FM / tensorboard

/usr/local/lib64/python3.6/site-packages/torch/utils/tensorboard/

/usr/local/lib64/python3.6/site-packages/torch/utils/tensorboard

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
summary.py	29778	0644	edit dl rm
writer.py	46866	0644	edit dl rm
_caffe2_graph.py	26596	0644	edit dl rm
_convert_np.py	885	0644	edit dl rm
_embedding.py	2884	0644	edit dl rm
_onnx_graph.py	1759	0644	edit dl rm
_proto_graph.py	1688	0644	edit dl rm
_pytorch_graph.py	12983	0644	edit dl rm
_utils.py	4083	0644	edit dl rm
__init__.py	485	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/utils/tensorboard/_proto_graph.py (1688B)

from tensorboard.compat.proto.node_def_pb2 import NodeDef
from tensorboard.compat.proto.attr_value_pb2 import AttrValue
from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto

def attr_value_proto(dtype, shape, s):
    """Creates a dict of objects matching
    https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/attr_value.proto
    specifically designed for a NodeDef. The values have been
    reverse engineered from standard TensorBoard logged data.
    """
    attr = {}
    if s is not None:
        attr['attr'] = AttrValue(s=s.encode(encoding='utf_8'))
    if shape is not None:
        shapeproto = tensor_shape_proto(shape)
        attr['_output_shapes'] = AttrValue(list=AttrValue.ListValue(shape=[shapeproto]))
    return attr

def tensor_shape_proto(outputsize):
    """Creates an object matching
    https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/tensor_shape.proto
    """
    return TensorShapeProto(dim=[TensorShapeProto.Dim(size=d) for d in outputsize])

def node_proto(name,
               op='UnSpecified',
               input=None,
               dtype=None,
               shape=None,  # type: tuple
               outputsize=None,
               attributes=''
               ):
    """Creates an object matching
    https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/node_def.proto
    """
    if input is None:
        input = []
    if not isinstance(input, list):
        input = [input]
    return NodeDef(
        name=name.encode(encoding='utf_8'),
        op=op,
        input=input,
        attr=attr_value_proto(dtype, outputsize, attributes)
    )

from tensorboard.compat.proto.node_def_pb2 import NodeDef from tensorboard.compat.proto.attr_value_pb2 import AttrValue from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto def attr_value_proto(dtype, shape, s): """Creates a dict of objects matching https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/attr_value.proto specifically designed for a NodeDef. The values have been reverse engineered from standard TensorBoard logged data. """ attr = {} if s is not None: attr['attr'] = AttrValue(s=s.encode(encoding='utf_8')) if shape is not None: shapeproto = tensor_shape_proto(shape) attr['_output_shapes'] = AttrValue(list=AttrValue.ListValue(shape=[shapeproto])) return attr def tensor_shape_proto(outputsize): """Creates an object matching https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/tensor_shape.proto """ return TensorShapeProto(dim=[TensorShapeProto.Dim(size=d) for d in outputsize]) def node_proto(name, op='UnSpecified', input=None, dtype=None, shape=None, # type: tuple outputsize=None, attributes='' ): """Creates an object matching https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/node_def.proto """ if input is None: input = [] if not isinstance(input, list): input = [input] return NodeDef( name=name.encode(encoding='utf_8'), op=op, input=input, attr=attr_value_proto(dtype, outputsize, attributes) )