def test_tensor_array_scalar_cast():
outer_dim = 3
inner_shape = (1,)
shape = (outer_dim,) + inner_shape
num_items = np.prod(np.array(shape))
arr = np.arange(num_items).reshape(shape)
t_arr = TensorArray(arr)
for t_arr_elem, arr_elem in zip(t_arr, arr):
assert float(t_arr_elem) == float(arr_elem)
arr = np.arange(1).reshape((1, 1, 1))
t_arr = TensorArray(arr)
assert float(t_arr) == float(arr)
def test_dict_pandas():
input_data = {"x": np.array([1, 2, 3])}
expected_output = pd.DataFrame({"x": TensorArray(input_data["x"])})
actual_output = convert_batch_type_to_pandas(input_data)
assert expected_output.equals(actual_output)
output_array = convert_pandas_to_batch_type(actual_output,
type=DataType.NUMPY)
assert np.array_equal(output_array, input_data["x"])
def test_dict_multi_dim_to_pandas():
tensor = np.arange(12).reshape((3, 2, 2))
input_data = {"x": tensor}
expected_output = pd.DataFrame({"x": TensorArray(tensor)})
actual_output = convert_batch_type_to_pandas(input_data)
assert expected_output.equals(actual_output)
output_array = convert_pandas_to_batch_type(actual_output,
type=DataType.NUMPY)
assert np.array_equal(output_array, input_data["x"])
def test_numpy_object_pandas():
input_data = np.array([[1, 2, 3], [1]], dtype=object)
expected_output = pd.DataFrame(
{TENSOR_COLUMN_NAME: TensorArray(input_data)})
actual_output = convert_batch_type_to_pandas(input_data)
assert expected_output.equals(actual_output)
assert np.array_equal(
convert_pandas_to_batch_type(actual_output, type=DataType.NUMPY),
input_data)
def test_numpy_multi_dim_pandas():
input_data = np.arange(12).reshape((3, 2, 2))
expected_output = pd.DataFrame(
{TENSOR_COLUMN_NAME: TensorArray(input_data)})
actual_output = convert_batch_type_to_pandas(input_data)
assert expected_output.equals(actual_output)
assert np.array_equal(
convert_pandas_to_batch_type(actual_output, type=DataType.NUMPY),
input_data)
def test_arrow_tensor_pandas():
np_array = np.array([1, 2, 3])
df = pd.DataFrame({"x": TensorArray(np_array)})
input_data = pa.Table.from_arrays([ArrowTensorArray.from_numpy(np_array)],
names=["x"])
expected_output = df
actual_output = convert_batch_type_to_pandas(input_data)
assert expected_output.equals(actual_output)
assert convert_pandas_to_batch_type(actual_output,
type=DataType.ARROW).equals(input_data)
def test_dict_pandas_multi_column():
array_dict = {"x": np.array([1, 2, 3]), "y": np.array([4, 5, 6])}
expected_output = pd.DataFrame(
{k: TensorArray(v)
for k, v in array_dict.items()})
actual_output = convert_batch_type_to_pandas(array_dict)
assert expected_output.equals(actual_output)
output_dict = convert_pandas_to_batch_type(actual_output,
type=DataType.NUMPY)
for k, v in output_dict.items():
assert np.array_equal(v, array_dict[k])
def _predict_pandas(
self, data: pd.DataFrame,
dtype: Union[TensorDtype, Dict[str, TensorDtype]]) -> pd.DataFrame:
tensors = convert_pandas_to_batch_type(data, DataType.NUMPY)
# Single numpy array.
if isinstance(tensors, np.ndarray):
column_name = data.columns[0]
if isinstance(dtype, dict):
dtype = dtype[column_name]
model_input = self._array_to_tensor(tensors, dtype)
else:
model_input = {
k: self._array_to_tensor(
v, dtype=dtype[k] if isinstance(dtype, dict) else dtype)
for k, v in tensors.items()
}
output = self._model_predict(model_input)
# Handle model multi-output. For example if model outputs 2 images.
if isinstance(output, dict):
return pd.DataFrame(
{k: TensorArray(self._tensor_to_array(v))
for k, v in output})
elif isinstance(output, list) or isinstance(output, tuple):
tensor_name = "output_"
output_dict = {}
for i in range(len(output)):
output_dict[tensor_name + str(i + 1).zfill(5)] = TensorArray(
self._tensor_to_array(output[i]))
return pd.DataFrame(output_dict)
else:
return pd.DataFrame(
{"predictions": TensorArray(self._tensor_to_array(output))},
columns=["predictions"],
)
def test_tensor_array_dataframe_repr():
outer_dim = 3
inner_shape = (2, 2)
shape = (outer_dim,) + inner_shape
num_items = np.prod(np.array(shape))
arr = np.arange(num_items).reshape(shape)
t_arr = TensorArray(arr)
df = pd.DataFrame({"a": t_arr})
expected_repr = """ a
0 [[ 0, 1], [ 2, 3]]
1 [[ 4, 5], [ 6, 7]]
2 [[ 8, 9], [10, 11]]"""
assert repr(df) == expected_repr
def preprocess(df: pd.DataFrame) -> pd.DataFrame:
"""
User Pytorch code to transform user image. Note we still use pandas as
intermediate format to hold images as shorthand of python dictionary.
"""
preprocess = transforms.Compose(
[
transforms.ToTensor(),
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
df["image"] = TensorArray([preprocess(x.to_numpy()) for x in df["image"]])
return df
def convert_batch_type_to_pandas(data: DataBatchType) -> pd.DataFrame:
"""Convert the provided data to a Pandas DataFrame.
Args:
data: Data of type DataBatchType
Returns:
A pandas Dataframe representation of the input data.
"""
from ray.air.util.tensor_extensions.pandas import TensorArray
if isinstance(data, pd.DataFrame):
return data
elif isinstance(data, np.ndarray):
return pd.DataFrame({TENSOR_COLUMN_NAME: TensorArray(data)})
elif isinstance(data, dict):
tensor_dict = {}
for k, v in data.items():
if not isinstance(v, np.ndarray):
raise ValueError(
"All values in the provided dict must be of type "
f"np.ndarray. Found type {type(v)} for key {k} "
f"instead.")
# Convert numpy arrays to TensorArray.
tensor_dict[k] = TensorArray(v)
return pd.DataFrame(tensor_dict)
elif pyarrow is not None and isinstance(data, pyarrow.Table):
return data.to_pandas()
else:
raise ValueError(
f"Received data of type: {type(data)}, but expected it to be one "
f"of {DataBatchType}")
def preprocess_image_with_label(df: pd.DataFrame) -> pd.DataFrame:
"""
User Pytorch code to transform user image. Note we still use TensorArray as
intermediate format to hold images for now.
"""
preprocess = transforms.Compose([
transforms.ToTensor(),
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
df["image"] = TensorArray(
[preprocess(image.to_numpy()) for image in df["image"]])
# Fix fixed synthetic value for perf benchmark purpose
df["label"] = df["label"].map(lambda _: 1)
return df
def test_tensor_array_reductions():
outer_dim = 3
inner_shape = (2, 2, 2)
shape = (outer_dim,) + inner_shape
num_items = np.prod(np.array(shape))
arr = np.arange(num_items).reshape(shape)
df = pd.DataFrame({"one": list(range(outer_dim)), "two": TensorArray(arr)})
# Reduction tests, using NumPy as the groundtruth.
for name, reducer in TensorArray.SUPPORTED_REDUCERS.items():
np_kwargs = {}
if name in ("std", "var"):
# Pandas uses a ddof default of 1 while NumPy uses 0.
# Give NumPy a ddof kwarg of 1 in order to ensure equivalent
# standard deviation calculations.
np_kwargs["ddof"] = 1
np.testing.assert_equal(df["two"].agg(name), reducer(arr, axis=0, **np_kwargs))
def test_tensor_array_array_protocol():
outer_dim = 3
inner_shape = (2, 2, 2)
shape = (outer_dim,) + inner_shape
num_items = np.prod(np.array(shape))
arr = np.arange(num_items).reshape(shape)
t_arr = TensorArray(arr)
np.testing.assert_array_equal(
np.asarray(t_arr, dtype=np.float32), arr.astype(np.float32)
)
t_arr_elem = t_arr[0]
np.testing.assert_array_equal(
np.asarray(t_arr_elem, dtype=np.float32), arr[0].astype(np.float32)
)
def test_tensor_array_ops():
outer_dim = 3
inner_shape = (2, 2, 2)
shape = (outer_dim,) + inner_shape
num_items = np.prod(np.array(shape))
arr = np.arange(num_items).reshape(shape)
df = pd.DataFrame({"one": [1, 2, 3], "two": TensorArray(arr)})
def apply_arithmetic_ops(arr):
return 2 * (arr + 1) / 3
def apply_comparison_ops(arr):
return arr % 2 == 0
def apply_logical_ops(arr):
return arr & (3 * arr) | (5 * arr)
# Op tests, using NumPy as the groundtruth.
np.testing.assert_equal(apply_arithmetic_ops(arr), apply_arithmetic_ops(df["two"]))
np.testing.assert_equal(apply_comparison_ops(arr), apply_comparison_ops(df["two"]))
np.testing.assert_equal(apply_logical_ops(arr), apply_logical_ops(df["two"]))
def untensorize(torch_tensor):
numpy_array = torch_tensor.cpu().detach().numpy()
return TensorArray(numpy_array)
