970b1301b5
* Removed back phase transformations related to IRv7 * Fixed setting value for the input port using the 'set_value' method * Removed front and middle phase transformations related to IRv7 * Cleanup the rest of the Model Optimizer transformations from IRv7 specific transformations * Final cleanup of the deprecated IR v7 related code * Removed 'blobs_as_input' usage in the Model Optimizer. * Removed function '_fuse_add' from the Model Optimizer since it is not used anymore. * Removed 'keep_in_IR' node attribute for FakeQuantize ops in the MO * Disabled failing gpu_engine.user_context test
79 lines
3.0 KiB
Python
79 lines
3.0 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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from mo.graph.graph import Node, Graph
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from mo.graph.perm_inputs import PermuteInputs
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from mo.ops.op import Op
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from mo.utils.broadcasting import bi_directional_shape_broadcasting, uni_directional_shape_broadcasting, \
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uni_directional_broadcasting, bi_directional_broadcasting
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from mo.utils.error import Error
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class Broadcast(Op):
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""" Broadcast tensor to a given shape with optional axis parameter
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Inputs:
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[0] - tensor to be broadcasted
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[1] - shape to be broadcast to
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[2] - optional axis parameter that which axis are allowed to be broadcasted
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"""
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op = 'Broadcast'
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enabled = True
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def __init__(self, graph: Graph, attrs: dict):
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super().__init__(graph, {
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'type': __class__.op,
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'op': __class__.op,
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'version': 'opset3',
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'mode': 'numpy',
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'in_ports_count': 3,
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'out_ports_count': 1,
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'force_precision_in_ports':
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{1: 'int64',
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2: 'int64',
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},
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'infer': __class__.infer,
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}, attrs)
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def supported_attrs(self):
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return ['mode']
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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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input_shape = node.in_port(0).data.get_shape()
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input_value = node.in_port(0).data.get_value()
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target_shape = node.in_port(1).data.get_value()
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assert target_shape is not None, 'Output shape is not defined for node "{}"'.format(node_name)
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assert node.has_and_set('mode'), 'Broadcasting mode is not defined for node "{}"'.format(node_name)
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if node.mode == 'numpy':
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node.out_port(0).data.set_shape(uni_directional_shape_broadcasting(input_shape, target_shape))
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elif node.mode == 'bidirectional':
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node.out_port(0).data.set_shape(bi_directional_shape_broadcasting(input_shape, target_shape))
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else:
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raise Error('The node "{}" has unsupported mode "{}"'.format(node_name, node.mode))
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PermuteInputs().set_input_permutation(node.in_node(1), node, 'output:0', 'shape')
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if input_value is not None and not node.has_and_set('stop_value_propagation'):
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if node.mode == 'numpy':
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node.out_port(0).data.set_value(uni_directional_broadcasting(input_value, target_shape))
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elif node.mode == 'bidirectional':
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node.out_port(0).data.set_value(bi_directional_broadcasting(input_value, target_shape))
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