def decode_raw(input_bytes,
out_type,
little_endian=True,
fixed_length=None,
name=None):
"""Convert raw byte strings into tensors.
Args:
input_bytes:
Each element of the input Tensor is converted to an array of bytes.
out_type:
`DType` of the output. Acceptable types are `half`, `float`, `double`,
`int32`, `uint16`, `uint8`, `int16`, `int8`, `int64`.
little_endian:
Whether the `input_bytes` data is in little-endian format. Data will be
converted into host byte order if necessary.
fixed_length:
If set, the first `fixed_length` bytes of each element will be converted.
Data will be zero-padded or truncated to the specified length.
`fixed_length` must be a multiple of the size of `out_type`.
`fixed_length` must be specified if the elements of `input_bytes` are of
variable length.
name: A name for the operation (optional).
Returns:
A `Tensor` object storing the decoded bytes.
"""
if fixed_length is not None:
return gen_parsing_ops.decode_padded_raw(input_bytes,
fixed_length=fixed_length,
out_type=out_type,
little_endian=little_endian,
name=name)
else:
return gen_parsing_ops.decode_raw(input_bytes,
out_type,
little_endian=little_endian,
name=name)
def decode_raw(input_bytes,
out_type,
little_endian=True,
fixed_length=None,
name=None):
r"""Convert raw bytes from input tensor into numeric tensors.
The input tensor is interpreted as a sequence of bytes. These bytes are then
decoded as numbers in the format specified by `out_type`.
>>> tf.io.decode_raw(tf.constant("1"), tf.uint8)
<tf.Tensor: shape=(1,), dtype=uint8, numpy=array([49], dtype=uint8)>
>>> tf.io.decode_raw(tf.constant("1,2"), tf.uint8)
<tf.Tensor: shape=(3,), dtype=uint8, numpy=array([49, 44, 50], dtype=uint8)>
Note that the rank of the output tensor is always one more than the input one:
>>> tf.io.decode_raw(tf.constant(["1","2"]), tf.uint8).shape
TensorShape([2, 1])
>>> tf.io.decode_raw(tf.constant([["1"],["2"]]), tf.uint8).shape
TensorShape([2, 1, 1])
This is because each byte in the input is converted to a new value on the
output (if output type is `uint8` or `int8`, otherwise chunks of inputs get
coverted to a new value):
>>> tf.io.decode_raw(tf.constant("123"), tf.uint8)
<tf.Tensor: shape=(3,), dtype=uint8, numpy=array([49, 50, 51], dtype=uint8)>
>>> tf.io.decode_raw(tf.constant("1234"), tf.uint8)
<tf.Tensor: shape=(4,), dtype=uint8, numpy=array([49, 50, 51, 52], ...
>>> # chuncked output
>>> tf.io.decode_raw(tf.constant("12"), tf.uint16)
<tf.Tensor: shape=(1,), dtype=uint16, numpy=array([12849], dtype=uint16)>
>>> tf.io.decode_raw(tf.constant("1234"), tf.uint16)
<tf.Tensor: shape=(2,), dtype=uint16, numpy=array([12849, 13363], ...
>>> # int64 output
>>> tf.io.decode_raw(tf.constant("12345678"), tf.int64)
<tf.Tensor: ... numpy=array([4050765991979987505])>
>>> tf.io.decode_raw(tf.constant("1234567887654321"), tf.int64)
<tf.Tensor: ... numpy=array([4050765991979987505, 3544952156018063160])>
The operation allows specifying endianness via the `little_endian` parameter.
>>> tf.io.decode_raw(tf.constant("\x0a\x0b"), tf.int16)
<tf.Tensor: shape=(1,), dtype=int16, numpy=array([2826], dtype=int16)>
>>> hex(2826)
'0xb0a'
>>> tf.io.decode_raw(tf.constant("\x0a\x0b"), tf.int16, little_endian=False)
<tf.Tensor: shape=(1,), dtype=int16, numpy=array([2571], dtype=int16)>
>>> hex(2571)
'0xa0b'
If the elements of `input_bytes` are of different length, you must specify
`fixed_length`:
>>> tf.io.decode_raw(tf.constant([["1"],["23"]]), tf.uint8, fixed_length=4)
<tf.Tensor: shape=(2, 1, 4), dtype=uint8, numpy=
array([[[49, 0, 0, 0]],
[[50, 51, 0, 0]]], dtype=uint8)>
If the `fixed_length` value is larger that the length of the `out_type` dtype,
multiple values are generated:
>>> tf.io.decode_raw(tf.constant(["1212"]), tf.uint16, fixed_length=4)
<tf.Tensor: shape=(1, 2), dtype=uint16, numpy=array([[12849, 12849]], ...
Note: There is currently a bug in `fixed_length` that can result in data loss:
>>> # truncated to length of type as it matches fixed_length
>>> tf.io.decode_raw(tf.constant(["1212"]), tf.uint16, fixed_length=2)
<tf.Tensor: shape=(1, 1), dtype=uint16, numpy=array([[12849]], dtype=uint16)>
>>> # ignores the second component
>>> tf.io.decode_raw(tf.constant(["12","34"]), tf.uint16, fixed_length=2)
<tf.Tensor: shape=(2, 1), dtype=uint16, numpy=
array([[12849],
[ 0]], dtype=uint16)>
>>> tf.io.decode_raw(tf.constant(["12","34"]), tf.uint16, fixed_length=4)
<tf.Tensor: shape=(2, 2), dtype=uint16, numpy=
array([[12849, 0],
[ 0, 0]], dtype=uint16)>
This will be fixed on a future release of TensorFlow.
Args:
input_bytes:
Each element of the input Tensor is converted to an array of bytes.
Currently, this must be a tensor of strings (bytes), although semantically
the operation should support any input.
out_type:
`DType` of the output. Acceptable types are `half`, `float`, `double`,
`int32`, `uint16`, `uint8`, `int16`, `int8`, `int64`.
little_endian:
Whether the `input_bytes` data is in little-endian format. Data will be
converted into host byte order if necessary.
fixed_length:
If set, the first `fixed_length` bytes of each element will be converted.
Data will be zero-padded or truncated to the specified length.
`fixed_length` must be a multiple of the size of `out_type`.
`fixed_length` must be specified if the elements of `input_bytes` are of
variable length.
name: A name for the operation (optional).
Returns:
A `Tensor` object storing the decoded bytes.
"""
if fixed_length is not None:
return gen_parsing_ops.decode_padded_raw(input_bytes,
fixed_length=fixed_length,
out_type=out_type,
little_endian=little_endian,
name=name)
else:
return gen_parsing_ops.decode_raw(input_bytes,
out_type,
little_endian=little_endian,
name=name)