#
# 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
from zoo.pipeline.api.onnx.onnx_helper import OnnxHelper
import zoo.pipeline.api.keras.layers as zlayers
[docs]class AveragePoolMapper (OperatorMapper):
def __init__(self, node, _params, _all_tensors):
super(AveragePoolMapper, self).__init__(node, _params, _all_tensors)
def _to_tensor(self):
assert len(self.model_inputs) == 1, "AveragePool accept single input only"
rank = len(self.model_inputs[0].zvalue.shape)
if (rank == 4): # NCHW
poolSize = [int(i) for i in self.onnx_attr['kernel_shape']]
strides = [int(i) for i in self.onnx_attr['strides']]
count_include_pad = bool(self.onnx_attr['count_include_pad'])\
if "count_include_pad" in self.onnx_attr else False
dim_ordering = "th"
border_mode, pads = OnnxHelper.get_padds(self.onnx_attr)
averagepool2d = zlayers.AveragePooling2D(pool_size=poolSize,
strides=strides,
dim_ordering=dim_ordering,
pads=pads,
count_include_pad=count_include_pad)
return averagepool2d(self.model_inputs[0].zvalue)
else:
raise Exception("not supported.")