def extract(cls, node):
# some Dropout flavors doesn't have is_test attribute; when it is missing, interpret it as 1
is_test = onnx_attr(node, 'is_test', 'i', 1)
if len(node.out_nodes()) > 1:
raise Error(
'Dropout node {} has more than one consumer. Unsupported.',
node.name)
if not is_test:
raise Error(
'Dropout node {} has is_test: 0. This means training mode which is not supported.',
node.name)
Identity.update_node_stat(node)
return cls.enabled
def extract(cls, node):
pb = node.parameters
collect_until_token(pb, b'<Dim>')
dim = read_binary_integer32_token(pb)
collect_until_token(pb, b'<BlockDim>')
block_dim = read_binary_integer32_token(pb)
collect_until_token(pb, b'<TimePeriod>')
time_period = read_binary_integer32_token(pb)
collect_until_token(pb, b'<DropoutProportion>')
dropout_proporion = read_binary_float_token(pb)
# collect_until_token(pb, b'<Continuous>')
Identity.update_node_stat(node, {})
return cls.enabled
def extract(cls, node: Node):
Identity.update_node_stat(node, {})
return cls.enabled
def extract(cls, node: Node):
Identity.update_node_stat(node, {'op': 'StopGradient'})
return cls.enabled
def extract(cls, node: Node):
Identity.update_node_stat(node, {
'data_type': tf_dtype_extractor(node.pb.attr["T"].type),
})
return cls.enabled