233 lines
7.5 KiB
Python
233 lines
7.5 KiB
Python
# ******************************************************************************
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# Copyright 2017-2020 Intel Corporation
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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import pytest
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import ngraph as ng
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from tests.runtime import get_runtime
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from tests.test_ngraph.util import run_op_node, run_op_numeric_data
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from tests import xfail_issue_34323, xfail_issue_35929
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def test_concat():
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a = np.array([[1, 2], [3, 4]])
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b = np.array([[5, 6]])
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axis = 0
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expected = np.concatenate((a, b), axis=0)
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runtime = get_runtime()
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parameter_a = ng.parameter(list(a.shape), name="A", dtype=np.float32)
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parameter_b = ng.parameter(list(b.shape), name="B", dtype=np.float32)
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node = ng.concat([parameter_a, parameter_b], axis)
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computation = runtime.computation(node, parameter_a, parameter_b)
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result = computation(a, b)
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assert np.allclose(result, expected)
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@xfail_issue_34323
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@pytest.mark.parametrize(
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"val_type, value", [(bool, False), (bool, np.empty((2, 2), dtype=bool))]
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)
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def test_constant_from_bool(val_type, value):
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expected = np.array(value, dtype=val_type)
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result = run_op_numeric_data(value, ng.constant, val_type)
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assert np.allclose(result, expected)
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@pytest.mark.parametrize(
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"val_type, value",
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[
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pytest.param(np.float32, np.float32(0.1234), marks=xfail_issue_34323),
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pytest.param(np.float64, np.float64(0.1234), marks=xfail_issue_35929),
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pytest.param(np.int8, np.int8(-63), marks=xfail_issue_34323),
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pytest.param(np.int16, np.int16(-12345), marks=xfail_issue_34323),
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pytest.param(np.int32, np.int32(-123456), marks=xfail_issue_34323),
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pytest.param(np.int64, np.int64(-1234567), marks=xfail_issue_34323),
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pytest.param(np.uint8, np.uint8(63), marks=xfail_issue_34323),
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pytest.param(np.uint16, np.uint16(12345), marks=xfail_issue_34323),
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pytest.param(np.uint32, np.uint32(123456), marks=xfail_issue_34323),
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pytest.param(np.uint64, np.uint64(1234567), marks=xfail_issue_34323),
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],
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)
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def test_constant_from_scalar(val_type, value):
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expected = np.array(value, dtype=val_type)
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result = run_op_numeric_data(value, ng.constant, val_type)
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assert np.allclose(result, expected)
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@pytest.mark.parametrize(
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"val_type",
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[
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pytest.param(np.float32, marks=xfail_issue_34323),
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pytest.param(np.float64, marks=xfail_issue_35929),
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],
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)
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def test_constant_from_float_array(val_type):
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np.random.seed(133391)
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input_data = np.array(-1 + np.random.rand(2, 3, 4) * 2, dtype=val_type)
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result = run_op_numeric_data(input_data, ng.constant, val_type)
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assert np.allclose(result, input_data)
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@xfail_issue_34323
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@pytest.mark.parametrize(
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"val_type, range_start, range_end",
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[
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(np.int8, -8, 8),
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(np.int16, -64, 64),
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(np.int32, -1024, 1024),
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(np.int64, -16383, 16383),
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(np.uint8, 0, 8),
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(np.uint16, 0, 64),
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(np.uint32, 0, 1024),
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(np.uint64, 0, 16383),
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],
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)
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def test_constant_from_integer_array(val_type, range_start, range_end):
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np.random.seed(133391)
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input_data = np.array(
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np.random.randint(range_start, range_end, size=(2, 2)), dtype=val_type
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)
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result = run_op_numeric_data(input_data, ng.constant, val_type)
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assert np.allclose(result, input_data)
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def test_broadcast_numpy():
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data_shape = [16, 1, 1]
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target_shape_shape = [4]
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data_parameter = ng.parameter(data_shape, name="Data", dtype=np.float32)
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target_shape_parameter = ng.parameter(
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target_shape_shape, name="Target_shape", dtype=np.int64
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)
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node = ng.broadcast(data_parameter, target_shape_parameter)
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assert node.get_type_name() == "Broadcast"
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assert node.get_output_size() == 1
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def test_broadcast_bidirectional():
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data_shape = [16, 1, 1]
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target_shape_shape = [4]
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data_parameter = ng.parameter(data_shape, name="Data", dtype=np.float32)
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target_shape_parameter = ng.parameter(
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target_shape_shape, name="Target_shape", dtype=np.int64
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)
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node = ng.broadcast(data_parameter, target_shape_parameter, "BIDIRECTIONAL")
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assert node.get_type_name() == "Broadcast"
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assert node.get_output_size() == 1
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def test_gather():
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input_data = np.array(
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[1.0, 1.1, 1.2, 2.0, 2.1, 2.2, 3.0, 3.1, 3.2], np.float32
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).reshape((3, 3))
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input_indices = np.array([0, 2], np.int32).reshape(1, 2)
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input_axes = np.array([1], np.int32)
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expected = np.array([1.0, 1.2, 2.0, 2.2, 3.0, 3.2], dtype=np.float32).reshape(
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(3, 1, 2)
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)
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result = run_op_node([input_data], ng.gather, input_indices, input_axes)
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assert np.allclose(result, expected)
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def test_transpose():
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input_tensor = np.arange(3 * 3 * 224 * 224, dtype=np.int32).reshape(
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(3, 3, 224, 224)
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)
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input_order = np.array([0, 2, 3, 1], dtype=np.int32)
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result = run_op_node([input_tensor], ng.transpose, input_order)
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expected = np.transpose(input_tensor, input_order)
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assert np.allclose(result, expected)
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@pytest.mark.xfail(
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reason="Tile operation has a form that is not supported. Tile_2 should be converted to TileIE operation."
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)
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def test_tile():
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input_tensor = np.arange(6, dtype=np.int32).reshape((2, 1, 3))
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repeats = np.array([2, 1], dtype=np.int32)
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result = run_op_node([input_tensor], ng.tile, repeats)
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expected = np.array([0, 1, 2, 0, 1, 2, 3, 4, 5, 3, 4, 5]).reshape((2, 2, 3))
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assert np.allclose(result, expected)
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@pytest.mark.xfail(
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reason="RuntimeError: Check 'shape_size(get_input_shape(0)) == shape_size(output_shape)'"
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)
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def test_strided_slice():
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input_tensor = np.arange(2 * 3 * 4, dtype=np.float32).reshape((2, 3, 4))
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begin = np.array([1, 0], dtype=np.int32)
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end = np.array([0, 0], dtype=np.int32)
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strides = np.array([1, 1], dtype=np.int32)
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begin_mask = np.array([0, 0, 0], dtype=np.int32)
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end_mask = np.array([0, 0, 0], dtype=np.int32)
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new_axis_mask = np.array([0, 1, 0], dtype=np.int32)
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shrink_axis_mask = np.array([1, 0, 0], dtype=np.int32)
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ellipsis_mask = np.array([0, 0, 0], dtype=np.int32)
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result = run_op_node(
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[input_tensor],
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ng.strided_slice,
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begin,
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end,
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strides,
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begin_mask,
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end_mask,
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new_axis_mask,
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shrink_axis_mask,
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ellipsis_mask,
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)
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expected = np.array(
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[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], dtype=np.float32
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).reshape((1, 3, 4))
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assert np.allclose(result, expected)
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def test_reshape_v1():
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A = np.arange(1200, dtype=np.float32).reshape((2, 5, 5, 24))
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shape = np.array([0, -1, 4], dtype=np.int32)
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special_zero = True
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expected_shape = np.array([2, 150, 4])
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expected = np.reshape(A, expected_shape)
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result = run_op_node([A], ng.reshape, shape, special_zero)
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assert np.allclose(result, expected)
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@xfail_issue_34323
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def test_shape_of():
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input_tensor = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)
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result = run_op_node([input_tensor], ng.shape_of)
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assert np.allclose(result, [3, 3])
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