dec7df17ed
* Remove unnnecessary ir_version checks in the MO * Cleaned up 'backend_attrs_v2' function * Small clean up from the 'TFCustomSubgraphCall' * Clean up the MO extractor attributes mapping * Renamed PreluOp to PReLU
75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import numpy as np
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from mo.middle.passes.convert_data_type import data_type_str_to_np, np_data_type_to_destination_type, \
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precision_to_destination_type
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from mo.ops.op import Op
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class Const(Op):
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"""
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Operation producing constant value stored in the attribute 'value' of shape 'shape'.
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"""
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op = 'Const'
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def __init__(self, graph, attrs: dict = None):
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super().__init__(graph, {
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'type': self.op,
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'op': self.op,
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'version': 'opset1',
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'infer': self.infer,
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'value': None,
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'shape': None,
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'data_type': None,
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'out_ports_count': 1,
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'type_infer': self.type_infer,
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}, attrs)
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if not isinstance(self.attrs['value'], np.ndarray):
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self.attrs['value'] = np.array(self.attrs['value'])
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self.attrs['shape'] = np.array(self.attrs['value'].shape, dtype=np.int64)
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if 'force_shape' in self.attrs and self.attrs['force_shape'] is not None:
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self.attrs['shape'] = np.array(self.attrs['force_shape'], dtype=np.int64)
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self.attrs['data_type'] = self.attrs['value'].dtype
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if 'force_type' in self.attrs and self.attrs['force_type'] is not None:
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self.attrs['data_type'] = data_type_str_to_np(self.attrs['force_type'])
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def supported_attrs(self):
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return [
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'offset',
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'size',
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('shape', lambda node: ','.join([str(i) for i in node.shape])),
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('element_type', lambda node: precision_to_destination_type(node.force_type)
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if node.has_valid('force_type') else np_data_type_to_destination_type(node.value.dtype)),
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]
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@staticmethod
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def type_infer(node):
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node.out_port(0).set_data_type(node.value.dtype, override=True)
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if node.has_valid('force_type'):
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node.out_port(0).set_data_type(node.data_type, override=True)
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@staticmethod
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def infer(node):
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# no broadcast, copy as-is (tensor or scalar) or apply broadcast depending on value and shape
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output_value = node.value if isinstance(node.value, np.ndarray) or len(node.shape) == 0 \
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else np.full(node.shape, node.value)
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node.out_port(0).data.set_value(output_value)
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