Anton Chetverikov
6b54e738d7
* Allign attribute values in spec * Fix wrong attribute name in spec * Add `get_boolean_attr` function * Add get_type function * Update conv attrs * Update copyright year * Add missed attrs, update copyright year * Fix year in copyright * Update ir parser for RegionYolo layer * Remove wrong changes for BinaryConvolution * Remove get_type function as it no more needed * Update check for reduce ops * Fix error in reduce attrs * Update ir_engine to work with bool attrs * Update DetectionOutput operation * Update PSROIPooling * remove redundant attrs from spec * Update get_boolean_attr function * Update Reduce operations * Update DetectionOutput specification * Update specification for missed attrs * Apply comments * Fixconst renumbering logic * Fix typo * Change default value to fix broken shape inference * Add additional asserts * Add comment * model-optimizer/mo/utils/ir_reader/layer_to_class.py * Sort imports * Sort imports * Update year in copyright * Update const * Remove changes from const restoring * Rename function * remove unnecessary changes * model-optimizer/mo/front/extractor_test.py * Fix year in copyright * Add soft_get * Fix exclude-pad attribute name for AvgPool operation * Update exclude_pad attribute values * Remove useless comment * Update examples in specification * Remove file added by mistake * Resolve comments * Resolve comments * Add return value * Allign global_pool attribute
88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
"""
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Copyright (C) 2018-2021 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.front.extractor import bool_to_str
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from mo.graph.graph import Node, Graph
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from mo.middle.passes.convert_data_type import np_data_type_to_destination_type
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from mo.ops.op import Op
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class Bucketize(Op):
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op = 'Bucketize'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'kind': 'op',
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'type': __class__.op,
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'op': __class__.op,
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'version': 'opset3',
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'type_infer': self.type_infer,
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'infer': self.infer,
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'in_ports_count': 2,
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'out_ports_count': 1,
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}
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super().__init__(graph, mandatory_props, attrs)
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def backend_attrs(self):
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version = self.get_opset()
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if version == "extension":
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return [('with_right_bound', lambda node: bool_to_str(node, 'with_right_bound'))]
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else:
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return [
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('with_right_bound', lambda node: bool_to_str(node, 'with_right_bound')),
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('output_type', lambda node: np_data_type_to_destination_type(node.output_type)),
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]
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@staticmethod
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def type_infer(node):
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# the output is always integer since the layer outputs a bucket index
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if node.get_opset() == "extension":
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node.out_port(0).set_data_type(np.int32)
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else:
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assert node.output_type in [np.int64, np.int32], \
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'Bucketize `output_type` attribute must be int32 or int64, `{}` found'.format(np.dtype(node.output_type).name)
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node.out_port(0).set_data_type(node.output_type)
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@staticmethod
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def infer(node: Node):
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node_name = node.soft_get('name', node.id)
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assert node.with_right_bound is not None, \
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"Attribute \"with_right_bound\" is not defined"
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assert len(node.in_nodes()) == 2, \
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"Incorrect number of inputs for {} node".format(node.id)
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if node.get_opset() == "extension":
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output_type = np.int32
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else:
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assert node.has_valid('output_type'), \
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'`output_type` attribute is not set for Bucketize node `{}`'.format(node_name)
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assert node.output_type in [np.int64, np.int32], \
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'Bucketize `output_type` attribute must be int32 or int64, `{}` found'.format(np.dtype(node.output_type).name)
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output_type = node.output_type
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output_shape = node.in_port(0).data.get_shape()
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node.out_port(0).data.set_shape(output_shape)
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input_value = node.in_port(0).data.get_value()
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buckets_value = node.in_port(1).data.get_value()
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# compute if all input is constant
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if input_value is not None and buckets_value is not None:
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node.out_port(0).data.set_value(np.array(np.digitize(input_value, buckets_value, right=node.with_right_bound), dtype=node.output_type))
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