abaf61d059
* Improve op support for detectron mask rcnn * Initial commit * Fix for reading processed list * Format code * Cleanup * cleanup * Cleanup * cleanup test * Add comment * Add rt_info * fix type * More fixes for detectron * Fix build * Add tests for if * Revert changes in index * Add comment * Fix test * Fix get_axes_range * Add tests and fix if type alignment * Fix code style --------- Co-authored-by: Mateusz <mateusz.mikolajczyk@intel.com>
41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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import numpy as np
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from pytorch_layer_test_class import PytorchLayerTest
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class TestIf(PytorchLayerTest):
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def _prepare_input(self):
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return (np.random.randn(1, 3, 224, 224).astype(np.float32), self.y)
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def create_model(self):
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import torch
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import torch.nn.functional as F
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class prim_if(torch.nn.Module):
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def __init__(self):
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super(prim_if, self).__init__()
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def forward(self, x, y):
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if y > 0:
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res = x.new_empty((0, 10), dtype=torch.uint8)
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else:
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res = torch.zeros(x.shape[:2], dtype=torch.bool)
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return res.to(torch.bool)
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ref_net = None
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return prim_if(), ref_net, "prim::If"
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@pytest.mark.parametrize("y", [np.array(1),
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np.array(-1)
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_if(self, y, ie_device, precision, ir_version):
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self.y = y
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self._test(*self.create_model(), ie_device, precision, ir_version)
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