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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.extractor import FrontExtractorOp
from mo.front.onnx.extractors.utils import onnx_attr
class AffineFrontExtractor(FrontExtractorOp):
# Affine operation will be transformed to ImageScalar and further will be converted to Mul->Add seq
op = 'Affine'
enabled = True
@staticmethod
def extract(node):
dst_type = lambda x: np.array(x)
scale = onnx_attr(node, 'alpha', 'f', default=None, dst_type=dst_type)
bias = onnx_attr(node, 'beta', 'f', default=None, dst_type=dst_type)
node['scale'] = scale
node['bias'] = bias
node['op'] = 'ImageScaler'
return __class__.enabled
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