MLPY/Lib/site-packages/tensorboard/util/tensor_util.py

# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utilities to manipulate TensorProtos."""

import numpy as np

from tensorboard.compat.proto import tensor_pb2
from tensorboard.compat.tensorflow_stub import dtypes, compat, tensor_shape


def ExtractBitsFromFloat16(x):
    return np.asarray(x, dtype=np.float16).view(np.uint16).item()


def SlowAppendFloat16ArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.half_val.extend(
        [ExtractBitsFromFloat16(x) for x in proto_values]
    )


def ExtractBitsFromBFloat16(x):
    return (
        np.asarray(x, dtype=dtypes.bfloat16.as_numpy_dtype)
        .view(np.uint16)
        .item()
    )


def SlowAppendBFloat16ArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.half_val.extend(
        [ExtractBitsFromBFloat16(x) for x in proto_values]
    )


def SlowAppendFloat32ArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.float_val.extend([x.item() for x in proto_values])


def SlowAppendFloat64ArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.double_val.extend([x.item() for x in proto_values])


def SlowAppendIntArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.int_val.extend([x.item() for x in proto_values])


def SlowAppendInt64ArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.int64_val.extend([x.item() for x in proto_values])


def SlowAppendQIntArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.int_val.extend([x[0].item() for x in proto_values])


def SlowAppendUInt32ArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.uint32_val.extend([x.item() for x in proto_values])


def SlowAppendUInt64ArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.uint64_val.extend([x.item() for x in proto_values])


def SlowAppendComplex64ArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.scomplex_val.extend(
        [v.item() for x in proto_values for v in [x.real, x.imag]]
    )


def SlowAppendComplex128ArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.dcomplex_val.extend(
        [v.item() for x in proto_values for v in [x.real, x.imag]]
    )


def SlowAppendObjectArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])


def SlowAppendBoolArrayToTensorProto(tensor_proto, proto_values):
    tensor_proto.bool_val.extend([x.item() for x in proto_values])


_NP_TO_APPEND_FN = {
    np.float16: SlowAppendFloat16ArrayToTensorProto,
    np.float32: SlowAppendFloat32ArrayToTensorProto,
    np.float64: SlowAppendFloat64ArrayToTensorProto,
    np.int32: SlowAppendIntArrayToTensorProto,
    np.int64: SlowAppendInt64ArrayToTensorProto,
    np.uint8: SlowAppendIntArrayToTensorProto,
    np.uint16: SlowAppendIntArrayToTensorProto,
    np.uint32: SlowAppendUInt32ArrayToTensorProto,
    np.uint64: SlowAppendUInt64ArrayToTensorProto,
    np.int8: SlowAppendIntArrayToTensorProto,
    np.int16: SlowAppendIntArrayToTensorProto,
    np.complex64: SlowAppendComplex64ArrayToTensorProto,
    np.complex128: SlowAppendComplex128ArrayToTensorProto,
    np.object_: SlowAppendObjectArrayToTensorProto,
    np.bool_: SlowAppendBoolArrayToTensorProto,
    dtypes.qint8.as_numpy_dtype: SlowAppendQIntArrayToTensorProto,
    dtypes.quint8.as_numpy_dtype: SlowAppendQIntArrayToTensorProto,
    dtypes.qint16.as_numpy_dtype: SlowAppendQIntArrayToTensorProto,
    dtypes.quint16.as_numpy_dtype: SlowAppendQIntArrayToTensorProto,
    dtypes.qint32.as_numpy_dtype: SlowAppendQIntArrayToTensorProto,
    # NOTE(touts): Intentionally no way to feed a DT_BFLOAT16.
}

BACKUP_DICT = {
    dtypes.bfloat16.as_numpy_dtype: SlowAppendBFloat16ArrayToTensorProto
}


def GetFromNumpyDTypeDict(dtype_dict, dtype):
    # NOTE: dtype_dict.get(dtype) always returns None.
    for key, val in dtype_dict.items():
        if key == dtype:
            return val
    for key, val in BACKUP_DICT.items():
        if key == dtype:
            return val
    return None


def GetNumpyAppendFn(dtype):
    # numpy dtype for strings are variable length. We can not compare
    # dtype with a single constant (np.string does not exist) to decide
    # dtype is a "string" type. We need to compare the dtype.type to be
    # sure it's a string type.
    if dtype.type == np.string_ or dtype.type == np.unicode_:
        return SlowAppendObjectArrayToTensorProto
    return GetFromNumpyDTypeDict(_NP_TO_APPEND_FN, dtype)


def _GetDenseDimensions(list_of_lists):
    """Returns the inferred dense dimensions of a list of lists."""
    if not isinstance(list_of_lists, (list, tuple)):
        return []
    elif not list_of_lists:
        return [0]
    else:
        return [len(list_of_lists)] + _GetDenseDimensions(list_of_lists[0])


def _FlattenToStrings(nested_strings):
    if isinstance(nested_strings, (list, tuple)):
        for inner in nested_strings:
            for flattened_string in _FlattenToStrings(inner):
                yield flattened_string
    else:
        yield nested_strings


_TENSOR_CONTENT_TYPES = frozenset(
    [
        dtypes.float32,
        dtypes.float64,
        dtypes.int32,
        dtypes.uint8,
        dtypes.int16,
        dtypes.int8,
        dtypes.int64,
        dtypes.qint8,
        dtypes.quint8,
        dtypes.qint16,
        dtypes.quint16,
        dtypes.qint32,
        dtypes.uint32,
        dtypes.uint64,
    ]
)


class _Message:
    def __init__(self, message):
        self._message = message

    def __repr__(self):
        return self._message


def _FirstNotNone(l):
    for x in l:
        if x is not None:
            return x
    return None


def _NotNone(v):
    if v is None:
        return _Message("None")
    else:
        return v


def _FilterTuple(v):
    if not isinstance(v, (list, tuple)):
        return v
    if isinstance(v, tuple):
        if not any(isinstance(x, (list, tuple)) for x in v):
            return None
    if isinstance(v, list):
        if not any(isinstance(x, (list, tuple)) for x in v):
            return _FirstNotNone(
                [None if isinstance(x, (list, tuple)) else x for x in v]
            )
    return _FirstNotNone([_FilterTuple(x) for x in v])


def _FilterInt(v):
    if isinstance(v, (list, tuple)):
        return _FirstNotNone([_FilterInt(x) for x in v])
    return (
        None
        if isinstance(v, (compat.integral_types, tensor_shape.Dimension))
        else _NotNone(v)
    )


def _FilterFloat(v):
    if isinstance(v, (list, tuple)):
        return _FirstNotNone([_FilterFloat(x) for x in v])
    return None if isinstance(v, compat.real_types) else _NotNone(v)


def _FilterComplex(v):
    if isinstance(v, (list, tuple)):
        return _FirstNotNone([_FilterComplex(x) for x in v])
    return None if isinstance(v, compat.complex_types) else _NotNone(v)


def _FilterStr(v):
    if isinstance(v, (list, tuple)):
        return _FirstNotNone([_FilterStr(x) for x in v])
    if isinstance(v, compat.bytes_or_text_types):
        return None
    else:
        return _NotNone(v)


def _FilterBool(v):
    if isinstance(v, (list, tuple)):
        return _FirstNotNone([_FilterBool(x) for x in v])
    return None if isinstance(v, bool) else _NotNone(v)


_TF_TO_IS_OK = {
    dtypes.bool: [_FilterBool],
    dtypes.complex128: [_FilterComplex],
    dtypes.complex64: [_FilterComplex],
    dtypes.float16: [_FilterFloat],
    dtypes.float32: [_FilterFloat],
    dtypes.float64: [_FilterFloat],
    dtypes.int16: [_FilterInt],
    dtypes.int32: [_FilterInt],
    dtypes.int64: [_FilterInt],
    dtypes.int8: [_FilterInt],
    dtypes.qint16: [_FilterInt, _FilterTuple],
    dtypes.qint32: [_FilterInt, _FilterTuple],
    dtypes.qint8: [_FilterInt, _FilterTuple],
    dtypes.quint16: [_FilterInt, _FilterTuple],
    dtypes.quint8: [_FilterInt, _FilterTuple],
    dtypes.string: [_FilterStr],
    dtypes.uint16: [_FilterInt],
    dtypes.uint8: [_FilterInt],
}


def _Assertconvertible(values, dtype):
    # If dtype is None or not recognized, assume it's convertible.
    if dtype is None or dtype not in _TF_TO_IS_OK:
        return
    fn_list = _TF_TO_IS_OK.get(dtype)
    mismatch = _FirstNotNone([fn(values) for fn in fn_list])
    if mismatch is not None:
        raise TypeError(
            "Expected %s, got %s of type '%s' instead."
            % (dtype.name, repr(mismatch), type(mismatch).__name__)
        )


def make_tensor_proto(values, dtype=None, shape=None, verify_shape=False):
    """Create a TensorProto.

    Args:
      values:         Values to put in the TensorProto.
      dtype:          Optional tensor_pb2 DataType value.
      shape:          List of integers representing the dimensions of tensor.
      verify_shape:   Boolean that enables verification of a shape of values.

    Returns:
      A `TensorProto`. Depending on the type, it may contain data in the
      "tensor_content" attribute, which is not directly useful to Python programs.
      To access the values you should convert the proto back to a numpy ndarray
      with `tensor_util.MakeNdarray(proto)`.

      If `values` is a `TensorProto`, it is immediately returned; `dtype` and
      `shape` are ignored.

    Raises:
      TypeError:  if unsupported types are provided.
      ValueError: if arguments have inappropriate values or if verify_shape is
       True and shape of values is not equals to a shape from the argument.

    make_tensor_proto accepts "values" of a python scalar, a python list, a
    numpy ndarray, or a numpy scalar.

    If "values" is a python scalar or a python list, make_tensor_proto
    first convert it to numpy ndarray. If dtype is None, the
    conversion tries its best to infer the right numpy data
    type. Otherwise, the resulting numpy array has a convertible data
    type with the given dtype.

    In either case above, the numpy ndarray (either the caller provided
    or the auto converted) must have the convertible type with dtype.

    make_tensor_proto then converts the numpy array to a tensor proto.

    If "shape" is None, the resulting tensor proto represents the numpy
    array precisely.

    Otherwise, "shape" specifies the tensor's shape and the numpy array
    can not have more elements than what "shape" specifies.
    """
    if isinstance(values, tensor_pb2.TensorProto):
        return values

    if dtype:
        dtype = dtypes.as_dtype(dtype)

    is_quantized = dtype in [
        dtypes.qint8,
        dtypes.quint8,
        dtypes.qint16,
        dtypes.quint16,
        dtypes.qint32,
    ]

    # We first convert value to a numpy array or scalar.
    if isinstance(values, (np.ndarray, np.generic)):
        if dtype:
            nparray = values.astype(dtype.as_numpy_dtype)
        else:
            nparray = values
    elif callable(getattr(values, "__array__", None)) or isinstance(
        getattr(values, "__array_interface__", None), dict
    ):
        # If a class has the __array__ method, or __array_interface__ dict, then it
        # is possible to convert to numpy array.
        nparray = np.asarray(values, dtype=dtype)

        # This is the preferred way to create an array from the object, so replace
        # the `values` with the array so that _FlattenToStrings is not run.
        values = nparray
    else:
        if values is None:
            raise ValueError("None values not supported.")
        # if dtype is provided, forces numpy array to be the type
        # provided if possible.
        if dtype and dtype.is_numpy_compatible:
            np_dt = dtype.as_numpy_dtype
        else:
            np_dt = None
        # If shape is None, numpy.prod returns None when dtype is not set, but raises
        # exception when dtype is set to np.int64
        if shape is not None and np.prod(shape, dtype=np.int64) == 0:
            nparray = np.empty(shape, dtype=np_dt)
        else:
            _Assertconvertible(values, dtype)
            nparray = np.array(values, dtype=np_dt)
            # check to them.
            # We need to pass in quantized values as tuples, so don't apply the shape
            if (
                list(nparray.shape) != _GetDenseDimensions(values)
                and not is_quantized
            ):
                raise ValueError(
                    """Argument must be a dense tensor: %s"""
                    """ - got shape %s, but wanted %s."""
                    % (values, list(nparray.shape), _GetDenseDimensions(values))
                )

        # python/numpy default float type is float64. We prefer float32 instead.
        if (nparray.dtype == np.float64) and dtype is None:
            nparray = nparray.astype(np.float32)
        # python/numpy default int type is int64. We prefer int32 instead.
        elif (nparray.dtype == np.int64) and dtype is None:
            downcasted_array = nparray.astype(np.int32)
            # Do not down cast if it leads to precision loss.
            if np.array_equal(downcasted_array, nparray):
                nparray = downcasted_array

    # if dtype is provided, it must be convertible with what numpy
    # conversion says.
    numpy_dtype = dtypes.as_dtype(nparray.dtype)
    if numpy_dtype is None:
        raise TypeError("Unrecognized data type: %s" % nparray.dtype)

    # If dtype was specified and is a quantized type, we convert
    # numpy_dtype back into the quantized version.
    if is_quantized:
        numpy_dtype = dtype

    if dtype is not None and (
        not hasattr(dtype, "base_dtype")
        or dtype.base_dtype != numpy_dtype.base_dtype
    ):
        raise TypeError(
            "Inconvertible types: %s vs. %s. Value is %s"
            % (dtype, nparray.dtype, values)
        )

    # If shape is not given, get the shape from the numpy array.
    if shape is None:
        shape = nparray.shape
        is_same_size = True
        shape_size = nparray.size
    else:
        shape = [int(dim) for dim in shape]
        shape_size = np.prod(shape, dtype=np.int64)
        is_same_size = shape_size == nparray.size

        if verify_shape:
            if not nparray.shape == tuple(shape):
                raise TypeError(
                    "Expected Tensor's shape: %s, got %s."
                    % (tuple(shape), nparray.shape)
                )

        if nparray.size > shape_size:
            raise ValueError(
                "Too many elements provided. Needed at most %d, but received %d"
                % (shape_size, nparray.size)
            )

    tensor_proto = tensor_pb2.TensorProto(
        dtype=numpy_dtype.as_datatype_enum,
        tensor_shape=tensor_shape.as_shape(shape).as_proto(),
    )

    if is_same_size and numpy_dtype in _TENSOR_CONTENT_TYPES and shape_size > 1:
        if nparray.size * nparray.itemsize >= (1 << 31):
            raise ValueError(
                "Cannot create a tensor proto whose content is larger than 2GB."
            )
        tensor_proto.tensor_content = nparray.tobytes()
        return tensor_proto

    # If we were not given values as a numpy array, compute the proto_values
    # from the given values directly, to avoid numpy trimming nulls from the
    # strings. Since values could be a list of strings, or a multi-dimensional
    # list of lists that might or might not correspond to the given shape,
    # we flatten it conservatively.
    if numpy_dtype == dtypes.string and not isinstance(values, np.ndarray):
        proto_values = _FlattenToStrings(values)

        # At this point, values may be a list of objects that we could not
        # identify a common type for (hence it was inferred as
        # np.object/dtypes.string).  If we are unable to convert it to a
        # string, we raise a more helpful error message.
        #
        # Ideally, we'd be able to convert the elements of the list to a
        # common type, but this type inference requires some thinking and
        # so we defer it for now.
        try:
            str_values = [compat.as_bytes(x) for x in proto_values]
        except TypeError:
            raise TypeError(
                "Failed to convert object of type %s to Tensor. "
                "Contents: %s. Consider casting elements to a "
                "supported type." % (type(values), values)
            )
        tensor_proto.string_val.extend(str_values)
        return tensor_proto

    # TensorFlow expects C order (a.k.a., eigen row major).
    proto_values = nparray.ravel()

    append_fn = GetNumpyAppendFn(proto_values.dtype)
    if append_fn is None:
        raise TypeError(
            "Element type not supported in TensorProto: %s" % numpy_dtype.name
        )
    append_fn(tensor_proto, proto_values)

    return tensor_proto


def make_ndarray(tensor):
    """Create a numpy ndarray from a tensor.

    Create a numpy ndarray with the same shape and data as the tensor.

    Args:
      tensor: A TensorProto.

    Returns:
      A numpy array with the tensor contents.

    Raises:
      TypeError: if tensor has unsupported type.
    """
    shape = [d.size for d in tensor.tensor_shape.dim]
    num_elements = np.prod(shape, dtype=np.int64)
    tensor_dtype = dtypes.as_dtype(tensor.dtype)
    dtype = tensor_dtype.as_numpy_dtype

    if tensor.tensor_content:
        return (
            np.frombuffer(tensor.tensor_content, dtype=dtype)
            .copy()
            .reshape(shape)
        )
    elif tensor_dtype == dtypes.float16 or tensor_dtype == dtypes.bfloat16:
        # the half_val field of the TensorProto stores the binary representation
        # of the fp16: we need to reinterpret this as a proper float16
        if len(tensor.half_val) == 1:
            tmp = np.array(tensor.half_val[0], dtype=np.uint16)
            tmp.dtype = tensor_dtype.as_numpy_dtype
            return np.repeat(tmp, num_elements).reshape(shape)
        else:
            tmp = np.fromiter(tensor.half_val, dtype=np.uint16)
            tmp.dtype = tensor_dtype.as_numpy_dtype
            return tmp.reshape(shape)
    elif tensor_dtype == dtypes.float32:
        if len(tensor.float_val) == 1:
            return np.repeat(
                np.array(tensor.float_val[0], dtype=dtype), num_elements
            ).reshape(shape)
        else:
            return np.fromiter(tensor.float_val, dtype=dtype).reshape(shape)
    elif tensor_dtype == dtypes.float64:
        if len(tensor.double_val) == 1:
            return np.repeat(
                np.array(tensor.double_val[0], dtype=dtype), num_elements
            ).reshape(shape)
        else:
            return np.fromiter(tensor.double_val, dtype=dtype).reshape(shape)
    elif tensor_dtype in [
        dtypes.int32,
        dtypes.uint8,
        dtypes.uint16,
        dtypes.int16,
        dtypes.int8,
        dtypes.qint32,
        dtypes.quint8,
        dtypes.qint8,
        dtypes.qint16,
        dtypes.quint16,
    ]:
        if len(tensor.int_val) == 1:
            return np.repeat(
                np.array(tensor.int_val[0], dtype=dtype), num_elements
            ).reshape(shape)
        else:
            return np.fromiter(tensor.int_val, dtype=dtype).reshape(shape)
    elif tensor_dtype == dtypes.int64:
        if len(tensor.int64_val) == 1:
            return np.repeat(
                np.array(tensor.int64_val[0], dtype=dtype), num_elements
            ).reshape(shape)
        else:
            return np.fromiter(tensor.int64_val, dtype=dtype).reshape(shape)
    elif tensor_dtype == dtypes.string:
        if len(tensor.string_val) == 1:
            return np.repeat(
                np.array(tensor.string_val[0], dtype=dtype), num_elements
            ).reshape(shape)
        else:
            return np.array(list(tensor.string_val), dtype=dtype).reshape(shape)
    elif tensor_dtype == dtypes.complex64:
        it = iter(tensor.scomplex_val)
        if len(tensor.scomplex_val) == 2:
            return np.repeat(
                np.array(
                    complex(tensor.scomplex_val[0], tensor.scomplex_val[1]),
                    dtype=dtype,
                ),
                num_elements,
            ).reshape(shape)
        else:
            return np.array(
                [complex(x[0], x[1]) for x in zip(it, it)], dtype=dtype
            ).reshape(shape)
    elif tensor_dtype == dtypes.complex128:
        it = iter(tensor.dcomplex_val)
        if len(tensor.dcomplex_val) == 2:
            return np.repeat(
                np.array(
                    complex(tensor.dcomplex_val[0], tensor.dcomplex_val[1]),
                    dtype=dtype,
                ),
                num_elements,
            ).reshape(shape)
        else:
            return np.array(
                [complex(x[0], x[1]) for x in zip(it, it)], dtype=dtype
            ).reshape(shape)
    elif tensor_dtype == dtypes.bool:
        if len(tensor.bool_val) == 1:
            return np.repeat(
                np.array(tensor.bool_val[0], dtype=dtype), num_elements
            ).reshape(shape)
        else:
            return np.fromiter(tensor.bool_val, dtype=dtype).reshape(shape)
    else:
        raise TypeError("Unsupported tensor type: %s" % tensor.dtype)