#
# 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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from zoo.pipeline.api.onnx.mapper.operator_mapper import OperatorMapper
import zoo.pipeline.api.keras.layers as zlayers
import numpy as np
from zoo.pipeline.api.autograd import Parameter
import bigdl.nn.layer as blayer
import zoo.pipeline.api.autograd as autograd
[docs]class UnsqueezeMapper(OperatorMapper):
def __init__(self, node, initializer, _all_tensors):
super(UnsqueezeMapper, self).__init__(node, initializer, _all_tensors)
def _extract_model_inputs(self):
"""
:return: list of OnnxInput
"""
input = self._input_list[0]
if isinstance(input.zvalue, np.ndarray):
self.is_batch = False
return [self._to_zoo_input(input, is_constant=True)]
else:
self.is_batch = True
return [self._to_zoo_input(input)]
def _to_tensor(self):
data = self.model_inputs[0].zvalue
dim = sorted(tuple([int(i) for i in self.onnx_attr['axes']]))
for i in dim:
data = autograd.expand_dims(data, axis=i)
return data