def extract(cls, node):
attrs = {
'data_type': node.value.dtype,
'value': node.value,
}
Const.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
pb_tensor = node.pb.attr["value"].tensor
shape = tf_tensor_shape(pb_tensor.tensor_shape)
attrs = {
'shape': shape,
'value': tf_tensor_content(pb_tensor.dtype, shape, pb_tensor),
'data_type': tf_dtype_extractor(pb_tensor.dtype),
}
Const.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
pb_value = onnx_attr(node, 'value', 't')
value = numpy_helper.to_array(pb_value)
attrs = {
'data_type': value.dtype,
'value': value,
}
Const.update_node_stat(node, attrs)
return cls.enabled
def replace_sub_graph(self, graph: Graph, match: dict):
node = match['op']
if not node.has_valid('value'):
log.debug("No value in FakeConst node {}".format(node.id))
return
node_value = node.value
extracted_attrs = {
'data_type': tf_dtype_extractor(node.pb.attr['dtype'].type),
'shape': int64_array(node_value.shape),
'value': node_value
}
Const.update_node_stat(node, extracted_attrs)
log.debug(
'FakeConst op was translated to Const op with shape = {} and value.shape = {}'
''.format(extracted_attrs['shape'],
extracted_attrs['value'].shape))
def extract(cls, node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
shape = list(attrs.tuple('shape', int, None))
zero_shapes = []
for i, s in enumerate(shape):
if s == 0:
shape[i] = 1
zero_shapes.append(i)
update_attrs = {
'shape': np.ndarray(shape),
'value': np.zeros(shape),
'zero_shapes': zero_shapes
}
# update the attributes of the node
Const.update_node_stat(node, update_attrs)
return cls.enabled
def extract(cls, node):
if 'value' in node.symbol_dict:
Const.update_node_stat(node, {'value': node.symbol_dict['value']})
else:
Parameter.update_node_stat(node, {})
return cls.enabled
def extract(cls, node):
value = to_array(node.pb_init)
attrs = {'data_type': value.dtype, 'value': value}
Const.update_node_stat(node, attrs)
return cls.enabled
