#
# Copyright 2018 Analytics Zoo Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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import zoo.pipeline.api.keras.layers as zlayers
import zoo.pipeline.api.autograd as zautograd
from zoo.pipeline.api.onnx.onnx_helper import OnnxHelper
import zoo.pipeline.api.onnx.mapper
import importlib
import numpy as np
[docs]class OperatorMapper(object):
# converting NodeProto message
# we don't differentiate the data input and weights here, they are all included into inputs.
def __init__(self, node, initializer, inputs):
self.node = node
self.op_name = node.op_type
node_name = node.name.strip()
# it would be None if user doesn't set it manually
self.node_name = node_name if node_name else None
self.onnx_attr = OnnxHelper.parse_attr(node.attribute) # dict(name: value)
self._initializer = initializer
self._input_list = inputs
self.model_inputs = self._extract_model_inputs()
self.model_trainable_values = self._extract_trainable_values()
self.output = node.output[0]
[docs] @staticmethod
def of(node, _params, inputs):
m = importlib.import_module("zoo.pipeline.api.onnx.mapper." + node.op_type.lower())
cls = getattr(m, node.op_type + "Mapper")
return cls(node, _params, inputs)
def _to_zoo_input(self, input, is_constant=None):
is_parameter = True if input.name in self._initializer else False
if isinstance(input.zvalue, zautograd.Variable) or isinstance(input.zvalue,
zautograd.Parameter):
return input
if isinstance(input.zvalue, np.ndarray):
if is_parameter or is_constant:
shape = input.zvalue.shape
else:
shape = input.zvalue.shape[1:]
elif isinstance(input.zvalue, list):
if is_parameter or is_constant:
shape = input.zvalue
else:
shape = input.zvalue[1:]
else:
raise Exception("not supported type " + str(type(input.zvalue)))
input.data = input.zvalue
if is_constant:
input.zvalue = zautograd.Parameter(shape=shape, init_weight=input.zvalue,
trainable=False)
elif is_parameter:
input.zvalue = zautograd.Parameter(shape=shape, init_weight=input.zvalue, )
else:
input.zvalue = zlayers.Input(
shape=shape, name=input.name)
return input
[docs] def to_tensor(self):
"""
Convert a node to tensor
"""
out_tensor = self._to_tensor()
if self.node_name:
out_tensor.set_name(self.node_name)
assert isinstance(out_tensor, zautograd.Variable) or isinstance(out_tensor,
zautograd.Parameter)
if self.model_trainable_values:
out_tensor.node.element().set_weights(
self.to_zoo_format(self.model_trainable_values))
return out_tensor
def _to_tensor(self):
raise Exception("Please define the content")
def _extract_model_inputs(self):
"""
:return: list of OnnxInput
"""
return [self._to_zoo_input(i) for i in self._input_list]
def _extract_trainable_values(self):
"""
:return: list of ndarray for weights
"""
return None