def make_const_node(data: Tensor, name: str = None) -> NodeDef:
"""
Create a TF graph node containing a constant value.
The resulting node is equivalent to using `tf.constant` on the
default graph.
Args:
data: Numpy-array containing the data, shape, and datatype
name: Optional name of the node
Returns:
Graph node for adding to a TF Graph instance
"""
dtype = as_dtype(data.dtype).as_datatype_enum
tensor_content = data.tobytes()
tensor_dim = [TensorShapeProto.Dim(size=size) for size in data.shape]
tensor_shape = TensorShapeProto(dim=tensor_dim)
tensor_proto = TensorProto(tensor_content=tensor_content,
tensor_shape=tensor_shape,
dtype=dtype)
node_def = NodeDef(op='Const',
name=name or 'Const',
attr={
'value': AttrValue(tensor=tensor_proto),
'dtype': AttrValue(type=dtype)
})
return node_def
def test_extract_shape():
shape = (3, 1, 4)
tensor_proto = TensorProto(
dtype=types_pb2.DT_FLOAT,
tensor_shape=TensorShapeProto(
dim=[TensorShapeProto.Dim(size=n) for n in shape]),
)
result = extract_shape(tensor_proto)
assert result == (3, 1, 4)
def ndarray_to_tensor_proto(ndarray: np.ndarray) -> TensorProto:
dtype = DataType(ndarray.dtype.type)
proto = TensorProto(
dtype=dtype.enum,
tensor_shape=TensorShapeProto(
dim=[TensorShapeProto.Dim(size=d) for d in ndarray.shape]),
)
proto = write_values_to_tensor_proto(tensor_proto=proto,
values=ndarray.ravel(),
dtype=dtype)
return proto
def test_write_values_to_tensor_proto_on_numerics():
dtype = DataType(types_pb2.DT_FLOAT)
array = np.array([0.314, 0.159, 0.268])
tensor_proto = TensorProto(
dtype=dtype.enum,
tensor_shape=TensorShapeProto(
dim=[TensorShapeProto.Dim(size=len(array))]))
write_values_to_tensor_proto(tensor_proto, array.ravel(), dtype)
np.testing.assert_almost_equal(tensor_proto.float_val,
[0.314, 0.159, 0.268])
def _create_const_node(self, name, value):
tensor_content = value.tobytes()
dt = tf.as_dtype(value.dtype).as_datatype_enum
tensor_shape = TensorShapeProto(
dim=[TensorShapeProto.Dim(size=s) for s in value.shape])
tensor_proto = TensorProto(tensor_content=tensor_content,
tensor_shape=tensor_shape,
dtype=dt)
node = tf.compat.v1.NodeDef(name=name, op='Const',
attr={'value': tf.compat.v1.AttrValue(tensor=tensor_proto),
'dtype': tf.compat.v1.AttrValue(type=dt)})
return node
def test_write_values_to_tensor_proto_on_strings():
dtype = DataType(types_pb2.DT_STRING)
array = np.array(["Ceci", "n'est", "pas", "une", "string"])
tensor_proto = TensorProto(
dtype=dtype.enum,
tensor_shape=TensorShapeProto(
dim=[TensorShapeProto.Dim(size=len(array))]))
write_values_to_tensor_proto(tensor_proto, array.ravel(), dtype)
assert tensor_proto.string_val == [
b"Ceci", b"n'est", b"pas", b"une", b"string"
]
def _create_const_node(self, name, value):
for i in range(len(value)):
fixed_name = name
if i > 0:
original_name = name + ":" + str(i)
fixed_name = original_name.replace(":", "_")
self._rename_node_dict[original_name] = fixed_name
tensor_content = value[i].tobytes()
dt = tf.as_dtype(value[i].dtype).as_datatype_enum
tensor_shape = TensorShapeProto(
dim=[TensorShapeProto.Dim(size=s) for s in value[i].shape])
tensor_proto = TensorProto(tensor_content=tensor_content,
tensor_shape=tensor_shape,
dtype=dt)
node = tf.compat.v1.NodeDef(
name=fixed_name,
op='Const',
attr={
'value': tf.compat.v1.AttrValue(tensor=tensor_proto),
'dtype': tf.compat.v1.AttrValue(type=dt)
})
self.op_dict[fixed_name] = Operator(node)
def _gen_transpose_def(name, perm, input_node):
perm_node = tf.NodeDef(name='{}/perm'.format(name), op='Const')
perm_node.attr['dtype'].type = tf.int32.as_datatype_enum
tensor_def = perm_node.attr['value'].tensor
tensor_def.dtype = tf.int32.as_datatype_enum
tensor_def.tensor_shape.dim.append(TensorShapeProto.Dim(size=4))
tensor_def.tensor_content = np.array(perm, dtype=np.int32).tobytes()
transpose_node = tf.NodeDef(name=name, op='Transpose')
transpose_node.input.append(input_node.name)
transpose_node.input.append(perm_node.name)
transpose_node.attr['T'].type = input_node.attr['T'].type
transpose_node.attr['Tperm'].type = tf.int32.as_datatype_enum
return transpose_node, perm_node
def create_const_for_anchor_generator():
"""Creates a 'Const' node as an input to 'MultipleGridAnchorGenerator'
Note the 'MultipleGridAnchorGenerator' TRT plugin node requires a
[1.0, 1.0] array as input.
Reference: https://stackoverflow.com/a/56296195/7596504
"""
import numpy as np
import tensorflow as tf
from tensorflow.core.framework.tensor_pb2 import TensorProto
from tensorflow.core.framework.tensor_shape_pb2 import TensorShapeProto
value = np.array([1.0, 1.0], dtype=np.float32)
dt = tf.as_dtype(value.dtype).as_datatype_enum
tensor_shape = TensorShapeProto(
dim=[TensorShapeProto.Dim(size=s) for s in value.shape])
tensor_proto = TensorProto(tensor_content=value.tobytes(),
tensor_shape=tensor_shape,
dtype=dt)
return tf.NodeDef(name='const_for_anchors',
op='Const',
attr={
'value': tf.AttrValue(tensor=tensor_proto),
'dtype': tf.AttrValue(type=dt)
})