def __init__(self, name=None, shape=None, dtype=None):
super(TensorType, self).__init__(name, shape)
if dtype is None:
self.dtype = types.fp32
elif is_builtin(dtype):
self.dtype = dtype
else:
# Assume dtype is numpy type
try:
self.dtype = numpy_type_to_builtin_type(dtype)
except TypeError:
raise TypeError("dtype={} is unsupported".format(dtype))
def __init__(self, name=None, shape=None, dtype=None, default_value=None):
"""
Specify a (dense) tensor input.
Parameters
----------
name: str
Input name. Must match an input name in the model (usually the
Placeholder name for TensorFlow or the input name for PyTorch).
The ``name`` is required except for a TensorFlow model in which there is
exactly one input Placeholder.
shape: (1) list of positive int or RangeDim, or (2) EnumeratedShapes
The shape of the input.
For TensorFlow:
* The ``shape`` is optional. If omitted, the shape is inferred from
TensorFlow graph's Placeholder shape.
For PyTorch:
* The ``shape`` is required.
dtype: np.generic or mil.type type
Numpy ``dtype`` (for example, ``np.int32``). Default is ``np.float32``.
default_value: np.ndarray
If provided, the input is considered optional. At runtime, if the
input is not provided, ``default_value`` is used.
Limitations:
* If ``default_value`` is ``np.ndarray``, all
elements are required to have the same value.
* The ``default_value`` may not be specified if ``shape`` is
``EnumeratedShapes``.
Examples
--------
* ``ct.TensorType(name="input", shape=(1, 2, 3))` implies `dtype ==
np.float32``
* ``ct.TensorType(name="input", shape=(1, 2, 3), dtype=np.int32)``
* ``ct.TensorType(name="input", shape=(1, 2, 3),
dtype=ct.converters.mil.types.fp32)``
"""
super(TensorType, self).__init__(name, shape)
if dtype is None:
self.dtype = types.fp32
elif is_builtin(dtype):
self.dtype = dtype
else:
# Assume dtype is numpy type
try:
self.dtype = numpy_type_to_builtin_type(dtype)
except TypeError:
raise TypeError("dtype={} is unsupported".format(dtype))
if default_value is not None:
if isinstance(shape, EnumeratedShapes):
msg = 'TensorType input {} has EnumeratedShapes and ' +\
'may not be optional'
raise ValueError(msg.format(name))
if not isinstance(default_value, np.ndarray):
msg = 'TensorType {} default_value is not np.ndarray'
raise ValueError(msg.format(name))
default_fill_val = default_value.flatten()[0]
if not np.all(default_value == default_fill_val):
msg = 'TensorType {} default_value can only have ' +\
'same entries'
raise ValueError(msg.format(name))
if not self.shape.has_symbolic and \
list(default_value.shape) != list(self.shape.symbolic_shape):
msg = 'TensorType {} default_value shape {} != ' +\
'TensorType.shape {}'
raise ValueError(
msg.format(name, default_value.shape,
self.shape.to_list()))
if numpy_type_to_builtin_type(default_value.dtype) != self.dtype:
msg = 'TensorType {} default_value dtype {} != ' +\
'TensorType.dtype {}'
raise ValueError(
msg.format(name, default_value.dtype,
self.dtype.__type_info__()))
self.default_value = default_value
