146 lines
5.6 KiB
Python
146 lines
5.6 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 logging as log
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import numpy as np
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from mo.graph.graph import Node, Graph
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from mo.ops.op import Op
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class Slice(Op):
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op = 'Slice'
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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': 'Slice',
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'in_ports_count': 3,
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'out_ports_count': 1,
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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 ['start', 'end', 'axis']
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@staticmethod
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def infer(node: Node):
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input_shape = node.in_port(0).data.get_shape()
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axis = None
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steps = None
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if len(node.in_nodes()) == 1:
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# Caffe or ONNX before 10 opset
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if node.has('start') and node.has('end') and node.has('axis'):
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# ONNX case
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if node.has_valid('start') and node.has_valid('end') and node.has('axis'):
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start = node.start
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end = node.end
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axis = node.axis
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else:
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log.warning('Incorrect slice operation: no starts or end attr')
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return
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else:
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# Caffe case
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from mo.front.common.partial_infer.slice import caffe_slice_infer
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caffe_slice_infer(node)
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elif len(node.in_nodes()) >= 3:
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if node.has('format') and node['format'] == 'onnx':
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# ONNX 10 opset case
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starts_node = node.in_node(1)
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ends_node = node.in_node(2)
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if starts_node.has_valid('value') and ends_node.has_valid('value'):
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start = np.array(node.in_node(1).value, dtype=np.int64)
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end = np.array(node.in_node(2).value, dtype=np.int64)
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if 3 in node.in_nodes():
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if node.in_node(3).has_valid('value'):
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axis = np.array(node.in_node(3).value, dtype=np.int64)
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else:
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log.warning('Incorrect slice operation: axes should be const')
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return
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if 4 in node.in_nodes():
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if node.in_node(4).has_valid('value'):
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steps = np.array(node.in_node(4).value, dtype=np.int64)
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else:
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log.warning('Incorrect slice operation: steps should be const')
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return
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else:
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log.warning('Incorrect slice operation: no starts or ends attr')
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return
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else:
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# TF case
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start_node = node.in_node(1)
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size_node = node.in_node(2)
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if start_node.has_valid('value') and size_node.has_valid('value'):
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start = np.array(node.in_node(1).value, dtype=np.int64)
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size = np.array(node.in_node(2).value, dtype=np.int64)
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end = start + size
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axis = None
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# Check for situation when size[i] == -1 in TF
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for i in range(start.size):
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if end[i] < start[i]:
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end[i] = input_shape[i]
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# Delete edges to start, size nodes
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node.graph.remove_edge(node.in_node(1).id, node.id)
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node.graph.remove_edge(node.in_node(2).id, node.id)
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node['start'] = start
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node['end'] = end
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node['axis'] = None
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else:
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log.warning('Incorrect slice operation: no starts or end attr')
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return
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else:
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log.warning('Incorrect number of input nodes in slice operation')
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return
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# Update end param
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node.end = end
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value = node.in_node(0).value
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# If value is None create dummy value for shape propagation
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if value is None:
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value = np.zeros(input_shape)
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# Following ONNX and TF specification, in case of unknown axis, axises should be in greater order
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if axis is None:
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axis = [x for x in range(len(start))]
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if steps is None:
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steps = np.ones(start.size, dtype=np.int64)
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# Calculate output value for slice operation
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slice_idx = [None for x in range(len(node.in_node().shape))]
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shrink_axis_mask = [False for x in range(len(node.in_node().shape))]
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for id in range(len(axis)):
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# Ranged for output value for specified axis
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slice_idx[axis[id]] = slice(start[id], end[id], steps[id])
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for axis, s in enumerate(slice_idx):
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if s is None:
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slice_idx[axis] = slice(0, input_shape[axis], 1)
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# Add new parameters to node
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node['slices'] = np.array(slice_idx)
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node['shrink_axis_mask'] = np.array(shrink_axis_mask)
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value = value[tuple(slice_idx)]
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node.out_node().value = value.copy() if node.in_node(0).value is not None else None
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node.out_node().shape = np.array(value.shape)
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