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>
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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import torch
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from pytorch_layer_test_class import PytorchLayerTest
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@pytest.mark.parametrize('input_tensor',
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[
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[2, 1, 3], [3, 7], [1, 1, 4, 4]
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])
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class TestLen(PytorchLayerTest):
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def _prepare_input(self):
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input_tensor = self.input_tensor * 10
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return (input_tensor.astype(np.int64),)
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def create_model(self):
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class aten_len(torch.nn.Module):
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def forward(self, input_tensor):
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return torch.as_tensor(len(input_tensor), dtype=torch.int)
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ref_net = None
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return aten_len(), ref_net, "aten::len"
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def create_model_int_list(self):
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class aten_len(torch.nn.Module):
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def forward(self, input_tensor):
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int_list = input_tensor.size()
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return torch.as_tensor(len(int_list), dtype=torch.int)
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ref_net = None
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return aten_len(), ref_net, "aten::len"
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_len(self, ie_device, precision, ir_version, input_tensor):
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self.input_tensor = np.random.randn(*input_tensor).astype(np.float32)
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self._test(*self.create_model(), ie_device, precision, ir_version)
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_len_int_list(self, ie_device, precision, ir_version, input_tensor):
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self.input_tensor = np.random.randn(*input_tensor).astype(np.float32)
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self._test(*self.create_model_int_list(),
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ie_device, precision, ir_version, use_convert_model=True)
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class TestLenEmpty(PytorchLayerTest):
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def _prepare_input(self):
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input_tensor = np.random.randn(1, 2, 3) * 10
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return (input_tensor.astype(np.int64),)
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def create_model_empty(self):
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class aten_len(torch.nn.Module):
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def forward(self, input_tensor):
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# len of empty slice
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return torch.as_tensor(len(input_tensor[0:0]), dtype=torch.int)
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ref_net = None
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return aten_len(), ref_net, "aten::len"
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_len_empty(self, ie_device, precision, ir_version):
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self._test(*self.create_model_empty(),
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ie_device, precision, ir_version)
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