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_tensor"), [
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np.random.rand(7, 5, 5, 4),
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np.random.rand(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_tensor, k, dim, largest, sort, ie_device, precision, ir_version):
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self.input_tensor = input_tensor
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self._test(*self.create_model(k, dim, largest, sort), ie_device, precision, ir_version)
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