def extract(cls, node):
attrs = {
'op': __class__.op,
'dim': node.module.shape,
}
Reshape.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
dim = onnx_attr(node, 'shape', 'ints', None)
if dim is not None:
dim = np.array(dim, dtype=np.int64)
Reshape.update_node_stat(node, {'dim': dim})
else:
Reshape.update_node_stat(node)
return cls.enabled
def onnx_reshape_ext(node):
''' Extract ONNX Reshape op of different versions.
Support both latest Reshape and Reshape-1.
The first one has 2 arguments, Reshape-1 has one input and shape is coded in attribute.
'''
dim = onnx_attr(node, 'shape', 'ints', None)
if dim is not None:
dim = np.array(dim, dtype=np.int64)
Reshape.update_node_stat(node, {'dim': dim})
else:
Reshape.update_node_stat(node)
return node.graph.node[node.id]
def extract(cls, node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
dim = attrs.tuple("shape", int, None)
reverse = attrs.bool("reverse", False)
update_attrs = {'dim': int64_array(dim), 'reverse': reverse}
for d in dim:
if d in [-2, -3, -4] or reverse:
MXReshape.update_node_stat(node, update_attrs)
return cls.enabled
# update the attributes of the node
Reshape.update_node_stat(node, update_attrs)
return cls.enabled
def extract(node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
dim = attrs.tuple("shape", int, None)
update_attrs = {'dim': np.array(dim)}
for d in dim:
if d in [-2, -3, -4]:
log.error(
'The attribute "shape" of the operation "{}" contains value "{}" which is not supported.'
.format(node.soft_get('name'), d))
return False
# update the attributes of the node
Reshape.update_node_stat(node, update_attrs)
return __class__.enabled
def extract(cls, node):
param = node.pb.reshape_param
if param.axis != 0:
log.error('The operation "Reshape" has attribute "axis" with unsupported value "{}"'.format(param['axis']))
return False
if param.num_axes != -1:
log.error('The operation "Reshape" has attribute "num_axes" with unsupported value "{}"'.format(
param['num_axes']))
return False
Reshape.update_node_stat(node, {
'dim': list(param.shape.dim),
})
return cls.enabled
def replace_pattern(graph: Graph, match: dict):
flatten = match['reshape']
out_shape = flatten.out_port(0).data.get_shape()
const_id = graph.unique_id(flatten.id + '/DimData')
graph.add_node(
const_id, **{
'kind': 'data',
'value': out_shape,
'shape': np.array(out_shape.shape),
'name': flatten.id + '/DimData'
})
flatten.add_input_port(1, skip_if_exist=True)
graph.add_edge(const_id, flatten.id, **{'in': 1})
flatten['force_precision_in_ports'] = {1: 'int64'}
# TODO workaround for nGraph only!!!
flatten.in_node(1)['value'] = flatten.out_node(0)['shape']
Reshape.update_node_stat(flatten)
flatten['force_precision_in_ports'] = {1: 'int64'}
def extract(cls, node: Node):
Reshape.update_node_stat(node, {'special_zero': False})
return cls.enabled
def extract(node):
Reshape.update_node_stat(node, {})
return __class__.enabled