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"""
Copyright (c) 2018 Intel Corporation
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.
"""
import numpy as np
from mo.front.common.partial_infer.utils import convert_tf_padding_to_str
from mo.front.extractor import FrontExtractorOp
from mo.front.tf.extractors.utils import tf_data_format_spatial, tf_data_format_channel, tf_data_format_batch, \
tf_int_list
from mo.ops.pooling import Pooling
class AvgPoolFrontExtractor(FrontExtractorOp):
op = 'AvgPool'
enabled = True
@staticmethod
def extract(node):
attrs = create_pooling_attrs(node, 'avg')
attrs.update({'op': __class__.op})
# update the attributes of the node
Pooling.update_node_stat(node, attrs)
return __class__.enabled
class MaxPoolFrontExtractor(FrontExtractorOp):
op = 'MaxPool'
enabled = True
@staticmethod
def extract(node):
attrs = create_pooling_attrs(node, 'max')
attrs.update({'op': __class__.op})
# update the attributes of the node
Pooling.update_node_stat(node, attrs)
return __class__.enabled
class MaxPool3DFrontExtractor(FrontExtractorOp):
op = 'MaxPool3D'
enabled = True
@staticmethod
def extract(node):
attrs = create_pooling_attrs(node, 'max')
attrs.update({'op': __class__.op})
# update the attributes of the node
Pooling.update_node_stat(node, attrs)
return __class__.enabled
def create_pooling_attrs(node, pool_method):
data_format = node.pb.attr["data_format"]
attrs = {
'auto_pad': convert_tf_padding_to_str(node.pb.attr['padding']),
'window': tf_int_list(node.pb.attr["ksize"].list),
'spatial_dims': tf_data_format_spatial(data_format),
'pad': None, # will be inferred when input shape is known
'stride': tf_int_list(node.pb.attr["strides"].list),
'pad_spatial_shape': None,
'output_spatial_shape': None,
'pool_method': pool_method,
'type': 'Pooling',
'layout': data_format.s.decode(),
'exclude_pad': 'true',
}
return attrs
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