Fix Slice issue in MO IR Reader (#13784)
* Fix slice issue in MO IR Reader * Add unit test * Fix slice test
This commit is contained in:
Maxim Vafin
GitHub
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commit
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@ -3,7 +3,8 @@
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import numpy as np
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from openvino.tools.mo.front.common.partial_infer.utils import get_shape_from_slice, shape_array, dynamic_dimension_value, \
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from openvino.tools.mo.front.common.partial_infer.utils import get_shape_from_slice, shape_array, \
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dynamic_dimension_value, \
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dynamic_dimension, is_dynamic_slice
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from openvino.tools.mo.graph.graph import Node, Graph
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from openvino.tools.mo.ops.op import Op
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@ -56,7 +57,6 @@ class CaffeSlice(Op):
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}, attrs)
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class TFSlice(Op):
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"""
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TFSlice differs from Slice in ONNX, Caffe and MXNet.
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@ -97,6 +97,37 @@ class MXSlice(Op):
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}, attrs)
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def slice_infer(node: Node, steps_idx: int, axes_idx: int):
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input_value = node.in_port(0).data.get_value()
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input_shape = node.in_port(0).data.get_shape()
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starts = node.in_port(1).data.get_value()
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ends = node.in_port(2).data.get_value()
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if node.is_in_port_connected(steps_idx):
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steps = node.in_port(steps_idx).data.get_value()
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else:
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steps = np.ones(len(starts), dtype=np.int64)
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if node.is_in_port_connected(axes_idx):
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axes = node.in_port(axes_idx).data.get_value()
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else:
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axes = [x for x in range(len(starts))]
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if starts is None or ends is None or steps is None or axes is None:
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node.out_port(0).data.set_shape(shape_array([dynamic_dimension_value] * len(input_shape)))
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return
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slice_idx = [slice(0, in_shape, 1) for in_shape in input_shape]
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for i in range(len(axes)):
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# Ranged for output value for specified axis
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slice_idx[axes[i]] = slice(starts[i], ends[i], steps[i])
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if input_value is None or any(is_dynamic_slice(s) for s in slice_idx):
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output_shape = get_shape_from_slice(input_shape, slice_idx)
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node.out_port(0).data.set_shape(output_shape)
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else:
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node.out_port(0).data.set_value(input_value[tuple(slice_idx)])
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class Slice(Op):
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"""
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Semantic of Slice is identical to Slice in ONNX opset >= 10.
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@ -117,31 +148,26 @@ class Slice(Op):
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@staticmethod
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def infer(node: Node):
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input_value = node.in_port(0).data.get_value()
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input_shape = node.in_port(0).data.get_shape()
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slice_infer(node, 4, 3)
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starts = node.in_port(1).data.get_value()
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ends = node.in_port(2).data.get_value()
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if node.is_in_port_connected(4):
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steps = node.in_port(4).data.get_value()
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else:
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steps = np.ones(len(starts), dtype=np.int64)
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if node.is_in_port_connected(3):
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axes = node.in_port(3).data.get_value()
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else:
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axes = [x for x in range(len(starts))]
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class OvSlice(Op):
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"""
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Semantic of OvSlice is identical to Slice in Openvino opset8.
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It is introduced for usage in MO IR Reader.
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"""
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op = 'OvSlice'
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enabled = False
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if starts is None or ends is None or steps is None or axes is None:
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node.out_port(0).data.set_shape(shape_array([dynamic_dimension_value] * len(input_shape)))
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return
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def __init__(self, graph: Graph, attrs: dict = None):
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super().__init__(graph, {
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'type': None,
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'op': self.op,
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'in_ports_count': 5,
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'out_ports_count': 1,
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'infer': self.infer
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}, attrs)
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slice_idx = [slice(0, in_shape, 1) for in_shape in input_shape]
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for i in range(len(axes)):
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# Ranged for output value for specified axis
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slice_idx[axes[i]] = slice(starts[i], ends[i], steps[i])
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if input_value is None or any(is_dynamic_slice(s) for s in slice_idx):
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output_shape = get_shape_from_slice(input_shape, slice_idx)
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node.out_port(0).data.set_shape(output_shape)
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else:
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node.out_port(0).data.set_value(input_value[tuple(slice_idx)])
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@staticmethod
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def infer(node: Node):
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slice_infer(node, 3, 4)
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@ -27,6 +27,7 @@ from openvino.tools.mo.ops.pooling import Pooling
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from openvino.tools.mo.ops.psroipooling import DeformablePSROIPoolingOp
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from openvino.tools.mo.ops.scatter import Scatter
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from openvino.tools.mo.ops.scatternd import ScatterNDBase
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from openvino.tools.mo.ops.slice import OvSlice
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from openvino.tools.mo.ops.split import Split, VariadicSplit
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from openvino.tools.mo.utils.class_registration import update_registration
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from openvino.tools.mo.utils.import_extensions import import_by_path
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@ -47,6 +48,7 @@ custom_ops = {
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'MaxPool': Pooling,
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'Multiply': Mul,
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'Power': Pow,
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'Slice': OvSlice,
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'Split': Split,
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'Subtract': Sub,
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'VariadicSplit': VariadicSplit,
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@ -6,10 +6,10 @@ import unittest
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import numpy as np
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from generator import generator, generate
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from openvino.tools.mo.front.common.partial_infer.utils import int64_array, dynamic_dimension_value, shape_array, strict_compare_tensors
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from openvino.tools.mo.front.common.partial_infer.utils import int64_array, dynamic_dimension_value, shape_array, \
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strict_compare_tensors
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from openvino.tools.mo.graph.graph import Node
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from openvino.tools.mo.ops.slice import Slice
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from openvino.tools.mo.utils.error import Error
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from openvino.tools.mo.ops.slice import Slice, OvSlice
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from unit_tests.utils.graph import build_graph, valued_const_with_data, valued_data, regular_op_with_empty_data, \
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connect, shaped_data, shaped_const_with_data
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@ -109,3 +109,100 @@ class TestSliceOp(unittest.TestCase):
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if expected_value is not None:
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self.assertTrue(strict_compare_tensors(slice_node.out_node().value, expected_value))
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self.assertTrue(strict_compare_tensors(slice_node.out_node().shape, expected_shape))
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@generator
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class TestOvSliceOp(unittest.TestCase):
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@generate(*[
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# standard case
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([[4, 5, 6, 7], [2, 3, 5, 6], [5, 6, 8, 9], [5, 6, 8, 9]], [4, 4], [0, 1], [3, 2], [0, 1], [1, 1],
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[[5], [3], [6]], [3, 1]),
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# negative bounds
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([[4, 5, 6, 7], [2, 3, 5, 6], [5, 6, 8, 9], [5, 6, 8, 9]], [4, 4], [0, 1], [3, -2], [0, 1], [1, 1],
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[[5], [3], [6]], [3, 1]),
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# unusual order of axes
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([[4, 5, 6, 7], [2, 3, 5, 6], [5, 6, 8, 9], [5, 6, 8, 9]], [4, 4], [0, 1], [3, -2], [1, 0], [1, 1],
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[[2, 3, 5]], [1, 3]),
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# when only input_shape is defined without values (one from bottom element is shape)
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(None, [4, 5, 6], [1, 2], [4, 3], [0, 1], [1, 1], None, [3, 1, 6]),
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# boundary case
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(None, [4, 5, 6], [0, 2], [np.iinfo(np.int32).max, 3], [0, 1], [1, 1], None, [4, 1, 6]),
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# boundary case
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(None, [4, 5, 6], [np.iinfo(np.int32).min, 2], [3, 3], [0, 1], [1, 1], None, [3, 1, 6],),
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# 1D input
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([1, 3, 224, 224], [4], [1], [2], [0], [1], [3], [1]),
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# 1D input with negative starts
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(None, [4], [-1], [1], [0], [-1], None, [2]),
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# 1D input with negative ends
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(None, [4], [1], [-1], [0], [1], None, [2]),
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# with rounding (e.g. take from 1st to 3rd with step 4 should give shape 1 not 0)
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(None, [4], [1], [3], [0], [4], None, [1]),
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# with rounding and negative steps (e.g. take from 1st to 3rd with step 4 should give shape 1 not 0)
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(None, [10], [7], [3], [0], [-7], None, [1]),
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# reversing the sequence of elements
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(None, [10], [-1], [np.iinfo(np.int32).min], [0], [-1], None, [10]),
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# dynamic dimensions cases
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# starts are non-constant
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([[4, 5, 6, 7], [2, 3, 5, 6], [5, 6, 8, 9], [5, 6, 8, 9]], [4, 4], None, [3, 2], [0, 1], [1, 1], None,
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[dynamic_dimension_value, dynamic_dimension_value]),
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# ends are non-constant
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([[4, 5, 6, 7], [2, 3, 5, 6], [5, 6, 8, 9], [5, 6, 8, 9]], [4, 4], [0, 1], None, [0, 1], [1, 1], None,
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[dynamic_dimension_value, dynamic_dimension_value]),
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# axes are non-constant
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([[4, 5, 6, 7], [2, 3, 5, 6], [5, 6, 8, 9], [5, 6, 8, 9]], [4, 4], [0, 1], [3, -2], None, [1, 1], None,
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[dynamic_dimension_value, dynamic_dimension_value]),
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# steps are non-constant
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([[4, 5, 6, 7], [2, 3, 5, 6], [5, 6, 8, 9], [5, 6, 8, 9]], [4, 4], [0, 1], [3, -2], [0, 1], None, None,
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[dynamic_dimension_value, dynamic_dimension_value]),
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# negative steps and since after normalization starts < ends output shape has 0-size dimension
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(None, [20], [1], [-1], [0], [-2], None, [0]),
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# since starts == ends output shape has 0-size dimension
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(None, [4], [1], [1], [0], [1], None, [0]),
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# since starts > ends output shape has 0-size dimension
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(None, [4], [2], [1], [0], [1], None, [0])
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])
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def test_ov_slice_infer(self, inp_value, inp_shape, starts, ends, axes, steps, expected_value, expected_shape):
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if inp_value is None:
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input_node = shaped_data('data_1', int64_array(inp_shape))
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else:
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input_node = valued_data('data_1', int64_array(inp_value))
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if inp_value is not None and inp_shape is not None:
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assert np.array_equal(np.array(inp_value).shape, inp_shape)
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def convert_args(val, name=''):
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if val is not None:
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return valued_const_with_data(name, int64_array(val))
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else:
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return shaped_const_with_data(name, [0]) # fake shape
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starts = convert_args(starts, 'starts')
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ends = convert_args(ends, 'ends')
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steps = convert_args(steps, 'steps')
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axes = convert_args(axes, 'axes')
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if expected_shape is not None:
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expected_shape = shape_array(expected_shape)
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nodes = {
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**input_node,
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**regular_op_with_empty_data('slice', {'op': 'OvSlice'}),
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**starts,
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**ends,
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**steps,
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**axes,
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}
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graph = build_graph(nodes,
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[('data_1', 'slice'),
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*connect('starts', '1:slice'),
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*connect('ends', '2:slice'),
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*connect('steps', '3:slice'),
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*connect('axes', '4:slice'),
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*connect('slice', 'slice_d')])
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graph.stage = 'middle'
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slice_node = Node(graph, 'slice')
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OvSlice.infer(slice_node)
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if expected_value is not None:
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self.assertTrue(strict_compare_tensors(slice_node.out_node().value, expected_value))
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self.assertTrue(strict_compare_tensors(slice_node.out_node().shape, expected_shape))
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