equal(x, tensor_)
assert torch.equal(y, tensor_)
converter = TensorConverter(x_dtype=torch.long)
x, y = converter.input_proc((np_, np_), np_, trainer=trainer) # noqa
assert isinstance(x, tuple)
assert len(x) == 2
assert x[0].dtype == torch.long
assert x[1].dtype == torch.long
converter = TensorConverter(x_dtype=(torch.long, torch.float32), y_dtype=torch.long)
x, y = converter.input_proc((np_, np_), np_, trainer=trainer) # noqa
assert isinstance(x, tuple)
assert len(x) == 2
assert x[0].dtype == torch.long
assert x[1].dtype == torch.float32
assert y.dtype == torch.long
converter = TensorConverter(x_dtype=(torch.long, torch.float32))
x, y = converter.input_proc((pd_, pd_), pd_, trainer=trainer) # noqa
assert isinstance(x, tuple)
assert len(x) == 2
assert x[0].dtype == torch.long
assert x[1].dtype == torch.float32
# for tensor input, dtype change will never be executed
converter = TensorConverter(x_dtype=(torch.long, torch.long))
x, y = converter.input_proc((tensor_, tensor_), tensor_, trainer=trainer) # noqa
assert isinstance(x, tuple)
assert len(x) == 2
assert x[0].dtype == torch.float32
assert x[1].dtype == torch.float32