4078bd9c19
* test * fixed tests * typo * fixed tests * rest of the tests * fixed rsub test * tmp fix * Revert "tmp fix" This reverts commit b8bf1e9492e13497895da488612c9a137ef840bc. * fixed test params * reset thirdparty/pugixml * Revert "fixed rsub test" This reverts commit 9b6be34b8666936e8124b6622fcc5185b640de92. * fixed typo * fixed test data * reset test_rsub * removed unused param * reverrted runner * simplified call * fixed random * changed logical to auto mode * Revert "fixed random" This reverts commit 8a4f20b24641144f823a7e1f1ff92038634acf32. * fixed test_all * replaced random_sample with randn * fixed rebase issue * reverted logical splitting * Update tests/layer_tests/pytorch_tests/test_repeat_interleave.py Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> * Update tests/layer_tests/pytorch_tests/test_all.py Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> * Apply suggestions from code review Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> * fixed merge conflict --------- Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import os
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import numpy as np
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import pytest
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from pytorch_layer_test_class import PytorchLayerTest
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class TestTopK(PytorchLayerTest):
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def _prepare_input(self):
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return (self.input_tensor,)
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def create_model(self, k, dim, largest, sort):
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import torch
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class aten_topk(torch.nn.Module):
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def __init__(self, k, dim, largest, sort):
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super(aten_topk, self).__init__()
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self.k = k
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self.dim = dim
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self.largest = largest
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self.sort = sort
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def forward(self, input_tensor):
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if self.dim is None:
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return torch.topk(input_tensor, k=self.k, largest=self.largest, sorted=self.sort)
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else:
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return torch.topk(input_tensor, k=self.k, dim=self.dim, largest=self.largest, sorted=self.sort)
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ref_net = None
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return aten_topk(k, dim, largest, sort), ref_net, "aten::topk"
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@pytest.mark.parametrize(("input_shape"), [
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[7, 5, 5, 4],
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[5, 6, 6, 7, 8]
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])
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@pytest.mark.parametrize(("k"), [
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3,
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1,
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2,
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])
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@pytest.mark.parametrize(("dim"), [
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0,
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2,
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-1,
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None,
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])
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@pytest.mark.parametrize(("largest"), [
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True,
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False,
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])
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# For False it is hard to test because in Pytorch implementation
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# there is not promise on the order of output values
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@pytest.mark.parametrize(("sort"), [
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True,
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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@pytest.mark.skipif(os.getenv("GITHUB_ACTIONS") == 'true', reason="Ticket - 115085")
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def test_topK(self, input_shape, k, dim, largest, sort, ie_device, precision, ir_version):
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self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
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self._test(*self.create_model(k, dim, largest, sort), ie_device, precision, ir_version)
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