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>
87 lines
2.3 KiB
Python
87 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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from pytorch_layer_test_class import PytorchLayerTest
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class TestStack2D(PytorchLayerTest):
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def _prepare_input(self):
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return self.input_tensors
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def create_model(self, dim):
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import torch
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class aten_stack(torch.nn.Module):
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def __init__(self, dim):
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super(aten_stack, self).__init__()
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self.dim = dim
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def forward(self, x, y):
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inputs = [x, y]
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return torch.stack(inputs, self.dim)
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ref_net = None
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return aten_stack(dim), ref_net, "aten::stack"
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@pytest.mark.parametrize("input_shape",
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[
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[1, 3, 3],
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[4, 4, 2],
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[8, 1, 1, 9]
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])
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@pytest.mark.parametrize("dim", ([
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0, 1, 2,
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]))
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_stack2D(self, input_shape, dim, ie_device, precision, ir_version):
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self.input_tensors = [
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np.random.randn(*input_shape).astype(np.float32),
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np.random.randn(*input_shape).astype(np.float32),
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]
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self._test(*self.create_model(dim), ie_device, precision, ir_version)
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class TestStack3D(PytorchLayerTest):
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def _prepare_input(self):
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return self.input_tensors
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def create_model(self, dim):
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import torch
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class aten_stack(torch.nn.Module):
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def __init__(self, dim):
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super(aten_stack, self).__init__()
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self.dim = dim
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def forward(self, x, y, z):
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inputs = [x, y, z]
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return torch.stack(inputs, self.dim)
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ref_net = None
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return aten_stack(dim), ref_net, "aten::stack"
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@pytest.mark.parametrize("input_shape",
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[
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[1, 3, 3],
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[4, 4, 2],
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[8, 1, 1, 9]
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])
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@pytest.mark.parametrize("dim", ([
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0, 1, 2,
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]))
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_stack3D(self, input_shape, dim, ie_device, precision, ir_version):
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self.input_tensors = [
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np.random.randn(*input_shape).astype(np.float32),
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np.random.randn(*input_shape).astype(np.float32),
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np.random.randn(*input_shape).astype(np.float32)
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]
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self._test(*self.create_model(dim), ie_device, precision, ir_version)
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