iliya mironov
642a62d0e2
* Add axis support * Update dequant extractor * Update qdeq ops * Refactoring quantize * Update dequantize resolver * Update dequantize op * Refactoring dequantize * Some fixes for quantize and dequantize * Update unit tests * Reafctoring quantize/dequantize axis support * Move quantize/dequantize resolvers to middle * hot fix * Fix unit tests * Fix unit tests * Update quintize resolver comment * Refactoring code according to code review * Fix according to review * Change order for transforms quantize pipline
83 lines
3.9 KiB
Python
83 lines
3.9 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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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, explicit_broadcasting, explicit_shape_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 axes_mapping tensor
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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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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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elif node.mode == 'explicit':
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axes_mapping = node.in_port(2).data.get_value()
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assert axes_mapping is not None, 'Broadcast(mode="explicit") with dynamic axes_mapping input ' \
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'is not supported. Node: `{}`'.format(node_name)
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PermuteInputs().set_input_permutation(node.in_node(2), node, 'output:0', 'axis')
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axes_mapping = node.in_port(2).data.get_value()
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node.out_port(0).data.set_value(explicit_broadcasting(input_value, target_shape, axes_mapping))
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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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else:
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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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elif node.mode == 'explicit':
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axes_mapping = node.in_port(2).data.get_value()
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assert axes_mapping is not None, 'Broadcast(mode="explicit") with dynamic axes_mapping input ' \
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'is not supported. Node: `{}`'.format(node_name)
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PermuteInputs().set_input_permutation(node.in_node(2), node, 'output:0', 'axis')
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axes_mapping = node.in_port(2).data.get_value()
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new_shape,_ = explicit_shape_broadcasting(input_shape, target_shape, axes_mapping)
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node.out_port(0).data.set_shape(new_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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