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/_onnx_graph.py (1759B)

def load_onnx_graph(fname):
    import onnx
    m = onnx.load(fname)
    g = m.graph
    return parse(g)

def parse(graph):
    nodes_proto = []
    nodes = []
    import itertools
    for node in itertools.chain(graph.input, graph.output):
        nodes_proto.append(node)

for node in nodes_proto:
        print(node.name)
        shapeproto = TensorShapeProto(
            dim=[TensorShapeProto.Dim(size=d.dim_value) for d in node.type.tensor_type.shape.dim])
        nodes.append(NodeDef(
            name=node.name.encode(encoding='utf_8'),
            op='Variable',
            input=[],
            attr={
                'dtype': AttrValue(type=node.type.tensor_type.elem_type),
                'shape': AttrValue(shape=shapeproto),
            })
        )

for node in graph.node:
        _attr = []
        for s in node.attribute:
            _attr.append(' = '.join([str(f[1]) for f in s.ListFields()]))
        attr = ', '.join(_attr).encode(encoding='utf_8')
        print(node.output[0])
        nodes.append(NodeDef(
            name=node.output[0].encode(encoding='utf_8'),
            op=node.op_type,
            input=node.input,
            attr={'parameters': AttrValue(s=attr)},
        ))

# two pass token replacement, appends opname to object id
    mapping = {}
    for node in nodes:
        mapping[node.name] = node.op + '_' + node.name

return GraphDef(node=nodes, versions=VersionDef(producer=22))

from tensorboard.compat.proto.graph_pb2 import GraphDef from tensorboard.compat.proto.node_def_pb2 import NodeDef from tensorboard.compat.proto.versions_pb2 import VersionDef from tensorboard.compat.proto.attr_value_pb2 import AttrValue from tensorboard.compat.proto.tensor_shape_pb2 import TensorShapeProto def load_onnx_graph(fname): import onnx m = onnx.load(fname) g = m.graph return parse(g) def parse(graph): nodes_proto = [] nodes = [] import itertools for node in itertools.chain(graph.input, graph.output): nodes_proto.append(node) for node in nodes_proto: print(node.name) shapeproto = TensorShapeProto( dim=[TensorShapeProto.Dim(size=d.dim_value) for d in node.type.tensor_type.shape.dim]) nodes.append(NodeDef( name=node.name.encode(encoding='utf_8'), op='Variable', input=[], attr={ 'dtype': AttrValue(type=node.type.tensor_type.elem_type), 'shape': AttrValue(shape=shapeproto), }) ) for node in graph.node: _attr = [] for s in node.attribute: _attr.append(' = '.join([str(f[1]) for f in s.ListFields()])) attr = ', '.join(_attr).encode(encoding='utf_8') print(node.output[0]) nodes.append(NodeDef( name=node.output[0].encode(encoding='utf_8'), op=node.op_type, input=node.input, attr={'parameters': AttrValue(s=attr)}, )) # two pass token replacement, appends opname to object id mapping = {} for node in nodes: mapping[node.name] = node.op + '_' + node.name return GraphDef(node=nodes, versions=VersionDef(producer=22))