Mikhail RyzhovandMaxim Vafin
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>
111 lines
3.7 KiB
Python
111 lines
3.7 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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class TestRsub(PytorchLayerTest):
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def _prepare_input(self):
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return self.input_data
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def create_model(self, second_type="float"):
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class aten_rsub_float(torch.nn.Module):
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def forward(self, x, y:float, alpha: float):
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return torch.rsub(x, y, alpha=alpha)
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class aten_rsub_int(torch.nn.Module):
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def forward(self, x, y:int, alpha: float):
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return torch.rsub(x, y, alpha=alpha)
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model_cls = {
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"float": aten_rsub_float,
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"int": aten_rsub_int
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}
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model = model_cls[second_type]
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ref_net = None
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return model(), ref_net, "aten::rsub"
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@pytest.mark.parametrize('input_data',
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[
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[[2, 3, 4], np.array(5).astype(np.float32), [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_rsub(self, ie_device, precision, ir_version, input_data):
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self.input_data = []
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for input in input_data:
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if type(input) is list:
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self.input_data.append(np.random.randn(*input).astype(np.float32))
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else:
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self.input_data.append(input)
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self._test(*self.create_model(second_type="float"), ie_device, precision, ir_version, use_convert_model=True)
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@pytest.mark.parametrize('input_data',
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[
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[[2, 3, 4], np.array(5).astype(int), [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_rsub(self, ie_device, precision, ir_version, input_data):
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self.input_data = []
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for input in input_data:
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if type(input) is list:
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self.input_data.append(np.random.randn(*input).astype(np.float32))
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else:
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self.input_data.append(input)
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self._test(*self.create_model(second_type="int"), ie_device, precision, ir_version, use_convert_model=True)
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class TestRsubTypes(PytorchLayerTest):
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def _prepare_input(self):
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return (torch.randn(self.lhs_shape).to(self.lhs_type).numpy(),
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np.array([1]).astype(self.rhs_type))
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def create_model(self, lhs_type, rhs_type):
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class aten_rsub(torch.nn.Module):
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def __init__(self, lhs_type, rhs_type):
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super().__init__()
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self.lhs_type = lhs_type
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if rhs_type == np.int32:
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self.forward = self.forward2
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else:
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self.forward = self.forward1
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def forward1(self, lhs, rhs:float):
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return torch.rsub(lhs.to(self.lhs_type), rhs, alpha=2)
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def forward2(self, lhs, rhs:int):
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return torch.rsub(lhs.to(self.lhs_type), rhs, alpha=2)
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ref_net = None
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return aten_rsub(lhs_type, rhs_type), ref_net, "aten::rsub"
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@pytest.mark.parametrize(("lhs_type", "rhs_type"),
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[[torch.int32, np.int32],
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[torch.int32, np.float32],
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[torch.int64, np.int32],
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[torch.int64, np.float32],
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[torch.float32, np.int32],
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[torch.float32, np.float32],
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])
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@pytest.mark.parametrize(("lhs_shape"), [[2, 3], [3], [2, 3, 4]])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_rsub_types(self, ie_device, precision, ir_version, lhs_type, lhs_shape, rhs_type):
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self.lhs_type = lhs_type
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self.lhs_shape = lhs_shape
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self.rhs_type = rhs_type
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self._test(*self.create_model(lhs_type, rhs_type),
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ie_device, precision, ir_version) |